WorldWideScience

Sample records for surveillance modeling network

  1. Space Surveillance Network Scheduling Under Uncertainty: Models and Benefits

    Science.gov (United States)

    Valicka, C.; Garcia, D.; Staid, A.; Watson, J.; Rintoul, M.; Hackebeil, G.; Ntaimo, L.

    2016-09-01

    Advances in space technologies continue to reduce the cost of placing satellites in orbit. With more entities operating space vehicles, the number of orbiting vehicles and debris has reached unprecedented levels and the number continues to grow. Sensor operators responsible for maintaining the space catalog and providing space situational awareness face an increasingly complex and demanding scheduling requirements. Despite these trends, a lack of advanced tools continues to prevent sensor planners and operators from fully utilizing space surveillance resources. One key challenge involves optimally selecting sensors from a network of varying capabilities for missions with differing requirements. Another open challenge, the primary focus of our work, is building robust schedules that effectively plan for uncertainties associated with weather, ad hoc collections, and other target uncertainties. Existing tools and techniques are not amenable to rigorous analysis of schedule optimality and do not adequately address the presented challenges. Building on prior research, we have developed stochastic mixed-integer linear optimization models to address uncertainty due to weather's effect on collection quality. By making use of the open source Pyomo optimization modeling software, we have posed and solved sensor network scheduling models addressing both forms of uncertainty. We present herein models that allow for concurrent scheduling of collections with the same sensor configuration and for proactively scheduling against uncertain ad hoc collections. The suitability of stochastic mixed-integer linear optimization for building sensor network schedules under different run-time constraints will be discussed.

  2. GEIS Surveillance Network Program

    Science.gov (United States)

    2013-10-01

    resistance surveillance, diarrhea etiology and antimicrobial resistance surveillance, sexually transmitted illness surveillance, and capacity building...vomiting, diarrhea , joint pains, general malaise. Of the samples analyzed, only 53.7% had an associated etiology: Malaria (47.0%, EBV (39.7...immunity to the disease. Many adults in those situations are reservoirs, facilitating continuing disease transmission to those without immunity. In Kenya

  3. Inferring epidemic network topology from surveillance data.

    Science.gov (United States)

    Wan, Xiang; Liu, Jiming; Cheung, William K; Tong, Tiejun

    2014-01-01

    The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases.

  4. Public Health Disease Surveillance Networks.

    Science.gov (United States)

    Morse, Stephen S

    2014-02-01

    Zoonotic infections are important sources of human disease; most known emerging infections are zoonotic (e.g., HIV, Ebola virus, severe acute respiratory syndrome, Nipah virus, and enteropathogenic Escherichia coli) and originated as natural infections of other species that acquired opportunities to come in contact with humans. There are also serious infectious diseases classically considered zoonotic, such as influenza, rabies, bubonic plague, brucellosis, and leptospirosis. More recently, it has been recognized that wildlife constitutes a particularly important source of novel zoonoses. With all this microbial movement, surveillance is considered the first line of public health defense. The zoonotic origin of many human and livestock infections argues strongly for the synergistic value of a One Health approach, which provides the capability to identify pathogens crossing into new species and could provide earlier warning of potential epidemics. This article discusses public health surveillance and major recent surveillance initiatives and reviews progress toward implementing a One Health surveillance framework. Networks discussed include global intergovernmental organizations and recent combined efforts of these organizations; Web-based nongovernmental systems (e.g., ProMED, the Program for Monitoring Emerging Diseases); and networks of bilateral or multilateral government programs (e.g., the CDC's Global Disease Detection [GDD] platform; the U.S. Department of Defense's Global Emerging Infections Surveillance and Response System [GEIS]; regional and subregional networks; and the U.S. Agency for International Development's Emerging Pandemic Threats [EPT] program and its surveillance component, PREDICT). Syndromic surveillance also has potential to complement existing systems. New technologies are enabling revolutionary capabilities for global surveillance, but in addition to serious technical needs, both sustainability and data-sharing mechanisms remain

  5. European surveillance network for influenza in pigs

    NARCIS (Netherlands)

    Simon, Gaëlle; Larsen, Lars E.; Dürrwald, Ralf; Foni, Emanuela; Harder, Timm; Reeth, Van Kristien; Markowska-Daniel, Iwona; Reid, Scott M.; Dan, Adam; Maldonado, Jaime; Huovilainen, Anita; Billinis, Charalambos; Davidson, Irit; Agüero, Montserrat; Vila, Thaïs; Hervé, Séverine; Breum, Solvej Østergaard; Chiapponi, Chiara; Urbaniak, Kinga; Kyriakis, Constantinos S.; Brown, Ian H.; Loeffen, Willie; Meulen, Van der Karen; Schlegel, Michael; Bublot, Michel; Kellam, Paul; Watson, Simon; Lewis, Nicola S.; Pybus, Oliver G.; Webby, Richard; Chen, Hualan; Vincent, Amy L.

    2014-01-01

    Swine influenza causes concern for global veterinary and public health officials. In continuing two previous networks that initiated the surveillance of swine influenza viruses (SIVs) circulating in European pigs between 2001 and 2008, a third European Surveillance Network for Influenza in Pigs (

  6. Disease Surveillance on Complex Social Networks.

    Directory of Open Access Journals (Sweden)

    Jose L Herrera

    2016-07-01

    Full Text Available As infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine whether and when individuals become infected, and thus who might serve as early and accurate surveillance sensors. Here, we evaluate three strategies for selecting sensors-sampling the most connected, random, and friends of random individuals-in three complex social networks-a simple scale-free network, an empirical Venezuelan college student network, and an empirical Montreal wireless hotspot usage network. Across five different surveillance goals-early and accurate detection of epidemic emergence and peak, and general situational awareness-we find that the optimal choice of sensors depends on the public health goal, the underlying network and the reproduction number of the disease (R0. For diseases with a low R0, the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak. However, identifying network hubs is often impractical, and they can be misleading if monitored for general situational awareness, if the underlying network has significant community structure, or if R0 is high or unknown. Taking a theoretical approach, we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible. By contrast, the friends-of-random strategy offers a more practical and robust alternative. It can be readily implemented without prior knowledge of the network, and by identifying sensors with higher than average, but not the highest, epidemiological risk, it provides reasonably early and accurate information.

  7. Airport Surveillance Radar : Model 8 -

    Data.gov (United States)

    Department of Transportation — The Airport Surveillance Radar Model 8 (ASR-8) is a short-range (60 nautical mile (nmi)), analog radar system used to detect and report the presence and location of...

  8. Airport Surveillance Radar : Model 7 -

    Data.gov (United States)

    Department of Transportation — The Airport Surveillance Radar Model 7 (ASR-7) is a short-range (60 nautical miles (nmi)) analog radar system used to detect and report the presence and location of...

  9. Epidemiological modelling for the assessment of bovine tuberculosis surveillance in the dairy farm network in Emilia-Romagna (Italy

    Directory of Open Access Journals (Sweden)

    Gianluigi Rossi

    2015-06-01

    Our analysis showed that slaughterhouse inspection is the most effective surveillance component in reducing the time for disease detection, while routine surveillance in reducing the number of multi-farms epidemics. On the other hand, testing exchanged cattle improved the performance of the surveillance system only marginally.

  10. Google AdWords as a Network of Grey Surveillance

    OpenAIRE

    Roberts, Harold M

    2010-01-01

    Google's AdWords processes information about what sorts of content users are browsing for about a quarter of all web site visits. The significance of AdWords' use of this vast amount of personal data lies not in its use for such obviously authoritarian purposes but instead as a network of grey surveillance with Google acting as the hub and the various publishers, advertisers, and users watching (and controlling) each other in distinct ways. Google's model of collective intelligence in its s...

  11. European surveillance network for influenza in pigs 3 (ESNIP 3)

    DEFF Research Database (Denmark)

    Reid, Scott M.; Simon, Gaëlle; Larsen, Lars Erik

    Objectives: The “European surveillance network for influenza in pigs (ESNIP) 3” continues a surveillance network previously established during concerted actions ESNIP 1 and ESNIP 2. Running from 2010-2013, ESNIP 3 represents the only organised surveillance network for influenza in pigs in Europe...... influenza virus (SIV) experimentally and in the field. Three work packages aim to increase knowledge of the epidemiology and evolution of SIV in European pigs to inform changes in disease trends and variation in contemporary viruses through organised field surveillance programmes. Results: An inventory...... will improve SI diagnosis by updating reagents employed in the recommended techniques to define minimum datasets for standardised epidemiological analyses. These approaches will aid pandemic preparedness and planning for human influenza whilst providing an evidence base for decisions relating to veterinary...

  12. Modeling the dynamics of backyard chicken flows in traditional trade networks in Thailand: implications for surveillance and control of avian influenza.

    Science.gov (United States)

    Wiratsudakul, Anuwat; Paul, Mathilde Cécile; Bicout, Dominique Joseph; Tiensin, Thanawat; Triampo, Wannapong; Chalvet-Monfray, Karine

    2014-06-01

    In Southeast Asia, traditional poultry marketing chains have been threatened by epidemics caused by the highly pathogenic avian influenza H5N1 (HPAI H5N1) virus. In Thailand, the trade of live backyard chickens is based on the activities of traders buying chickens from villages and supplying urban markets with chicken meat. This study aims to quantify the flows of chickens traded during a 1-year period in a province of Thailand. A compartmental stochastic dynamic model was constructed to illustrate trade flows of live chickens from villages to slaughterhouses. Live poultry movements present important temporal variations with increased activities during the 15 days preceding the Chinese New Year and, to a lesser extent, other festivals (Qingming Festival, Thai New Year, Hungry Ghost Festival, and International New Year). The average distance of poultry movements ranges from 4 to 25 km, defining a spatial scale for the risk of avian influenza that spread through traditional poultry marketing chains. Some characteristics of traditional poultry networks in Thailand, such as overlapping chicken supply zones, may facilitate disease diffusion over longer distances through combined expansion and relocation processes. This information may be of use in tailoring avian influenza and other emerging infectious poultry disease surveillance and control programs provided that the cost-effectiveness of such scenarios is also evaluated in further studies.

  13. Intelligent network video understanding modern video surveillance systems

    CERN Document Server

    Nilsson, Fredrik

    2008-01-01

    Offering ready access to the security industry's cutting-edge digital future, Intelligent Network Video provides the first complete reference for all those involved with developing, implementing, and maintaining the latest surveillance systems. Pioneering expert Fredrik Nilsson explains how IP-based video surveillance systems provide better image quality, and a more scalable and flexible system at lower cost. A complete and practical reference for all those in the field, this volume:Describes all components relevant to modern IP video surveillance systemsProvides in-depth information about ima

  14. Antimicrobial resistance surveillance in the AFHSC-GEIS network.

    Science.gov (United States)

    Meyer, William G; Pavlin, Julie A; Hospenthal, Duane; Murray, Clinton K; Jerke, Kurt; Hawksworth, Anthony; Metzgar, David; Myers, Todd; Walsh, Douglas; Wu, Max; Ergas, Rosa; Chukwuma, Uzo; Tobias, Steven; Klena, John; Nakhla, Isabelle; Talaat, Maha; Maves, Ryan; Ellis, Michael; Wortmann, Glenn; Blazes, David L; Lindler, Luther

    2011-03-04

    International infectious disease surveillance has been conducted by the United States (U.S.) Department of Defense (DoD) for many years and has been consolidated within the Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System (AFHSC-GEIS) since 1998. This includes activities that monitor the presence of antimicrobial resistance among pathogens. AFHSC-GEIS partners work within DoD military treatment facilities and collaborate with host-nation civilian and military clinics, hospitals and university systems. The goals of these activities are to foster military force health protection and medical diplomacy. Surveillance activities include both community-acquired and health care-associated infections and have promoted the development of surveillance networks, centers of excellence and referral laboratories. Information technology applications have been utilized increasingly to aid in DoD-wide global surveillance for diseases significant to force health protection and global public health. This section documents the accomplishments and activities of the network through AFHSC-GEIS partners in 2009.

  15. Interval algebra: an effective means of scheduling surveillance radar networks

    CSIR Research Space (South Africa)

    Focke, RW

    2015-05-01

    Full Text Available Interval Algebra provides an effective means to schedule surveillance radar networks, as it is a temporal ordering constraint language. Thus it provides a solution to a part of resource management, which is included in the revised Data Fusion...

  16. Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network.

    Science.gov (United States)

    Budiharto, Widodo

    2015-01-01

    For specific purpose, vision-based surveillance robot that can be run autonomously and able to acquire images from its dynamic environment is very important, for example, in rescuing disaster victims in Indonesia. In this paper, we propose architecture for intelligent surveillance robot that is able to avoid obstacles using 3 ultrasonic distance sensors based on backpropagation neural network and a camera for face recognition. 2.4 GHz transmitter for transmitting video is used by the operator/user to direct the robot to the desired area. Results show the effectiveness of our method and we evaluate the performance of the system.

  17. Automated Negotiation for Resource Assignment in Wireless Surveillance Sensor Networks

    Directory of Open Access Journals (Sweden)

    Enrique de la Hoz

    2015-11-01

    Full Text Available Due to the low cost of CMOS IP-based cameras, wireless surveillance sensor networks have emerged as a new application of sensor networks able to monitor public or private areas or even country borders. Since these networks are bandwidth intensive and the radioelectric spectrum is limited, especially in unlicensed bands, it is mandatory to assign frequency channels in a smart manner. In this work, we propose the application of automated negotiation techniques for frequency assignment. Results show that these techniques are very suitable for the problem, being able to obtain the best solutions among the techniques with which we have compared them.

  18. Automated Negotiation for Resource Assignment in Wireless Surveillance Sensor Networks.

    Science.gov (United States)

    de la Hoz, Enrique; Gimenez-Guzman, Jose Manuel; Marsa-Maestre, Ivan; Orden, David

    2015-11-24

    Due to the low cost of CMOS IP-based cameras, wireless surveillance sensor networks have emerged as a new application of sensor networks able to monitor public or private areas or even country borders. Since these networks are bandwidth intensive and the radioelectric spectrum is limited, especially in unlicensed bands, it is mandatory to assign frequency channels in a smart manner. In this work, we propose the application of automated negotiation techniques for frequency assignment. Results show that these techniques are very suitable for the problem, being able to obtain the best solutions among the techniques with which we have compared them.

  19. Epidemiological models to support animal disease surveillance activities

    DEFF Research Database (Denmark)

    Willeberg, Preben; Paisley, Larry; Lind, Peter

    2011-01-01

    Epidemiological models have been used extensively as a tool in improving animal disease surveillance activities. A review of published papers identified three main groups of model applications: models for planning surveillance, models for evaluating the performance of surveillance systems...... and models for interpreting surveillance data as part of ongoing control or eradication programmes. Two Danish examples are outlined. The first illustrates how models were used in documenting country freedom from disease (trichinellosis) and the second demonstrates how models were of assistance in predicting...

  20. Camera location optimisation for traffic surveillance in urban road networks with multiple user classes

    Science.gov (United States)

    Lu, Xiao-Shan; Huang, Hai-Jun; Long, Jiancheng

    2013-12-01

    New sensor technologies (e.g. surveillance cameras, loop detectors) enable the synthesis of disaggregated vehicle information from multiple locations. This article studies the camera location problem for traffic surveillance in urban road networks with multiple user classes. All users are differentiated by their own acceptance degree of camera monitoring and make their route choices in a logit-based stochastic user equilibrium manner. A bi-level programming model is proposed to formulate the problem and solved by the sensitivity analysis based branch and bound method. Numerical examples are presented to illustrate the model application and show the effectiveness of the solution method.

  1. Disease surveillance using a hidden Markov model

    Directory of Open Access Journals (Sweden)

    Wright Graeme

    2009-08-01

    Full Text Available Abstract Background Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data. Methods A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS algorithms and a negative binomial cusum. Results Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms. Conclusion Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.

  2. Epidemiological models to support animal disease surveillance activities

    DEFF Research Database (Denmark)

    Willeberg, Preben; Paisley, Larry; Lind, Peter

    2011-01-01

    Epidemiological models have been used extensively as a tool in improving animal disease surveillance activities. A review of published papers identified three main groups of model applications: models for planning surveillance, models for evaluating the performance of surveillance systems and mod...

  3. Energy-aware scheduling of surveillance in wireless multimedia sensor networks.

    Science.gov (United States)

    Wang, Xue; Wang, Sheng; Ma, Junjie; Sun, Xinyao

    2010-01-01

    Wireless sensor networks involve a large number of sensor nodes with limited energy supply, which impacts the behavior of their application. In wireless multimedia sensor networks, sensor nodes are equipped with audio and visual information collection modules. Multimedia contents are ubiquitously retrieved in surveillance applications. To solve the energy problems during target surveillance with wireless multimedia sensor networks, an energy-aware sensor scheduling method is proposed in this paper. Sensor nodes which acquire acoustic signals are deployed randomly in the sensing fields. Target localization is based on the signal energy feature provided by multiple sensor nodes, employing particle swarm optimization (PSO). During the target surveillance procedure, sensor nodes are adaptively grouped in a totally distributed manner. Specially, the target motion information is extracted by a forecasting algorithm, which is based on the hidden Markov model (HMM). The forecasting results are utilized to awaken sensor node in the vicinity of future target position. According to the two properties, signal energy feature and residual energy, the sensor nodes decide whether to participate in target detection separately with a fuzzy control approach. Meanwhile, the local routing scheme of data transmission towards the observer is discussed. Experimental results demonstrate the efficiency of energy-aware scheduling of surveillance in wireless multimedia sensor network, where significant energy saving is achieved by the sensor awakening approach and data transmission paths are calculated with low computational complexity.

  4. Sharing, caring, and surveilling: an actor-partner interdependence model examination of Facebook relational maintenance strategies.

    Science.gov (United States)

    McEwan, Bree

    2013-12-01

    Abstract Relational maintenance is connected to high quality friendships. Friendship maintenance behaviors may occur online via social networking sites. This study utilized an Actor-Partner Interdependence Model to examine how Facebook maintenance and surveillance affect friendship quality. Bryant and Marmo's (2012) Facebook maintenance scale was evaluated, revealing two factors: sharing and caring. Facebook surveillance was also measured. For friendship satisfaction and liking, significant positive actor and partner effects emerged for caring; significant negative actor, partner, and interaction effects emerged for sharing; and significant positive actor effects emerged for surveillance. For friendship closeness, significant positive actor effects emerged for caring and surveillance.

  5. Coastal Surveillance Baseline Model Development

    Science.gov (United States)

    2015-02-27

    that were considered too numerous for inclusion in this report. Therefore, the Access was reconfigured to cover only the first two weeks of...radar (SAR). One can also define jammers and detailed antenna models, as well as a variety of effects associated with specific types of atmospheric...line loss and temperature, antenna noise) to help simulate real-world RF situations more accurately if necessary. C.1.2 Radar Cross-Section Property

  6. Particle swarm optimization based space debris surveillance network scheduling

    Science.gov (United States)

    Jiang, Hai; Liu, Jing; Cheng, Hao-Wen; Zhang, Yao

    2017-02-01

    The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.

  7. Critical Infrastructure Surveillance Using SecureWireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Michael Niedermeier

    2015-11-01

    Full Text Available In this work, a secure wireless sensor network (WSN for the surveillance, monitoring and protection of critical infrastructures was developed. To guarantee the security of the system, the main focus was the implementation of a unique security concept, which includes both security on the communication level, as well as mechanisms that ensure the functional safety during its operation. While there are many theoretical approaches in various subdomains of WSNs—like network structures, communication protocols and security concepts—the construction, implementation and real-life application of these devices is still rare. This work deals with these aforementioned aspects, including all phases from concept-generation to operation of a secure wireless sensor network. While the key focus of this paper lies on the security and safety features of the WSN, the detection, localization and classification capabilities resulting from the interaction of the nodes’ different sensor types are also described.

  8. [Influenza sentinel surveillance network improvement in Senegal and results].

    Science.gov (United States)

    Thiam, D; Niang, M; Dia, N; Sarr, F D; Goudiab, D; Senghor, M-L; Kiori, D; Faye, T; Espié, E; Ba, I O; Richard, V

    2015-02-01

    Influenza surveillance in Senegal was initially restricted to the identification of circulating strains. The network has recently been enhanced (i) to include epidemiological data from Dakar and other regions and (ii) to extend virological surveillance to other respiratory viruses. Epidemiological data from the sentinel sites is transmitted daily by mobile phone. The data include those for other febrile syndromes similar to influenza-like illnesses (ILI), corresponding to integrated approach. Also, clinical samples are randomly selected and analyzed for influenza and other respiratory viruses. There were 180,192 declared visits to the 11 sentinel sites between week 11-2012 and week 52-2013; 24% of the visits were for fever syndromes and 25% of the cases of fever syndrome were ILI. Rhinoviruses were the most frequent cause of ILI (19%), before adenoviruses (18%), enteroviruses (18%) and influenza A viruses (13%). Co-circulation and co-infection were frequent and were responsible for ILI peaks. In conclusion, it is clear that the greatest advantage of this system is the ease with which it can be implemented, thanks to the availability of mobile phones and mobile phone networks. We recommend this solution for other African countries, because it performs very well and provides rapid benefits in terms of public health decision-making.

  9. Time series modeling for syndromic surveillance

    Directory of Open Access Journals (Sweden)

    Mandl Kenneth D

    2003-01-01

    Full Text Available Abstract Background Emergency department (ED based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. Methods Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. Results Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. Conclusions Time series methods applied to historical ED utilization data are an important tool

  10. Surveillance

    DEFF Research Database (Denmark)

    Albrechtslund, Anders; Coeckelbergh, Mark; Matzner, Tobias;

    Studying surveillance involves raising questions about the very nature of concepts such as information, technology, identity, space and power. Besides the maybe all too obvious ethical issues often discussed with regard to surveillance, there are several other angles and approaches that we should...... like to encourage. Therefore, our panel will focus on the philosophical, yet non-ethical issues of surveillance in order to stimulate an intense debate with the audience on the ethical implications of our enquiries. We also hope to provide a broader and deeper understanding of surveillance....

  11. Random effect modelling of patient-related risk factors in orthopaedic procedures: results from the Dutch nosocomial infection surveillance network 'PREZIES'.

    NARCIS (Netherlands)

    Muilwijk, J.; Walenkamp, G.H.; Voss, A.; Wille, J.C.; Hof, S. van den

    2006-01-01

    In the Dutch surveillance for surgical site infections (SSIs), data from 70277 orthopaedic procedures with 1895 SSIs were collected between 1996 and 2003. The aims of this study were: (1) to analyse the trends in SSIs associated with Gram-positive and Gram-negative bacteria; (2) to estimate patient-

  12. A Fast Method for Embattling Optimization of Ground-Based Radar Surveillance Network

    Science.gov (United States)

    Jiang, H.; Cheng, H.; Zhang, Y.; Liu, J.

    A growing number of space activities have created an orbital debris environment that poses increasing impact risks to existing space systems and human space flight. For the safety of in-orbit spacecraft, a lot of observation facilities are needed to catalog space objects, especially in low earth orbit. Surveillance of Low earth orbit objects are mainly rely on ground-based radar, due to the ability limitation of exist radar facilities, a large number of ground-based radar need to build in the next few years in order to meet the current space surveillance demands. How to optimize the embattling of ground-based radar surveillance network is a problem to need to be solved. The traditional method for embattling optimization of ground-based radar surveillance network is mainly through to the detection simulation of all possible stations with cataloged data, and makes a comprehensive comparative analysis of various simulation results with the combinational method, and then selects an optimal result as station layout scheme. This method is time consuming for single simulation and high computational complexity for the combinational analysis, when the number of stations increases, the complexity of optimization problem will be increased exponentially, and cannot be solved with traditional method. There is no better way to solve this problem till now. In this paper, target detection procedure was simplified. Firstly, the space coverage of ground-based radar was simplified, a space coverage projection model of radar facilities in different orbit altitudes was built; then a simplified objects cross the radar coverage model was established according to the characteristics of space objects orbit motion; after two steps simplification, the computational complexity of the target detection was greatly simplified, and simulation results shown the correctness of the simplified results. In addition, the detection areas of ground-based radar network can be easily computed with the

  13. Regional Initiatives in Support of Surveillance in East Africa: The East Africa Integrated Disease Surveillance Network (EAIDSNet Experience

    Directory of Open Access Journals (Sweden)

    Maurice Ope

    2013-01-01

    Full Text Available The East African Integrated Disease Surveillance Network (EAIDSNet was formed in response to a growing frequency of cross-border malaria outbreaks in the 1990s and a growing recognition that fragmented disease interventions, coupled with weak laboratory capacity, were making it difficult to respond in a timely manner to the outbreaks of malaria and other infectious diseases. The East Africa Community (EAC partner states, with financial support from the Rockefeller Foundation, established EAIDSNet in 2000 to develop and strengthen the communication channels necessary for integrated cross-border disease surveillance and control efforts. The objective of this paper is to review the regional EAIDSNet initiative and highlight achievements and challenges in its implementation. Major accomplishments of EAIDSNet include influencing the establishment of a Department of Health within the EAC Secretariat to support a regional health agenda; successfully completing a regional field simulation exercise in pandemic influenza preparedness; and piloting a web-based portal for linking animal and human health disease surveillance. The strategic direction of EAIDSNet was shaped, in part, by lessons learned following a visit to the more established Mekong Basin Disease Surveillance (MBDS regional network. Looking to the future, EAIDSNet is collaborating with the East, Central and Southern Africa Health Community (ECSA-HC, EAC partner states, and the World Health Organization to implement the World Bank-funded East Africa Public Health Laboratory Networking Project (EAPHLNP. The network has also begun lobbying East African countries for funding to support EAIDSNet activities.

  14. Enhanced Differentiated Surveillance for Static and Random Mobile Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xue-Qin Zhu

    2011-10-01

    Full Text Available Wireless integrated sensor networks, which include collecting, processing data and communication, are used more and more widely for its low cost and convenient deployment. Nowadays the researches of sensor networks are fairly active. The security is one of the key questions in sensor networks. Intrusion detection is a kind of network security technologies used to detect any behavior that will damage or attempt to damage system confidentiality, integrality or availability, and it can provide the reasonable supplement to intrusion prevention mechanism, and construct a second wall of defense for network and system.  This paper mainly focuses on the energy efficient intrusion detection technology. According to the characteristics of sensor network and the specialty of the invasions in sensor network, this paper presents an intrusion detection model based on statistics anomaly in sensor networks. The algorithm establishes models for the normal state of the nodes, and makes decisions through the deviation degree of observed value. The algorithm is fault-tolerant for non-invasion anomaly when the communication between nodes break down or the accident wrongly create anomaly.

  15. Surveillance of avian influenza in the Caribbean through the Caribbean Animal Health Network: surveillance tools and epidemiologic studies.

    Science.gov (United States)

    Lefrançois, T; Hendrikx, P; Ehrhardt, N; Millien, M; Gomez, L; Gouyet, L; Gaidet, N; Gerbier, G; Vachiéry, N; Petitclerc, F; Carasco-Lacombe, C; Pinarello, V; Ahoussou, S; Levesque, A; Gongora, H V; Trotman, M

    2010-03-01

    The Caribbean region is considered to be at risk for avian influenza (AI) due to a large backyard poultry system, an important commercial poultry production system, the presence of migratory birds, and disparities in the surveillance systems. The Caribbean Animal Health Network (CaribVET) has developed tools to implement AI surveillance in the region with the goals to have 1) a regionally harmonized surveillance protocol and specific web pages for AI surveillance on www.caribvet.net, and 2) an active and passive surveillance for AI in domestic and wild birds. A diagnostic network for the Caribbean, including technology transfer and AI virus molecular diagnostic capability in Guadeloupe (real-time reverse transcription-polymerase chain reaction for the AI virus matrix gene), was developed. Between 2006 and 2009, 627 samples from four Caribbean countries were tested for three circumstances: importation purposes, following a clinical suspicion of AI, or through an active survey of wild birds (mainly waders) during the southward and northward migration periods in Guadeloupe. None of the samples tested were positive, suggesting a limited role of these species in the AI virus ecology in the Caribbean. Following low pathogenic H5N2 outbreaks in the Dominican Republic in 2007, a questionnaire was developed to collect data for a risk analysis of AI spread in the region through fighting cocks. The infection pathway of the Martinique commercial poultry sector by AI, through introduction of infected cocks, was designed, and recommendations were provided to the Caribbean Veterinary Services to improve cock movement control and biosecurity measures. The CaribVET and its organization allowed interaction between diagnostic and surveillance tools on the one hand and epidemiologic studies on the other, both of them developed in congruence with regional strategies. Together, these CaribVET activities contribute to strengthening surveillance of avian influenza virus (AIV) in the

  16. Wireless Mesh Networks to Support Video Surveillance: Architecture, Protocol, and Implementation Issues

    Directory of Open Access Journals (Sweden)

    Licandro Francesco

    2007-01-01

    Full Text Available Current video-surveillance systems typically consist of many video sources distributed over a wide area, transmitting live video streams to a central location for processing and monitoring. The target of this paper is to present an experience of implementation of a large-scale video-surveillance system based on a wireless mesh network infrastructure, discussing architecture, protocol, and implementation issues. More specifically, the paper proposes an architecture for a video-surveillance system, and mainly centers its focus on the routing protocol to be used in the wireless mesh network, evaluating its impact on performance at the receiver side. A wireless mesh network was chosen to support a video-surveillance application in order to reduce the overall system costs and increase scalability and performance. The paper analyzes the performance of the network in order to choose design parameters that will achieve the best trade-off between video encoding quality and the network traffic generated.

  17. Wireless Mesh Networks to Support Video Surveillance: Architecture, Protocol, and Implementation Issues

    Directory of Open Access Journals (Sweden)

    Francesco Licandro

    2007-03-01

    Full Text Available Current video-surveillance systems typically consist of many video sources distributed over a wide area, transmitting live video streams to a central location for processing and monitoring. The target of this paper is to present an experience of implementation of a large-scale video-surveillance system based on a wireless mesh network infrastructure, discussing architecture, protocol, and implementation issues. More specifically, the paper proposes an architecture for a video-surveillance system, and mainly centers its focus on the routing protocol to be used in the wireless mesh network, evaluating its impact on performance at the receiver side. A wireless mesh network was chosen to support a video-surveillance application in order to reduce the overall system costs and increase scalability and performance. The paper analyzes the performance of the network in order to choose design parameters that will achieve the best trade-off between video encoding quality and the network traffic generated.

  18. WHO global rotavirus surveillance network: a strategic review of the first 5 years, 2008-2012.

    Science.gov (United States)

    Agócs, Mary M; Serhan, Fatima; Yen, Catherine; Mwenda, Jason M; de Oliveira, Lúcia H; Teleb, Nadia; Wasley, Annemarie; Wijesinghe, Pushpa R; Fox, Kimberley; Tate, Jacqueline E; Gentsch, Jon R; Parashar, Umesh D; Kang, Gagandeep

    2014-07-25

    Since 2008, the World Health Organization (WHO) has coordinated the Global Rotavirus Surveillance Network, a network of sentinel surveillance hospitals and laboratories that report to ministries of health (MoHs) and WHO clinical features and rotavirus testing data for children aged strategic review to assess surveillance network performance, provide recommendations for strengthening the network, and assess the network's utility as a platform for other vaccine-preventable disease surveillance. The strategic review team determined that during 2011 and 2012, a total of 79 sites in 37 countries met reporting and testing inclusion criteria for data analysis. Of the 37 countries with sites meeting inclusion criteria, 13 (35%) had introduced rotavirus vaccine nationwide. All 79 sites included in the analysis were meeting 2008 network objectives of documenting presence of disease and describing disease epidemiology, and all countries were using the rotavirus surveillance data for vaccine introduction decisions, disease burden estimates, and advocacy; countries were in the process of assessing the use of this surveillance platform for other vaccine-preventable diseases. However, the review also indicated that the network would benefit from enhanced management, standardized data formats, linkage of clinical data with laboratory data, and additional resources to support network functions. In November 2013, WHO's Strategic Advisory Group of Experts on Immunization (SAGE) endorsed the findings and recommendations made by the review team and noted potential opportunities for using the network as a platform for other vaccine-preventable disease surveillance. WHO will work to implement the recommendations to improve the network's functions and to provide higher quality surveillance data for use in decisions related to vaccine introduction and vaccination program sustainability.

  19. Power, surveillance and digital network media in organizations

    DEFF Research Database (Denmark)

    Tække, Jesper

    With the focus on organizations, this article describes power in relation to mediated surveillance using Luhmann’s systems theory, poststructuralist theory and theory of media sociography. It aims to sketch out the main issues in contemporary surveillance discourse and illustrate the current situ...... and surveillance do digital media provide and how the realization of this potential feeds back into organizations’ power structures.......With the focus on organizations, this article describes power in relation to mediated surveillance using Luhmann’s systems theory, poststructuralist theory and theory of media sociography. It aims to sketch out the main issues in contemporary surveillance discourse and illustrate the current...... situation, as well as discuss surveillance from the perspective of poststructuralist theory in relation to Luhmann’s concepts of trust, risk and especially power. The underlying media sociographical question is which storing-, retrieving-, localisation- and temporal possibilities for communication...

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

    Science.gov (United States)

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

    2014-04-01

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

  1. Literacies for Surveillance: Social Network Sites and Background Investigations

    Directory of Open Access Journals (Sweden)

    Sarah Jackson Young

    2015-09-01

    Full Text Available In September 2013, civilian contractor Aaron Alexis entered the Washington Navy Yard and murdered twelve people before being fatally shot by police. This incident, together with an incident three months earlier involving Edward Snowden, caused the U.S. government to critically examine their background investigation (BI process; because both Snowden and Alexis had supposedly slipped through the cracks of their investigations, there must be some flaw in the BI procedure. The U.S. Committee on Oversight and Reform concluded that rules forbidding “background checkers from looking at the Internet or social media when performing checks” was one of the main factors contributing to defective BIs (Report, 2014. Since the report’s release, the Director of National Intelligence has been debating and trialing whether information from the Internet should be used to form a data double for BIs (Kopp, 2014; Rockwell, 2014. Using this conversation as a discussion catalyst, I argue that due to the nature of the data double, if the United States were to adopt the use of social networking sites (SNSs for security clearance purposes, neglecting to take into account basic principles of SNSs into the process of BIs may lead to misinformation and unfavorable adjudication. Ultimately, being literate about the social practices involved in SNSs and surveillance would benefit not only investigators, but anyone, including academics, looking at individuals in online spaces.

  2. LWT Based Sensor Node Signal Processing in Vehicle Surveillance Distributed Sensor Network

    Science.gov (United States)

    Cha, Daehyun; Hwang, Chansik

    Previous vehicle surveillance researches on distributed sensor network focused on overcoming power limitation and communication bandwidth constraints in sensor node. In spite of this constraints, vehicle surveillance sensor node must have signal compression, feature extraction, target localization, noise cancellation and collaborative signal processing with low computation and communication energy dissipation. In this paper, we introduce an algorithm for light-weight wireless sensor node signal processing based on lifting scheme wavelet analysis feature extraction in distributed sensor network.

  3. [Cost estimation of an epidemiological surveillance network for animal diseases in Central Africa: a case study of the Chad network].

    Science.gov (United States)

    Ouagal, M; Berkvens, D; Hendrikx, P; Fecher-Bourgeois, F; Saegerman, C

    2012-12-01

    In sub-Saharan Africa, most epidemiological surveillance networks for animal diseases were temporarily funded by foreign aid. It should be possible for national public funds to ensure the sustainability of such decision support tools. Taking the epidemiological surveillance network for animal diseases in Chad (REPIMAT) as an example, this study aims to estimate the network's cost by identifying the various costs and expenditures for each level of intervention. The network cost was estimated on the basis of an analysis of the operational organisation of REPIMAT, additional data collected in surveys and interviews with network field workers and a market price listing for Chad. These costs were then compared with those of other epidemiological surveillance networks in West Africa. The study results indicate that REPIMAT costs account for 3% of the State budget allocated to the Ministry of Livestock. In Chad in general, as in other West African countries, fixed costs outweigh variable costs at every level of intervention. The cost of surveillance principally depends on what is needed for surveillance at the local level (monitoring stations) and at the intermediate level (official livestock sectors and regional livestock delegations) and on the cost of the necessary equipment. In African countries, the cost of surveillance per square kilometre depends on livestock density.

  4. Constitution and monitoring of an epidemiological surveillance network with sentinel general practitioners.

    Science.gov (United States)

    Chauvin, P

    1994-08-01

    The Réseau National Télé-informatique de surveillance et d'information sur les Maladies Transmissibles (RNTMT) (French communicable diseases computerised surveillance network) comprises a network of sentinel general practitioners (SGP). These benevolent volunteers are responsible for the weekly epidemiological surveillance. Since its creation, 1,700 SGPs have participated in the RNTMT, representing a total of more than 120,000 connections to the RNTMT telematic service center. The principal motivation of these benevolent SGPs was to 'actively participate in public health', although only a minority of them (17.6%) had any training in this field. Such a system, based on the benevolent and voluntary activity of SGPs, requires a good understanding of SGPs' attitudes towards epidemiological surveillance in general and the tool used, in order to quantitatively and qualitatively follow their participation and to provide regular and useful feedback to the surveillance actors.

  5. EVALUATION & TRENDS OF SURVEILLANCE SYSTEM NETWORK IN UBIQUITOUS COMPUTING ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    Sunil Kr Singh

    2015-03-01

    Full Text Available With the emergence of ubiquitous computing, whole scenario of computing has been changed. It affected many inter disciplinary fields. This paper visions the impact of ubiquitous computing on video surveillance system. With increase in population and highly specific security areas, intelligent monitoring is the major requirement of modern world .The paper describes the evolution of surveillance system from analog to multi sensor ubiquitous system. It mentions the demand of context based architectures. It draws the benefit of merging of cloud computing to boost the surveillance system and at the same time reducing cost and maintenance. It analyzes some surveillance system architectures which are made for ubiquitous deployment. It provides major challenges and opportunities for the researchers to make surveillance system highly efficient and make them seamlessly embed in our environments.

  6. Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques

    Science.gov (United States)

    Stottler, D.

    There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.

  7. A Surveillance Model for Human Avian Influenza with a Comprehensive Surveillance System for Local-Priority Communicable Diseases in South Sulawesi, Indonesia

    Science.gov (United States)

    Hanafusa, Shigeki; Muhadir, Andi; Santoso, Hari; Tanaka, Kohtaroh; Anwar, Muhammad; Sulistyo, Erwan Tri; Hachiya, Masahiko

    2012-01-01

    The government of Indonesia and the Japan International Cooperation Agency launched a three-year project (2008–2011) to strengthen the surveillance of human avian influenza cases through a comprehensive surveillance system of local-priority communicable diseases in South Sulawesi Province. Based on findings from preliminary and baseline surveys, the project developed a technical protocol for surveillance and response activities in local settings, consistent with national guidelines. District surveillance officers (DSOs) and rapid-response-team members underwent training to improve surveillance and response skills. A network-based early warning and response system for weekly reports and a short message service (SMS) gateway for outbreak reports, both encompassing more than 20 probable outbreak diseases, were introduced to support existing paper-based systems. Two further strategies were implemented to optimize project outputs: a simulation exercise and a DSO-centered model. As a result, the timeliness of weekly reports improved from 33% in 2009 to 82% in 2011. In 2011, 65 outbreaks were reported using the SMS, with 64 subsequent paper-based reports. All suspected human avian influenza outbreaks up to September 2011 were reported in the stipulated format. A crosscutting approach using human avian influenza as the core disease for coordinating surveillance activities improved the overall surveillance system for communicable diseases. PMID:23532690

  8. Enabling analytical and Modeling Tools for Enhanced Disease Surveillance

    Energy Technology Data Exchange (ETDEWEB)

    Dawn K. Manley

    2003-04-01

    Early detection, identification, and warning are essential to minimize casualties from a biological attack. For covert attacks, sick people are likely to provide the first indication of an attack. An enhanced medical surveillance system that synthesizes distributed health indicator information and rapidly analyzes the information can dramatically increase the number of lives saved. Current surveillance methods to detect both biological attacks and natural outbreaks are hindered by factors such as distributed ownership of information, incompatible data storage and analysis programs, and patient privacy concerns. Moreover, because data are not widely shared, few data mining algorithms have been tested on and applied to diverse health indicator data. This project addressed both integration of multiple data sources and development and integration of analytical tools for rapid detection of disease outbreaks. As a first prototype, we developed an application to query and display distributed patient records. This application incorporated need-to-know access control and incorporated data from standard commercial databases. We developed and tested two different algorithms for outbreak recognition. The first is a pattern recognition technique that searches for space-time data clusters that may signal a disease outbreak. The second is a genetic algorithm to design and train neural networks (GANN) that we applied toward disease forecasting. We tested these algorithms against influenza, respiratory illness, and Dengue Fever data. Through this LDRD in combination with other internal funding, we delivered a distributed simulation capability to synthesize disparate information and models for earlier recognition and improved decision-making in the event of a biological attack. The architecture incorporates user feedback and control so that a user's decision inputs can impact the scenario outcome as well as integrated security and role-based access-control for communicating

  9. Predicting costs of alien species surveillance across varying transportation networks

    Science.gov (United States)

    Laura Blackburn; Rebecca Epanchin-Niell; Alexandra Thompson; Andrew Liebhold; Jacqueline Beggs

    2017-01-01

    Efforts to detect and eradicate invading populations before they establish are a critical component of national biosecurity programmes. An essential element for maximizing the efficiency of these efforts is the balancing of expenditures on surveillance (e.g. trapping) versus treatment (e.g. eradication). Identifying the optimal allocation of resources towards...

  10. Activity-Based Scene Decomposition for Topology Inference of Video Surveillance Network

    Directory of Open Access Journals (Sweden)

    Hongguang Zhang

    2014-01-01

    Full Text Available The topology inference is the study of spatial and temporal relationships among cameras within a video surveillance network. We propose a novel approach to understand activities based on the visual coverage of a video surveillance network. In our approach, an optimal camera placement scheme is firstly presented by using a binary integer programming algorithm in order to maximize the surveillance coverage. Then, each camera view is decomposed into regions based on the Histograms of Color Optical Flow (HCOF, according to the spatial-temporal distribution of activity patterns observed in a training set of video sequences. We conduct experiments by using hours of video sequences captured at an office building with seven camera views, all of which are sparse scenes with complex activities. The results of real scene experiment show that the features of histograms of color optic flow offer important contextual information for spatial and temporal topology inference of a camera network.

  11. Collaborative 3D Target Tracking in Distributed Smart Camera Networks for Wide-Area Surveillance

    Directory of Open Access Journals (Sweden)

    Xenofon Koutsoukos

    2013-05-01

    Full Text Available With the evolution and fusion of wireless sensor network and embedded camera technologies, distributed smart camera networks have emerged as a new class of systems for wide-area surveillance applications. Wireless networks, however, introduce a number of constraints to the system that need to be considered, notably the communication bandwidth constraints. Existing approaches for target tracking using a camera network typically utilize target handover mechanisms between cameras, or combine results from 2D trackers in each camera into 3D target estimation. Such approaches suffer from scale selection, target rotation, and occlusion, drawbacks typically associated with 2D tracking. In this paper, we present an approach for tracking multiple targets directly in 3D space using a network of smart cameras. The approach employs multi-view histograms to characterize targets in 3D space using color and texture as the visual features. The visual features from each camera along with the target models are used in a probabilistic tracker to estimate the target state. We introduce four variations of our base tracker that incur different computational and communication costs on each node and result in different tracking accuracy. We demonstrate the effectiveness of our proposed trackers by comparing their performance to a 3D tracker that fuses the results of independent 2D trackers. We also present performance analysis of the base tracker along Quality-of-Service (QoS and Quality-of-Information (QoI metrics, and study QoS vs. QoI trade-offs between the proposed tracker variations. Finally, we demonstrate our tracker in a real-life scenario using a camera network deployed in a building.

  12. HybVOR: A Voronoi-Based 3D GIS Approach for Camera Surveillance Network Placement

    Directory of Open Access Journals (Sweden)

    Reda Yaagoubi

    2015-05-01

    Full Text Available As a consequence of increasing safety concerns, camera surveillance has been widely adopted as a way to monitor public spaces. One of the major challenges of camera surveillance is to design an optimal method for camera network placement in order to ensure the greater possible coverage. In addition, this method must consider the landscape of the monitored environment to take into account the existing objects that may influence the deployment of such a network. In this paper, a new Voronoi-based 3D GIS oriented approach named “HybVOR” is proposed for surveillance camera network placement. The “HybVOR” approach aims to achieve a coverage near 100% through three main phases. First, a Voronoi Diagram from buildings’ footprints is generated and cameras are placed on the Voronoi Edges. Second, the level of coverage is assessed by calculating a viewshed based on a raster Digital Surface Model of the region of interest. Finally, the visibility of the main buildings’ entrances is evaluated based on a 3D vector model that contains these features. The effectiveness of the “HybVOR” approach is demonstrated through a case study that corresponds to an area of interest in Jeddah Seaport in the Kingdom of Saudi Arabia.

  13. [Microbiological Surveillance of Measles and Rubella in Spain. Laboratory Network].

    Science.gov (United States)

    Echevarría, Juan Emilio; Fernández García, Aurora; de Ory, Fernando

    2015-01-01

    The Laboratory is a fundamental component on the surveillance of measles and rubella. Cases need to be properly confirmed to ensure an accurate estimation of the incidence. Strains should be genetically characterized to know the transmission pattern of these viruses and frequently, outbreaks and transmission chains can be totally discriminated only after that. Finally, the susceptibility of the population is estimated on the basis of sero-prevalence surveys. Detection of specific IgM response is the base of the laboratory diagnosis of these diseases. It should be completed with genomic detection by RT-PCR to reach an optimal efficiency, especially when sampling is performed early in the course of the disease. Genotyping is performed by genomic sequencing according to reference protocols of the WHO. Laboratory surveillance of measles and rubella in Spain is organized as a net of regional laboratories with different capabilities. The National Center of Microbiology as National Reference Laboratory (NRL), supports regional laboratories ensuring the availability of all required techniques in the whole country and watching for the quality of the results. The NRL is currently working in the implementation of new molecular techniques based on the analysis of genomic hypervariable regions for the strain characterization at sub-genotypic levels and use them in the surveillance.

  14. Reassembling Surveillance Creep

    DEFF Research Database (Denmark)

    Bøge, Ask Risom; Lauritsen, Peter

    2017-01-01

    We live in societies in which surveillance technologies are constantly introduced, are transformed, and spread to new practices for new purposes. How and why does this happen? In other words, why does surveillance “creep”? This question has received little attention either in theoretical...... development or in empirical analyses. Accordingly, this article contributes to this special issue on the usefulness of Actor-Network Theory (ANT) by suggesting that ANT can advance our understanding of ‘surveillance creep’. Based on ANT’s model of translation and a historical study of the Danish DNA database......, we argue that surveillance creep involves reassembling the relations in surveillance networks between heterogeneous actors such as the watchers, the watched, laws, and technologies. Second, surveillance creeps only when these heterogeneous actors are adequately interested and aligned. However...

  15. Dynamic Network Models

    CERN Document Server

    Armbruster, Benjamin

    2011-01-01

    We analyze random networks that change over time. First we analyze a dynamic Erdos-Renyi model, whose edges change over time. We describe its stationary distribution, its convergence thereto, and the SI contact process on the network, which has relevance for connectivity and the spread of infections. Second, we analyze the effect of node turnover, when nodes enter and leave the network, which has relevance for network models incorporating births, deaths, aging, and other demographic factors.

  16. From planning to practice: building the national network for the surveillance of severe maternal morbidity

    Directory of Open Access Journals (Sweden)

    Bahamondes Maria V

    2011-05-01

    Full Text Available Abstract Background Improving maternal health is one of the Millennium Development Goals for 2015. Recently some progress has been achieved in reducing mortality. On the other hand, in developed regions, maternal death is a relatively rare event compared to the number of cases of morbidity; hence studying maternal morbidity has become more relevant. Electronic surveillance systems may improve research by facilitating complete data reporting and reducing the time required for data collection and analysis. Therefore the purpose of this study was to describe the methods used in elaborating and implementing the National Network for the Surveillance of Severe Maternal Morbidity in Brazil. Methods The project consisted of a multicenter, cross-sectional study for the surveillance of severe maternal morbidity including near-miss, in Brazil. Results Following the development of a conceptual framework, centers were selected for inclusion in the network, consensus meetings were held among the centers, an electronic data collection system was identified, specific software and hardware tools were developed, research material was prepared, and the implementation process was initiated and analyzed. Conclusion The conceptual framework developed for this network was based on the experience acquired in various studies carried out in the area over recent years and encompasses maternal and perinatal health. It is innovative especially in the context of a developing country. The implementation of the project represents the first step towards this planned management. The system online elaborated for this surveillance network may be used in further studies in reproductive and perinatal health.

  17. CLAM - CoLlAborative eMbedded networks for submarine surveillance: An overview

    NARCIS (Netherlands)

    Meratnia, Nirvana; Havinga, Paul J.M.; Casari, Paolo; Petrioli, Chiara; Grythe, Knut; Husoy, Thor; Zorzi, Michele

    2011-01-01

    This paper provides an overview of the CLAM project, which aims at developing a collaborative embedded monitoring and control platform for submarine surveillance by combining cutting edge acoustic vector sensor technology and 1D, 2D, 3D sensor arrays, underwater wireless sensor networks protocol des

  18. Romantic Partner Monitoring After Breakups: Attachment, Dependence, Distress, and Post-Dissolution Online Surveillance via Social Networking Sites.

    Science.gov (United States)

    Fox, Jesse; Tokunaga, Robert S

    2015-09-01

    Romantic relationship dissolution can be stressful, and social networking sites make it difficult to separate from a romantic partner online as well as offline. An online survey (N = 431) tested a model synthesizing attachment, investment model variables, and post-dissolution emotional distress as predictors of interpersonal surveillance (i.e., "Facebook stalking") of one's ex-partner on Facebook after a breakup. Results indicated that anxious attachment predicted relational investment but also seeking relationship alternatives; avoidant attachment was negatively related to investment but positively related to seeking alternatives. Investment predicted commitment, whereas seeking alternatives was negatively related to commitment. Commitment predicted emotional distress after the breakup. Distress predicted partner monitoring immediately following the breakup, particularly for those who did not initiate the breakup, as well as current partner monitoring. Given their affordances, social media are discussed as potentially unhealthy enablers for online surveillance after relationship termination.

  19. Modeling worldwide highway networks

    Science.gov (United States)

    Villas Boas, Paulino R.; Rodrigues, Francisco A.; da F. Costa, Luciano

    2009-12-01

    This Letter addresses the problem of modeling the highway systems of different countries by using complex networks formalism. More specifically, we compare two traditional geographical models with a modified geometrical network model where paths, rather than edges, are incorporated at each step between the origin and the destination vertices. Optimal configurations of parameters are obtained for each model and used for the comparison. The highway networks of Australia, Brazil, India, and Romania are considered and shown to be properly modeled by the modified geographical model.

  20. Brain Network Modelling

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther

    Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

  1. Social networking sites in romantic relationships: attachment, uncertainty, and partner surveillance on facebook.

    Science.gov (United States)

    Fox, Jesse; Warber, Katie M

    2014-01-01

    Social networking sites serve as both a source of information and a source of tension between romantic partners. Previous studies have investigated the use of Facebook for monitoring former and current romantic partners, but why certain individuals engage in this behavior has not been fully explained. College students (N=328) participated in an online survey that examined two potential explanatory variables for interpersonal electronic surveillance (IES) of romantic partners: attachment style and relational uncertainty. Attachment style predicted both uncertainty and IES, with preoccupieds and fearfuls reporting the highest levels. Uncertainty did not predict IES, however. Future directions for research on romantic relationships and online surveillance are explored.

  2. Influenza and respiratory disease surveillance: the US military’s global laboratory‐based network

    OpenAIRE

    Jeremy Sueker, J.; Blazes, David L.; Matthew C Johns; Patrick J Blair; Paul A Sjoberg; Tjaden, Jeffrey A.; Montgomery, Joel M.; Pavlin, Julie A; Schnabel, David C; Angelia A Eick; Tobias, Steven; Quintana, Miguel; Vest, Kelly G; Burke, Ronald L.; Lindler, Luther E.

    2010-01-01

    Please cite this paper as: Jeremy Sueker et al. (2010) Influenza and respiratory disease surveillance: the US military’s global laboratory‐based network. Influenza and Other Respiratory Viruses 4(3), 155–161. The US Department of Defense influenza surveillance system now spans nearly 500 sites in 75 countries, including active duty US military and dependent populations as well as host‐country civilian and military personnel. This system represents a major part of the US Government’s contribut...

  3. Modeling Epidemic Network Failures

    DEFF Research Database (Denmark)

    Ruepp, Sarah Renée; Fagertun, Anna Manolova

    2013-01-01

    This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....

  4. Infectious disease surveillance in animal movement networks: An approach based on the friendship paradox.

    Science.gov (United States)

    Amaku, Marcos; Grisi-Filho, José Henrique de Hildebrand; Negreiros, Rísia Lopes; Dias, Ricardo Augusto; Ferreira, Fernando; Ferreira Neto, José Soares; Cipullo, Rafael Ishibashi; Marques, Fernando Silveira; Ossada, Raul

    2015-10-01

    The network of animal movements among livestock premises is an important topological structure for the spread of infectious diseases. The central focus of this study was to analyze strategies for selecting premises based on the friendship paradox ("your friends have more friends than you do") - in which premises that neighbor randomly selected premises are sampled for surveillance or control - to determine whether these strategies are viable alternatives for the surveillance and control of diseases in scenarios with insufficient data on animal movement. To test the effectiveness of these strategies, we performed three sets of simulations. In the first set, we examined the risk of spreading an infectious disease using the cattle movement network of the state of Mato Grosso, Brazil. All tested strategies based on the friendship paradox have comparable performance to the hub control strategy (controlling premises that sold more animals) and superior performance to random sampling in terms of both reducing the risk of purchasing infected animals and the number of premises that need to be controlled. In the second and third sets of simulations, we observed that the friendship paradox strategies were more sensitive than the random sampling strategy to detect cases and disease, respectively. The survey of the entire animal movement network to identify animal premises with a key role in trade is not always possible, either because the data are insufficient or because informal trade is significant. If surveying the network is not possible, all approaches based on knowledge of the network become useless. As an alternative, knowing that there is a hidden movement network that follows rules inherent to all networks, such as the friendship paradox, can be used to our advantage. Strategies based on the friendship paradox do not assume knowledge of the animal movement network and therefore may be viable alternatives for the surveillance or control of infectious diseases in the

  5. Data mining techniques in sensor networks summarization, interpolation and surveillance

    CERN Document Server

    Appice, Annalisa; Fumarola, Fabio; Malerba, Donato

    2013-01-01

    Sensor networks comprise of a number of sensors installed across a spatially distributed network, which gather information and periodically feed a central server with the measured data. The server monitors the data, issues possible alarms and computes fast aggregates. As data analysis requests may concern both present and past data, the server is forced to store the entire stream. But the limited storage capacity of a server may reduce the amount of data stored on the disk. One solution is to compute summaries of the data as it arrives, and to use these summaries to interpolate the real data.

  6. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  7. Using visual analytics model for pattern matching in surveillance data

    Science.gov (United States)

    Habibi, Mohammad S.

    2013-03-01

    In a persistent surveillance system huge amount of data is collected continuously and significant details are labeled for future references. In this paper a method to summarize video data as a result of identifying events based on these tagged information is explained, leading to concise description of behavior within a section of extended recordings. An efficient retrieval of various events thus becomes the foundation for determining a pattern in surveillance system observations, both in its extended and fragmented versions. The patterns consisting of spatiotemporal semantic contents are extracted and classified by application of video data mining on generated ontology, and can be matched based on analysts interest and rules set forth for decision making. The proposed extraction and classification method used in this paper uses query by example for retrieving similar events containing relevant features, and is carried out by data aggregation. Since structured data forms majority of surveillance information this Visual Analytics model employs KD-Tree approach to group patterns in variant space and time, thus making it convenient to identify and match any abnormal burst of pattern detected in a surveillance video. Several experimental video were presented to viewers to analyze independently and were compared with the results obtained in this paper to demonstrate the efficiency and effectiveness of the proposed technique.

  8. Modeling Network Interdiction Tasks

    Science.gov (United States)

    2015-09-17

    minimize the operating costs for manufacturing 50 the item. This simple example illustrates the hierarchical structure that can be modeled using...fixed. The resulting model is linearized and the product of the dual variable and the (1−γij) term replaced with βij. This allows certain...the standard network interdiction model based on its tight linear programming relaxation. 2.3.3 Network Disruption. In practice, whenever an object is

  9. Wisconsin’s Environmental Public Health Tracking Network: Information Systems Design for Childhood Cancer Surveillance

    Science.gov (United States)

    Hanrahan, Lawrence P.; Anderson, Henry A.; Busby, Brian; Bekkedal, Marni; Sieger, Thomas; Stephenson, Laura; Knobeloch, Lynda; Werner, Mark; Imm, Pamela; Olson, Joseph

    2004-01-01

    In this article we describe the development of an information system for environmental childhood cancer surveillance. The Wisconsin Cancer Registry annually receives more than 25,000 incident case reports. Approximately 269 cases per year involve children. Over time, there has been considerable community interest in understanding the role the environment plays as a cause of these cancer cases. Wisconsin’s Public Health Information Network (WI-PHIN) is a robust web portal integrating both Health Alert Network and National Electronic Disease Surveillance System components. WI-PHIN is the information technology platform for all public health surveillance programs. Functions include the secure, automated exchange of cancer case data between public health–based and hospital-based cancer registrars; web-based supplemental data entry for environmental exposure confirmation and hypothesis testing; automated data analysis, visualization, and exposure–outcome record linkage; directories of public health and clinical personnel for role-based access control of sensitive surveillance information; public health information dissemination and alerting; and information technology security and critical infrastructure protection. For hypothesis generation, cancer case data are sent electronically to WI-PHIN and populate the integrated data repository. Environmental data are linked and the exposure–disease relationships are explored using statistical tools for ecologic exposure risk assessment. For hypothesis testing, case–control interviews collect exposure histories, including parental employment and residential histories. This information technology approach can thus serve as the basis for building a comprehensive system to assess environmental cancer etiology. PMID:15471739

  10. Design and Implementation of Integrated Surveillance and Modeling Systems for Climate-Sensitive Diseases

    Science.gov (United States)

    Wimberly, M. C.; Merkord, C. L.; Davis, J. K.; Liu, Y.; Henebry, G. M.; Hildreth, M. B.

    2016-12-01

    Climatic variations have a multitude of effects on human health, ranging from the direct impacts of extreme heat events to indirect effects on the vectors and hosts that transmit infectious diseases. Disease surveillance has traditionally focused on monitoring human cases, and in some instances tracking populations sizes and infection rates of arthropod vectors and zoonotic hosts. For climate-sensitive diseases, there is a potential to strengthen surveillance and obtain early indicators of future outbreaks by monitoring environmental risk factors using broad-scale sensor networks that include earth-observing satellites as well as ground stations. We highlight the opportunities and challenges of this integration by presenting modeling results and discussing lessons learned from two projects focused on surveillance and forecasting of mosquito-borne diseases. The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessement (EPIDEMIA) project integrates malaria case surveillance with remotely-sensed environmental data for early detection of malaria epidemics in the Amhara region of Ethiopia and has been producing weekly forecast reports since 2015. The South Dakota Mosquito Information System (SDMIS) project similarly combines entomological surveillance with environmental monitoring to generate weekly maps for West Nile virus (WNV) in the north-central United States. We are currently implementing a new disease forecasting and risk reporting framework for the state of South Dakota during the 2016 WNV transmission season. Despite important differences in disease ecology and geographic setting, our experiences with these projects highlight several important lessons learned that can inform future efforts at disease early warning based on climatic predictors. These include the need to engage end users in system design from the outset, the critical role of automated workflows to facilitate the timely integration of multiple data streams

  11. Big Data for Infectious Disease Surveillance and Modeling

    DEFF Research Database (Denmark)

    Bansal, Shweta; Chowell, Gerardo; Simonsen, Lone

    2016-01-01

    for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special......We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data...... issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity...

  12. European Surveillance Network for Influenza in Pigs: Surveillance Programs, Diagnostic Tools and Swine Influenza Virus Subtypes Identified in 14 European Countries from 2010 to 2013

    DEFF Research Database (Denmark)

    Simon, Gaelle; Larsen, Lars Erik; Duerrwald, Ralf

    2014-01-01

    Swine influenza causes concern for global veterinary and public health officials. In continuing two previous networks that initiated the surveillance of swine influenza viruses (SIVs) circulating in European pigs between 2001 and 2008, a third European Surveillance Network for Influenza in Pigs......, mainly conducted through passive surveillance programs, resulted in the examination of more than 9 000 herds in 17 countries. Influenza A viruses were detected in 31% of herds examined from which 1887 viruses were preliminary characterized. The dominating subtypes were the three European enzootic SIVs......: avian-like swine H1N1 (53.6%), human-like reassortant swine H1N2 (13%) and human-like reassortant swine H3N2 (9.1%), as well as pandemic A/H1N1 2009 (H1N1pdm) virus (10.3%). Viruses from these four lineages co-circulated in several countries but with very different relative levels of incidence...

  13. A WiMAX Networked UAV Telemetry System for Net-Centric Remote Sensing and Range Surveillance Project

    Data.gov (United States)

    National Aeronautics and Space Administration — A WiMAX networked UAV Telemetry System (WNUTS) is designed for net-centric remote sensing and launch range surveillance applications. WNUTS integrates a MIMO powered...

  14. Modeling Epidemic Network Failures

    DEFF Research Database (Denmark)

    Ruepp, Sarah Renée; Fagertun, Anna Manolova

    2013-01-01

    the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used......This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...

  15. The Caribbean animal health network: new tools for harmonization and reinforcement of animal disease surveillance.

    Science.gov (United States)

    Gongora, Victor; Trotman, Mark; Thomas, Reginald; Max, Millien; Zamora, Pastor Alfonso; Lepoureau, Maria Teresa Frias; Phanord, Siméon; Quirico, Jocelyn; Douglas, Kirk; Pegram, Rupert; Martinez, Dominique; Petitclerc, Martial; Chouin, Emilie; Marchal, Céline; Chavernac, David; Doyen, David; Vachiéry, Nathalie; Molia, Sophie; Hendrikx, Pascal; Lefrançois, Thierry

    2008-12-01

    The Caribbean Animal Health Network (CaribVET) is a collaboration of veterinary services, diagnostic laboratories, research institutes, universities, and regional/international organizations to improve animal health in the Caribbean. New tools were used by the network to develop regional animal health activities: (1) A steering committee, a coordination unit, and working groups on specific diseases or activities were established. The working group on avian influenza used a collaborative Web site to develop a regionally harmonized avian influenza surveillance protocol and performance indicators. (2) A specific network was implemented on West Nile virus (WNV) to describe the WNV status of the Caribbean countries, to perform a technology transfer of WNV diagnostics, and to establish a surveillance system. (3) The CaribVET Web site (http://www.caribvet.net) encompasses information on surveillance systems, diagnostic laboratories, conferences, bibliography, and diseases of major concern in the region. It is a participatory Web site allowing registered users to add or edit information, pages, or data. An online notification system of sanitary information was set up for Guadeloupe to improve knowledge on animal diseases and facilitate early alert.

  16. Modeling Evolving Innovation Networks

    OpenAIRE

    Koenig, Michael D.; Battiston, Stefano; Schweitzer, Frank

    2007-01-01

    We develop a new framework for modeling innovation networks which evolve over time. The nodes in the network represent firms, whereas the directed links represent unilateral interactions between the firms. Both nodes and links evolve according to their own dynamics and on different time scales. The model assumes that firms produce knowledge based on the knowledge exchange with other firms, which involves both costs and benefits for the participating firms. In order to increase their knowledge...

  17. Strengthening systems for communicable disease surveillance: creating a laboratory network in Rwanda

    Directory of Open Access Journals (Sweden)

    Ndihokubwayo Jean B

    2011-06-01

    Full Text Available Abstract Background The recent emergence of a novel strain of influenza virus with pandemic potential underscores the need for quality surveillance and laboratory services to contribute to the timely detection and confirmation of public health threats. To provide a framework for strengthening disease surveillance and response capacities in African countries, the World Health Organization Regional Headquarters for Africa (AFRO developed Integrated Disease Surveillance and Response (IDSR aimed at improving national surveillance and laboratory systems. IDSR emphasizes the linkage of information provided by public health laboratories to the selection of relevant, appropriate and effective public health responses to disease outbreaks. Methods We reviewed the development of Rwanda's National Reference Laboratory (NRL to understand essential structures involved in creating a national public health laboratory network. We reviewed documents describing the NRL's organization and record of test results, conducted site visits, and interviewed health staff in the Ministry of Health and in partner agencies. Findings were developed by organizing thematic categories and grouping examples within them. We purposefully sought to identify success factors as well as challenges inherent in developing a national public health laboratory system. Results Among the identified success factors were: a structured governing framework for public health surveillance; political commitment to promote leadership for stronger laboratory capacities in Rwanda; defined roles and responsibilities for each level; coordinated approaches between technical and funding partners; collaboration with external laboratories; and use of performance results in advocacy with national stakeholders. Major challenges involved general infrastructure, human resources, and budgetary constraints. Conclusions Rwanda's experience with collaborative partnerships contributed to creation of a functional

  18. HybVOR: A Voronoi-Based 3D GIS Approach for Camera Surveillance Network Placement

    OpenAIRE

    Reda Yaagoubi; Mabrouk El Yarmani; Abdullah Kamel; Walid Khemiri

    2015-01-01

    As a consequence of increasing safety concerns, camera surveillance has been widely adopted as a way to monitor public spaces. One of the major challenges of camera surveillance is to design an optimal method for camera network placement in order to ensure the greater possible coverage. In addition, this method must consider the landscape of the monitored environment to take into account the existing objects that may influence the deployment of such a network. In this paper, a new Voronoi-bas...

  19. Using networks to combine "big data" and traditional surveillance to improve influenza predictions.

    Science.gov (United States)

    Davidson, Michael W; Haim, Dotan A; Radin, Jennifer M

    2015-01-29

    Seasonal influenza infects approximately 5-20% of the U.S. population every year, resulting in over 200,000 hospitalizations. The ability to more accurately assess infection levels and predict which regions have higher infection risk in future time periods can instruct targeted prevention and treatment efforts, especially during epidemics. Google Flu Trends (GFT) has generated significant hope that "big data" can be an effective tool for estimating disease burden and spread. The estimates generated by GFT come in real-time--two weeks earlier than traditional surveillance data collected by the U.S. Centers for Disease Control and Prevention (CDC). However, GFT had some infamous errors and is significantly less accurate at tracking laboratory-confirmed cases than syndromic influenza-like illness (ILI) cases. We construct an empirical network using CDC data and combine this with GFT to substantially improve its performance. This improved model predicts infections one week into the future as well as GFT predicts the present and does particularly well in regions that are most likely to facilitate influenza spread and during epidemics.

  20. Foodborne Diseases Active Surveillance Network-2 Decades of Achievements, 1996-2015.

    Science.gov (United States)

    Henao, Olga L; Jones, Timothy F; Vugia, Duc J; Griffin, Patricia M

    2015-09-01

    The Foodborne Diseases Active Surveillance Network (FoodNet) provides a foundation for food safety policy and illness prevention in the United States. FoodNet conducts active, population-based surveillance at 10 US sites for laboratory-confirmed infections of 9 bacterial and parasitic pathogens transmitted commonly through food and for hemolytic uremic syndrome. Through FoodNet, state and federal scientists collaborate to monitor trends in enteric illnesses, identify their sources, and implement special studies. FoodNet's major contributions include establishment of reliable, active population-based surveillance of enteric diseases; development and implementation of epidemiologic studies to determine risk and protective factors for sporadic enteric infections; population and laboratory surveys that describe the features of gastrointestinal illnesses, medical care-seeking behavior, frequency of eating various foods, and laboratory practices; and development of a surveillance and research platform that can be adapted to address emerging issues. The importance of FoodNet's ongoing contributions probably will grow as clinical, laboratory, and informatics technologies continue changing rapidly.

  1. The Italian National Seismic Network and the earthquake and tsunami monitoring and surveillance systems

    Science.gov (United States)

    Michelini, Alberto; Margheriti, Lucia; Cattaneo, Marco; Cecere, Gianpaolo; D'Anna, Giuseppe; Delladio, Alberto; Moretti, Milena; Pintore, Stefano; Amato, Alessandro; Basili, Alberto; Bono, Andrea; Casale, Paolo; Danecek, Peter; Demartin, Martina; Faenza, Licia; Lauciani, Valentino; Mandiello, Alfonso Giovanni; Marchetti, Alessandro; Marcocci, Carlo; Mazza, Salvatore; Mariano Mele, Francesco; Nardi, Anna; Nostro, Concetta; Pignone, Maurizio; Quintiliani, Matteo; Rao, Sandro; Scognamiglio, Laura; Selvaggi, Giulio

    2016-11-01

    The Istituto Nazionale di Geofisica e Vulcanologia (INGV) is an Italian research institution, with focus on Earth Sciences. INGV runs the Italian National Seismic Network (Rete Sismica Nazionale, RSN) and other networks at national scale for monitoring earthquakes and tsunami as a part of the National Civil Protection System coordinated by the Italian Department of Civil Protection (Dipartimento di Protezione Civile, DPC). RSN is composed of about 400 stations, mainly broadband, installed in the Country and in the surrounding regions; about 110 stations feature also co-located strong motion instruments, and about 180 have GPS receivers and belong to the National GPS network (Rete Integrata Nazionale GPS, RING). The data acquisition system was designed to accomplish, in near-real-time, automatic earthquake detection, hypocenter and magnitude determination, moment tensors, shake maps and other products of interest for DPC. Database archiving of all parametric results are closely linked to the existing procedures of the INGV seismic monitoring environment and surveillance procedures. INGV is one of the primary nodes of ORFEUS (Observatories & Research Facilities for European Seismology) EIDA (European Integrated Data Archive) for the archiving and distribution of continuous, quality checked seismic data. The strong motion network data are archived and distributed both in EIDA and in event based archives; GPS data, from the RING network are also archived, analyzed and distributed at INGV. Overall, the Italian earthquake surveillance service provides, in quasi real-time, hypocenter parameters to the DPC. These are then revised routinely by the analysts of the Italian Seismic Bulletin (Bollettino Sismico Italiano, BSI). The results are published on the web, these are available to both the scientific community and the general public. The INGV surveillance includes a pre-operational tsunami alert service since INGV is one of the Tsunami Service providers of the North

  2. Simulation of a World-Wide Seismic Surveillance Network

    Science.gov (United States)

    1974-12-31

    APPROACHES DESIGN PARAMETERS BY FACILITY AND ELEMENT SUMMARY OF ANALYSIS ELEMENTS DETECTION TRUTH TABLE TIMING ERROR DIAGNOSTIC SUMMARY OF THE RSE ...storage element ( RSE ), and the data collection processor (DCP). The simulator functions as follows; • Earth model imputs are converted to...detection bulletins by the SDP • Detection bulletins from the SDP are written on the RSE and stored for pickup by the communications processor • Incoming

  3. A test of syndromic surveillance using a severe acute respiratory syndrome model.

    Science.gov (United States)

    Wallace, David J; Arquilla, Bonnie; Heffernan, Richard; Kramer, Martin; Anderson, Todd; Bernstein, David; Augenbraun, Michael

    2009-05-01

    We describe a field simulation that was conducted using volunteers to assess the ability of 3 hospitals in a network to manage a large influx of patients with a potentially communicable disease. This drill provided the opportunity to evaluate the ability of the New York City Department of Health and Mental Hygiene's (NYC-DOHMH) emergency department chief complaint syndromic surveillance system to detect a cluster of patients with febrile respiratory illness. The evaluation was a prospective simulation. The clinical picture was modeled on severe acute respiratory syndrome symptoms. Forty-four volunteers participated in the drill as mock patients. Records from 42 patients (95%) were successfully transmitted to the NYC-DOHMH. The electronic chief complaint for 24 (57%) of these patients indicated febrile or respiratory illness. The drill did not generate a statistical signal in the NYC-DOHMH SaTScan analysis. The 42 drill patients were classified in 8 hierarchical categories based on chief complaints: sepsis (2), cold (3), diarrhea (2), respiratory (20), fever/flu (4), vomit (3), and other (8). The number of respiratory visits, while elevated on the day of the drill, did not appear particularly unusual when compared with the 14-day baseline period used for spatial analyses. This drill with a cluster of patients with febrile respiratory illness failed to trigger a signal from the NYC-DOHMH emergency department chief complaint syndromic surveillance system. This highlighted several limitations and challenges to syndromic surveillance monitoring.

  4. Models of educational institutions' networking

    OpenAIRE

    Shilova Olga Nikolaevna

    2015-01-01

    The importance of educational institutions' networking in modern sociocultural conditions and a definition of networking in education are presented in the article. The results of research levels, methods and models of educational institutions' networking are presented and substantially disclosed.

  5. Models of educational institutions' networking

    OpenAIRE

    Shilova Olga Nikolaevna

    2015-01-01

    The importance of educational institutions' networking in modern sociocultural conditions and a definition of networking in education are presented in the article. The results of research levels, methods and models of educational institutions' networking are presented and substantially disclosed.

  6. New Method for Real Time Influenza Surveillance in Primary Care: A Wisconsin Research and Education Network (WREN) Supported Study.

    Science.gov (United States)

    Temte, Jonathan L; Barlow, Shari; Schemmel, Amber; Temte, Emily; Hahn, David L; Reisdorf, Erik; Shult, Peter; Tamerius, John

    2017-01-01

    The goal of public health infectious disease surveillance systems is to provide accurate laboratory results in near-real time. When it comes to influenza surveillance, most current systems are encumbered with inherent delays encountered in the real-life chaos of medical practice. To combat this, we implemented and tested near-real-time surveillance using a rapid influenza detection test (RIDT) coupled with immediate, wireless transmission of results to public health entities. A network of 19 primary care clinics across Wisconsin were recruited, including 4 sites already involved in ongoing influenza surveillance and 15 sites that were new to surveillance activities. Each site was provided with a Quidel Sofia Influenza A+B RIDT analyzer attached to a wireless router. Influenza test results, along with patient age, were transmitted immediately to a cloud-based server, automatically compiled, and forwarded to the surveillance team daily. Weekly counts of positive influenza A and B cases were compared with positive polymerase chain reaction (PCR) detections from an independent surveillance system within the state. Following Institutional Review Board (IRB) and institutional approvals, we recruited 19 surveillance sites, installed equipment, and trained staff within 4 months. Of the 1119 cases tested between September 15, 2013 and June 28, 2014, 316 were positive for influenza. The system provided early detection of the influenza outbreak in Wisconsin. The influenza peak between January 12 and 25, 2014, as well as the epidemic curve, closely matched that derived from the established PCR laboratory network (r = 0.927; P surveillance. Results from the initial year strongly support this approach to highly accurate and timely influenza surveillance. © Copyright 2017 by the American Board of Family Medicine.

  7. Spatio-temporal Background Models for Outdoor Surveillance

    Directory of Open Access Journals (Sweden)

    Pless Robert

    2005-01-01

    Full Text Available Video surveillance in outdoor areas is hampered by consistent background motion which defeats systems that use motion to identify intruders. While algorithms exist for masking out regions with motion, a better approach is to develop a statistical model of the typical dynamic video appearance. This allows the detection of potential intruders even in front of trees and grass waving in the wind, waves across a lake, or cars moving past. In this paper we present a general framework for the identification of anomalies in video, and a comparison of statistical models that characterize the local video dynamics at each pixel neighborhood. A real-time implementation of these algorithms runs on an 800 MHz laptop, and we present qualitative results in many application domains.

  8. Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts

    Directory of Open Access Journals (Sweden)

    Abusalah Ahmad

    2009-04-01

    Full Text Available Abstract Background Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods. Methods Our new methods for modeling daily chief complaint counts are based on a seasonal-trend decomposition procedure based on loess (STL and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, we develop a new synoptic-scale (days to weeks outbreak detection method and carry out a simulation study to compare detection performance to four well-known methods for nine outbreak scenarios. Result The components of the STL decomposition reveal insights into the variability of the Indiana ED data. Day-of-the-week components tend to peak Sunday or Monday, fall steadily to a minimum Thursday or Friday, and then rise to the peak. Yearly-seasonal components show seasonal influenza, some with bimodal peaks. Some inter-annual components increase slightly due to increasing patient populations. A new outbreak detection method based on the decomposition modeling performs well with 90 days or more of data. Control limits were set empirically so that all methods had a specificity of 97%. STL had the largest sensitivity in all nine outbreak scenarios. The STL method also exhibited a well-behaved false positive rate when run on the data with no outbreaks injected. Conclusion The STL decomposition method for chief complaint counts leads to a rapid and accurate detection method for disease outbreaks, and requires only 90 days of historical data to be put into

  9. Advancing environmental health surveillance in the US through a national human biomonitoring network.

    Science.gov (United States)

    Latshaw, Megan Weil; Degeberg, Ruhiyyih; Patel, Surili Sutaria; Rhodes, Blaine; King, Ewa; Chaudhuri, Sanwat; Nassif, Julianne

    2016-09-17

    The United States lacks a comprehensive, nationally-coordinated, state-based environmental health surveillance system. This lack of infrastructure leads to: • varying levels of understanding of chemical exposures at the state & local levels • often inefficient public health responses to chemical exposure emergencies (such as those that occurred in the Flint drinking water crisis, the Gold King mine spill, the Elk river spill and the Gulf Coast oil spill) • reduced ability to measure the impact of public health interventions or environmental policies • less efficient use of resources for cleaning up environmental contamination Establishing the National Biomonitoring Network serves as a step toward building a national, state-based environmental health surveillance system. The Network builds upon CDC investments in emergency preparedness and environmental public health tracking, which have created advanced chemical analysis and information sharing capabilities in the state public health systems. The short-term goal of the network is to harmonize approaches to human biomonitoring in the US, thus increasing the comparability of human biomonitoring data across states and communities. The long-term goal is to compile baseline data on exposures at the state level, similar to data found in CDC's National Report on Human Exposure to Environmental Chemicals. Barriers to success for this network include: available resources, effective risk communication strategies, data comparability & sharing, and political will. Anticipated benefits include high quality data on which to base public health and environmental decisions, data with which to assess the success of public health interventions, improved risk assessments for chemicals, and new ways to prioritize environmental health research.

  10. Directed network discovery with dynamic network modelling.

    Science.gov (United States)

    Anzellotti, Stefano; Kliemann, Dorit; Jacoby, Nir; Saxe, Rebecca

    2017-05-01

    Cognitive tasks recruit multiple brain regions. Understanding how these regions influence each other (the network structure) is an important step to characterize the neural basis of cognitive processes. Often, limited evidence is available to restrict the range of hypotheses a priori, and techniques that sift efficiently through a large number of possible network structures are needed (network discovery). This article introduces a novel modelling technique for network discovery (Dynamic Network Modelling or DNM) that builds on ideas from Granger Causality and Dynamic Causal Modelling introducing three key changes: (1) efficient network discovery is implemented with statistical tests on the consistency of model parameters across participants, (2) the tests take into account the magnitude and sign of each influence, and (3) variance explained in independent data is used as an absolute (rather than relative) measure of the quality of the network model. In this article, we outline the functioning of DNM, we validate DNM in simulated data for which the ground truth is known, and we report an example of its application to the investigation of influences between regions during emotion recognition, revealing top-down influences from brain regions encoding abstract representations of emotions (medial prefrontal cortex and superior temporal sulcus) onto regions engaged in the perceptual analysis of facial expressions (occipital face area and fusiform face area) when participants are asked to switch between reporting the emotional valence and the age of a face. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. A Simulation Model to Determine Sensitivity and Timeliness of Surveillance Strategies

    DEFF Research Database (Denmark)

    Schulz, Jana; Staubach, C.; Conraths, F. J.

    2016-01-01

    Animal surveillance systems need regular evaluation. We developed an easily applicable simulation model of the German wild boar population to investigate two evaluation attributes: the sensitivity and timeliness (i.e. the ability to detect a disease outbreak rapidly) of a surveillance system...... is therefore vital. Our non-epidemic simulation model is based on real data and evaluates the currently implemented German surveillance system for CSF in wild boar. The results show that active surveillance for CSF fulfils the requirements of detecting an outbreak with 95% confidence within one year after...

  12. A Bayesian hierarchical model for accident and injury surveillance.

    Science.gov (United States)

    MacNab, Ying C

    2003-01-01

    This article presents a recent study which applies Bayesian hierarchical methodology to model and analyse accident and injury surveillance data. A hierarchical Poisson random effects spatio-temporal model is introduced and an analysis of inter-regional variations and regional trends in hospitalisations due to motor vehicle accident injuries to boys aged 0-24 in the province of British Columbia, Canada, is presented. The objective of this article is to illustrate how the modelling technique can be implemented as part of an accident and injury surveillance and prevention system where transportation and/or health authorities may routinely examine accidents, injuries, and hospitalisations to target high-risk regions for prevention programs, to evaluate prevention strategies, and to assist in health planning and resource allocation. The innovation of the methodology is its ability to uncover and highlight important underlying structure of the data. Between 1987 and 1996, British Columbia hospital separation registry registered 10,599 motor vehicle traffic injury related hospitalisations among boys aged 0-24 who resided in British Columbia, of which majority (89%) of the injuries occurred to boys aged 15-24. The injuries were aggregated by three age groups (0-4, 5-14, and 15-24), 20 health regions (based of place-of-residence), and 10 calendar years (1987 to 1996) and the corresponding mid-year population estimates were used as 'at risk' population. An empirical Bayes inference technique using penalised quasi-likelihood estimation was implemented to model both rates and counts, with spline smoothing accommodating non-linear temporal effects. The results show that (a) crude rates and ratios at health region level are unstable, (b) the models with spline smoothing enable us to explore possible shapes of injury trends at both the provincial level and the regional level, and (c) the fitted models provide a wealth of information about the patterns (both over space and time

  13. Coevolutionary modeling in network formation

    KAUST Repository

    Al-Shyoukh, Ibrahim

    2014-12-03

    Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

  14. Community monitoring for youth violence surveillance: testing a prediction model.

    Science.gov (United States)

    Henry, David B; Dymnicki, Allison; Kane, Candice; Quintana, Elena; Cartland, Jenifer; Bromann, Kimberly; Bhatia, Shaun; Wisnieski, Elise

    2014-08-01

    Predictive epidemiology is an embryonic field that involves developing informative signatures for disorder and tracking them using surveillance methods. Through such efforts assistance can be provided to the planning and implementation of preventive interventions. Believing that certain minor crimes indicative of gang activity are informative signatures for the emergence of serious youth violence in communities, in this study we aim to predict outbreaks of violence in neighborhoods from pre-existing levels and changes in reports of minor offenses. We develop a prediction equation that uses publicly available neighborhood-level data on disorderly conduct, vandalism, and weapons violations to predict neighborhoods likely to have increases in serious violent crime. Data for this study were taken from the Chicago Police Department ClearMap reporting system, which provided data on index and non-index crimes for each of the 844 Chicago census tracts. Data were available in three month segments for a single year (fall 2009, winter, spring, and summer 2010). Predicted change in aggravated battery and overall violent crime correlated significantly with actual change. The model was evaluated by comparing alternative models using randomly selected training and test samples, producing favorable results with reference to overfitting, seasonal variation, and spatial autocorrelation. A prediction equation based on winter and spring levels of the predictors had area under the curve ranging from .65 to .71 for aggravated battery, and .58 to .69 for overall violent crime. We discuss future development of such a model and its potential usefulness in violence prevention and community policing.

  15. Model Diagnostics for Bayesian Networks

    Science.gov (United States)

    Sinharay, Sandip

    2006-01-01

    Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…

  16. The KIzSS network, a sentinel surveillance system for infectious diseases in day care centers: study protocol

    Directory of Open Access Journals (Sweden)

    Enserink Remko

    2012-10-01

    Full Text Available Abstract Background Day care-associated infectious diseases are widely recognized as a public health problem but rarely studied. Insights into their dynamics and their association with the day care setting are important for effective decision making in management of infectious disease control. This paper describes the purpose, design and potential of our national multi-center, day care-based sentinel surveillance network for infectious diseases (the KIzSS network. The aim of the KIzSS network is to acquire a long-term insight into the syndromic and microbiological aspects of day care-related infectious diseases and associated disease burden and to model these aspects with day care setting characteristics. Methods/design The KIzSS network applies a prospective cohort design, following day care centers rather than individual children or staff members over time. Data on infectious disease symptoms and related morbidity (children and staff, medical consumption, absenteeism and circulating enteric pathogens (children are collected on a daily, weekly or monthly basis. Every two years, a survey is performed to assess the characteristics of participating day care centers. Discussion The KIzSS network offers a unique potential to study infectious disease dynamics in the day care setting over a sustained period of time. The created (biodatabases will help us to assess day care-related disease burden of infectious diseases among attending children and staff and their relation with the day care setting. This will support the much needed development of evidence-based and pragmatic guidelines for infectious disease control in day care centers.

  17. Towards One Health Knowledge Networks: A Southern African Centre of Infectious Disease Surveillance case study

    Directory of Open Access Journals (Sweden)

    Eric Beda

    2012-06-01

    Full Text Available The dynamic nature of new information and/or knowledge is a big challenge for information systems. Early knowledge management systems focused entirely on technologies for storing, searching and retrieving data; these systems have proved a failure. Juirsica and Mylopoulos1 suggested that in order to build effective technologies for knowledge management, we need to further our understanding of how individuals, groups and organisations use knowledge. As the focus on knowledge management for organisations and consortia alike is moving towards a keen appreciation of how deeply knowledge is embedded in people’s experiences, there is a general realisation that knowledge cannot be stored or captured digitally. This puts more emphasis in creating enabling environments for interactions that stimulate knowledge sharing.Our work aims at developing an un-obtrusive intelligent system that glues together effective contemporary and traditional technologies to aid these interactions and manage the information captured. In addition this system will include tools to aid propagating a repository of scientific information relevant to surveillance of infectious diseases to complement knowledge shared and/or acts as a point of reference.This work is ongoing and based on experiences in developing a knowledge network management system for the Southern African Centre of Infectious Disease Surveillance (SACIDS, A One Health consortium of southern African academic and research institutions involved with infectious diseases of humans and animals in partnership with world-renowned centres of research in industrialised countries.

  18. Malaria Modeling and Surveillance for the Greater Mekong Subregion

    Science.gov (United States)

    Kiang, Richard; Adimi, Farida; Soika, Valerii; Nigro, Joseph

    2005-01-01

    At 4,200 km, the Mekong River is the tenth longest river in the world. It directly and indirectly influences the lives of hundreds of millions of inhabitants in its basin. The riparian countries - Thailand, Myanmar, Cambodia, Laos, Vietnam, and a small part of China - form the Greater Mekong Subregion (GMS). This geographical region has the misfortune of being the world's epicenter of falciparum malaria, which is the most severe form of malaria caused by Plasmodium falciparum. Depending on the country, approximately 50 to 90% of all malaria cases are due to this species. In the Malaria Modeling and Surveillance Project, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) Identifying the potential breeding sites for major vector species; 2) Implementing a malaria transmission model to identify the key factors that sustain or intensify malaria transmission; and 3) Implementing a risk algorithm to predict the occurrence of malaria and its transmission intensity. The potential benefits are: 1) Increased warning time for public health organizations to respond to malaria outbreaks; 2) Optimized utilization of pesticide and chemoprophylaxis; 3) Reduced likelihood of pesticide and drug resistance; and 4) Reduced damage to environment. Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. These parameters are extracted from NASA Earth science data sets. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records.

  19. Malaria Modeling and Surveillance for the Greater Mekong Subregion

    Science.gov (United States)

    Kiang, Richard; Adimi, Farida; Soika, Valerii; Nigro, Joseph

    2005-01-01

    At 4,200 km, the Mekong River is the tenth longest river in the world. It directly and indirectly influences the lives of hundreds of millions of inhabitants in its basin. The riparian countries - Thailand, Myanmar, Cambodia, Laos, Vietnam, and a small part of China - form the Greater Mekong Subregion (GMS). This geographical region has the misfortune of being the world's epicenter of falciparum malaria, which is the most severe form of malaria caused by Plasmodium falciparum. Depending on the country, approximately 50 to 90% of all malaria cases are due to this species. In the Malaria Modeling and Surveillance Project, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) Identifying the potential breeding sites for major vector species; 2) Implementing a malaria transmission model to identify the key factors that sustain or intensify malaria transmission; and 3) Implementing a risk algorithm to predict the occurrence of malaria and its transmission intensity. The potential benefits are: 1) Increased warning time for public health organizations to respond to malaria outbreaks; 2) Optimized utilization of pesticide and chemoprophylaxis; 3) Reduced likelihood of pesticide and drug resistance; and 4) Reduced damage to environment. Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. These parameters are extracted from NASA Earth science data sets. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records.

  20. A semantic autonomous video surveillance system for dense camera networks in Smart Cities.

    Science.gov (United States)

    Calavia, Lorena; Baladrón, Carlos; Aguiar, Javier M; Carro, Belén; Sánchez-Esguevillas, Antonio

    2012-01-01

    This paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network.

  1. A radar-enabled collaborative sensor network integrating COTS technology for surveillance and tracking.

    Science.gov (United States)

    Kozma, Robert; Wang, Lan; Iftekharuddin, Khan; McCracken, Ernest; Khan, Muhammad; Islam, Khandakar; Bhurtel, Sushil R; Demirer, R Murat

    2012-01-01

    The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios.

  2. Intraclass correlation coefficients in the Brazilian network for surveillance of severe maternal morbidity study

    Directory of Open Access Journals (Sweden)

    Haddad Samira M

    2012-09-01

    Full Text Available Abstract Background The purpose of the study was to evaluate intraclass correlation coefficients (ICC of variables concerning personal characteristics, structure, outcome and process in the Brazilian Network for Surveillance of Severe Maternal Morbidity study conducted to identify severe maternal morbidity/near miss cases using the World Health Organization criteria. Method It was a cross-sectional, multicenter study involving 27 hospitals providing care for pregnant women in Brazil. Cluster size and the mean size of the primary sampling unit were described. Estimated prevalence rates, ICC, their respective 95% confidence intervals, the design effect and the mean cluster size were presented for each variable. Results Overall, 9,555 cases of severe maternal morbidity (woman admitted with potentially life-threatening conditions, near miss events or death were included in the study. ICC ranged from Conclusions These results may be used to design new cluster trials in maternal and perinatal health and to help calculate sample sizes.

  3. A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking

    Directory of Open Access Journals (Sweden)

    R. Murat Demirer

    2012-01-01

    Full Text Available The feasibility of using Commercial Off-The-Shelf (COTS sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost ( < $50 US miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios.

  4. Programme of the Community Network of Reference Laboratories for Human Influenza to improve Influenza Surveillance in Europe.

    NARCIS (Netherlands)

    Meijer, Adam; Brown, Caroline; Hungnes, Olav; Schweiger, Brunhilde; Valette, Martine; Werf, Sylvie van der; Zambon, Maria

    2006-01-01

    All laboratories participating in the Community Network of Reference Laboratories for Human Influenza in Europe (CNRL) co-ordinated by the European Influenza Surveillance Scheme (EISS) should be able to perform a range of influenza diagnostics. This includes direct detection, culture, typing, subtyp

  5. Programme of the community network of reference laboratories for human influenza to improve influenza surveillance in Europe.

    NARCIS (Netherlands)

    Meijer, A.; Brown, C.; Hungnes, O.; Schweiger, B.; Valette, M.; Werf, S. van der; Zambon, M.

    2006-01-01

    All laboratories participating in the Community Network of Reference Laboratories for Human Influenza in Europe (CNRL) co-ordinated by the European Influenza Surveillance Scheme (EISS) should be able to perform a range of influenza diagnostics. This includes direct detection, culture, typing, subtyp

  6. The potential of the European network of congenital anomaly registers (EUROCAT) for drug safety surveillance : a descriptive study

    NARCIS (Netherlands)

    Meijer, Willemijn M.; Cornel, Martina C.; Dolk, Helen; de Walle, Hermien E. K.; Armstrong, Nicola C.; de Jong-van den Berg, Lolkje T. W.

    Background European Surveillance of Congenital Anomalies (EUROCAT) is a network of population-based congenital anomaly registries in Europe surveying more than I million births per year, or 25% of the births in the European Union. This paper describes the potential of the EUROCAT collaboration for

  7. Mining and modeling character networks

    CERN Document Server

    Bonato, Anthony; Elenberg, Ethan R; Gleich, David F; Hou, Yangyang

    2016-01-01

    We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure, but may be of relatively small order with a high multiplicity of edges. Building on recent work of beveridge, we consider graph extraction, visualization, and network statistics for three novels: Twilight by Stephanie Meyer, Steven King's The Stand, and J.K. Rowling's Harry Potter and the Goblet of Fire. Coupling with 800 character networks from films found in the http://moviegalaxies.com/ database, we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees (also known as the Chung-Lu model), the configuration model, and the preferential attachment model. Using machine learning techniques based on motif (or small subgraph) counts, we determine that the Chung-Lu model best fits character networks and we ...

  8. Interaction between research and diagnosis and surveillance of avian influenza within the Caribbean animal health network (CaribVET).

    Science.gov (United States)

    Lefrançois, T; Hendrikx, P; Vachiéry, N; Ehrhardt, N; Millien, M; Gomez, L; Gouyet, L; Gerbier, G; Gongora, V; Shaw, J; Trotman, M

    2010-04-01

    The Caribbean region is considered to be at risk for avian influenza (AI) because of predominance of the backyard poultry system, important commercial poultry production, migratory birds and disparities in the surveillance systems. The Caribbean animal health network (CaribVET) has developed tools to implement AI surveillance in the region: (i) a regionally harmonized surveillance protocol, (ii) specific web pages for AI surveillance on http://www.caribvet.net, and (iii) a diagnostic network for the Caribbean including AI virus molecular diagnostic capability in Guadeloupe and technology transfer. Altogether 303 samples from four Caribbean countries were tested between June 2006 and March 2009 by real time PCR either for importation purposes or following clinical suspicion. Following AI H5N2 outbreaks in the Dominican Republic in 2007, a questionnaire was developed to collect data for risk analysis of AI spread in the region through fighting cocks. The infection pathway of Martinique commercial poultry sector by AI through introduction of infected cocks was designed and recommendations were provided to the Caribbean veterinary services to improve fighting cock movement controls and biosecurity measures. Altogether, these CaribVET activities contribute to strengthen surveillance of AI in the Caribbean region and may allow the development of research studies on AI risk analysis.

  9. Description and analysis of the poultry trading network in the Lake Alaotra region, Madagascar: implications for the surveillance and control of Newcastle disease.

    Science.gov (United States)

    Rasamoelina-Andriamanivo, H; Duboz, R; Lancelot, R; Maminiaina, O F; Jourdan, M; Rakotondramaro, T M C; Rakotonjanahary, S N; de Almeida, R Servan; Rakotondravao; Durand, B; Chevalier, V

    2014-07-01

    Madagascar's 36.5-million-head poultry industry holds a foremost place in its economy and the livelihood of its people. Unfortunately, regular Newcastle disease outbreaks associated with high mortality causes high losses for smallholders and threatens their livelihood. Therefore, Madagascar is seeking concrete, achievable and sustainable methods for the surveillance and the control of Newcastle disease. In this paper, we present and analyze the results of a field study conducted in Madagascar between December 2009 and December 2010. The study area was the Lac Alaotra region, a landlocked area in the north-eastern part of the country's center. Poultry trading is suspected of playing a major role in the spread of avian diseases, especially in developing countries characterized by many live-bird markets and middlemen. Therefore, the goals of our study were to: (i) describe and analyze smallholders' poultry trading network in the Lake Alaotra region using social network analysis; (ii) assess the role of the network in the spread of Newcastle disease; and (iii) propose the implementation of a targeted disease surveillance based on the characteristics of the poultry trading network. We focused our field study on the harvesting of two data sets. The first is a complete description of the poultry trading network in the landlocked area of Lac Alaotra, including a description of the poultry movements between groups of villages. The second set of data measures the occurrence of outbreaks in the same area by combining a participatory approach with an event-based surveillance method. These data were used to determine the attributes of the network, and to statistically assess the association between the position of nodes and the occurrence of outbreaks. By using social network analysis techniques combined with a classification method and a logistic model, we finally identified 3 nodes (set of villages), of the 387 in the initial network, to focus on for surveillance and control

  10. Evaluation of a prognostic model for risk of relapse in stage I seminoma surveillance

    DEFF Research Database (Denmark)

    Chung, Peter; Daugaard, Gedske; Tyldesley, Scott

    2015-01-01

    A prognostic model for relapse risk in stage I seminoma managed by surveillance after orchiectomy has been developed but has not been independently validated. Individual data on 685 stage I seminoma surveillance patients managed between 1998 and 2005 at three cancer centers were retrospectively...... useful, highly discriminating prognostic model remains elusive in stage I seminoma surveillance as we were unable to validate the previously developed model. However, primary tumor size retained prognostic importance and a scale of relapse risk based on the unit increment of tumor size was developed...

  11. A micro-Doppler sonar for acoustic surveillance in sensor networks

    Science.gov (United States)

    Zhang, Zhaonian

    Wireless sensor networks have been employed in a wide variety of applications, despite the limited energy and communication resources at each sensor node. Low power custom VLSI chips implementing passive acoustic sensing algorithms have been successfully integrated into an acoustic surveillance unit and demonstrated for detection and location of sound sources. In this dissertation, I explore active and passive acoustic sensing techniques, signal processing and classification algorithms for detection and classification in a multinodal sensor network environment. I will present the design and characterization of a continuous-wave micro-Doppler sonar to image objects with articulated moving components. As an example application for this system, we use it to image gaits of humans and four-legged animals. I will present the micro-Doppler gait signatures of a walking person, a dog and a horse. I will discuss the resolution and range of this micro-Doppler sonar and use experimental results to support the theoretical analyses. In order to reduce the data rate and make the system amenable to wireless sensor networks, I will present a second micro-Doppler sonar that uses bandpass sampling for data acquisition. Speech recognition algorithms are explored for biometric identifications from one's gait, and I will present and compare the classification performance of the two systems. The acoustic micro-Doppler sonar design and biometric identification results are the first in the field as the previous work used either video camera or microwave technology. I will also review bearing estimation algorithms and present results of applying these algorithms for bearing estimation and tracking of moving vehicles. Another major source of the power consumption at each sensor node is the wireless interface. To address the need of low power communications in a wireless sensor network, I will also discuss the design and implementation of ultra wideband transmitters in a three dimensional

  12. Battlefield Acoustic Sensing, Multimodal Sensing, and Networked Sensing for Intelligence, Surveillance, and Reconnaissance (ISR) Applications

    Science.gov (United States)

    2015-09-01

    Intelligence, Surveillance, and Reconnaissance ( ISR ) Applications by Latasha Solomon, Wesley Wang, and Miriam Häge...Surveillance, and Reconnaissance ( ISR ) Applications by Latasha Solomon Sensors and Electron Devices Directorate, ARL Wesley Wang...Intelligence, Surveillance, and Reconnaissance ( ISR ) Applications 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S

  13. Joint space-time geostatistical model for air quality surveillance

    Science.gov (United States)

    Russo, A.; Soares, A.; Pereira, M. J.

    2009-04-01

    Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.

  14. An epidemiological network model for disease outbreak detection.

    Directory of Open Access Journals (Sweden)

    Ben Y Reis

    2007-06-01

    Full Text Available BACKGROUND: Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that improve the overall detection capabilities of these systems can have a broad practical impact. Furthermore, most current generation surveillance systems are vulnerable to dramatic and unpredictable shifts in the health-care data that they monitor. These shifts can occur during major public events, such as the Olympics, as a result of population surges and public closures. Shifts can also occur during epidemics and pandemics as a result of quarantines, the worried-well flooding emergency departments or, conversely, the public staying away from hospitals for fear of nosocomial infection. Most surveillance systems are not robust to such shifts in health-care utilization, either because they do not adjust baselines and alert-thresholds to new utilization levels, or because the utilization shifts themselves may trigger an alarm. As a result, public-health crises and major public events threaten to undermine health-surveillance systems at the very times they are needed most. METHODS AND FINDINGS: To address this challenge, we introduce a class of epidemiological network models that monitor the relationships among different health-care data streams instead of monitoring the data streams themselves. By extracting the extra information present in the relationships between the data streams, these models have the potential to improve the detection capabilities of a system. Furthermore, the models' relational nature has the potential to increase a system's robustness to unpredictable baseline shifts. We implemented these models and evaluated their effectiveness using historical emergency department data from five hospitals in a single metropolitan area, recorded over a period of 4.5 y by the Automated Epidemiological Geotemporal Integrated Surveillance real-time public health-surveillance

  15. European surveillance network for influenza in pigs: surveillance programs, diagnostic tools and Swine influenza virus subtypes identified in 14 European countries from 2010 to 2013.

    Directory of Open Access Journals (Sweden)

    Gaëlle Simon

    Full Text Available Swine influenza causes concern for global veterinary and public health officials. In continuing two previous networks that initiated the surveillance of swine influenza viruses (SIVs circulating in European pigs between 2001 and 2008, a third European Surveillance Network for Influenza in Pigs (ESNIP3, 2010-2013 aimed to expand widely the knowledge of the epidemiology of European SIVs. ESNIP3 stimulated programs of harmonized SIV surveillance in European countries and supported the coordination of appropriate diagnostic tools and subtyping methods. Thus, an extensive virological monitoring, mainly conducted through passive surveillance programs, resulted in the examination of more than 9 000 herds in 17 countries. Influenza A viruses were detected in 31% of herds examined from which 1887 viruses were preliminary characterized. The dominating subtypes were the three European enzootic SIVs: avian-like swine H1N1 (53.6%, human-like reassortant swine H1N2 (13% and human-like reassortant swine H3N2 (9.1%, as well as pandemic A/H1N1 2009 (H1N1pdm virus (10.3%. Viruses from these four lineages co-circulated in several countries but with very different relative levels of incidence. For instance, the H3N2 subtype was not detected at all in some geographic areas whereas it was still prevalent in other parts of Europe. Interestingly, H3N2-free areas were those that exhibited highest frequencies of circulating H1N2 viruses. H1N1pdm viruses were isolated at an increasing incidence in some countries from 2010 to 2013, indicating that this subtype has become established in the European pig population. Finally, 13.9% of the viruses represented reassortants between these four lineages, especially between previous enzootic SIVs and H1N1pdm. These novel viruses were detected at the same time in several countries, with increasing prevalence. Some of them might become established in pig herds, causing implications for zoonotic infections.

  16. European surveillance network for influenza in pigs: surveillance programs, diagnostic tools and Swine influenza virus subtypes identified in 14 European countries from 2010 to 2013.

    Science.gov (United States)

    Simon, Gaëlle; Larsen, Lars E; Dürrwald, Ralf; Foni, Emanuela; Harder, Timm; Van Reeth, Kristien; Markowska-Daniel, Iwona; Reid, Scott M; Dan, Adam; Maldonado, Jaime; Huovilainen, Anita; Billinis, Charalambos; Davidson, Irit; Agüero, Montserrat; Vila, Thaïs; Hervé, Séverine; Breum, Solvej Østergaard; Chiapponi, Chiara; Urbaniak, Kinga; Kyriakis, Constantinos S; Brown, Ian H; Loeffen, Willie

    2014-01-01

    Swine influenza causes concern for global veterinary and public health officials. In continuing two previous networks that initiated the surveillance of swine influenza viruses (SIVs) circulating in European pigs between 2001 and 2008, a third European Surveillance Network for Influenza in Pigs (ESNIP3, 2010-2013) aimed to expand widely the knowledge of the epidemiology of European SIVs. ESNIP3 stimulated programs of harmonized SIV surveillance in European countries and supported the coordination of appropriate diagnostic tools and subtyping methods. Thus, an extensive virological monitoring, mainly conducted through passive surveillance programs, resulted in the examination of more than 9 000 herds in 17 countries. Influenza A viruses were detected in 31% of herds examined from which 1887 viruses were preliminary characterized. The dominating subtypes were the three European enzootic SIVs: avian-like swine H1N1 (53.6%), human-like reassortant swine H1N2 (13%) and human-like reassortant swine H3N2 (9.1%), as well as pandemic A/H1N1 2009 (H1N1pdm) virus (10.3%). Viruses from these four lineages co-circulated in several countries but with very different relative levels of incidence. For instance, the H3N2 subtype was not detected at all in some geographic areas whereas it was still prevalent in other parts of Europe. Interestingly, H3N2-free areas were those that exhibited highest frequencies of circulating H1N2 viruses. H1N1pdm viruses were isolated at an increasing incidence in some countries from 2010 to 2013, indicating that this subtype has become established in the European pig population. Finally, 13.9% of the viruses represented reassortants between these four lineages, especially between previous enzootic SIVs and H1N1pdm. These novel viruses were detected at the same time in several countries, with increasing prevalence. Some of them might become established in pig herds, causing implications for zoonotic infections.

  17. Italian network for obesity and cardiovascular disease surveillance: A pilot project

    Directory of Open Access Journals (Sweden)

    Palmieri Luigi

    2008-09-01

    Full Text Available Abstract Background Also in Mediterranean countries, which are considered a low risk population for cardiovascular disease (CVD, the increase in body mass index (BMI has become a public health priority. To evaluate the feasibility of a CVD and obesity surveillance network, forty General Practitioners (GPs were engaged to perform a screening to assess obesity, cardiovascular risk, lifestyle habits and medication use. Methods A total of 1,046 women and 1,044 men aged 35–74 years were randomly selected from GPs' lists stratifying by age decade and gender. Anthropometric and blood pressure measurements were performed by GPs using standardized methodologies. BMI was computed and categorized in normal weight (BMI 18.5–24.9 kg/m2, overweight (BMI 25.0–29.9 kg/m2 and obese (BMI ≥ 30 kg/m2. Food frequency (per day: fruits and vegetables; per week: meat, cheese, fish, pulses, chocolate, fried food, sweet, wholemeal food, rotisserie food and sugar drink and physical activity (at work and during leisure time were investigated through a questionnaire. CVD risk was assessed using the Italian CUORE Project risk function. Results The percentage of missing values was very low. Prevalence of overweight was 34% in women and 50% in men; prevalence of obesity was 23% in both men and women. Level of physical activity was mostly low or very low. BMI was inversely associated with consumption of pulses, rotisserie food, chocolate, sweets and physical activity during leisure time and directly associated with consumption of meat. Mean value of total cardiovascular risk was 4% in women and 11% in men. One percent of women and 16% of men were at high cardiovascular risk (≥ 20% in 10 years. Normal weight persons were four times more likely to be at low risk than obese persons. Conclusion This study demonstrated the feasibility of a surveillance network of GPs in Italy focusing on obesity and other CVD risk factors. It also provided information on lifestyle habits

  18. Internet Network Resource Information Model

    Institute of Scientific and Technical Information of China (English)

    陈传峰; 李增智; 唐亚哲; 刘康平

    2002-01-01

    The foundation of any network management systens is a database that con-tains information about the network resources relevant to the management tasks. A networkinformation model is an abstraction of network resources, including both managed resources andmanaging resources. In the SNMP-based management framework, management information isdefined almost exclusively from a "device" viewpoint, namely, managing a network is equiva-lent to managing a collection of individual nodes. Aiming at making use of recent advances indistributed computing and in object-oriented analysis and design, the Internet management ar-chitecture can also be based on the Open Distributed Processing Reference Model (RM-ODP).The purpose of this article is to provide an Internet Network Resource Information Model.First, a layered management information architecture will be discussed. Then the Internetnetwork resource information model is presented. The information model is specified usingObject-Z.

  19. Complex Networks in Psychological Models

    Science.gov (United States)

    Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

    We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

  20. Network Simulation Models

    Science.gov (United States)

    2008-12-01

    well, then a Euclidean distance would be appropriate. The quadratic assignment procedure ( QAP ) (Krackhardt, 1987) could be used to compare the...Networks. Journal of Applied Psychology, 71(1): 50-55. Krackhardt, D. (1987). QAP Partialling as a Test of Spuriousness. Social Networks, 9, 171-186

  1. Assortative model for social networks

    Science.gov (United States)

    Catanzaro, Michele; Caldarelli, Guido; Pietronero, Luciano

    2004-09-01

    In this Brief Report we present a version of a network growth model, generalized in order to describe the behavior of social networks. The case of study considered is the preprint archive at cul.arxiv.org. Each node corresponds to a scientist, and a link is present whenever two authors wrote a paper together. This graph is a nice example of degree-assortative network, that is, to say a network where sites with similar degree are connected to each other. The model presented is one of the few able to reproduce such behavior, giving some insight on the microscopic dynamics at the basis of the graph structure.

  2. Developing Personal Network Business Models

    DEFF Research Database (Denmark)

    Saugstrup, Dan; Henten, Anders

    2006-01-01

    on the 'state of the art' in the field of business modeling. Furthermore, the paper suggests three generic business models for PNs: a service oriented model, a self-organized model, and a combination model. Finally, examples of relevant services and applications in relation to three different cases......The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...... are presented and analyzed in light of business modeling of PN....

  3. Network models in anatomical systems.

    Science.gov (United States)

    Esteve-Altava, Borja; Marugán-Lobón, Jesús; Botella, Héctor; Rasskin-Gutman, Diego

    2011-01-01

    Network theory has been extensively used to model the underlying structure of biological processes. From genetics to ecology, network thinking is changing our understanding of complex systems, specifically how their internal structure determines their overall behavior. Concepts such as hubs, scale-free or small-world networks, common in the complexity literature, are now used more and more in sociology, neurosciences, as well as other anthropological fields. Even though the use of network models is nowadays so widely applied, few attempts have been carried out to enrich our understanding in the classical morphological sciences such as in comparative anatomy or physical anthropology. The purpose of this article is to introduce the usage of network tools in morphology; specifically by building anatomical networks, dealing with the most common analyses and problems, and interpreting their outcome.

  4. Growth surveillance in the context of the Primary Public Healthcare Service Network in Brazil: literature review

    Directory of Open Access Journals (Sweden)

    Dixis Figueroa Pedraza

    2016-03-01

    Full Text Available Abstract Objectives: to identify and analyze the scientific literature on child growth monitoring in the context of the primary public healthcare service network in Brazil, focusing on the main problems detected in studies. Methods: the review was based on searches ofSciELO, Lilacs and PubMed databases to identify articles published between 2006 and 2014. The articles were categorized according to the analytical categories of structure (items needed to carry out primary activities or work processes (set of activities and procedures used in the management of resources. Results: of the 16 articles included in this review, only six dealt with structure and, in these, thetraining of professionals and availability of protocols were the most frequently identified problems. Processes, addressed in 15 articles, highlighted the underutilization of Child Health Handbook to record growth measurements and the adoption of guidelines on the basis of notes taken. Conclusions: the difficulties found demonstrate the everyday circumstances of the public health service which have a detrimental effect on growth surveillance.

  5. Telecommunications network modelling, planning and design

    CERN Document Server

    Evans, Sharon

    2003-01-01

    Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.

  6. Campus network security model study

    Science.gov (United States)

    Zhang, Yong-ku; Song, Li-ren

    2011-12-01

    Campus network security is growing importance, Design a very effective defense hacker attacks, viruses, data theft, and internal defense system, is the focus of the study in this paper. This paper compared the firewall; IDS based on the integrated, then design of a campus network security model, and detail the specific implementation principle.

  7. Neural network modeling of emotion

    Science.gov (United States)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  8. Nonequilibrium model on Apollonian networks.

    Science.gov (United States)

    Lima, F W S; Moreira, André A; Araújo, Ascânio D

    2012-11-01

    We investigate the majority-vote model with two states (-1,+1) and a noise parameter q on Apollonian networks. The main result found here is the presence of the phase transition as a function of the noise parameter q. Previous results on the Ising model in Apollonian networks have reported no presence of a phase transition. We also studied the effect of redirecting a fraction p of the links of the network. By means of Monte Carlo simulations, we obtained the exponent ratio γ/ν, β/ν, and 1/ν for several values of rewiring probability p. The critical noise q{c} and U were also calculated. Therefore, the results presented here demonstrate that the majority-vote model belongs to a different universality class than equilibrium Ising model on Apollonian network.

  9. Surveillance system and method having an operating mode partitioned fault classification model

    Science.gov (United States)

    Bickford, Randall L. (Inventor)

    2005-01-01

    A system and method which partitions a parameter estimation model, a fault detection model, and a fault classification model for a process surveillance scheme into two or more coordinated submodels together providing improved diagnostic decision making for at least one determined operating mode of an asset.

  10. A surveillance model for sexually transmitted infections in India

    Directory of Open Access Journals (Sweden)

    Partha Haldar

    2015-01-01

    Full Text Available The strategy for prevention and control of sexually transmitted infections (STIs in India is based on syndromic case management delivered through designated STI/reproductive tract infection (RTI centers (DSRCs situated in medical colleges, district hospitals, and STI-clinics of targeted interventions programs. Laboratory tests for enhanced syndromic management are available at some sites. To ensure country-level planning and effective local implementation of STI services, reliable and consistent epidemiologic information is required on the distribution of STI cases, rate and trends of newly acquired infections, and STI prevalence in specific population groups. The present STI management information system is inadequate to meet these requirements because it is based on syndromic data and limited laboratory investigations on STIs reported passively by DSRCs and laboratories. Geographically representative information on the etiology of STI syndromes and antimicrobial susceptibility of STI pathogens although essential for optimizing available treatment options, is deficient. Surveillance must provide high quality information on: (a prevalence of STIs such as syphilis, trichomoniasis, gonorrhea, and chlamydia among high-risk groups; syphilis in the general population and pregnant antenatal women; (b demographic characteristics such as age, sex, new/recurrent episode, and type of syndromically diagnosed STI cases; (c proportion of acute infections such as urethral discharge (UD in men and nonherpetic genital ulcer disease (GUD in men and women; (d etiology of STI syndromes; and (e gonococcal antimicrobial susceptibility. We describe here a framework for an STI sentinel surveillance system in India, building on the existing STI reporting systems and infrastructure, an overview of the components of the proposed surveillance system, and operational challenges in its implementation.

  11. Reducing surgical site infection incidence through a network: results from the French ISO-RAISIN surveillance system.

    Science.gov (United States)

    Astagneau, P; L'Hériteau, F; Daniel, F; Parneix, P; Venier, A-G; Malavaud, S; Jarno, P; Lejeune, B; Savey, A; Metzger, M-H; Bernet, C; Fabry, J; Rabaud, C; Tronel, H; Thiolet, J-M; Coignard, B

    2009-06-01

    Surgical-site infections (SSIs) are a key target for nosocomial infection control programmes. We evaluated the impact of an eight-year national SSI surveillance system named ISO-RAISIN (infection du site opératoire - Réseau Alerte Investigation Surveillance des Infections). Consecutive patients undergoing surgery were enrolled during a three-month period each year and surveyed for 30 days following surgery. A standardised form was completed for each patient including SSI diagnosis according to standard criteria, and several risk factors such as wound class, American Society of Anesthesiologists (ASA) score, operation duration, elective/emergency surgery, and type of surgery. From 1999 to 2006, 14,845 SSIs were identified in 964,128 patients (overall crude incidence: 1.54%) operated on in 838 participating hospitals. The crude overall SSI incidence decreased from 2.04% to 1.26% (P<0.001; relative reduction: -38%) and the National Nosocomial Infections Surveillance system (NNIS)-0 adjusted SSI incidence from 1.10% to 0.74% (P<0.001; relative reduction: -33%). The most significant SSI incidence reduction was observed for hernia repair and caesarean section, and to a lesser extent, cholecystectomy, hip prosthesis arthroplasty, and mastectomy. Active surveillance striving for a benchmark throughout a network is an effective strategy to reduce SSI incidence.

  12. Algebraic Statistics for Network Models

    Science.gov (United States)

    2014-02-19

    AFRL-OSR-VA-TR-2014-0070 (DARPA) Algebraic Statistics for Network Models SONJA PETROVIC PENNSYLVANIA STATE UNIVERSITY 02/19/2014 Final Report...DARPA GRAPHS Phase I Algebraic Statistics for Network Models FA9550-12-1-0392 Sonja Petrović petrovic@psu.edu1 Department of Statistics Pennsylvania...Department of Statistics, Heinz College , Machine Learning Department, Cylab Carnegie Mellon University 1. Abstract This project focused on the family of

  13. Impact of postdischarge surveillance on surgical site infection rates for several surgical procedures: results from the nosocomial surveillance network in The Netherlands.

    NARCIS (Netherlands)

    Manniën, Judith; Wille, Jan C; Snoeren, Ruud L M M; Hof, Susan van den

    2006-01-01

    OBJECTIVE: To compare the number of surgical site infections (SSIs) registered after hospital discharge with respect to various surgical procedures and to identify the procedures for which postdischarge surveillance (PDS) is most important. DESIGN: Prospective SSI surveillance with voluntary PDS.

  14. Surveillance Angels

    NARCIS (Netherlands)

    Rothkrantz, L.J.M.

    2014-01-01

    The use of sensor networks has been proposed for military surveillance and environmental monitoring applications. Those systems are composed of a heterogeneous set of sensors to observe the environment. In centralised systems the observed data will be conveyed to the control room to process the

  15. Surveillance Angels

    NARCIS (Netherlands)

    Rothkrantz, L.J.M.

    2014-01-01

    The use of sensor networks has been proposed for military surveillance and environmental monitoring applications. Those systems are composed of a heterogeneous set of sensors to observe the environment. In centralised systems the observed data will be conveyed to the control room to process the data

  16. Adaptive Probabilistic Tracking Embedded in Smart Cameras for Distributed Surveillance in a 3D Model

    Directory of Open Access Journals (Sweden)

    Sven Fleck

    2006-12-01

    Full Text Available Tracking applications based on distributed and embedded sensor networks are emerging today, both in the fields of surveillance and industrial vision. Traditional centralized approaches have several drawbacks, due to limited communication bandwidth, computational requirements, and thus limited spatial camera resolution and frame rate. In this article, we present network-enabled smart cameras for probabilistic tracking. They are capable of tracking objects adaptively in real time and offer a very bandwidthconservative approach, as the whole computation is performed embedded in each smart camera and only the tracking results are transmitted, which are on a higher level of abstraction. Based on this, we present a distributed surveillance system. The smart cameras' tracking results are embedded in an integrated 3D environment as live textures and can be viewed from arbitrary perspectives. Also a georeferenced live visualization embedded in Google Earth is presented.

  17. A bootstrap based space-time surveillance model with an application to crime occurrences

    Science.gov (United States)

    Kim, Youngho; O'Kelly, Morton

    2008-06-01

    This study proposes a bootstrap-based space-time surveillance model. Designed to find emerging hotspots in near-real time, the bootstrap based model is characterized by its use of past occurrence information and bootstrap permutations. Many existing space-time surveillance methods, using population at risk data to generate expected values, have resulting hotspots bounded by administrative area units and are of limited use for near-real time applications because of the population data needed. However, this study generates expected values for local hotspots from past occurrences rather than population at risk. Also, bootstrap permutations of previous occurrences are used for significant tests. Consequently, the bootstrap-based model, without the requirement of population at risk data, (1) is free from administrative area restriction, (2) enables more frequent surveillance for continuously updated registry database, and (3) is readily applicable to criminology and epidemiology surveillance. The bootstrap-based model performs better for space-time surveillance than the space-time scan statistic. This is shown by means of simulations and an application to residential crime occurrences in Columbus, OH, year 2000.

  18. Improving the modeling of disease data from the government surveillance system: a case study on malaria in the Brazilian Amazon.

    Directory of Open Access Journals (Sweden)

    Denis Valle

    Full Text Available The study of the effect of large-scale drivers (e.g., climate of human diseases typically relies on aggregate disease data collected by the government surveillance network. The usual approach to analyze these data, however, often ignores a changes in the total number of individuals examined, b the bias towards symptomatic individuals in routine government surveillance, and; c the influence that observations can have on disease dynamics. Here, we highlight the consequences of ignoring the problems listed above and develop a novel modeling framework to circumvent them, which is illustrated using simulations and real malaria data. Our simulations reveal that trends in the number of disease cases do not necessarily imply similar trends in infection prevalence or incidence, due to the strong influence of concurrent changes in sampling effort. We also show that ignoring decreases in the pool of infected individuals due to the treatment of part of these individuals can hamper reliable inference on infection incidence. We propose a model that avoids these problems, being a compromise between phenomenological statistical models and mechanistic disease dynamics models; in particular, a cross-validation exercise reveals that it has better out-of-sample predictive performance than both of these alternative models. Our case study in the Brazilian Amazon reveals that infection prevalence was high in 2004-2008 (prevalence of 4% with 95% CI of 3-5%, with outbreaks (prevalence up to 18% occurring during the dry season of the year. After this period, infection prevalence decreased substantially (0.9% with 95% CI of 0.8-1.1%, which is due to a large reduction in infection incidence (i.e., incidence in 2008-2010 was approximately one fifth of the incidence in 2004-2008.We believe that our approach to modeling government surveillance disease data will be useful to advance current understanding of large-scale drivers of several diseases.

  19. Advances in theoretical models of network science

    Institute of Scientific and Technical Information of China (English)

    FANG Jin-qing; BI Qiao; LI Yong

    2007-01-01

    In this review article, we will summarize the main advances in network science investigated by the CIAE Group of Complex Network in this field. Several theoretical models of network science were proposed and their topological and dynamical properties are reviewed and compared with the other models. Our models mainly include a harmonious unifying hybrid preferential model, a large unifying hybrid network model, a quantum interference network, a hexagonal nanowire network, and a small-world network with the same degree. The models above reveal some new phenomena and findings, which are useful for deeply understanding and investigating complex networks and their applications.

  20. Using spatially explicit surveillance models to provide confidence in the eradication of an invasive ant.

    Science.gov (United States)

    Ward, Darren F; Anderson, Dean P; Barron, Mandy C

    2016-10-10

    Effective detection plays an important role in the surveillance and management of invasive species. Invasive ants are very difficult to eradicate and are prone to imperfect detection because of their small size and cryptic nature. Here we demonstrate the use of spatially explicit surveillance models to estimate the probability that Argentine ants (Linepithema humile) have been eradicated from an offshore island site, given their absence across four surveys and three surveillance methods, conducted since ant control was applied. The probability of eradication increased sharply as each survey was conducted. Using all surveys and surveillance methods combined, the overall median probability of eradication of Argentine ants was 0.96. There was a high level of confidence in this result, with a high Credible Interval Value of 0.87. Our results demonstrate the value of spatially explicit surveillance models for the likelihood of eradication of Argentine ants. We argue that such models are vital to give confidence in eradication programs, especially from highly valued conservation areas such as offshore islands.

  1. Structure learning for Bayesian networks as models of biological networks.

    Science.gov (United States)

    Larjo, Antti; Shmulevich, Ilya; Lähdesmäki, Harri

    2013-01-01

    Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.

  2. Current approaches to gene regulatory network modelling

    Directory of Open Access Journals (Sweden)

    Brazma Alvis

    2007-09-01

    Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

  3. Network model of security system

    Directory of Open Access Journals (Sweden)

    Adamczyk Piotr

    2016-01-01

    Full Text Available The article presents the concept of building a network security model and its application in the process of risk analysis. It indicates the possibility of a new definition of the role of the network models in the safety analysis. Special attention was paid to the development of the use of an algorithm describing the process of identifying the assets, vulnerability and threats in a given context. The aim of the article is to present how this algorithm reduced the complexity of the problem by eliminating from the base model these components that have no links with others component and as a result and it was possible to build a real network model corresponding to reality.

  4. A new transient network model for associative polymer networks

    NARCIS (Netherlands)

    Wientjes, R.H.W.; Jongschaap, R.J.J.; Duits, M.H.G.; Mellema, J.

    1999-01-01

    A new model for the linear viscoelastic behavior of polymer networks is developed. In this model the polymer system is described as a network of spring segments connected via sticky points (as in the Lodge model). [Lodge, A. S., “A network theory of flow birefringence and stress in concentrated poly

  5. State synergies and disease surveillance: creating an electronic health data communication model for cancer reporting and comparative effectiveness research in kentucky.

    Science.gov (United States)

    Reams, Christopher; Powell, Mallory; Edwards, Rob

    2014-01-01

    agencies, and a major research university in leveraging existing networks, infrastructure, and federally awarded funding to implement interoperable health information systems for disease surveillance. Project assessment through quasi-qualitative interviews with key stakeholders facilitated evaluation of attitudes and beliefs for continued use of the cancer surveillance model. In Kentucky, the cancer reporting initiative leveraged and enhanced a solid foundation for statewide collaboration to achieve better health and improved disease surveillance through a learning health system. Leveraging the Meaningful Use (MU) program as an overarching policy and structural driver is imperative. The cancer reporting initiative in Kentucky suggests that future surveillance and reporting initiatives will require locally adaptable solutions and that there is a need for increased technical assistance in rural settings. Kentucky's experience also indicates that stakeholders should be diligent in identifying state-level criteria that align with MU for vetting EHR vendors.

  6. Target-Centric Network Modeling

    DEFF Research Database (Denmark)

    Mitchell, Dr. William L.; Clark, Dr. Robert M.

    In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence repor....... Working through these cases, students will learn to manage and evaluate realistic intelligence accounts.......In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...

  7. Evaluating Social Media Networks in Medicines Safety Surveillance: Two Case Studies

    NARCIS (Netherlands)

    P.M. Coloma (Preciosa); B. Becker (Benedikt); M.C.J.M. Sturkenboom (Miriam); E.M. Van Mulligen (Erik M.); J.A. Kors (Jan)

    2015-01-01

    textabstractIntroduction: There is growing interest in whether social media can capture patient-generated information relevant for medicines safety surveillance that cannot be found in traditional sources. Objective: The aim of this study was to evaluate the potential contribution of mining social m

  8. Evaluation of the surveillance of surgical site infections within the Dutch PREZIES network

    NARCIS (Netherlands)

    Manniën, Judith

    2008-01-01

    Surgical site infections (SSI) are the most-common healthcare-associated infections among surgical patients and have severe adverse consequences. Surveillance is the ongoing systematic collection, analysis, interpretation, and feedback of data, and has been accepted worldwide as a primary step

  9. CNEM: Cluster Based Network Evolution Model

    Directory of Open Access Journals (Sweden)

    Sarwat Nizamani

    2015-01-01

    Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks

  10. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo

    2015-09-15

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

  11. Ising model for distribution networks

    CERN Document Server

    Hooyberghs, H; Giuraniuc, C; Van Schaeybroeck, B; Indekeu, J O

    2012-01-01

    An elementary Ising spin model is proposed for demonstrating cascading failures (break-downs, blackouts, collapses, avalanches, ...) that can occur in realistic networks for distribution and delivery by suppliers to consumers. A ferromagnetic Hamiltonian with quenched random fields results from policies that maximize the gap between demand and delivery. Such policies can arise in a competitive market where firms artificially create new demand, or in a solidary environment where too high a demand cannot reasonably be met. Network failure in the context of a policy of solidarity is possible when an initially active state becomes metastable and decays to a stable inactive state. We explore the characteristics of the demand and delivery, as well as the topological properties, which make the distribution network susceptible of failure. An effective temperature is defined, which governs the strength of the activity fluctuations which can induce a collapse. Numerical results, obtained by Monte Carlo simulations of t...

  12. Modelling the effect of surveillance programmes on spread of bovine herpesvirus 1 between certified cattle herds.

    Science.gov (United States)

    Graat, E A; de Jong, M C; Frankena, K; Franken, P

    2001-04-02

    For the eradication of an infectious agent, like bovine herpesvirus 1 (BHV-1), surveillance and certification can be used to reduce the transmission between herds. The goal of surveillance is that a certified herd that becomes infected is detected timely so that infection of several other certified herds is prevented. What counts is whether the reproduction ratio R, i.e. the average number of certified herds infected by one infected certified herd can be kept below 1. To support policy makers in making decisions about the minimal demands for a surveillance programme in an eradication campaign of BHV-1 in cattle, two mathematical models were investigated. With these models, the basic reproduction ratio between herds was calculated. The surveillance programmes were characterised with sample size, sampling frequency, test sensitivity, herd size, vaccination status, and contacts between herds. When R between herds is below 1, then the surveillance programme is sufficiently good to prevent spread of infection, provided that R is estimated well. In the model based on bulk milk testing sample size was replaced by a threshold at which bulk milk can be found positive. The R between herds was mainly influenced by the vaccination status, sampling frequency, and contacts between herds. Herd size moderately affected the outcome. Test sensitivity and sample size, however, were of minor importance. If herds of 50 cows became free of BHV-1 without vaccination, then spread of infection between herds might be prevented when animals within herds are sampled once a year (milk or blood samples). This frequency needs to be intensified, being twice a year, for larger herds and/or herds with extensive contacts with other herds. When bulk milk is sampled instead, sampling should be done at least every 5 months and more intensively, being each month, with larger herd sizes and more contacts between herds.

  13. Nonparametric Bayesian Modeling of Complex Networks

    DEFF Research Database (Denmark)

    Schmidt, Mikkel Nørgaard; Mørup, Morten

    2013-01-01

    Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...... for complex networks can be derived and point out relevant literature....

  14. Medical Surveillance System & Medical Effect Modeling Thrust Areas

    Science.gov (United States)

    2007-06-01

    Equations ( PFE ) developed for this project model physiological systems in biological organisms as 1D liquid or gas flows. Special attention is given...in the model to capturing 2D viscous effects and branching effects. Multiple PFE representations of physiological systems (e.g. the respiratory and

  15. Building up a collaborative network for the surveillance of HIV genetic diversity in Italy: A pilot study

    Directory of Open Access Journals (Sweden)

    Nunzia Sanarico

    2015-12-01

    Full Text Available INTRODUCTION: Prevalence of infection with HIV-1 non-B subtypes in Italy has been reported to raise, due to increased migration flows and travels. HIV-1 variants show different biological and immunological properties that impact on disease progression rate, response to antiretroviral therapy (ART and sensitivity of diagnostic tests with important implications for public health. Therefore, a constant surveillance of the dynamics of HIV variants in Italy should be a high public health priority. Organization of surveillance studies requires building up a platform constituted of a network of clinical centers, laboratories and institutional agencies, able to properly collect samples for the investigation of HIV subtypes heterogeneity and to provide a database with reliable demographic, clinical, immunological and virological data. AIM: We here report our experience in building up such a platform, co-ordinated by the National AIDS Center of the Istituto Superiore di Sanita, taking advantage of a pilot study aimed at evaluating HIV subtypes diversity in populations of HIV-infected migrant people in Italy. MATERIALS AND METHODS: Four hundred and thirty four HIV-infected migrants were enrolled in 9 Italian clinical centers located throughout the Italian territory. Standard Operating Procedures (SOPs for sample collection were provided by the National AIDS Center to each clinical center. In addition, clinical centers were required to fill up a case report form (crf for each patient, which included demographic, clinical, immunological and virological information. RESULTS: All centers properly collected and stored samples from each enrolled individual. Overall, the required information was correctly provided for more than 90% of the patients. However, some fields of the crf, particularly those including information on the last HIV-negative antibody test and presence of co-infections, were properly filled up in less than 80% of the enrolled migrants. Centers

  16. Mathematical Modelling Plant Signalling Networks

    KAUST Repository

    Muraro, D.

    2013-01-01

    During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.

  17. Analysing trends and forecasting malaria epidemics in Madagascar using a sentinel surveillance network: a web-based application.

    Science.gov (United States)

    Girond, Florian; Randrianasolo, Laurence; Randriamampionona, Lea; Rakotomanana, Fanjasoa; Randrianarivelojosia, Milijaona; Ratsitorahina, Maherisoa; Brou, Télesphore Yao; Herbreteau, Vincent; Mangeas, Morgan; Zigiumugabe, Sixte; Hedje, Judith; Rogier, Christophe; Piola, Patrice

    2017-02-13

    The use of a malaria early warning system (MEWS) to trigger prompt public health interventions is a key step in adding value to the epidemiological data routinely collected by sentinel surveillance systems. This study describes a system using various epidemic thresholds and a forecasting component with the support of new technologies to improve the performance of a sentinel MEWS. Malaria-related data from 21 sentinel sites collected by Short Message Service are automatically analysed to detect malaria trends and malaria outbreak alerts with automated feedback reports. Roll Back Malaria partners can, through a user-friendly web-based tool, visualize potential outbreaks and generate a forecasting model. The system already demonstrated its ability to detect malaria outbreaks in Madagascar in 2014. This approach aims to maximize the usefulness of a sentinel surveillance system to predict and detect epidemics in limited-resource environments.

  18. Random Boolean network models and the yeast transcriptional network

    Science.gov (United States)

    Kauffman, Stuart; Peterson, Carsten; Samuelsson, Björn; Troein, Carl

    2003-12-01

    The recently measured yeast transcriptional network is analyzed in terms of simplified Boolean network models, with the aim of determining feasible rule structures, given the requirement of stable solutions of the generated Boolean networks. We find that for ensembles of generated models, those with canalyzing Boolean rules are remarkably stable, whereas those with random Boolean rules are only marginally stable. Furthermore, substantial parts of the generated networks are frozen, in the sense that they reach the same state regardless of initial state. Thus, our ensemble approach suggests that the yeast network shows highly ordered dynamics.

  19. Bodygraphic Injury Surveillance System

    Science.gov (United States)

    Tsuboi, Toshiki; Kitamura, Koji; Nishida, Yoshihumi; Motomura, Yoichi; Takano, Tachio; Yamanaka, Tatsuhiro; Mizoguchi, Hiroshi

    This paper proposes a new technology,``a bodygraphic injury surveillance system (BISS)'' that not only accumulates accident situation data but also represents injury data based on a human body coordinate system in a standardized and multilayered way. Standardized and multilayered representation of injury enables accumulation, retrieval, sharing, statistical analysis, and modeling causalities of injury across different fields such as medicine, engineering, and industry. To confirm the effectiveness of the developed system, the authors collected 3,685 children's injury data in cooperation with a hospital. As new analyses based on the developed BISS, this paper shows bodygraphically statistical analysis and childhood injury modeling using the developed BISS and Bayesian network technology.

  20. Malaria Modeling and Surveillance in Thailand and Indonesia

    Science.gov (United States)

    Kiang, Richard; Adimi, Farida; Soebiyanto, Radina

    2008-01-01

    This viewgraph presentation reviews the modeling of malaria transmission in Thailand and Indonesia to assist in the understanding and reducing the incidence of the deadly disease. Satellite observations are being integrated into this work, and this is described herein.

  1. Plant Growth Models Using Artificial Neural Networks

    Science.gov (United States)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  2. Generalization performance of regularized neural network models

    DEFF Research Database (Denmark)

    Larsen, Jan; Hansen, Lars Kai

    1994-01-01

    Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...

  3. Scalable Capacity Bounding Models for Wireless Networks

    OpenAIRE

    Du, Jinfeng; Medard, Muriel; Xiao, Ming; Skoglund, Mikael

    2014-01-01

    The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper (resp. lower) bounding models, which can be used to calculate the outer (resp. inner) bounds for the capacity region of the original noisy network. Based on the network equivalence framework, this paper presents scalable upper and lower bounding models for wireless networks with potentially many nodes. A channel decoupling method is proposed...

  4. Brand Marketing Model on Social Networks

    Directory of Open Access Journals (Sweden)

    Jolita Jezukevičiūtė

    2014-04-01

    Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.

  5. Projectbeschrijving Surveillance Ziekenhuisinfecties 1996-1999

    NARCIS (Netherlands)

    Berg JMJ van den; Boer AS de; Mintjes-de Groot AJ; Sprenger MJW; Cucic S; Pelt W van; Centraal Begeleidingsorgaan; CIE

    1996-01-01

    In the Project Surveillance Hospital Acquired Infections a surveillance system in a national network of hospitals is being developed and implemented. In the project surveillance of hospital acquired infections is implemented in components: surveillance of surgical wound infections, surveillance of i

  6. Surveillance of poisoning and drug overdose through hospital discharge coding, poison control center reporting, and the Drug Abuse Warning Network.

    Science.gov (United States)

    Blanc, P D; Jones, M R; Olson, K R

    1993-01-01

    There is no gold standard for determining poisoning incidence. We wished to compare four measures of poisoning incidence: International Classification of Diseases 9th Revision (ICD-9) principal (N-code) and supplemental external cause of injury (E-code) designations, poison control center (PCC) reporting, and detection by the Drug Abuse Warning Network (DAWN). We studied a case series at two urban hospitals. We assigned ICD-9 N-code and E-code classifications, determining whether these matched with medical records. We ascertained PCC and DAWN system reporting. A total of 724 subjects met entry criteria; 533 were studied (74%). We matched poisoning N-codes for 278 patients (52%), E-code by cause in 306 patients (57%), and E-code by intent in 171 patients (32%). A total of 383 patients (72%) received any poisoning N-code or any E-code. We found that PCC and DAWN reporting occurred for 123 of all patients (23%) and 399 of 487 eligible patients (82%), respectively. In multiple logistic regression, factors of age, hospital admission, suicidal intent, principal poisoning or overdose type, and mixed drug overdose were statistically significant predictors of case match or report varying by surveillance measure. Our findings indicate that common surveillance measures of poisoning and drug overdose may systematically undercount morbidity.

  7. Evaluation of a prognostic model for risk of relapse in stage I seminoma surveillance.

    Science.gov (United States)

    Chung, Peter; Daugaard, Gedske; Tyldesley, Scott; Atenafu, Eshetu G; Panzarella, Tony; Kollmannsberger, Christian; Warde, Padraig

    2015-01-01

    A prognostic model for relapse risk in stage I seminoma managed by surveillance after orchiectomy has been developed but has not been independently validated. Individual data on 685 stage I seminoma surveillance patients managed between 1998 and 2005 at three cancer centers were retrospectively analyzed. Variables including age and pathology of the primary tumor: small vessel invasion, tumor size, and invasion of rete testis were analyzed. Specifically median tumor size and rete testis invasion was tested to evaluate the performance of the published model. Median follow-up was 3.85 years (0.1-10.29), 88 patients relapsed and 5-year relapse-free rate was 85%. In univariate analysis, median tumor size (seminoma surveillance as we were unable to validate the previously developed model. However, primary tumor size retained prognostic importance and a scale of relapse risk based on the unit increment of tumor size was developed to help guide patients and clinicians in decision making. © 2014 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

  8. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

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

  9. Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control.

    Science.gov (United States)

    VanderWaal, Kimberly L; Picasso, Catalina; Enns, Eva A; Craft, Meggan E; Alvarez, Julio; Fernandez, Federico; Gil, Andres; Perez, Andres; Wells, Scott

    2016-01-01

    Movement of livestock between premises is one of the foremost factors contributing to the spread of infectious diseases of livestock. In part to address this issue, the origin and destination for all cattle movements in Uruguay are registered by law. This information has great potential to be used in assessing the risk of disease spread in the Uruguayan cattle population. Here, we analyze cattle movements from 2008 to 2013 using network analysis in order to understand the flows of animals in the Uruguayan cattle industry and to identify targets for surveillance and control measures. Cattle movements were represented as seasonal and annual networks in which farms represented nodes and nodes were linked based on the frequency and quantity of cattle moved. At the farm level, the distribution of the number of unique farms each farm is connected to through outgoing and incoming movements, as well as the number of animals moved, was highly right-skewed; the majority of farms had few to no contacts, whereas the 10% most highly connected farms accounted for 72-83% of animals moved annually. This extreme level of heterogeneity in movement patterns indicates that some farms may be disproportionately important for pathogen spread. Different production types exhibited characteristic patterns of farm-level connectivity, with some types, such a dairies, showing consistently higher levels of centrality. In addition, the observed networks were characterized by lower levels of connectivity and higher levels of heterogeneity than random networks of the same size and density, both of which have major implications for disease dynamics and control strategies. This represents the first in-depth analysis of farm-level livestock movements within South America, and highlights the importance of collecting livestock movement data in order to understand the vulnerability of livestock trade networks to invasion by infectious diseases.

  10. Extending Wireless Broadband Network Architectures with Home Gateways, Localization, and Physical Environment Surveillance

    DEFF Research Database (Denmark)

    Jelling Kristoffersen, Kåre; Kjærgaard, Mikkel Baun; Chen, Jianjun;

    2005-01-01

    Adding content is seen as the next important step in the development and deployment of broadband networks. This paper proposes three different ways of adding content to wireless broadband networks. First, the home gateway is presented as the focal point for delivery of digital services to future...

  11. MODELS FOR NETWORK DYNAMICS - A MARKOVIAN FRAMEWORK

    NARCIS (Netherlands)

    LEENDERS, RTAJ

    1995-01-01

    A question not very often addressed in social network analysis relates to network dynamics and focuses on how networks arise and change. It alludes to the idea that ties do not arise or vanish randomly, but (partly) as a consequence of human behavior and preferences. Statistical models for modeling

  12. Modelling delay propagation within an airport network

    NARCIS (Netherlands)

    Pyrgiotis, N.; Malone, K.M.; Odoni, A.

    2013-01-01

    We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and capture

  13. Modelling delay propagation within an airport network

    NARCIS (Netherlands)

    Pyrgiotis, N.; Malone, K.M.; Odoni, A.

    2013-01-01

    We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and

  14. A system dynamics model for communications networks

    Science.gov (United States)

    Awcock, A. J.; King, T. E. G.

    1985-09-01

    An abstract model of a communications network in system dynamics terminology is developed as implementation of this model by a FORTRAN program package developed at RSRE is discussed. The result of this work is a high-level simulation package in which the performance of adaptive routing algorithms and other network controls may be assessed for a network of arbitrary topology.

  15. An optimization model of UAV route planning for road segment surveillance

    Institute of Scientific and Technical Information of China (English)

    刘晓锋; 关志伟; 宋裕庆; 陈大山

    2014-01-01

    Unmanned aerial vehicle (UAV) was introduced to take road segment traffic surveillance. Considering the limited UAV maximum flight distance, UAV route planning problem was studied. First, a multi-objective optimization model of planning UAV route for road segment surveillance was proposed, which aimed to minimize UAV cruise distance and minimize the number of UAVs used. Then, an evolutionary algorithm based on Pareto optimality technique was proposed to solve multi-objective UAV route planning problem. At last, a UAV flight experiment was conducted to test UAV route planning effect, and a case with three scenarios was studied to analyze the impact of different road segment lengths on UAV route planning. The case results show that the optimized cruise distance and the number of UAVs used decrease by an average of 38.43% and 33.33%, respectively. Additionally, shortening or extending the length of road segments has different impacts on UAV route planning.

  16. Network simulation modeling of equine infectious anemia in the non-racehorse population in Japan.

    Science.gov (United States)

    Hayama, Yoko; Kobayashi, Sota; Nishida, Takeshi; Muroga, Norihiko; Tsutsui, Toshiyuki

    2012-01-01

    An equine infectious anemia (EIA) transmission model was developed by constructing a network structure of horse movement patterns in a non-racehorse population. This model was then used to evaluate the effectiveness and efficiency of several EIA surveillance strategies. Because EIA had not been detected in Japan since 1993, it was appropriate to review the current surveillance strategy, which aims to eradicate EIA by intensive testing, and to consider alternative strategies suitable for the current EIA status in Japan. The non-racehorse population was divided into four sectors based on horse usage: the equestrian sector, private owner sector, exhibition sector, and fattening sector. To evaluate the risk of disease spread within and between sectors accompanied by horse movements, a stochastic individual-based network model was developed based on a previous survey of horse movement patterns. Surveillance parameters such as targeting sectors and frequency of testing were added into the model to compare surveillance strategies. The disease spread heterogeneously among sectors. Infection occurred mainly in the equestrian sector; the infection was less disseminated in other sectors. Therefore, we considered that the equestrian sector posed a higher risk of disease dissemination within and between sectors through horse movements. However, surveillance strategies targeting only the equestrian sector were not effective enough for early detection of the disease. Alternatively, targeting horses that moved permanently and those in the private owner sector in addition to the equestrian sector is recommended to achieve effectiveness equivalent to that of the current surveillance. In terms of surveillance efficacy, by increasing the testing interval (once yearly to once every 3 years), this testing scheme could reduce the number of tested horses to 44% of the current surveillance, while maintaining almost equivalent effectiveness. Intensive strategies targeting high

  17. Network models in epidemiology: an overview

    Science.gov (United States)

    Lloyd, Alun L.; Valeika, Steve

    In this chapter we shall discuss the development and use of network models in epidemiology. While network models have long been discussed in the theoretical epidemiology literature, they have recently received a large amount of attention amongst the statistical physics community. This has been fueled by the desire to better understand the structure of social and large-scale technological networks, and the increases in computational power that have made the simulation of reasonably-sized network models a feasible proposition. A main aim of this review is to bridge the epidemiologic and statistical physics approaches to network models for infectious diseases, highlighting the important contributions made by both research communities.

  18. Modeling gene regulatory networks: A network simplification algorithm

    Science.gov (United States)

    Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

    2016-12-01

    Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

  19. Eight challenges for network epidemic models

    Directory of Open Access Journals (Sweden)

    Lorenzo Pellis

    2015-03-01

    Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.

  20. An evolutionary model of social networks

    Science.gov (United States)

    Ludwig, M.; Abell, P.

    2007-07-01

    Social networks in communities, markets, and societies self-organise through the interactions of many individuals. In this paper we use a well-known mechanism of social interactions — the balance of sentiment in triadic relations — to describe the development of social networks. Our model contrasts with many existing network models, in that people not only establish but also break up relations whilst the network evolves. The procedure generates several interesting network features such as a variety of degree distributions and degree correlations. The resulting network converges under certain conditions to a steady critical state where temporal disruptions in triangles follow a power-law distribution.

  1. The model of social crypto-network

    Directory of Open Access Journals (Sweden)

    Марк Миколайович Орел

    2015-06-01

    Full Text Available The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks

  2. Conceptual Model for Automatic Early Warning Information System of Infectious Diseases Based on Internet Reporting Surveillance System

    Institute of Scientific and Technical Information of China (English)

    JIA-QI MA; LI-PING WANG; XUAO-PENG QI; XIAO-MING SHI; GONG-HUAN YANG

    2007-01-01

    Objective To establish a conceptual model of automatic early warning of infectious diseases based on internet reporting surveillance system,with a view to realizing an automated warning system on a daily basis and timely identifying potential outbreaks of infectious diseases. Methods The statistic conceptual model was established using historic surveillance data with movable percentile method.Results Based on the infectious disease surveillance information platform,the conceptualmodelfor early warning was established.The parameter,threshold,and revised sensitivity and specificity of early warning value were changed to realize dynamic alert of infectious diseases on a daily basis.Conclusion The instructive conceptual model of dynamic alert can be used as a validating tool in institutions of infectious disease surveillance in different districts.

  3. Performance of data acceptance criteria over 50 months from an automatic real-time environmental radiation surveillance network

    Energy Technology Data Exchange (ETDEWEB)

    Casanovas, R., E-mail: ramon.casanovas@urv.cat [Unitat de Fisica Medica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain); Morant, J.J. [Servei de Proteccio Radiologica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain); Lopez, M. [Unitat de Fisica Medica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain); Servei de Proteccio Radiologica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain); Hernandez-Giron, I. [Unitat de Fisica Medica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain); Batalla, E. [Servei de Coordinacio d' Activitats Radioactives, Departament d' Economia i Finances, Generalitat de Catalunya, ES-08018 Barcelona (Spain); Salvado, M. [Unitat de Fisica Medica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain)

    2011-08-15

    The automatic real-time environmental radiation surveillance network of Catalonia (Spain) comprises two subnetworks; one with 9 aerosol monitors and the other with 8 Geiger monitors together with 2 water monitors located in the Ebre river. Since September 2006, several improvements were implemented in order to get better quality and quantity of data, allowing a more accurate data analysis. However, several causes (natural causes, equipment failure, artificial external causes and incidents in nuclear power plants) may produce radiological measured values mismatched with the own station background, whether spurious without significance or true radiological values. Thus, data analysis for a 50-month period was made and allowed to establish an easily implementable statistical criterion to find those values that require special attention. This criterion proved a very useful tool for creating a properly debugged database and to give a quick response to equipment failures or possible radiological incidents. This paper presents the results obtained from the criterion application, including the figures for the expected, raw and debugged data, percentages of missing data grouped by causes and radiological measurements from the networks. Finally, based on the discussed information, recommendations for the improvement of the network are identified to obtain better radiological information and analysis capabilities. - Highlights: > Causes producing data mismatching with the own stations background are described. > Causes may be natural, equipment failure, external or nuclear plants incidents. > These causes can produce either spurious or true radiological data. > A criterion to find these data was implemented and tested for a 50-month period. > Recommendations for the improvement of the network are identified.

  4. Expansion of syndromic vaccine preventable disease surveillance to include bacterial meningitis and Japanese encephalitis: evaluation of adapting polio and measles laboratory networks in Bangladesh, China and India, 2007-2008.

    Science.gov (United States)

    Cavallaro, Kathleen F; Sandhu, Hardeep S; Hyde, Terri B; Johnson, Barbara W; Fischer, Marc; Mayer, Leonard W; Clark, Thomas A; Pallansch, Mark A; Yin, Zundong; Zuo, Shuyan; Hadler, Stephen C; Diorditsa, Serguey; Hasan, A S M Mainul; Bose, Anindya S; Dietz, Vance

    2015-02-25

    Surveillance for acute flaccid paralysis with laboratory confirmation has been a key strategy in the global polio eradication initiative, and the laboratory platform established for polio testing has been expanded in many countries to include surveillance for cases of febrile rash illness to identify measles and rubella cases. Vaccine-preventable disease surveillance is essential to detect outbreaks, define disease burden, guide vaccination strategies and assess immunization impact. Vaccines now exist to prevent Japanese encephalitis (JE) and some etiologies of bacterial meningitis. We evaluated the feasibility of expanding polio-measles surveillance and laboratory networks to detect bacterial meningitis and JE, using surveillance for acute meningitis-encephalitis syndrome in Bangladesh and China and acute encephalitis syndrome in India. We developed nine syndromic surveillance performance indicators based on international surveillance guidelines and calculated scores using supervisory visit reports, annual reports, and case-based surveillance data. Scores, variable by country and targeted disease, were highest for the presence of national guidelines, sustainability, training, availability of JE laboratory resources, and effectiveness of using polio-measles networks for JE surveillance. Scores for effectiveness of building on polio-measles networks for bacterial meningitis surveillance and specimen referral were the lowest, because of differences in specimens and techniques. Polio-measles surveillance and laboratory networks provided useful infrastructure for establishing syndromic surveillance and building capacity for JE diagnosis, but were less applicable for bacterial meningitis. Laboratory-supported surveillance for vaccine-preventable bacterial diseases will require substantial technical and financial support to enhance local diagnostic capacity. Published by Elsevier Ltd.

  5. Navy Network Dependability: Models, Metrics, and Tools

    Science.gov (United States)

    2010-01-01

    surveillance; TCDL = Tactical Common Data Link; UHF = ultra high frequency; UFO = ultra-high-frequency follow-on; WGS = Wideband Gapfiller Satellite. RAND...VOICE (DMR VALUES) UFO OE-82 UHF LOS VOICE (DMR VALUES) UHF SATCOM VOICE DMR UHF LOS VOICE DMR TVs ADMS KIV-7 COMSEC ADNS SW ADNS II HW ISNS SW ISNS HW...Data Link; UHF = ultra high frequency; UFO = ultra-high-frequency follow-on; WGS = Wideband Gapfiller Satellite. RAND MG1003-1.1 4 Navy Network

  6. A dynamic network model for interbank market

    Science.gov (United States)

    Xu, Tao; He, Jianmin; Li, Shouwei

    2016-12-01

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

  7. Modeling Diagnostic Assessments with Bayesian Networks

    Science.gov (United States)

    Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego

    2007-01-01

    This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

  8. Modeling Diagnostic Assessments with Bayesian Networks

    Science.gov (United States)

    Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego

    2007-01-01

    This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

  9. Dermatology in public health--a model for surveillance of common skin diseases.

    Science.gov (United States)

    Stenberg, Berndt; Meding, Birgitta; Svensson, Ake

    2010-06-01

    The aim was to establish a baseline prevalence of skin conditions of public health importance in the general population and taking the validity of the questions into account. Our model is intended for future surveillance of skin conditions. The suggested questions have for the first time been used in Swedish population surveys. A random sample was taken from the general population aged 16 to 84 years of the participating areas. During the past 12 months, hand eczema was reported by 9.4%, childhood eczema by 15.7% and nickel allergy by 13.7% of the population. Hand and childhood eczema questions have previously been validated. Taking the validity into account, the actual population prevalence of hand eczema (11.7%) is underestimated, and the prevalence of atopic childhood eczema (10.0%) is overestimated based on the results of the questionnaire. In addition to presenting prevalence, population survey results can be used for risk analyses. A 10-fold risk of hand eczema in individuals with childhood eczema and self-reported nickel sensitivity is shown in our study. Questionnaires can be used for epidemiologic surveillance so long as the questions are validated and that the validity is taken into account when estimating the occurrence of the conditions. Public health surveys such as this one lay the basis for future epidemiological surveillance of skin conditions that can be subject to interventions. We propose that these, or similar, questions should be used regularly in population surveys and supplemented by questions on skin exposure.

  10. Bayesian Network Webserver: a comprehensive tool for biological network modeling.

    Science.gov (United States)

    Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

    2013-11-01

    The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

  11. Ice cloud modeling upgrade to GRC's Advanced Surveillance Test-bed

    Science.gov (United States)

    Daniell, Anthony L.; Robbins, Howard M.

    1995-09-01

    The development and evaluation of signal and data processing algorithms for IR surveillance systems is critically dependent on realistic simulations of targets and background. The Advanced Surveillance Testbed has been developed by GRC to perform such simulations. It has recently been upgraded to include models for the scattering of sunlight from high-altitude clouds of ice-crystals. The ice cloud models in the Advanced Surveillance Testbed are designed primarily for the SWIR and MWIR bands. The ice clouds are assumed to have nominally flat upper surfaces, and to be composed of hexagonal crystals (plates, columns, or intermediates), with any of several alternative distributions of shapes and sizes. The ice- crystals are assumed to rotate randomly about their hexagonal axes, but the user can choose from several models for the orientation of this axis: random isotropic, random horizonal, or nominally vertical. A single scattering model is used, with the small-angle forward scattering removed by renormalization. The scattering is calculated by geometrical optics, using algorithms based on the papers published by Liou, Takano, Cai, and Coleman. However, the GRC implementation includes some innovations that greatly increase its computational efficiency. In the SWIR band, the refractive index is highly variable. Its imaginary part varies by orders of magnitude, and its real part can be less than unity, causing total external reflections. Therefore, it is necessary to perform the computations for multiple IR wavelengths and combine the results. The calculations include two-way atmospheric transmission for the relevant wavelengths and the assumed cloud altitude. The model and its utility will be discussed.

  12. Smart City Surveillance Through Low-Cost Fiber Sensors in Metropolitan Optical Networks

    Science.gov (United States)

    Bourmpos, Michail; Argyris, Apostolos; Syvridis, Dimitris

    2014-05-01

    A continuously growing number of municipalities has optical fiber networks supporting communications at their disposal. These fiber installations can also be utilized to convey low data optical signals from a large number of deployed sensing elements, usually positioned in critical infrastructure locations, providing a variety of useful information. Such information can be used in the context of a "smart city" to provide citizens with higher-level services or even to proactively ensure public security and safety. This work demonstrates a fiber sensing network based on low-cost fiber Bragg grating sensors that are able to appropriately oversee diverse monitoring parameters.

  13. Model to Track Wild Birds for Avian Influenza by Means of Population Dynamics and Surveillance Information

    Science.gov (United States)

    Alba, Anna; Bicout, Dominique J.; Vidal, Francesc; Curcó, Antoni; Allepuz, Alberto; Napp, Sebastián; García-Bocanegra, Ignacio; Costa, Taiana; Casal, Jordi

    2012-01-01

    Design, sampling and data interpretation constitute an important challenge for wildlife surveillance of avian influenza viruses (AIV). The aim of this study was to construct a model to improve and enhance identification in both different periods and locations of avian species likely at high risk of contact with AIV in a specific wetland. This study presents an individual-based stochastic model for the Ebre Delta as an example of this appliance. Based on the Monte-Carlo method, the model simulates the dynamics of the spread of AIV among wild birds in a natural park following introduction of an infected bird. Data on wild bird species population, apparent AIV prevalence recorded in wild birds during the period of study, and ecological information on factors such as behaviour, contact rates or patterns of movements of waterfowl were incorporated as inputs of the model. From these inputs, the model predicted those species that would introduce most of AIV in different periods and those species and areas that would be at high risk as a consequence of the spread of these AIV incursions. This method can serve as a complementary tool to previous studies to optimize the allocation of the limited AI surveillance resources in a local complex ecosystem. However, this study indicates that in order to predict the evolution of the spread of AIV at the local scale, there is a need for further research on the identification of host factors involved in the interspecies transmission of AIV. PMID:22952962

  14. Model to track wild birds for avian influenza by means of population dynamics and surveillance information.

    Directory of Open Access Journals (Sweden)

    Anna Alba

    Full Text Available Design, sampling and data interpretation constitute an important challenge for wildlife surveillance of avian influenza viruses (AIV. The aim of this study was to construct a model to improve and enhance identification in both different periods and locations of avian species likely at high risk of contact with AIV in a specific wetland. This study presents an individual-based stochastic model for the Ebre Delta as an example of this appliance. Based on the Monte-Carlo method, the model simulates the dynamics of the spread of AIV among wild birds in a natural park following introduction of an infected bird. Data on wild bird species population, apparent AIV prevalence recorded in wild birds during the period of study, and ecological information on factors such as behaviour, contact rates or patterns of movements of waterfowl were incorporated as inputs of the model. From these inputs, the model predicted those species that would introduce most of AIV in different periods and those species and areas that would be at high risk as a consequence of the spread of these AIV incursions. This method can serve as a complementary tool to previous studies to optimize the allocation of the limited AI surveillance resources in a local complex ecosystem. However, this study indicates that in order to predict the evolution of the spread of AIV at the local scale, there is a need for further research on the identification of host factors involved in the interspecies transmission of AIV.

  15. 76 FR 25695 - Public Health Information Network (PHIN) Messaging Guide for Syndromic Surveillance

    Science.gov (United States)

    2011-05-05

    ... HUMAN SERVICES Centers for Disease Control and Prevention Public Health Information Network (PHIN... submit written comments to the following address: Public Health Informatics and Technology Program Office... representative from the Public Health Informatics and Technology Program Office to schedule your visit....

  16. Bayesian estimation of the network autocorrelation model

    NARCIS (Netherlands)

    Dittrich, D.; Leenders, R.T.A.J.; Mulder, J.

    2017-01-01

    The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of

  17. Object Oriented Modeling Of Social Networks

    NARCIS (Netherlands)

    Zeggelink, Evelien P.H.; Oosten, Reinier van; Stokman, Frans N.

    1996-01-01

    The aim of this paper is to explain principles of object oriented modeling in the scope of modeling dynamic social networks. As such, the approach of object oriented modeling is advocated within the field of organizational research that focuses on networks. We provide a brief introduction into the f

  18. Agent-based modeling and network dynamics

    CERN Document Server

    Namatame, Akira

    2016-01-01

    The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

  19. A positive event dependence model for self-controlled case series with applications in postmarketing surveillance.

    Science.gov (United States)

    Simpson, Shawn E

    2013-03-01

    A primary objective in the application of postmarketing drug safety surveillance is to ascertain the relationship between time-varying drug exposures and recurrent adverse events (AEs) related to health outcomes. The self-controlled case series (SCCS) method is one approach to analysis in this context. It is based on a conditional Poisson regression model, which assumes that events at different time points are conditionally independent given the covariate process. This requirement is problematic when the occurrence of an event can alter the future event risk. In a clinical setting, for example, patients who have a first myocardial infarction (MI) may be at higher subsequent risk for a second. In this work, we propose the positive dependence self-controlled case series (PD-SCCS) method: a generalization of SCCS that allows the occurrence of an event to increase the future event risk, yet maintains the advantages of the original model by controlling for fixed baseline covariates and relying solely on data from cases. As in the SCCS model, individual-level baseline parameters drop out of the PD-SCCS likelihood. Data sources used for postmarketing surveillance can contain tens of millions of people, so in this situation it is particularly advantageous that PD-SCCS avoids doing a costly estimation of individual parameters. We develop expressions for large sample inference and optimization for PD-SCCS and compare the results of our generalized model with the more restrictive SCCS approach. Copyright © 2013, The International Biometric Society.

  20. Road-traffic pollution and asthma – using modelled exposure assessment for routine public health surveillance

    Directory of Open Access Journals (Sweden)

    Daly Mark

    2004-10-01

    Full Text Available Abstract Asthma is a common disease and appears to be increasing in prevalence. There is evidence linking air pollution, including that from road-traffic, with asthma. Road traffic is also on the increase. Routine surveillance of the impact of road-traffic pollution on asthma, and other diseases, would be useful in informing local and national government policy in terms of managing the environmental health risk. Several methods for exposure assessment have been used in studies examining the association between asthma and road traffic pollution. These include comparing asthma prevalence in areas designated as high and low pollution areas, using distance from main roads as a proxy for exposure to road traffic pollution, using traffic counts to estimate exposure, using vehicular miles travelled and using modelling techniques. Although there are limitations to all these methods, the modelling approach has the advantage of incorporating several variables and may be used for prospective health impact assessment. The modelling approach is already in routine use in the United Kingdom in support of the government's strategy for air quality management. Combining information from such models with routinely collected health data would form the basis of a routine public health surveillance system. Such a system would facilitate prospective health impact assessment, enabling policy decisions concerned with road-traffic to be made with knowledge of the potential implications. It would also allow systematic monitoring of the health impacts when the policy decisions and plans have been implemented.

  1. Edge exchangeable models for network data

    CERN Document Server

    Crane, Harry

    2016-01-01

    Exchangeable models for vertex labeled graphs cannot replicate the large sample behaviors of sparsity and power law degree distributions observed in many network datasets. Out of this mathematical impossibility emerges the question of how network data can be modeled in a way that reflects known empirical behaviors and respects basic statistical principles. We address this question by observing that edges, not vertices, act as the statistical units in most network datasets, making a theory of edge labeled networks more natural for most applications. Within this context we introduce the new invariance principle of {\\em edge exchangeability}, which unlike its vertex exchangeable counterpart can produce networks with sparse and/or power law structure. We characterize the class of all edge exchangeable network models and identify a particular two parameter family of models with suitable theoretical properties for statistical inference. We discuss issues of estimation from edge exchangeable models and compare our a...

  2. Evolutionary Phylogenetic Networks: Models and Issues

    Science.gov (United States)

    Nakhleh, Luay

    Phylogenetic networks are special graphs that generalize phylogenetic trees to allow for modeling of non-treelike evolutionary histories. The ability to sequence multiple genetic markers from a set of organisms and the conflicting evolutionary signals that these markers provide in many cases, have propelled research and interest in phylogenetic networks to the forefront in computational phylogenetics. Nonetheless, the term 'phylogenetic network' has been generically used to refer to a class of models whose core shared property is tree generalization. Several excellent surveys of the different flavors of phylogenetic networks and methods for their reconstruction have been written recently. However, unlike these surveys, this chapte focuses specifically on one type of phylogenetic networks, namely evolutionary phylogenetic networks, which explicitly model reticulate evolutionary events. Further, this chapter focuses less on surveying existing tools, and addresses in more detail issues that are central to the accurate reconstruction of phylogenetic networks.

  3. Modeling Network Evolution Using Graph Motifs

    CERN Document Server

    Conway, Drew

    2011-01-01

    Network structures are extremely important to the study of political science. Much of the data in its subfields are naturally represented as networks. This includes trade, diplomatic and conflict relationships. The social structure of several organization is also of interest to many researchers, such as the affiliations of legislators or the relationships among terrorist. A key aspect of studying social networks is understanding the evolutionary dynamics and the mechanism by which these structures grow and change over time. While current methods are well suited to describe static features of networks, they are less capable of specifying models of change and simulating network evolution. In the following paper I present a new method for modeling network growth and evolution. This method relies on graph motifs to generate simulated network data with particular structural characteristic. This technique departs notably from current methods both in form and function. Rather than a closed-form model, or stochastic ...

  4. Queuing theory models for computer networks

    Science.gov (United States)

    Galant, David C.

    1989-01-01

    A set of simple queuing theory models which can model the average response of a network of computers to a given traffic load has been implemented using a spreadsheet. The impact of variations in traffic patterns and intensities, channel capacities, and message protocols can be assessed using them because of the lack of fine detail in the network traffic rates, traffic patterns, and the hardware used to implement the networks. A sample use of the models applied to a realistic problem is included in appendix A. Appendix B provides a glossary of terms used in this paper. This Ames Research Center computer communication network is an evolving network of local area networks (LANs) connected via gateways and high-speed backbone communication channels. Intelligent planning of expansion and improvement requires understanding the behavior of the individual LANs as well as the collection of networks as a whole.

  5. 视频传感器网络中无盲区监视优化%Coverage Optimization of Occlusion-Free Surveillance for Video Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    蒋一波; 王万良; 陈伟杰; 郑建炜; 姚信威

    2012-01-01

    This paper studies the problem of surveillance area of WVSN (wireless video sensor network) with obstacles, which is finding the optimized view-orientations for all video sensors to maximize multimedia coverage. In this paper, the characteristics of direction adjustable sensing model are discussed and the relation between sensors and obstacles is analyzed. Then, a coverage enhancement algorithm PFOFSA (potential field based occlusion-free surveillance algorithm) is proposed, which could effectively minimize the negative effect of occlusion and overlapping in the sensing field. In PFOFSA, three kinds of virtual centroid point are defined for simplifying the structure of effective region, occlusion region and overlapping region. Two kinds of relationship between virtual force and orientation rotation are constructed and made comparison in experiments. Finally, a set of simulation results are performed to demonstrate the effectiveness of PFOFSA. According to the experiments, PFOFSA could maximize the coverage of WVSN with obstacles and make higher coverage rate than other algorithms.%针对监控区域存在障碍物的情况,从无线视频传感节点的有向感知特性出发,讨论了视频传感器网络覆盖效果与监控区域之间的相互关系.在此基础上,定义了视频传感器网络的无盲区覆盖模型.基于虚拟势场的工作原理,提出了一种适用于无盲区覆盖模型的覆盖率动态优化算法PFOFSA(potential field based occlusion-free surveillance algorithm).设计了PFOFSA中虚拟力的相互作用方法与监控节点运动规则,通过监控区域、重叠区域和遮挡区域之间的相互作用,逐步消除网络中的感知重叠区和盲区,优化视频无线传感器网络的覆盖率.最后,通过一系列的仿真实验分析了不同监控区域参数对PFOFSA算法的影响,验证了算法的有效性.

  6. Leveraging social networking sites for disease surveillance and public sensing: the case of the 2013 avian influenza A(H7N9) outbreak in China

    Science.gov (United States)

    Zhang, Emma Xuxiao; Yang, Yinping; Di Shang, Richard; Simons, Joseph John Pyne; Quek, Boon Kiat; Yin, Xiao Feng; See, Wanhan; Oh, Olivia Seen Huey; Nandar, Khine Sein Tun; Ling, Vivienne Ruo Yun; Chan, Pei Pei; Wang, Zhaoxia; Goh, Rick Siow Mong; James, Lyn

    2015-01-01

    We conducted in-depth analysis on the use of a popular Chinese social networking and microblogging site, Sina Weibo, to monitor an avian influenza A(H7N9) outbreak in China and to assess the value of social networking sites in the surveillance of disease outbreaks that occur overseas. Two data sets were employed for our analysis: a line listing of confirmed cases obtained from conventional public health information channels and case information from Weibo posts. Our findings showed that the level of activity on Weibo corresponded with the number of new cases reported. In addition, the reporting of new cases on Weibo was significantly faster than those of conventional reporting sites and non-local news media. A qualitative review of the functions of Weibo also revealed that Weibo enabled timely monitoring of other outbreak-relevant information, provided access to additional crowd-sourced epidemiological information and was leveraged by the local government as an interactive platform for risk communication and monitoring public sentiment on the policy response. Our analysis demonstrated the potential for social networking sites to be used by public health agencies to enhance traditional communicable disease surveillance systems for the global surveillance of overseas public health threats. Social networking sites also can be used by governments for calibration of response policies and measures and for risk communication. PMID:26306219

  7. Leveraging social networking sites for disease surveillance and public sensing: the case of the 2013 avian influenza A(H7N9) outbreak in China.

    Science.gov (United States)

    Zhang, Emma Xuxiao; Yang, Yinping; Di Shang, Richard; Simons, Joseph John Pyne; Quek, Boon Kiat; Yin, Xiao Feng; See, Wanhan; Oh, Olivia Seen Huey; Nandar, Khine Sein Tun; Ling, Vivienne Ruo Yun; Chan, Pei Pei; Wang, Zhaoxia; Goh, Rick Siow Mong; James, Lyn; Tey, Jeannie Su Hui

    2015-01-01

    We conducted in-depth analysis on the use of a popular Chinese social networking and microblogging site, Sina Weibo, to monitor an avian influenza A(H7N9) outbreak in China and to assess the value of social networking sites in the surveillance of disease outbreaks that occur overseas. Two data sets were employed for our analysis: a line listing of confirmed cases obtained from conventional public health information channels and case information from Weibo posts. Our findings showed that the level of activity on Weibo corresponded with the number of new cases reported. In addition, the reporting of new cases on Weibo was significantly faster than those of conventional reporting sites and non-local news media. A qualitative review of the functions of Weibo also revealed that Weibo enabled timely monitoring of other outbreak-relevant information, provided access to additional crowd-sourced epidemiological information and was leveraged by the local government as an interactive platform for risk communication and monitoring public sentiment on the policy response. Our analysis demonstrated the potential for social networking sites to be used by public health agencies to enhance traditional communicable disease surveillance systems for the global surveillance of overseas public health threats. Social networking sites also can be used by governments for calibration of response policies and measures and for risk communication.

  8. Leveraging social networking sites for disease surveillance and public sensing: the case of the 2013 avian influenza A(H7N9 outbreak in China

    Directory of Open Access Journals (Sweden)

    Emma Xuxiao Zhang

    2015-05-01

    Full Text Available We conducted in-depth analysis on the use of a popular Chinese social networking and microblogging site, Sina Weibo, to monitor an avian influenza A(H7N9 outbreak in China and to assess the value of social networking sites in the surveillance of disease outbreaks that occur overseas. Two data sets were employed for our analysis: a line listing of confirmed cases obtained from conventional public health information channels and case information from Weibo posts. Our findings showed that the level of activity on Weibo corresponded with the number of new cases reported. In addition, the reporting of new cases on Weibo was significantly faster than those of conventional reporting sites and non-local news media. A qualitative review of the functions of Weibo also revealed that Weibo enabled timely monitoring of other outbreak-relevant information, provided access to additional crowd-sourced epidemiological information and was leveraged by the local government as an interactive platform for risk communication and monitoring public sentiment on the policy response. Our analysis demonstrated the potential for social networking sites to be used by public health agencies to enhance traditional communicable disease surveillance systems for the global surveillance of overseas public health threats. Social networking sites also can be used by governments for calibration of response policies and measures and for risk communication.

  9. Networking dual-surveillance/dual-pair-tele-paths for critical urban areas

    Institute of Scientific and Technical Information of China (English)

    Hsu; Li-Yen

    2008-01-01

    Providing a reliable and efficient communication infrastructure for critical regions is generally a strategy used to enhance regional safety or to sustain regional development. For such purposes, the spider-web network is prototyped as a cellular telecommunication infrastructure for its advantages including being interference-free, possessing fault-tolerance, having security management, and countering radio's multipath effects. Besides, it is used to develop area-based-street-SCADA (supervisory control and ...

  10. Probabilistic daily ILI syndromic surveillance with a spatio-temporal Bayesian hierarchical model.

    Directory of Open Access Journals (Sweden)

    Ta-Chien Chan

    Full Text Available BACKGROUND: For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty. METHODS AND FINDINGS: Based on the Bayesian posterior probability of influenza-like illness (ILI visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006-2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1-2 days prior to the rise of ILI visits. CONCLUSIONS: This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs.

  11. Random graph models for dynamic networks

    CERN Document Server

    Zhang, Xiao; Newman, M E J

    2016-01-01

    We propose generalizations of a number of standard network models, including the classic random graph, the configuration model, and the stochastic block model, to the case of time-varying networks. We assume that the presence and absence of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. In addition to computing equilibrium properties of these models, we demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data. This allows us, for instance, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate our methods with a selection of applications, both to computer-generated test networks and real-world examples.

  12. Modelling the structure of complex networks

    DEFF Research Database (Denmark)

    Herlau, Tue

    networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

  13. Biodiversity: modelling angiosperms as networks.

    Science.gov (United States)

    Gottlieb, O R; Borin, M R

    2000-11-01

    In the neotropics, one of the last biological frontiers, the major ecological concern should not involve local strategies, but global effects often responsible for irreparable damage. For a holistic approach, angiosperms are ideal model systems dominating most land areas of the present world in an astonishing variety of form and function. Recognition of biogeographical patterns requires new methodologies and entails several questions, such as their nature, dynamics and mechanism. Demographical patterns of families, modelled via species dominance, reveal the existence of South American angiosperm networks converging at the central Brazilian plateau. Biodiversity of habitats, measured via taxonomic uniqueness, reveal higher creative power at this point of convergence than in more peripheral regions. Compositional affinities of habitats, measured via bioconnectivity, reveal the decisive role of ecotones in the exchange or redistribution of information, energy and organisms among the ecosystems. Forming dynamic boundaries, ecotones generate and relay evolutionary novelty, and integrate all neotropical ecosystems into a single vegetation net. Connectivity in such plant webs may depend on mycorrhizal links. If sufficiently general such means of metabolic transfer will require revision of the chemical composition of many plants.

  14. Network of marine environmental observation, surveillance and control in the canary islands waters (red acomar)

    Science.gov (United States)

    Rueda, M. J.; Villagarcía, M. G.; Barrera, C.; Pérez, J.; Cianca, A.; Godoy, J.; Maroto, L.; Cardona, L.; Llinás, O.

    2003-04-01

    On January 2003 it has began the experimental core deployment of a Marine Obsevational network in Gran Canaria Island sorrounding waters, as a first step of a network which will spread to the whole Canarian Archipelago. The network initially consists of 6 buoys, 3 to 5 are expected to be permanently operative whereas the rest will be under maintenance and improvement. In the beginning each buoy has a double mission: on one hand to contribute to the general observation of oceanographic/ meteorologic parameters of general interest; on the other, to provide specific interesting data to a specific user at least. Some users are the aquaculture enterprises that develop their productive activity in cages moored at sea, the water management companies, public institutions in charge of management of environmentally protected areas and organisers of sailing competitions. Each buoy is composed of a common sensors assembly (position: GPS, compass; meteorology: speed and direction of wind, air temperature, relative humidity; oceanography: water temperature, conductivity, pH, oxygen) and a specific sensor set-up (turbidity, chlorophyll, nutrients, hydrocarbons) depending on the each buoy function. Power, control and processing elements are also included in the buoy. The basic observational program consists of a reading cycle of all the parameters each hour, though it is also possible the programming of specific cycles or the request of a needed demand. Data are transmitted via VHF to a proximal point in land which is linked to a specific user, who acts as a local control element. From each point, the data are sent via a mobile or fixed telephone line to the central control, located at the ICCM. Upon arrival, the data undergo several quality and transformation processes in order to be able to publish those parameters of general interest in the project web (http://iccm.rcanaria.es), and those specific for each user according to their particular protocol. Additionally, it is

  15. Modeling of hysteresis in gene regulatory networks.

    Science.gov (United States)

    Hu, J; Qin, K R; Xiang, C; Lee, T H

    2012-08-01

    Hysteresis, observed in many gene regulatory networks, has a pivotal impact on biological systems, which enhances the robustness of cell functions. In this paper, a general model is proposed to describe the hysteretic gene regulatory network by combining the hysteresis component and the transient dynamics. The Bouc-Wen hysteresis model is modified to describe the hysteresis component in the mammalian gene regulatory networks. Rigorous mathematical analysis on the dynamical properties of the model is presented to ensure the bounded-input-bounded-output (BIBO) stability and demonstrates that the original Bouc-Wen model can only generate a clockwise hysteresis loop while the modified model can describe both clockwise and counter clockwise hysteresis loops. Simulation studies have shown that the hysteresis loops from our model are consistent with the experimental observations in three mammalian gene regulatory networks and two E.coli gene regulatory networks, which demonstrate the ability and accuracy of the mathematical model to emulate natural gene expression behavior with hysteresis. A comparison study has also been conducted to show that this model fits the experiment data significantly better than previous ones in the literature. The successful modeling of the hysteresis in all the five hysteretic gene regulatory networks suggests that the new model has the potential to be a unified framework for modeling hysteresis in gene regulatory networks and provide better understanding of the general mechanism that drives the hysteretic function.

  16. Situational awareness of influenza activity based on multiple streams of surveillance data using multivariate dynamic linear model.

    Directory of Open Access Journals (Sweden)

    Eric H Y Lau

    Full Text Available BACKGROUND: Multiple sources of influenza surveillance data are becoming more available; however integration of these data streams for situational awareness of influenza activity is less explored. METHODS AND RESULTS: We applied multivariate time-series methods to sentinel outpatient and school absenteeism surveillance data in Hong Kong during 2004-2009. School absenteeism data and outpatient surveillance data experienced interruptions due to school holidays and changes in public health guidelines during the pandemic, including school closures and the establishment of special designated flu clinics, which in turn provided 'drop-in' fever counts surveillance data. A multivariate dynamic linear model was used to monitor influenza activity throughout epidemics based on all available data. The inferred level followed influenza activity closely at different times, while the inferred trend was less competent with low influenza activity. Correlations between inferred level and trend from the multivariate model and reference influenza activity, measured by the product of weekly laboratory influenza detection rates and weekly general practitioner influenza-like illness consultation rates, were calculated and compared with those from univariate models. Over the whole study period, there was a significantly higher correlation (ρ = 0.82, p≤0.02 for the inferred trend based on the multivariate model compared to other univariate models, while the inferred trend from the multivariate model performed as well as the best univariate model in the pre-pandemic and the pandemic period. The inferred trend and level from the multivariate model was able to match, if not outperform, the best univariate model albeit with missing data plus drop-in and drop-out of different surveillance data streams. An overall influenza index combining level and trend was constructed to demonstrate another potential use of the method. CONCLUSIONS: Our results demonstrate the

  17. Commercial Online Social Network Data and Statin Side-Effect Surveillance: A Pilot Observational Study of Aggregate Mentions on Facebook.

    Science.gov (United States)

    Huesch, Marco D

    2017-07-26

    Surveillance of the safety of prescribed drugs after marketing approval has been secured remains fraught with complications. Formal ascertainment by providers and reporting to adverse-event registries, formal surveys by manufacturers, and mining of electronic medical records are all well-known approaches with varying degrees of difficulty, cost, and success. Novel approaches may be a useful adjunct, especially approaches that mine or sample internet-based methods such as online social networks. A novel commercial software-as-a-service data-mining product supplied by Sysomos from Datasift/Facebook was used to mine all mentions on Facebook of statins and stain-related side effects in the US in the 1-month period 9 January 2017 through 8 February 2017. A total of 4.3% of all 25,700 mentions of statins also mentioned typical stain-related side effects. Multiple methodological weaknesses stymie interpretation of this percentage, which is however not inconsistent with estimates that 5-20% of patients taking statins will experience typical side effects at some time. Future work on pharmacovigilance may be informed by this novel commercial tool, but the inability to mine the full text of a posting poses serious challenges to content categorization.

  18. Nonconsensus opinion model on directed networks

    NARCIS (Netherlands)

    Qu, B.; Li, Q.; Havlin, S.; Stanley, E.; Wang, H.

    2014-01-01

    Dynamic social opinion models have been widely studied on undirected networks, and most of them are based on spin interaction models that produce a consensus. In reality, however, many networks such as Twitter and the World Wide Web are directed and are composed of both unidirectional and bidirectio

  19. Radio channel modeling in body area networks

    NARCIS (Netherlands)

    An, L.; Bentum, M.J.; Meijerink, A.; Scanlon, W.G.

    2010-01-01

    A body area network (BAN) is a network of bodyworn or implanted electronic devices, including wireless sensors which can monitor body parameters or to detect movements. One of the big challenges in BANs is the propagation channel modeling. Channel models can be used to understand wave propagation in

  20. Radio channel modeling in body area networks

    NARCIS (Netherlands)

    An, L.; Bentum, M.J.; Meijerink, A.; Scanlon, W.G.

    2009-01-01

    A body area network (BAN) is a network of bodyworn or implanted electronic devices, including wireless sensors which can monitor body parameters or to de- tect movements. One of the big challenges in BANs is the propagation channel modeling. Channel models can be used to understand wave propagation

  1. Ground-based remote sensing profiling and numerical weather prediction model to manage nuclear power plants meteorological surveillance in Switzerland

    Directory of Open Access Journals (Sweden)

    B. Calpini

    2011-08-01

    Full Text Available The meteorological surveillance of the four nuclear power plants in Switzerland is of first importance in a densely populated area such as the Swiss Plateau. The project "Centrales Nucléaires et Météorologie" CN-MET aimed at providing a new security tool based on one hand on the development of a high resolution numerical weather prediction (NWP model. The latter is providing essential nowcasting information in case of a radioactive release from a nuclear power plant in Switzerland. On the other hand, the model input over the Swiss Plateau is generated by a dedicated network of surface and upper air observations including remote sensing instruments (wind profilers and temperature/humidity passive microwave radiometers. This network is built upon three main sites ideally located for measuring the inflow/outflow and central conditions of the main wind field in the planetary boundary layer over the Swiss Plateau, as well as a number of surface automatic weather stations (AWS. The network data are assimilated in real-time into the fine grid NWP model using a rapid update cycle of eight runs per day (one forecast every three hours. This high resolution NWP model has replaced the former security tool based on in situ observations (in particular one meteorological mast at each of the power plants and a local dispersion model. It is used to forecast the dynamics of the atmosphere in the planetary boundary layer (typically the first 4 km above ground layer and over a time scale of 24 h. This tool provides at any time (e.g. starting at the initial time of a nuclear power plant release the best picture of the 24-h evolution of the air mass over the Swiss Plateau and furthermore generates the input data (in the form of simulated values substituting in situ observations required for the local dispersion model used at each of the nuclear power plants locations. This paper is presenting the concept and two validation studies as well as the results of an

  2. A simulation of wide area surveillance (WAS) systems and algorithm for digital elevation model (DEM) extraction

    Science.gov (United States)

    Cheng, Beato T.

    2010-04-01

    With the advances in focal plane, electronics and memory storage technologies, wide area and persistence surveillance capabilities have become a reality in airborne ISR. A WAS system offers many benefits in comparison with the traditional airborne image capturing systems that provide little data overlap, both in terms of space and time. Unlike a fix-mount surveillance camera, a persistence WAS system can be deployed anywhere as desired, although the platform typically has to be in motion, say circling above an area of interest. Therefore, WAS is a perfect choice for surveillance that can provide near real time capabilities such as change detection and target tracking. However, the performance of a WAS system is still limited by the available technologies: the optics that control the field-of-view, the electronics and mechanical subsystems that control the scanning, the focal plane data throughput, and the dynamics of the platform all play key roles in the success of the system. It is therefore beneficial to develop a simulated version that can capture the essence of the system, in order to help provide insights into the design of an optimized system. We describe an approach to the simulation of a generic WAS system that allows focal plane layouts, scanning patterns, flight paths and platform dynamics to be defined by a user. The system generates simulated image data of the area ground coverage from reference databases (e.g. aerial imagery, and elevation data), based on the sensor model. The simulated data provides a basis for further algorithm development, such as image stitching/mosaic, registration, and geolocation. We also discuss an algorithm to extract the terrain elevation from the simulated data, and to compare that with the original DEM data.

  3. The Effect of Participating in a Surgical Site Infection (SSI) Surveillance Network on the Time Trend of SSI Rates: A Systematic Review.

    Science.gov (United States)

    Abbas, Mohamed; Tartari, Ermira; Allegranzi, Benedetta; Pittet, Didier; Harbarth, Stephan

    2017-08-24

    This systematic literature review reveals that participating in a surgical site infection (SSI) surveillance network is associated with short-term reductions in SSI rates: relative risk [RR] for year 2, 0.80 (95% confidence interval [CI], 0.79-0.82); year 3 RR, 0.92 (95% CI, 0.90-0.94); year 4 RR, 0.98 (95% CI, 0.96-1.00). Infect Control Hosp Epidemiol 2017;1-3.

  4. Network models in economics and finance

    CERN Document Server

    Pardalos, Panos; Rassias, Themistocles

    2014-01-01

    Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis  that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.

  5. Unified Hybrid Network Theoretical Model Trilogy

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    The first of the unified hybrid network theoretical model trilogy (UHNTF) is the harmonious unification hybrid preferential model (HUHPM), seen in the inner loop of Fig. 1, the unified hybrid ratio is defined.

  6. A Network Formation Model Based on Subgraphs

    CERN Document Server

    Chandrasekhar, Arun

    2016-01-01

    We develop a new class of random-graph models for the statistical estimation of network formation that allow for substantial correlation in links. Various subgraphs (e.g., links, triangles, cliques, stars) are generated and their union results in a network. We provide estimation techniques for recovering the rates at which the underlying subgraphs were formed. We illustrate the models via a series of applications including testing for incentives to form cross-caste relationships in rural India, testing to see whether network structure is used to enforce risk-sharing, testing as to whether networks change in response to a community's exposure to microcredit, and show that these models significantly outperform stochastic block models in matching observed network characteristics. We also establish asymptotic properties of the models and various estimators, which requires proving a new Central Limit Theorem for correlated random variables.

  7. Gossip spread in social network Models

    Science.gov (United States)

    Johansson, Tobias

    2017-04-01

    Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.

  8. Strategic games on a hierarchical network model

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Among complex network models, the hierarchical network model is the one most close to such real networks as world trade web, metabolic network, WWW, actor network, and so on. It has not only the property of power-law degree distribution, but growth based on growth and preferential attachment, showing the scale-free degree distribution property. In this paper, we study the evolution of cooperation on a hierarchical network model, adopting the prisoner's dilemma (PD) game and snowdrift game (SG) as metaphors of the interplay between connected nodes. BA model provides a unifying framework for the emergence of cooperation. But interestingly, we found that on hierarchical model, there is no sign of cooperation for PD game, while the frequency of cooperation decreases as the common benefit decreases for SG. By comparing the scaling clustering coefficient properties of the hierarchical network model with that of BA model, we found that the former amplifies the effect of hubs. Considering different performances of PD game and SG on complex network, we also found that common benefit leads to cooperation in the evolution. Thus our study may shed light on the emergence of cooperation in both natural and social environments.

  9. Spectrum sharing between a surveillance radar and secondary Wi-Fi networks

    Science.gov (United States)

    Hessar, Farzad; Roy, Sumit

    2016-06-01

    Co-existence between unlicensed networks that share spectrum spatio-temporally with terrestrial (e.g. Air Traffic Control) and shipborne radars in 3-GHz band is attracting significant interest. Similar to every primary-secondary coexistence scenario, interference from unlicensed devices to a primary receiver must be within acceptable bounds. In this work, we formulate the spectrum sharing problem between a pulsed, search radar (primary) and 802.11 WLAN as the secondary. We compute the protection region for such a search radar for a) a single secondary user (initially) as well as b) a random spatial distribution of multiple secondary users. Furthermore, we also analyze the interference to the WiFi devices from the radar's transmissions to estimate the impact on achievable WLAN throughput as a function of distance to the primary radar.

  10. A nomadic access mechanism for enabling dynamic video surveillance over IEEE 802.15.4 networks

    Science.gov (United States)

    Garcia-Sanchez, Felipe; Garcia-Sanchez, Antonio-Javier; Losilla, Fernando; Garcia-Haro, Joan

    2010-12-01

    IEEE 802.15.4 networking technology is designed to be the common standard for integrating WSN applications in heterogeneous environments. However, applications considering mobile nodes along with strict temporal requirements, such as those required for video transmission, are an unexplored field for this technology. These applications involve different challenges and issues that the direct employment of the IEEE 802.15.4 standard does not resolve. Therefore, in this paper a cross-layer mechanism consisting of application and medium access arbitration is presented, enabling the efficient connection and operation of mobile nodes together with the transmission of video flows. The proposed mechanism is evaluated via simulation and its feasibility checked by means of a first prototype. The study of power consumption is also taken into account and so are the quality of service parameters and the human quality perception of the received video stream. The results obtained are presented and further discussed.

  11. Incidence and Trends of Infections with Pathogens Transmitted Commonly Through Food and the Effect of Increasing Use of Culture-Independent Diagnostic Tests on Surveillance - Foodborne Diseases Active Surveillance Network, 10 U.S. Sites, 2013-2016.

    Science.gov (United States)

    Marder, Ellyn P; Cieslak, Paul R; Cronquist, Alicia B; Dunn, John; Lathrop, Sarah; Rabatsky-Ehr, Therese; Ryan, Patricia; Smith, Kirk; Tobin-D'Angelo, Melissa; Vugia, Duc J; Zansky, Shelley; Holt, Kristin G; Wolpert, Beverly J; Lynch, Michael; Tauxe, Robert; Geissler, Aimee L

    2017-04-21

    Foodborne diseases represent a substantial public health concern in the United States. CDC's Foodborne Diseases Active Surveillance Network (FoodNet) monitors cases reported from 10 U.S. sites* of laboratory-diagnosed infections caused by nine enteric pathogens commonly transmitted through food. This report describes preliminary surveillance data for 2016 on the nine pathogens and changes in incidences compared with 2013-2015. In 2016, FoodNet identified 24,029 infections, 5,512 hospitalizations, and 98 deaths caused by these pathogens. The use of culture-independent diagnostic tests (CIDTs) by clinical laboratories to detect enteric pathogens has been steadily increasing since FoodNet began surveying clinical laboratories in 2010 (1). CIDTs complicate the interpretation of FoodNet surveillance data because pathogen detection could be affected by changes in health care provider behaviors or laboratory testing practices (2). Health care providers might be more likely to order CIDTs because these tests are quicker and easier to use than traditional culture methods, a circumstance that could increase pathogen detection (3). Similarly, pathogen detection could also be increasing as clinical laboratories adopt DNA-based syndromic panels, which include pathogens not often included in routine stool culture (4,5). In addition, CIDTs do not yield isolates, which public health officials rely on to distinguish pathogen subtypes, determine antimicrobial resistance, monitor trends, and detect outbreaks. To obtain isolates for infections identified by CIDTs, laboratories must perform reflex culture(†); if clinical laboratories do not, the burden of culturing falls to state public health laboratories, which might not be able to absorb that burden as the adoption of these tests increases (2). Strategies are needed to preserve access to bacterial isolates for further characterization and to determine the effect of changing trends in testing practices on surveillance.

  12. Towards reproducible descriptions of neuronal network models.

    Directory of Open Access Journals (Sweden)

    Eilen Nordlie

    2009-08-01

    Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.

  13. Symbolic regression of generative network models

    CERN Document Server

    Menezes, Telmo

    2014-01-01

    Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied "out of the box" to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world netwo...

  14. Complex networks analysis in socioeconomic models

    CERN Document Server

    Varela, Luis M; Ausloos, Marcel; Carrete, Jesus

    2014-01-01

    This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including res...

  15. Multi-Instance Learning Models for Automated Support of Analysts in Simulated Surveillance Environments

    Science.gov (United States)

    Birisan, Mihnea; Beling, Peter

    2011-01-01

    New generations of surveillance drones are being outfitted with numerous high definition cameras. The rapid proliferation of fielded sensors and supporting capacity for processing and displaying data will translate into ever more capable platforms, but with increased capability comes increased complexity and scale that may diminish the usefulness of such platforms to human operators. We investigate methods for alleviating strain on analysts by automatically retrieving content specific to their current task using a machine learning technique known as Multi-Instance Learning (MIL). We use MIL to create a real time model of the analysts' task and subsequently use the model to dynamically retrieve relevant content. This paper presents results from a pilot experiment in which a computer agent is assigned analyst tasks such as identifying caravanning vehicles in a simulated vehicle traffic environment. We compare agent performance between MIL aided trials and unaided trials.

  16. R0-modeling as a tool for early warning and surveillance of exotic vector borne diseases in Denmark

    DEFF Research Database (Denmark)

    Bødker, Rene

    2011-01-01

    the surveillance on periods and areas with high R0 estimates even if the actual value of these estimates are difficult to interpret. Furthermore running R0 models on historic outbreaks in Europe may be used to fit estimates for R0 for these data. When comparing the model R0 to the observed value of R0 a correction...

  17. Internet and Surveillance

    DEFF Research Database (Denmark)

    The Internet has been transformed in the past years from a system primarily oriented on information provision into a medium for communication and community-building. The notion of “Web 2.0”, social software, and social networking sites such as Facebook, Twitter and MySpace have emerged...... institutions have a growing interest in accessing this personal data. Here, contributors explore this changing landscape by addressing topics such as commercial data collection by advertising, consumer sites and interactive media; self-disclosure in the social web; surveillance of file-sharers; privacy...... in the age of the internet; civil watch-surveillance on social networking sites; and networked interactive surveillance in transnational space. This book is a result of a research action launched by the intergovernmental network COST (European Cooperation in Science and Technology)....

  18. Boolean networks as modelling framework

    Directory of Open Access Journals (Sweden)

    Florian eGreil

    2012-08-01

    Full Text Available In a network, the components of a given system are represented as nodes, the interactions are abstracted as links between the nodes. Boolean networks refer to a class of dynamics on networks, in fact it is the simplest possible dynamics where each node has a value 0 or 1. This allows to investigate extensively the dynamics both analytically and by numerical experiments. The present article focuses on the theoretical concept of relevant components and the immediate application in plant biology, references for more in-depths treatment of the mathematical details are also given.

  19. Implementing network constraints in the EMPS model

    Energy Technology Data Exchange (ETDEWEB)

    Helseth, Arild; Warland, Geir; Mo, Birger; Fosso, Olav B.

    2010-02-15

    This report concerns the coupling of detailed market and network models for long-term hydro-thermal scheduling. Currently, the EPF model (Samlast) is the only tool available for this task for actors in the Nordic market. A new prototype for solving the coupled market and network problem has been developed. The prototype is based on the EMPS model (Samkjoeringsmodellen). Results from the market model are distributed to a detailed network model, where a DC load flow detects if there are overloads on monitored lines or intersections. In case of overloads, network constraints are generated and added to the market problem. Theoretical and implementation details for the new prototype are elaborated in this report. The performance of the prototype is tested against the EPF model on a 20-area Nordic dataset. (Author)

  20. Modelling Microwave Devices Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Andrius Katkevičius

    2012-04-01

    Full Text Available Artificial neural networks (ANN have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics.Article in Lithuanian

  1. Delivery Time Reliability Model of Logistics Network

    OpenAIRE

    Liusan Wu; Qingmei Tan; Yuehui Zhang

    2013-01-01

    Natural disasters like earthquake and flood will surely destroy the existing traffic network, usually accompanied by delivery delay or even network collapse. A logistics-network-related delivery time reliability model defined by a shortest-time entropy is proposed as a means to estimate the actual delivery time reliability. The less the entropy is, the stronger the delivery time reliability remains, and vice versa. The shortest delivery time is computed separately based on two different assum...

  2. Modeling Evolution of Weighted Clique Networks

    Institute of Scientific and Technical Information of China (English)

    杨旭华; 蒋峰岭; 陈胜勇; 王万良

    2011-01-01

    We propose a weighted clique network evolution model, which expands continuously by the addition of a new clique (maximal complete sub-graph) at. each time step. And the cliques in the network overlap with each other. The structural expansion of the weighted clique network is combined with the edges' weight and vertices' strengths dynamical evolution. The model is based on a weight-driven dynamics and a weights' enhancement mechanism combining with the network growth. We study the network properties, which include the distribution of vertices' strength and the distribution o~ edges' weight, and find that both the distributions follow the scale-free distribution. At the same time, we also find that the relationship between strength and degree of a vertex are linear correlation during the growth of the network. On the basis of mean-field theory, we study the weighted network model and prove that both vertices' strength and edges' weight of this model follow the scale-free distribution. And we exploit an algorithm to forecast the network dynamics, which can be used to reckon the distributions and the corresponding scaling exponents. Furthermore, we observe that mean-field based theoretic results are consistent with the statistical data of the model, which denotes the theoretical result in this paper is effective.

  3. Modeling Evolution of Weighted Clique Networks

    Science.gov (United States)

    Yang, Xu-Hua; Jiang, Feng-Ling; Chen, Sheng-Yong; Wang, Wan-Liang

    2011-11-01

    We propose a weighted clique network evolution model, which expands continuously by the addition of a new clique (maximal complete sub-graph) at each time step. And the cliques in the network overlap with each other. The structural expansion of the weighted clique network is combined with the edges' weight and vertices' strengths dynamical evolution. The model is based on a weight-driven dynamics and a weights' enhancement mechanism combining with the network growth. We study the network properties, which include the distribution of vertices' strength and the distribution of edges' weight, and find that both the distributions follow the scale-free distribution. At the same time, we also find that the relationship between strength and degree of a vertex are linear correlation during the growth of the network. On the basis of mean-field theory, we study the weighted network model and prove that both vertices' strength and edges' weight of this model follow the scale-free distribution. And we exploit an algorithm to forecast the network dynamics, which can be used to reckon the distributions and the corresponding scaling exponents. Furthermore, we observe that mean-field based theoretic results are consistent with the statistical data of the model, which denotes the theoretical result in this paper is effective.

  4. Survey of propagation Model in wireless Network

    Directory of Open Access Journals (Sweden)

    Hemant Kumar Sharma

    2011-05-01

    Full Text Available To implementation of mobile ad hoc network wave propagation models are necessary to determine propagation characteristic through a medium. Wireless mobile ad hoc networks are self creating and self organizing entity. Propagation study provides an estimation of signal characteristics. Accurate prediction of radio propagation behaviour for MANET is becoming a difficult task. This paper presents investigation of propagation model. Radio wave propagation mechanisms are absorption, reflection, refraction, diffraction and scattering. This paper discuss free space model, two rays model, and cost 231 hata and its variants and fading model, and summarized the advantages and disadvantages of these model. This study would be helpful in choosing the correct propagation model.

  5. Modelling of virtual production networks

    Directory of Open Access Journals (Sweden)

    2011-03-01

    Full Text Available Nowadays many companies, especially small and medium-sized enterprises (SMEs, specialize in a limited field of production. It requires forming virtual production networks of cooperating enterprises to manufacture better, faster and cheaper. Apart from that, some production orders cannot be realized, because there is not a company of sufficient production potential. In this case the virtual production networks of cooperating companies can realize these production orders. These networks have larger production capacity and many different resources. Therefore it can realize many more production orders together than each of them separately. Such organization allows for executing high quality product. The maintenance costs of production capacity and used resources are not so high. In this paper a methodology of rapid prototyping of virtual production networks is proposed. It allows to execute production orders on time considered existing logistic constraints.

  6. Modeling the interdependent network based on two-mode networks

    Science.gov (United States)

    An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

    2017-10-01

    Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

  7. Introducing Synchronisation in Deterministic Network Models

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.;

    2006-01-01

    The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading....... The suggested models are intended for incorporation into an existing analysis tool a.k.a. CyNC based on the MATLAB/SimuLink framework for graphical system analysis and design....

  8. Neural network models of protein domain evolution

    OpenAIRE

    Sylvia Nagl

    2000-01-01

    Protein domains are complex adaptive systems, and here a novel procedure is presented that models the evolution of new functional sites within stable domain folds using neural networks. Neural networks, which were originally developed in cognitive science for the modeling of brain functions, can provide a fruitful methodology for the study of complex systems in general. Ethical implications of developing complex systems models of biomolecules are discussed, with particular reference to molecu...

  9. Homophyly/Kinship Model: Naturally Evolving Networks

    Science.gov (United States)

    Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

    2015-10-01

    It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

  10. An Analysis of USSPACECOM’s Space Surveillance Network (SSN) Sensor Tasking Methodology

    Science.gov (United States)

    1992-12-01

    models via a Call to SOP. Nertj t. usor is onlay required to pass a tim (mus anOte) for the calculat ion and the stlite E? poslttion sand velocity are...Useful in initializing in preparation for a call to Select.-TimeInterval. Procedure GetCurrentTime(var time : time-set); ..ýids the current system time

  11. A latent process model for forecasting multiple time series in environmental public health surveillance.

    Science.gov (United States)

    Morrison, Kathryn T; Shaddick, Gavin; Henderson, Sarah B; Buckeridge, David L

    2016-08-15

    This paper outlines a latent process model for forecasting multiple health outcomes arising from a common environmental exposure. Traditionally, surveillance models in environmental health do not link health outcome measures, such as morbidity or mortality counts, to measures of exposure, such as air pollution. Moreover, different measures of health outcomes are treated as independent, while it is known that they are correlated with one another over time as they arise in part from a common underlying exposure. We propose modelling an environmental exposure as a latent process, and we describe the implementation of such a model within a hierarchical Bayesian framework and its efficient computation using integrated nested Laplace approximations. Through a simulation study, we compare distinct univariate models for each health outcome with a bivariate approach. The bivariate model outperforms the univariate models in bias and coverage of parameter estimation, in forecast accuracy and in computational efficiency. The methods are illustrated with a case study using healthcare utilization and air pollution data from British Columbia, Canada, 2003-2011, where seasonal wildfires produce high levels of air pollution, significantly impacting population health. Copyright © 2016 John Wiley & Sons, Ltd.

  12. Simple model for river network evolution

    Energy Technology Data Exchange (ETDEWEB)

    Leheny, R.L. [The James Franck Institute and The Department of Physics, The University of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637 (United States)

    1995-11-01

    We simulate the evolution of a drainage basin by erosion from precipitation and avalanching on hillslopes. The avalanches create a competition in growth between neighboring basins and play the central role in driving the evolution. The simulated landscapes form drainage systems that share many qualitative features with Glock`s model for natural network evolution and maintain statistical properties that characterize real river networks. We also present results from a second model with a modified, mass conserving avalanche scheme. Although the terrains from these two models are qualitatively dissimilar, their drainage networks share the same general evolution and statistical features.

  13. Characterization and Modeling of Network Traffic

    DEFF Research Database (Denmark)

    Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur

    2011-01-01

    This paper attempts to characterize and model backbone network traffic, using a small number of statistics. In order to reduce cost and processing power associated with traffic analysis. The parameters affecting the behaviour of network traffic are investigated and the choice is that inter...

  14. Modeling the hydrodynamics in tidal networks

    NARCIS (Netherlands)

    Alebregtse, N.C.

    2016-01-01

    This thesis covers tidal propagation through networks of channels. Such networks are widespread and are often subject to discordant human and natural interests. First, the effect of a secondary channel on the tides in a main channel is explained with the use of an idealized model and the responsible

  15. Simple models of human brain functional networks.

    Science.gov (United States)

    Vértes, Petra E; Alexander-Bloch, Aaron F; Gogtay, Nitin; Giedd, Jay N; Rapoport, Judith L; Bullmore, Edward T

    2012-04-10

    Human brain functional networks are embedded in anatomical space and have topological properties--small-worldness, modularity, fat-tailed degree distributions--that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.

  16. Network Design Models for Container Shipping

    DEFF Research Database (Denmark)

    Reinhardt, Line Blander; Kallehauge, Brian; Nielsen, Anders Nørrelund

    This paper presents a study of the network design problem in container shipping. The paper combines the network design and fleet assignment problem into a mixed integer linear programming model minimizing the overall cost. The major contributions of this paper is that the time of a vessel route...

  17. Cyber threat model for tactical radio networks

    Science.gov (United States)

    Kurdziel, Michael T.

    2014-05-01

    The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.

  18. Multiscaling in an YX model of networks.

    Science.gov (United States)

    Holme, Petter; Wu, Zhi-Xi; Minnhagen, Petter

    2009-09-01

    We investigate a Hamiltonian model of networks. The model is a mirror formulation of the XY model (hence the name)--instead of letting the XY spins vary, keeping the coupling topology static, we keep the spins conserved and sample different underlying networks. Our numerical simulations show complex scaling behaviors with various exponents as the system grows and temperature approaches zero, but no finite-temperature universal critical behavior. The ground-state and low-order excitations for sparse, finite graphs are a fragmented set of isolated network clusters. Configurations of higher energy are typically more connected. The connected networks of lowest energy are stretched out giving the network large average distances. For the finite sizes we investigate, there are three regions--a low-energy regime of fragmented networks, an intermediate regime of stretched-out networks, and a high-energy regime of compact, disordered topologies. Scaling up the system size, the borders between these regimes approach zero temperature algebraically, but different network-structural quantities approach their T=0 values with different exponents. We argue this is a, perhaps rare, example of a statistical-physics model where finite sizes show a more interesting behavior than the thermodynamic limit.

  19. Designing Network-based Business Model Ontology

    DEFF Research Database (Denmark)

    Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz

    2015-01-01

    Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....

  20. Stochastic discrete model of karstic networks

    Science.gov (United States)

    Jaquet, O.; Siegel, P.; Klubertanz, G.; Benabderrhamane, H.

    Karst aquifers are characterised by an extreme spatial heterogeneity that strongly influences their hydraulic behaviour and the transport of pollutants. These aquifers are particularly vulnerable to contamination because of their highly permeable networks of conduits. A stochastic model is proposed for the simulation of the geometry of karstic networks at a regional scale. The model integrates the relevant physical processes governing the formation of karstic networks. The discrete simulation of karstic networks is performed with a modified lattice-gas cellular automaton for a representative description of the karstic aquifer geometry. Consequently, more reliable modelling results can be obtained for the management and the protection of karst aquifers. The stochastic model was applied jointly with groundwater modelling techniques to a regional karst aquifer in France for the purpose of resolving surface pollution issues.

  1. A Model for Telestrok Network Evaluation

    DEFF Research Database (Denmark)

    Storm, Anna; Günzel, Franziska; Theiss, Stephan

    2011-01-01

    Different telestroke network concepts have been implemented worldwide to enable fast and efficient treatment of stroke patients in underserved rural areas. Networks could demonstrate the improvement in clinical outcome, but have so far excluded a cost-effectiveness analysis. With health economic...... was developed from the third-party payer perspective. In principle, it enables telestroke networks to conduct cost-effectiveness studies, because the majority of the required data can be extracted from health insurance companies’ databases and the telestroke network itself. The model presents a basis...

  2. Radar Scan Strategies for the Patrick Air Force Base Weather Surveillance Radar, Model-74C, Replacement

    Science.gov (United States)

    Short, David

    2008-01-01

    The 45th Weather Squadron (45 WS) is replacing the Weather Surveillance Radar, Model 74C (WSR-74C) at Patrick Air Force Base (PAFB), with a Doppler, dual polarization radar, the Radtec 43/250. A new scan strategy is needed for the Radtec 43/250, to provide high vertical resolution data over the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) launch pads, while taking advantage of the new radar's advanced capabilities for detecting severe weather phenomena associated with convection within the 45 WS area of responsibility. The Applied Meteorology Unit (AMU) developed several scan strategies customized for the operational needs of the 45 WS. The AMU also developed a plan for evaluating the scan strategies in the period prior to operational acceptance, currently scheduled for November 2008.

  3. Hierarchical ensemble of background models for PTZ-based video surveillance.

    Science.gov (United States)

    Liu, Ning; Wu, Hefeng; Lin, Liang

    2015-01-01

    In this paper, we study a novel hierarchical background model for intelligent video surveillance with the pan-tilt-zoom (PTZ) camera, and give rise to an integrated system consisting of three key components: background modeling, observed frame registration, and object tracking. First, we build the hierarchical background model by separating the full range of continuous focal lengths of a PTZ camera into several discrete levels and then partitioning the wide scene at each level into many partial fixed scenes. In this way, the wide scenes captured by a PTZ camera through rotation and zoom are represented by a hierarchical collection of partial fixed scenes. A new robust feature is presented for background modeling of each partial scene. Second, we locate the partial scenes corresponding to the observed frame in the hierarchical background model. Frame registration is then achieved by feature descriptor matching via fast approximate nearest neighbor search. Afterwards, foreground objects can be detected using background subtraction. Last, we configure the hierarchical background model into a framework to facilitate existing object tracking algorithms under the PTZ camera. Foreground extraction is used to assist tracking an object of interest. The tracking outputs are fed back to the PTZ controller for adjusting the camera properly so as to maintain the tracked object in the image plane. We apply our system on several challenging scenarios and achieve promising results.

  4. PORTENT: predator aware situation assessment for wireless sensor network surveillance applications

    Science.gov (United States)

    Ghataoura, D. S.; Yang, Y.; Mitchell, J. E.; Matich, G. E.

    2010-04-01

    In this paper, we propose a distributed predator aware situation assessment system (PORTENT) to model and detect potential events occurring within an uncertain environment. PORTENT draws inspiration from how the mammalian brain detects and makes rational decisions through assessing fragmented signals of threat, within uncertainty, at different speeds. PORTENT represents the faster system using standard signal detection theory and the slower more accurate system as the integration of sensory data over time, until a certain level of confidence is reached. We also consider strategies to how both these systems could be combined optimally, to enhance PORTENT situation assessment performance. Our experimental simulations to verify the PORTENT concept demonstrates the effectiveness of our approach.

  5. Model-based control of networked systems

    CERN Document Server

    Garcia, Eloy; Montestruque, Luis A

    2014-01-01

    This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled.   The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control.   Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...

  6. Complex networks repair strategies: Dynamic models

    Science.gov (United States)

    Fu, Chaoqi; Wang, Ying; Gao, Yangjun; Wang, Xiaoyang

    2017-09-01

    Network repair strategies are tactical methods that restore the efficiency of damaged networks; however, unreasonable repair strategies not only waste resources, they are also ineffective for network recovery. Most extant research on network repair focuses on static networks, but results and findings on static networks cannot be applied to evolutionary dynamic networks because, in dynamic models, complex network repair has completely different characteristics. For instance, repaired nodes face more severe challenges, and require strategic repair methods in order to have a significant effect. In this study, we propose the Shell Repair Strategy (SRS) to minimize the risk of secondary node failures due to the cascading effect. Our proposed method includes the identification of a set of vital nodes that have a significant impact on network repair and defense. Our identification of these vital nodes reduces the number of switching nodes that face the risk of secondary failures during the dynamic repair process. This is positively correlated with the size of the average degree and enhances network invulnerability.

  7. Modeling trust context in networks

    CERN Document Server

    Adali, Sibel

    2013-01-01

    We make complex decisions every day, requiring trust in many different entities for different reasons. These decisions are not made by combining many isolated trust evaluations. Many interlocking factors play a role, each dynamically impacting the others.? In this brief, 'trust context' is defined as the system level description of how the trust evaluation process unfolds.Networks today are part of almost all human activity, supporting and shaping it. Applications increasingly incorporate new interdependencies and new trust contexts. Social networks connect people and organizations throughout

  8. Graphical Model Theory for Wireless Sensor Networks

    Energy Technology Data Exchange (ETDEWEB)

    Davis, William B.

    2002-12-08

    Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.

  9. Modelling subtle growth of linguistic networks

    CERN Document Server

    Kulig, Andrzej; Kwapien, Jaroslaw; Oswiecimka, Pawel

    2014-01-01

    We investigate properties of evolving linguistic networks defined by the word-adjacency relation. Such networks belong to the category of networks with accelerated growth but their shortest path length appears to reveal the network size dependence of different functional form than the ones known so far. We thus compare the networks created from literary texts with their artificial substitutes based on different variants of the Dorogovtsev-Mendes model and observe that none of them is able to properly simulate the novel asymptotics of the shortest path length. Then, we identify grammar induced local chain-like linear growth as a missing element in this model and extend it by incorporating such effects. It is in this way that a satisfactory agreement with the empirical result is obtained.

  10. Network models of dissolution of porous media

    CERN Document Server

    Budek, Agnieszka

    2012-01-01

    We investigate the chemical dissolution of porous media using a network model in which the system is represented as a series of interconnected pipes with the diameter of each segment increasing in proportion to the local reactant consumption. Moreover, the topology of the network is allowed to change dynamically during the simulation: as the diameters of the eroding pores become comparable with the interpore distances, the pores are joined together thus changing the interconnections within the network. With this model, we investigate different growth regimes in an evolving porous medium, identifying the mechanisms responsible for the emergence of specific patterns. We consider both the random and regular network and study the effect of the network geometry on the patterns. Finally, we consider practically important problem of finding an optimum flow rate that gives a maximum increase in permeability for a given amount of reactant.

  11. Modeling Network Traffic in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Sheng Ma

    2004-12-01

    Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.

  12. EPS Biomarkers Improve Stratification of NCCN Active Surveillance Candidates: Performance of Secretion Capacity and TMPRSS2:ERG Models

    Science.gov (United States)

    Whelan, Christopher; Kawachi, Mark; Smith, David D.; Linehan, Jennifer; Babilonia, Gail; Mejia, Rosa; Wilson, Timothy; Smith, Steven

    2014-01-01

    Purpose Active surveillance (AS) is a viable patient option for prostate cancer where a clinical determination of low-risk and presumably organ-confined disease can be made. In an effort to standardize risk stratification schemes, the National Comprehensive Cancer Network (NCCN) has provided guidelines for the AS option. Our purpose was to determine the effectiveness of expressed prostatic secretion (EPS) biomarkers in detecting occult risk factors in NCCN AS candidates. Materials and Methods EPS specimens were obtained prior to Robot-Assisted Radical Prostatectomy (RARP). Secretion capacity biomarkers: total RNA and EPS specimen volume were measured by standard techniques. RNA expression biomarkers: TXNRD1-mRNA, PSA-mRNA, TMPRSS2:ERG fusion mRNA and PCA3-mRNAs were measured by quantitative reverse-transcription PCR. Results Of the 528 patients from whom EPS was collected, 216 were eligible for AS under NCCN guidelines. Variable Selection in logistic regression identified two models, one featuring Type III and Type VI TMPRSS2:ERG variants, and one featuring two secretion capacity biomarkers. Of the two high performing models, the secretion capacity model was the most effective in detecting patients within this group that were upstaged or both upstaged and upgraded. It reduced the risk of upstaging in patients with a negative test by nearly 8 fold, and reduced the risk of being both upstaged and upgraded by about 5 fold, while doubling the prevalence upstaging in the positive test group. Conclusions Non-invasive EPS testing may improve patient acceptance of AS by dramatically reducing the presence of occult risk factors among patients eligible for AS under NCCN guidelines. PMID:23669563

  13. A spatio-velocity model based semantic event detection algorithm for traffic surveillance video

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Detection of vehicle events is a research hotspot in digital traffic.In this paper,an approach is proposed to detect vehicle events with semantic analysis of traffic surveillance video using spatio-velocity statistic models.The approach includes two successive phases:trajectory clustering and semantic events detection.For trajectory clustering,a statistic model of vehicle trajectories are presented,for which a spatio-velocity model is trained by analyzing the trajectories of moving vehicles in the scene.Based on the trajectory,which represents both the position of the vehicle and its instantaneous velocity,a trajectory similarity measure is proposed.Then,an improved hierarchical clustering algorithm is adopted to cluster the trajectories according to different spatial and velocity distributions.In each cluster,trajectories that are spatially close have similar velocities of motion and represent one type of activity pattern.For the semantic events detection phase,statistic models of semantic regions in the scene are generated by estimating the probability density and velocity distributions of each type of activity pattern.Finally,semantic events are detected by the proposed spatio-velocity statistic models.The paper also presents experiments using real video sequence to verify the effectiveness of the proposed method.

  14. Modelling the scorpion stings using surveillance data in El Bayadh Province, Algeria

    Directory of Open Access Journals (Sweden)

    Schehrazad Selmane

    2016-12-01

    Full Text Available Objective: To examine some epidemiological features of scorpion envenomations, analyse and interpret the recorded data, find any relationship between the incidence of scorpion stings and climatic factors, and finally develop a statistical model to estimate the variability among future cases in El Bayadh Province, Algeria. Methods: To assess the effects of climate variability on the scorpion envenomations, we applied the count data regression models to the monthly recorded scorpion stings in El Bayadh Province from 2001 to 2012. Results: The epidemiological analysis of data revealed that scorpion stings occured mainly in rural areas, round the clock, all year long with the highest seasonal incidence in summer, and the lowest in winter, all ages with male predominance. The ends of upper and lower limbs were the most affected parts of the human body. The majority of cases (95.7% were classified as mild envenomations and systemic toxicity was observed in 4.3% of cases. The use of count data regression models showed that the negative binomial regression was appropriate to forecast cases and the fitted data agreed considerably with the actual data. Moreover, the model had predicted the monthly scorpion sting cases for the year of 2013, with satisfactory accuracy. Conclusions: This study shows an optimism for forecasting scorpion stings by modelling and calibration with surveillance data and climate information. This knowledge could help to contain any unusual situation and assist health decision-makers to strengthen the prevention and control measures and to be in a state of readiness.

  15. IP Network Management Model Based on NGOSS

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jin-yu; LI Hong-hui; LIU Feng

    2004-01-01

    This paper addresses a management model for IP network based on Next Generation Operation Support System (NGOSS). It makes the network management on the base of all the operation actions of ISP, It provides QoS to user service through the whole path by providing end-to-end Service Level Agreements (SLA) management through whole path. Based on web and coordination technology, this paper gives an implement architecture of this model.

  16. A Tumor Surveillance Model: A Non-Coding RNA Senses Neoplastic Cells and Its Protein Partner Signals Cell Death

    Directory of Open Access Journals (Sweden)

    Yong Sun Lee

    2012-10-01

    Full Text Available nc886 (= pre-miR-886 or vtRNA2-1 is a non-coding RNA that has been recently identified as a natural repressor for the activity of PKR (Protein Kinase R. The suppression of nc886 activates PKR and thereby provokes a cell death pathway. When combined with the fact that nc886 is suppressed in a wide range of cancer cells, the nc886-PKR relationship suggests a tumor surveillance model. When neoplastic cells develop and nc886 decreases therein, PKR is released from nc886 and becomes the active phosphorylated form, which initiates an apoptotic cascade to eliminate those cells. The nc886-PKR pathway is distinct from conventional mechanisms, such as the immune surveillance hypothesis or intrinsic mechanisms that check/proofread the genomic integrity, and thus represents a novel example of tumor surveillance.

  17. Model-driven SOA for sensor networks

    Science.gov (United States)

    Ibbotson, John; Gibson, Christopher; Geyik, Sahin; Szymanski, Boleslaw K.; Mott, David; Braines, David; Klapiscak, Tom; Bergamaschi, Flavio

    2011-06-01

    Our previous work has explored the application of enterprise middleware techniques at the edge of the network to address the challenges of delivering complex sensor network solutions over heterogeneous communications infrastructures. In this paper, we develop this approach further into a practicable, semantically rich, model-based design and analysis approach that considers the sensor network and its contained services as a service-oriented architecture. The proposed model enables a systematic approach to service composition, analysis (using domain-specific techniques), and deployment. It also enables cross intelligence domain integration to simplify intelligence gathering, allowing users to express queries in structured natural language (Controlled English).

  18. Modelling food safety and economic consequences of surveillance and control stratigegies for Salmonella in pigs and pork

    DEFF Research Database (Denmark)

    Baptista, Filipa M.; Hisham Beshara Halasa, Tariq; Alban, Liza R.;

    2011-01-01

    Targets for maximum acceptable levels of Salmonella in pigs and pork are to be decided. A stochastic simulation model accounting for herd and abattoir information was used to evaluate food safety and economic consequences of different surveillance and control strategies, based among others on Dan...

  19. River Network Modeling Beyond Discharge at Gauges

    Science.gov (United States)

    David, C. H.; Famiglietti, J. S.; Salas, F. R.; Whiteaker, T. L.; Maidment, D. R.; Tolle, K.

    2014-12-01

    Over the past two decades, the estimation of water flow in river networks within hydro-meteorological models has mostly focused on simulations of natural processes and on their verification at available river gauges. Despite valuable existing skills in hydrologic modeling the accounting for anthropogenic actions in current models remains limited. The emerging availability of datasets containing measured dam outflows and reported irrigation withdrawals motivates their inclusion into simulations of flow in river networks. However, the development of advanced river network models accounting for such datasets of anthropogenic influences requires a detailed data model and a thorough handling of the various data types, sources and time scales. This contribution details the development of a consistent data model suitable for accounting some observations of anthropogenic modifications of the surface water cycle and presents the impact of such inclusion on simulations using the Routing Application for Parallel computatIon of Discharge (RAPID).

  20. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  1. Kitaev spin models from topological nanowire networks

    NARCIS (Netherlands)

    Kells, G.; Lahtinen, V.; Vala, J.

    2014-01-01

    We show that networks of superconducting topological nanowires can realize the physics of exactly solvable Kitaev spin models on trivalent lattices. This connection arises from the low-energy theory of both systems being described by a tight-binding model of Majorana modes. In Kitaev spin models the

  2. Current crisis or artifact of surveillance: insights into rebound chlamydia rates from dynamic modelling

    Directory of Open Access Journals (Sweden)

    Vickers David M

    2010-03-01

    Full Text Available Abstract Background After initially falling in the face of intensified control efforts, reported rates of sexually transmitted chlamydia in many developed countries are rising. Recent hypotheses for this phenomenon have broadly focused on improved case finding or an increase in the prevalence. Because of many complex interactions behind the spread of infectious diseases, dynamic models of infection transmission are an effective means to guide learning, and assess quantitative conjectures of epidemiological processes. The objective of this paper is to bring a unique and robust perspective to observed chlamydial patterns through analyzing surveillance data with mathematical models of infection transmission. Methods This study integrated 25-year testing volume data from the Canadian province of Saskatchewan with one susceptible-infected-treated-susceptible and three susceptible-infected-treated-removed compartmental models. Calibration of model parameters to fit observed 25-year case notification data, after being combined with testing records, placed constraints on model behaviour and allowed for an approximation of chlamydia prevalence to be estimated. Model predictions were compared to observed case notification trends, and extensive sensitivity analyses were performed to confirm the robustness of model results. Results Model predictions accurately mirrored historic chlamydial trends including an observed rebound in the mid 1990s. For all models examined, the results repeatedly highlighted that increased testing volumes, rather than changes in the sensitivity and specificity of testing technologies, sexual behaviour, or truncated immunological responses brought about by treatment can, explain the increase in observed chlamydia case notifications. Conclusions Our results highlight the significant impact testing volume can have on observed incidence rates, and that simple explanations for these observed increases appear to have been dismissed in

  3. Fractal modeling of natural fracture networks

    Energy Technology Data Exchange (ETDEWEB)

    Ferer, M.; Dean, B.; Mick, C.

    1995-06-01

    West Virginia University will implement procedures for a fractal analysis of fractures in reservoirs. This procedure will be applied to fracture networks in outcrops and to fractures intersecting horizontal boreholes. The parameters resulting from this analysis will be used to generate synthetic fracture networks with the same fractal characteristics as the real networks. Recovery from naturally fractured, tight-gas reservoirs is controlled by the fracture network. Reliable characterization of the actual fracture network in the reservoir is severely limited. The location and orientation of fractures intersecting the borehole can be determined, but the length of these fractures cannot be unambiguously determined. Because of the lack of detailed information about the actual fracture network, modeling methods must represent the porosity and permeability associated with the fracture network, as accurately as possible with very little a priori information. In the sections following, the authors will (1) present fractal analysis of the MWX site, using the box-counting procedure; (2) review evidence testing the fractal nature of fracture distributions and discuss the advantages of using the fractal analysis over a stochastic analysis; and (3) present an efficient algorithm for producing a self-similar fracture networks which mimic the real MWX outcrop fracture network.

  4. A simple model for studying interacting networks

    Science.gov (United States)

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

    2011-03-01

    Many specific physical networks (e.g., internet, power grid, interstates), have been characterized in considerable detail, but in isolation from each other. Yet, each of these networks supports the functions of the others, and so far, little is known about how their interactions affect their structure and functionality. To address this issue, we consider two coupled model networks. Each network is relatively simple, with a fixed set of nodes, but dynamically generated set of links which has a preferred degree, κ . In the stationary state, the degree distribution has exponential tails (far from κ), an attribute which we can explain. Next, we consider two such networks with different κ 's, reminiscent of two social groups, e.g., extroverts and introverts. Finally, we let these networks interact by establishing a controllable fraction of cross links. The resulting distribution of links, both within and across the two model networks, is investigated and discussed, along with some potential consequences for real networks. Supported in part by NSF-DMR-0705152 and 1005417.

  5. Automated modelling of signal transduction networks

    Directory of Open Access Journals (Sweden)

    Aach John

    2002-11-01

    Full Text Available Abstract Background Intracellular signal transduction is achieved by networks of proteins and small molecules that transmit information from the cell surface to the nucleus, where they ultimately effect transcriptional changes. Understanding the mechanisms cells use to accomplish this important process requires a detailed molecular description of the networks involved. Results We have developed a computational approach for generating static models of signal transduction networks which utilizes protein-interaction maps generated from large-scale two-hybrid screens and expression profiles from DNA microarrays. Networks are determined entirely by integrating protein-protein interaction data with microarray expression data, without prior knowledge of any pathway intermediates. In effect, this is equivalent to extracting subnetworks of the protein interaction dataset whose members have the most correlated expression profiles. Conclusion We show that our technique accurately reconstructs MAP Kinase signaling networks in Saccharomyces cerevisiae. This approach should enhance our ability to model signaling networks and to discover new components of known networks. More generally, it provides a method for synthesizing molecular data, either individual transcript abundance measurements or pairwise protein interactions, into higher level structures, such as pathways and networks.

  6. A survey of statistical network models

    CERN Document Server

    Goldenberg, Anna; Fienberg, Stephen E; Airoldi, Edoardo M

    2009-01-01

    Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry poin...

  7. Syndromic surveillance models using Web data: the case of scarlet fever in the UK.

    Science.gov (United States)

    Samaras, Loukas; García-Barriocanal, Elena; Sicilia, Miguel-Angel

    2012-03-01

    Recent research has shown the potential of Web queries as a source for syndromic surveillance, and existing studies show that these queries can be used as a basis for estimation and prediction of the development of a syndromic disease, such as influenza, using log linear (logit) statistical models. Two alternative models are applied to the relationship between cases and Web queries in this paper. We examine the applicability of using statistical methods to relate search engine queries with scarlet fever cases in the UK, taking advantage of tools to acquire the appropriate data from Google, and using an alternative statistical method based on gamma distributions. The results show that using logit models, the Pearson correlation factor between Web queries and the data obtained from the official agencies must be over 0.90, otherwise the prediction of the peak and the spread of the distributions gives significant deviations. In this paper, we describe the gamma distribution model and show that we can obtain better results in all cases using gamma transformations, and especially in those with a smaller correlation factor.

  8. Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

    Directory of Open Access Journals (Sweden)

    Lei Qin

    2014-05-01

    Full Text Available We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences.

  9. [The Brazilian National Health Surveillance Agency performance evaluation at the management contract model].

    Science.gov (United States)

    Moreira, Elka Maltez de Miranda; Costa, Ediná Alves

    2010-11-01

    The Brazilian National Health Surveillance Agency (Anvisa) is supervised by the Ministry of Health by means of a management contract, a performance evaluation tool. This case study was aimed at describing and analyzing Anvisa's performance evaluation model based on the agency's institutional purpose, according to the following analytical categories: the management contract formalization, evaluation tools, evaluators and institutional performance. Semi-structured interviews and document analysis revealed that Anvisa signed only one management contract with the Ministry of Health in 1999, updated by four additive terms. The Collegiate Board of Directors and the Advisory Center for Strategic Management play the role of Anvisa's internal evaluators and an Assessing Committee, comprising the Ministry of Health, constitutes its external evaluator. Three phases were identified in the evaluation model: the structuring of the new management model (1999-2000), legitimation regarding the productive segment (2001-2004) and widespread legitimation (2005). The best performance was presented in 2000 (86.05%) and the worst in 2004 (40.00%). The evaluation model was shown to have contributed little towards the agency's institutional purpose and the effectiveness measurement of the implemented actions.

  10. A global network for the control of snail-borne disease using satellite surveillance and geographic information systems.

    Science.gov (United States)

    Malone, J B; Bergquist, N R; Huh, O K; Bavia, M E; Bernardi, M; El Bahy, M M; Fuentes, M V; Kristensen, T K; McCarroll, J C; Yilma, J M; Zhou, X N

    2001-04-27

    At a team residency sponsored by the Rockefeller Foundation in Bellagio, Italy, 10-14 April 2000 an organizational plan was conceived to create a global network of collaborating health workers and earth scientists dedicated to the development of computer-based models that can be used for improved control programs for schistosomiasis and other snail-borne diseases of medical and veterinary importance. The models will be assembled using GIS methods, global climate model data, sensor data from earth observing satellites, disease prevalence data, the distribution and abundance of snail hosts, and digital maps of key environmental factors that affect development and propagation of snail-borne disease agents. A work plan was developed for research collaboration and data sharing, recruitment of new contributing researchers, and means of access of other medical scientists and national control program managers to GIS models that may be used for more effective control of snail-borne disease. Agreement was reached on the use of compatible GIS formats, software, methods and data resources, including the definition of a 'minimum medical database' to enable seamless incorporation of results from each regional GIS project into a global model. The collaboration plan calls for linking a 'central resource group' at the World Health Organization, the Food and Agriculture Organization, Louisiana State University and the Danish Bilharziasis Laboratory with regional GIS networks to be initiated in Eastern Africa, Southern Africa, West Africa, Latin America and Southern Asia. An Internet site, www.gnosisGIS.org, (GIS Network On Snail-borne Infections with special reference to Schistosomiasis), has been initiated to allow interaction of team members as a 'virtual research group'. When completed, the site will point users to a toolbox of common resources resident on computers at member organizations, provide assistance on routine use of GIS health maps in selected national disease control

  11. A quantum-implementable neural network model

    Science.gov (United States)

    Chen, Jialin; Wang, Lingli; Charbon, Edoardo

    2017-10-01

    A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

  12. Network Modeling and Simulation (NEMSE)

    Science.gov (United States)

    2013-07-01

    Emulator (CORE) modules, hardware components, wireless cards with links modifiable using Radio Frequency (RF) cabling and variable attenuators, and GNU ...universal Network Interface Card (NIC) cards - Cardbus, PCI, or miniPCI. o GNU radio : Free & open-source software development toolkit that provides...Layer Emulator (FPLE), GNU Radio , CORE & EMANE, Tech Warrior, CASCON (CAS Connectivity), and Rate Adaptive Video Coding (RAVC). Also described is the

  13. A Model of Network Porosity

    Science.gov (United States)

    2016-11-09

    standpoint remains more of an art than a science. Even when well executed, the ongoing evolution of the network may violate initial, security-critical design... Internet and compromises the LAN (this step compromises only the LAN). We describe this as a functional information flow between the Internet and the...vulnerabilities will stem from the delivery of email, access to the Internet , or access to an internal document or data repository. If any of these are

  14. Telestroke network business model strategies.

    Science.gov (United States)

    Fanale, Christopher V; Demaerschalk, Bart M

    2012-10-01

    Our objective is to summarize the evidence that supports the reliability of telemedicine for diagnosis and efficacy in acute stroke treatment, identify strategies for funding the development of a telestroke network, and to present issues with respect to economic sustainability, cost effectiveness, and the status of reimbursement for telestroke. Copyright © 2012 National Stroke Association. Published by Elsevier Inc. All rights reserved.

  15. Human thermal modeling to augment MWIR image analysis in surveillance applications

    Science.gov (United States)

    Woodyard, R. L.; Skipper, J. A.

    2014-05-01

    The interpretation of thermal imagery can be augmented with information derived from human thermal modeling to better infer human activity during, or prior to, data capture. This additional insight into human activity could prove useful in security and surveillance applications. We have implemented Tanabe's 65 NM thermocomfort model to predict skin surface temperature under a wide variety of environmental, activity and body parameters. Here, humans are modeled as sixteen segments (head, chest, upper leg, etc.), wherein spherical geometry is assumed for the head and cylindrical geometry is assumed for all other segments. Each segment is comprised of four layers: core, muscle, fat, and skin. Clothing is modeled as an additional layer (or layers) of resistance. Users supply input parameters via our custom MATLAB graphical user interface that includes a robust clothing database based on McCullough's A Database for Determining the Evaporative Resistance of Clothing, and then Tanabe's bioheat equations are solved to predict skin temperatures of each body segment. As an initial step of model validation, we compared our computed thermal resistances with literature values. Our evaporative and dry resistance on a per segment basis agreed with literature values. The dry resistance of each segment varied no more than .03 [m2°C/W]. Model validation will be extended to compare the results of our human subject trials (known body parameters, clothing, environmental factors and activity levels) to model outputs. Agreement would further substantiate the propagation of model- predicted skin temperatures through the thermal imager's transfer function to predict human heat signatures in thermal imagery.

  16. Model Predictive Control of Sewer Networks

    Science.gov (United States)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.

    2017-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.

  17. Performance modeling, stochastic networks, and statistical multiplexing

    CERN Document Server

    Mazumdar, Ravi R

    2013-01-01

    This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan

  18. Network Modeling and Simulation A Practical Perspective

    CERN Document Server

    Guizani, Mohsen; Khan, Bilal

    2010-01-01

    Network Modeling and Simulation is a practical guide to using modeling and simulation to solve real-life problems. The authors give a comprehensive exposition of the core concepts in modeling and simulation, and then systematically address the many practical considerations faced by developers in modeling complex large-scale systems. The authors provide examples from computer and telecommunication networks and use these to illustrate the process of mapping generic simulation concepts to domain-specific problems in different industries and disciplines. Key features: Provides the tools and strate

  19. Modeling acquaintance networks based on balance theory

    Directory of Open Access Journals (Sweden)

    Vukašinović Vida

    2014-09-01

    Full Text Available An acquaintance network is a social structure made up of a set of actors and the ties between them. These ties change dynamically as a consequence of incessant interactions between the actors. In this paper we introduce a social network model called the Interaction-Based (IB model that involves well-known sociological principles. The connections between the actors and the strength of the connections are influenced by the continuous positive and negative interactions between the actors and, vice versa, the future interactions are more likely to happen between the actors that are connected with stronger ties. The model is also inspired by the social behavior of animal species, particularly that of ants in their colony. A model evaluation showed that the IB model turned out to be sparse. The model has a small diameter and an average path length that grows in proportion to the logarithm of the number of vertices. The clustering coefficient is relatively high, and its value stabilizes in larger networks. The degree distributions are slightly right-skewed. In the mature phase of the IB model, i.e., when the number of edges does not change significantly, most of the network properties do not change significantly either. The IB model was found to be the best of all the compared models in simulating the e-mail URV (University Rovira i Virgili of Tarragona network because the properties of the IB model more closely matched those of the e-mail URV network than the other models

  20. Evaluation of control and surveillance strategies for classical swine fever using a simulation model.

    Science.gov (United States)

    Dürr, S; Zu Dohna, H; Di Labio, E; Carpenter, T E; Doherr, M G

    2013-01-01

    Classical swine fever (CSF) outbreaks can cause enormous losses in naïve pig populations. How to best minimize the economic damage and number of culled animals caused by CSF is therefore an important research area. The baseline CSF control strategy in the European Union and Switzerland consists of culling all animals in infected herds, movement restrictions for animals, material and people within a given distance to the infected herd and epidemiological tracing of transmission contacts. Additional disease control measures such as pre-emptive culling or vaccination have been recommended based on the results from several simulation models; however, these models were parameterized for areas with high animal densities. The objective of this study was to explore whether pre-emptive culling and emergency vaccination should also be recommended in low- to moderate-density areas such as Switzerland. Additionally, we studied the influence of initial outbreak conditions on outbreak severity to improve the efficiency of disease prevention and surveillance. A spatial, stochastic, individual-animal-based simulation model using all registered Swiss pig premises in 2009 (n=9770) was implemented to quantify these relationships. The model simulates within-herd and between-herd transmission (direct and indirect contacts and local area spread). By varying the four parameters (a) control measures, (b) index herd type (breeding, fattening, weaning or mixed herd), (c) detection delay for secondary cases during an outbreak and (d) contact tracing probability, 112 distinct scenarios were simulated. To assess the impact of scenarios on outbreak severity, daily transmission rates were compared between scenarios. Compared with the baseline strategy (stamping out and movement restrictions) vaccination and pre-emptive culling neither reduced outbreak size nor duration. Outbreaks starting in a herd with weaning piglets or fattening pigs caused higher losses regarding to the number of culled

  1. Distributed Bayesian Networks for User Modeling

    DEFF Research Database (Denmark)

    Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang

    2006-01-01

    by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...... efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms....

  2. Optimal transportation networks models and theory

    CERN Document Server

    Bernot, Marc; Morel, Jean-Michel

    2009-01-01

    The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.

  3. Modeling Emergence in Neuroprotective Regulatory Networks

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Haack, Jereme N.; McDermott, Jason E.; Stevens, S.L.; Stenzel-Poore, Mary

    2013-01-05

    The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling in silico experimentation to test disease triggers and potential drug therapies. Techniques that focus on modeling emergence, such as agent-based modeling and multi-agent simulations, are of particular interest as they support the discovery of pathways that may have never been observed in the past. Thus far, these techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatory networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks that can advance the discovery of acute treatments for stroke and other diseases.

  4. Flood routing modelling with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    R. Peters

    2006-01-01

    Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.

  5. International migration network: topology and modeling.

    Science.gov (United States)

    Fagiolo, Giorgio; Mastrorillo, Marina

    2013-07-01

    This paper studies international migration from a complex-network perspective. We define the international migration network (IMN) as the weighted-directed graph where nodes are world countries and links account for the stock of migrants originated in a given country and living in another country at a given point in time. We characterize the binary and weighted architecture of the network and its evolution over time in the period 1960-2000. We find that the IMN is organized around a modular structure with a small-world binary pattern displaying disassortativity and high clustering, with power-law distributed weighted-network statistics. We also show that a parsimonious gravity model of migration can account for most of observed IMN topological structure. Overall, our results suggest that socioeconomic, geographical, and political factors are more important than local-network properties in shaping the structure of the IMN.

  6. Internet and Surveillance

    DEFF Research Database (Denmark)

    The Internet has been transformed in the past years from a system primarily oriented on information provision into a medium for communication and community-building. The notion of “Web 2.0”, social software, and social networking sites such as Facebook, Twitter and MySpace have emerged in this co......The Internet has been transformed in the past years from a system primarily oriented on information provision into a medium for communication and community-building. The notion of “Web 2.0”, social software, and social networking sites such as Facebook, Twitter and MySpace have emerged...... in the age of the internet; civil watch-surveillance on social networking sites; and networked interactive surveillance in transnational space. This book is a result of a research action launched by the intergovernmental network COST (European Cooperation in Science and Technology)....

  7. Evaluation of models predicting insignificant prostate cancer to select men for active surveillance of prostate cancer.

    Science.gov (United States)

    Wong, L M; Neal, D E; Finelli, A; Davis, S; Bonner, C; Kapoor, J; Trachtenberg, J; Thomas, B; Hovens, C M; Costello, A J; Corcoran, N M

    2015-06-01

    In an era of personalized medicine, individualized risk assessment using easily available tools on the internet and the literature are appealing. However, uninformed use by clinicians and the public raises potential problems. Herein, we assess the performance of published models to predict insignificant prostate cancer (PCa), using a multi-national low-risk population that may be considered for active surveillance (AS) based on contemporary practice. Data on men suitable for AS but undergoing upfront radical prostatectomy were pooled from three international academic institutions in Cambridge (UK), Toronto (Canada) and Melbourne (Australia). Four predictive models identified from literature review were assessed for their ability to predict the presence of four definitions of insignificant PCa. Evaluation was performed using area under the curve (AUC) of receiver operating characteristic curves and Brier scores for discrimination, calibration curves and decision curve analysis. A cohort of 460 men meeting the inclusion criteria of all four nomograms was identified. The highest AUCs calculated for any of the four models ranged from 0.618 to 0.664, suggesting weak positive discrimination at best. Models had best discriminative ability for a definition of insignificant disease characterized by organ-confined Gleason score ⩽6 with a total volume ⩽0.5 ml or 1.3 ml. Calibration plots showed moderate range of predictive ability for the Kattan model though this model did not perform well at decision curve analysis. External assessment of models predicting insignificant PCa showed moderate performance at best. Uninformed interpretation may cause undue anxiety or false reassurance and they should be used with caution.

  8. Modeling, Optimization & Control of Hydraulic Networks

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat

    2014-01-01

    to check if the network is controllable. Afterward the pressure control problem in water supply systems is formulated as an optimal control problem. The goal is to minimize the power consumption in pumps and also to regulate the pressure drop at the end-users to a desired value. The formulated optimal...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...

  9. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    . Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...... variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...

  10. Accessing and Utilizing Remote Sensing Data for Vectorborne Infectious Diseases Surveillance and Modeling

    Science.gov (United States)

    Kiang, Richard; Adimi, Farida; Kempler, Steven

    2008-01-01

    intelligence based techniques. Conclusions: Remote sensing data relevant to the transmission of vectorborne infectious diseases can be conveniently accessed at NASA and some other websites. These data are useful for vectorborne infectious disease surveillance and modeling.

  11. Enhanced Gravity Model of trade: reconciling macroeconomic and network models

    CERN Document Server

    Almog, Assaf; Garlaschelli, Diego

    2015-01-01

    The bilateral trade relations between world countries form a complex network, the International Trade Network (ITN), which is involved in an increasing number of worldwide economic processes, including globalization, integration, industrial production, and the propagation of shocks and instabilities. Characterizing the ITN via a simple yet accurate model is an open problem. The classical Gravity Model of trade successfully reproduces the volume of trade between two connected countries using known macroeconomic properties such as GDP and geographic distance. However, it generates a network with an unrealistically homogeneous topology, thus failing to reproduce the highly heterogeneous structure of the real ITN. On the other hand, network models successfully reproduce the complex topology of the ITN, but provide no information about trade volumes. Therefore macroeconomic and network models of trade suffer from complementary limitations but are still largely incompatible. Here, we make an important step forward ...

  12. Hybrid neural network models of transducers

    Science.gov (United States)

    Xie, Shilin; Zhang, Xinong; Chen, Shenglai; Zhu, Changchun

    2011-10-01

    A hybrid neural network (NN) approach is proposed and applied to modeling of transducers in the paper. The modeling procedures are also presented in detail. First, the simulated studies on the modeling of single input-single output and multi input-multi output transducers are conducted respectively by use of the developed hybrid NN scheme. Secondly, the hybrid NN modeling approach is utilized to characterize a six-axis force sensor prototype based on the measured data. The results show that the hybrid NN approach can significantly improve modeling precision in comparison with the conventional modeling method. In addition, the method is superior to NN black-box modeling because the former possesses smaller network scale, higher convergence speed, higher model precision and better generalization performance.

  13. Preferential urn model and nongrowing complex networks.

    Science.gov (United States)

    Ohkubo, Jun; Yasuda, Muneki; Tanaka, Kazuyuki

    2005-12-01

    A preferential urn model, which is based on the concept "the rich get richer," is proposed. From a relationship between a nongrowing model for complex networks and the preferential urn model in regard to degree distributions, it is revealed that a fitness parameter in the nongrowing model is interpreted as an inverse local temperature in the preferential urn model. Furthermore, it is clarified that the preferential urn model with randomness generates a fat-tailed occupation distribution; the concept of the local temperature enables us to understand the fat-tailed occupation distribution intuitively. Since the preferential urn model is a simple stochastic model, it can be applied to research on not only the nongrowing complex networks, but also many other fields such as econophysics and social sciences.

  14. A Network Model of Credit Risk Contagion

    Directory of Open Access Journals (Sweden)

    Ting-Qiang Chen

    2012-01-01

    Full Text Available A network model of credit risk contagion is presented, in which the effect of behaviors of credit risk holders and the financial market regulators and the network structure are considered. By introducing the stochastic dominance theory, we discussed, respectively, the effect mechanisms of the degree of individual relationship, individual attitude to credit risk contagion, the individual ability to resist credit risk contagion, the monitoring strength of the financial market regulators, and the network structure on credit risk contagion. Then some derived and proofed propositions were verified through numerical simulations.

  15. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network....... Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...

  16. Grid architecture model of network centric warfare

    Institute of Scientific and Technical Information of China (English)

    Yan Tihua; Wang Baoshu

    2006-01-01

    NCW(network centric warfare) is an information warfare concentrating on network. A global network-centric warfare architecture with OGSA grid technology is put forward, which is a four levels system including the user level, the application level, the grid middleware layer and the resource level. In grid middleware layer, based on virtual hosting environment, a BEPL4WS grid service composition method is introduced. In addition, the NCW grid service model is built with the help of Eclipse-SDK-3.0.1 and Bpws4j.

  17. Autonomous surveillance for biosecurity.

    Science.gov (United States)

    Jurdak, Raja; Elfes, Alberto; Kusy, Branislav; Tews, Ashley; Hu, Wen; Hernandez, Emili; Kottege, Navinda; Sikka, Pavan

    2015-04-01

    The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

  18. Decomposed Implicit Models of Piecewise - Linear Networks

    Directory of Open Access Journals (Sweden)

    J. Brzobohaty

    1992-05-01

    Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.

  19. Stochastic modeling and analysis of telecoms networks

    CERN Document Server

    Decreusefond, Laurent

    2012-01-01

    This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an

  20. Model Reduction for Complex Hyperbolic Networks

    OpenAIRE

    Himpe, Christian; Ohlberger, Mario

    2013-01-01

    We recently introduced the joint gramian for combined state and parameter reduction [C. Himpe and M. Ohlberger. Cross-Gramian Based Combined State and Parameter Reduction for Large-Scale Control Systems. arXiv:1302.0634, 2013], which is applied in this work to reduce a parametrized linear time-varying control system modeling a hyperbolic network. The reduction encompasses the dimension of nodes and parameters of the underlying control system. Networks with a hyperbolic structure have many app...

  1. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo

    2017-04-10

    We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes the pressure field using a Darcy type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. We first introduce micro- and mesoscopic models and show how they are connected to the macroscopic PDE system. Then, we provide an overview of analytical results for the PDE model, focusing mainly on the existence of weak and mild solutions and analysis of the steady states. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on finite elements and study the qualitative properties of network structures for various parameter values.

  2. Network Design Models for Container Shipping

    DEFF Research Database (Denmark)

    Reinhardt, Line Blander; Kallehauge, Brian; Nielsen, Anders Nørrelund

    This paper presents a study of the network design problem in container shipping. The paper combines the network design and fleet assignment problem into a mixed integer linear programming model minimizing the overall cost. The major contributions of this paper is that the time of a vessel route...... is included in the calculation of the capacity and that a inhomogeneous fleet is modeled. The model also includes the cost of transshipment which is one of the major cost for the shipping companies. The concept of pseudo simple routes is introduced to expand the set of feasible routes. The linearization...

  3. Implementation of a network model of hysteresis

    Energy Technology Data Exchange (ETDEWEB)

    Gruosso, G. [Dipartimento Elettronica e Informazione, Politecnico di Milano, P.za Leonardo da Vinci 32, I-20133 Milan (Italy); Repetto, M. [Dipartimento Ingegneria Elettrica, Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Turin (Italy)]. E-mail: maurizio.repetto@polito.it

    2006-02-01

    A network model of hysteresis based on elementary cells made up with piece-wise linear resistors and a linear capacitor has been presented in the literature and its theoretical properties have been investigated. This model allows to simulate hysteresis in a circuit solver without requiring any modification to its source code. Despite its appealing features, some cautions must be used for the treatment of the interface between the model and the rest of the circuit and for the handling of nonlinear resistors which can introduce some convergence problems in the network solution. These topics are investigated and some results on a simple test case are presented and discussed.

  4. [Model of Analysis and Prevention of Accidents - MAPA: tool for operational health surveillance].

    Science.gov (United States)

    de Almeida, Ildeberto Muniz; Vilela, Rodolfo Andrade de Gouveia; da Silva, Alessandro José Nunes; Beltran, Sandra Lorena

    2014-12-01

    The analysis of work-related accidents is important for accident surveillance and prevention. Current methods of analysis seek to overcome reductionist views that see these occurrences as simple events explained by operator error. The objective of this paper is to analyze the Model of Analysis and Prevention of Accidents (MAPA) and its use in monitoring interventions, duly highlighting aspects experienced in the use of the tool. The descriptive analytical method was used, introducing the steps of the model. To illustrate contributions and or difficulties, cases where the tool was used in the context of service were selected. MAPA integrates theoretical approaches that have already been tried in studies of accidents by providing useful conceptual support from the data collection stage until conclusion and intervention stages. Besides revealing weaknesses of the traditional approach, it helps identify organizational determinants, such as management failings, system design and safety management involved in the accident. The main challenges lie in the grasp of concepts by users, in exploring organizational aspects upstream in the chain of decisions or at higher levels of the hierarchy, as well as the intervention to change the determinants of these events.

  5. Evaluation of EOR Processes Using Network Models

    DEFF Research Database (Denmark)

    Larsen, Jens Kjell; Krogsbøll, Anette

    1998-01-01

    The report consists of the following parts: 1) Studies of wetting properties of model fluids and fluid mixtures aimed at an optimal selection of candidates for micromodel experiments. 2) Experimental studies of multiphase transport properties using physical models of porous networks (micromodels)...

  6. Spectral stability of unitary network models

    Science.gov (United States)

    Asch, Joachim; Bourget, Olivier; Joye, Alain

    2015-08-01

    We review various unitary network models used in quantum computing, spectral analysis or condensed matter physics and establish relationships between them. We show that symmetric one-dimensional quantum walks are universal, as are CMV matrices. We prove spectral stability and propagation properties for general asymptotically uniform models by means of unitary Mourre theory.

  7. Modelling cooperative agents in infrastructure networks

    NARCIS (Netherlands)

    Ligtvoet, A.; Chappin, E.J.L.; Stikkelman, R.M.

    2010-01-01

    This paper describes the translation of concepts of cooperation into an agent-based model of an industrial network. It first addresses the concept of cooperation and how this could be captured as heuristical rules within agents. Then it describes tests using these heuristics in an abstract model of

  8. Live bird markets characterization and trading network analysis in Mali: Implications for the surveillance and control of avian influenza and Newcastle disease.

    Science.gov (United States)

    Molia, Sophie; Boly, Ismaël Ardho; Duboz, Raphaël; Coulibaly, Boubacar; Guitian, Javier; Grosbois, Vladimir; Fournié, Guillaume; Pfeiffer, Dirk Udo

    2016-03-01

    Live bird markets (LBMs) play an important role in the transmission of avian influenza (AI) and Newcastle disease (ND) viruses in poultry. Our study had two objectives: (1) characterizing LBMs in Mali with a focus on practices influencing the risk of transmission of AI and ND, and (2) identifying which LBMs should be targeted for surveillance and control based on properties of the live poultry trade network. Two surveys were conducted in 2009-2010: a descriptive study in all 96 LBMs of an area encompassing approximately 98% of the Malian poultry population and a network analysis study in Sikasso county, the main poultry supplying county for the capital city Bamako. Regarding LBMs' characteristics, risk factors for the presence of AI and ND viruses (being open every day, more than 2 days before a bird is sold, absence of zoning to segregate poultry-related work flow areas, waste removal or cleaning and disinfecting less frequently than on a daily basis, trash disposal of dead birds and absence of manure processing) were present in 80-100% of the LBMs. Furthermore, LBMs tended to have wide catchment areas because of consumers' preference for village poultry meat, thereby involving a large number of villages in their supply chain. In the poultry trade network from/to Sikasso county, 182 traders were involved and 685 links were recorded among 159 locations. The network had a heterogeneous degree distribution and four hubs were identified based on measures of in-degrees, out-degrees and betweenness: the markets of Medine and Wayerma and the fairs of Farakala and Niena. These results can be used to design biosecurity-improvement interventions and to optimize the prevention, surveillance and control of transmissible poultry diseases in Malian LBMs. Further studies should investigate potential drivers (seasonality, prices) of the poultry trade network and the acceptability of biosecurity and behavior-change recommendations in the Malian socio-cultural context.

  9. Delay and Disruption Tolerant Networking MACHETE Model

    Science.gov (United States)

    Segui, John S.; Jennings, Esther H.; Gao, Jay L.

    2011-01-01

    To verify satisfaction of communication requirements imposed by unique missions, as early as 2000, the Communications Networking Group at the Jet Propulsion Laboratory (JPL) saw the need for an environment to support interplanetary communication protocol design, validation, and characterization. JPL's Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in Simulator of Space Communication Networks (NPO-41373) NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various commercial, non-commercial, and in-house custom tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. As NASA is expanding its Space Communications and Navigation (SCaN) capabilities to support planned and future missions, building infrastructure to maintain services and developing enabling technologies, an important and broader role is seen for MACHETE in design-phase evaluation of future SCaN architectures. To support evaluation of the developing Delay Tolerant Networking (DTN) field and its applicability for space networks, JPL developed MACHETE models for DTN Bundle Protocol (BP) and Licklider/Long-haul Transmission Protocol (LTP). DTN is an Internet Research Task Force (IRTF) architecture providing communication in and/or through highly stressed networking environments such as space exploration and battlefield networks. Stressed networking environments include those with intermittent (predictable and unknown) connectivity, large and/or variable delays, and high bit error rates. To provide its services over existing domain specific protocols, the DTN protocols reside at the application layer of the TCP/IP stack, forming a store-and-forward overlay network. The key capabilities of the Bundle Protocol include custody-based reliability, the ability to cope with intermittent connectivity

  10. Genetic network models: a comparative study

    Science.gov (United States)

    van Someren, Eugene P.; Wessels, Lodewyk F. A.; Reinders, Marcel J. T.

    2001-06-01

    Currently, the need arises for tools capable of unraveling the functionality of genes based on the analysis of microarray measurements. Modeling genetic interactions by means of genetic network models provides a methodology to infer functional relationships between genes. Although a wide variety of different models have been introduced so far, it remains, in general, unclear what the strengths and weaknesses of each of these approaches are and where these models overlap and differ. This paper compares different genetic modeling approaches that attempt to extract the gene regulation matrix from expression data. A taxonomy of continuous genetic network models is proposed and the following important characteristics are suggested and employed to compare the models: inferential power; predictive power; robustness; consistency; stability and computational cost. Where possible, synthetic time series data are employed to investigate some of these properties. The comparison shows that although genetic network modeling might provide valuable information regarding genetic interactions, current models show disappointing results on simple artificial problems. For now, the simplest models are favored because they generalize better, but more complex models will probably prevail once their bias is more thoroughly understood and their variance is better controlled.

  11. Gonorrhoea and gonococcal antimicrobial resistance surveillance networks in the WHO European Region, including the independent countries of the former Soviet Union.

    Science.gov (United States)

    Unemo, Magnus; Ison, Catherine A; Cole, Michelle; Spiteri, Gianfranco; van de Laar, Marita; Khotenashvili, Lali

    2013-12-01

    Antimicrobial resistance (AMR) in Neisseria gonorrhoeae has emerged for essentially all antimicrobials following their introduction into clinical practice. During the latest decade, susceptibility to the last remaining options for antimicrobial monotherapy, the extended-spectrum cephalosporins (ESC), has markedly decreased internationally and treatment failures with these ESCs have been verified. In response to this developing situation, WHO and the European Centre for Disease Prevention and Control (ECDC) have published global and region-specific response plans, respectively. One main component of these action/response plans is to enhance the surveillance of AMR and treatment failures. This paper describes the perspectives from the diverse WHO European Region (53 countries), including the independent countries of the former Soviet Union, regarding gonococcal AMR surveillance networks. The WHO European Region has a high prevalence of resistance to all previously recommended antimicrobials, and most of the first strictly verified treatment failures with cefixime and ceftriaxone were also reported from Europe. In the European Union/European Economic Area (EU/EEA), the European gonococcal antimicrobial surveillance programme (Euro-GASP) funded by the ECDC is running. In 2011, the Euro-GASP included 21/31 (68%) EU/EEA countries, and the programme is further strengthened annually. However, in the non-EU/EEA countries, internationally reported and quality assured gonococcal AMR data are lacking in 87% of the countries and, worryingly, appropriate support for establishment of a GASP is still lacking. Accordingly, national and international support, including political and financial commitment, for gonococcal AMR surveillance in the non-EU/EEA countries of the WHO European Region is essential.

  12. Towards a Theoretical Model of Social Media Surveillance in Contemporary Society.

    OpenAIRE

    Fuchs, Christian; Trottier, D

    2015-01-01

    'Social media’ like Facebook or Twitter have become tremendously popular in recent years. Their popularity provides new opportunities for data collection by state and private companies, which requires a critical and theoretical focus on social media surveillance. The task of this paper is to outline a theoretical framework for defining social media surveillance in the context of contemporary society, identifying its principal characteristics, and understanding its broader societal implication...

  13. An integrated network model of psychotic symptoms.

    Science.gov (United States)

    Looijestijn, Jasper; Blom, Jan Dirk; Aleman, André; Hoek, Hans W; Goekoop, Rutger

    2015-12-01

    The full body of research on the nature of psychosis and its determinants indicates that a considerable number of factors are relevant to the development of hallucinations, delusions, and other positive symptoms, ranging from neurodevelopmental parameters and altered connectivity of brain regions to impaired cognitive functioning and social factors. We aimed to integrate these factors in a single mathematical model based on network theory. At the microscopic level this model explains positive symptoms of psychosis in terms of experiential equivalents of robust, high-frequency attractor states of neural networks. At the mesoscopic level it explains them in relation to global brain states, and at the macroscopic level in relation to social-network structures and dynamics. Due to the scale-free nature of biological networks, all three levels are governed by the same general laws, thereby allowing for an integrated model of biological, psychological, and social phenomena involved in the mediation of positive symptoms of psychosis. This integrated network model of psychotic symptoms (INMOPS) is described together with various possibilities for application in clinical practice.

  14. Research on Modeling of Genetic Networks Based on Information Measurement

    Institute of Scientific and Technical Information of China (English)

    ZHANG Guo-wei; SHAO Shi-huang; ZHANG Ying; LI Hai-ying

    2006-01-01

    As the basis of network of biology organism, the genetic network is concerned by many researchers.Current modeling methods to genetic network, especially the Boolean networks modeling method are analyzed. For modeling the genetic network, the information theory is proposed to mining the relations between elements in network. Through calculating the values of information entropy and mutual entropy in a case, the effectiveness of the method is verified.

  15. Bayesian network modelling of upper gastrointestinal bleeding

    Science.gov (United States)

    Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri

    2013-09-01

    Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.

  16. Modelling Users` Trust in Online Social Networks

    Directory of Open Access Journals (Sweden)

    Iacob Cătoiu

    2014-02-01

    Full Text Available Previous studies (McKnight, Lankton and Tripp, 2011; Liao, Lui and Chen, 2011 have shown the crucial role of trust when choosing to disclose sensitive information online. This is the case of online social networks users, who must disclose a certain amount of personal data in order to gain access to these online services. Taking into account privacy calculus model and the risk/benefit ratio, we propose a model of users’ trust in online social networks with four variables. We have adapted metrics for the purpose of our study and we have assessed their reliability and validity. We use a Partial Least Squares (PLS based structural equation modelling analysis, which validated all our initial assumptions, indicating that our three predictors (privacy concerns, perceived benefits and perceived risks explain 48% of the variation of users’ trust in online social networks, the resulting variable of our study. We also discuss the implications and further research opportunities of our study.

  17. Modeling epidemics dynamics on heterogenous networks.

    Science.gov (United States)

    Ben-Zion, Yossi; Cohen, Yahel; Shnerb, Nadav M

    2010-05-21

    The dynamics of the SIS process on heterogenous networks, where different local communities are connected by airlines, is studied. We suggest a new modeling technique for travelers movement, in which the movement does not affect the demographic parameters characterizing the metapopulation. A solution to the deterministic reaction-diffusion equations that emerges from this model on a general network is presented. A typical example of a heterogenous network, the star structure, is studied in detail both analytically and using agent-based simulations. The interplay between demographic stochasticity, spatial heterogeneity and the infection dynamics is shown to produce some counterintuitive effects. In particular it was found that, while movement always increases the chance of an outbreak, it may decrease the steady-state fraction of sick individuals. The importance of the modeling technique in estimating the outcomes of a vaccination campaign is demonstrated.

  18. The Kuramoto model in complex networks

    CERN Document Server

    Rodrigues, Francisco A; Ji, Peng; Kurths, Jürgen

    2016-01-01

    Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from social and physical to biological and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. This model has been traditionally studied in complete graphs. However, besides being intrinsically dynamical, complex systems present very heterogeneous structure, which can be represented as complex networks. This report is dedicated to review main contributions in the field of synchronization in networks of Kuramoto oscillators. In particular, we provide an overview of the impact of network patterns on the local and global dynamics of coupled phase oscillators. We cover many relevant topics, which encompass a description of the most used analytical approaches and the analysis of several numerical results. Furthermore, we discuss recent developments on variations of the Kuramoto model in net...

  19. Features and heterogeneities in growing network models

    Science.gov (United States)

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

    2012-06-01

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

  20. String networks with junctions in competition models

    Science.gov (United States)

    Avelino, P. P.; Bazeia, D.; Losano, L.; Menezes, J.; de Oliveira, B. F.

    2017-03-01

    In this work we give specific examples of competition models, with six and eight species, whose three-dimensional dynamics naturally leads to the formation of string networks with junctions, associated with regions that have a high concentration of enemy species. We study the two- and three-dimensional evolution of such networks, both using stochastic network and mean field theory simulations. If the predation, reproduction and mobility probabilities do not vary in space and time, we find that the networks attain scaling regimes with a characteristic length roughly proportional to t 1 / 2, where t is the physical time, thus showing that the presence of junctions, on its own, does not have a significant impact on their scaling properties.

  1. String networks with junctions in competition models

    CERN Document Server

    Avelino, P P; Losano, L; Menezes, J; de Oliveira, B F

    2016-01-01

    In this work we give specific examples of competition models, with six and eight species, whose three-dimensional dynamics naturally leads to the formation of string networks with junctions, associated with regions that have a high concentration of enemy species. We study the two- and three-dimensional evolution of such networks, both using stochastic network and mean field theory simulations. If the predation, reproduction and mobility probabilities do not vary in space and time, we find that the networks attain scaling regimes with a characteristic length roughly proportional to $t^{1/2}$, where $t$ is the physical time, thus showing that the presence of junctions, on its own, does not have a significant impact on their scaling properties.

  2. Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks

    CERN Document Server

    Santi, Paolo

    2012-01-01

    Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and

  3. Characteristics of patients patch tested in the European Surveillance System on Contact Allergies (ESSCA) network, 2009-2012

    DEFF Research Database (Denmark)

    Uter, Wolfgang; Gefeller, Olaf; Giménez-Arnau, Ana

    2015-01-01

    from 63 530 consultations collected by 53 departments from 12 countries participating in the European Surveillance System on Contact Allergies (ESSCA) ( www.essca-dc.org) between 2009 and 2012. RESULTS: Considerable variation in the prevalence of the MOAHLFA factors between departments was found...

  4. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik;

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored...... and controlled have thus become essential factors for efficient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona...

  5. Green Network Planning Model for Optical Backbones

    DEFF Research Database (Denmark)

    Gutierrez Lopez, Jose Manuel; Riaz, M. Tahir; Jensen, Michael

    2010-01-01

    Communication networks are becoming more essential for our daily lives and critically important for industry and governments. The intense growth in the backbone traffic implies an increment of the power demands of the transmission systems. This power usage might have a significant negative effect...... on the environment in general. In network planning there are existing planning models focused on QoS provisioning, investment minimization or combinations of both and other parameters. But there is a lack of a model for designing green optical backbones. This paper presents novel ideas to be able to define...

  6. Influenza sentinel surveillance network: a public health-primary care collaborative action to assess influenza A(H1N1)pmd09 in Catalonia, Spain.

    Science.gov (United States)

    Torner, Nuria; Baricot, Maretva; Martínez, Ana; Toledo, Diana; Godoy, Pere; Dominguez, Ángela

    2013-03-01

    The aim of this study was to evaluate the outcome of a collaborative action between Public Health services and Primary Care in the context of a case-control study on effectiveness of pharmaceutical and non-pharmaceutical measures to prevent hospitalization in a pandemic situation. To carry out this research the collaborative action of the primary care physicians members of the Influenza surveillance network was needed, they had to recall clinical information from influenza A(H1N1)pmd09 confirmed outpatient cases and negative outpatient controls matching their corresponding hospitalized confirmed case.   A survey questionnaire to assess involvement of Influenza Sentinel Surveillance Primary care physicians' Network of Catalonia (PIDIRAC) regarding the outpatient case and control outreach during the pandemic influenza season was performed. A total of 71,1% of completed surveys were received. Perception of pandemic activity was considered to be similar to seasonal influenza activity in 43.8% or higher but not unbearable in 37.5% of the replies. There was no nuisance reported from patients regarding neither the questions nor the surveyor. Collaborative research between Public Health services and Primary Care physicians enhances Public Health actions and research.

  7. PROJECT ACTIVITY ANALYSIS WITHOUT THE NETWORK MODEL

    Directory of Open Access Journals (Sweden)

    S. Munapo

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: This paper presents a new procedure for analysing and managing activity sequences in projects. The new procedure determines critical activities, critical path, start times, free floats, crash limits, and other useful information without the use of the network model. Even though network models have been successfully used in project management so far, there are weaknesses associated with the use. A network is not easy to generate, and dummies that are usually associated with it make the network diagram complex – and dummy activities have no meaning in the original project management problem. The network model for projects can be avoided while still obtaining all the useful information that is required for project management. What are required are the activities, their accurate durations, and their predecessors.

    AFRIKAANSE OPSOMMING: Die navorsing beskryf ’n nuwerwetse metode vir die ontleding en bestuur van die sekwensiële aktiwiteite van projekte. Die voorgestelde metode bepaal kritiese aktiwiteite, die kritieke pad, aanvangstye, speling, verhasing, en ander groothede sonder die gebruik van ’n netwerkmodel. Die metode funksioneer bevredigend in die praktyk, en omseil die administratiewe rompslomp van die tradisionele netwerkmodelle.

  8. Introduction to surveillance studies

    CERN Document Server

    Petersen, JK

    2012-01-01

    Introduction & OverviewIntroduction Brief History of Surveillance Technologies & TechniquesOptical SurveillanceAerial Surveillance Audio Surveillance Radio-Wave SurveillanceGlobal Positioning Systems Sensors Computers & the Internet Data Cards Biochemical Surveillance Animal Surveillance Biometrics Genetics Practical ConsiderationsPrevalence of Surveillance Effectiveness of Surveillance Freedom & Privacy IssuesConstitutional Freedoms Privacy Safeguards & Intrusions ResourcesReferences Glossary Index

  9. Security Modeling on the Supply Chain Networks

    Directory of Open Access Journals (Sweden)

    Marn-Ling Shing

    2007-10-01

    Full Text Available In order to keep the price down, a purchaser sends out the request for quotation to a group of suppliers in a supply chain network. The purchaser will then choose a supplier with the best combination of price and quality. A potential supplier will try to collect the related information about other suppliers so he/she can offer the best bid to the purchaser. Therefore, confidentiality becomes an important consideration for the design of a supply chain network. Chen et al. have proposed the application of the Bell-LaPadula model in the design of a secured supply chain network. In the Bell-LaPadula model, a subject can be in one of different security clearances and an object can be in one of various security classifications. All the possible combinations of (Security Clearance, Classification pair in the Bell-LaPadula model can be thought as different states in the Markov Chain model. This paper extends the work done by Chen et al., provides more details on the Markov Chain model and illustrates how to use it to monitor the security state transition in the supply chain network.

  10. An evolving model of online bipartite networks

    Science.gov (United States)

    Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang

    2013-12-01

    Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.

  11. Models and average properties of scale-free directed networks

    Science.gov (United States)

    Bernhardsson, Sebastian; Minnhagen, Petter

    2006-08-01

    We extend the merging model for undirected networks by Kim [Eur. Phys. J. B 43, 369 (2004)] to directed networks and investigate the emerging scale-free networks. Two versions of the directed merging model, friendly and hostile merging, give rise to two distinct network types. We uncover that some nontrivial features of these two network types resemble two levels of a certain randomization/nonspecificity in the link reshuffling during network evolution. Furthermore, the same features show up, respectively, in metabolic networks and transcriptional networks. We introduce measures that single out the distinguishing features between the two prototype networks, as well as point out features that are beyond the prototypes.

  12. Delivery Time Reliability Model of Logistics Network

    Directory of Open Access Journals (Sweden)

    Liusan Wu

    2013-01-01

    Full Text Available Natural disasters like earthquake and flood will surely destroy the existing traffic network, usually accompanied by delivery delay or even network collapse. A logistics-network-related delivery time reliability model defined by a shortest-time entropy is proposed as a means to estimate the actual delivery time reliability. The less the entropy is, the stronger the delivery time reliability remains, and vice versa. The shortest delivery time is computed separately based on two different assumptions. If a path is concerned without capacity restriction, the shortest delivery time is positively related to the length of the shortest path, and if a path is concerned with capacity restriction, a minimax programming model is built to figure up the shortest delivery time. Finally, an example is utilized to confirm the validity and practicality of the proposed approach.

  13. An improved network model for railway traffic

    Science.gov (United States)

    Li, Keping; Ma, Xin; Shao, Fubo

    In railway traffic, safety analysis is a key issue for controlling train operation. Here, the identification and order of key factors are very important. In this paper, a new network model is constructed for analyzing the railway safety, in which nodes are regarded as causation factors and links represent possible relationships among those factors. Our aim is to give all these nodes an importance order, and to find the in-depth relationship among these nodes including how failures spread among them. Based on the constructed network model, we propose a control method to ensure the safe state by setting each node a threshold. As the results, by protecting the Hub node of the constructed network, the spreading of railway accident can be controlled well. The efficiency of such a method is further tested with the help of numerical example.

  14. Methodically Modeling the Tor Network

    Science.gov (United States)

    2012-08-01

    iPlane [7] and CAIDA [3]. Third, determining a better client model would further increase confidence in experimental results. Producing a more robust...Bandwidth Speed Test. http://speedtest.net/. [3] CAIDA Data. http://www.caida.org/data. [4] DETER Testbed. http://www.isi.edu/deter. [5] Emulab

  15. Network Model Building (Process Mapping)

    OpenAIRE

    Blau, Gary; Yih, Yuehwern

    2004-01-01

    12 slides Provider Notes:See Project Planning Video (Windows Media) Posted at the bottom are Gary Blau's slides. Before watching, please note that "process mapping" and "modeling" are mentioned in the video and notes. Here they are meant to refer to the NSCORT "project plan"

  16. Self-organized Collaboration Network Model Based on Module Emerging

    Science.gov (United States)

    Yang, Hongyong; Lu, Lan; Liu, Qiming

    Recently, the studies of the complex network have gone deep into many scientific fields, such as computer science, physics, mathematics, sociology, etc. These researches enrich the realization for complex network, and increase understands for the new characteristic of complex network. Based on the evolvement characteristic of the author collaboration in the scientific thesis, a self-organized network model of the scientific cooperation network is presented by module emerging. By applying the theoretical analysis, it is shown that this network model is a scale-free network, and the strength degree distribution and the module degree distribution of the network nodes have the same power law. In order to make sure the validity of the theoretical analysis for the network model, we create the computer simulation and demonstration collaboration network. By analyzing the data of the network, the results of the demonstration network and the computer simulation are consistent with that of the theoretical analysis of the model.

  17. Bayesian Network Based XP Process Modelling

    Directory of Open Access Journals (Sweden)

    Mohamed Abouelela

    2010-07-01

    Full Text Available A Bayesian Network based mathematical model has been used for modelling Extreme Programmingsoftware development process. The model is capable of predicting the expected finish time and theexpected defect rate for each XP release. Therefore, it can be used to determine the success/failure of anyXP Project. The model takes into account the effect of three XP practices, namely: Pair Programming,Test Driven Development and Onsite Customer practices. The model’s predictions were validated againsttwo case studies. Results show the precision of our model especially in predicting the project finish time.

  18. Modeling of Network Identification Capability.

    Science.gov (United States)

    1986-07-01

    scalar moment is assumed to follow a Poisson distribution, as suggested by Lomnitz (1966). The A cumulative number of events occurring per year at or...Spectral Ratios from Point Sources in Plane-Layered Earth V Models," BSSA. 60, pp 1937-1987 Lomnitz . C. (1966). -Statistical Prediction of Earthquakes...Moment-Magritude Relations in Theory and Practice," J Geophy. Res., 89 (B7). pp. 6229-6235. Lomnitz , C. (1966), Statistical Prediction of Earthquakes

  19. The Evolving Demographic and Health Transition in Four Low- and Middle-Income Countries: Evidence from Four Sites in the INDEPTH Network of Longitudinal Health and Demographic Surveillance Systems.

    Directory of Open Access Journals (Sweden)

    Ayaga Bawah

    Full Text Available This paper contributes evidence documenting the continued decline in all-cause mortality and changes in the cause of death distribution over time in four developing country populations in Africa and Asia. We present levels and trends in age-specific mortality (all-cause and cause-specific from four demographic surveillance sites: Agincourt (South Africa, Navrongo (Ghana in Africa; Filabavi (Vietnam, Matlab (Bangladesh in Asia. We model mortality using discrete time event history analysis. This study illustrates how data from INDEPTH Network centers can provide a comparative, longitudinal examination of mortality patterns and the epidemiological transition. Health care systems need to be reconfigured to deal simultaneously with continuing challenges of communicable disease and increasing incidence of non-communicable diseases that require long-term care. In populations with endemic HIV, long-term care of HIV patients on ART will add to the chronic care needs of the community.

  20. Keystone Business Models for Network Security Processors

    Directory of Open Access Journals (Sweden)

    Arthur Low

    2013-07-01

    Full Text Available Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor” models nor the silicon intellectual-property licensing (“IP-licensing” models allow small technology companies to successfully compete. This article describes an alternative approach that produces an ongoing stream of novel network security processors for niche markets through continuous innovation by both large and small companies. This approach, referred to here as the "business ecosystem model for network security processors", includes a flexible and reconfigurable technology platform, a “keystone” business model for the company that maintains the platform architecture, and an extended ecosystem of companies that both contribute and share in the value created by innovation. New opportunities for business model innovation by participating companies are made possible by the ecosystem model. This ecosystem model builds on: i the lessons learned from the experience of the first author as a senior integrated circuit architect for providers of public-key cryptography solutions and as the owner of a semiconductor startup, and ii the latest scholarly research on technology entrepreneurship, business models, platforms, and business ecosystems. This article will be of interest to all technology entrepreneurs, but it will be of particular interest to owners of small companies that provide security solutions and to specialized security professionals seeking to launch their own companies.

  1. Psychometric Measurement Models and Artificial Neural Networks

    Science.gov (United States)

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  2. A generalized network model for polymeric liquids

    NARCIS (Netherlands)

    Jongschaap, R.J.J.; Kamphuis, H.; Doeksen, D.K.

    1983-01-01

    A kinetic model was developed for relating the molecular structure and the rheological behaviour of polymer-like materials in which bonds are being created and broken. In particular, the stress contribution of molecules that are not a part of the network was taken account of. In two limiting cases

  3. The Kuramoto model in complex networks

    Science.gov (United States)

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

    2016-01-01

    Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from social and physical to biological and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. This model has been traditionally studied in complete graphs. However, besides being intrinsically dynamical, complex systems present very heterogeneous structure, which can be represented as complex networks. This report is dedicated to review main contributions in the field of synchronization in networks of Kuramoto oscillators. In particular, we provide an overview of the impact of network patterns on the local and global dynamics of coupled phase oscillators. We cover many relevant topics, which encompass a description of the most used analytical approaches and the analysis of several numerical results. Furthermore, we discuss recent developments on variations of the Kuramoto model in networks, including the presence of noise and inertia. The rich potential for applications is discussed for special fields in engineering, neuroscience, physics and Earth science. Finally, we conclude by discussing problems that remain open after the last decade of intensive research on the Kuramoto model and point out some promising directions for future research.

  4. Modelling crime linkage with Bayesian networks

    NARCIS (Netherlands)

    J. de Zoete; M. Sjerps; D. Lagnado; N. Fenton

    2015-01-01

    When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model

  5. Dynamic Pathloss Model for Future Mobile Communication Networks

    DEFF Research Database (Denmark)

    Kumar, Ambuj; Mihovska, Albena Dimitrova; Prasad, Ramjee

    2016-01-01

    — Future mobile communication networks (MCNs) are expected to be more intelligent and proactive based on new capabilities that increase agility and performance. However, for any successful mobile network service, the dexterity in network deployment is a key factor. The efficiency of the network...... that incorporates the environmental dynamics factor in the propagation model for intelligent and proactively iterative networks...

  6. Unified Model for Generation Complex Networks with Utility Preferential Attachment

    Institute of Scientific and Technical Information of China (English)

    WU Jian-Jun; GAO Zi-You; SUN Hui-Jun

    2006-01-01

    In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics ofthis new network are given.

  7. A Model of Mental State Transition Network

    Science.gov (United States)

    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.

  8. Examining an elaborated sociocultural model of disordered eating among college women: the roles of social comparison and body surveillance.

    Science.gov (United States)

    Fitzsimmons-Craft, Ellen E; Bardone-Cone, Anna M; Bulik, Cynthia M; Wonderlich, Stephen A; Crosby, Ross D; Engel, Scott G

    2014-09-01

    Social comparison (i.e., body, eating, exercise) and body surveillance were tested as mediators of the thin-ideal internalization-body dissatisfaction relationship in the context of an elaborated sociocultural model of disordered eating. Participants were 219 college women who completed two questionnaire sessions 3 months apart. The cross-sectional elaborated sociocultural model (i.e., including social comparison and body surveillance as mediators of the thin-ideal internalization-body dissatisfaction relation) provided a good fit to the data, and the total indirect effect from thin-ideal internalization to body dissatisfaction through the mediators was significant. Social comparison emerged as a significant specific mediator while body surveillance did not. The mediation model did not hold prospectively; however, social comparison accounted for unique variance in body dissatisfaction and disordered eating 3 months later. Results suggest that thin-ideal internalization may not be "automatically" associated with body dissatisfaction and that it may be especially important to target comparison in prevention and intervention efforts.

  9. Surveillance of the environmental radioactivity; Journees organisees par la Section Environnement de la SFRP, surveillance de la radioactivite de l'environnement

    Energy Technology Data Exchange (ETDEWEB)

    Schneider, Th.; Gitzinger, C.; Jaunet, P.; Eberbach, F.; Clavel, B.; Hemidy, P.Y.; Perrier, G.; Kiper, Ch.; Peres, J.M.; Josset, M.; Calvez, M.; Leclerc, M.; Leclerc, E.; Aubert, C.; Levelut, M.N.; Debayle, Ch.; Mayer, St.; Renaud, Ph.; Leprieur, F.; Petitfrere, M.; Catelinois, O.; Monfort, M.; Baron, Y.; Target, A

    2008-07-01

    The objective of these days was to present the organisation of the surveillance of the environmental radioactivity and to allow an experience sharing and a dialog on this subject between the different actors of the radiation protection in france. The different presentations were as follow: evolution and stakes of the surveillance of radioactivity in environment; the part of the European commission, regulatory aspects; the implementation of the surveillance: the case of Germany; Strategy and logic of environmental surveillance around the EDF national centers of energy production; environmental surveillance: F.B.F.C. site of Romans on Isere; steps of the implementation 'analysis for release decree at the F.B.F.C./C.E.R.C.A. laboratory of Romans; I.R.S.N. and the environmental surveillance: situation and perspectives; the part of a non institutional actor, the citizenship surveillance done by A.C.R.O.; harmonization of sampling methods: the results of inter operators G.T. sampling; sustainable observatory of environment: data traceability and samples conservation; inter laboratories tests of radioactivity measurements; national network of environmental radioactivity measurement: laboratories agreements; the networks of environmental radioactivity telemetry: modernization positioning; programme of observation and surveillance of surface environment and installations of the H.A.-M.A.V.L. project (high activity and long life medium activity); Evolution of radionuclides concentration in environment and adaptation of measurements techniques to the surveillance needs; the national network of radioactivity measurement in environment; modes of data restoration of surveillance: the results of the Loire environment pilot action; method of sanitary impacts estimation in the area of ionizing radiations; the radiological impact of atmospheric nuclear tests in French Polynesia; validation of models by the measure; network of measurement and alert management of the atmospheric

  10. Hybrid simulation models of production networks

    CERN Document Server

    Kouikoglou, Vassilis S

    2001-01-01

    This book is concerned with a most important area of industrial production, that of analysis and optimization of production lines and networks using discrete-event models and simulation. The book introduces a novel approach that combines analytic models and discrete-event simulation. Unlike conventional piece-by-piece simulation, this method observes a reduced number of events between which the evolution of the system is tracked analytically. Using this hybrid approach, several models are developed for the analysis of production lines and networks. The hybrid approach combines speed and accuracy for exceptional analysis of most practical situations. A number of optimization problems, involving buffer design, workforce planning, and production control, are solved through the use of hybrid models.

  11. Model for the evolution of river networks

    Energy Technology Data Exchange (ETDEWEB)

    Leheny, R.L.; Nagel, S.R. (The James Franck Institute and the Department of Physics, The University of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637 (United States))

    1993-08-30

    We have developed a model, which includes the effects of erosion both from precipitation and from avalanching of soil on steep slopes, to simulate the formation and evolution of river networks. The avalanches provide a mechanism for competition in growth between neighboring river basins. The changing morphology follows many of the characteristics of evolution set forth by Glock. We find that during evolution the model maintains the statistical characteristics measured in natural river systems.

  12. Investigating complex networks with inverse models

    CERN Document Server

    Wens, Vincent

    2014-01-01

    Recent advances in neuroscience have motivated the study of network organization in spatially distributed dynamical systems from indirect measurements. However, the associated connectivity estimation, when combined with inverse modeling, is strongly affected by spatial leakage. We formulate this problem in a general framework and develop a new approach to model spatial leakage and limit its effects. It is analytically compared to existing regression-based methods used in electrophysiology, which are shown to yield biased estimates of amplitude and phase couplings.

  13. Surveillance Pleasures

    DEFF Research Database (Denmark)

    Albrechtslund, Anders

    and leisure have not been studied with the same intensity as e.g. policing, civil liberties and social sorting. This paper offers a study of trends in surveillance pleasures, i.e. watching and eavesdropping in popular culture. My focus is the existential aspects and ethical dilemmas of surveillance...

  14. Assessment of a syndromic surveillance system based on morbidity data: results from the Oscour network during a heat wave.

    Directory of Open Access Journals (Sweden)

    Loïc Josseran

    Full Text Available BACKGROUND: Syndromic surveillance systems have been developed in recent years and are now increasingly used by stakeholders to quickly answer questions and make important decisions. It is therefore essential to evaluate the quality and utility of such systems. This study was designed to assess a syndromic surveillance system based on emergency departments' (ED morbidity rates related to the health effects of heat waves. This study uses data collected during the 2006 heat wave in France. METHODS: Data recorded from 15 EDs in the Ile-de-France (Paris and surrounding area from June to August, 2006, were transmitted daily via the Internet to the French Institute for Public Health Surveillance. Items collected included diagnosis (ICD10, outcome, and age. Several aspects of the system have been evaluated (data quality, cost, flexibility, stability, and performance. Periods of heat wave are considered the most suitable time to evaluate the system. RESULTS: Data quality did not vary significantly during the period. Age, gender and outcome were completed in a comprehensive manner. Diagnoses were missing or uninformative for 37.5% of patients. Stability was recorded as being 99.49% for the period overall. The average cost per day over the study period was estimated to be euro287. Diagnoses of hyperthermia, malaise, dehydration, hyponatremia were correlated with increased temperatures. Malaise was most sensitive in younger and elderly adults but also the less specific. However, overall syndrome groups were more sensitive with comparable specificity than individual diagnoses. CONCLUSION: This system satisfactorily detected the health impact of hot days (observed values were higher than expected on more than 90% of days on which a heat alert was issued. Our findings should reassure stakeholders about the reliability of health impact assessments during or following such an event. These evaluations are essential to establish the validity of the results of

  15. Distance distribution in configuration-model networks

    Science.gov (United States)

    Nitzan, Mor; Katzav, Eytan; Kühn, Reimer; Biham, Ofer

    2016-06-01

    We present analytical results for the distribution of shortest path lengths between random pairs of nodes in configuration model networks. The results, which are based on recursion equations, are shown to be in good agreement with numerical simulations for networks with degenerate, binomial, and power-law degree distributions. The mean, mode, and variance of the distribution of shortest path lengths are also evaluated. These results provide expressions for central measures and dispersion measures of the distribution of shortest path lengths in terms of moments of the degree distribution, illuminating the connection between the two distributions.

  16. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  17. Empirical generalization assessment of neural network models

    DEFF Research Database (Denmark)

    Larsen, Jan; Hansen, Lars Kai

    1995-01-01

    competing models. Since all models are trained on the same data, a key issue is to take this dependency into account. The optimal split of the data set of size N into a cross-validation set of size Nγ and a training set of size N(1-γ) is discussed. Asymptotically (large data sees), γopt→1......This paper addresses the assessment of generalization performance of neural network models by use of empirical techniques. We suggest to use the cross-validation scheme combined with a resampling technique to obtain an estimate of the generalization performance distribution of a specific model...

  18. Global influenza surveillance with Laplacian multidimensional scaling

    Institute of Scientific and Technical Information of China (English)

    Xi-chuan ZHOU; Fang TANG; Qin LI; Sheng-dong HU; Guo-jun LI; Yun-jian JIA; Xin-ke LI; Yu-jie FENG

    2016-01-01

    The Global Influenza Surveillance Network is crucial for monitoring epidemic risk in participating countries. However, at present, the network has notable gaps in the developing world, principally in Africa and Asia where laboratory capabilities are limited. Moreover, for the last few years, various influenza viruses have been continuously emerging in the resource-limited countries, making these surveillance gaps a more imminent challenge. We present a spatial-transmission model to estimate epidemic risks in the countries where only partial or even no surveillance data are available. Motivated by the observation that countries in the same influenza transmission zone divided by the World Health Organization had similar transmission patterns, we propose to estimate the influenza epidemic risk of an unmonitored country by incorporating the surveillance data reported by countries of the same transmission zone. Experiments show that the risk estimates are highly correlated with the actual influenza morbidity trends for African and Asian countries. The proposed method may provide the much-needed capability to detect, assess, and notify potential influenza epidemics to the developing world.

  19. A Networks Approach to Modeling Enzymatic Reactions.

    Science.gov (United States)

    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.

  20. A improved Network Security Situation Awareness Model

    Directory of Open Access Journals (Sweden)

    Li Fangwei

    2015-08-01

    Full Text Available In order to reflect the situation of network security assessment performance fully and accurately, a new network security situation awareness model based on information fusion was proposed. Network security situation is the result of fusion three aspects evaluation. In terms of attack, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed. In terms of vulnerability, a improved Common Vulnerability Scoring System (CVSS was raised and maked the assessment more comprehensive. In terms of node weights, the method of calculating the combined weights and optimizing the result by Sequence Quadratic Program (SQP algorithm which reduced the uncertainty of fusion was raised. To verify the validity and necessity of the method, a testing platform was built and used to test through evaluating 2000 DAPRA data sets. Experiments show that the method can improve the accuracy of evaluation results.

  1. Network transmission model: A dynamic traffic model at network level (poster)

    NARCIS (Netherlands)

    Knoop, V.L.; Hoogendoorn, S.P.

    2014-01-01

    New IT techniques allow communication and coordination between traffic measures. To best use this, one needs to coordinate over longer distances. Optimization of the measures is not possible using traditional microscopic or macroscopic simulation models. The Network Fundamental Diagram (NFD)

  2. Characterizing Attention with Predictive Network Models.

    Science.gov (United States)

    Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

    2017-04-01

    Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Higher-dimensional models of networks

    CERN Document Server

    Spivak, David I

    2009-01-01

    Networks are often studied as graphs, where the vertices stand for entities in the world and the edges stand for connections between them. While relatively easy to study, graphs are often inadequate for modeling real-world situations, especially those that include contexts of more than two entities. For these situations, one typically uses hypergraphs or simplicial complexes. In this paper, we provide a precise framework in which graphs, hypergraphs, simplicial complexes, and many other categories, all of which model higher graphs, can be studied side-by-side. We show how to transform a hypergraph into its nearest simplicial analogue, for example. Our framework includes many new categories as well, such as one that models broadcasting networks. We give several examples and applications of these ideas.

  4. Threshold model of cascades in temporal networks

    CERN Document Server

    Karimi, Fariba

    2012-01-01

    Threshold models try to explain the consequences of social influence like the spread of fads and opinions. Along with models of epidemics, they constitute a major theoretical framework of social spreading processes. In threshold models on static networks, an individual changes her state if a certain fraction of her neighbors has done the same. When there are strong correlations in the temporal aspects of contact patterns, it is useful to represent the system as a temporal network. In such a system, not only contacts but also the time of the contacts are represented explicitly. There is a consensus that bursty temporal patterns slow down disease spreading. However, as we will see, this is not a universal truth for threshold models. In this work, we propose an extension of Watts' classic threshold model to temporal networks. We do this by assuming that an agent is influenced by contacts which lie a certain time into the past. I.e., the individuals are affected by contacts within a time window. In addition to th...

  5. The noisy voter model on complex networks

    CERN Document Server

    Carro, Adrián; Miguel, Maxi San

    2016-01-01

    We propose a new analytical method to study stochastic, binary-state models on complex networks. Moving beyond the usual mean-field theories, this alternative approach is based on the introduction of an uncorrelated network approximation, allowing to deal with the network structure as parametric heterogeneity. As an illustration, we study the noisy voter model, a modification of the original voter model including random changes of state. The proposed method is able to unfold the dependence of the model not only on the mean degree (the mean-field prediction) but also on more complex averages over the degree distribution. In particular, we find that the degree heterogeneity ---variance of the underlying degree distribution--- has a strong influence on the location of the critical point of a noise-induced, finite-size transition occurring in the model, on the local ordering of the system, and on the functional form of its temporal correlations. Finally, we show how this latter point opens the possibility of infe...

  6. Entanglement effects in model polymer networks

    Science.gov (United States)

    Everaers, R.; Kremer, K.

    The influence of topological constraints on the local dynamics in cross-linked polymer melts and their contribution to the elastic properties of rubber elastic systems are a long standing problem in statistical mechanics. Polymer networks with diamond lattice connectivity (Everaers and Kremer 1995, Everaers and Kremer 1996a) are idealized model systems which isolate the effect of topology conservation from other sources of quenched disorder. We study their behavior in molecular dynamics simulations under elongational strain. In our analysis we compare the measured, purely entropic shear moduli G to the predictions of statistical mechanical models of rubber elasticity, making extensive use of the microscopic structural and topological information available in computer simulations. We find (Everaers and Kremer 1995) that the classical models of rubber elasticity underestimate the true change in entropy in a deformed network significantly, because they neglect the tension along the contour of the strands which cannot relax due to entanglements (Everaers and Kremer (in preparation)). This contribution and the fluctuations in strained systems seem to be well described by the constrained mode model (Everaers 1998) which allows to treat the crossover from classical rubber elasticity to the tube model for polymer networks with increasing strand length within one transparant formalism. While this is important for the description of the effects we try to do a first quantitative step towards their explanation by topological considerations. We show (Everaers and Kremer 1996a) that for the comparatively short strand lengths of our diamond networks the topology contribution to the shear modulus is proportional to the density of entangled mesh pairs with non-zero Gauss linking number. Moreover, the prefactor can be estimated consistently within a rather simple model developed by Vologodskii et al. and by Graessley and Pearson, which is based on the definition of an entropic

  7. The National Athletic Treatment, Injury and Outcomes Network (NATION): Methods of the Surveillance Program, 2011–2012 Through 2013–2014

    Science.gov (United States)

    Dompier, Thomas P.; Marshall, Stephen W.; Kerr, Zachary Y.; Hayden, Ross

    2015-01-01

    Context Previous epidemiologic researchers have examined time-loss (TL) injuries in high school student-athletes, but little is known about the frequency of non–time-loss (NTL) injuries in these athletes. Objective To describe the methods of the National Athletic Treatment, Injury and Outcomes Network (NATION) Surveillance Program and provide descriptive epidemiology of TL and NTL injuries across athletes in 27 high school sports. Design Descriptive epidemiology study. Setting Aggregate injury and exposure data collected from 147 high schools in 26 states. Patients or Other Participants High school student-athletes participating in 13 boys' sports and 14 girls' sports during the 2011–2012 through 2013–2014 academic years. Main Outcome Measure(s) Athletic trainers documented injuries and exposures using commercially available injury-tracking software packages. Standard injury-tracking software was modified by the software vendors to conform to the surveillance needs of this project. The modified software exported a set of common data elements, stripped of personally identifiable information, to a centralized automated verification and validation system before they were included in the centralized research database. Dependent measures were injury and exposure frequencies and injury rates with 95% confidence intervals stratified by sport, sex, and injury type (TL or NTL). Results Over the 3-year period, a total of 2337 team seasons across 27 sports resulted in 47 014 injuries and 5 146 355 athlete-exposures. The NTL injuries accounted for 38 765 (82.45%) and TL injuries for 8249 (17.55%) of the total. Conclusions The NTL injuries accounted for a substantial amount of the total number of injuries sustained by high school student-athletes. This project demonstrates the feasibility of creating large-scale injury surveillance systems using commercially available injury-tracking software. PMID:26067620

  8. Surveillance in Programming Plagiarism beyond Techniques: An Incentive-Based Fishbone Model

    Science.gov (United States)

    Wang, Yanqing; Chen, Min; Liang, Yaowen; Jiang, Yu

    2013-01-01

    Lots of researches have showed that plagiarism becomes a severe problem in higher education around the world, especially in programming learning for its essence. Therefore, an effective strategy for plagiarism surveillance in program learning is much essential. Some literature focus on code similarity algorithm and the related tools can help to…

  9. Features and heterogeneities in growing network models

    CERN Document Server

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

    2011-01-01

    Many complex networks from the World-Wide-Web to biological networks are growing taking into account the heterogeneous features of the nodes. The feature of a node might be a discrete quantity such as a classification of a URL document as personal page, thematic website, news, blog, search engine, social network, ect. or the classification of a gene in a functional module. Moreover the feature of a node can be a continuous variable such as the position of a node in the embedding space. In order to account for these properties, in this paper we provide a generalization of growing network models with preferential attachment that includes the effect of heterogeneous features of the nodes. The main effect of heterogeneity is the emergence of an "effective fitness" for each class of nodes, determining the rate at which nodes acquire new links. Beyond the degree distribution, in this paper we give a full characterization of the other relevant properties of the model. We evaluate the clustering coefficient and show ...

  10. Modeling Dynamic Evolution of Online Friendship Network

    Institute of Scientific and Technical Information of China (English)

    吴联仁; 闫强

    2012-01-01

    In this paper,we study the dynamic evolution of friendship network in SNS (Social Networking Site).Our analysis suggests that an individual joining a community depends not only on the number of friends he or she has within the community,but also on the friendship network generated by those friends.In addition,we propose a model which is based on two processes:first,connecting nearest neighbors;second,strength driven attachment mechanism.The model reflects two facts:first,in the social network it is a universal phenomenon that two nodes are connected when they have at least one common neighbor;second,new nodes connect more likely to nodes which have larger weights and interactions,a phenomenon called strength driven attachment (also called weight driven attachment).From the simulation results,we find that degree distribution P(k),strength distribution P(s),and degree-strength correlation are all consistent with empirical data.

  11. Network Strategies in the Voter Model

    CERN Document Server

    Javarone, Marco Alberto

    2013-01-01

    We study a simple voter model with two competing parties. In particular, we represent the case of political elections, where people can choose to support one of the two competitors or to remain neutral. People interact in a social network and their opinion depends on those of their neighbors. Therefore, people may change opinion over time, i.e., they can support one competitor or none. The two competitors try to gain the people's consensus by interacting with their neighbors and also with other people. In particular, competitors define temporal connections, following a strategy, to interact with people they do not know, i.e., with all the people that are not their neighbors. We analyze the proposed model to investigate which network strategies are more advantageous, for the competitors, in order to gain the popular consensus. As result, we found that the best network strategy depends on the topology of the social network. Finally, we investigate how the charisma of competitors affects the outcomes of the prop...

  12. Models and Algorithm for Stochastic Network Designs

    Institute of Scientific and Technical Information of China (English)

    Anthony Chen; Juyoung Kim; Seungjae Lee; Jaisung Choi

    2009-01-01

    The network design problem (NDP) is one of the most difficult and challenging problems in trans-portation. Traditional NDP models are often posed as a deterministic bilevel program assuming that all rele-vant inputs are known with certainty. This paper presents three stochastic models for designing transporta-tion networks with demand uncertainty. These three stochastic NDP models were formulated as the ex-pected value model, chance-constrained model, and dependent-chance model in a bilevel programming framework using different criteria to hedge against demand uncertainty. Solution procedures based on the traffic assignment algorithm, genetic algorithm, and Monte-Cado simulations were developed to solve these stochastic NDP models. The nonlinear and nonconvex nature of the bilevel program was handled by the genetic algorithm and traffic assignment algorithm, whereas the stochastic nature was addressed through simulations. Numerical experiments were conducted to evaluate the applicability of the stochastic NDP models and the solution procedure. Results from the three experiments show that the solution procedures are quite robust to different parameter settings.

  13. Microbial growth modelling with artificial neural networks.

    Science.gov (United States)

    Jeyamkonda, S; Jaya, D S; Holle, R A

    2001-03-20

    There is a growing interest in modelling microbial growth as an alternative to time-consuming, traditional, microbiological enumeration techniques. Several statistical models have been reported to describe the growth of different microorganisms, but there are accuracy problems. An alternate technique 'artificial neural networks' (ANN) for modelling microbial growth is explained and evaluated. Published data were used to build separate general regression neural network (GRNN) structures for modelling growth of Aeromonas hydrophila, Shigella flexneri, and Brochothrix thermosphacta. Both GRNN and published statistical model predictions were compared against the experimental data using six statistical indices. For training data sets, the GRNN predictions were far superior than the statistical model predictions, whereas the GRNN predictions were similar or slightly worse than statistical model predictions for test data sets for all the three data sets. GRNN predictions can be considered good, considering its performance for unseen data. Graphical plots, mean relative percentage residual, mean absolute relative residual, and root mean squared residual were identified as suitable indices for comparing competing models. ANN can now become a vehicle whereby predictive microbiology can be applied in food product development and food safety risk assessment.

  14. Performance modeling, loss networks, and statistical multiplexing

    CERN Document Server

    Mazumdar, Ravi

    2009-01-01

    This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I

  15. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  16. The International Haemovigilance Network Database for the Surveillance of Adverse Reactions and Events in Donors and Recipients of Blood Components: technical issues and results.

    Science.gov (United States)

    Politis, C; Wiersum, J C; Richardson, C; Robillard, P; Jorgensen, J; Renaudier, P; Faber, J-C; Wood, E M

    2016-11-01

    The International Haemovigilance Network's ISTARE is an online database for surveillance of all adverse reactions (ARs) and adverse events (AEs) associated with donation of blood and transfusion of blood components, irrespective of severity or the harm caused. ISTARE aims to unify the collection and sharing of information with a view to harmonizing best practices for haemovigilance systems around the world. Adverse reactionss and adverse events are recorded by blood component, type of reaction, severity and imputability to transfusion, using internationally agreed standard definitions. From 2006 to 2012, 125 national sets of annual aggregated data were received from 25 countries, covering 132.8 million blood components issued. The incidence of all ARs was 77.5 per 100 000 components issued, of which 25% were severe (19.1 per 100 000). Of 349 deaths (0.26 per 100 000), 58% were due to the three ARs related to the respiratory system: transfusion-associated circulatory overload (TACO, 27%), transfusion-associated acute lung injury (TRALI, 19%) and transfusion-associated dyspnoea (TAD, 12%). Cumulatively, 594 477 donor complications were reported (rate 660 per 100 000), of which 2.9% were severe. ISTARE is a well-established surveillance tool offering important contributions to international efforts to maximize transfusion safety. © 2016 International Society of Blood Transfusion.

  17. The Spanish national health care-associated infection surveillance network (INCLIMECC): data summary January 1997 through December 2006 adapted to the new National Healthcare Safety Network Procedure-associated module codes.

    Science.gov (United States)

    Pérez, Cristina Díaz-Agero; Rodela, Ana Robustillo; Monge Jodrá, Vincente

    2009-12-01

    In 1997, a national standardized surveillance system (designated INCLIMECC [Indicadores Clínicos de Mejora Continua de la Calidad]) was established in Spain for health care-associated infection (HAI) in surgery patients, based on the National Nosocomial Infection Surveillance (NNIS) system. In 2005, in its procedure-associated module, the National Healthcare Safety Network (NHSN) inherited the NNIS program for surveillance of HAI in surgery patients and reorganized all surgical procedures. INCLIMECC actively monitors all patients referred to the surgical ward of each participating hospital. We present a summary of the data collected from January 1997 to December 2006 adapted to the new NHSN procedures. Surgical site infection (SSI) rates are provided by operative procedure and NNIS risk index category. Further quality indicators reported are surgical complications, length of stay, antimicrobial prophylaxis, mortality, readmission because of infection or other complication, and revision surgery. Because the ICD-9-CM surgery procedure code is included in each patient's record, we were able to reorganize our database avoiding the loss of extensive information, as has occurred with other systems.

  18. Network models of frequency modulated sweep detection.

    Directory of Open Access Journals (Sweden)

    Steven Skorheim

    Full Text Available Frequency modulated (FM sweeps are common in species-specific vocalizations, including human speech. Auditory neurons selective for the direction and rate of frequency change in FM sweeps are present across species, but the synaptic mechanisms underlying such selectivity are only beginning to be understood. Even less is known about mechanisms of experience-dependent changes in FM sweep selectivity. We present three network models of synaptic mechanisms of FM sweep direction and rate selectivity that explains experimental data: (1 The 'facilitation' model contains frequency selective cells operating as coincidence detectors, summing up multiple excitatory inputs with different time delays. (2 The 'duration tuned' model depends on interactions between delayed excitation and early inhibition. The strength of delayed excitation determines the preferred duration. Inhibitory rebound can reinforce the delayed excitation. (3 The 'inhibitory sideband' model uses frequency selective inputs to a network of excitatory and inhibitory cells. The strength and asymmetry of these connections results in neurons responsive to sweeps in a single direction of sufficient sweep rate. Variations of these properties, can explain the diversity of rate-dependent direction selectivity seen across species. We show that the inhibitory sideband model can be trained using spike timing dependent plasticity (STDP to develop direction selectivity from a non-selective network. These models provide a means to compare the proposed synaptic and spectrotemporal mechanisms of FM sweep processing and can be utilized to explore cellular mechanisms underlying experience- or training-dependent changes in spectrotemporal processing across animal models. Given the analogy between FM sweeps and visual motion, these models can serve a broader function in studying stimulus movement across sensory epithelia.

  19. A Comparative Assessment of Observational Medical Outcomes Partnership and Mini-Sentinel Common Data Models and Analytics: Implications for Active Drug Safety Surveillance.

    Science.gov (United States)

    Xu, Yihua; Zhou, Xiaofeng; Suehs, Brandon T; Hartzema, Abraham G; Kahn, Michael G; Moride, Yola; Sauer, Brian C; Liu, Qing; Moll, Keran; Pasquale, Margaret K; Nair, Vinit P; Bate, Andrew

    2015-08-01

    An often key component to coordinating surveillance activities across distributed networks is the design and implementation of a common data model (CDM). The purpose of this study was to evaluate two drug safety surveillance CDMs from an ecosystem perspective to better understand how differences in CDMs and analytic tools affect usability and interpretation of results. Humana claims data from 2007 to 2012 were mapped to Observational Medical Outcomes Partnership (OMOP) and Mini-Sentinel CDMs. Data were described and compared at the patient level by source code and mapped concepts. Study cohort construction and effect estimates were also compared using two different analytical methods--one based on a new user design implementing a high-dimensional propensity score (HDPS) algorithm and the other based on univariate self-controlled case series (SCCS) design--across six established positive drug-outcome pairs to learn how differences in CDMs and analytics influence steps in the database analytic process and results. Claims data for approximately 7.7 million Humana health plan members were transformed into the two CDMs. Three health outcome cohorts and two drug cohorts showed differences in cohort size and constituency between Mini-Sentinel and OMOP CDMs, which was a result of multiple factors. Overall, the implementation of the HDPS procedure on Mini-Sentinel CDM detected more known positive associations than that on OMOP CDM. The SCCS method results were comparable on both CDMs. Differences in the implementation of the HDPS procedure between the two CDMs were identified; analytic model and risk period specification had a significant impact on the performance of the HDPS procedure on OMOP CDM. Differences were observed between OMOP and Mini-Sentinel CDMs. The analysis of both CDMs at the data model level indicated that such conceptual differences had only a slight but not significant impact on identifying known safety associations. Our results show that differences at

  20. Systems biology of plant molecular networks: from networks to models

    NARCIS (Netherlands)

    Valentim, F.L.

    2015-01-01

    Developmental processes are controlled by regulatory networks (GRNs), which are tightly coordinated networks of transcription factors (TFs) that activate and repress gene expression within a spatial and temporal context. In Arabidopsis thaliana, the key components and network structures of the GRNs

  1. Advances in dynamic network modeling in complex transportation systems

    CERN Document Server

    Ukkusuri, Satish V

    2013-01-01

    This book focuses on the latest in dynamic network modeling, including route guidance and traffic control in transportation systems and other complex infrastructure networks. Covers dynamic traffic assignment, flow modeling, mobile sensor deployment and more.

  2. Influence of Deterministic Attachments for Large Unifying Hybrid Network Model

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    Large unifying hybrid network model (LUHPM) introduced the deterministic mixing ratio fd on the basis of the harmonious unification hybrid preferential model, to describe the influence of deterministic attachment to the network topology characteristics,

  3. New Federated Collaborative Networked Organization Model (FCNOM

    Directory of Open Access Journals (Sweden)

    Morcous M. Yassa

    2012-01-01

    Full Text Available Formation of Collaborative Networked Organization (CNO usually comes upon expected business opportunities and needs huge of negotiation during its lifecycle, especially to increase the Dynamic Virtual Organization (DVO configuration automation. Decision makers need more comprehensive information about CNO system to support their decisions. Unfortunately, there is no single formal modeling, tool, approach or any comprehensive methodology that covers all perspectives. In spite of there are some approaches to model CNO have been existed, these approaches model the CNO either with respect to the technology, or business without considering organizational behavior, federation modeling, and external environments. The aim of this paper is to propose an integrated framework that combines the existed modeling perspectives, as well as, proposes new ones. Also, it provides clear CNO boundaries. By using this approach the view of CNO environment becomes clear and unified. Also, it minimizes the negotiations within CNO components during its life cycle, supports DVO configuration automation, as well as, helps decision making for DVO, and achieves harmonization between CNO partners. The proposed FCNOM utilizes CommonKADS methodology organization model for describing CNO components. Insurance Collaborative Network has been used as an example to proof the proposed FCNOM model.

  4. Antiferromagnetic Ising Model in Hierarchical Networks

    Science.gov (United States)

    Cheng, Xiang; Boettcher, Stefan

    2015-03-01

    The Ising antiferromagnet is a convenient model of glassy dynamics. It can introduce geometric frustrations and may give rise to a spin glass phase and glassy relaxation at low temperatures [ 1 ] . We apply the antiferromagnetic Ising model to 3 hierarchical networks which share features of both small world networks and regular lattices. Their recursive and fixed structures make them suitable for exact renormalization group analysis as well as numerical simulations. We first explore the dynamical behaviors using simulated annealing and discover an extremely slow relaxation at low temperatures. Then we employ the Wang-Landau algorithm to investigate the energy landscape and the corresponding equilibrium behaviors for different system sizes. Besides the Monte Carlo methods, renormalization group [ 2 ] is used to study the equilibrium properties in the thermodynamic limit and to compare with the results from simulated annealing and Wang-Landau sampling. Supported through NSF Grant DMR-1207431.

  5. Patch test results with fragrance markers of the baseline series - analysis of the European Surveillance System on Contact Allergies (ESSCA) network 2009-2012

    DEFF Research Database (Denmark)

    Frosch, Peter J; Duus Johansen, Jeanne; Schuttelaar, Marie-Louise A;

    2015-01-01

    of patients consecutively patch tested between 2009 and 2012 in 12 European countries with fragrance allergens contained in the baseline series were collected by the European Surveillance System on Contact Allergies network and descriptively analysed. Four departments used the TRUE Test(®) system. RESULTS......BACKGROUND: Contact allergy to fragrances is common, and impairs quality of life, particularly in young women. OBJECTIVE: To provide current results on the prevalences of sensitization to fragrance allergens used as markers in the baseline series of most European countries. METHODS: Data......: Contact allergy to fragrances is common throughout Europe, with regional variation probably being explained by patch test technique, and differences in exposure and referral patterns. The current basic markers of fragrance sensitivity in the baseline series should be supplemented with additional fragrance...

  6. A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

    Science.gov (United States)

    Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher

    2005-01-01

    This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

  7. Development and application of a real-time network surveillance server based on uClinux%基于uClinux的实时网络监控服务器开发与应用

    Institute of Scientific and Technical Information of China (English)

    吴春祥

    2015-01-01

    Introduces a developing method of a real-time network multimedia server based on uClinux, the multimedia server can control management and data access to remote data surveillance camera. Applying it to large scale network surveillance system, users can access the distributed network surveillance server cluster system composed of the real-time data, then read and save (or write) them to large capacity storage devices, increasing the number of concurrent users type connection, improve the stability and reliability of network surveillance system.%介绍了一种基于uClinux的实时网络监控服务器开发方法,该服务器能对实时对远程网络终端、监控摄像头等进行控制管理和数据访问。将其应用到大型网络监控系统中,用户可访问由多台服务器组成的分布式网络监控服务器集群,实时读取数据然后将其存写到大容量存储设备中,增加并发式连接的用户数量,提高远程实时网络监控系统稳定性和可靠性。

  8. An UAV Allocation Method for Traffic Surveillance in Sparse Road Network%稀疏路网条件下的无人飞机交通监控部署方法

    Institute of Scientific and Technical Information of China (English)

    刘晓锋; 常云涛; 王珣

    2012-01-01

    将无人飞机技术应用到稀疏路网的交通监控当中,提出了无人飞机在有无续航里程约束条件下的交通监控部署方法.给出了无人飞机的监控路段和节点的选择方法;在无续航里程约束条件下,将无人飞机的交通监控问题转化为旅行商问题,并运用模拟退火算法予以求解;在有续航里程约束条件下,运用K -means聚类方法,将无人飞机的监控区域划分成若干子监控区,从而将该问题转化为无续航里程约束的无人飞机交通监控部署问题.以新疆库库高速公路和路网为例,对稀疏路网条件下的无人飞机交通监控部署方法进行实证分析和试飞试验.试验结果表明:无人飞机在稀疏路网条件下是一种有效的交通监控设备,可用于我国西部稀疏路网的交通监控.%Unmanned Aerial Vehicle (UAV) technology was introduced into the traffic surveillance in sparse road network and an UAV allocation method for traffic surveillance with/without UAV continuous flight distance constraint was proposed. First, the method of choosing the surveillance road segments and nodes was proposed. Then, UAV traffic surveillance problem without continuous flight distance constraint was formulated as a traveling salesman problem, and the simulated annealing algorithm was introduced to solve this problem. As for UAV traffic surveillance problem with continuous flight distance constraint, K-means clustering algorithm was used to divide the UAV surveillance area into multiple sub-zones to convert this problem into UAV traffic surveillance scenario without continuous flight distance constraint. Finally, taking Korla-Kuqa expressway of Xinjiang and its road network as example, the proposed UAV-based traffic surveillance allocation method for sparse road network was demonstrated and validated by using several field experiments. The experimental results show that UAV is an effective and useful tool for traffic surveillance in sparse

  9. Risk-based surveillance for avian influenza control along poultry market chains in South China: The value of social network analysis.

    Science.gov (United States)

    Martin, Vincent; Zhou, Xiaoyan; Marshall, Edith; Jia, Beibei; Fusheng, Guo; FrancoDixon, Mary Ann; DeHaan, Nicoline; Pfeiffer, Dirk U; Soares Magalhães, Ricardo J; Gilbert, Marius

    2011-12-01

    Over the past two decades, the poultry sector in China went through a phase of tremendous growth as well as rapid intensification and concentration. Highly pathogenic avian influenza virus (HPAIV) subtype H5N1 was first detected in 1996 in Guangdong province, South China and started spreading throughout Asia in early 2004. Since then, control of the disease in China has relied heavily on wide-scale preventive vaccination combined with movement control, quarantine and stamping out. This strategy has been successful in drastically reducing the number of outbreaks during the past 5years. However, HPAIV H5N1 is still circulating and is regularly isolated in traditional live bird markets (LBMs) where viral infection can persist, which represent a public health hazard for people visiting them. The use of social network analysis in combination with epidemiological surveillance in South China has identified areas where the success of current strategies for HPAI control in the poultry production sector may benefit from better knowledge of poultry trading patterns and the LBM network configuration as well as their capacity for maintaining HPAIV H5N1 infection. We produced a set of LBM network maps and estimated the associated risk of HPAIV H5N1 within LBMs and along poultry market chains, providing new insights into how live poultry trade and infection are intertwined. More specifically, our study provides evidence that several biosecurity factors such as daily cage cleaning, daily cage disinfection or manure processing contribute to a reduction in HPAIV H5N1 presence in LBMs. Of significant importance is that the results of our study also show the association between social network indicators and the presence of HPAIV H5N1 in specific network configurations such as the one represented by the counties of origin of the birds traded in LBMs. This new information could be used to develop more targeted and effective control interventions.

  10. Prevalence and genetic mechanisms of antimicrobial resistance in Staphylococcus species: A multicentre report of the indian council of medical research antimicrobial resistance surveillance network.

    Science.gov (United States)

    Rajkumar, Sunanda; Sistla, Sujatha; Manoharan, Meerabai; Sugumar, Madhan; Nagasundaram, Niveditha; Parija, Subhash Chandra; Ray, Pallab; Bakthavatchalam, Yamuna Devi; Veeraraghavan, Balaji; Kapil, Arti; Walia, Kamini; Ohri, V C

    2017-01-01

    Routine surveillance of antimicrobial resistance (AMR) is an essential component of measures aimed to tackle the growing threat of resistant microbes in public health. This study presents a 1-year multicentre report on AMR in Staphylococcus species as part of Indian Council of Medical Research-AMR surveillance network. Staphylococcus species was routinely collected in the nodal and regional centres of the network and antimicrobial susceptibility testing was performed against a panel of antimicrobials. Minimum inhibitory concentration (MIC) values of vancomycin (VAN), daptomycin, tigecycline and linezolid (LNZ) against selected methicillin-resistant Staphylococcus aureus(MRSA) isolates were determined by E-test and MIC creep, if any, was determined. Resistant genotypes were determined by polymerase chain reaction for those isolates showing phenotypic resistance. The prevalence of MRSA was found to be range from moderate (21%) to high (45%) among the centres with an overall prevalence of 37.3%. High prevalence of resistance was observed with commonly used antimicrobials such as ciprofloxacin and erythromycin in all the centres. Resistance to LNZ was not encountered except for a single case. Full-blown resistance to VAN in S. aureus was not observed; however, a few VAN-intermediate S. aureus isolates were documented. The most common species of coagulase negative staphylococci (CoNS) identified was Staphylococcus haemolyticus and Staphylococcus epidermidis. Resistance among CoNS was relatively higher than S. aureus. Most phenotypically resistant organisms possessed the corresponding resistance genes. There were localised differences in the prevalence of resistance between the centres. The efficacy of the anti-MRSA antimicrobials was very high; however, almost all these antimicrobials showed evidence of creeping MIC.

  11. Electronic circuits modeling using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrejević Miona V.

    2003-01-01

    Full Text Available In this paper artificial neural networks (ANN are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.

  12. Modeling social influence through network autocorrelation : constructing the weight matrix

    NARCIS (Netherlands)

    Leenders, RTAJ

    2002-01-01

    Many physical and social phenomena are embedded within networks of interdependencies, the so-called 'context' of these phenomena. In network analysis, this type of process is typically modeled as a network autocorrelation model. Parameter estimates and inferences based on autocorrelation models, hin

  13. A Universal Model of Commuting Networks

    CERN Document Server

    Lenormand, Maxime; Gargiulo, Floriana; Deffuant, Guillaume

    2012-01-01

    We test a recently proposed model of commuting networks on 80 case studies from different regions of the world (Europe and United-States) and with geographic units of different sizes (municipality, county, region). The model takes as input the number of commuters coming in and out of each geographic unit and generates the matrix of commuting flows betwen the geographic units. We show that the single parameter of the model, which rules the compromise between the influence of the distance and job opportunities, follows a universal law that depends only on the average surface of the geographic units. We verified that the law derived from a part of the case studies yields accurate results on other case studies. We also show that our model significantly outperforms the two other approaches proposing a universal commuting model (Balcan et al. (2009); Simini et al. (2012)), particularly when the geographic units are small (e.g. municipalities).

  14. Dual random circuit breaker network model with equivalent thermal circuit network

    Science.gov (United States)

    Kim, Kwanyong; Yoon, Seong Jun; Choi, Woo Young

    2014-02-01

    A SPICE-based dual random circuit breaker (RCB) network model with an equivalent thermal circuit network has been proposed in order to emulate resistance switching (RS) of unipolar resistive random access memory (RRAM). The dual RCB network model consists of the electrical RCB network model for the forming and set operations and the equivalent thermal circuit network model for the reset operation. In addition, the proposed model can explain the effects of heat dissipation on the memory and threshold RS with the variation in electrode thickness.

  15. A network model for Ebola spreading.

    Science.gov (United States)

    Rizzo, Alessandro; Pedalino, Biagio; Porfiri, Maurizio

    2016-04-01

    The availability of accurate models for the spreading of infectious diseases has opened a new era in management and containment of epidemics. Models are extensively used to plan for and execute vaccination campaigns, to evaluate the risk of international spreadings and the feasibility of travel bans, and to inform prophylaxis campaigns. Even when no specific therapeutical protocol is available, as for the Ebola Virus Disease (EVD), models of epidemic spreading can provide useful insight to steer interventions in the field and to forecast the trend of the epidemic. Here, we propose a novel mathematical model to describe EVD spreading based on activity driven networks (ADNs). Our approach overcomes the simplifying assumption of homogeneous mixing, which is central to most of the mathematically tractable models of EVD spreading. In our ADN-based model, each individual is not bound to contact every other, and its network of contacts varies in time as a function of an activity potential. Our model contemplates the possibility of non-ideal and time-varying intervention policies, which are critical to accurately describe EVD spreading in afflicted countries. The model is calibrated from field data of the 2014 April-to-December spreading in Liberia. We use the model as a predictive tool, to emulate the dynamics of EVD in Liberia and offer a one-year projection, until December 2015. Our predictions agree with the current vision expressed by professionals in the field, who consider EVD in Liberia at its final stage. The model is also used to perform a what-if analysis to assess the efficacy of timely intervention policies. In particular, we show that an earlier application of the same intervention policy would have greatly reduced the number of EVD cases, the duration of the outbreak, and the infrastructures needed for the implementation of the intervention.

  16. Evolutionary algorithms in genetic regulatory networks model

    CERN Document Server

    Raza, Khalid

    2012-01-01

    Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of complex biological processes. Modeling GRNs is significantly important in order to reveal fundamental cellular processes, examine gene functions and understanding their complex relationships. Understanding the interactions between genes gives rise to develop better method for drug discovery and diagnosis of the disease since many diseases are characterized by abnormal behaviour of the genes. In this paper we have reviewed various evolutionary algorithms-based approach for modeling GRNs and discussed various opportunities and challenges.

  17. Modeling online social networks based on preferential linking

    Institute of Scientific and Technical Information of China (English)

    Hu Hai-Bo; Guo Jin-Li; Chen Jun

    2012-01-01

    We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation,preferential acceptance,and preferential attachment.Based on the linear preference,we propose an analyzable model,which illustrates the mechanism of network growth and reproduces the process of network evolution.Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network.This work provides a possible bridge between the micro-mechanisms of network growth and the macrostructures of online social networks.

  18. Multiple Social Networks, Data Models and Measures for

    DEFF Research Database (Denmark)

    Magnani, Matteo; Rossi, Luca

    2017-01-01

    Multiple Social Network Analysis is a discipline defining models, measures, methodologies, and algorithms to study multiple social networks together as a single social system. It is particularly valuable when the networks are interconnected, e.g., the same actors are present in more than one...... network....

  19. Network Modeling of Crohn's Disease Incidence.

    Directory of Open Access Journals (Sweden)

    Jean-Marc Victor

    Full Text Available Numerous genetic and environmental risk factors play a role in human complex genetic disorders (CGD. However, their complex interplay remains to be modelled and explained in terms of disease mechanisms.Crohn's Disease (CD was modeled as a modular network of patho-physiological functions, each summarizing multiple gene-gene and gene-environment interactions. The disease resulted from one or few specific combinations of module functional states. Network aging dynamics was able to reproduce age-specific CD incidence curves as well as their variations over the past century in Western countries. Within the model, we translated the odds ratios (OR associated to at-risk alleles in terms of disease propensities of the functional modules. Finally, the model was successfully applied to other CGD including ulcerative colitis, ankylosing spondylitis, multiple sclerosis and schizophrenia.Modeling disease incidence may help to understand disease causative chains, to delineate the potential of personalized medicine, and to monitor epidemiological changes in CGD.

  20. A Packet Routing Model for Computer Networks

    Directory of Open Access Journals (Sweden)

    O. Osunade

    2012-05-01

    Full Text Available The quest for reliable data transmission in today’s computer networks and internetworks forms the basis for which routing schemes need be improved upon. The persistent increase in the size of internetwork leads to a dwindling performance of the present routing algorithms which are meant to provide optimal path for forwarding packets from one network to the other. A mathematical and analytical routing model framework is proposed to address the routing needs to a substantial extent. The model provides schemes typical of packet sources, queuing system within a buffer, links and bandwidth allocation and time-based bandwidth generator in routing chunks of packets to their destinations. Principal to the choice of link are such design considerations as least-congested link in a set of links, normalized throughput, mean delay and mean waiting time and the priority of packets in a set of prioritized packets. These performance metrics were targeted and the resultant outcome is a fair, load-balanced network.

  1. Towards a Realistic Model for Failure Propagation in Interdependent Networks

    CERN Document Server

    Sturaro, Agostino; Conti, Mauro; Das, Sajal K

    2015-01-01

    Modern networks are becoming increasingly interdependent. As a prominent example, the smart grid is an electrical grid controlled through a communications network, which in turn is powered by the electrical grid. Such interdependencies create new vulnerabilities and make these networks more susceptible to failures. In particular, failures can easily spread across these networks due to their interdependencies, possibly causing cascade effects with a devastating impact on their functionalities. In this paper we focus on the interdependence between the power grid and the communications network, and propose a novel realistic model, HINT (Heterogeneous Interdependent NeTworks), to study the evolution of cascading failures. Our model takes into account the heterogeneity of such networks as well as their complex interdependencies. We compare HINT with previously proposed models both on synthetic and real network topologies. Experimental results show that existing models oversimplify the failure evolution and network...

  2. A hybrid neural network model for consciousness

    Institute of Scientific and Technical Information of China (English)

    蔺杰; 金小刚; 杨建刚

    2004-01-01

    A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers,physical mnemonic layer and abstract thinking layer,which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness:(1)the reception process whereby cerebral subsystems group distributed signals into coherent object patterns;(2)the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and(3)the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework,various sorts of human actions can be explained,leading to a general approach for analyzing brain functions.

  3. A hybrid neural network model for consciousness

    Institute of Scientific and Technical Information of China (English)

    蔺杰; 金小刚; 杨建刚

    2004-01-01

    A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers, physical mnemonic layer and abstract thinking layer, which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness: (l) the reception process whereby cerebral subsystems group distributed signals into coherent object patterns; (2) the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and (3) the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework, various sorts of human actions can be explained, leading to a general approach for analyzing brain functions.

  4. Neural Network Program Package for Prosody Modeling

    Directory of Open Access Journals (Sweden)

    J. Santarius

    2004-04-01

    Full Text Available This contribution describes the programme for one part of theautomatic Text-to-Speech (TTS synthesis. Some experiments (for example[14] documented the considerable improvement of the naturalness ofsynthetic speech, but this approach requires completing the inputfeature values by hand. This completing takes a lot of time for bigfiles. We need to improve the prosody by other approaches which useonly automatically classified features (input parameters. Theartificial neural network (ANN approach is used for the modeling ofprosody parameters. The program package contains all modules necessaryfor the text and speech signal pre-processing, neural network training,sensitivity analysis, result processing and a module for the creationof the input data protocol for Czech speech synthesizer ARTIC [1].

  5. Modelling Traffic in IMS Network Nodes

    Directory of Open Access Journals (Sweden)

    BA Alassane

    2013-07-01

    Full Text Available IMS is well integrated with existing voice and data networks, while adopting many of their keycharacteristics.The Call Session Control Functions (CSCFs servers are the key part of the IMS structure. They are themain components responsible for processing and routing signalling messages.When CSCFs servers (P-CSCF, I-CSCF, S-CSCF are running on the same host, the SIP message can beinternally passed between SIP servers using a single operating system mechanism like a queue. It increasesthe reliability of the network [5], [6]. We have proposed in a last work for each type of service (between ICSCFand S-CSCF (call, data, multimedia.[23], to use less than two servers well dimensioned andrunning on the same operating system.Instead dimensioning servers, in order to increase performance, we try to model traffic on IMS nodes,particularly on entries nodes; it will provide results on separation of incoming flows, and then offer moresatisfactory service.

  6. Aeronautical telecommunications network advances, challenges, and modeling

    CERN Document Server

    Musa, Sarhan M

    2015-01-01

    Addresses the Challenges of Modern-Day Air Traffic Air traffic control (ATC) directs aircraft in the sky and on the ground to safety, while the Aeronautical Telecommunications Network (ATN) comprises all systems and phases that assist in aircraft departure and landing. The Aeronautical Telecommunications Network: Advances, Challenges, and Modeling focuses on the development of ATN and examines the role of the various systems that link aircraft with the ground. The book places special emphasis on ATC-introducing the modern ATC system from the perspective of the user and the developer-and provides a thorough understanding of the operating mechanism of the ATC system. It discusses the evolution of ATC, explaining its structure and how it works; includes design examples; and describes all subsystems of the ATC system. In addition, the book covers relevant tools, techniques, protocols, and architectures in ATN, including MIPv6, air traffic control (ATC), security of air traffic management (ATM), very-high-frequenc...

  7. Logic integer programming models for signaling networks.

    Science.gov (United States)

    Haus, Utz-Uwe; Niermann, Kathrin; Truemper, Klaus; Weismantel, Robert

    2009-05-01

    We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this, we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in molecular biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included.

  8. Quantifying robustness of biochemical network models

    Directory of Open Access Journals (Sweden)

    Iglesias Pablo A

    2002-12-01

    Full Text Available Abstract Background Robustness of mathematical models of biochemical networks is important for validation purposes and can be used as a means of selecting between different competing models. Tools for quantifying parametric robustness are needed. Results Two techniques for describing quantitatively the robustness of an oscillatory model were presented and contrasted. Single-parameter bifurcation analysis was used to evaluate the stability robustness of the limit cycle oscillation as well as the frequency and amplitude of oscillations. A tool from control engineering – the structural singular value (SSV – was used to quantify robust stability of the limit cycle. Using SSV analysis, we find very poor robustness when the model's parameters are allowed to vary. Conclusion The results show the usefulness of incorporating SSV analysis to single parameter sensitivity analysis to quantify robustness.

  9. Combining logistic regression and neural networks to create predictive models.

    OpenAIRE

    Spackman, K. A.

    1992-01-01

    Neural networks are being used widely in medicine and other areas to create predictive models from data. The statistical method that most closely parallels neural networks is logistic regression. This paper outlines some ways in which neural networks and logistic regression are similar, shows how a small modification of logistic regression can be used in the training of neural network models, and illustrates the use of this modification for variable selection and predictive model building wit...

  10. Optimizing neural network models: motivation and case studies

    OpenAIRE

    Harp, S A; T. Samad

    2012-01-01

    Practical successes have been achieved  with neural network models in a variety of domains, including energy-related industry. The large, complex design space presented by neural networks is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeling technology; at the same time, however, the lack of a systematic design approach implies that the best neural network models generally  rem...

  11. TCP-IP Model in Data Communication and Networking

    OpenAIRE

    Pranab Bandhu Nath; Md.Mofiz Uddin

    2015-01-01

    The Internet protocol suite is the computer networking model and set of communications protocols used on the Internet and similar computer networks. It is commonly known as TCP/IP, because it’s most important protocols, the Transmission Control Protocol (TCP) and the Internet Protocol (IP), were the first networking protocols defined in this standard. Often also called the Internet model, it was originally also known as the DoD model, because the development of the networking mode...

  12. Modeling of regional warehouse network generation

    Directory of Open Access Journals (Sweden)

    Popov Pavel Vladimirovich

    2016-08-01

    Full Text Available One of the factors that has a significant impact on the socio-economic development of the Russian Federation’s regions is the logistics infrastructure. It provides integrated transportation and distribution service of material flows. One of the main elements of logistics infrastructure is a storage infrastructure, which includes distribution center, distribution-and-sortout and sortout warehouses. It is the most expedient to place distribution center in the vicinity of the regional center. One of the tasks of the distribution network creation within the regions of the Russian Federation is to determine the location, capacity and number of stores. When determining regional network location of general purpose warehouses methodological approaches to solving the problems of location of production and non-production can be used which depend on various economic factors. The mathematical models for solving relevant problems are the deployment models. However, the existing models focus on the dimensionless power storage. The purpose of the given work is to develop a model to determine the optimal location of general-purpose warehouses on the Russian Federation area. At the first stage of the work, the authors assess the main economic indicators influencing the choice of the location of general purpose warehouses. An algorithm for solving the first stage, based on ABC, discriminant and cluster analysis were proposed by the authors in earlier papers. At the second stage the specific locations of general purpose warehouses and their power is chosen to provide the cost minimization for the construction and subsequent maintenance of warehouses and transportation heterogeneous products. In order to solve this problem the authors developed a mathematical model that takes into account the possibility of delivery in heterogeneous goods from suppliers and manufacturers in the distribution and storage sorting with specified set of capacities. The model allows

  13. Epidemic model with isolation in multilayer networks

    CERN Document Server

    Zuzek, L G Alvarez; Braunstein, L A

    2014-01-01

    The Susceptible-Infected-Recovered (SIR) model has successfully mimicked the propagation of such airborne diseases as influenza A (H1N1). Although the SIR model has recently been studied in a multilayer networks configuration, in almost all the research the dynamic movement of infected individuals, e.g., how they are often kept in isolation, is disregarded. We study the SIR model in two multilayer networks and use an isolation parameter, indicating time period, to measure the effect of isolating infected individuals from both layers. This isolation reduces the transmission of the disease because the time in which infection can spread is reduced. In this scenario we find that the epidemic threshold increases with the isolation time and the isolation parameter and the impact of the propagation is reduced. We also find that when isolation is total there is a threshold for the isolation parameter above which the disease never becomes an epidemic. We also find that regular epidemic models always overestimate the e...

  14. Parsimonious modeling with information filtering networks

    Science.gov (United States)

    Barfuss, Wolfram; Massara, Guido Previde; Di Matteo, T.; Aste, Tomaso

    2016-12-01

    We introduce a methodology to construct parsimonious probabilistic models. This method makes use of information filtering networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small subparts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust, even for the estimation of inverse covariance of high-dimensional, noisy, and short time series. Applied to financial data our method results are computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big data sets with large numbers of variables. Examples of practical application for forecasting, stress testing, and risk allocation in financial systems are also provided.

  15. Bayesian Recurrent Neural Network for Language Modeling.

    Science.gov (United States)

    Chien, Jen-Tzung; Ku, Yuan-Chu

    2016-02-01

    A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.

  16. Social network models predict movement and connectivity in ecological landscapes

    Science.gov (United States)

    Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.

    2011-01-01

    Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

  17. Inferring gene regression networks with model trees

    Directory of Open Access Journals (Sweden)

    Aguilar-Ruiz Jesus S

    2010-10-01

    Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear

  18. Secure transfer of surveillance data over Internet using Virtual Private Network technology. Field trial between STUK and IAEA

    Energy Technology Data Exchange (ETDEWEB)

    Smartt, H.; Martinez, R.; Caskey, S. [Sandia National Laboratories (United States); Honkamaa, T.; Ilander, T.; Poellaenen, R. [Radiation and Nuclear Safety Authority, Helsinki (Finland); Jeremica, N.; Ford, G. [Nokia (Finland)

    2000-01-01

    One of the primary concerns of employing remote monitoring technologies for IAEA safeguards applications is the high cost of data transmission. Transmitting data over the Internet has been shown often to be less expensive than other data transmission methods. However, data security of the Internet is often considered to be at a low level. Virtual Private Networks has emerged as a solution to this problem. A field demonstration was implemented to evaluate the use of Virtual Private Networks (via the Internet) as a means for data transmission. Evaluation points included security, reliability and cost. The existing Finnish Remote Environmental Monitoring System, located at the STUK facility in Helsinki, Finland, served as the field demonstration system. Sandia National Laboratories (SNL) established a Virtual Private Network between STUK (Radiation and Nuclear Safety Authority) Headquarters in Helsinki, Finland, and IAEA Headquarters in Vienna, Austria. Data from the existing STUK Remote Monitoring System was viewed at the IAEA via this network. The Virtual Private Network link was established in a proper manner, which guarantees the data security. Encryption was verified using a network sniffer. No problems were? encountered during the test. In the test system, fixed costs were higher than in the previous system, which utilized telephone lines. On the other hand transmission and operating costs are very low. Therefore, with low data amounts, the test system is not cost-effective, but if the data amount is tens of Megabytes per day the use of Virtual Private Networks and Internet will be economically justifiable. A cost-benefit analysis should be performed for each site due to significant variables. (orig.)

  19. Neural Networks for Electrohydrodynamic Effect Modelling

    Directory of Open Access Journals (Sweden)

    Jolanta Gancarz

    2004-01-01

    Full Text Available This paper presents currently achieved results concerning methods of electrohydrodynamic effect used in geophysics simulated with feedforward networks trained with backpropagation algorithm, radial basis function networks and generalized regression networks.

  20. Neural Networks For Electrohydrodynamic Effect Modelling

    Directory of Open Access Journals (Sweden)

    Wiesław Wajs

    2004-01-01

    Full Text Available This paper presents currently achieved results concerning methods of electrohydrodynamiceffect used in geophysics simulated with feedforward networks trained with backpropagation algorithm, radial basis function networks and generalized regression networks.

  1. Development of the Observational Surveillance

    OpenAIRE

    Rieutort, Delphine

    2015-01-01

    Impact of population on the environment, and conversely, is obvious and represents a real challenge for Public Health since 2000. It has been shown an increase in cancer prevalence, respiratory disease or even reproductive disorders, for which multifactorial origins are strongly suspected. In this context, surveillance has become an essential tool to decision making in public health, and surveillance networks of health events are multiplying, giving rise to numerous databases (sometimes consi...

  2. Modeling and Robustness of Knowledge Network in Supply Chain

    Institute of Scientific and Technical Information of China (English)

    王道平; 沈睿芳

    2014-01-01

    The growth and evolution of the knowledge network in supply chain can be characterized by dynamic growth clustering and non-homogeneous degree distribution. The networks with the above characteristics are also known as scale-free networks. In this paper, the knowledge network model in supply chain is established, in which the preferential attachment mechanism based on the node strength is adopted to simulate the growth and evolution of the network. The nodes in the network have a certain preference in the choice of a knowledge partner. On the basis of the network model, the robustness of the three network models based on different preferential attachment strategies is in-vestigated. The robustness is also referred to as tolerances when the nodes are subjected to random destruction and malicious damage. The simulation results of this study show that the improved network has higher connectivity and stability.

  3. Assessment of distributed arterial network models.

    Science.gov (United States)

    Segers, P; Stergiopulos, N; Verdonck, P; Verhoeven, R

    1997-11-01

    The aim of this study is to evaluate the relative importance of elastic non-linearities, viscoelasticity and resistance vessel modelling on arterial pressure and flow wave contours computed with distributed arterial network models. The computational results of a non-linear (time-domain) and a linear (frequency-domain) mode were compared using the same geometrical configuration and identical upstream and downstream boundary conditions and mechanical properties. pressures were computed at the ascending aorta, brachial and femoral artery. In spite of the identical problem definition, computational differences were found in input impedance modulus (max. 15-20%), systolic pressure (max. 5%) and pulse pressure (max. 10%). For the brachial artery, the ratio of pulse pressure to aortic pulse pressure was practically identical for both models (3%), whereas for the femoral artery higher values are found for the linear model (+10%). The aortic/brachial pressure transfer function indicates that pressure harmonic amplification is somewhat higher in the linear model for frequencies lower than 6 Hz while the opposite is true for higher frequencies. These computational disparities were attributed to conceptual model differences, such as the treatment of geometric tapering, rather than to elastic or convective non-linearities. Compared to the effect of viscoelasticity, the discrepancy between the linear and non-linear model is of the same importance. At peripheral locations, the correct representation of terminal impedance outweight the computational differences between the linear and non-linear models.

  4. Fundamentals of complex networks models, structures and dynamics

    CERN Document Server

    Chen, Guanrong; Li, Xiang

    2014-01-01

    Complex networks such as the Internet, WWW, transportationnetworks, power grids, biological neural networks, and scientificcooperation networks of all kinds provide challenges for futuretechnological development. In particular, advanced societies havebecome dependent on large infrastructural networks to an extentbeyond our capability to plan (modeling) and to operate (control).The recent spate of collapses in power grids and ongoing virusattacks on the Internet illustrate the need for knowledge aboutmodeling, analysis of behaviors, optimized planning and performancecontrol in such networks. F

  5. Stochastic simulation of HIV population dynamics through complex network modelling

    NARCIS (Netherlands)

    Sloot, P.M.A.; Ivanov, S.V.; Boukhanovsky, A.V.; van de Vijver, D.A.M.C.; Boucher, C.A.B.

    2008-01-01

    We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and

  6. Stochastic simulation of HIV population dynamics through complex network modelling

    NARCIS (Netherlands)

    Sloot, P. M. A.; Ivanov, S. V.; Boukhanovsky, A. V.; van de Vijver, D. A. M. C.; Boucher, C. A. B.

    We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and

  7. A Search Model with a Quasi-Network

    DEFF Research Database (Denmark)

    Ejarque, Joao Miguel

    This paper adds a quasi-network to a search model of the labor market. Fitting the model to an average unemployment rate and to other moments in the data implies the presence of the network is not noticeable in the basic properties of the unemployment and job finding rates. However, the network c...

  8. A Search Model with a Quasi-Network

    DEFF Research Database (Denmark)

    Ejarque, Joao Miguel

    This paper adds a quasi-network to a search model of the labor market. Fitting the model to an average unemployment rate and to other moments in the data implies the presence of the network is not noticeable in the basic properties of the unemployment and job finding rates. However, the network...

  9. A network-oriented business modeling environment

    Science.gov (United States)

    Bisconti, Cristian; Storelli, Davide; Totaro, Salvatore; Arigliano, Francesco; Savarino, Vincenzo; Vicari, Claudia

    The development of formal models related to the organizational aspects of an enterprise is fundamental when these aspects must be re-engineered and digitalized, especially when the enterprise is involved in the dynamics and value flows of a business network. Business modeling provides an opportunity to synthesize and make business processes, business rules and the structural aspects of an organization explicit, allowing business managers to control their complexity and guide an enterprise through effective decisional and strategic activities. This chapter discusses the main results of the TEKNE project in terms of software components that enable enterprises to configure, store, search and share models of any aspects of their business while leveraging standard and business-oriented technologies and languages to bridge the gap between the world of business people and IT experts and to foster effective business-to-business collaborations.

  10. Neural network models of categorical perception.

    Science.gov (United States)

    Damper, R I; Harnad, S R

    2000-05-01

    Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.

  11. Networks model of the East Turkistan terrorism

    Science.gov (United States)

    Li, Ben-xian; Zhu, Jun-fang; Wang, Shun-guo

    2015-02-01

    The presence of the East Turkistan terrorist network in China can be traced back to the rebellions on the BAREN region in Xinjiang in April 1990. This article intends to research the East Turkistan networks in China and offer a panoramic view. The events, terrorists and their relationship are described using matrices. Then social network analysis is adopted to reveal the network type and the network structure characteristics. We also find the crucial terrorist leader. Ultimately, some results show that the East Turkistan network has big hub nodes and small shortest path, and that the network follows a pattern of small world network with hierarchical structure.

  12. Modeling Transmission Line Networks Using Quantum Graphs

    Science.gov (United States)

    Koch, Trystan; Antonsen, Thomas

    Quantum graphs--one dimensional edges, connecting nodes, that support propagating Schrödinger wavefunctions--have been studied extensively as tractable models of wave chaotic behavior (Smilansky and Gnutzmann 2006, Berkolaiko and Kuchment 2013). Here we consider the electrical analog, in which the graph represents an electrical network where the edges are transmission lines (Hul et. al. 2004) and the nodes contain either discrete circuit elements or intricate circuit elements best represented by arbitrary scattering matrices. Including these extra degrees of freedom at the nodes leads to phenomena that do not arise in simpler graph models. We investigate the properties of eigenfrequencies and eigenfunctions on these graphs, and relate these to the statistical description of voltages on the transmission lines when driving the network externally. The study of electromagnetic compatibility, the effect of external radiation on complicated systems with numerous interconnected cables, motivates our research into this extension of the graph model. Work supported by the Office of Naval Research (N0014130474) and the Air Force Office of Scientific Research.

  13. Pruning Boltzmann networks and hidden Markov models

    DEFF Research Database (Denmark)

    Pedersen, Morten With; Stork, D.

    1996-01-01

    We present sensitivity-based pruning algorithms for general Boltzmann networks. Central to our methods is the efficient calculation of a second-order approximation to the true weight saliencies in a cross-entropy error. Building upon previous work which shows a formal correspondence between linear...... Boltzmann chains and hidden Markov models (HMMs), we argue that our method can be applied to HMMs as well. We illustrate pruning on Boltzmann zippers, which are equivalent to two HMMs with cross-connection links. We verify that our second-order approximation preserves the rank ordering of weight saliencies...

  14. Compartmentalization analysis using discrete fracture network models

    Energy Technology Data Exchange (ETDEWEB)

    La Pointe, P.R.; Eiben, T.; Dershowitz, W. [Golder Associates, Redmond, VA (United States); Wadleigh, E. [Marathon Oil Co., Midland, TX (United States)

    1997-08-01

    This paper illustrates how Discrete Fracture Network (DFN) technology can serve as a basis for the calculation of reservoir engineering parameters for the development of fractured reservoirs. It describes the development of quantitative techniques for defining the geometry and volume of structurally controlled compartments. These techniques are based on a combination of stochastic geometry, computational geometry, and graph the theory. The parameters addressed are compartment size, matrix block size and tributary drainage volume. The concept of DFN models is explained and methodologies to compute these parameters are demonstrated.

  15. Traffic chaotic dynamics modeling and analysis of deterministic network

    Science.gov (United States)

    Wu, Weiqiang; Huang, Ning; Wu, Zhitao

    2016-07-01

    Network traffic is an important and direct acting factor of network reliability and performance. To understand the behaviors of network traffic, chaotic dynamics models were proposed and helped to analyze nondeterministic network a lot. The previous research thought that the chaotic dynamics behavior was caused by random factors, and the deterministic networks would not exhibit chaotic dynamics behavior because of lacking of random factors. In this paper, we first adopted chaos theory to analyze traffic data collected from a typical deterministic network testbed — avionics full duplex switched Ethernet (AFDX, a typical deterministic network) testbed, and found that the chaotic dynamics behavior also existed in deterministic network. Then in order to explore the chaos generating mechanism, we applied the mean field theory to construct the traffic dynamics equation (TDE) for deterministic network traffic modeling without any network random factors. Through studying the derived TDE, we proposed that chaotic dynamics was one of the nature properties of network traffic, and it also could be looked as the action effect of TDE control parameters. A network simulation was performed and the results verified that the network congestion resulted in the chaotic dynamics for a deterministic network, which was identical with expectation of TDE. Our research will be helpful to analyze the traffic complicated dynamics behavior for deterministic network and contribute to network reliability designing and analysis.

  16. Effect of mobility models on infrastructure based wireless networks ...

    African Journals Online (AJOL)

    Effect of mobility models on infrastructure based wireless networks. ... In this paper, the effect of handoff procedure on the performance of random mobile nodes in wireless networks was investigated. Mobility of node is defined ... Article Metrics.

  17. An Optimal Design Model for New Water Distribution Networks in ...

    African Journals Online (AJOL)

    An Optimal Design Model for New Water Distribution Networks in Kigali City. ... a Linear Programming Problem (LPP) which involves the design of a new network of water distribution considering the cost in the form of unit price ... Article Metrics.

  18. A Model of Genetic Variation in Human Social Networks

    CERN Document Server

    Fowler, James H; Christakis, Nicholas A

    2008-01-01

    Social networks influence the evolution of cooperation and they exhibit strikingly systematic patterns across a wide range of human contexts. Both of these facts suggest that variation in the topological attributes of human social networks might have a genetic basis. While genetic variation accounts for a significant portion of the variation in many complex social behaviors, the heritability of egocentric social network attributes is unknown. Here we show that three of these attributes (in-degree, transitivity, and centrality) are heritable. We then develop a "mirror network" method to test extant network models and show that none accounts for observed genetic variation in human social networks. We propose an alternative "attract and introduce" model that generates significant heritability as well as other important network features, and we show that this model with two simple forms of heterogeneity is well suited to the modeling of real social networks in humans. These results suggest that natural selection ...

  19. Bus transport network model with ideal n-depth clique network topology

    Science.gov (United States)

    Yang, Xu-Hua; Chen, Guang; Sun, Bao; Chen, Sheng-Yong; Wang, Wan-Liang

    2011-11-01

    We propose an ideal n-depth clique network model. In this model, the original network is composed of cliques (maximal complete subgraphs) that overlap with each other. The network expands continuously by the addition of new cliques. The final diameter of the network can be set in advance, namely, it is controllable. Assuming that the diameter of the network is n, the network exhibits a logistic structure with (n+1) layers. In this structure, the 0th layer represents the original network and each node of the (m)th layer (1≤m≤n) corresponds to a clique in the (m-1)th layer. In the growth process of the network, we ensure that any (m)th layer network is composed of overlapping cliques. Any node in an (m)th layer network corresponds to an m-depth community in the original network, and the diameter of an m-depth community is m. Therefore, the (n-1)th layer network will contain only one clique, the (n)th layer network will contain only one node, and the diameter of the corresponding original network is n. Then an ideal n-depth clique network will be obtained. Based on the ideal n-depth clique network model, we construct a bus transport network model with an ideal n-depth clique network topology (ICNBTN). Moreover, our study compares this model with the real bus transport network (RealBTN) of three major cities in China and a recently introduced bus transport network model (BTN) whose network properties correspond well with those of real BTNs. The network properties of the ICNBTN are much closer to those of the RealBTN than those of the BTN are. At the same time, the ICNBTN has higher clustering extent of bus routes, smaller network diameter, which corresponds to shorter maximum transfer times in a bus network, and lower average shortest path time coefficient than the BTN and the RealBTN. Therefore, the ICNBTN can achieve higher transfer efficiency for a bus transport system.

  20. An Improved Car-Following Model in Vehicle Networking Based on Network Control

    Directory of Open Access Journals (Sweden)

    D. Y. Kong

    2014-01-01

    Full Text Available Vehicle networking is a system to realize information interoperability between vehicles and people, vehicles and roads, vehicles and vehicles, and cars and transport facilities, through the network information exchange, in order to achieve the effective monitoring of the vehicle and traffic flow. Realizing information interoperability between vehicles and vehicles, which can affect the traffic flow, is an important application of network control system (NCS. In this paper, a car-following model using vehicle networking theory is established, based on network control principle. The car-following model, which is an improvement of the traditional traffic model, describes the traffic in vehicle networking condition. The impact that vehicle networking has on the traffic flow is quantitatively assessed in a particular scene of one-way, no lane changing highway. The examples show that the capacity of the road is effectively enhanced by using vehicle networking.

  1. Readiness of the Belgian network of sentinel general practitioners to deliver electronic health record data for surveillance purposes: results of survey study

    Directory of Open Access Journals (Sweden)

    Vanthomme Katrien

    2010-06-01

    Full Text Available Abstract Background In order to proceed from a paper based registration to a surveillance system that is based on extraction of electronic health records (EHR, knowledge is needed on the number and representativeness of sentinel GPs using a government-certified EHR system and the quality of EHR data for research, expressed in the compliance rate with three criteria: recording of home visits, use of prescription module and diagnostic subject headings. Methods Data were collected by annual postal surveys between 2005 and 2009 among all sentinel GPs. We tested relations between four key GP characteristics (age, gender, language community, practice organisation and use of a certified EHR system by multivariable logistic regression. The relation between EHR software package, GP characteristics and compliance with three quality criteria was equally measured by multivariable logistic regression. Results A response rate of 99% was obtained. Of 221 sentinel GPs, 55% participated in the surveillance without interruption from 2005 onwards, i.e. all five years, and 78% were participants in 2009. Sixteen certified EHR systems were used among 91% of the Dutch and 63% of the French speaking sentinel GPs. The EHR software package was strongly related to the community and only one EHR system was used by a comparable number of sentinel GPs in both communities. Overall, the prescription module was always used and home visits were usually recorded. Uniform subject headings were only sometimes used and the compliance with this quality criterion was almost exclusively related to the EHR software package in use. Conclusions The challenge is to progress towards a sentinel network of GPs delivering care-based data that are (partly extracted from well performing EHR systems and still representative for Belgian general practice.

  2. A novel mathematical model for coverage in wireless sensor network

    Institute of Scientific and Technical Information of China (English)

    YAN Zhen-ya; ZHENG Bao-yu

    2006-01-01

    Coverage problem is one of the fundamental issues in the design of wireless sensor network, which has a great impact on the performance of sensor network. In this article,coverage problem was investigated using a mathematical model named Birth-death process. In this model, sensor nodes joining into networks at every period of time is considered as the rebirth of network and the quitting of sensor nodes from the networks is considered as the death of the network. In the end, an analytical solution is used to investigate the appropriate rate to meet the coverage requirement.

  3. PageRank model of opinion formation on Ulam networks

    CERN Document Server

    Chakhmakhchyan, L

    2013-01-01

    We consider a PageRank model of opinion formation on Ulam networks, generated by the intermittency map and the typical Chirikov map. The Ulam networks generated by these maps have certain similarities with such scale-free networks as the World Wide Web (WWW), showing an algebraic decay of the PageRank probability. We find that the opinion formation process on Ulam networks have certain similarities but also distinct features comparing to the WWW. We attribute these distinctions to internal differences in network structure of the Ulam and WWW networks. We also analyze the process of opinion formation in the frame of generalized Sznajd model which protects opinion of small communities.

  4. Brazilian network for the surveillance of maternal potentially life threatening morbidity and maternal near-miss and a multidimensional evaluation of their long term consequences

    Directory of Open Access Journals (Sweden)

    Surita Fernanda G

    2009-09-01

    Full Text Available Abstract Background It has been suggested that the study of women who survive life-threatening complications related to pregnancy (maternal near-miss cases may represent a practical alternative to surveillance of maternal morbidity/mortality since the number of cases is higher and the woman herself is able to provide information on the difficulties she faced and the long-term repercussions of the event. These repercussions, which may include sexual dysfunction, postpartum depression and posttraumatic stress disorder, may persist for prolonged periods of time, affecting women's quality of life and resulting in adverse effects to them and their babies. Objective The aims of the present study are to create a nationwide network of scientific cooperation to carry out surveillance and estimate the frequency of maternal near-miss cases, to perform a multicenter investigation into the quality of care for women with severe complications of pregnancy, and to carry out a multidimensional evaluation of these women up to six months. Methods/Design This project has two components: a multicenter, cross-sectional study to be implemented in 27 referral obstetric units in different geographical regions of Brazil, and a concurrent cohort study of multidimensional analysis. Over 12 months, investigators will perform prospective surveillance to identify all maternal complications. The population of the cross-sectional component will consist of all women surviving potentially life-threatening conditions (severe maternal complications or life-threatening conditions (the maternal near miss criteria and maternal deaths according to the new WHO definition and criteria. Data analysis will be performed in case subgroups according to the moment of occurrence and determining cause. Frequencies of near-miss and other severe maternal morbidity and the association between organ dysfunction and maternal death will be estimated. A proportion of cases identified in the cross

  5. Brazilian network for the surveillance of maternal potentially life threatening morbidity and maternal near-miss and a multidimensional evaluation of their long term consequences.

    Science.gov (United States)

    Cecatti, Jose G; Souza, João P; Parpinelli, Mary A; Haddad, Samira M; Camargo, Rodrigo S; Pacagnella, Rodolfo C; Silveira, Carla; Zanardi, Dulce T; Costa, Maria L; Pinto e Silva, João L; Passini, Renato; Surita, Fernanda G; Sousa, Maria H; Calderon, Iracema M P; Say, Lale; Pattinson, Robert C

    2009-09-24

    It has been suggested that the study of women who survive life-threatening complications related to pregnancy (maternal near-miss cases) may represent a practical alternative to surveillance of maternal morbidity/mortality since the number of cases is higher and the woman herself is able to provide information on the difficulties she faced and the long-term repercussions of the event. These repercussions, which may include sexual dysfunction, postpartum depression and posttraumatic stress disorder, may persist for prolonged periods of time, affecting women's quality of life and resulting in adverse effects to them and their babies. The aims of the present study are to create a nationwide network of scientific cooperation to carry out surveillance and estimate the frequency of maternal near-miss cases, to perform a multicenter investigation into the quality of care for women with severe complications of pregnancy, and to carry out a multidimensional evaluation of these women up to six months. This project has two components: a multicenter, cross-sectional study to be implemented in 27 referral obstetric units in different geographical regions of Brazil, and a concurrent cohort study of multidimensional analysis. Over 12 months, investigators will perform prospective surveillance to identify all maternal complications. The population of the cross-sectional component will consist of all women surviving potentially life-threatening conditions (severe maternal complications) or life-threatening conditions (the maternal near miss criteria) and maternal deaths according to the new WHO definition and criteria. Data analysis will be performed in case subgroups according to the moment of occurrence and determining cause. Frequencies of near-miss and other severe maternal morbidity and the association between organ dysfunction and maternal death will be estimated. A proportion of cases identified in the cross-sectional study will comprise the cohort of women for the

  6. Feature network models for proximity data : statistical inference, model selection, network representations and links with related models

    NARCIS (Netherlands)

    Frank, Laurence Emmanuelle

    2006-01-01

    Feature Network Models (FNM) are graphical structures that represent proximity data in a discrete space with the use of features. A statistical inference theory is introduced, based on the additivity properties of networks and the linear regression framework. Considering features as predictor variab

  7. VEPCO network model reconciliation of LANL and MZA model data

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1992-12-15

    The LANL DC load flow model of the VEPCO transmission network shows 210 more substations than the AC load flow model produced by MZA utility Consultants. MZA was requested to determine the source of the difference. The AC load flow model used for this study utilizes 2 standard network algorithms (Decoupled or Newton). The solution time of each is affected by the number of substations. The more substations included, the longer the model will take to solve. In addition, the ability of the algorithms to converge to a solution is affected by line loadings and characteristics. Convergence is inhibited by numerous lightly loaded and electrically short lines. The MZA model reduces the total substations to 343 by creating equivalent loads and generation. Most of the omitted substations are lightly loaded and rated at 115 kV. The MZA model includes 16 substations not included in the LANL model. These represent new generation including Non-Utility Generator (NUG) sites, additional substations and an intertie (Wake, to CP and L). This report also contains data from the Italian State AC power flow model and the Duke Power Company AC flow model.

  8. A scale-free neural network for modelling neurogenesis

    Science.gov (United States)

    Perotti, Juan I.; Tamarit, Francisco A.; Cannas, Sergio A.

    2006-11-01

    In this work we introduce a neural network model for associative memory based on a diluted Hopfield model, which grows through a neurogenesis algorithm that guarantees that the final network is a small-world and scale-free one. We also analyze the storage capacity of the network and prove that its performance is larger than that measured in a randomly dilute network with the same connectivity.

  9. NSME: a framework for network worm modeling and simulation

    OpenAIRE

    Lin, Siming; Cheng, Xueqi

    2006-01-01

    Various worms have a devastating impact on Internet. Packet level network modeling and simulation has become an approach to find effective countermeasures against worm threat. However, current alternatives are not fit enough for this purpose. For instance, they mostly focus on the details of lower layers of the network so that the abstraction of application layer is very coarse. In our work, we propose a formal description of network and worm models, and define network virtualization level...

  10. Modeling management of research and education networks

    NARCIS (Netherlands)

    Galagan, D.V.

    2004-01-01

    Computer networks and their services have become an essential part of research and education. Nowadays every modern R&E institution must have a computer network and provide network services to its students and staff. In addition to its internal computer network, every R&E institution must have a con

  11. Awareness about a Life-Threatening Condition: Ectopic Pregnancy in a Network for Surveillance of Severe Maternal Morbidity in Brazil

    Directory of Open Access Journals (Sweden)

    Edilberto Alves Rocha Filho

    2014-01-01

    Full Text Available Objective. To assess occurrence of severe maternal complications associated with ectopic pregnancy (EP. Method. A multicenter cross-sectional study was conducted, with prospective surveillance of potentially life-threatening conditions (PLTC, maternal near miss (MNM, and maternal death (MD. EP complications, patient sociodemographic/obstetric characteristics, and conditions of severity management were assessed, estimating prevalence ratios with respective 95% CI. Factors independently associated with greater severity were identified using multiple regression analysis. Results. Of the 9.555 severe maternal morbidity patients, 312 women (3.3% had complications after EP: 286 (91.7% PLTC, 25 (8.0% MNM, and 1 (0.3% MD. Severe maternal outcome ratio (SMOR was 0.3/1000 LB among EP cases and 10.8/1000 LB among other causes. Complicated EP patients faced a higher risk of blood transfusion, laparotomy, and lower risk of ICU admission and prolonged hospitalization than women developing complications resulting from other causes. Substandard care was the most common in more severe maternal morbidity and EP cases (22.7% MNM and MD versus 15% PLTC, although not significant. Conclusion. Increased maternal morbidity due to EP raised awareness about the condition and its impact on female reproductive life. No important risk factors for greater severity were identified. Care providers should develop specific guidelines and interventions to prevent severe maternal morbidity.

  12. Formation of Modularity in a Model of Evolving Networks

    CERN Document Server

    Li, Menghui; Lai, Choy-Heng

    2011-01-01

    Modularity structures are common in various social and biological networks. However, its dynamical origin remains an open question. In this work, we set up a toy dynamical model describing the evolution of a social network. Based on the observations of real social networks, we introduced a strategy of link-creating/deleting according to the local dynamics in the model. Thus the coevolution of the dynamics and topology naturally determines the network properties. It is found that for a small coupling strength, the networked system cannot reach any synchronization and the network topology is homogeneous. Interestingly, when the coupling strength is large enough, the networked system spontaneously forms communities with different dynamical states. Meanwhile, the network topology becomes heterogeneous with modular structures. It is further shown that in certain parameter regime, both the degree and the community size in the formed network follow power-law distribution. These results are consistent with the charac...

  13. Boolean network model predicts knockout mutant phenotypes of fission yeast.

    Directory of Open Access Journals (Sweden)

    Maria I Davidich

    Full Text Available BOOLEAN NETWORKS (OR: networks of switches are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene regulatory networks in living cells. For example, the temporal sequence of cell cycle activation patterns in yeasts S. pombe and S. cerevisiae are faithfully reproduced by Boolean network models. An interesting question is whether this simple model class could also predict a more complex cellular phenomenology as, for example, the cell cycle dynamics under various knockout mutants instead of the wild type dynamics, only. Here we show that a Boolean network model for the cell cycle control network of yeast S. pombe correctly predicts viability of a large number of known mutants. So far this had been left to the more detailed differential equation models of the biochemical kinetics of the yeast cell cycle network and was commonly thought to be out of reach for models as simplistic as Boolean networks. The new results support our vision that Boolean networks may complement other mathematical models in systems biology to a larger extent than expected so far, and may fill a gap where simplicity of the model and a preference for an overall dynamical blueprint of cellular regulation, instead of biochemical details, are in the focus.

  14. Boolean Network Model Predicts Knockout Mutant Phenotypes of Fission Yeast

    Science.gov (United States)

    Davidich, Maria I.; Bornholdt, Stefan

    2013-01-01

    Boolean networks (or: networks of switches) are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene regulatory networks in living cells. For example, the temporal sequence of cell cycle activation patterns in yeasts S. pombe and S. cerevisiae are faithfully reproduced by Boolean network models. An interesting question is whether this simple model class could also predict a more complex cellular phenomenology as, for example, the cell cycle dynamics under various knockout mutants instead of the wild type dynamics, only. Here we show that a Boolean network model for the cell cycle control network of yeast S. pombe correctly predicts viability of a large number of known mutants. So far this had been left to the more detailed differential equation models of the biochemical kinetics of the yeast cell cycle network and was commonly thought to be out of reach for models as simplistic as Boolean networks. The new results support our vision that Boolean networks may complement other mathematical models in systems biology to a larger extent than expected so far, and may fill a gap where simplicity of the model and a preference for an overall dynamical blueprint of cellular regulation, instead of biochemical details, are in the focus. PMID:24069138

  15. A graph model for opportunistic network coding

    KAUST Repository

    Sorour, Sameh

    2015-08-12

    © 2015 IEEE. Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase in complexity. In this paper, we design a simple IDNC-like graph model for a specific subclass of ONC, by introducing a more generalized definition of its vertices and the notion of vertex aggregation in order to represent the storage of non-instantly-decodable packets in ONC. Based on this representation, we determine the set of pairwise vertex adjacency conditions that can populate this graph with edges so as to guarantee decodability or aggregation for the vertices of each clique in this graph. We then develop the algorithmic procedures that can be applied on the designed graph model to optimize any performance metric for this ONC subclass. A case study on reducing the completion time shows that the proposed framework improves on the performance of IDNC and gets very close to the optimal performance.

  16. Determining Application Runtimes Using Queueing Network Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Elliott, Michael L. [Univ. of San Francisco, CA (United States)

    2006-12-14

    Determination of application times-to-solution for large-scale clustered computers continues to be a difficult problem in high-end computing, which will only become more challenging as multi-core consumer machines become more prevalent in the market. Both researchers and consumers of these multi-core systems desire reasonable estimates of how long their programs will take to run (time-to-solution, or TTS), and how many resources will be consumed in the execution. Currently there are few methods of determining these values, and those that do exist are either overly simplistic in their assumptions or require great amounts of effort to parameterize and understand. One previously untried method is queuing network modeling (QNM), which is easy to parameterize and solve, and produces results that typically fall within 10 to 30% of the actual TTS for our test cases. Using characteristics of the computer network (bandwidth, latency) and communication patterns (number of messages, message length, time spent in communication), the QNM model of the NAS-PB CG application was applied to MCR and ALC, supercomputers at LLNL, and the Keck Cluster at USF, with average errors of 2.41%, 3.61%, and -10.73%, respectively, compared to the actual TTS observed. While additional work is necessary to improve the predictive capabilities of QNM, current results show that QNM has a great deal of promise for determining application TTS for multi-processor computer systems.

  17. Marketing communications model for innovation networks

    Directory of Open Access Journals (Sweden)

    Tiago João Freitas Correia

    2015-10-01

    Full Text Available Innovation is an increasingly relevant concept for the success of any organization, but it also represents a set of internal and external considerations, barriers and challenges to overcome. Along the concept of innovation, new paradigms emerge such as open innovation and co-creation that are simultaneously innovation modifiers and intensifiers in organizations, promoting organizational openness and stakeholder integration within the value creation process. Innovation networks composed by a multiplicity of agents in co-creative work perform as innovation mechanisms to face the increasingly complexity of products, services and markets. Technology, especially the Internet, is an enabler of all process among organizations supported by co-creative platforms for innovation. The definition of marketing communication strategies that promote motivation and involvement of all stakeholders in synergic creation and external promotion is the central aspect of this research. The implementation of the projects is performed by participative workshops with stakeholders from Madan Parque through IDEAS(REVOLUTION methodology and the operational model LinkUp parameterized for the project. The project is divided into the first part, the theoretical framework, and the second part where a model is developed for the marketing communication strategies that appeal to the Madan Parque case study. Keywords: Marketing Communication; Open Innovation, Technology; Innovation Networks; Incubator; Co-Creation.

  18. Multilevel method for modeling large-scale networks.

    Energy Technology Data Exchange (ETDEWEB)

    Safro, I. M. (Mathematics and Computer Science)

    2012-02-24

    Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

  19. Infinite multiple membership relational modeling for complex networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Schmidt, Mikkel Nørgaard; Hansen, Lars Kai

    2011-01-01

    Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiple-membership latent feature model for networks. Contrary to exist......Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiple-membership latent feature model for networks. Contrary...... to existing multiplemembership models that scale quadratically in the number of vertices the proposed model scales linearly in the number of links admitting multiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership...

  20. Models of neural networks with fuzzy activation functions

    Science.gov (United States)

    Nguyen, A. T.; Korikov, A. M.

    2017-02-01

    This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc.

  1. Topological evolution of virtual social networks by modeling social activities

    Science.gov (United States)

    Sun, Xin; Dong, Junyu; Tang, Ruichun; Xu, Mantao; Qi, Lin; Cai, Yang

    2015-09-01

    With the development of Internet and wireless communication, virtual social networks are becoming increasingly important in the formation of nowadays' social communities. Topological evolution model is foundational and critical for social network related researches. Up to present most of the related research experiments are carried out on artificial networks, however, a study of incorporating the actual social activities into the network topology model is ignored. This paper first formalizes two mathematical abstract concepts of hobbies search and friend recommendation to model the social actions people exhibit. Then a social activities based topology evolution simulation model is developed to satisfy some well-known properties that have been discovered in real-world social networks. Empirical results show that the proposed topology evolution model has embraced several key network topological properties of concern, which can be envisioned as signatures of real social networks.

  2. A fusion networking model for smart grid power distribution backbone communication network based on PTN

    Directory of Open Access Journals (Sweden)

    Wang Hao

    2016-01-01

    Full Text Available In current communication network for distribution in Chinese power grid systems, the fiber communication backbone network for distribution and TD-LTE power private wireless backhaul network of power grid are both bearing by the SDH optical transmission network, which also carries the communication network of transformer substation and main electric. As the data traffic of the distribution communication and TD-LTE power private wireless network grow rapidly in recent years, it will have a big impact with the SDH network’s bearing capacity which is mainly used for main electric communication in high security level. This paper presents a fusion networking model which use a multiple-layer PTN network as the unified bearing of the TD-LTE power private wireless backhaul network and fiber communication backbone network for distribution. Network dataflow analysis shows that this model can greatly reduce the capacity pressure of the traditional SDH network as well as ensure the reliability of the transmission of the communication network for distribution and TD-LTE power private wireless network.

  3. Modeling stochasticity in biochemical reaction networks

    Science.gov (United States)

    Constantino, P. H.; Vlysidis, M.; Smadbeck, P.; Kaznessis, Y. N.

    2016-03-01

    Small biomolecular systems are inherently stochastic. Indeed, fluctuations of molecular species are substantial in living organisms and may result in significant variation in cellular phenotypes. The chemical master equation (CME) is the most detailed mathematical model that can describe stochastic behaviors. However, because of its complexity the CME has been solved for only few, very small reaction networks. As a result, the contribution of CME-based approaches to biology has been very limited. In this review we discuss the approach of solving CME by a set of differential equations of probability moments, called moment equations. We present different approaches to produce and to solve these equations, emphasizing the use of factorial moments and the zero information entropy closure scheme. We also provide information on the stability analysis of stochastic systems. Finally, we speculate on the utility of CME-based modeling formalisms, especially in the context of synthetic biology efforts.

  4. Modelling transcriptional networks in leaf senescence.

    Science.gov (United States)

    Penfold, Christopher A; Buchanan-Wollaston, Vicky

    2014-07-01

    The process of leaf senescence is induced by an extensive range of developmental and environmental signals and controlled by multiple, cross-linking pathways, many of which overlap with plant stress-response signals. Elucidation of this complex regulation requires a step beyond a traditional one-gene-at-a-time analysis. Application of a more global analysis using statistical and mathematical tools of systems biology is an approach that is being applied to address this problem. A variety of modelling methods applicable to the analysis of current and future senescence data are reviewed and discussed using some senescence-specific examples. Network modelling with a senescence transcriptome time course followed by testing predictions with gene-expression data illustrates the application of systems biology tools.

  5. Network transmission model: A dynamic traffic model at network level (poster)

    NARCIS (Netherlands)

    Knoop, V.L.; Hoogendoorn, S.P.

    2014-01-01

    New IT techniques allow communication and coordination between traffic measures. To best use this, one needs to coordinate over longer distances. Optimization of the measures is not possible using traditional microscopic or macroscopic simulation models. The Network Fundamental Diagram (NFD) describ

  6. Completely random measures for modelling block-structured sparse networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Schmidt, Mikkel Nørgaard; Mørup, Morten

    2016-01-01

    Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world network...... is not significantly more difficult to implement than existing approaches to block-modelling and performs well on real network datasets.......Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks...... [2014] proposed the use of a different notion of exchangeability due to Kallenberg [2006] and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re...

  7. Logistics of community smallpox control through contact tracing and ring vaccination: a stochastic network model

    Directory of Open Access Journals (Sweden)

    Portnoy Diane L

    2004-08-01

    Full Text Available Abstract Background Previous smallpox ring vaccination models based on contact tracing over a network suggest that ring vaccination would be effective, but have not explicitly included response logistics and limited numbers of vaccinators. Methods We developed a continuous-time stochastic simulation of smallpox transmission, including network structure, post-exposure vaccination, vaccination of contacts of contacts, limited response capacity, heterogeneity in symptoms and infectiousness, vaccination prior to the discontinuation of routine vaccination, more rapid diagnosis due to public awareness, surveillance of asymptomatic contacts, and isolation of cases. Results We found that even in cases of very rapidly spreading smallpox, ring vaccination (when coupled with surveillance is sufficient in most cases to eliminate smallpox quickly, assuming that 95% of household contacts are traced, 80% of workplace or social contacts are traced, and no casual contacts are traced, and that in most cases the ability to trace 1–5 individuals per day per index case is sufficient. If smallpox is assumed to be transmitted very quickly to contacts, it may at times escape containment by ring vaccination, but could be controlled in these circumstances by mass vaccination. Conclusions Small introductions of smallpox are likely to be easily contained by ring vaccination, provided contact tracing is feasible. Uncertainties in the nature of bioterrorist smallpox (infectiousness, vaccine efficacy support continued planning for ring vaccination as well as mass vaccination. If initiated, ring vaccination should be conducted without delays in vaccination, should include contacts of contacts (whenever there is sufficient capacity and should be accompanied by increased public awareness and surveillance.

  8. Surveillance of Autism.

    Science.gov (United States)

    Boyle, Coleen A.; Bertrand, Jacquelyn; Yeargin-Allsopp, Marshalyn

    1999-01-01

    This article describes the autism surveillance activities of the Center for Disease Control and Prevention. It considers why surveillance to track prevalence of autistic disorders is needed, how such surveillance is conducted, and the special challenges of autism surveillance. (DB)

  9. Road network safety evaluation using Bayesian hierarchical joint model.

    Science.gov (United States)

    Wang, Jie; Huang, Helai

    2016-05-01

    Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model. A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well.

  10. An Efficient Multitask Scheduling Model for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hongsheng Yin

    2014-01-01

    Full Text Available The sensor nodes of multitask wireless network are constrained in performance-driven computation. Theoretical studies on the data processing model of wireless sensor nodes suggest satisfying the requirements of high qualities of service (QoS of multiple application networks, thus improving the efficiency of network. In this paper, we present the priority based data processing model for multitask sensor nodes in the architecture of multitask wireless sensor network. The proposed model is deduced with the M/M/1 queuing model based on the queuing theory where the average delay of data packets passing by sensor nodes is estimated. The model is validated with the real data from the Huoerxinhe Coal Mine. By applying the proposed priority based data processing model in the multitask wireless sensor network, the average delay of data packets in a sensor nodes is reduced nearly to 50%. The simulation results show that the proposed model can improve the throughput of network efficiently.

  11. Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance

    Science.gov (United States)

    Chan, Emily H.; Sahai, Vikram; Conrad, Corrie; Brownstein, John S.

    2011-01-01

    Background A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics. Methodology/Principal Findings Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003–2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99. Conclusions/Significance Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance. PMID:21647308

  12. Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance.

    Directory of Open Access Journals (Sweden)

    Emily H Chan

    2011-05-01

    Full Text Available BACKGROUND: A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics. METHODOLOGY/PRINCIPAL FINDINGS: Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003-2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99. CONCLUSIONS/SIGNIFICANCE: Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance.

  13. Resolving structural variability in network models and the brain.

    Directory of Open Access Journals (Sweden)

    Florian Klimm

    2014-03-01

    Full Text Available Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful

  14. An information theoretic approach for combining neural network process models.

    Science.gov (United States)

    Sridhar, D V.; Bartlett, E B.; Seagrave, R C.

    1999-07-01

    Typically neural network modelers in chemical engineering focus on identifying and using a single, hopefully optimal, neural network model. Using a single optimal model implicitly assumes that one neural network model can extract all the information available in a given data set and that the other candidate models are redundant. In general, there is no assurance that any individual model has extracted all relevant information from the data set. Recently, Wolpert (Neural Networks, 5(2), 241 (1992)) proposed the idea of stacked generalization to combine multiple models. Sridhar, Seagrave and Barlett (AIChE J., 42, 2529 (1996)) implemented the stacked generalization for neural network models by integrating multiple neural networks into an architecture known as stacked neural networks (SNNs). SNNs consist of a combination of the candidate neural networks and were shown to provide improved modeling of chemical processes. However, in Sridhar's work SNNs were limited to using a linear combination of artificial neural networks. While a linear combination is simple and easy to use, it can utilize only those model outputs that have a high linear correlation to the output. Models that are useful in a nonlinear sense are wasted if a linear combination is used. In this work we propose an information theoretic stacking (ITS) algorithm for combining neural network models. The ITS algorithm identifies and combines useful models regardless of the nature of their relationship to the actual output. The power of the ITS algorithm is demonstrated through three examples including application to a dynamic process modeling problem. The results obtained demonstrate that the SNNs developed using the ITS algorithm can achieve highly improved performance as compared to selecting and using a single hopefully optimal network or using SNNs based on a linear combination of neural networks.

  15. Application of Species Distribution Modeling for Avian Influenza surveillance in the United States considering the North America Migratory Flyways

    Science.gov (United States)

    Belkhiria, Jaber; Alkhamis, Moh A.; Martínez-López, Beatriz

    2016-09-01

    Highly Pathogenic Avian Influenza (HPAI) has recently (2014–2015) re-emerged in the United States (US) causing the largest outbreak in US history with 232 outbreaks and an estimated economic impact of $950 million. This study proposes to use suitability maps for Low Pathogenic Avian Influenza (LPAI) to identify areas at high risk for HPAI outbreaks. LPAI suitability maps were based on wild bird demographics, LPAI surveillance, and poultry density in combination with environmental, climatic, and socio-economic risk factors. Species distribution modeling was used to produce high-resolution (cell size: 500m x 500m) maps for Avian Influenza (AI) suitability in each of the four North American migratory flyways (NAMF). Results reveal that AI suitability is heterogeneously distributed throughout the US with higher suitability in specific zones of the Midwest and coastal areas. The resultant suitability maps adequately predicted most of the HPAI outbreak areas during the 2014–2015 epidemic in the US (i.e. 89% of HPAI outbreaks were located in areas identified as highly suitable for LPAI). Results are potentially useful for poultry producers and stakeholders in designing risk-based surveillance, outreach and intervention strategies to better prevent and control future HPAI outbreaks in the US.

  16. Geocoding capacity of birth defects surveillance programs: results from the National Birth Defects Prevention Network Geocoding Survey.

    Science.gov (United States)

    Wang, Ying; O'Leary, Leslie A; Rickard, Russel S; Mason, Craig A

    2010-01-01

    A Web-based survey focusing on geocoding of birth defects data was developed and administrated to gain an understanding of the capacity of state birth defects programs to geocode maternal residence and to identify barriers to geocoding birth defects data. The survey consisted of 21 questions related to geocoding of maternal residence, type of software used, barriers to geocoding, and data linkage. In August 2007, an e-mail with a Web link to the survey was sent to all state birth defects program contacts in the United States, including the District of Columbia, Puerto Rico, and the Centers for Disease Control and Prevention (CDC) requesting they complete the online survey. By October 2007, 39 (74%) out of 53 birth defects program contacts completed the survey. Although nearly all birth defects programs collect maternal residential data, many are not currently geocoding that data. Results indicated that 97% of the programs that completed the survey reported they collected data on maternal residence, 53% of which reported that the birth defects surveillance data were geocoded to the street address level using maternal residential address at delivery. Twenty six percent of the programs that do not currently geocode the data identified "Software and address reference file are not available" as the most significant barrier to geocoding; another 16% chose "Lack of funding" as the most significant barrier to geocoding. Since geocoding is an important component of spatial analyses used to detect potential clusters of birth defects, leveraging resources to overcome the barriers that prevent programs from geocoding is important.

  17. Efficient large-scale graph data optimization for intelligent video surveillance

    Science.gov (United States)

    Shang, Quanhong; Zhang, Shujun; Wang, Yanbo; Sun, Chen; Wang, Zepeng; Zhang, Luming

    2017-08-01

    Society is rapidly accepting the use of a wide variety of cameras Location and applications: site traffic monitoring, parking Lot surveillance, car and smart space. These ones here the camera provides data every day in an analysis Effective way. Recent advances in sensor technology Manufacturing, communications and computing are stimulating.The development of new applications that can change the traditional Vision system incorporating universal smart camera network. This Analysis of visual cues in multi camera networks makes wide Applications ranging from smart home and office automation to large area surveillance and traffic surveillance. In addition, dense Camera networks, most of which have large overlapping areas of cameras. In the view of good research, we focus on sparse camera networks. One Sparse camera network using large area surveillance. As few cameras as possible, most cameras do not overlap Each other’s field of vision. This task is challenging Lack of knowledge of topology Network, the specific changes in appearance and movement Track different opinions of the target, as well as difficulties Understanding complex events in a network. In this review in this paper, we present a comprehensive survey of recent studies Results to solve the problem of topology learning, Object appearance modeling and global activity understanding sparse camera network. In addition, some of the current open Research issues are discussed.

  18. A last updating evolution model for online social networks

    Science.gov (United States)

    Bu, Zhan; Xia, Zhengyou; Wang, Jiandong; Zhang, Chengcui

    2013-05-01

    As information technology has advanced, people are turning to electronic media more frequently for communication, and social relationships are increasingly found on online channels. However, there is very limited knowledge about the actual evolution of the online social networks. In this paper, we propose and study a novel evolution network model with the new concept of “last updating time”, which exists in many real-life online social networks. The last updating evolution network model can maintain the robustness of scale-free networks and can improve the network reliance against intentional attacks. What is more, we also found that it has the “small-world effect”, which is the inherent property of most social networks. Simulation experiment based on this model show that the results and the real-life data are consistent, which means that our model is valid.

  19. Multi-agent Based Modeling of Manufacturing Network

    Institute of Scientific and Technical Information of China (English)

    GUO Yuming; SUN Yanming; ZHENG Shixiong

    2006-01-01

    An intelligent manufacturing system is modeled currently from the viewpoint of manufacturing applications, and the network platform's influence to manufacturing applications is not considered adequately. However any bottleneck in service oriented architecture (SOA) for the manufacturing network can affect the agility of the IT environment. In this paper, to achieve a trade-off between manufacturing resources and network resources, the manufacturing network is modeled with multi-agent, in which two kinds of basic elements, the manufacturing application unit and the network carrier of manufacturing information, are presented. And their main characters are described by colored petri net. The manufacturing application model drives the network platform that inversely provides this application model technology supports. The proposed multi-agent system is demonstrated through an example integration scenario involving production plan, resources management and execution subsystems. And the result suggests that analyzing and designing the system architecture of networked manufacturing should give due attention to the operation system as well as manufacturing applications.

  20. Domestic violence surveillance system: a model Sistema de vigilancia de violencia doméstica: un modelo

    Directory of Open Access Journals (Sweden)

    Rafael Espinosa

    2008-01-01

    Full Text Available OBJECTIVE: To develop a domestic violence surveillance system. MATERIAL AND METHODS: The strategies included implementation of a standard digitalized reporting and analysis system along with advocacy with community decision makers, strengthening inter-institutional attention networks, consultation for constructing internal flow charts, sensitizing and training network teams in charge of providing health care in cases of domestic violence and supporting improved public policy prevention initiatives. RESULTS: A total of 6 893 cases were observed using 2004 and 2005 surveillance system data. The system reports that 80% of the affected were women, followed by 36% children under 14 years. The identified aggressors were mainly females' partners. The system was useful for improving victim services. CONCLUSIONS: Findings indicate that significant gains were made in facilitating the attention and treatment of victims of domestic violence, improving the procedural response process and enhancing the quality of information provided to policy-making bodies.OBJETIVO: Desarrollar un sistema de vigilancia sobre violencia doméstica. MATERIAL Y MÉTODOS: Las estrategias incluyeron la implementación de un sistema de análisis y reporte digitalizado estándar, a la par de hacer conciencia entre los tomadores de decisiones a nivel comunitario, fortalecer redes de atención interinstitucionales, consultoría para el diseño de diagramas de flujo internos, equipos de sensibilización y entrenamiento a cargo de proveer cuidados de salud en casos de violencia doméstica y de dar a poyo a iniciativas de prevención como parte de políticas públicas mejoradas. RESULTADOS: Se observó un total de 6893 casos a partir de datos de 2004 y 2005 de un sistema de vigilancia. El sistema informa que 80% de las víctimas fueron mujeres, seguidas de 36% de niños menores de 14 años. Los agresores identificados fueron principalmente los compañeros de las mujeres. El sistema