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

DEFF Research Database (Denmark)

Simon, G.; Reid, S. M.; Larsen, Lars Erik

and seeks to strengthen formal interactions with human and avian surveillance networks. Materials and Methods: The project consortium comprises 24 participants, contributing a variety of specialism’s and skills ensuring multi-disciplinary cutting-edge outputs. Most partners are actively working with swine...... 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...... of the programmes that are currently active in fifteen of the partners showed that passive surveillance was primarily used. Detected virus strains will be characterised by antigenic cartography (informing better evidence-based approaches for selection of vaccine strains) and genetically through full genome...

An Interfacing System for Radiation Surveillance Using a Radio Communication Network

International Nuclear Information System (INIS)

Arunsiri, T.; Punnachaiya, S.; Pattarasumun, A.

1998-01-01

The development of an interfacing system for environmental radiation surveillance using radio communication network is aimed to improve a way by which environmental radiation measurement is transmitted and reported from the regional area monitoring station network. This also includes an automatic warning of beacon status via the radio link network to the center of environmental radiation control when an abnormal radiation level is detected. The interfacing system was developed by simulating the EGAT radio link network, the NT 2612, and can be separated into two parts. The first part was for a mobile station which can manage the output data from the radiation measurement system in the standard form of RS-232, IEEE-488, BCD and analog signal. This was accomplished by modulating the signal in selected baud rates ranging from 150 to 9600 bps using an economical radio packet capable of identifying and recalling the station code number. The other part is the linking system between the output data and the microcomputer equipped with a software to manage and evaluate the data from 10 surveillance stations for convenient handing of data output, statistical analysis and transmitting warning signal. Data transmission was tested using a baud rate of 1200 bps and was found to contain no detectable error when digital signal was transmitted while analog signal transmission resulted in deviations of less than ± 0.003%. The development of this radio link system provides a future trend for the environmental radiation monitoring network for countries with nuclear power plants or neighboring countries needed to continuously monitor for any abnormal radiation level in the environment. In case that the radiation surveillance system detects a high level of radiation, a warning signal will be transmitted and appropriate actions may be immediately exercised to control impacts of radiation on environment and living things according to international guidelines

Pervasive surveillance-agent system based on wireless sensor networks: design and deployment

International Nuclear Information System (INIS)

Martínez, José F; Bravo, Sury; García, Ana B; Corredor, Iván; Familiar, Miguel S; López, Lourdes; Hernández, Vicente; Da Silva, Antonio

2010-01-01

Nowadays, proliferation of embedded systems is enhancing the possibilities of gathering information by using wireless sensor networks (WSNs). Flexibility and ease of installation make these kinds of pervasive networks suitable for security and surveillance environments. Moreover, the risk for humans to be exposed to these functions is minimized when using these networks. In this paper, a virtual perimeter surveillance agent, which has been designed to detect any person crossing an invisible barrier around a marked perimeter and send an alarm notification to the security staff, is presented. This agent works in a state of 'low power consumption' until there is a crossing on the perimeter. In our approach, the 'intelligence' of the agent has been distributed by using mobile nodes in order to discern the cause of the event of presence. This feature contributes to saving both processing resources and power consumption since the required code that detects presence is the only system installed. The research work described in this paper illustrates our experience in the development of a surveillance system using WNSs for a practical application as well as its evaluation in real-world deployments. This mechanism plays an important role in providing confidence in ensuring safety to our environment

The French surveillance network of Creutzfeldt-Jakob disease. Epidemiological data in France and worldwide.

Science.gov (United States)

Brandel, J-P; Peckeu, L; Haïk, S

2013-09-01

France, involved for a long time in the epidemiological surveillance of transmissible spongiform encephalopathy (TSE), created a national network of surveillance in 1991, because of the description of the first cases of Creutzfeldt-Jakob disease (CJD) linked to a treatment by growth hormone of human origin and the observation of cases of cats infected with the agent of the bovine spongiform encephalopathy in the United Kingdom (UK). The French surveillance network is integrated into the European network of surveillance since its creation in 1993. As in other countries, sporadic CJD is the most frequent form of TSE in France with an annual mortality rate of 1.44 per million. Genetic forms are most often associated with a mutation at codon 200. Among the cases of iatrogenic CJD, 13 cases of CJD after duramater grafts were observed and 119 related to treatment with growth hormone. France is the country worst affected in Europe and the world by this latter form, before the USA and UK. Since 1996, 27 cases of variant of CJD (vCJD) has been observed, making France the second country in the world most affected after the UK. No cases of transfusion-associated vCJD have been observed. Copyright © 2013. Published by Elsevier SAS.

Multi-site cholera surveillance within the African Cholera Surveillance Network shows endemicity in Mozambique, 2011–2015

Science.gov (United States)

Langa, José Paulo; Dengo Baloi, Liliana; Wood, Richard; Ouedraogo, Issaka; Njanpop-Lafourcade, Berthe-Marie; Inguane, Dorteia; Elias Chitio, Jucunu; Mhlanga, Themba; Gujral, Lorna; D. Gessner, Bradford; Munier, Aline; A. Mengel, Martin

2017-01-01

Background Mozambique suffers recurrent annual cholera outbreaks especially during the rainy season between October to March. The African Cholera Surveillance Network (Africhol) was implemented in Mozambique in 2011 to generate accurate detailed surveillance data to support appropriate interventions for cholera control and prevention in the country. Methodology/Principal findings Africhol was implemented in enhanced surveillance zones located in the provinces of Sofala (Beira), Zambézia (District Mocuba), and Cabo Delgado (Pemba City). Data were also analyzed from the three outbreak areas that experienced the greatest number of cases during the time period under observation (in the districts of Cuamba, Montepuez, and Nampula). Rectal swabs were collected from suspected cases for identification of Vibrio cholerae, as well as clinical, behavioral, and socio-demographic variables. We analyzed factors associated with confirmed, hospitalized, and fatal cholera using multivariate logistic regression models. A total of 1,863 suspected cases and 23 deaths (case fatality ratio (CFR), 1.2%) were reported from October 2011 to December 2015. Among these suspected cases, 52.2% were tested of which 23.5% were positive for Vibrio cholerae O1 Ogawa. Risk factors independently associated with the occurrence of confirmed cholera were living in Nampula city district, the year 2014, human immunodeficiency virus infection, and the primary water source for drinking. Conclusions/Significance Cholera was endemic in Mozambique during the study period with a high CFR and identifiable risk factors. The study reinforces the importance of continued cholera surveillance, including a strong laboratory component. The results enhanced our understanding of the need to target priority areas and at-risk populations for interventions including oral cholera vaccine (OCV) use, and assess the impact of prevention and control strategies. Our data were instrumental in informing integrated prevention and

Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network

Directory of Open Access Journals (Sweden)

Widodo Budiharto

2015-01-01

Full Text Available 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.

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.

Space Surveillance Network: New Way Proposed To Support Commercial and Foreign Entities

National Research Council Canada - National Science Library

Shays, Christopher

2002-01-01

DOD uses the U.S. space surveillance network to track active and inactive satellites and space debris generated from launch vehicles and satellite breakups, and the agency catalogs and provides these data to DOD organizations, U.S...

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

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.

Reporting and Surveillance for Norovirus Outbreaks

Science.gov (United States)

... Vaccine Surveillance Network (NVSN) Foodborne Diseases Active Surveillance Network (FoodNet) National Outbreak Reporting System (NORS) Estimates of Foodborne Illness in the United States CDC's Vessel Sanitation Program CDC Feature: Surveillance for Norovirus Outbreaks Top ...

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

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

Reactor pressure vessel embrittlement: Insights from neural network modelling

Science.gov (United States)

Mathew, J.; Parfitt, D.; Wilford, K.; Riddle, N.; Alamaniotis, M.; Chroneos, A.; Fitzpatrick, M. E.

2018-04-01

Irradiation embrittlement of steel pressure vessels is an important consideration for the operation of current and future light water nuclear reactors. In this study we employ an ensemble of artificial neural networks in order to provide predictions of the embrittlement using two literature datasets, one based on US surveillance data and the second from the IVAR experiment. We use these networks to examine trends with input variables and to assess various literature models including compositional effects and the role of flux and temperature. Overall, the networks agree with the existing literature models and we comment on their more general use in predicting irradiation embrittlement.

A Belief Network Decision Support Method Applied to Aerospace Surveillance and Battle Management Projects

National Research Council Canada - National Science Library

Staker, R

2003-01-01

This report demonstrates the application of a Bayesian Belief Network decision support method for Force Level Systems Engineering to a collection of projects related to Aerospace Surveillance and Battle Management...

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.

Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks.

Science.gov (United States)

Jarama, Ángel J; López-Araquistain, Jaime; Miguel, Gonzalo de; Besada, Juan A

2017-09-21

In this paper, a complete and rigorous mathematical model for secondary surveillance radar systematic errors (biases) is developed. The model takes into account the physical effects systematically affecting the measurement processes. The azimuth biases are calculated from the physical error of the antenna calibration and the errors of the angle determination dispositive. Distance bias is calculated from the delay of the signal produced by the refractivity index of the atmosphere, and from clock errors, while the altitude bias is calculated taking into account the atmosphere conditions (pressure and temperature). It will be shown, using simulated and real data, that adapting a classical bias estimation process to use the complete parametrized model results in improved accuracy in the bias estimation.

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

Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks

Directory of Open Access Journals (Sweden)

Ángel J. Jarama

2017-09-01

Full Text Available In this paper, a complete and rigorous mathematical model for secondary surveillance radar systematic errors (biases is developed. The model takes into account the physical effects systematically affecting the measurement processes. The azimuth biases are calculated from the physical error of the antenna calibration and the errors of the angle determination dispositive. Distance bias is calculated from the delay of the signal produced by the refractivity index of the atmosphere, and from clock errors, while the altitude bias is calculated taking into account the atmosphere conditions (pressure and temperature. It will be shown, using simulated and real data, that adapting a classical bias estimation process to use the complete parametrized model results in improved accuracy in the bias estimation.

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...... 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)....

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.

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

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

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.

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.

Comparative economic evaluation of data from the ACRIN National CT Colonography Trial with three cancer intervention and surveillance modeling network microsimulations.

Science.gov (United States)

Vanness, David J; Knudsen, Amy B; Lansdorp-Vogelaar, Iris; Rutter, Carolyn M; Gareen, Ilana F; Herman, Benjamin A; Kuntz, Karen M; Zauber, Ann G; van Ballegooijen, Marjolein; Feuer, Eric J; Chen, Mei-Hsiu; Johnson, C Daniel

2011-11-01

To estimate the cost-effectiveness of computed tomographic (CT) colonography for colorectal cancer (CRC) screening in average-risk asymptomatic subjects in the United States aged 50 years. Enrollees in the American College of Radiology Imaging Network National CT Colonography Trial provided informed consent, and approval was obtained from the institutional review board at each site. CT colonography performance estimates from the trial were incorporated into three Cancer Intervention and Surveillance Modeling Network CRC microsimulations. Simulated survival and lifetime costs for screening 50-year-old subjects in the United States with CT colonography every 5 or 10 years were compared with those for guideline-concordant screening with colonoscopy, flexible sigmoidoscopy plus either sensitive unrehydrated fecal occult blood testing (FOBT) or fecal immunochemical testing (FIT), and no screening. Perfect and reduced screening adherence scenarios were considered. Incremental cost-effectiveness and net health benefits were estimated from the U.S. health care sector perspective, assuming a 3% discount rate. CT colonography at 5- and 10-year screening intervals was more costly and less effective than FOBT plus flexible sigmoidoscopy in all three models in both 100% and 50% adherence scenarios. Colonoscopy also was more costly and less effective than FOBT plus flexible sigmoidoscopy, except in the CRC-SPIN model assuming 100% adherence (incremental cost-effectiveness ratio: $26,300 per life-year gained). CT colonography at 5- and 10-year screening intervals and colonoscopy were net beneficial compared with no screening in all model scenarios. The 5-year screening interval was net beneficial over the 10-year interval except in the MISCAN model when assuming 100% adherence and willingness to pay $50,000 per life-year gained. All three models predict CT colonography to be more costly and less effective than non-CT colonographic screening but net beneficial compared with no

Epidemiological models to support animal disease surveillance activities

DEFF Research Database (Denmark)

Willeberg, Preben; Paisley, Larry; Lind, Peter

2011-01-01

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...... the risk of future cases, detected and undetected, of a waning infection of bovine spongiform encephalopathy. Both studies were successful in advancing European policy changes to reduce the cost of surveillance to appropriate levels given the magnitude of the respective hazards....

Scoring sensor observations to facilitate the exchange of space surveillance data

Science.gov (United States)

Weigel, M.; Fiedler, H.; Schildknecht, T.

2017-08-01

In this paper, a scoring metric for space surveillance sensor observations is introduced. A scoring metric allows for direct comparison of data quantity and data quality, and makes transparent the effort made by different sensor operators. The concept might be applied to various sensor types like tracking and surveillance radar, active optical laser tracking, or passive optical telescopes as well as combinations of different measurement types. For each measurement type, a polynomial least squares fit is performed on the measurement values contained in the track. The track score is the average sum over the polynomial coefficients uncertainties and scaled by reference measurement accuracy. Based on the newly developed scoring metric, an accounting model and a rating model are introduced. Both models facilitate the exchange of observation data within a network of space surveillance sensors operators. In this paper, optical observations are taken as an example for analysis purposes, but both models can also be utilized for any other type of observations. The rating model has the capability to distinguish between network participants with major and minor data contribution to the network. The level of sanction on data reception is defined by the participants themselves enabling a high flexibility. The more elaborated accounting model translates the track score to credit points earned for data provision and spend for data reception. In this model, data reception is automatically limited for participants with low contribution to the network. The introduced method for observation scoring is first applied for transparent data exchange within the Small Aperture Robotic Telescope Network (SMARTnet). Therefore a detailed mathematical description is presented for line of sight measurements from optical telescopes, as well as numerical simulations for different network setups.

Deep learning-based fine-grained car make/model classification for visual surveillance

Science.gov (United States)

Gundogdu, Erhan; Parıldı, Enes Sinan; Solmaz, Berkan; Yücesoy, Veysel; Koç, Aykut

2017-10-01

Fine-grained object recognition is a potential computer vision problem that has been recently addressed by utilizing deep Convolutional Neural Networks (CNNs). Nevertheless, the main disadvantage of classification methods relying on deep CNN models is the need for considerably large amount of data. In addition, there exists relatively less amount of annotated data for a real world application, such as the recognition of car models in a traffic surveillance system. To this end, we mainly concentrate on the classification of fine-grained car make and/or models for visual scenarios by the help of two different domains. First, a large-scale dataset including approximately 900K images is constructed from a website which includes fine-grained car models. According to their labels, a state-of-the-art CNN model is trained on the constructed dataset. The second domain that is dealt with is the set of images collected from a camera integrated to a traffic surveillance system. These images, which are over 260K, are gathered by a special license plate detection method on top of a motion detection algorithm. An appropriately selected size of the image is cropped from the region of interest provided by the detected license plate location. These sets of images and their provided labels for more than 30 classes are employed to fine-tune the CNN model which is already trained on the large scale dataset described above. To fine-tune the network, the last two fully-connected layers are randomly initialized and the remaining layers are fine-tuned in the second dataset. In this work, the transfer of a learned model on a large dataset to a smaller one has been successfully performed by utilizing both the limited annotated data of the traffic field and a large scale dataset with available annotations. Our experimental results both in the validation dataset and the real field show that the proposed methodology performs favorably against the training of the CNN model from scratch.

The monocular visual imaging technology model applied in the airport surface surveillance

Science.gov (United States)

Qin, Zhe; Wang, Jian; Huang, Chao

2013-08-01

At present, the civil aviation airports use the surface surveillance radar monitoring and positioning systems to monitor the aircrafts, vehicles and the other moving objects. Surface surveillance radars can cover most of the airport scenes, but because of the terminals, covered bridges and other buildings geometry, surface surveillance radar systems inevitably have some small segment blind spots. This paper presents a monocular vision imaging technology model for airport surface surveillance, achieving the perception of scenes of moving objects such as aircrafts, vehicles and personnel location. This new model provides an important complement for airport surface surveillance, which is different from the traditional surface surveillance radar techniques. Such technique not only provides clear objects activities screen for the ATC, but also provides image recognition and positioning of moving targets in this area. Thereby it can improve the work efficiency of the airport operations and avoid the conflict between the aircrafts and vehicles. This paper first introduces the monocular visual imaging technology model applied in the airport surface surveillance and then the monocular vision measurement accuracy analysis of the model. The monocular visual imaging technology model is simple, low cost, and highly efficient. It is an advanced monitoring technique which can make up blind spot area of the surface surveillance radar monitoring and positioning systems.

The Health and Occupation Research Network: An Evolving Surveillance System.

Science.gov (United States)

Carder, Melanie; Hussey, Louise; Money, Annemarie; Gittins, Matthew; McNamee, Roseanne; Stocks, Susan Jill; Sen, Dil; Agius, Raymond M

2017-09-01

Vital to the prevention of work-related ill-health (WRIH) is the availability of good quality data regarding WRIH burden and risks. Physician-based surveillance systems such as The Health and Occupation Research (THOR) network in the UK are often established in response to limitations of statutory, compensation-based systems for addressing certain epidemiological aspects of disease surveillance. However, to fulfil their purpose, THOR and others need to have methodologic rigor in capturing and ascertaining cases. This article describes how data collected by THOR and analogous systems can inform WRIH incidence, trends, and other determinants. An overview of the different strands of THOR research is provided, including methodologic advancements facilitated by increased data quantity/quality over time and the value of the research outputs for informing Government and other policy makers. In doing so, the utility of data collected by systems such as THOR to address a wide range of research questions, both in relation to WRIH and to wider issues of public and social health, is demonstrated.

The Health and Occupation Research Network: An Evolving Surveillance System

Directory of Open Access Journals (Sweden)

Melanie Carder

2017-09-01

Full Text Available Vital to the prevention of work-related ill-health (WRIH is the availability of good quality data regarding WRIH burden and risks. Physician-based surveillance systems such as The Health and Occupation Research (THOR network in the UK are often established in response to limitations of statutory, compensation-based systems for addressing certain epidemiological aspects of disease surveillance. However, to fulfil their purpose, THOR and others need to have methodologic rigor in capturing and ascertaining cases. This article describes how data collected by THOR and analogous systems can inform WRIH incidence, trends, and other determinants. An overview of the different strands of THOR research is provided, including methodologic advancements facilitated by increased data quantity/quality over time and the value of the research outputs for informing Government and other policy makers. In doing so, the utility of data collected by systems such as THOR to address a wide range of research questions, both in relation to WRIH and to wider issues of public and social health, is demonstrated.

PWR surveillance based on correspondence between empirical models and physical

International Nuclear Information System (INIS)

Zwingelstein, G.; Upadhyaya, B.R.; Kerlin, T.W.

1976-01-01

An on line surveillance method based on the correspondence between empirical models and physicals models is proposed for pressurized water reactors. Two types of empirical models are considered as well as the mathematical models defining the correspondence between the physical and empirical parameters. The efficiency of this method is illustrated for the surveillance of the Doppler coefficient for Oconee I (an 886 MWe PWR) [fr

Molecular surveillance of norovirus, 2005-16: an epidemiological analysis of data collected from the NoroNet network.

NARCIS (Netherlands)

van Beek, Janko; de Graaf, Miranda; Al-Hello, Haider; Allen, David J; Ambert-Balay, Katia; Botteldoorn, Nadine; Brytting, Mia; Buesa, Javier; Cabrerizo, Maria; Chan, Martin; Cloak, Fiona; Di Bartolo, Ilaria; Guix, Susana; Hewitt, Joanne; Iritani, Nobuhiro; Jin, Miao; Johne, Reimar; Lederer, Ingeborg; Mans, Janet; Martella, Vito; Maunula, Leena; McAllister, Georgina; Niendorf, Sandra; Niesters, Hubert G; Podkolzin, Alexander T; Poljsak-Prijatelj, Mateja; Rasmussen, Lasse Dam; Reuter, Gábor; Tuite, Gráinne; Kroneman, Annelies; Vennema, Harry; Koopmans, Marion P G

2018-01-01

The development of a vaccine for norovirus requires a detailed understanding of global genetic diversity of noroviruses. We analysed their epidemiology and diversity using surveillance data from the NoroNet network.

Validation of intensive care unit-acquired infection surveillance in the Italian SPIN-UTI network.

Science.gov (United States)

Masia, M D; Barchitta, M; Liperi, G; Cantù, A P; Alliata, E; Auxilia, F; Torregrossa, V; Mura, I; Agodi, A

2010-10-01

Validity is one of the most critical factors concerning surveillance of nosocomial infections (NIs). This article describes the first validation study of the Italian Nosocomial Infections Surveillance in Intensive Care Units (ICUs) project (SPIN-UTI) surveillance data. The objective was to validate infection data and thus to determine the sensitivity, specificity, and positive and negative predictive values of NI data reported on patients in the ICUs participating in the SPIN-UTI network. A validation study was performed at the end of the surveillance period. All medical records including all clinical and laboratory data were reviewed retrospectively by the trained physicians of the validation team and a positive predictive value (PPV), a negative predictive value (NPV), sensitivity and specificity were calculated. Eight ICUs (16.3%) were randomly chosen from all 49 SPIN-UTI ICUs for the validation study. In total, the validation team reviewed 832 patient charts (27.3% of the SPIN-UTI patients). The PPV was 83.5% and the NPV was 97.3%. The overall sensitivity was 82.3% and overall specificity was 97.2%. Over- and under-reporting of NIs were related to misinterpretation of the case definitions and deviations from the protocol despite previous training and instructions. The results of this study are useful to identify methodological problems within a surveillance system and have been used to plan retraining for surveillance personnel and to design and implement the second phase of the SPIN-UTI project. Copyright 2010 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved.

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

2017-03-01

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. Copyright © 2016 Elsevier GmbH. All rights reserved.

Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding.

Science.gov (United States)

Zhang, Xianguo; Huang, Tiejun; Tian, Yonghong; Gao, Wen

2014-02-01

The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.

Decreased rates of nosocomial endometritis and urinary tract infection after vaginal delivery in a French surveillance network, 1997-2003.

Science.gov (United States)

Ayzac, Louis; Caillat-Vallet, Emmanuelle; Girard, Raphaële; Chapuis, Catherine; Depaix, Florence; Dumas, Anne-Marie; Gignoux, Chantal; Haond, Catherine; Lafarge-Leboucher, Joëlle; Launay, Carine; Tissot-Guerraz, Françoise; Vincent, Agnès; Fabry, Jacques

2008-06-01

To identify independent risk factors for endometritis and urinary tract infection (UTI) after vaginal delivery, and to monitor changes in nosocomial infection rates and derive benchmarks for prevention. Prospective study. We analyzed routine surveillance data for all vaginal deliveries between January 1997 and December 2003 at 66 maternity units participating in the Mater Sud-Est surveillance network. Adjusted odds ratios for risk of endometritis or UTI were obtained using a logistic regression model. The overall incidence rates were 0.5% for endometritis and 0.3% for UTI. There was a significant decrease in the incidence and risk of endometritis but not of UTI during the 7-year period. Significant risk factors for endometritis were fever during labor, parity of 1, and instrumental delivery and/or manual removal of the placenta. Significant risk factors for UTI were urinary infection on admission, premature rupture of membranes (more than 12 hours before admission), blood loss of more than 800 mL, parity of 1, instrumental delivery, and receipt of more than 5 vaginal digital examinations. Each maternity unit received a poster showing graphs of the number of expected and observed cases of UTI and endometritis associated with vaginal deliveries, which enabled each maternity unit to determine their rank within the network and to initiate prevention programs. Although routine surveillance means additional work for maternity units, our results demonstrate the usefulness of regular targeted monitoring of risk factors and of the most common nosocomial infections in obstetrics. Most of the information needed for monitoring is already present in the patients' records.

Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012.

Directory of Open Access Journals (Sweden)

William J Love

2016-11-01

Full Text Available Surveillance of antimicrobial resistance (AMR is an important component of public health. Antimicrobial drug use generates selective pressure that may lead to resistance against to the administered drug, and may also select for collateral resistances to other drugs. Analysis of AMR surveillance data has focused on resistance to individual drugs but joint distributions of resistance in bacterial populations are infrequently analyzed and reported. New methods are needed to characterize and communicate joint resistance distributions. Markov networks are a class of graphical models that define connections, or edges, between pairs of variables with non-zero partial correlations and are used here to describe AMR resistance relationships. The graphical least absolute shrinkage and selection operator is used to estimate sparse Markov networks from AMR surveillance data. The method is demonstrated using a subset of Escherichia coli isolates collected by the National Antimicrobial Resistance Monitoring System between 2004 and 2012 which included AMR results for 16 drugs from 14418 isolates. Of the 119 possible unique edges, 33 unique edges were identified at least once during the study period and graphical density ranged from 16.2% to 24.8%. Two frequent dense subgraphs were noted, one containing the five β-lactam drugs and the other containing both sulfonamides, three aminoglycosides, and tetracycline. Density did not appear to change over time (p = 0.71. Unweighted modularity did not appear to change over time (p = 0.18, but a significant decreasing trend was noted in the modularity of the weighted networks (p < 0.005 indicating relationships between drugs of different classes tended to increase in strength and frequency over time compared to relationships between drugs of the same class. The current method provides a novel method to study the joint resistance distribution, but additional work is required to unite the underlying biological and genetic

Future directions for the European influenza reference laboratory network in influenza surveillance.

Science.gov (United States)

Goddard, N; Rebelo-de-Andrade, H; Meijer, A; McCauley, J; Daniels, R; Zambon, M

2015-07-30

By defining strategic objectives for the network of influenza laboratories that have national influenza centre status or national function within European Union Member States, Iceland and Norway, it is possible to align their priorities in undertaking virological surveillance of influenza. This will help maintain and develop the network to meet and adapt to new challenges over the next 3-5 years and underpin a longer-term strategy over 5-10 years. We analysed the key activities undertaken by influenza reference laboratories in Europe and categorised them into a framework of four key strategic objectives areas: enhancing laboratory capability, ensuring laboratory capacity, providing emergency response and translating laboratory data into information for public health action. We make recommendations on the priority areas for future development.

Sensor signal analysis by neural networks for surveillance in nuclear reactors

International Nuclear Information System (INIS)

Keyvan, S.; Rabelo, L.C.

1992-01-01

The application of neural networks as a tool for reactor diagnostics is examined here. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of the degradation of a pump shaft are analyzed as a semi-benchmark test to study the feasibility of neural networks for monitoring and surveillance in nuclear reactors. The Adaptive Resonance Theory (ART 2 and ART 2-A) paradigm of neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques, and is capable of distinguishing these signals apart and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data, and provides an evaluation on the performance of ART 2-A and ART 2 for reactor signal analysis. The selection of ART 2 is due to its desired design principles such as unsupervised learning, stability-plasticity, search-direct access, and the match-reset tradeoffs

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.

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

: 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......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...

Big Data for Infectious Disease Surveillance and Modeling

OpenAIRE

Bansal, Shweta; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro; Viboud, Cécile

2016-01-01

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 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 socia...

Molecular surveillance of norovirus, 2005-16 : an epidemiological analysis of data collected from the NoroNet network

NARCIS (Netherlands)

van Beek, Janko; de Graaf, Miranda; Al-Hello, Haider; Allen, David J; Ambert-Balay, Katia; Botteldoorn, Nadine; Brytting, Mia; Buesa, Javier; Cabrerizo, Maria; Chan, Martin; Cloak, Fiona; Di Bartolo, Ilaria; Guix, Susana; Hewitt, Joanne; Iritani, Nobuhiro; Jin, Miao; Johne, Reimar; Lederer, Ingeborg; Mans, Janet; Martella, Vito; Maunula, Leena; McAllister, Georgina; Niendorf, Sandra; Niesters, Hubert G; Podkolzin, Alexander T; Poljsak-Prijatelj, Mateja; Rasmussen, Lasse Dam; Reuter, Gábor; Tuite, Gráinne; Kroneman, Annelies; Vennema, Harry; Koopmans, Marion P G

BACKGROUND: The development of a vaccine for norovirus requires a detailed understanding of global genetic diversity of noroviruses. We analysed their epidemiology and diversity using surveillance data from the NoroNet network. METHODS: We included genetic sequences of norovirus specimens obtained

Modeling of Food and Nutrition Surveillance in Primary Health Care

Directory of Open Access Journals (Sweden)

Santuzza Arreguy Silva VITORINO

Full Text Available ABSTRACT Objective: To describe the modeling stages of food and nutrition surveillance in the Primary Health Care of the Unified Health Care System, considering its activities, objectives, and goals Methods: Document analysis and semi-structured interviews were used for identifying the components, describe the intervention, and identify potential assessment users. Results: The results include identification of the objectives and goals of the intervention, the required inputs, activities, and expected effects. The intervention was then modeled based on these data. The use of the theoretical logic model optimizes times, resources, definition of the indicators that require monitoring, and the aspects that require assessment, identifying more clearly the contribution of the intervention to the results Conclusion: Modeling enabled the description of food and nutrition surveillance based on its components and may guide the development of viable plans to monitor food and nutrition surveillance actions so that modeling can be established as a local intersectoral planning instrument.

What is a missing link among wireless persistent surveillance?

Science.gov (United States)

Hsu, Charles; Szu, Harold

2011-06-01

The next generation surveillance system will equip with versatile sensor devices and information focus capable of conducting regular and irregular surveillance and security environments worldwide. The community of the persistent surveillance must invest the limited energy and money effectively into researching enabling technologies such as nanotechnology, wireless networks, and micro-electromechanical systems (MEMS) to develop persistent surveillance applications for the future. Wireless sensor networks can be used by the military for a number of purposes such as monitoring militant activity in remote areas and force protection. Being equipped with appropriate sensors these networks can enable detection of enemy movement, identification of enemy force and analysis of their movement and progress. Among these sensor network technologies, covert communication is one of the challenging tasks in the persistent surveillance because it is highly demanded to provide secured sensor nodes and linkage for fear of deliberate sabotage. Due to the matured VLSI/DSP technologies, affordable COTS of UWB technology with noise-like direct sequence (DS) time-domain pulses is a potential solution to support low probability of intercept and low probability of detection (LPI/LPD) data communication and transmission. This paper will describe a number of technical challenges in wireless persistent surveillance development include covert communication, network control and routing, collaborating signal and information processing, and etc. The paper concludes by presenting Hermitian Wavelets to enhance SNR in support of secured communication.

The EUVAC-NET project: creation and operation of a surveillance community network for vaccine preventable infectious diseases.

Science.gov (United States)

Glismann, S; Rønne, T; Tozzi, A

2001-06-01

The EUVAC-NET network is in charge of the epidemiological surveillance and control of vaccine preventable diseases. It is coordinated by the SSI in Denmark, in collaboration with the ISS in Italy. The two main diseases targeted by the network are measles and pertussis. A collaboration is planned with the PHLS for the monitoring of Haemophilus influenzae b. EUVAC-NET includes the Member States of the European Union, and Iceland, Norway and Switzerland.

A network control theory approach to modeling and optimal control of zoonoses: case study of brucellosis transmission in sub-Saharan Africa.

Science.gov (United States)

Roy, Sandip; McElwain, Terry F; Wan, Yan

2011-10-01

Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis) and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations), and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk) and points in the spread network (e.g., transhumance vs. sedentary herds). In addition, a preliminary

A network control theory approach to modeling and optimal control of zoonoses: case study of brucellosis transmission in sub-Saharan Africa.

Directory of Open Access Journals (Sweden)

Sandip Roy

2011-10-01

Full Text Available BACKGROUND: Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. METHODOLOGY/PRINCIPAL FINDINGS: A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations, and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. CONCLUSIONS/SIGNIFICANCE: The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk and points in

Poliomyelitis surveillance: the model used in India for polio eradication.

Science.gov (United States)

Banerjee, K.; Hlady, W. G.; Andrus, J. K.; Sarkar, S.; Fitzsimmons, J.; Abeykoon, P.

2000-01-01

Poliomyelitis surveillance in India previously involved the passive reporting of clinically suspected cases. The capacity for detecting the disease was limited because there was no surveillance of acute flaccid paralysis (AFP). In October 1997, 59 specially trained Surveillance Medical Officers were deployed throughout the country to establish active AFP surveillance; 11,533 units were created to report weekly on the occurrence of AFP cases at the district, state and national levels; timely case investigation and the collection of stool specimens from AFP cases was undertaken; linkages were made to support the polio laboratory network; and extensive training of government counterparts of the Surveillance Medical Officers was conducted. Data reported at the national level are analysed and distributed weekly. Annualized rates of non-polio AFP increased from 0.22 per 100,000 children aged under 15 years in 1997 to 1.39 per 100,000 in 1999. The proportion of cases with two adequate stools collected within two weeks of the onset of paralysis increased from 34% in 1997 to 68% in 1999. The number of polio cases associated with the isolation of wild poliovirus decreased from 211 in the first quarter of 1998 to 77 in the first quarter of 1999. Widespread transmission of wild poliovirus types 1 and 3 persists throughout the country; type 2 occurs only in Bihar and Uttar Pradesh. In order to achieve polio eradication in India during 2000, extra national immunization days and house-to-house mopping-up rounds should be organized. PMID:10812728

User interface using a 3D model for video surveillance

Science.gov (United States)

Hata, Toshihiko; Boh, Satoru; Tsukada, Akihiro; Ozaki, Minoru

1998-02-01

These days fewer people, who must carry out their tasks quickly and precisely, are required in industrial surveillance and monitoring applications such as plant control or building security. Utilizing multimedia technology is a good approach to meet this need, and we previously developed Media Controller, which is designed for the applications and provides realtime recording and retrieval of digital video data in a distributed environment. In this paper, we propose a user interface for such a distributed video surveillance system in which 3D models of buildings and facilities are connected to the surveillance video. A novel method of synchronizing camera field data with each frame of a video stream is considered. This method records and reads the camera field data similarity to the video data and transmits it synchronously with the video stream. This enables the user interface to have such useful functions as comprehending the camera field immediately and providing clues when visibility is poor, for not only live video but also playback video. We have also implemented and evaluated the display function which makes surveillance video and 3D model work together using Media Controller with Java and Virtual Reality Modeling Language employed for multi-purpose and intranet use of 3D model.

Radiological assessment of the French environment in 2008. Synthesis of the IRSN's surveillance networks

International Nuclear Information System (INIS)

Chaptal-Gradoz, N.; Chevreuil, M.; D'amico, D.; Debayle, Ch.; Leprieur, F.; Manificat, G.; Peres, J.M.; Pierrard, O.; Tournieux, D.; Veran-Viguie, M.P.; Renaud, Ph.; Roussel-Debet, S.; Masson, O.; Pourcelot, L.; Fayolle, C.; Loyen, J.; Robe, M.Ch.; Picolo, J.L.; Gallerand, M.O.

2009-01-01

While providing many maps, graphs and tables, this report presents and comments the very large amount of data acquired in 2008 within the frame of the control by the IRSN of radioactivity levels on the French national territory by means of its different surveillance networks. After a presentation of these networks (objectives, organization, sample collection, analysis, and preparation methodologies) and of the different radionuclides present in the French environment, results are presented by installation type (electricity production nuclear centres, nuclear fuel reprocessing centres, nuclear medicine centres, nuclear waste storage centres, research centres, nuclear naval bases, etc.) and environment component (air, water ways, rain waters, continental or coastal environment, biological media, etc.)

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

Instrument surveillance and calibration verification through plant wide monitoring using autoassociative neural networks

International Nuclear Information System (INIS)

Wrest, D.J.; Hines, J.W.; Uhrig, R.E.

1996-01-01

The approach to instrument surveillance and calibration verification (ISCV) through plant wide monitoring proposed in this paper is an autoassociative neural network (AANN) which will utilize digitized data presently available in the Safety Parameter Display computer system from Florida Power Corporations Crystal River number 3 nuclear power plant. An autoassociative neural network is one in which the outputs are trained to emulate the inputs over an appropriate dynamic range. The relationships between the different variables are embedded in the weights by the training process. As a result, the output can be a correct version of an input pattern that has been distorted by noise, missing data, or non-linearities. Plant variables that have some degree of coherence with each other constitute the inputs to the network. Once the network has been trained with normal operational data it has been shown to successfully monitor the selected plant variables to detect sensor drift or failure by simply comparing the network inputs with the outputs. The AANN method of monitoring many variables not only indicates that there is a sensor failure, it clearly indicates the signal channel in which the signal error has occurred. (author). 11 refs, 8 figs, 2 tabs

Instrument surveillance and calibration verification through plant wide monitoring using autoassociative neural networks

Energy Technology Data Exchange (ETDEWEB)

Wrest, D J; Hines, J W; Uhrig, R E [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering

1997-12-31

The approach to instrument surveillance and calibration verification (ISCV) through plant wide monitoring proposed in this paper is an autoassociative neural network (AANN) which will utilize digitized data presently available in the Safety Parameter Display computer system from Florida Power Corporations Crystal River number 3 nuclear power plant. An autoassociative neural network is one in which the outputs are trained to emulate the inputs over an appropriate dynamic range. The relationships between the different variables are embedded in the weights by the training process. As a result, the output can be a correct version of an input pattern that has been distorted by noise, missing data, or non-linearities. Plant variables that have some degree of coherence with each other constitute the inputs to the network. Once the network has been trained with normal operational data it has been shown to successfully monitor the selected plant variables to detect sensor drift or failure by simply comparing the network inputs with the outputs. The AANN method of monitoring many variables not only indicates that there is a sensor failure, it clearly indicates the signal channel in which the signal error has occurred. (author). 11 refs, 8 figs, 2 tabs.

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.

UAV Trajectory Modeling Using Neural Networks

Science.gov (United States)

Xue, Min

2017-01-01

Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural

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...

Expressed prostatic secretion 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 S

2014-01-01

Active surveillance is a viable patient option for prostate cancer provided that a clinical determination of low risk and presumably organ confined disease can be made. To standardize risk stratification schemes the NCCN (National Comprehensive Cancer Network®) provides guidelines for the active surveillance option. We determined the effectiveness of expressed prostatic secretion biomarkers for detecting occult risk factors in NCCN active surveillance candidates. Expressed prostatic secretion specimens were obtained before robot-assisted radical prostatectomy. Secretion capacity biomarkers, including total RNA and expressed prostatic secretion specimen volume, were measured by standard techniques. RNA expression biomarkers, including TXNRD1 mRNA, prostate specific antigen mRNA, TMPRSS2:ERG fusion mRNA and PCA3 mRNA, were measured by quantitative reverse-transcription polymerase chain reaction. Of the 528 patients from whom expressed prostatic secretions were collected 216 were eligible for active surveillance under NCCN guidelines. Variable selection on logistic regression identified 2 models, including one featuring types III and VI TMPRSS2:ERG variants, and one featuring 2 secretion capacity biomarkers. Of the 2 high performing models the secretion capacity model was most effective for detecting cases in this group that were up-staged or up-staged plus upgraded. It decreased the risk of up-staging in patients with a negative test almost eightfold and decreased the risk of up-staging plus upgrading about fivefold while doubling the prevalence of up-staging in the positive test group. Noninvasive expressed prostatic secretion testing may improve patient acceptance of active surveillance by dramatically reducing the presence of occult risk factors among those eligible for active surveillance under NCCN guidelines. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

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.

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.

Big Data for Infectious Disease Surveillance and Modeling.

Science.gov (United States)

Bansal, Shweta; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro; Viboud, Cécile

2016-12-01

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 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 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 and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts. Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

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

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

Auto-Associative Recurrent Neural Networks and Long Term Dependencies in Novelty Detection for Audio Surveillance Applications

Science.gov (United States)

Rossi, A.; Montefoschi, F.; Rizzo, A.; Diligenti, M.; Festucci, C.

2017-10-01

Machine Learning applied to Automatic Audio Surveillance has been attracting increasing attention in recent years. In spite of several investigations based on a large number of different approaches, little attention had been paid to the environmental temporal evolution of the input signal. In this work, we propose an exploration in this direction comparing the temporal correlations extracted at the feature level with the one learned by a representational structure. To this aim we analysed the prediction performances of a Recurrent Neural Network architecture varying the length of the processed input sequence and the size of the time window used in the feature extraction. Results corroborated the hypothesis that sequential models work better when dealing with data characterized by temporal order. However, so far the optimization of the temporal dimension remains an open issue.

RENDAC: Integrated System Data for the Information Control the Environmental Radiological Surveillance the National Network in Cuban Republic

International Nuclear Information System (INIS)

Valdes Ramos, M.; Prendes Alonso, M.

1998-01-01

With the objective to evaluate, process, control and to store the information that is generated in the National Environmental Radiological Surveillance Network, it is designed and I program the on-line RENDAC system that allows to capture and evaluate the parameters that characterize the environmental radiological situation

A model surveillance program based on regulatory experience

International Nuclear Information System (INIS)

Conte, R.J.

1980-01-01

A model surveillance program is presented based on regulatory experience. The program consists of three phases: Program Delineation, Data Acquistion and Data Analysis. Each phase is described in terms of key quality assurance elements and some current philosophies is the United States Licensing Program. Other topics include the application of these ideas to test equipment used in the surveillance progam and audits of the established program. Program Delineation discusses the establishment of administrative controls for organization and the description of responsibilities using the 'Program Coordinator' concept, with assistance from Data Acquisition and Analysis Teams. Ideas regarding frequency of surveillance testing are also presented. The Data Acquisition Phase discusses various methods for acquiring data including operator observations, test procedures, operator logs, and computer output, for trending equipment performance. The Data Analysis Phase discusses the process for drawing conclusions regarding component/equipment service life, proper application, and generic problems through the use of trend analysis and failure rate data. (orig.)

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.

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

Directory of Open Access Journals (Sweden)

Fleck Sven

2007-01-01

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.

Influenza surveillance

Directory of Open Access Journals (Sweden)

Karolina Bednarska

2016-04-01

Full Text Available Influenza surveillance was established in 1947. From this moment WHO (World Health Organization has been coordinating international cooperation, with a goal of monitoring influenza virus activity, effective diagnostic of the circulating viruses and informing society about epidemics or pandemics, as well as about emergence of new subtypes of influenza virus type A. Influenza surveillance is an important task, because it enables people to prepare themselves for battle with the virus that is constantly mutating, what leads to circulation of new and often more virulent strains of influenza in human population. As vaccination is the most effective method of fighting the virus, one of the major tasks of GISRS is developing an optimal antigenic composition of the vaccine for the current epidemic season. European Influenza Surveillance Network (EISN has also developed over the years. EISN is running integrated epidemiological and virological influenza surveillance, to provide appropriate data to public health experts in member countries, to enable them undertaking relevant activities based on the current information about influenza activity. In close cooperation with GISRS and EISN are National Influenza Centres - national institutions designated by the Ministry of Health in each country.

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.

Water radiological surveillance (II)

International Nuclear Information System (INIS)

Pablo San Martin de, M.

2008-01-01

This paper summarizes the characteristics of the Environmental Surveillance Radiological Networks (ESRN) currently operating in CEDEX. In the first part, the Spanish Continental Waters ESRN has been presented. This second one describes Spanish Costal Waters ESRN and the High Sensitivity Networks in Continental and Marine Waters. It also presents the Radiological Surveillance of Drinking Waters that CEDEX carries out in waters of public consumption management by the Canal de Isabel II (CYII) and by the Mancomunity of Canals Taibilla (M.C.T.). The legislation applicable in each case is reviewed as well. Due to its extension the article has been divided into two parts. As Spanish Continental Waters ESRN has been reviewed in the first part, the others ESRN are discussed in this second one. (Author) 10 refs

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

Science.gov (United States)

Donfrancesco, Chiara; Lo Noce, Cinzia; Brignoli, Ovidio; Riccardi, Gabriele; Ciccarelli, Paola; Dima, Francesco; Palmieri, Luigi; Giampaoli, Simona

2008-09-29

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. 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 > or = 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. 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 (> or = 20% in 10 years). Normal weight persons were four times more likely to be at low risk than obese persons. 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, such as diet and physical activity.

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

Twitter web-service for soft agent reporting in persistent surveillance systems

Science.gov (United States)

Rababaah, Haroun; Shirkhodaie, Amir

2010-04-01

Persistent surveillance is an intricate process requiring monitoring, gathering, processing, tracking, and characterization of many spatiotemporal events occurring concurrently. Data associated with events can be readily attained by networking of hard (physical) sensors. Sensors may have homogeneous or heterogeneous (hybrid) sensing modalities with different communication bandwidth requirements. Complimentary to hard sensors are human observers or "soft sensors" that can report occurrences of evolving events via different communication devices (e.g., texting, cell phones, emails, instant messaging, etc.) to the command control center. However, networking of human observers in ad-hoc way is rather a difficult task. In this paper, we present a Twitter web-service for soft agent reporting in persistent surveillance systems (called Web-STARS). The objective of this web-service is to aggregate multi-source human observations in hybrid sensor networks rapidly. With availability of Twitter social network, such a human networking concept can not only be realized for large scale persistent surveillance systems (PSS), but also, it can be employed with proper interfaces to expedite rapid events reporting by human observers. The proposed technique is particularly suitable for large-scale persistent surveillance systems with distributed soft and hard sensor networks. The efficiency and effectiveness of the proposed technique is measured experimentally by conducting several simulated persistent surveillance scenarios. It is demonstrated that by fusion of information from hard and soft agents improves understanding of common operating picture and enhances situational awareness.

Participatory Surveillance and Photo Sharing Practices

DEFF Research Database (Denmark)

Albrechtslund, Anders; Damkjaer, Maja Sonne; Bøge, Ask Risom

2019-01-01

-material perspective on photo-sharing practices. Information, Communication & Society, 19(4), 475–488. Sontag, S. (1977). On Photography. Picador. Steeves, V., & Jones, O. (2010). Editorial: Surveillance, Children and Childhood. Surveillance & Society, 7(3/4), 187–191....... that parents do not generally plan to store or organize their photos, and even less their children’s photos. This seems to indicate a shift from a pre-digital perception of photos as objects to be packaged, accumulated, framed etc. which can age and disappear (see Sontag, 1977) to something perceived less....... References: Albrechtslund, A. (2008). Online Social Networking as Participatory Surveillance. First Monday, 13(3). Fotel, T., & Thomsen, T. U. (2002). The Surveillance of Children’s Mobility. Surveillance & Society, 1(4), 535-554. Lobinger, K. (2016). Photographs as things–photographs of things. A texto...

The development of passive health surveillance by a sentinel ...

African Journals Online (AJOL)

SASPREN), a volunteer network of family practitioners in South Africa, to develop a health surveillance system through the surveillance of important health events. Motivation. The incidence of important preventable diseases and the burden of disease ...

Surveillance, Snowden, and Big Data: Capacities, consequences, critique

Directory of Open Access Journals (Sweden)

David Lyon

2014-07-01

Full Text Available The Snowden revelations about National Security Agency surveillance, starting in 2013, along with the ambiguous complicity of internet companies and the international controversies that followed provide a perfect segue into contemporary conundrums of surveillance and Big Data. Attention has shifted from late C20th information technologies and networks to a C21st focus on data, currently crystallized in “Big Data.” Big Data intensifies certain surveillance trends associated with information technology and networks, and is thus implicated in fresh but fluid configurations. This is considered in three main ways: One, the capacities of Big Data (including metadata intensify surveillance by expanding interconnected datasets and analytical tools. Existing dynamics of influence, risk-management, and control increase their speed and scope through new techniques, especially predictive analytics. Two, while Big Data appears to be about size, qualitative change in surveillance practices is also perceptible, accenting consequences. Important trends persist – the control motif, faith in technology, public-private synergies, and user-involvement – but the future-orientation increasingly severs surveillance from history and memory and the quest for pattern-discovery is used to justify unprecedented access to data. Three, the ethical turn becomes more urgent as a mode of critique. Modernity's predilection for certain definitions of privacy betrays the subjects of surveillance who, so far from conforming to the abstract, disembodied image of both computing and legal practices, are engaged and embodied users-in-relation whose activities both fuel and foreclose surveillance.

Smart sensing surveillance system

Science.gov (United States)

Hsu, Charles; Chu, Kai-Dee; O'Looney, James; Blake, Michael; Rutar, Colleen

2010-04-01

Unattended ground sensor (UGS) networks have been widely used in remote battlefield and other tactical applications over the last few decades due to the advances of the digital signal processing. The UGS network can be applied in a variety of areas including border surveillance, special force operations, perimeter and building protection, target acquisition, situational awareness, and force protection. In this paper, a highly-distributed, fault-tolerant, and energyefficient Smart Sensing Surveillance System (S4) is presented to efficiently provide 24/7 and all weather security operation in a situation management environment. The S4 is composed of a number of distributed nodes to collect, process, and disseminate heterogeneous sensor data. Nearly all S4 nodes have passive sensors to provide rapid omnidirectional detection. In addition, Pan- Tilt- Zoom- (PTZ) Electro-Optics EO/IR cameras are integrated to selected nodes to track the objects and capture associated imagery. These S4 camera-connected nodes will provide applicable advanced on-board digital image processing capabilities to detect and track the specific objects. The imaging detection operations include unattended object detection, human feature and behavior detection, and configurable alert triggers, etc. In the S4, all the nodes are connected with a robust, reconfigurable, LPI/LPD (Low Probability of Intercept/ Low Probability of Detect) wireless mesh network using Ultra-wide band (UWB) RF technology, which can provide an ad-hoc, secure mesh network and capability to relay network information, communicate and pass situational awareness and messages. The S4 utilizes a Service Oriented Architecture such that remote applications can interact with the S4 network and use the specific presentation methods. The S4 capabilities and technologies have great potential for both military and civilian applications, enabling highly effective security support tools for improving surveillance activities in densely crowded

A review of zoonotic disease surveillance supported by the Armed Forces Health Surveillance Center.

Science.gov (United States)

Burke, R L; Kronmann, K C; Daniels, C C; Meyers, M; Byarugaba, D K; Dueger, E; Klein, T A; Evans, B P; Vest, K G

2012-05-01

The Armed Forces Health Surveillance Center (AFHSC), Division of Global Emerging Infections Surveillance and Response System conducts disease surveillance through a global network of US Department of Defense research laboratories and partnerships with foreign ministries of agriculture, health and livestock development in over 90 countries worldwide. In 2010, AFHSC supported zoonosis survey efforts were organized into four main categories: (i) development of field assays for animal disease surveillance during deployments and in resource limited environments, (ii) determining zoonotic disease prevalence in high-contact species which may serve as important reservoirs of diseases and sources of transmission, (iii) surveillance in high-risk human populations which are more likely to become exposed and subsequently infected with zoonotic pathogens and (iv) surveillance at the human-animal interface examining zoonotic disease prevalence and transmission within and between human and animal populations. These efforts have aided in the detection, identification and quantification of the burden of zoonotic diseases such as anthrax, brucellosis, Crimean Congo haemorrhagic fever, dengue fever, Hantaan virus, influenza, Lassa fever, leptospirosis, melioidosis, Q fever, Rift Valley fever, sandfly fever Sicilian virus, sandfly fever Naples virus, tuberculosis and West Nile virus, which are of military and public health importance. Future zoonotic surveillance efforts will seek to develop local capacity for zoonotic surveillance focusing on high risk populations at the human-animal interface. © 2011 Blackwell Verlag GmbH.

Infectieziekten Surveillance Informatie Systeem

NARCIS (Netherlands)

Sprenger MJW; van Pelt W; CIE

1994-01-01

In the Netherlands an electronic network has been proposed for structured data transfer and communication concerning the control of infectious diseases. This project has been baptized ISIS (Infectious diseases Surveillance Information System). It is an initiative of the Dutch Government. ISIS

Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

Science.gov (United States)

Young, Alistair A.; Li, Xiaosong

2014-01-01

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases. PMID:24505382

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.

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.

Converging requirements and emerging challenges to public health diseases surveillance and bio surveillance

International Nuclear Information System (INIS)

Rao, V.; Abel, T.

2009-01-01

Disease surveillance systems are a critical component of an early warning system for public health agencies to prepare and respond to major public health catastrophes. With a growing emphasis for more robust early indicator and warning systems to track emerging and dangerous diseases of suspicious nature, considerable emphasis is now placed on deployment of more expanded electronic disease surveillance systems. The architectural considerations for bio surveillance information system are based on collection, analysis and dissemination of human, veterinary and agricultural related disease surveillance to broader regional areas likely to be affected in the event of an emerging disease, or due to bioterrorism and better coordinate plans, preparations and response by governmental agencies and multilateral forums. The diseases surveillance systems architectures by intent and design could as well support biological threat monitoring and threat reduction initiatives. As an illustrative sample set, this paper will describe the comparative informatics requirements for a disease surveillance systems developed by CSC for the US Centers for Diseases Control and Prevention (CDC) currently operational nationwide, and biological weapons threat assessment developed as part of the Threat Agent Detection and Response (TADR) Network under the US Biological Threat Reduction Program and deployed at Uzbekistan, Kazakhstan, Georgia, and Azerbaijan.(author)

Modeling the citation network by network cosmology.

Science.gov (United States)

Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

2015-01-01

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.

Next Generation Space Surveillance System-of-Systems

Science.gov (United States)

McShane, B.

2014-09-01

International economic and military dependence on space assets is pervasive and ever-growing in an environment that is now congested, contested, and competitive. There are a number of natural and man-made risks that need to be monitored and characterized to protect and preserve the space environment and the assets within it. Unfortunately, today's space surveillance network (SSN) has gaps in coverage, is not resilient, and has a growing number of objects that get lost. Risks can be efficiently and effectively mitigated, gaps closed, resiliency improved, and performance increased within a next generation space surveillance network implemented as a system-of-systems with modern information architectures and analytic techniques. This also includes consideration for the newest SSN sensors (e.g. Space Fence) which are born Net-Centric out-of-the-box and able to seamlessly interface with the JSpOC Mission System, global information grid, and future unanticipated users. Significant opportunity exists to integrate legacy, traditional, and non-traditional sensors into a larger space system-of-systems (including command and control centers) for multiple clients through low cost sustainment, modification, and modernization efforts. Clients include operations centers (e.g. JSpOC, USSTRATCOM, CANSPOC), Intelligence centers (e.g. NASIC), space surveillance sensor sites (e.g. AMOS, GEODSS), international governments (e.g. Germany, UK), space agencies (e.g. NASA), and academic institutions. Each has differing priorities, networks, data needs, timeliness, security, accuracy requirements and formats. Enabling processes and technologies include: Standardized and type accredited methods for secure connections to multiple networks, machine-to-machine interfaces for near real-time data sharing and tip-and-queue activities, common data models for analytical processing across multiple radar and optical sensor types, an efficient way to automatically translate between differing client and

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

A large-scale study of a poultry trading network in Bangladesh: implications for control and surveillance of avian influenza viruses.

Science.gov (United States)

Moyen, N; Ahmed, G; Gupta, S; Tenzin, T; Khan, R; Khan, T; Debnath, N; Yamage, M; Pfeiffer, D U; Fournie, G

2018-01-12

Since its first report in 2007, avian influenza (AI) has been endemic in Bangladesh. While live poultry marketing is widespread throughout the country and known to influence AI dissemination and persistence, trading patterns have not been described. The aim of this study is to assess poultry trading practices and features of the poultry trading networks which could promote AI spread, and their potential implications for disease control and surveillance. Data on poultry trading practices was collected from 849 poultry traders during a cross-sectional survey in 138 live bird markets (LBMs) across 17 different districts of Bangladesh. The quantity and origins of traded poultry were assessed for each poultry type in surveyed LBMs. The network of contacts between farms and LBMs resulting from commercial movements of live poultry was constructed to assess its connectivity and to identify the key premises influencing it. Poultry trading practices varied according to the size of the LBMs and to the type of poultry traded. Industrial broiler chickens, the most commonly traded poultry, were generally sold in LBMs close to their production areas, whereas ducks and backyard chickens were moved over longer distances, and their transport involved several intermediates. The poultry trading network composed of 445 nodes (73.2% were LBMs) was highly connected and disassortative. However, the removal of only 5.6% of the nodes (25 LBMs with the highest betweenness scores), reduced the network's connectedness, and the maximum size of output and input domains by more than 50%. Poultry types need to be discriminated in order to understand the way in which poultry trading networks are shaped, and the level of risk of disease spread that these networks may promote. Knowledge of the network structure could be used to target control and surveillance interventions to a small number of LBMs.

Modelling computer networks

International Nuclear Information System (INIS)

Max, G

2011-01-01

Traffic models in computer networks can be described as a complicated system. These systems show non-linear features and to simulate behaviours of these systems are also difficult. Before implementing network equipments users wants to know capability of their computer network. They do not want the servers to be overloaded during temporary traffic peaks when more requests arrive than the server is designed for. As a starting point for our study a non-linear system model of network traffic is established to exam behaviour of the network planned. The paper presents setting up a non-linear simulation model that helps us to observe dataflow problems of the networks. This simple model captures the relationship between the competing traffic and the input and output dataflow. In this paper, we also focus on measuring the bottleneck of the network, which was defined as the difference between the link capacity and the competing traffic volume on the link that limits end-to-end throughput. We validate the model using measurements on a working network. The results show that the initial model estimates well main behaviours and critical parameters of the network. Based on this study, we propose to develop a new algorithm, which experimentally determines and predict the available parameters of the network modelled.

Human tracking over camera networks: a review

Science.gov (United States)

Hou, Li; Wan, Wanggen; Hwang, Jenq-Neng; Muhammad, Rizwan; Yang, Mingyang; Han, Kang

2017-12-01

In recent years, automated human tracking over camera networks is getting essential for video surveillance. The tasks of tracking human over camera networks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, ranging from security surveillance to retail and health care. This review paper surveys the most widely used techniques and recent advances for human tracking over camera networks. Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across non-overlapping cameras. The core techniques of human tracking within a camera are discussed based on two aspects, i.e., generative trackers and discriminative trackers. The core techniques of human tracking across non-overlapping cameras are then discussed based on the aspects of human re-identification, camera-link model-based tracking and graph model-based tracking. Our survey aims to address existing problems, challenges, and future research directions based on the analyses of the current progress made toward human tracking techniques over camera networks.

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.

Privacy Implications of Surveillance Systems

DEFF Research Database (Denmark)

Thommesen, Jacob; Andersen, Henning Boje

2009-01-01

This paper presents a model for assessing the privacy „cost‟ of a surveillance system. Surveillance systems collect and provide personal information or observations of people by means of surveillance technologies such as databases, video or location tracking. Such systems can be designed for vari......This paper presents a model for assessing the privacy „cost‟ of a surveillance system. Surveillance systems collect and provide personal information or observations of people by means of surveillance technologies such as databases, video or location tracking. Such systems can be designed...... for various purposes, even as a service for those being observed, but in any case they will to some degree invade their privacy. The model provided here can indicate how invasive any particular system may be – and be used to compare the invasiveness of different systems. Applying a functional approach......, the model is established by first considering the social function of privacy in everyday life, which in turn lets us determine which different domains will be considered as private, and finally identify the different types of privacy invasion. This underlying model (function – domain – invasion) then serves...

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

A Hidden Markov Model for Analysis of Frontline Veterinary Data for Emerging Zoonotic Disease Surveillance

Science.gov (United States)

Robertson, Colin; Sawford, Kate; Gunawardana, Walimunige S. N.; Nelson, Trisalyn A.; Nathoo, Farouk; Stephen, Craig

2011-01-01

Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines. PMID:21949763

Semantic web technologies for video surveillance metadata

OpenAIRE

Poppe, Chris; Martens, Gaëtan; De Potter, Pieterjan; Van de Walle, Rik

2012-01-01

Video surveillance systems are growing in size and complexity. Such systems typically consist of integrated modules of different vendors to cope with the increasing demands on network and storage capacity, intelligent video analytics, picture quality, and enhanced visual interfaces. Within a surveillance system, relevant information (like technical details on the video sequences, or analysis results of the monitored environment) is described using metadata standards. However, different module...

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

Science.gov (United States)

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

2006-09-01

European Surveillance of Congenital Anomalies (EUROCAT) is a network of population-based congenital anomaly registries in Europe surveying more than 1 million births per year, or 25% of the births in the European Union. This paper describes the potential of the EUROCAT collaboration for pharmacoepidemiology and drug safety surveillance. The 34 full members and 6 associate members of the EUROCAT network were sent a questionnaire about their data sources on drug exposure and on drug coding. Available data on drug exposure during the first trimester available in the central EUROCAT database for the years 1996-2000 was summarised for 15 out of 25 responding full members. Of the 40 registries, 29 returned questionnaires (25 full and 4 associate members). Four of these registries do not collect data on maternal drug use. Of the full members, 15 registries use the EUROCAT drug code, 4 use the international ATC drug code, 3 registries use another coding system and 7 use a combination of these coding systems. Obstetric records are the most frequently used sources of drug information for the registries, followed by interviews with the mother. Only one registry uses pharmacy data. Percentages of cases with drug exposure (excluding vitamins/minerals) varied from 4.4% to 26.0% among different registries. The categories of drugs recorded varied widely between registries. Practices vary widely between registries regarding recording drug exposure information. EUROCAT has the potential to be an effective collaborative framework to contribute to post-marketing drug surveillance in relation to teratogenic effects, but work is needed to implement ATC drug coding more widely, and to diversify the sources of information used to determine drug exposure in each registry.

Wallops Ship Surveillance System

Science.gov (United States)

Smith, Donna C.

2011-01-01

Approved as a Wallops control center backup system, the Wallops Ship Surveillance Software is a day-of-launch risk analysis tool for spaceport activities. The system calculates impact probabilities and displays ship locations relative to boundary lines. It enables rapid analysis of possible flight paths to preclude the need to cancel launches and allow execution of launches in a timely manner. Its design is based on low-cost, large-customer- base elements including personal computers, the Windows operating system, C/C++ object-oriented software, and network interfaces. In conformance with the NASA software safety standard, the system is designed to ensure that it does not falsely report a safe-for-launch condition. To improve the current ship surveillance method, the system is designed to prevent delay of launch under a safe-for-launch condition. A single workstation is designated the controller of the official ship information and the official risk analysis. Copies of this information are shared with other networked workstations. The program design is divided into five subsystems areas: 1. Communication Link -- threads that control the networking of workstations; 2. Contact List -- a thread that controls a list of protected item (ocean vessel) information; 3. Hazard List -- threads that control a list of hazardous item (debris) information and associated risk calculation information; 4. Display -- threads that control operator inputs and screen display outputs; and 5. Archive -- a thread that controls archive file read and write access. Currently, most of the hazard list thread and parts of other threads are being reused as part of a new ship surveillance system, under the SureTrak project.

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 (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.

Risk based surveillance for vector borne diseases

DEFF Research Database (Denmark)

Bødker, Rene

of samples and hence early detection of outbreaks. Models for vector borne diseases in Denmark have demonstrated dramatic variation in outbreak risk during the season and between years. The Danish VetMap project aims to make these risk based surveillance estimates available on the veterinarians smart phones...... in Northern Europe. This model approach may be used as a basis for risk based surveillance. In risk based surveillance limited resources for surveillance are targeted at geographical areas most at risk and only when the risk is high. This makes risk based surveillance a cost effective alternative...... sample to a diagnostic laboratory. Risk based surveillance models may reduce this delay. An important feature of risk based surveillance models is their ability to continuously communicate the level of risk to veterinarians and hence increase awareness when risk is high. This is essential for submission...

Automated video surveillance: teaching an old dog new tricks

Science.gov (United States)

McLeod, Alastair

1993-12-01

The automated video surveillance market is booming with new players, new systems, new hardware and software, and an extended range of applications. This paper reviews available technology, and describes the features required for a good automated surveillance system. Both hardware and software are discussed. An overview of typical applications is also given. A shift towards PC-based hybrid systems, use of parallel processing, neural networks, and exploitation of modern telecomms are introduced, highlighting the evolution modern video surveillance systems.

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...

Wireless sensor network for mobile surveillance systems; 2005BU1-TRSP

NARCIS (Netherlands)

Maris, M.G.; Dijk, G.J.A. van

2005-01-01

Guarding safety and security within industrial, commercial and military areas is an important issue nowadays. A specific challenge lies in the design of portable surveillance systems that can be rapidly deployed, installed and easily operated. Conventional surveillance systems typically employ

Smart sensing surveillance system

Science.gov (United States)

Hsu, Charles; Chu, Kai-Dee; O'Looney, James; Blake, Michael; Rutar, Colleen

2010-04-01

An effective public safety sensor system for heavily-populated applications requires sophisticated and geographically-distributed infrastructures, centralized supervision, and deployment of large-scale security and surveillance networks. Artificial intelligence in sensor systems is a critical design to raise awareness levels, improve the performance of the system and adapt to a changing scenario and environment. In this paper, a highly-distributed, fault-tolerant, and energy-efficient Smart Sensing Surveillance System (S4) is presented to efficiently provide a 24/7 and all weather security operation in crowded environments or restricted areas. Technically, the S4 consists of a number of distributed sensor nodes integrated with specific passive sensors to rapidly collect, process, and disseminate heterogeneous sensor data from near omni-directions. These distributed sensor nodes can cooperatively work to send immediate security information when new objects appear. When the new objects are detected, the S4 will smartly select the available node with a Pan- Tilt- Zoom- (PTZ) Electro-Optics EO/IR camera to track the objects and capture associated imagery. The S4 provides applicable advanced on-board digital image processing capabilities to detect and track the specific objects. The imaging detection operations include unattended object detection, human feature and behavior detection, and configurable alert triggers, etc. Other imaging processes can be updated to meet specific requirements and operations. In the S4, all the sensor nodes are connected with a robust, reconfigurable, LPI/LPD (Low Probability of Intercept/ Low Probability of Detect) wireless mesh network using Ultra-wide band (UWB) RF technology. This UWB RF technology can provide an ad-hoc, secure mesh network and capability to relay network information, communicate and pass situational awareness and messages. The Service Oriented Architecture of S4 enables remote applications to interact with the S4

Air quality monitoring at Seoul, Korea as a part of East-Asian air surveillance network

International Nuclear Information System (INIS)

Hashimoto, Y.; Sekine, Y.; Kim, H.K.; Otoshi, T.

1989-01-01

Global scale air pollution study is a recent trend due to a perception that air pollution is changing climate and other essential earth's conditions that could seriously affect our lives. One of the important tasks which can contribute to protect our natural environment must be to know about the present and changing air quality. For this purpose, a regional air monitoring plan was designed by a research group and has proceeded to set up stations in the eastern Asia including Japan, Korea and China to get continuous data which can contribute to world wide data base of air quality. This project was initiated at Seoul, Korea in April, 1986 by the method of National Air Surveillance Network, Japan. Airborne particles were collected by so-called Hi-vol and Lo-vol, and their components were analyzed by neutron activation analysis and others. The results of Seoul sampling as a first step of this network plan are presented

Facebook use during relationship termination: uncertainty reduction and surveillance.

Science.gov (United States)

Tong, Stephanie Tom

2013-11-01

Many studies document how individuals use Facebook to meet partners or develop and maintain relationships. Less is known about information-seeking behaviors during the stages of relationship termination. Relational dissolution is a socially embedded activity, and affordances of social network sites offer many advantages in reducing uncertainty after a breakup. A survey collected responses from 110 individuals who use Facebook to gather information about their romantic ex-partners. Results indicated that after breakup, partners may take advantage of the system's information visibility and the relative invisibility of movement depending on relational factors (initiator role and breakup uncertainty), social factors (perceived network approval of Facebook surveillance), and individual privacy concerns. This investigation addresses questions such as what type of information-seeking foci do individuals employ and how do individuals use Facebook as a form of surveillance? What factors motivate surveillance behavior?

Influenza epidemiology and influenza vaccine effectiveness during the 2014–2015 season: annual report from the Global Influenza Hospital Surveillance Network

Directory of Open Access Journals (Sweden)

Joan Puig-Barberà

2016-08-01

Full Text Available Abstract The Global Influenza Hospital Surveillance Network (GIHSN has established a prospective, active surveillance, hospital-based epidemiological study to collect epidemiological and virological data for the Northern and Southern Hemispheres over several consecutive seasons. It focuses exclusively on severe cases of influenza requiring hospitalization. A standard protocol is shared between sites allowing comparison and pooling of results. During the 2014–2015 influenza season, the GIHSN included seven coordinating sites from six countries (St. Petersburg and Moscow, Russian Federation; Prague, Czech Republic; Istanbul, Turkey; Beijing, China; Valencia, Spain; and Rio de Janeiro, Brazil. Here, we present the detailed epidemiological and influenza vaccine effectiveness findings for the Northern Hemisphere 2014–2015 influenza season.

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.

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.

Self-surveillance

DEFF Research Database (Denmark)

Albrechtslund, Anders

Gadgets and applications are increasingly being developed and used for tracking, quantifying, and documenting everyday life activities and especially health and fitness devices such as GPS-enabled sports watches are well-known and popular. However, self-surveillance practices involving networked...... pressure, fitness activities, sleep cycles, etc. can be broadcasted, e.g. as tweets on Twitter or status updates on Facebook. Such quantification practices with monitoring technologies become co-producing when individuals constitute themselves as subjects engaging in self-tracking, self-care, and self...

History and evolution of surveillance in public health

Directory of Open Access Journals (Sweden)

Varun Kumar

2014-01-01

Full Text Available The modern concept of surveillance has evolved over the centuries. Public health surveillance provides the scientific database essential for decision making and appropriate public health action. It is considered as the best public health tool to prevent the occurrence of epidemics and is the backbone of public health programs and provides information so that effective action can be taken in controlling and preventing diseases of public health importance. This article reviews the history of evolution of public health surveillance from historical perspective: from Hippocrates, Black Death and quarantine, recording of vital events for the first time, first field investigation, legislations that were developed over time and modern concepts in public health surveillance. Eradication of small pox is an important achievement in public health surveillance but the recent Severe Acute Respiratory Syndrome (SARS and Influenza pandemics suggest still there is a room for improvement. Recently new global disease surveillance networks like FluNet and DengueNet were developed as internet sites for monitoring influenza and dengue information. In spite of these developments, global public health surveillance still remains unevenly distributed. There is a need for increased international cooperation to address the global needs of public health surveillance.

Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet): case definition in surveillance for childhood-onset Duchenne/Becker muscular dystrophy.

Science.gov (United States)

Mathews, Katherine D; Cunniff, Chris; Kantamneni, Jiji R; Ciafaloni, Emma; Miller, Timothy; Matthews, Dennis; Cwik, Valerie; Druschel, Charlotte; Miller, Lisa; Meaney, F John; Sladky, John; Romitti, Paul A

2010-09-01

The Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet) is a multisite collaboration to determine the prevalence of childhood-onset Duchenne/Becker muscular dystrophy and to characterize health care and health outcomes in this population. MD STARnet uses medical record abstraction to identify patients with Duchenne/Becker muscular dystrophy born January 1, 1982 or later who resided in 1 of the participating sites. Critical diagnostic elements of each abstracted record are reviewed independently by >4 clinicians and assigned to 1 of 6 case definition categories (definite, probable, possible, asymptomatic, female, not Duchenne/Becker muscular dystrophy) by consensus. As of November 2009, 815 potential cases were reviewed. Of the cases included in analysis, 674 (82%) were either ''definite'' or ''probable'' Duchenne/Becker muscular dystrophy. These data reflect a change in diagnostic testing, as case assignment based on genetic testing increased from 67% in the oldest cohort (born 1982-1987) to 94% in the cohort born 2004 to 2009.

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.

Collaborative networks: Reference modeling

NARCIS (Netherlands)

Camarinha-Matos, L.M.; Afsarmanesh, H.

2008-01-01

Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of

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

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.

Real-Time Surveillance in Emergencies Using the Early Warning Alert and Response Network.

Science.gov (United States)

Cordes, Kristina M; Cookson, Susan T; Boyd, Andrew T; Hardy, Colleen; Malik, Mamunur Rahman; Mala, Peter; El Tahir, Khalid; Everard, Marthe; Jasiem, Mohamad; Husain, Farah

2017-11-01

Humanitarian emergencies often result in population displacement and increase the risk for transmission of communicable diseases. To address the increased risk for outbreaks during humanitarian emergencies, the World Health Organization developed the Early Warning Alert and Response Network (EWARN) for early detection of epidemic-prone diseases. The US Centers for Disease Control and Prevention has worked with the World Health Organization, ministries of health, and other partners to support EWARN through the implementation and evaluation of these systems and the development of standardized guidance. Although protocols have been developed for the implementation and evaluation of EWARN, a need persists for standardized training and additional guidance on supporting these systems remotely when access to affected areas is restricted. Continued collaboration between partners and the Centers for Disease Control and Prevention for surveillance during emergencies is necessary to strengthen capacity and support global health security.

A Semantic Autonomous Video Surveillance System for Dense Camera Networks in Smart Cities

Directory of Open Access Journals (Sweden)

Antonio Sánchez-Esguevillas

2012-08-01

Full Text Available 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.

Silhouettes of War: Technologies of U.S. Soldiering and Surveillance

Directory of Open Access Journals (Sweden)

Jessica J. Behm

2010-03-01

Full Text Available This paper forwards a theory of silhouetting in relation to technological augmenta-tion in U.S. Military uniforms and suggests that the increasing utilization of metamaterials, nanotechnology, and surveillance technologies operates under a rhetoric of invisibility that complicates the technologies' visible destruction. Methodologically, the paper attends to three general technological developments in the evolution of the U.S. Army uniform: the design of the new Army Combat Uniform (ACU; the technological advances in the uniform, including embedded wearables, biometric identification devices, and 3D combat enhancement systems; and the bio-networking, GPS, and digital communication arrays that physically link digital uniforms to a larger geopolitical network of U.S. military strategy and surveillance. Throughout, the work traces the aforementioned theory of silhouet-ting in relation to select sociopolitical consequences of linking digitally enhanced soldiers into a transnational grid of surveillance.

Deployment Design of Wireless Sensor Network for Simple Multi-Point Surveillance of a Moving Target

Science.gov (United States)

Tsukamoto, Kazuya; Ueda, Hirofumi; Tamura, Hitomi; Kawahara, Kenji; Oie, Yuji

2009-01-01

In this paper, we focus on the problem of tracking a moving target in a wireless sensor network (WSN), in which the capability of each sensor is relatively limited, to construct large-scale WSNs at a reasonable cost. We first propose two simple multi-point surveillance schemes for a moving target in a WSN and demonstrate that one of the schemes can achieve high tracking probability with low power consumption. In addition, we examine the relationship between tracking probability and sensor density through simulations, and then derive an approximate expression representing the relationship. As the results, we present guidelines for sensor density, tracking probability, and the number of monitoring sensors that satisfy a variety of application demands. PMID:22412326

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.

Progress with enhancing veterinary surveillance in the United Kingdom.

Science.gov (United States)

Lysons, R E; Gibbens, J C; Smith, L H

2007-01-27

The UK has experienced various animal health events that have had national impact in recent years. In response, a ;Veterinary Surveillance Strategy' (VSS) was published in 2003, with the objective of enhancing and coordinating national veterinary surveillance practice in a way that would enable important animal health events to be detected and assessed more rapidly and reliably. The VSS adopts an integrated UK-wide approach, which includes widespread engagement with interested parties both within government and beyond. It proposes enhancing surveillance through improved collaboration; transparent and defensible prioritisation of government resources to surveillance; deriving better value from existing resources, and assuring quality of the surveillance reports and source data. This article describes progress with implementing the VSS, in particular the methodology for developing a functional network and creating an effective, quality-assured, information management system, RADAR.

A neighbourhood evolving network model

International Nuclear Information System (INIS)

Cao, Y.J.; Wang, G.Z.; Jiang, Q.Y.; Han, Z.X.

2006-01-01

Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabasi-Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development

Surveillance, Privacy and Trans-Atlantic Relations

DEFF Research Database (Denmark)

Recent revelations, by Edward Snowden and others, of the vast network of government spying enabled by modern technology have raised major concerns both in the European Union and the United States on how to protect privacy in the face of increasing governmental surveillance. This book brings...

Big Data for Infectious Disease Surveillance and Modeling

DEFF Research Database (Denmark)

Bansal, Shweta; Chowell, Gerardo; Simonsen, Lone

2016-01-01

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...

Fragmentation, integration and macroprudential surveillance of the US financial industry: Insights from network science.

Science.gov (United States)

Gandica, Yerali; Geraci, Marco Valerio; Béreau, Sophie; Gnabo, Jean-Yves

2018-01-01

Drawing on recent contributions inferring financial interconnectedness from market data, our paper provides new insights on the evolution of the US financial industry over a long period of time by using several tools coming from network science. Relying on a Time-Varying Parameter Vector AutoRegressive (TVP-VAR) approach on stock market returns to retrieve unobserved directed links among financial institutions, we reconstruct a fully dynamic network in the sense that connections are let to evolve through time. The financial system analysed consists of a large set of 155 financial institutions that are all the banks, broker-dealers, insurance and real estate companies listed in the Standard & Poors' 500 index over the 1993-2014 period. Looking alternatively at the individual, then sector-, community- and system-wide levels, we show that network sciences' tools are able to support well-known features of the financial markets such as the dramatic fall of connectivity following Lehman Brothers' collapse. More importantly, by means of less traditional metrics, such as sectoral interface or measurements based on contagion processes, our results document the co-existence of both fragmentation and integration phases between firms independently from the sectors they belong to, and doing so, question the relevance of existing macroprudential surveillance frameworks which have been mostly developed on a sectoral basis. Overall, our results improve our understanding of the US financial landscape and may have important implications for risk monitoring as well as macroprudential policy design.

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.

A nurse-led model at public academic hospitals maintains high adherence to colorectal cancer surveillance guidelines.

Science.gov (United States)

Symonds, Erin L; Simpson, Kalindra; Coats, Michelle; Chaplin, Angela; Saxty, Karen; Sandford, Jayne; Young Am, Graeme P; Cock, Charles; Fraser, Robert; Bampton, Peter A

2018-06-18

To examine the compliance of colorectal cancer surveillance decisions for individuals at greater risk with current evidence-based guidelines and to determine whether compliance differs between surveillance models. Prospective auditing of compliance of surveillance decisions with evidence-based guidelines (NHMRC) in two decision-making models: nurse coordinator-led decision making in public academic hospitals and physician-led decision making in private non-academic hospitals. Selected South Australian hospitals participating in the Southern Co-operative Program for the Prevention of Colorectal Cancer (SCOOP). Proportions of recall recommendations that matched NHMRC guideline recommendations (March-May 2015); numbers of surveillance colonoscopies undertaken more than 6 months ahead of schedule (January-December 2015); proportions of significant neoplasia findings during the 15 years of SCOOP operation (2000-2015). For the nurse-led/public academic hospital model, the recall interval recommendation following 398 of 410 colonoscopies (97%) with findings covered by NHMRC guidelines corresponded to the guideline recommendations; for the physician-led/private non-academic hospital model, this applied to 257 of 310 colonoscopies (83%) (P < 0.001). During 2015, 27% of colonoscopies in public academic hospitals (mean, 27 months; SD, 13 months) and 20% of those in private non-academic hospitals (mean, 23 months; SD, 12 months) were performed more than 6 months earlier than scheduled, in most cases because of patient-related factors (symptoms, faecal occult blood test results). The ratio of the numbers of high risk adenomas to cancers increased from 6.6:1 during 2001-2005 to 16:1 during 2011-2015. The nurse-led/public academic hospital model for decisions about colorectal cancer surveillance intervals achieves a high degree of compliance with guideline recommendations, which should relieve burdening of colonoscopy resources.

Regional Disease Surveillance Meeting - Final Paper

Energy Technology Data Exchange (ETDEWEB)

Lesperance, Ann M.; Mahy, Heidi A.

2006-08-08

On June 1, 2006, public health officials working in surveillance, epidemiological modeling, and information technology communities from the Seattle/Tacoma area and State of Washington met with members of the Pacific Northwest National Laboratory (PNNL) to discuss the current state of disease surveillance and gaps and needs to improve the current systems. The meeting also included a discussion of PNNL initiatives that might be appropriate to enhance disease surveillance and the current tools being used for disease surveillance. Participants broke out into two groups to identify critical gaps and needs for improving a surveillance system, and discuss the requirements for developing improved surveillance. Each group developed a list of key priorities summarizing the requirements for improved surveillance. The objective of this meeting was to work towards the development of an improved disease surveillance system.

Ambient Surveillance by Probabilistic-Possibilistic Perception

NARCIS (Netherlands)

Bittermann, M.S.; Ciftcioglu, O.

2013-01-01

A method for quantifying ambient surveillance is presented, which is based on probabilistic-possibilistic perception. The human surveillance of a scene through observing camera sensed images on a monitor is modeled in three steps. First immersion of the observer is simulated by modeling perception

Social Networks for Surveillance and Security: ‘Using Online Techniques to make something happen in the real or cyber world’

OpenAIRE

Harbisher, Ben

2017-01-01

This chapter examines the use of Social Networks for Surveillance and Security in relation to the deployment of intelligence resources in the UK. The chapter documents the rise of Military Intelligence agencies during both World Wars (such as GCHQ and MI5), and the subsequent use of these institutions to maintain order during peacetime. In addition to the way in which military organisations have used clandestine techniques such as double agents, spies, and various programmes designed for cond...

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.

Coevolutionary modeling in network formation

KAUST Repository

Al-Shyoukh, Ibrahim; Chasparis, Georgios; Shamma, Jeff S.

2014-01-01

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.

Modeling online social signed networks

Science.gov (United States)

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

2018-04-01

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

Disaster prevention surveillance system

International Nuclear Information System (INIS)

Nara, Satoru; Kamiya, Eisei

2001-01-01

Fuji Electric Co., Ltd. has supplied many management systems to nuclear reactor institution. 'The nuclear countermeasures-against-calamities special-measures' was enforced. A nuclear entrepreneur has devised the measure about expansion prevention and restoration of a calamity while it endeavors after prevention of generating of a nuclear calamity. Our company have supplied the 'disaster prevention surveillance system' to the Japan Atomic Energy Research Institute Tokai Research Establishment aiming at strengthening of the monitoring function at the time (after the accident) of the accident used as one of the above-mentioned measures. A 'disaster prevention surveillance system' can share the information on the accident spot in an on-site command place, an activity headquarters, and support organizations, when the serious accident happens. This system is composed of various sensors (temperature, pressure and radiation), cameras, computers and network. (author)

A flexible data fusion architecture for persistent surveillance using ultra-low-power wireless sensor networks

Science.gov (United States)

Hanson, Jeffrey A.; McLaughlin, Keith L.; Sereno, Thomas J.

2011-06-01

We have developed a flexible, target-driven, multi-modal, physics-based fusion architecture that efficiently searches sensor detections for targets and rejects clutter while controlling the combinatoric problems that commonly arise in datadriven fusion systems. The informational constraints imposed by long lifetime requirements make systems vulnerable to false alarms. We demonstrate that our data fusion system significantly reduces false alarms while maintaining high sensitivity to threats. In addition, mission goals can vary substantially in terms of targets-of-interest, required characterization, acceptable latency, and false alarm rates. Our fusion architecture provides the flexibility to match these trade-offs with mission requirements unlike many conventional systems that require significant modifications for each new mission. We illustrate our data fusion performance with case studies that span many of the potential mission scenarios including border surveillance, base security, and infrastructure protection. In these studies, we deployed multi-modal sensor nodes - including geophones, magnetometers, accelerometers and PIR sensors - with low-power processing algorithms and low-bandwidth wireless mesh networking to create networks capable of multi-year operation. The results show our data fusion architecture maintains high sensitivities while suppressing most false alarms for a variety of environments and targets.

Statistical Models for Social Networks

NARCIS (Netherlands)

Snijders, Tom A. B.; Cook, KS; Massey, DS

2011-01-01

Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For

The surveillance of the electricity wholesale market and emission trading market

International Nuclear Information System (INIS)

Luedemann, Volker

2015-01-01

The Regulation on Wholesale Market Integrity and Transparency (REMIT) and the German Law on the Establishment of a Market Transparency Office for Wholesale Trade in Electricity and Gas (MTS-G) have fundamentally changed the surveillance of electricity wholesale trade in Germany. From now on the Federal Network Agency and the Federal Cartel Office will be jointly responsible for monitoring the electricity wholesale trade for suspicious market phenomena and abusive behaviour. The REMIT specifies that the electricity trade must be surveilled ''with due consideration to interactions'' with the emission trade system. However, occurrences observed in recent years have shown that the emission trading system is in need of reform. This has also been recognised and has prompted extensive corrective action by the regulatory authorities of the European Union. These changes have yet to be transposed into the national surveillance regimes. The present article explains why the new role accorded to the Federal Network Agency under the REMIT fails to eliminate the structural shortcomings of the old surveillance system. At least the decision to put the collection and evaluation of data exclusively in the hands of the market transparency office and the cooperation this will prompt between the supervisory authorities responsible will make the task of surveilling the energy wholesale trading market a lot easier for the authorities. The energy transition and its exigencies will yet lead to further changes in the market and its surveillance regime.

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...

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....

Camera Control and Geo-Registration for Video Sensor Networks

Science.gov (United States)

Davis, James W.

With the use of large video networks, there is a need to coordinate and interpret the video imagery for decision support systems with the goal of reducing the cognitive and perceptual overload of human operators. We present computer vision strategies that enable efficient control and management of cameras to effectively monitor wide-coverage areas, and examine the framework within an actual multi-camera outdoor urban video surveillance network. First, we construct a robust and precise camera control model for commercial pan-tilt-zoom (PTZ) video cameras. In addition to providing a complete functional control mapping for PTZ repositioning, the model can be used to generate wide-view spherical panoramic viewspaces for the cameras. Using the individual camera control models, we next individually map the spherical panoramic viewspace of each camera to a large aerial orthophotograph of the scene. The result provides a unified geo-referenced map representation to permit automatic (and manual) video control and exploitation of cameras in a coordinated manner. The combined framework provides new capabilities for video sensor networks that are of significance and benefit to the broad surveillance/security community.

Enhancing Syndromic Surveillance With Online Respondent-Driven Detection

NARCIS (Netherlands)

Stein, Mart L; van Steenbergen, Jim E; Buskens, Vincent; van der Heijden, Peter G M; Koppeschaar, Carl E; Bengtsson, Linus; Thorson, Anna; Kretzschmar, MEE

OBJECTIVES: We investigated the feasibility of combining an online chain recruitment method (respondent-driven detection) and participatory surveillance panels to collect previously undetected information on infectious diseases via social networks of participants. METHODS: In 2014, volunteers from 2

Enhancing syndromic surveillance with online respondent-driven detection

NARCIS (Netherlands)

Stein, Mart L.; Van Steenbergen, Jim E.; Buskens, Vincent; Van Der Heijden, Peter G M; Koppeschaar, Carl E.; Bengtsson, Linus; Thorson, Anna; Kretzschmar, Mirjam E E

2015-01-01

Objectives. We investigated the feasibility of combining an online chain recruitment method (respondent-driven detection) and participatory surveillance panels to collect previously undetected information on infectious diseases via social networks of participants. Methods. In 2014, volunteers from 2

The Need for European Surveillance of CDI.

Science.gov (United States)

Wiuff, Camilla; Banks, A-Lan; Fitzpatrick, Fidelma; Cottom, Laura

2018-01-01

Since the turn of the millennium, the epidemiology of Clostridium difficile infection (CDI) has continued to challenge. Over the last decade there has been a growing awareness that improvements to surveillance are needed. The increasing rate of CDI and emergence of ribotype 027 precipitated the implementation of mandatory national surveillance of CDI in the UK. Changes in clinical presentation, severity of disease, descriptions of new risk factors and the occurrence of outbreaks all emphasised the importance of early diagnosis and surveillance.However a lack of consensus on case definitions, clinical guidelines and optimal laboratory diagnostics across Europe has lead to the underestimation of CDI and impeded comparison between countries. These inconsistencies have prevented the true burden of disease from being appreciated.Acceptance that a multi-country surveillance programme and optimised diagnostic strategies are required not only to detect and control CDI in Europe, but for a better understanding of the epidemiology, has built the foundations for a more robust, unified surveillance. The concerted efforts of the European Centre for Disease Prevention and Control (ECDC) CDI networks, has lead to the development of an over-arching long-term CDI surveillance strategy for 2014-2020. Fulfilment of the ECDC priorities and targets will no doubt be challenging and will require significant investment however the hope is that both a national and Europe-wide picture of CDI will finally be realised.

Real-Time Surveillance of Infectious Diseases: Taiwan's Experience.

Science.gov (United States)

Jian, Shu-Wan; Chen, Chiu-Mei; Lee, Cheng-Yi; Liu, Ding-Ping

Integration of multiple surveillance systems advances early warning and supports better decision making during infectious disease events. Taiwan has a comprehensive network of laboratory, epidemiologic, and early warning surveillance systems with nationwide representation. Hospitals and clinical laboratories have deployed automatic reporting mechanisms since 2014 and have effectively improved timeliness of infectious disease and laboratory data reporting. In June 2016, the capacity of real-time surveillance in Taiwan was externally assessed and was found to have a demonstrated and sustainable capability. We describe Taiwan's disease surveillance system and use surveillance efforts for influenza and Zika virus as examples of surveillance capability. Timely and integrated influenza information showed a higher level and extended pattern of influenza activity during the 2015-16 season, which ensured prompt information dissemination and the coordination of response operations. Taiwan also has well-developed disease detection systems and was the first country to report imported cases of Zika virus from Miami Beach and Singapore. This illustrates a high level of awareness and willingness among health workers to report emerging infectious diseases, and highlights the robust and sensitive nature of Taiwan's surveillance system. These 2 examples demonstrate the flexibility of the surveillance systems in Taiwan to adapt to emerging infectious diseases and major communicable diseases. Through participation in the GHSA, Taiwan can more actively collaborate with national counterparts and use its expertise to strengthen global and regional surveillance capacity in the Asia Pacific and in Southeast Asia, in order to advance a world safe and secure from infectious disease.

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.

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.

Network bandwidth utilization forecast model on high bandwidth networks

Energy Technology Data Exchange (ETDEWEB)

Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2015-03-30

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 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

A fiber Bragg grating acceleration sensor for ground surveillance

Science.gov (United States)

Jiang, Shaodong; Zhang, Faxiang; Lv, Jingsheng; Ni, Jiasheng; Wang, Chang

2017-10-01

Ground surveillance system is a kind of intelligent monitoring equipment for detecting and tracking the ground target. This paper presents a fiber Bragg grating (FBG) acceleration sensor for ground surveillance, which has the characteristics of no power supply, anti-electromagnetic interference, easy large-scale networking, and small size. Which make it able to achieve the advantage of the ground surveillance system while avoiding the shortcoming of the electric sensing. The sensor has a double cantilever beam structure with a sensitivity of 1000 pm/g. Field experiment has been carried out on a flood beach to examine the sensor performance. The result shows that the detection distance on the walking of personnel reaches 70m, and the detection distance on the ordinary motor vehicle reaches 200m. The performance of the FBG sensor can satisfy the actual needs of the ground surveillance system.

RMBNToolbox: random models for biochemical networks

Directory of Open Access Journals (Sweden)

Niemi Jari

2007-05-01

Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

Infection 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, 2012-2015.

Science.gov (United States)

Huang, Jennifer Y; Henao, Olga L; Griffin, Patricia M; Vugia, Duc J; Cronquist, Alicia B; Hurd, Sharon; Tobin-D'Angelo, Melissa; Ryan, Patricia; Smith, Kirk; Lathrop, Sarah; Zansky, Shelley; Cieslak, Paul R; Dunn, John; Holt, Kristin G; Wolpert, Beverly J; Patrick, Mary E

2016-04-15

To evaluate progress toward prevention of enteric and foodborne illnesses in the United States, the Foodborne Diseases Active Surveillance Network (FoodNet) monitors the incidence of laboratory-confirmed infections caused by nine pathogens transmitted commonly through food in 10 U.S. sites. This report summarizes preliminary 2015 data and describes trends since 2012. In 2015, FoodNet reported 20,107 confirmed cases (defined as culture-confirmed bacterial infections and laboratory-confirmed parasitic infections), 4,531 hospitalizations, and 77 deaths. FoodNet also received reports of 3,112 positive culture-independent diagnostic tests (CIDTs) without culture-confirmation, a number that has markedly increased since 2012. Diagnostic testing practices for enteric pathogens are rapidly moving away from culture-based methods. The continued shift from culture-based methods to CIDTs that do not produce the isolates needed to distinguish between strains and subtypes affects the interpretation of public health surveillance data and ability to monitor progress toward prevention efforts. Expanded case definitions and strategies for obtaining bacterial isolates are crucial during this transition period.

Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning.

Science.gov (United States)

Munkhdalai, Tsendsuren; Liu, Feifan; Yu, Hong

2018-04-25

Medication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance. Existing observational studies have mainly relied on structured EHR data to obtain ADE information; however, ADEs are often buried in the EHR narratives and not recorded in structured data. To unlock ADE-related information from EHR narratives, there is a need to extract relevant entities and identify relations among them. In this study, we focus on relation identification. This study aimed to evaluate natural language processing and machine learning approaches using the expert-annotated medical entities and relations in the context of drug safety surveillance, and investigate how different learning approaches perform under different configurations. We have manually annotated 791 EHR notes with 9 named entities (eg, medication, indication, severity, and ADEs) and 7 different types of relations (eg, medication-dosage, medication-ADE, and severity-ADE). Then, we explored 3 supervised machine learning systems for relation identification: (1) a support vector machines (SVM) system, (2) an end-to-end deep neural network system, and (3) a supervised descriptive rule induction baseline system. For the neural network system, we exploited the state-of-the-art recurrent neural network (RNN) and attention models. We report the performance by macro-averaged precision, recall, and F1-score across the relation types. Our results show that the SVM model achieved the best average F1-score of 89.1% on test data, outperforming the long short-term memory (LSTM) model with attention (F1-score of 65.72%) as well as the rule induction baseline system (F1-score of 7.47%) by a large margin. The bidirectional LSTM model with attention achieved the best performance among different RNN models. With the inclusion of additional features in the LSTM model, its performance can be boosted to an average F1-score of 77.35%. It shows that

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.

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.

Forum: social network for the surveillance and prevention of workplace accidents.

Science.gov (United States)

Vilela, R A G; Almeida, I M; Nunes da Silva, A; Gomes, M H P; Prado, H; Buoso, E; Dias, M D; Cavalcante, S; Lacorte, L E

2012-01-01

In 2008, academic researchers and public service officials created a university extension studies platform based on online and on-site meetings denominated "Work-Related Accidents Forum: Analysis, Prevention, and Other Relevant Aspects. Its aim was to help public agents and social partners to propagate a systemic approach that would be helpful in the surveillance and prevention of work-related accidents. This article describes and analyses such a platform. Online access is free and structured to: support dissemination of updated concepts; support on-site meetings and capacity to build educational activities; and keep a permanent space for debate among the registered participants. The desired result is the propagation of a social-technical-systemic view of work-related accidents that replaces the current traditional view that emphasizes human error and results in blaming the victims. The Forum uses an educational approach known as permanent health education, which is based on the experience and needs of workers and encourages debate among participants. The forum adopts a problematizing pedagogy that starts from the requirements and experiences of the social actors and stimulates support and discussions among them in line with an ongoing health educational approach. The current challenge is to turn the platform into a social networking website in order to broaden its links with society.

LoRaWAN-Based Energy-Efficient Surveillance by Drones for Intelligent Transportation Systems

Directory of Open Access Journals (Sweden)

Vishal Sharma

2018-03-01

Full Text Available Urban networks aim at facilitating users for better experience and services through smart platforms such as the Intelligent Transportation System (ITS. ITS focuses on information acquisition, sensing, contrivance control, data processing and forwarding to ground devices via user-specific application-interfaces. The utility of ITS is further improved via the Internet of Things (IoT, which supports “Connectivity to All”. One of the key applications of IoT-ITS is urban surveillance. Current surveillance in IoT-ITS is performed via fixed infrastructure-based sensing applications which consume an excessive amount of energy leading to several overheads and failures in the network. Such issues can be overcome by the utilization of on-demand nodes, such as drones, etc. However, drones-assisted surveillance requires efficient communication setup as drones are battery operated and any extemporaneous maneuver during monitoring may result in loss of drone or complete failure of the network. The novelty in terms of network layout can be procured by the utilization of drones with LoRaWAN, which is the protocol designated for Low-Power Wide Area Networks (LPWAN. However, even this architectural novelty alone cannot ascertain the formation of fail-safe, highly resilient, low-overhead, and non-redundant network, which is additionally the problem considered in this paper. To resolve such problem, this paper uses drones as LoRaWAN gateway and proposes a communication strategy based on the area stress, resilient factor, and energy consumption that avail in the efficient localization, improved coverage and energy-efficient surveillance with lower overheads, lower redundancy, and almost zero-isolations. The proposed approach is numerically simulated and the results show that the proposed approach can conserve a maximum of 39.2% and a minimum of 12.6% of the total network energy along with an improvement in the area stress between 89.7% and 53.0% for varying

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.

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. Copyright �

Generalized Network Psychometrics : Combining Network and Latent Variable Models

NARCIS (Netherlands)

Epskamp, S.; Rhemtulla, M.; Borsboom, D.

2017-01-01

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between

Survey of Clostridium difficile infection surveillance systems in Europe, 2011.

Science.gov (United States)

Kola, Axel; Wiuff, Camilla; Akerlund, Thomas; van Benthem, Birgit H; Coignard, Bruno; Lyytikäinen, Outi; Weitzel-Kage, Doris; Suetens, Carl; Wilcox, Mark H; Kuijper, Ed J; Gastmeier, Petra

2016-07-21

To develop a European surveillance protocol for Clostridium difficile infection (CDI), existing national CDI surveillance systems were assessed in 2011. A web-based electronic form was provided for all national coordinators of the European CDI Surveillance Network (ECDIS-Net). Of 35 national coordinators approached, 33 from 31 European countries replied. Surveillance of CDI was in place in 14 of the 31 countries, comprising 18 different nationwide systems. Three of 14 countries with CDI surveillance used public health notification of cases as the route of reporting, and in another three, reporting was limited to public health notification of cases of severe CDI. The CDI definitions published by the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) and the European Centre for Disease Prevention and Control (ECDC) were widely used, but there were differing definitions to distinguish between community- and healthcare-associated cases. All CDI surveillance systems except one reported annual national CDI rates (calculated as number of cases per patient-days). Only four surveillance systems regularly integrated microbiological data (typing and susceptibility testing results). Surveillance methods varied considerably between countries, which emphasises the need for a harmonised European protocol to allow consistent monitoring of the CDI epidemiology at European level. The results of this survey were used to develop a harmonised EU-wide hospital-based CDI surveillance protocol. This article is copyright of The Authors, 2016.

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.

Death surveillance as an indicator of the quality of health care for women and children.

Science.gov (United States)

Melo, Cristiane Magalhães de; Aquino, Talita Iasmim Soares; Soares, Marcela Quaresma; Bevilacqua, Paula Dias

2017-10-01

The study aimed to evaluate the implementation of a regional death surveillance network, reflecting on challenges and potentialities of performance as observatory of violence against women. The research involved nine municipalities of a health region set at the Zona da Mata, Minas Gerais, Brazil. We followed the meetings of the regional death surveillance committee and conducted semi-structured interviews with professional members of the committee and municipal health managers. Furthermore, we analyzed information concerning investigations conducted and, in one municipality, we analyzed the notifications of deaths and cases of violence against women. The results point to some difficulties: lack of recognition of the death surveillance activity; work overload; failure in communication between institutions and poor resources, infrastructure and professional training. There were also improvements, namely: greater interaction between municipalities; increased investigations and greater awareness of the importance of death surveillance among workers. We identified cases of domestic, obstetric and institutional violence through the investigation of deaths. The experience as a regional committee reinforces the strategy of strengthening death surveillance and the network of care for women in situation of violence.

A scenario tree model for the Canadian Notifiable Avian Influenza Surveillance System and its application to estimation of probability of freedom and sample size determination.

Science.gov (United States)

Christensen, Jette; Stryhn, Henrik; Vallières, André; El Allaki, Farouk

2011-05-01

In 2008, Canada designed and implemented the Canadian Notifiable Avian Influenza Surveillance System (CanNAISS) with six surveillance activities in a phased-in approach. CanNAISS was a surveillance system because it had more than one surveillance activity or component in 2008: passive surveillance; pre-slaughter surveillance; and voluntary enhanced notifiable avian influenza surveillance. Our objectives were to give a short overview of two active surveillance components in CanNAISS; describe the CanNAISS scenario tree model and its application to estimation of probability of populations being free of NAI virus infection and sample size determination. Our data from the pre-slaughter surveillance component included diagnostic test results from 6296 serum samples representing 601 commercial chicken and turkey farms collected from 25 August 2008 to 29 January 2009. In addition, we included data from a sub-population of farms with high biosecurity standards: 36,164 samples from 55 farms sampled repeatedly over the 24 months study period from January 2007 to December 2008. All submissions were negative for Notifiable Avian Influenza (NAI) virus infection. We developed the CanNAISS scenario tree model, so that it will estimate the surveillance component sensitivity and the probability of a population being free of NAI at the 0.01 farm-level and 0.3 within-farm-level prevalences. We propose that a general model, such as the CanNAISS scenario tree model, may have a broader application than more detailed models that require disease specific input parameters, such as relative risk estimates. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Eight challenges for network epidemic models

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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.

Complex networks-based energy-efficient evolution model for wireless sensor networks

Energy Technology Data Exchange (ETDEWEB)

Zhu Hailin [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China)], E-mail: zhuhailin19@gmail.com; Luo Hong [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China); Peng Haipeng; Li Lixiang; Luo Qun [Information Secure Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China)

2009-08-30

Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

Complex networks-based energy-efficient evolution model for wireless sensor networks

International Nuclear Information System (INIS)

Zhu Hailin; Luo Hong; Peng Haipeng; Li Lixiang; Luo Qun

2009-01-01

Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

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.

Strengthening foodborne disease surveillance in the WHO African

African Journals Online (AJOL)

OMS

2012-06-04

Jun 4, 2012 ... region including acute aflatoxicosis in Kenya in 2004 and bromide poisoning in ... Global Food Infections Network (GFN), has been supporting countries to strengthen ... The surveillance system uses standard case definitions for classifying .... Figure 4: Participating countries and training sites for foodborne.

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.

Transportation Network Topologies

Science.gov (United States)

Holmes, Bruce J.; Scott, John

2004-01-01

A discomforting reality has materialized on the transportation scene: our existing air and ground infrastructures will not scale to meet our nation's 21st century demands and expectations for mobility, commerce, safety, and security. The consequence of inaction is diminished quality of life and economic opportunity in the 21st century. Clearly, new thinking is required for transportation that can scale to meet to the realities of a networked, knowledge-based economy in which the value of time is a new coin of the realm. This paper proposes a framework, or topology, for thinking about the problem of scalability of the system of networks that comprise the aviation system. This framework highlights the role of integrated communication-navigation-surveillance systems in enabling scalability of future air transportation networks. Scalability, in this vein, is a goal of the recently formed Joint Planning and Development Office for the Next Generation Air Transportation System. New foundations for 21st thinking about air transportation are underpinned by several technological developments in the traditional aircraft disciplines as well as in communication, navigation, surveillance and information systems. Complexity science and modern network theory give rise to one of the technological developments of importance. Scale-free (i.e., scalable) networks represent a promising concept space for modeling airspace system architectures, and for assessing network performance in terms of scalability, efficiency, robustness, resilience, and other metrics. The paper offers an air transportation system topology as framework for transportation system innovation. Successful outcomes of innovation in air transportation could lay the foundations for new paradigms for aircraft and their operating capabilities, air transportation system architectures, and airspace architectures and procedural concepts. The topology proposed considers air transportation as a system of networks, within which

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...... to the suggestion of suitable network models. An existing model for flow control is presented and an inherent weakness is revealed and remedied. Examples are given and numerically analysed through deterministic network modelling. Results are presented to highlight the properties of the suggested models...

Modeling Network Interdiction Tasks

Science.gov (United States)

2015-09-17

118 xiii Table Page 36 Computation times for weighted, 100-node random networks for GAND Approach testing in Python ...in Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 38 Accuracy measures for weighted, 100-node random networks for GAND...networks [15:p. 1]. A common approach to modeling network interdiction is to formulate the problem in terms of a two-stage strategic game between two

Secure and Efficient Reactive Video Surveillance for Patient Monitoring

Directory of Open Access Journals (Sweden)

An Braeken

2016-01-01

Full Text Available Video surveillance is widely deployed for many kinds of monitoring applications in healthcare and assisted living systems. Security and privacy are two promising factors that align the quality and validity of video surveillance systems with the caliber of patient monitoring applications. In this paper, we propose a symmetric key-based security framework for the reactive video surveillance of patients based on the inputs coming from data measured by a wireless body area network attached to the human body. Only authenticated patients are able to activate the video cameras, whereas the patient and authorized people can consult the video data. User and location privacy are at each moment guaranteed for the patient. A tradeoff between security and quality of service is defined in order to ensure that the surveillance system gets activated even in emergency situations. In addition, the solution includes resistance against tampering with the device on the patient’s side.

Formal and informal surveillance systems: how to build links

Directory of Open Access Journals (Sweden)

S. Desvaux

2015-11-01

Full Text Available Within the framework of highly pathogenic avian influenza (HPAI surveillance in Vietnam, interviews were carried out with poultry farmers and local animal health operators in two municipalities of the Red River delta with a view to documenting the circulation of health information concerning poultry (content of the information; method, scope and speed of circulation; actors involved; actions triggered as a result of the information received; economic and social incentives for disseminating or withholding information. The main results show that (i active informal surveillance networks exist, (ii the alert levels vary and the measures applied by the poultry farmers are myriad and often far-removed from the official recommendations, and (iii the municipal veterinarian is at the interface between the formal and the informal surveillance systems. The conclusions emphasize the need for the authorities to separate distinctly surveillance and control activities, and to regionalize control strategies, taking into account epidemiological specificities and social dynamics at local level.

Space Surveillance Network and Analysis Model (SSNAM) Performance Improvements

National Research Council Canada - National Science Library

Butkus, Albert; Roe, Kevin; Mitchell, Barbara L; Payne, Timothy

2007-01-01

... capacity by sensor, models for sensors yet to be created, user defined weather conditions, National Aeronautical and Space Administration catalog growth model including space debris, and solar flux just to name a few...

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

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.

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.

Surveillance for travel-related disease--GeoSentinel Surveillance System, United States, 1997-2011.

Science.gov (United States)

Harvey, Kira; Esposito, Douglas H; Han, Pauline; Kozarsky, Phyllis; Freedman, David O; Plier, D Adam; Sotir, Mark J

2013-07-19

In 2012, the number of international tourist arrivals worldwide was projected to reach a new high of 1 billion arrivals, a 48% increase from 674 million arrivals in 2000. International travel also is increasing among U.S. residents. In 2009, U.S. residents made approximately 61 million trips outside the country, a 5% increase from 1999. Travel-related morbidity can occur during or after travel. Worldwide, 8% of travelers from industrialized to developing countries report becoming ill enough to seek health care during or after travel. Travelers have contributed to the global spread of infectious diseases, including novel and emerging pathogens. Therefore, surveillance of travel-related morbidity is an essential component of global public health surveillance and will be of greater importance as international travel increases worldwide. September 1997-December 2011. GeoSentinel is a clinic-based global surveillance system that tracks infectious diseases and other adverse health outcomes in returned travelers, foreign visitors, and immigrants. GeoSentinel comprises 54 travel/tropical medicine clinics worldwide that electronically submit demographic, travel, and clinical diagnosis data for all patients evaluated for an illness or other health condition that is presumed to be related to international travel. Clinical information is collected by physicians with expertise or experience in travel/tropical medicine. Data collected at all sites are entered electronically into a database, which is housed at and maintained by CDC. The GeoSentinel network membership program comprises 235 additional clinics in 40 countries on six continents. Although these network members do not report surveillance data systematically, they can report unusual or concerning diagnoses in travelers and might be asked to perform enhanced surveillance in response to specific health events or concerns. During September 1997-December 2011, data were collected on 141,789 patients with confirmed or

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. .

Enteric disease surveillance under the AFHSC-GEIS: Current efforts, landscape analysis and vision forward

Directory of Open Access Journals (Sweden)

Kasper Matthew R

2011-03-01

Full Text Available Abstract The mission of the Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System (AFHSC-GEIS is to support global public health and to counter infectious disease threats to the United States Armed Forces, including newly identified agents or those increasing in incidence. Enteric diseases are a growing threat to U.S. forces, which must be ready to deploy to austere environments where the risk of exposure to enteropathogens may be significant and where routine prevention efforts may be impractical. In this report, the authors review the recent activities of AFHSC-GEIS partner laboratories in regards to enteric disease surveillance, prevention and response. Each partner identified recent accomplishments, including support for regional networks. AFHSC/GEIS partners also completed a Strengths, Weaknesses, Opportunities and Threats (SWOT survey as part of a landscape analysis of global enteric surveillance efforts. The current strengths of this network include excellent laboratory infrastructure, equipment and personnel that provide the opportunity for high-quality epidemiological studies and test platforms for point-of-care diagnostics. Weaknesses include inconsistent guidance and a splintered reporting system that hampers the comparison of data across regions or longitudinally. The newly chartered Enterics Surveillance Steering Committee (ESSC is intended to provide clear mission guidance, a structured project review process, and central data management and analysis in support of rationally directed enteric disease surveillance efforts.

Distributed data processing for public health surveillance

Directory of Open Access Journals (Sweden)

Yih Katherine

2006-09-01

Full Text Available Abstract Background Many systems for routine public health surveillance rely on centralized collection of potentially identifiable, individual, identifiable personal health information (PHI records. Although individual, identifiable patient records are essential for conditions for which there is mandated reporting, such as tuberculosis or sexually transmitted diseases, they are not routinely required for effective syndromic surveillance. Public concern about the routine collection of large quantities of PHI to support non-traditional public health functions may make alternative surveillance methods that do not rely on centralized identifiable PHI databases increasingly desirable. Methods The National Bioterrorism Syndromic Surveillance Demonstration Program (NDP is an example of one alternative model. All PHI in this system is initially processed within the secured infrastructure of the health care provider that collects and holds the data, using uniform software distributed and supported by the NDP. Only highly aggregated count data is transferred to the datacenter for statistical processing and display. Results Detailed, patient level information is readily available to the health care provider to elucidate signals observed in the aggregated data, or for ad hoc queries. We briefly describe the benefits and disadvantages associated with this distributed processing model for routine automated syndromic surveillance. Conclusion For well-defined surveillance requirements, the model can be successfully deployed with very low risk of inadvertent disclosure of PHI – a feature that may make participation in surveillance systems more feasible for organizations and more appealing to the individuals whose PHI they hold. It is possible to design and implement distributed systems to support non-routine public health needs if required.

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.

Mathematical models used to inform study design or surveillance systems in infectious diseases: a systematic review.

Science.gov (United States)

Herzog, Sereina A; Blaizot, Stéphanie; Hens, Niel

2017-12-18

Mathematical models offer the possibility to investigate the infectious disease dynamics over time and may help in informing design of studies. A systematic review was performed in order to determine to what extent mathematical models have been incorporated into the process of planning studies and hence inform study design for infectious diseases transmitted between humans and/or animals. We searched Ovid Medline and two trial registry platforms (Cochrane, WHO) using search terms related to infection, mathematical model, and study design from the earliest dates to October 2016. Eligible publications and registered trials included mathematical models (compartmental, individual-based, or Markov) which were described and used to inform the design of infectious disease studies. We extracted information about the investigated infection, population, model characteristics, and study design. We identified 28 unique publications but no registered trials. Focusing on compartmental and individual-based models we found 12 observational/surveillance studies and 11 clinical trials. Infections studied were equally animal and human infectious diseases for the observational/surveillance studies, while all but one between humans for clinical trials. The mathematical models were used to inform, amongst other things, the required sample size (n = 16), the statistical power (n = 9), the frequency at which samples should be taken (n = 6), and from whom (n = 6). Despite the fact that mathematical models have been advocated to be used at the planning stage of studies or surveillance systems, they are used scarcely. With only one exception, the publications described theoretical studies, hence, not being utilised in real studies.

Developing a new national approach to surveillance for ventilator-associated events: executive summary.

Science.gov (United States)

Magill, Shelley S; Klompas, Michael; Balk, Robert; Burns, Suzanne M; Deutschman, Clifford S; Diekema, Daniel; Fridkin, Scott; Greene, Linda; Guh, Alice; Gutterman, David; Hammer, Beth; Henderson, David; Hess, Dean R; Hill, Nicholas S; Horan, Teresa; Kollef, Marin; Levy, Mitchell; Septimus, Edward; VanAntwerpen, Carole; Wright, Don; Lipsett, Pamela

2013-11-01

In September 2011, the Centers for Disease Control and Prevention (CDC) convened a Ventilator-Associated Pneumonia (VAP) Surveillance Definition Working Group to organize a formal process for leaders and experts of key stakeholder organizations to discuss the challenges of VAP surveillance definitions and to propose new approaches to VAP surveillance in adult patients (Table 1). The charges to the Working Group were to (1) critically review a draft, streamlined VAP surveillance definition developed for use in adult patients; (2) suggest modifications to enhance the reliability and credibility of the surveillance definition within the critical care and infection prevention communities; and (3) propose a final adult surveillance definition algorithm to be implemented in the CDC's National Healthcare Safety Network (NHSN), taking into consideration the potential future use of the definition algorithm in public reporting, interfacility comparisons, and pay-for-reporting and pay-for-performance programs. Published by Mosby, Inc.

Development and implementation of the quality control panel of RT-PCR and real-time RT-PCR for avian influenza A (H5N1 surveillance network in mainland China

Directory of Open Access Journals (Sweden)

Wang Wei

2011-03-01

Full Text Available Abstract Background Reverse transcription PCR (RT-PCR and real time RT-PCR (rRT-PCR have been indispensable methods for influenza surveillance, especially for determination of avian influenza. The movement of testing beyond reference lab introduced the need of quality control, including the implementation of an evaluation system for validating personal training and sample proficiency testing. Methods We developed a panel with lysates of seasonal influenza virus (H1N1, H3N2 and B, serials of diluted H5N1 virus lysates, and in-vitro transcribed H5 hemaglutinin (HA and an artificial gene RNAs for RT-PCR and rRT-PCR quality control assessment. The validations of stability and reproducibility were performed on the panel. Additionally, the panel was implemented to assess the detection capability of Chinese human avian influenza networks. Results The panel has relatively high stability and good reproducibility demonstrated by kappa's tests. In the implementation of panel on Chinese human avian influenza networks, the results suggested that there were a relatively low number of discrepancies for both concise and reproducibility in Chinese avian influenza virus net works. Conclusions A quality control panel of RT-PCR and real-time RT-PCR for avian influenza A (H5N1 surveillance network was developed. An availably statistical data, which are used to assess the detection capability of networks on avian influenza virus (H5N1, can be obtained relatively easily through implementation of the panel on networks.

Analysis of Food Insecurity and Surveillance Based on the FANP ...

African Journals Online (AJOL)

Michael Horsfall

In this paper, we seek to use the Fuzzy analytical network process (FANP) for analysis of food insecurity surveillance and selecting the best strategies for ... population in different research reports (Bickel et. all, ..... Inability of financial motivation.

Brand Marketing Model on Social Networks

OpenAIRE

Jolita Jezukevičiūtė; Vida Davidavičienė

2014-01-01

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 exploratoryanalys...

Availability Issues in Wireless Visual Sensor Networks

Science.gov (United States)

Costa, Daniel G.; Silva, Ivanovitch; Guedes, Luiz Affonso; Vasques, Francisco; Portugal, Paulo

2014-01-01

Wireless visual sensor networks have been considered for a large set of monitoring applications related with surveillance, tracking and multipurpose visual monitoring. When sensors are deployed over a monitored field, permanent faults may happen during the network lifetime, reducing the monitoring quality or rendering parts or the entire network unavailable. In a different way from scalar sensor networks, camera-enabled sensors collect information following a directional sensing model, which changes the notions of vicinity and redundancy. Moreover, visual source nodes may have different relevancies for the applications, according to the monitoring requirements and cameras' poses. In this paper we discuss the most relevant availability issues related to wireless visual sensor networks, addressing availability evaluation and enhancement. Such discussions are valuable when designing, deploying and managing wireless visual sensor networks, bringing significant contributions to these networks. PMID:24526301

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....

Evaluation of the cost-effectiveness of bovine brucellosis surveillance in a disease-free country using stochastic scenario tree modelling.

Directory of Open Access Journals (Sweden)

Viviane Hénaux

Full Text Available Surveillance systems of exotic infectious diseases aim to ensure transparency about the country-specific animal disease situation (i.e. demonstrate disease freedom and to identify any introductions. In a context of decreasing resources, evaluation of surveillance efficiency is essential to help stakeholders make relevant decisions about prioritization of measures and funding allocation. This study evaluated the efficiency (sensitivity related to cost of the French bovine brucellosis surveillance system using stochastic scenario tree models. Cattle herds were categorized into three risk groups based on the annual number of purchases, given that trading is considered as the main route of brucellosis introduction in cattle herds. The sensitivity in detecting the disease and the costs of the current surveillance system, which includes clinical (abortion surveillance, programmed serological testing and introduction controls, were compared to those of 19 alternative surveillance scenarios. Surveillance costs included veterinary fees and laboratory analyses. The sensitivity over a year of the current surveillance system was predicted to be 91±7% at a design prevalence of 0.01% for a total cost of 14.9±1.8 million €. Several alternative surveillance scenarios, based on clinical surveillance and random or risk-based serological screening in a sample (20% of the population, were predicted to be at least as sensitive but for a lower cost. Such changes would reduce whole surveillance costs by 20 to 61% annually, and the costs for farmers only would be decreased from about 12.0 million € presently to 5.3-9.0 million € (i.e. 25-56% decrease. Besides, fostering the evolution of the surveillance system in one of these directions would be in agreement with the European regulations and farmers perceptions on brucellosis risk and surveillance.

Evaluation of the cost-effectiveness of bovine brucellosis surveillance in a disease-free country using stochastic scenario tree modelling.

Science.gov (United States)

Hénaux, Viviane; Calavas, Didier

2017-01-01

Surveillance systems of exotic infectious diseases aim to ensure transparency about the country-specific animal disease situation (i.e. demonstrate disease freedom) and to identify any introductions. In a context of decreasing resources, evaluation of surveillance efficiency is essential to help stakeholders make relevant decisions about prioritization of measures and funding allocation. This study evaluated the efficiency (sensitivity related to cost) of the French bovine brucellosis surveillance system using stochastic scenario tree models. Cattle herds were categorized into three risk groups based on the annual number of purchases, given that trading is considered as the main route of brucellosis introduction in cattle herds. The sensitivity in detecting the disease and the costs of the current surveillance system, which includes clinical (abortion) surveillance, programmed serological testing and introduction controls, were compared to those of 19 alternative surveillance scenarios. Surveillance costs included veterinary fees and laboratory analyses. The sensitivity over a year of the current surveillance system was predicted to be 91±7% at a design prevalence of 0.01% for a total cost of 14.9±1.8 million €. Several alternative surveillance scenarios, based on clinical surveillance and random or risk-based serological screening in a sample (20%) of the population, were predicted to be at least as sensitive but for a lower cost. Such changes would reduce whole surveillance costs by 20 to 61% annually, and the costs for farmers only would be decreased from about 12.0 million € presently to 5.3-9.0 million € (i.e. 25-56% decrease). Besides, fostering the evolution of the surveillance system in one of these directions would be in agreement with the European regulations and farmers perceptions on brucellosis risk and surveillance.

A model of coauthorship networks

Science.gov (United States)

Zhou, Guochang; Li, Jianping; Xie, Zonglin

2017-10-01

A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world property

Faulty node detection in wireless sensor networks using a recurrent neural network

Science.gov (United States)

Atiga, Jamila; Mbarki, Nour Elhouda; Ejbali, Ridha; Zaied, Mourad

2018-04-01

The wireless sensor networks (WSN) consist of a set of sensors that are more and more used in surveillance applications on a large scale in different areas: military, Environment, Health ... etc. Despite the minimization and the reduction of the manufacturing costs of the sensors, they can operate in places difficult to access without the possibility of reloading of battery, they generally have limited resources in terms of power of emission, of processing capacity, data storage and energy. These sensors can be used in a hostile environment, such as, for example, on a field of battle, in the presence of fires, floods, earthquakes. In these environments the sensors can fail, even in a normal operation. It is therefore necessary to develop algorithms tolerant and detection of defects of the nodes for the network of sensor without wires, therefore, the faults of the sensor can reduce the quality of the surveillance if they are not detected. The values that are measured by the sensors are used to estimate the state of the monitored area. We used the Non-linear Auto- Regressive with eXogeneous (NARX), the recursive architecture of the neural network, to predict the state of a node of a sensor from the previous values described by the functions of time series. The experimental results have verified that the prediction of the State is enhanced by our proposed model.

Brand marketing model on social networks

OpenAIRE

Jezukevičiūtė, Jolita; Davidavičienė, Vida

2014-01-01

Paper analyzes the brand and its marketing solutions on social networks. This analysis led to the creation of improved brand marketing model on social networks, which will contribute to the rapid and cheap organization brand recognition, increase competitive advantage and enhance consumer loyalty. Therefore, the brand and a variety of social networks are becoming a hot research area for brand marketing model on social networks. The world‘s most successful brand marketing models exploratory an...

Evaluation of outbreak detection performance using multi-stream syndromic surveillance for influenza-like illness in rural Hubei Province, China: a temporal simulation model based on healthcare-seeking behaviors.

Directory of Open Access Journals (Sweden)

Yunzhou Fan

Full Text Available BACKGROUND: Syndromic surveillance promotes the early detection of diseases outbreaks. Although syndromic surveillance has increased in developing countries, performance on outbreak detection, particularly in cases of multi-stream surveillance, has scarcely been evaluated in rural areas. OBJECTIVE: This study introduces a temporal simulation model based on healthcare-seeking behaviors to evaluate the performance of multi-stream syndromic surveillance for influenza-like illness. METHODS: Data were obtained in six towns of rural Hubei Province, China, from April 2012 to June 2013. A Susceptible-Exposed-Infectious-Recovered model generated 27 scenarios of simulated influenza A (H1N1 outbreaks, which were converted into corresponding simulated syndromic datasets through the healthcare-behaviors model. We then superimposed converted syndromic datasets onto the baselines obtained to create the testing datasets. Outbreak performance of single-stream surveillance of clinic visit, frequency of over the counter drug purchases, school absenteeism, and multi-stream surveillance of their combinations were evaluated using receiver operating characteristic curves and activity monitoring operation curves. RESULTS: In the six towns examined, clinic visit surveillance and school absenteeism surveillance exhibited superior performances of outbreak detection than over the counter drug purchase frequency surveillance; the performance of multi-stream surveillance was preferable to signal-stream surveillance, particularly at low specificity (Sp <90%. CONCLUSIONS: The temporal simulation model based on healthcare-seeking behaviors offers an accessible method for evaluating the performance of multi-stream surveillance.

Evaluation of outbreak detection performance using multi-stream syndromic surveillance for influenza-like illness in rural Hubei Province, China: a temporal simulation model based on healthcare-seeking behaviors.

Science.gov (United States)

Fan, Yunzhou; Wang, Ying; Jiang, Hongbo; Yang, Wenwen; Yu, Miao; Yan, Weirong; Diwan, Vinod K; Xu, Biao; Dong, Hengjin; Palm, Lars; Nie, Shaofa

2014-01-01

Syndromic surveillance promotes the early detection of diseases outbreaks. Although syndromic surveillance has increased in developing countries, performance on outbreak detection, particularly in cases of multi-stream surveillance, has scarcely been evaluated in rural areas. This study introduces a temporal simulation model based on healthcare-seeking behaviors to evaluate the performance of multi-stream syndromic surveillance for influenza-like illness. Data were obtained in six towns of rural Hubei Province, China, from April 2012 to June 2013. A Susceptible-Exposed-Infectious-Recovered model generated 27 scenarios of simulated influenza A (H1N1) outbreaks, which were converted into corresponding simulated syndromic datasets through the healthcare-behaviors model. We then superimposed converted syndromic datasets onto the baselines obtained to create the testing datasets. Outbreak performance of single-stream surveillance of clinic visit, frequency of over the counter drug purchases, school absenteeism, and multi-stream surveillance of their combinations were evaluated using receiver operating characteristic curves and activity monitoring operation curves. In the six towns examined, clinic visit surveillance and school absenteeism surveillance exhibited superior performances of outbreak detection than over the counter drug purchase frequency surveillance; the performance of multi-stream surveillance was preferable to signal-stream surveillance, particularly at low specificity (Sp performance of multi-stream surveillance.

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...... reporting formats, along with a tested process that facilitates the production of a wide range of analytical products for civilian, military, and hybrid intelligence environments. Readers will learn how to perform the specific actions of problem definition modeling, target network modeling......, 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...

A Complex Network Approach to Distributional Semantic Models.

Directory of Open Access Journals (Sweden)

Akira Utsumi

Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

Science.gov (United States)

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Modeling Renewable Penertration Using a Network Economic Model

Science.gov (United States)

Lamont, A.

2001-03-01

This paper evaluates the accuracy of a network economic modeling approach in designing energy systems having renewable and conventional generators. The network approach models the system as a network of processes such as demands, generators, markets, and resources. The model reaches a solution by exchanging prices and quantity information between the nodes of the system. This formulation is very flexible and takes very little time to build and modify models. This paper reports an experiment designing a system with photovoltaic and base and peak fossil generators. The level of PV penetration as a function of its price and the capacities of the fossil generators were determined using the network approach and using an exact, analytic approach. It is found that the two methods agree very closely in terms of the optimal capacities and are nearly identical in terms of annual system costs.

Stakeholder perspectives on dissemination and implementation of a prospective surveillance model of rehabilitation for breast cancer treatment.

Science.gov (United States)

Stout, Nicole L; Andrews, Kimberly; Binkley, Jill M; Schmitz, Kathryn H; Smith, Robert A

2012-04-15

The prospective surveillance model proposes a paradigm shift in the delivery of care for patients with breast cancer. The model is based on clinical research and clinical practice experience that was reviewed and discussed at a multidisciplinary meeting. The model identifies critical physical sequelae of treatment as well as timeframes for identification of and surveillance for these issues. Although the model of ongoing assessment for physical impairment and early rehabilitative intervention creates a framework for care, broad support and active dissemination among a variety of stakeholders will be required to transform patient care. Translating research findings to transform practice often occurs on a protracted timeline. The authors sought participation from a variety of stakeholder representatives throughout the process of creating this model in an effort to ensure that it reflects the realities of the patient experience and care delivery, to incorporate their input regarding the construct and viability of the model, and to potentiate effective and efficient strategies for implementation. This article summarizes comments from stakeholder representatives concerning the prospective surveillance model for rehabilitation for women treated for breast cancer. Concerns addressed include the scope of impairments included in the model, the potential creation of barriers to exercise and participation in community exercise programs, and cost and feasibility issues. Stakeholder disseminations strategies are also presented. Overall, there is recognition by the stakeholder group that this model calls attention to important unmet needs and defines a crucial opportunity to improve care for breast cancer survivors. Copyright © 2012 American Cancer Society.

Impact of clinical surveillance during a foot-and-mouth disease epidemic

DEFF Research Database (Denmark)

Hisham Beshara Halasa, Tariq; Boklund, Anette

duration, number of infected herds and the economic losses from an epidemic. The stochastic spatial simulation model DTU-DADS was enhanced to include simulation of surveillance of herds within the protection and surveillance zones and the model was used to model spread of FMD between herds. A queuing......The objectives of this study were to assess, whether the current surveillance capacity is sufficient to fulfill EU and Danish regulations to control a hypothetical foot-and-mouth disease (FMD) epidemic in Denmark, and whether enlarging the protection and/or surveillance zones could reduce epidemic...... showed that the default surveillance capacity is sufficient to survey herds within one week of the zones establishment, as the regulations demand. Extra resources for surveillance did not reduce the costs of the epidemics, but fewer resources could result in larger epidemics and costs. Furthermore...

Surveillance Jumps on the Network

Science.gov (United States)

Raths, David

2011-01-01

Internet protocol (IP) network-based cameras and digital video management software are maturing, and many issues that have surrounded them, including bandwidth, data storage, ease of use, and integration are starting to become clearer as the technology continues to evolve. Prices are going down and the number of features is going up. Many school…

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...

An evolving network model with community structure

International Nuclear Information System (INIS)

Li Chunguang; Maini, Philip K

2005-01-01

Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

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

Research on the model of home networking

Science.gov (United States)

Yun, Xiang; Feng, Xiancheng

2007-11-01

It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network

A video imaging system and related control hardware for nuclear safeguards surveillance applications

International Nuclear Information System (INIS)

Whichello, J.V.

1987-03-01

A novel video surveillance system has been developed for safeguards applications in nuclear installations. The hardware was tested at a small experimental enrichment facility located at the Lucas Heights Research Laboratories. The system uses digital video techniques to store, encode and transmit still television pictures over the public telephone network to a receiver located in the Australian Safeguards Office at Kings Cross, Sydney. A decoded, reconstructed picture is then obtained using a second video frame store. A computer-controlled video cassette recorder is used automatically to archive the surveillance pictures. The design of the surveillance system is described with examples of its operation

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

International Nuclear Information System (INIS)

Casanovas, R.; Morant, J.J.; Lopez, M.; Hernandez-Giron, I.; Batalla, E.; Salvado, M.

2011-01-01

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.

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.

The critical role of acute flaccid paralysis surveillance in the Global Polio Eradication Initiative.

Science.gov (United States)

Tangermann, Rudolf H; Lamoureux, Christine; Tallis, Graham; Goel, Ajay

2017-05-01

Acute flaccid paralysis (AFP) surveillance is a key strategy used by the Global Polio Eradication Initiative (GPEI) to measure progress towards reaching the global eradication goal. Supported by a global polio laboratory network, AFP surveillance is conducted in 179 of 194 WHO member states. Active surveillance visits to priority health facilities are used to assure all children polio laboratories. The quality of AFP surveillance is regularly monitored with standardized surveillance quality indicators. In highest risk countries and areas, the sensitivity of AFP surveillance is enhanced by environmental surveillance (testing of sewage samples). Genetic sequencing of detected poliovirus isolates yields programmatically important information on polio transmission pathways. AFP surveillance is one of the most valuable assets of the GPEI, with the potential to serve as a platform to build integrated disease surveillance systems. Continued support to maintain AFP surveillance systems will be essential, to reliably monitor the completion of global polio eradication, and to assure that a key resource for building surveillance capacity is transitioned post-eradication to support other health priorities. © The Author 2017. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Automated processing of thermal infrared images of Osservatorio Vesuviano permanent surveillance network by using Matlab code

Science.gov (United States)

Sansivero, Fabio; Vilardo, Giuseppe; Caputo, Teresa

2017-04-01

The permanent thermal infrared surveillance network of Osservatorio Vesuviano (INGV) is composed of 6 stations which acquire IR frames of fumarole fields in the Campi Flegrei caldera and inside the Vesuvius crater (Italy). The IR frames are uploaded to a dedicated server in the Surveillance Center of Osservatorio Vesuviano in order to process the infrared data and to excerpt all the information contained. In a first phase the infrared data are processed by an automated system (A.S.I.R.A. Acq- Automated System of IR Analysis and Acquisition) developed in Matlab environment and with a user-friendly graphic user interface (GUI). ASIRA daily generates time-series of residual temperature values of the maximum temperatures observed in the IR scenes after the removal of seasonal effects. These time-series are displayed in the Surveillance Room of Osservatorio Vesuviano and provide information about the evolution of shallow temperatures field of the observed areas. In particular the features of ASIRA Acq include: a) efficient quality selection of IR scenes, b) IR images co-registration in respect of a reference frame, c) seasonal correction by using a background-removal methodology, a) filing of IR matrices and of the processed data in shared archives accessible to interrogation. The daily archived records can be also processed by ASIRA Plot (Matlab code with GUI) to visualize IR data time-series and to help in evaluating inputs parameters for further data processing and analysis. Additional processing features are accomplished in a second phase by ASIRA Tools which is Matlab code with GUI developed to extract further information from the dataset in automated way. The main functions of ASIRA Tools are: a) the analysis of temperature variations of each pixel of the IR frame in a given time interval, b) the removal of seasonal effects from temperature of every pixel in the IR frames by using an analytic approach (removal of sinusoidal long term seasonal component by using a

High-Performance Computer Modeling of the Cosmos-Iridium Collision

Energy Technology Data Exchange (ETDEWEB)

Olivier, S; Cook, K; Fasenfest, B; Jefferson, D; Jiang, M; Leek, J; Levatin, J; Nikolaev, S; Pertica, A; Phillion, D; Springer, K; De Vries, W

2009-08-28

This paper describes the application of a new, integrated modeling and simulation framework, encompassing the space situational awareness (SSA) enterprise, to the recent Cosmos-Iridium collision. This framework is based on a flexible, scalable architecture to enable efficient simulation of the current SSA enterprise, and to accommodate future advancements in SSA systems. In particular, the code is designed to take advantage of massively parallel, high-performance computer systems available, for example, at Lawrence Livermore National Laboratory. We will describe the application of this framework to the recent collision of the Cosmos and Iridium satellites, including (1) detailed hydrodynamic modeling of the satellite collision and resulting debris generation, (2) orbital propagation of the simulated debris and analysis of the increased risk to other satellites (3) calculation of the radar and optical signatures of the simulated debris and modeling of debris detection with space surveillance radar and optical systems (4) determination of simulated debris orbits from modeled space surveillance observations and analysis of the resulting orbital accuracy, (5) comparison of these modeling and simulation results with Space Surveillance Network observations. We will also discuss the use of this integrated modeling and simulation framework to analyze the risks and consequences of future satellite collisions and to assess strategies for mitigating or avoiding future incidents, including the addition of new sensor systems, used in conjunction with the Space Surveillance Network, for improving space situational awareness.

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.

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

Complex networks under dynamic repair model

Science.gov (United States)

Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao

2018-01-01

Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.

Adaptive-network models of collective dynamics

Science.gov (United States)

Zschaler, G.

2012-09-01

Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

Linear approximation model network and its formation via ...

Indian Academy of Sciences (India)

To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked ...

ACP Facility Safety Surveillance System Installation

International Nuclear Information System (INIS)

You, Gil Sung; Kook, D. H.; Choung, W. M.; Ku, J. H.; Cho, I. J.; You, G. S.; Kwon, K. C.; Lee, W. K.; Lee, E. P.

2006-10-01

The Advanced spent fuel Conditioning Process is under development for effective management of spent fuel by converting UO 2 into U-metal. For demonstration of this process, α-γ type new hotcell was built in the IMEF basement. All facilities which treat radioactive materials must manage CCTV system which is under control of Health Physics department. Three main points (including hotcell rear door area) have each camera, but operators who are in charge of facility management need to check the safety of the facility immediately through the network in his office. This needs introduce additional network cameras installation and this new surveillance system is expected to update the whole safety control ability with existing system

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...

Spatial Epidemic Modelling in Social Networks

Science.gov (United States)

Simoes, Joana Margarida

2005-06-01

The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.

Optimized and Executive Survey of Physical Node Capture Attack in Wireless Sensor Network

OpenAIRE

Bhavana Butani; Piyush Kumar Shukla; Sanjay Silakari

2014-01-01

Wireless sensor networks (WSNs) are novel large-scale wireless networks that consist of distributed, self organizing, low-power, low-cost, tiny sensor devices to cooperatively collect information through infrastructure less wireless networks. These networks are envisioned to play a crucial role in variety of applications like critical military surveillance applications, forest fire monitoring, commercial applications such as building security monitoring, traffic surveillance, habitat monitori...

Modeling of fluctuating reaction networks

International Nuclear Information System (INIS)

Lipshtat, A.; Biham, O.

2004-01-01

Full Text:Various dynamical systems are organized as reaction networks, where the population size of one component affects the populations of all its neighbors. Such networks can be found in interstellar surface chemistry, cell biology, thin film growth and other systems. I cases where the populations of reactive species are large, the network can be modeled by rate equations which provide all reaction rates within mean field approximation. However, in small systems that are partitioned into sub-micron size, these populations strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations in the master equation grows exponentially with the number of reactive species, severely limiting its feasibility for complex networks. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in complex reaction networks. The method is examplified in the context of reaction network on dust grains. Its applicability for genetic networks will be discussed. 1. Efficient simulations of gas-grain chemistry in interstellar clouds. Azi Lipshtat and Ofer Biham, Phys. Rev. Lett. 93 (2004), 170601. 2. Modeling of negative autoregulated genetic networks in single cells. Azi Lipshtat, Hagai B. Perets, Nathalie Q. Balaban and Ofer Biham, Gene: evolutionary genomics (2004), In press

Semantic-based surveillance video retrieval.

Science.gov (United States)

Hu, Weiming; Xie, Dan; Fu, Zhouyu; Zeng, Wenrong; Maybank, Steve

2007-04-01

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene.

Deterministic ripple-spreading model for complex networks.

Science.gov (United States)

Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Hines, Evor L; Di Paolo, Ezequiel

2011-04-01

This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.

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.

Laboratory-based surveillance in the molecular era: the TYPENED model, a joint data-sharing platform for clinical and public health laboratories.

Science.gov (United States)

Niesters, H G; Rossen, J W; van der Avoort, H; Baas, D; Benschop, K; Claas, E C; Kroneman, A; van Maarseveen, N; Pas, S; van Pelt, W; Rahamat-Langendoen, J C; Schuurman, R; Vennema, H; Verhoef, L; Wolthers, K; Koopmans, M

2013-01-24

Laboratory-based surveillance, one of the pillars of monitoring infectious disease trends, relies on data produced in clinical and/or public health laboratories. Currently, diagnostic laboratories worldwide submit strains or samples to a relatively small number of reference laboratories for characterisation and typing. However, with the introduction of molecular diagnostic methods and sequencing in most of the larger diagnostic and university hospital centres in high-income countries, the distinction between diagnostic and reference/public health laboratory functions has become less clear-cut. Given these developments, new ways of networking and data sharing are needed. Assuming that clinical and public health laboratories may be able to use the same data for their own purposes when sequence-based testing and typing are used, we explored ways to develop a collaborative approach and a jointly owned database (TYPENED) in the Netherlands. The rationale was that sequence data - whether produced to support clinical care or for surveillance -can be aggregated to meet both needs. Here we describe the development of the TYPENED approach and supporting infrastructure, and the implementation of a pilot laboratory network sharing enterovirus sequences and metadata.

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.

An acoustical model based monitoring network

NARCIS (Netherlands)

Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der

2010-01-01

In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the

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

How to model wireless mesh networks topology

International Nuclear Information System (INIS)

Sanni, M L; Hashim, A A; Anwar, F; Ali, S; Ahmed, G S M

2013-01-01

The specification of network connectivity model or topology is the beginning of design and analysis in Computer Network researches. Wireless Mesh Networks is an autonomic network that is dynamically self-organised, self-configured while the mesh nodes establish automatic connectivity with the adjacent nodes in the relay network of wireless backbone routers. Researches in Wireless Mesh Networks range from node deployment to internetworking issues with sensor, Internet and cellular networks. These researches require modelling of relationships and interactions among nodes including technical characteristics of the links while satisfying the architectural requirements of the physical network. However, the existing topology generators model geographic topologies which constitute different architectures, thus may not be suitable in Wireless Mesh Networks scenarios. The existing methods of topology generation are explored, analysed and parameters for their characterisation are identified. Furthermore, an algorithm for the design of Wireless Mesh Networks topology based on square grid model is proposed in this paper. The performance of the topology generated is also evaluated. This research is particularly important in the generation of a close-to-real topology for ensuring relevance of design to the intended network and validity of results obtained in Wireless Mesh Networks researches

A generic flexible and robust approach for intelligent real-time video-surveillance systems

Science.gov (United States)

Desurmont, Xavier; Delaigle, Jean-Francois; Bastide, Arnaud; Macq, Benoit

2004-05-01

In this article we present a generic, flexible and robust approach for an intelligent real-time video-surveillance system. A previous version of the system was presented in [1]. The goal of these advanced tools is to provide help to operators by detecting events of interest in visual scenes and highlighting alarms and compute statistics. The proposed system is a multi-camera platform able to handle different standards of video inputs (composite, IP, IEEE1394 ) and which can basically compress (MPEG4), store and display them. This platform also integrates advanced video analysis tools, such as motion detection, segmentation, tracking and interpretation. The design of the architecture is optimised to playback, display, and process video flows in an efficient way for video-surveillance application. The implementation is distributed on a scalable computer cluster based on Linux and IP network. It relies on POSIX threads for multitasking scheduling. Data flows are transmitted between the different modules using multicast technology and under control of a TCP-based command network (e.g. for bandwidth occupation control). We report here some results and we show the potential use of such a flexible system in third generation video surveillance system. We illustrate the interest of the system in a real case study, which is the indoor surveillance.

Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china.

Science.gov (United States)

Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng

2015-01-01

Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

[Strengthen the cancer surveillance to promote cancer prevention and control in China].

Science.gov (United States)

He, J

2018-01-23

Cancer is a major chronic disease threatening the people's health in China. We reviewed the latest advances on cancer surveillance, prevention and control in our country, which may provide important clues for future cancer control. We used data from the National Central Cancer Registry, to describe and analyze the latest cancer statistics in China. We summarized updated informations on cancer control policies, conducting network, as well as programs in the country. We provided important suggestions on the future strategies of cancer prevention and control. The overall cancer burden in China has been increasing during the past decades. In 2014, there were about 3 804 000 new cancer cases and 2 296 000 cancer deaths in China. The age-standardized cancer incidence and mortality rates were 190.63/100 000 and 106.98/100 000, respectively. China has formed a comprehensive network on cancer prevention and control. Nationwide population-based cancer surveillance has been built up. The population coverage of cancer surveillance has been expanded, and the data quality has been improved. As the aging population is increasing and unhealthy life styles persist in our country, there will be an unnegligible cancer burden in China. Based on the comprehensive rationale of cancer control and prevention, National Cancer Center of China will perform its duty for future precise cancer control and prevention, based on cancer surveillance statistics.

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.

Entropy Characterization of Random Network Models

Directory of Open Access Journals (Sweden)

Pedro J. Zufiria

2017-06-01

Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.

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

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.

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....

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.

Multiplicative Attribute Graph Model of Real-World Networks

Energy Technology Data Exchange (ETDEWEB)

Kim, Myunghwan [Stanford Univ., CA (United States); Leskovec, Jure [Stanford Univ., CA (United States)

2010-10-20

Large scale real-world network data, such as social networks, Internet andWeb graphs, is ubiquitous in a variety of scientific domains. The study of such social and information networks commonly finds patterns and explain their emergence through tractable models. In most networks, especially in social networks, nodes also have a rich set of attributes (e.g., age, gender) associatedwith them. However, most of the existing network models focus only on modeling the network structure while ignoring the features of nodes in the network. Here we present a class of network models that we refer to as the Multiplicative Attribute Graphs (MAG), which naturally captures the interactions between the network structure and node attributes. We consider a model where each node has a vector of categorical features associated with it. The probability of an edge between a pair of nodes then depends on the product of individual attributeattribute similarities. The model yields itself to mathematical analysis as well as fit to real data. We derive thresholds for the connectivity, the emergence of the giant connected component, and show that the model gives rise to graphs with a constant diameter. Moreover, we analyze the degree distribution to show that the model can produce networks with either lognormal or power-law degree distribution depending on certain conditions.

Developing Personal Network Business Models

DEFF Research Database (Denmark)

Saugstrup, Dan; Henten, Anders

2006-01-01

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...

On-line surveillance system for Borssele nuclear power plant monitoring and diagnostics

International Nuclear Information System (INIS)

Tuerkcan, E.; Ciftcioglu, Oe.

1993-08-01

An operating on-line surveillance and diagnostic system is described where information processing for monitoring and fault diagnosis and plant maintenance are addressed. The surveillance system by means of its realtime multiprocessing, multitasking execution capabilities can perform plant-wide and wide-range monitoring for enhanced plant safety and operational reliability as well as enhanced maintenance. At the same time the system provides the possibilities for goal-oriented research and development such as estimation, filtering, verification and validation and neural networks. (orig./HP)

A novel Direct Small World network model

Directory of Open Access Journals (Sweden)

LIN Tao

2016-10-01

Full Text Available There is a certain degree of redundancy and low efficiency of existing computer networks.This paper presents a novel Direct Small World network model in order to optimize networks.In this model,several nodes construct a regular network.Then,randomly choose and replot some nodes to generate Direct Small World network iteratively.There is no change in average distance and clustering coefficient.However,the network performance,such as hops,is improved.The experiments prove that compared to traditional small world network,the degree,average of degree centrality and average of closeness centrality are lower in Direct Small World network.This illustrates that the nodes in Direct Small World networks are closer than Watts-Strogatz small world network model.The Direct Small World can be used not only in the communication of the community information,but also in the research of epidemics.

Reduced risk of surgical site infections through surveillance in a network

NARCIS (Netherlands)

Geubbels, Eveline L. P. E.; Nagelkerke, Nico J. D.; Mintjes-de Groot, A. Joke; Vandenbroucke-Grauls, Christina M. J. E.; Grobbee, Diederick E.; de Boer, Annette S.

2006-01-01

OBJECTIVE: To estimate the effect of multicentre surveillance for nosocomial infections on patients' risk of surgical site infection (SSI). DESIGN: Prospective multi-centre cohort study, from January 1996 to December 2000. SETTING: Acute care hospitals in The Netherlands. STUDY PARTICIPANTS: All 50

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

Science.gov (United States)

Hamdan, H. G. Muhammad; Khalifah, O. O.

2017-11-01

Object detection and tracking is a field of research that has many applications in the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT). In this paper, a traffic intensity monitoring system is implemented based on the Macroscopic Urban Traffic model is proposed using computer vision as its source. The input of this program is extracted from a traffic surveillance camera which has another program running a neural network classification which can identify and differentiate the vehicle type is implanted. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated. relevant articles in literature searches, great care should be taken in constructing both. Lastly the limitation and the future work is concluded.

Non-consensus Opinion Models on Complex Networks

Science.gov (United States)

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

2013-04-01

Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, 2009). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor W to each individual's original opinion when determining their future opinion (NCO W model). We find that as W increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, 2011), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not

PulseNet International: Vision for the implementation of whole genome sequencing (WGS) for global food-borne disease surveillance.

NARCIS (Netherlands)

Nadon, Celine; Van Walle, Ivo; Gerner-Smidt, Peter; Campos, Josefina; Chinen, Isabel; Concepcion-Acevedo, Jeniffer; Gilpin, Brent; Smith, Anthony M; Man Kam, Kai; Perez, Enrique; Trees, Eija; Kubota, Kristy; Takkinen, Johanna; Nielsen, Eva Møller; Carleton, Heather

2017-01-01

PulseNet International is a global network dedicated to laboratory-based surveillance for food-borne diseases. The network comprises the national and regional laboratory networks of Africa, Asia Pacific, Canada, Europe, Latin America and the Caribbean, the Middle East, and the United States. The

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 I M; Voss, Andreas; Wille, Jan C; Hof, Susan 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

Attaching Hollywood to a Surveillant Assemblage: Normalizing Discourses of Video Surveillance

Directory of Open Access Journals (Sweden)

Randy K Lippert

2015-10-01

Full Text Available This article examines video surveillance images in Hollywood film. It moves beyond previous accounts of video surveillance in relation to film by theoretically situating the use of these surveillance images in a broader “surveillant assemblage”. To this end, scenes from a sample of thirty-five (35 films of several genres are examined to discern dominant discourses and how they lend themselves to normalization of video surveillance. Four discourses are discovered and elaborated by providing examples from Hollywood films. While the films provide video surveillance with a positive associative association it is not without nuance and limitations. Thus, it is found that some forms of resistance to video surveillance are shown while its deterrent effect is not. It is ultimately argued that Hollywood film is becoming attached to a video surveillant assemblage discursively through these normalizing discourses as well as structurally to the extent actual video surveillance technology to produce the images is used.

Security and Privacy in Video Surveillance: Requirements and Challenges

DEFF Research Database (Denmark)

Mahmood Rajpoot, Qasim; Jensen, Christian D.

2014-01-01

observed by the system. Several techniques to protect the privacy of individuals have therefore been proposed, but very little research work has focused on the specific security requirements of video surveillance data (in transit or in storage) and on authorizing access to this data. In this paper, we...... present a general model of video surveillance systems that will help identify the major security and privacy requirements for a video surveillance system and we use this model to identify practical challenges in ensuring the security of video surveillance data in all stages (in transit and at rest). Our...... study shows a gap between the identified security requirements and the proposed security solutions where future research efforts may focus in this domain....

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

Science.gov (United States)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

Network structure exploration via Bayesian nonparametric models

International Nuclear Information System (INIS)

Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z

2015-01-01

Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)

Using Stereo Vision to Support the Automated Analysis of Surveillance Videos

Science.gov (United States)

Menze, M.; Muhle, D.

2012-07-01

Video surveillance systems are no longer a collection of independent cameras, manually controlled by human operators. Instead, smart sensor networks are developed, able to fulfil certain tasks on their own and thus supporting security personnel by automated analyses. One well-known task is the derivation of people's positions on a given ground plane from monocular video footage. An improved accuracy for the ground position as well as a more detailed representation of single salient people can be expected from a stereoscopic processing of overlapping views. Related work mostly relies on dedicated stereo devices or camera pairs with a small baseline. While this set-up is helpful for the essential step of image matching, the high accuracy potential of a wide baseline and the according good intersection geometry is not utilised. In this paper we present a stereoscopic approach, working on overlapping views of standard pan-tilt-zoom cameras which can easily be generated for arbitrary points of interest by an appropriate reconfiguration of parts of a sensor network. Experiments are conducted on realistic surveillance footage to show the potential of the suggested approach and to investigate the influence of different baselines on the quality of the derived surface model. Promising estimations of people's position and height are retrieved. Although standard matching approaches show helpful results, future work will incorporate temporal dependencies available from image sequences in order to reduce computational effort and improve the derived level of detail.

USING STEREO VISION TO SUPPORT THE AUTOMATED ANALYSIS OF SURVEILLANCE VIDEOS

Directory of Open Access Journals (Sweden)

M. Menze

2012-07-01

Full Text Available Video surveillance systems are no longer a collection of independent cameras, manually controlled by human operators. Instead, smart sensor networks are developed, able to fulfil certain tasks on their own and thus supporting security personnel by automated analyses. One well-known task is the derivation of people’s positions on a given ground plane from monocular video footage. An improved accuracy for the ground position as well as a more detailed representation of single salient people can be expected from a stereoscopic processing of overlapping views. Related work mostly relies on dedicated stereo devices or camera pairs with a small baseline. While this set-up is helpful for the essential step of image matching, the high accuracy potential of a wide baseline and the according good intersection geometry is not utilised. In this paper we present a stereoscopic approach, working on overlapping views of standard pan-tilt-zoom cameras which can easily be generated for arbitrary points of interest by an appropriate reconfiguration of parts of a sensor network. Experiments are conducted on realistic surveillance footage to show the potential of the suggested approach and to investigate the influence of different baselines on the quality of the derived surface model. Promising estimations of people’s position and height are retrieved. Although standard matching approaches show helpful results, future work will incorporate temporal dependencies available from image sequences in order to reduce computational effort and improve the derived level of detail.

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.

Modeling, robust and distributed model predictive control for freeway networks

NARCIS (Netherlands)

Liu, S.

2016-01-01

In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

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

DEFF Research Database (Denmark)

Bødker, Rene; Kristensen, Birgit; Græsbøll, Kaare

2011-01-01

local spread of exotic insect borne diseases of veterinary and human importance. R0 models for various vector borne diseases are continuously updated with spatial temperature data to quantify the present risk of autochthonous cases (R0>0) and the present risk of epidemics (R0>1) in case an infected...... surveillance to these limited periods of potential risk, thus dramatically reducing the number of samples collected and analysed. The risk estimated from the R0 modelling may be combined with the risk of introduction from neighbouring countries and trading partners to generate a truly risk based surveillance......Modelling the potential transmission intensity of insect borne diseases with climate driven R0 process models is frequently used to assess the potential for veterinary and human infections to become established in non endemic areas. Models are often based on mean temperatures of an arbitrary time...

Surveillance and Resilience in Theory and Practice

Directory of Open Access Journals (Sweden)

Charles D. Raab

2015-09-01

Full Text Available Surveillance is often used as a tool in resilience strategies towards the threat posed by terrorist attacks and other serious crime. “Resilience” is a contested term with varying and ambiguous meaning in governmental, business and social discourses, and it is not clear how it relates to other terms that characterise processes or states of being. Resilience is often assumed to have positive connotations, but critics view it with great suspicion, regarding it as a neo-liberal governmental strategy. However, we argue that surveillance, introduced in the name of greater security, may itself erode social freedoms and public goods such as privacy, paradoxically requiring societal resilience, whether precautionary or in mitigation of the harms it causes to the public goods of free societies. This article develops new models and extends existing ones to describe resilience processes unfolding over time and in anticipation of, or in reaction to, adversities of different kinds and severity, and explores resilience both on the plane of abstract analysis and in the context of societal responses to mass surveillance. The article thus focuses upon surveillance as a special field for conceptual analysis and modelling of situations, and for evaluating contemporary developments in “surveillance societies”.

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.

Tool wear modeling using abductive networks

Science.gov (United States)

Masory, Oren

1992-09-01

A tool wear model based on Abductive Networks, which consists of a network of `polynomial' nodes, is described. The model relates the cutting parameters, components of the cutting force, and machining time to flank wear. Thus real time measurements of the cutting force can be used to monitor the machining process. The model is obtained by a training process in which the connectivity between the network's nodes and the polynomial coefficients of each node are determined by optimizing a performance criteria. Actual wear measurements of coated and uncoated carbide inserts were used for training and evaluating the established model.

[Winter surveillance of cold exposure effects on health among the homeless population in the Paris area: data from the Coordinated Health Surveillance of Emergency Department network (Organisation de la surveillance coordonnée des urgences [Oscour(®)])].

Science.gov (United States)

Rouquette, A; Mandereau-Bruno, L; Baffert, E; Laaidi, K; Josseran, L; Isnard, H

2011-12-01

A program for helping homeless individuals in winter is implemented from November 1(st) to March 31(st) each year in France. Its aim is to prevent morbidity and mortality in this population during cold spells and periods of severe cold. A health surveillance system of the homeless population in the Paris area has been proposed to evaluate the effectiveness of the program and to alert decision-makers if an unusual increase in cold-weather effects is observed. The goal of this study was the creation of an indicator for the proposed surveillance system based on emergency department activity in the Paris area (Oscour(®) Network - Organisation de la surveillance coordonnée des urgences). The winter 2007-2008 computer medical files of 11 emergency departments in the Paris area were examined to confirm diagnosis and ascertain patient-homelessness for each patient visit which was selected from the Oscour(®) database by the patient chief-complaint or diagnosis code referring to hypothermia or frostbites. The proposed indicator is based on the maximization of three criteria: the positive predictive value, the proportion of people identified as being homeless and the number of emergency department visits. A Shewhart control chart was applied to the indicator for the four winters between 2005 and 2009 in the Paris area. Values beyond the statistical threshold would indicate a need for an adjustment to the program strategy. Two hundred and sixteen medical files were analyzed. An indicator was created, "number of emergency department visits of 15 to 69-years-old persons with chief-complaint or diagnosis code referring to hypothermia". It had a positive predictive value estimated near 85 % and identified 61.7 % people as being homeless. In the winter of 2008-2009, the statistical threshold was reached in December during the first cold spell, and again at the beginning of January during a period of severe cold. Our results support the use of this health indicator

Description and implementation of a surveillance network for bluetongue in the Balkans and in adjoining areas of south-eastern Europe.

Science.gov (United States)

Dall'Acqua, F; Paladini, C; Meiswinkel, R; Savini, L; Calistri, P

2006-01-01

During the recent severe outbreaks of bluetongue (BT) in the Mediterranean Basin, the BT virus (BTV) spread beyond its historical limits into the Balkan region. One of the primary impacts of BT is the cessation in livestock trade which can have severe economic and social consequences. The authors briefly describe the development of the collaborative East-BTnet programme which aims to assist all affected and at-risk Balkan states and adjoining countries in the management of BT, and in the development of individual national surveillance systems. The beneficiary countries involved, and led by the World organisation for animal health (Office International des Epizooties) Collaborating Centre for veterinary training, epidemiology, food safety and animal welfare of the Istituto Zooprofilattico dell'Abruzzo e del Molise 'G. Caporale' in collaboration with the Institute for the Protection and the Security of the Citizen, the European Commission Joint Research Centre (IPSC-JRC), were Albania, Bosnia-Herzegovina, Bulgaria, Croatia, Cyprus, the Former Yugoslavia Republic of Macedonia, Kosovo, Malta, Romania, Serbia and Montenegro, Slovenia and Turkey. A regional web-based surveillance network is a valuable tool for controlling and managing transboundary animal diseases such as BT. Its implementation in the Balkan region and in adjoining areas of south-eastern Europe is described and discussed.

Biological transportation networks: Modeling and simulation

KAUST Repository

Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.

2015-01-01

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

Information and communication technology in disease surveillance, India: a case study

Directory of Open Access Journals (Sweden)

Krishnan Sampath K

2010-12-01

Full Text Available Abstract India has made appreciable progress and continues to demonstrate a strong commitment for establishing and operating a disease surveillance programme responsive to the requirements of the International Health Regulations (IHR[2005]. Within five years of its launch, India has effectively used modern information and communication technology for collection, storage, transmission and management of data related to disease surveillance and effective response. Terrestrial and/or satellite based linkages are being established within all states, districts, state-run medical colleges, infectious disease hospitals, and public health laboratories. This network enables speedy data transfer, video conferencing, training and e-learning for outbreaks and programme monitoring. A 24x7 call centre is in operation to receive disease alerts. To complement these efforts, a media scanning and verification cell functions to receive reports of early warning signals. During the 2009 H1N1 outbreak, the usefulness of the information and communication technology (ICT network was well appreciated. India is using ICT as part of its Integrated Disease Surveillance Project (IDSP to help overcome the challenges in further expansion in hard-to-reach populations, to increase the involvement of the private sector, and to increase the use of other modes of communication like e-mail and voicemail.

Information and communication technology in disease surveillance, India: a case study.

Science.gov (United States)

Kant, Lalit; Krishnan, Sampath K

2010-12-03

India has made appreciable progress and continues to demonstrate a strong commitment for establishing and operating a disease surveillance programme responsive to the requirements of the International Health Regulations (IHR[2005]). Within five years of its launch, India has effectively used modern information and communication technology for collection, storage, transmission and management of data related to disease surveillance and effective response. Terrestrial and/or satellite based linkages are being established within all states, districts, state-run medical colleges, infectious disease hospitals, and public health laboratories. This network enables speedy data transfer, video conferencing, training and e-learning for outbreaks and programme monitoring. A 24x7 call centre is in operation to receive disease alerts. To complement these efforts, a media scanning and verification cell functions to receive reports of early warning signals. During the 2009 H1N1 outbreak, the usefulness of the information and communication technology (ICT) network was well appreciated. India is using ICT as part of its Integrated Disease Surveillance Project (IDSP) to help overcome the challenges in further expansion in hard-to-reach populations, to increase the involvement of the private sector, and to increase the use of other modes of communication like e-mail and voicemail.

Synergistic effects in threshold models on networks

Science.gov (United States)

Juul, Jonas S.; Porter, Mason A.

2018-01-01

Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

Modeling geomagnetic induced currents in Australian power networks

Science.gov (United States)

Marshall, R. A.; Kelly, A.; Van Der Walt, T.; Honecker, A.; Ong, C.; Mikkelsen, D.; Spierings, A.; Ivanovich, G.; Yoshikawa, A.

2017-07-01

Geomagnetic induced currents (GICs) have been considered an issue for high-latitude power networks for some decades. More recently, GICs have been observed and studied in power networks located in lower latitude regions. This paper presents the results of a model aimed at predicting and understanding the impact of geomagnetic storms on power networks in Australia, with particular focus on the Queensland and Tasmanian networks. The model incorporates a "geoelectric field" determined using a plane wave magnetic field incident on a uniform conducting Earth, and the network model developed by Lehtinen and Pirjola (1985). Model results for two intense geomagnetic storms of solar cycle 24 are compared with transformer neutral monitors at three locations within the Queensland network and one location within the Tasmanian network. The model is then used to assess the impacts of the superintense geomagnetic storm of 29-31 October 2003 on the flow of GICs within these networks. The model results show good correlation with the observations with coefficients ranging from 0.73 to 0.96 across the observing sites. For Queensland, modeled GIC magnitudes during the superstorm of 29-31 October 2003 exceed 40 A with the larger GICs occurring in the south-east section of the network. Modeled GICs in Tasmania for the same storm do not exceed 30 A. The larger distance spans and general east-west alignment of the southern section of the Queensland network, in conjunction with some relatively low branch resistance values, result in larger modeled GICs despite Queensland being a lower latitude network than Tasmania.

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.

Modeling Distillation Column Using ARX Model Structure and Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Reza Pirmoradi

2012-04-01

Full Text Available Distillation is a complex and highly nonlinear industrial process. In general it is not always possible to obtain accurate first principles models for high-purity distillation columns. On the other hand the development of first principles models is usually time consuming and expensive. To overcome these problems, empirical models such as neural networks can be used. One major drawback of empirical models is that the prediction is valid only inside the data domain that is sufficiently covered by measurement data. Modeling distillation columns by means of neural networks is reported in literature by using recursive networks. The recursive networks are proper for modeling purpose, but such models have the problems of high complexity and high computational cost. The objective of this paper is to propose a simple and reliable model for distillation column. The proposed model uses feed forward neural networks which results in a simple model with less parameters and faster training time. Simulation results demonstrate that predictions of the proposed model in all regions are close to outputs of the dynamic model and the error in negligible. This implies that the model is reliable in all regions.

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

International comparison of results of infection surveillance: The Netherlands versus Belgium

NARCIS (Netherlands)

Mertens, R.; van den Berg, J. M.; Veerman-Brenzikofer, M. L.; Kurz, X.; Jans, B.; Klazinga, N.

1994-01-01

To explore the potential benefit of comparing results from two national surveillance networks. Two prospective multicenter cohort studies of surgical wound infections (SWI). Thirty-five and 62 acute-care hospitals in The Netherlands (NL) and Belgium (B), respectively, from October 1, 1991, to June

Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks.

Directory of Open Access Journals (Sweden)

Mac Brown

Full Text Available Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

Privacy enabling technology for video surveillance

Science.gov (United States)

Dufaux, Frédéric; Ouaret, Mourad; Abdeljaoued, Yousri; Navarro, Alfonso; Vergnenègre, Fabrice; Ebrahimi, Touradj

2006-05-01

In this paper, we address the problem privacy in video surveillance. We propose an efficient solution based on transformdomain scrambling of regions of interest in a video sequence. More specifically, the sign of selected transform coefficients is flipped during encoding. We address more specifically the case of Motion JPEG 2000. Simulation results show that the technique can be successfully applied to conceal information in regions of interest in the scene while providing with a good level of security. Furthermore, the scrambling is flexible and allows adjusting the amount of distortion introduced. This is achieved with a small impact on coding performance and negligible computational complexity increase. In the proposed video surveillance system, heterogeneous clients can remotely access the system through the Internet or 2G/3G mobile phone network. Thanks to the inherently scalable Motion JPEG 2000 codestream, the server is able to adapt the resolution and bandwidth of the delivered video depending on the usage environment of the client.

Nonparametric Bayesian Modeling of Complex Networks

DEFF Research Database (Denmark)

Schmidt, Mikkel Nørgaard; Mørup, Morten

2013-01-01

an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......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...

Modelling and designing electric energy networks

International Nuclear Information System (INIS)

Retiere, N.

2003-11-01

The author gives an overview of his research works in the field of electric network modelling. After a brief overview of technological evolutions from the telegraph to the all-electric fly-by-wire aircraft, he reports and describes various works dealing with a simplified modelling of electric systems and with fractal simulation. Then, he outlines the challenges for the design of electric networks, proposes a design process, gives an overview of various design models, methods and tools, and reports an application in the design of electric networks for future jumbo jets

A global airport-based risk model for the spread of dengue infection via the air transport network.

Directory of Open Access Journals (Sweden)

Lauren Gardner

Full Text Available The number of travel-acquired dengue infections has seen a consistent global rise over the past decade. An increased volume of international passenger air traffic originating from regions with endemic dengue has contributed to a rise in the number of dengue cases in both areas of endemicity and elsewhere. This paper reports results from a network-based risk assessment model which uses international passenger travel volumes, travel routes, travel distances, regional populations, and predictive species distribution models (for the two vector species, Aedes aegypti and Aedes albopictus to quantify the relative risk posed by each airport in importing passengers with travel-acquired dengue infections. Two risk attributes are evaluated: (i the risk posed by through traffic at each stopover airport and (ii the risk posed by incoming travelers to each destination airport. The model results prioritize optimal locations (i.e., airports for targeted dengue surveillance. The model is easily extendible to other vector-borne diseases.

Economic Feasibility of a Siderostat-fed Liquid Mirror Telescope for Surveillance of Space

Science.gov (United States)

2015-04-01

by the Minister of National Defence, 2015 © Sa Majesté la Reine (en droit du Canada), telle que réprésentée par le ministre de la Défense nationale...Forces (CAF) conducts Surveillance of Space (SofS) in con- junction with international partners, primarily the United States. The constantly increasing...Surveillance Network (SSN) maintains a public catalog of almost 90001 RSOs [1], ranging in size down to about 10 cm in Low Earth Orbit (LEO) [2]. The

Modelling traffic congestion using queuing networks

Indian Academy of Sciences (India)

Flow-density curves; uninterrupted traffic; Jackson networks. ... ness - also suffer from a big handicap vis-a-vis the Indian scenario: most of these models do .... more well-known queuing network models and onsite data, a more exact Road Cell ...

Fixed SMRF Sensor Network Application Concepts

NARCIS (Netherlands)

Wit, J.J.M. de; Rossum, W.L. van; Smits, F.M.A.; Theije, P.A.M. de; Monni, S.; Huizing, A.G.

2010-01-01

Advantages of scalable multifunction RF (SMRF) sensors and networked operation of sensors are well-known. Some advantages are surveillance persistence, multipath resistance, and interference resistance. The particular benefits of applying multifunction RF sensors in a network still need to be

Using Acute Flaccid Paralysis Surveillance as a Platform for Vaccine-Preventable Disease Surveillance.

Science.gov (United States)

Wassilak, Steven G F; Williams, Cheryl L; Murrill, Christopher S; Dahl, Benjamin A; Ohuabunwo, Chima; Tangermann, Rudolf H

2017-07-01

Surveillance for acute flaccid paralysis (AFP) is a fundamental cornerstone of the global polio eradication initiative (GPEI). Active surveillance (with visits to health facilities) is a critical strategy of AFP surveillance systems for highly sensitive and timely detection of cases. Because of the extensive resources devoted to AFP surveillance, multiple opportunities exist for additional diseases to be added using GPEI assets, particularly because there is generally 1 district officer responsible for all disease surveillance. For this reason, integrated surveillance has become a standard practice in many countries, ranging from adding surveillance for measles and rubella to integrated disease surveillance for outbreak-prone diseases (integrated disease surveillance and response). This report outlines the current level of disease surveillance integration in 3 countries (Nepal, India, and Nigeria) and proposes that resources continue for long-term maintenance in resource-poor countries of AFP surveillance as a platform for surveillance of vaccine-preventable diseases and other outbreak-prone diseases. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.

Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China

Directory of Open Access Journals (Sweden)

Ruijing Gan

2015-01-01

Full Text Available Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM and back propagation artificial neural networks (BP-ANN to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method’s feasibility. The results showed that the proposal method has advantages over GM (1, 1 and GM (2, 1 in all the evaluation indexes.

Recurrent neural network based hybrid model for reconstructing gene regulatory network.

Science.gov (United States)

Raza, Khalid; Alam, Mansaf

2016-10-01

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Redefining syndromic surveillance

Directory of Open Access Journals (Sweden)

Rebecca Katz

2011-12-01

Full Text Available With growing concerns about international spread of disease and expanding use of early disease detection surveillance methods, the field of syndromic surveillance has received increased attention over the last decade. The purpose of this article is to clarify the various meanings that have been assigned to the term syndromic surveillance and to propose a refined categorization of the characteristics of these systems. Existing literature and conference proceedings were examined on syndromic surveillance from 1998 to 2010, focusing on low- and middle-income settings. Based on the 36 unique definitions of syndromic surveillance found in the literature, five commonly accepted principles of syndromic surveillance systems were identified, as well as two fundamental categories: specific and non-specific disease detection. Ultimately, the proposed categorization of syndromic surveillance distinguishes between systems that focus on detecting defined syndromes or outcomes of interest and those that aim to uncover non-specific trends that suggest an outbreak may be occurring. By providing an accurate and comprehensive picture of this field’s capabilities, and differentiating among system types, a unified understanding of the syndromic surveillance field can be developed, encouraging the adoption, investment in, and implementation of these systems in settings that need bolstered surveillance capacity, particularly low- and middle-income countries.

Modeling, Optimization & Control of Hydraulic Networks

DEFF Research Database (Denmark)

Tahavori, Maryamsadat

2014-01-01

. The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used......Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... 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...

A random spatial network model based on elementary postulates

Science.gov (United States)

Karlinger, Michael R.; Troutman, Brent M.

1989-01-01

A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

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.

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)

Compact and maintenance-free radio probes for environmental surveillance of the gamma dose rate

International Nuclear Information System (INIS)

Genrich, V.

1998-01-01

The author reports on his experience with the operation of radio data networks for the continuous observation of the gamma dose rate in nuclear installations. Practically at every location (within) the installation the hermetically sealed probes can record the measurement values. Moreover, the probes have proved successful in environmental surveillance where they typically work in the form of measurement rings in 10 to 30 km distance. All measurement data are organized in the form of a data base. They can be disposed of in the form of an SQL-server in the computer network (LAN) of the power plant or the institution in charge of environmental surveillance. In comparison to conventional, e.g. cable-bound measurement networks with the new radio transmission technology there are numerous advantages: - minimal cost for projection - minimal cost for installation due to simple fixing - quasi-mobile use with highest possible flexibility - maintenance-free operation and high degree of operating reliability. (orig.) [de

The force awakens: Birth of national surveillance state

Directory of Open Access Journals (Sweden)

Avramović Dragutin S.

2016-01-01

Full Text Available University of Yale professor of Constitutional Law Jack Balkin convincingly declared emergence of a new sort of the state called 'national surveillance state'. Although the very name announces quite clearly an Orwellian scenario, Balkin is in doubt which path that kind of state will follow - the authoritarian or the democratic one. Nevertheless quite optimistic approaches of J. Balkin, O. Kerr and other authors considering democratic type of the national surveillance state the author of this paper holds the opposite opinion. Taking as a starting point an anthropological feature that 'passion warps the rule even of the best men' (Aristotle, 1287a, the author doubts in democratic character of the national surveillance state. He criticizes Balkin's explanations that the problem could be solved by 'control of the controllers' or 'observation of the observers'. One who has supreme right to dispose over information (no matter which state body could it be, can always, or most often will abuse that right having in mind some interest, particularly when the interest can be vested within socially and politically acceptable tune, like the fight against terrorism, national interest or similar. Proper and firm normative framework could contribute to successful balance between privacy and security of citizens and eventually diminish potential misuse of surveillance of citizens. However, many people provide information for the 'Big Brother' by sacrificing their own privacy voluntarily, forming their own 'digital database' through different social networking. Balkin's generous but native belief that democratic national surveillance state is possible could hardly survive the test of the coming time and challenges. It is quite evident that, particularly the most developed states, fairly fast incline towards repressive national surveillance state. Maybe the process could be only decelerated by activities of NGOs, by developing awareness of every single citizen of

Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

Directory of Open Access Journals (Sweden)

Lan Liu

2017-01-01

Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when qmodel is effective, and the results may help to decide the SDN control strategy to defend against network malware and provide a theoretical basis to reduce and prevent network security incidents.

Creating, generating and comparing random network models with NetworkRandomizer.

Science.gov (United States)

Tosadori, Gabriele; Bestvina, Ivan; Spoto, Fausto; Laudanna, Carlo; Scardoni, Giovanni

2016-01-01

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

The plays and arts of surveillance: studying surveillance as entertainment

NARCIS (Netherlands)

Albrechtslund, Anders; Dubbeld, L.

2006-01-01

This paper suggests a direction in the development of Surveillance Studies that goes beyond current attention for the caring, productive and enabling aspects of surveillance practices. That is, surveillance could be considered not just as positively protective, but even as a comical, playful,

Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

Directory of Open Access Journals (Sweden)

Liang Jinghang

2012-08-01

Full Text Available Abstract Background Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs. As a logical model, probabilistic Boolean networks (PBNs consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n or O(nN2n for a sparse matrix. Results This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN. An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n, where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational efficiency of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a

Infectious diseases: Surveillance, genetic modification and simulation

Science.gov (United States)

Koh, H. L.; Teh, S.Y.; De Angelis, D. L.; Jiang, J.

2011-01-01

Infectious diseases such as influenza and dengue have the potential of becoming a worldwide pandemic that may exert immense pressures on existing medical infrastructures. Careful surveillance of these diseases, supported by consistent model simulations, provides a means for tracking the disease evolution. The integrated surveillance and simulation program is essential in devising effective early warning systems and in implementing efficient emergency preparedness and control measures. This paper presents a summary of simulation analysis on influenza A (H1N1) 2009 in Malaysia. This simulation analysis provides insightful lessons regarding how disease surveillance and simulation should be performed in the future. This paper briefly discusses the controversy over the experimental field release of genetically modified (GM) Aedes aegypti mosquito in Malaysia. Model simulations indicate that the proposed release of GM mosquitoes is neither a viable nor a sustainable control strategy. ?? 2011 WIT Press.

Network interconnections: an architectural reference model

NARCIS (Netherlands)

Butscher, B.; Lenzini, L.; Morling, R.; Vissers, C.A.; Popescu-Zeletin, R.; van Sinderen, Marten J.; Heger, D.; Krueger, G.; Spaniol, O.; Zorn, W.

1985-01-01

One of the major problems in understanding the different approaches in interconnecting networks of different technologies is the lack of reference to a general model. The paper develops the rationales for a reference model of network interconnection and focuses on the architectural implications for

LESSONS LEARNED ABOUT PUBLIC HEALTH FROM ONLINE CROWD SURVEILLANCE.

Science.gov (United States)

Hill, Shawndra; Merchant, Raina; Ungar, Lyle

2013-09-10

The Internet has forever changed the way people access information and make decisions about their healthcare needs. Patients now share information about their health at unprecedented rates on social networking sites such as Twitter and Facebook and on medical discussion boards. In addition to explicitly shared information about health conditions through posts, patients reveal data on their inner fears and desires about health when searching for health-related keywords on search engines. Data are also generated by the use of mobile phone applications that track users' health behaviors (e.g., eating and exercise habits) as well as give medical advice. The data generated through these applications are mined and repackaged by surveillance systems developed by academics, companies, and governments alike to provide insight to patients and healthcare providers for medical decisions. Until recently, most Internet research in public health has been surveillance focused or monitoring health behaviors. Only recently have researchers used and interacted with the crowd to ask questions and collect health-related data. In the future, we expect to move from this surveillance focus to the "ideal" of Internet-based patient-level interventions where healthcare providers help patients change their health behaviors. In this article, we highlight the results of our prior research on crowd surveillance and make suggestions for the future.

A general evolving model for growing bipartite networks

International Nuclear Information System (INIS)

Tian, Lixin; He, Yinghuan; Liu, Haijun; Du, Ruijin

2012-01-01

In this Letter, we propose and study an inner evolving bipartite network model. Significantly, we prove that the degree distribution of two different kinds of nodes both obey power-law form with adjustable exponents. Furthermore, the joint degree distribution of any two nodes for bipartite networks model is calculated analytically by the mean-field method. The result displays that such bipartite networks are nearly uncorrelated networks, which is different from one-mode networks. Numerical simulations and empirical results are given to verify the theoretical results. -- Highlights: ► We proposed a general evolving bipartite network model which was based on priority connection, reconnection and breaking edges. ► We prove that the degree distribution of two different kinds of nodes both obey power-law form with adjustable exponents. ► The joint degree distribution of any two nodes for bipartite networks model is calculated analytically by the mean-field method. ► The result displays that such bipartite networks are nearly uncorrelated networks, which is different from one-mode networks.

Model of community emergence in weighted social networks

Science.gov (United States)

Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.

2009-04-01

Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.

The surveillance of the electricity wholesale market and emission trading market; Die Ueberwachung von Stromgrosshandelsmarkt und Emissionshandelsmarkt

Energy Technology Data Exchange (ETDEWEB)

Luedemann, Volker [Hochschule Osnabrueck (Germany). Forschungszentrum Energiewirtschaft/Energierecht (fee); Hochschule Osnabrueck (Germany). Wirtschafts- und Wettbewerbsrecht; Konar, Selma [Sozietaet Becker Buettner Held, Muenchen (Germany)

2015-05-15

The Regulation on Wholesale Market Integrity and Transparency (REMIT) and the German Law on the Establishment of a Market Transparency Office for Wholesale Trade in Electricity and Gas (MTS-G) have fundamentally changed the surveillance of electricity wholesale trade in Germany. From now on the Federal Network Agency and the Federal Cartel Office will be jointly responsible for monitoring the electricity wholesale trade for suspicious market phenomena and abusive behaviour. The REMIT specifies that the electricity trade must be surveilled ''with due consideration to interactions'' with the emission trade system. However, occurrences observed in recent years have shown that the emission trading system is in need of reform. This has also been recognised and has prompted extensive corrective action by the regulatory authorities of the European Union. These changes have yet to be transposed into the national surveillance regimes. The present article explains why the new role accorded to the Federal Network Agency under the REMIT fails to eliminate the structural shortcomings of the old surveillance system. At least the decision to put the collection and evaluation of data exclusively in the hands of the market transparency office and the cooperation this will prompt between the supervisory authorities responsible will make the task of surveilling the energy wholesale trading market a lot easier for the authorities. The energy transition and its exigencies will yet lead to further changes in the market and its surveillance regime.

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.

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

R and D study on on-line criticality surveillance system (V)

International Nuclear Information System (INIS)

Yamada, Sumasu

2001-02-01

In view of necessity and importance of criticality surveillance systems for ensuring the safety of nuclear fuel manufacturing and reprocessing plants, 5-year basic studies and 4 year R and D studies on an on-line criticality surveillance system were carried out since 1991. This report is a summary of these series of studies. Noticing that the signal from a neutron detector is random in principle, these series of studies aimed to accumulate knowledge for developing an inexpensive criticality surveillance system with quick response based on the Auto-Regressive Moving Average (ARMA) model identification algorithm. During five-year basic studies on criticality surveillance system since 1991, we obtained knowledge required for developing a criticality surveillance system based on the ARMA model identification algorithm through 1) studies on recursive ARMA model identification algorithms most appropriate for estimating subcriticality form time series data under a steady state condition, 2) studies on pre-processing of signal from neutron detectors, 3) developing a new recursive ARMA model identification algorithm with small time delay to estimate time-dependent subcriticality, 4) proposing a basic concept for the elements required for an on-line criticality surveillance system, and 5) numerical analysis of data from the DCA experiments. During next four-year R and D studies on a criticality surveillance system since 1996, we 1) proposed modules required for a no-line criticality surveillance system, 2) revealed effectiveness of a adaptive digital filter (ADF) algorithm, as an important redundancy to the recursive ARMA model identification algorithm to be used in the signal processing module through numerical analysis of real data, 3) proposed a module of the Feynman-α method over γ ray signal and a fast signal processing module for γ ray signal, 4) developed a line-noise removal filter(Notch filter) and revealed its effectiveness for the DCA data corrupted with power

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.

Ripple-Spreading Network Model Optimization by Genetic Algorithm

Directory of Open Access Journals (Sweden)

Xiao-Bing Hu

2013-01-01

Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.

Constitutive modelling of composite biopolymer networks.

Science.gov (United States)

Fallqvist, B; Kroon, M

2016-04-21

The mechanical behaviour of biopolymer networks is to a large extent determined at a microstructural level where the characteristics of individual filaments and the interactions between them determine the response at a macroscopic level. Phenomena such as viscoelasticity and strain-hardening followed by strain-softening are observed experimentally in these networks, often due to microstructural changes (such as filament sliding, rupture and cross-link debonding). Further, composite structures can also be formed with vastly different mechanical properties as compared to the individual networks. In this present paper, we present a constitutive model presented in a continuum framework aimed at capturing these effects. Special care is taken to formulate thermodynamically consistent evolution laws for dissipative effects. This model, incorporating possible anisotropic network properties, is based on a strain energy function, split into an isochoric and a volumetric part. Generalisation to three dimensions is performed by numerical integration over the unit sphere. Model predictions indicate that the constitutive model is well able to predict the elastic and viscoelastic response of biological networks, and to an extent also composite structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessment of the accuracy and consistency in the application of standardized surveillance definitions: A summary of the American Journal of Infection Control and National Healthcare Safety Network case studies, 2010-2016.

Science.gov (United States)

Wright, Marc-Oliver; Allen-Bridson, Katherine; Hebden, Joan N

2017-06-01

The Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) surveillance definitions are the most widely used criteria for health care-associated infection (HAI) surveillance. NHSN participants agree to conduct surveillance in accordance with the NHSN protocol and criteria. To assess the application of these standardized surveillance specifications and offer infection preventionists (IPs) opportunities for ongoing education, a series of case studies, with questions related to NHSN definitions and criteria were published. Beginning in 2010, case studies with multiple-choice questions based on standard surveillance criteria and protocols were written and published in the American Journal of Infection Control with a link to an online survey. Participants anonymously submitted their responses before receiving the correct answers. The 22 case studies had 7,950 respondents who provided 27,790 responses to 75 questions during the first 6 years. Correct responses were selected 62.5% of the time (17,376 out of 27,290), but ranged widely (16%-87%). In a subset analysis, 93% of participants self-identified as IPs (3,387 out of 3,640), 4.5% were public health professionals (163 out of 3,640), and 2.5% were physicians (90 out of 3,640). IPs responded correctly (62%) more often than physicians (55%) (P = .006). Among a cohort of voluntary participants, accurate application of surveillance criteria to case studies was suboptimal, highlighting the need for continuing education, competency development, and auditing. Copyright © 2017 Association for Professionals in Infection Control and Epidemiology, Inc. All rights reserved.

The use of mobile phones for demographic surveillance of mobile pastoralists and their animals in Chad: proof of principle.

Science.gov (United States)

Jean-Richard, Vreni; Crump, Lisa; Moto Daugla, Doumagoum; Hattendorf, Jan; Schelling, Esther; Zinsstag, Jakob

2014-01-01

Demographic information is foundational for the planning and management of social programmes, in particular health services. The existing INDEPTH network surveillance sites are limited to coverage of sedentary populations. Including mobile populations in this approach would be expensive, time consuming and possibly low in accuracy. Very little is known about the demography of mobile pastoralists and their animals, so innovative approaches are urgently needed. To test and evaluate a mobile demographic surveillance system for mobile pastoralist households, including livestock herds, using mobile phones. Mobile pastoralist camps were monitored (10 for 12 months and 10 for 18 months) using biweekly mobile phone calls with camp leaders and their wives to conduct interviews about the households and livestock. The collected information was validated through personal visits, GPS data and a livestock demographic model. The study showed the feasibility of mobile phone surveillance for mobile pastoralist camps, providing usable, valid information on human and livestock population structures, pregnancy outcomes and herd dynamics, as well as migration patterns. The approach was low-cost and applicable with the existing local resources. Demographic surveillance in mobile populations is feasible using mobile phones. Expansion of the small-scale system into a full mobile demographic surveillance system is warranted and would likely lead to improved planning and provision of human and animal health care.

Bayesian latent feature modeling for modeling bipartite networks with overlapping groups

DEFF Research Database (Denmark)

Jørgensen, Philip H.; Mørup, Morten; Schmidt, Mikkel Nørgaard

2016-01-01

Bi-partite networks are commonly modelled using latent class or latent feature models. Whereas the existing latent class models admit marginalization of parameters specifying the strength of interaction between groups, existing latent feature models do not admit analytical marginalization...... by the notion of community structure such that the edge density within groups is higher than between groups. Our model further assumes that entities can have different propensities of generating links in one of the modes. The proposed framework is contrasted on both synthetic and real bi-partite networks...... feature representations in bipartite networks provides a new framework for accounting for structure in bi-partite networks using binary latent feature representations providing interpretable representations that well characterize structure as quantified by link prediction....

Surveillance and Critical Theory

Directory of Open Access Journals (Sweden)

Christian Fuchs

2015-09-01

Full Text Available In this comment, the author reflects on surveillance from a critical theory approach, his involvement in surveillance research and projects, and the status of the study of surveillance. The comment ascertains a lack of critical thinking about surveillance, questions the existence of something called “surveillance studies” as opposed to a critical theory of society, and reflects on issues such as Edward Snowden’s revelations, and Foucault and Marx in the context of surveillance.

Exploring the Utility of Model-based Meteorology Data for Heat-Related Health Research and Surveillance

Science.gov (United States)

Vaidyanathan, A.; Yip, F.

2017-12-01

Context: Studies that have explored the impacts of environmental exposure on human health have mostly relied on data from weather stations, which can be limited in geographic scope. For this assessment, we: (1) evaluated the performance of the meteorological data from the North American Land Data Assimilation System Phase 2 (NLDAS) model with measurements from weather stations for public health and specifically for CDC's Environmental Public Health Tracking Program, and (2) conducted a health assessment to explore the relationship between heat exposure and mortality, and examined region-specific differences in heat-mortality (H-M) relationships when using model-based estimates in place of measurements from weather stations.Methods: Meteorological data from the NLDAS Phase 2 model was evaluated against measurements from weather stations. A time-series analysis was conducted, using both station- and model-based data, to generate H-M relationships for counties in the U.S. The county-specific risk information was pooled to characterize regional relationships for both station- and model-based data, which were then compared to identify degrees of overlap and discrepancies between results generated using the two data sources. Results: NLDAS-based heat metrics were in agreement with those generated using weather station data. In general, the H-M relationship tended to be non-linear and varied by region, particularly the heat index value at which the health risks become positively significant. However, there was a high degree of overlap between region-specific H-M relationships generated from weather stations and the NLDAS model.Interpretation: Heat metrics from NLDAS model are available for all counties in the coterminous U.S. from 1979-2015. These data can facilitate health research and surveillance activities exploring health impacts associated with long-term heat exposures at finer geographic scales.Conclusion: High spatiotemporal coverage of environmental health data

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...

Global surveillance of emerging diseases: the ProMED-mail perspective

Directory of Open Access Journals (Sweden)

John P. Woodall

2001-01-01

Full Text Available The Internet is changing the way global disease surveillance is conducted. Countries and international organizations are increasingly placing their outbreak reports on the Internet, which speeds up distribution and therefore prevention and control. The World Health Organization (WHO has recognized the value of nongovernmental organizations and the media in reporting outbreaks, which it then attempts to verify through its country offices. However, WHO and other official sources are constrained in their reporting by the need for bureaucratic clearance. ProMED-mail has no such constraints, and posts outbreak reports 7 days a week. It is moderated by infectious disease specialists who add relevant comments. Thus, ProMED-mail complements official sources and provides early warning of outbreaks. Its network is more than 20,000 people in over 150 countries, who place their computers and time at the network's disposal and report on outbreaks of which they have knowledge. Regions and countries could benefit from adopting the ProMED-mail approach to complement their own disease surveillance systems.

Mathematical model for spreading dynamics of social network worms

International Nuclear Information System (INIS)

Sun, Xin; Liu, Yan-Heng; Han, Jia-Wei; Liu, Xue-Jie; Li, Bin; Li, Jin

2012-01-01

In this paper, a mathematical model for social network worm spreading is presented from the viewpoint of social engineering. This model consists of two submodels. Firstly, a human behavior model based on game theory is suggested for modeling and predicting the expected behaviors of a network user encountering malicious messages. The game situation models the actions of a user under the condition that the system may be infected at the time of opening a malicious message. Secondly, a social network accessing model is proposed to characterize the dynamics of network users, by which the number of online susceptible users can be determined at each time step. Several simulation experiments are carried out on artificial social networks. The results show that (1) the proposed mathematical model can well describe the spreading dynamics of social network worms; (2) weighted network topology greatly affects the spread of worms; (3) worms spread even faster on hybrid social networks

A comprehensive multi-local-world model for complex networks

International Nuclear Information System (INIS)

Fan Zhengping; Chen Guanrong; Zhang Yunong

2009-01-01

The nodes in a community within a network are much more connected to each other than to the others outside the community in the same network. This phenomenon has been commonly observed from many real-world networks, ranging from social to biological even to technical networks. Meanwhile, the number of communities in some real-world networks, such as the Internet and most social networks, are evolving with time. To model this kind of networks, the present Letter proposes a multi-local-world (MLW) model to capture and describe their essential topological properties. Based on the mean-field theory, the degree distribution of this model is obtained analytically, showing that the generated network has a novel topological feature as being not completely random nor completely scale-free but behaving somewhere between them. As a typical application, the MLW model is applied to characterize the Internet against some other models such as the BA, GBA, Fitness and HOT models, demonstrating the superiority of the new model.

Conditional predictive inference for online surveillance of spatial disease incidence

Science.gov (United States)

Corberán-Vallet, Ana; Lawson, Andrew B.

2012-01-01

This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina. PMID:21898522

Polio eradication in India: progress, but environmental surveillance and vigilance still needed.

Science.gov (United States)

Chatterjee, Animesh; Vidyant, Sanjukta; Dhole, Tapan N

2013-02-18

Poliomyelitis has appeared in epidemic form, become endemic on a global scale, and has been reduced to near elimination, all within the span of documented medical history. Nevertheless, effective vaccinations, global surveillance network, development of accurate viral diagnosis prompted the historical challenge, global polio eradication initiative (GPEI). Environmental surveillance of poliovirus means monitoring of wild polio virus (WPV) and vaccine derived polio virus (cVDPV) circulation in human populations by examining environmental specimens supposedly contaminated by human feces. The rationale for surveillance is based on the fact that PV-infected individuals, whether presenting with disease symptoms or not, shed large amounts of PV in the feces for several weeks. As the morbidity: infection ratio of PV infection is very low, and therefore this fact contributes to the sensitivity of poliovirus surveillance, which under optimal conditions can be better than that of the standard acute flaccid paralysis (AFP) surveillance. The World Health Organization (WHO) has included environmental surveillance of poliovirus in the new Strategic Plan of the Global Polio Eradication Initiative for years 2010-2012 to be increasingly used in PV surveillance, supplementing AFP surveillance and the strategic advisory group of experts on immunization (SAGE) recommended a switch from tOPV-bOPV to remove the threat of cVDPV2 and to accelerate the elimination of WPV type 1 and 3 as bOPV is a more immunogenic vaccine and to introduce one dose of IPV in their vaccination schedule prior to OPV cessation. Copyright © 2012 Elsevier Ltd. All rights reserved.

Agent based modeling of energy networks

International Nuclear Information System (INIS)

Gonzalez de Durana, José María; Barambones, Oscar; Kremers, Enrique; Varga, Liz

2014-01-01

Highlights: • A new approach for energy network modeling is designed and tested. • The agent-based approach is general and no technology dependent. • The models can be easily extended. • The range of applications encompasses from small to large energy infrastructures. - Abstract: Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed

Strategies for improving the surveillance of drinking water quality in distribution networks : application of emerging modeling approaches

OpenAIRE

Francisque, Alex

2009-01-01

Cette thèse est consacrée à l'amélioration de la surveillance de la qualité de l'eau potable en réseau de distribution (RD) et à son. Le principal RD de la ville de Québec (Canada) est étudié. La thèse comporte quatre chapitres. Le premier porte sur la qualité microbiologique de l'eau. Il introduit de nouvelles approches statistiques pour modéliser les comptes de bactéries hétérotrophes anaérobies et aérobies facultatives (BHAA) utilisées comme indicateur de la variabilité de la qualité de l'...

Ideology, Critique and Surveillance

Directory of Open Access Journals (Sweden)

Heidi Herzogenrath-Amelung

2013-11-01

Full Text Available The 2013 revelations concerning global surveillance programmes demonstrate in unprecedented clarity the need for Critical Theory of information and communication technologies (ICTs to address the mechanisms and implications of increasingly global, ubiquitous surveillance. This is all the more urgent because of the dominance of the “surveillance ideology” (the promise of security through surveillance that supports the political economy of surveillance. This paper asks which theoretical arguments and concepts can be useful for philosophically grounding a critique of this surveillance ideology. It begins by examining how the surveillance ideology works through language and introduces the concept of the ‘ideological packaging’ of ICTs to show how rhetoric surrounding the implementation of surveillance technologies reinforces the surveillance ideology. It then raises the problem of how ideology-critique can work if it relies on language itself and argues that Martin Heidegger’s philosophy can make a useful contribution to existing critical approaches to language.

Thermal conductivity model for nanofiber networks

Science.gov (United States)

Zhao, Xinpeng; Huang, Congliang; Liu, Qingkun; Smalyukh, Ivan I.; Yang, Ronggui

2018-02-01

Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

Thermal conductivity model for nanofiber networks

Energy Technology Data Exchange (ETDEWEB)

Zhao, Xinpeng [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Huang, Congliang [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China; Liu, Qingkun [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Smalyukh, Ivan I. [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Yang, Ronggui [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Buildings and Thermal Systems Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA

2018-02-28

Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

Infinite Multiple Membership Relational Modeling for Complex Networks

DEFF Research Database (Denmark)

Mørup, Morten; Schmidt, Mikkel Nørgaard; Hansen, Lars Kai

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 multiplemembership latent feature model for networks. Contrary to existing...... multiplemembership models that scale quadratically in the number of vertices the proposedmodel scales linearly in the number of links admittingmultiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show...

Modeling Epidemics Spreading on Social Contact Networks.

Science.gov (United States)

Zhang, Zhaoyang; Wang, Honggang; Wang, Chonggang; Fang, Hua

2015-09-01

Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.

Port Hamiltonian modeling of Power Networks

NARCIS (Netherlands)

van Schaik, F.; van der Schaft, Abraham; Scherpen, Jacquelien M.A.; Zonetti, Daniele; Ortega, R

2012-01-01

In this talk a full nonlinear model for the power network in port–Hamiltonian framework is derived to study its stability properties. For this we use the modularity approach i.e., we first derive the models of individual components in power network as port-Hamiltonian systems and then we combine all

Space Surveillance Catalog growth during SBIRS low deployment.

Science.gov (United States)

Hoult, C. P.; Wright, R. P.

The Space Surveillance Catalog is a database of all Resident Space Objects (RSOs) on Earth orbit. It is expected to grow in the future as more RSOs accumulate on orbit. Potentially still more dramatic growth could follow the deployment of the Space Based Infrared System Low Earth Orbit Component (SBTRS Low). SBIRS Low, currently about to enter development, offers the potential to detect and acquire much smaller debris RSOs than can be seen by the current ground-based Space Surveillance Network (SSN). SBIRS Low will host multicolor infrared/visible sensors on each satellite in a proliferated constellation on low Earth orbit, and if appropriately tasked, these sensors could provide significant space surveillance capability. Catalog growth during SBIRS Low deployment was analyzed using a highly aggregated code that numerically integrates the Markov equations governing the state transitions of RSOs from uncataloged to cataloged, and back again. It was assumed that all newly observed debris RSOs will be detected as by-products of routine Catalog maintenance, not including any post breakup searches, and if sufficient sensor resources are available, be acquired into the Catalog. Debris over the entire low to high altitude regime were considered.

Graphical Model Theory for Wireless Sensor Networks

International Nuclear Information System (INIS)

Davis, William B.

2002-01-01

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

A Simplified Network Model for Travel Time Reliability Analysis in a Road Network

Directory of Open Access Journals (Sweden)

Kenetsu Uchida

2017-01-01

Full Text Available This paper proposes a simplified network model which analyzes travel time reliability in a road network. A risk-averse driver is assumed in the simplified model. The risk-averse driver chooses a path by taking into account both a path travel time variance and a mean path travel time. The uncertainty addressed in this model is that of traffic flows (i.e., stochastic demand flows. In the simplified network model, the path travel time variance is not calculated by considering all travel time covariance between two links in the network. The path travel time variance is calculated by considering all travel time covariance between two adjacent links in the network. Numerical experiments are carried out to illustrate the applicability and validity of the proposed model. The experiments introduce the path choice behavior of a risk-neutral driver and several types of risk-averse drivers. It is shown that the mean link flows calculated by introducing the risk-neutral driver differ as a whole from those calculated by introducing several types of risk-averse drivers. It is also shown that the mean link flows calculated by the simplified network model are almost the same as the flows calculated by using the exact path travel time variance.

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.

Leaky Apps and Data Shots: Leakage and Insertion in NSA-surveillance

NARCIS (Netherlands)

van der Velden, L.

2015-01-01

The NSA disclosures have put the issue of surveillance at center stage, and with that, a range of technologies by which data are captured. This article aims to break up devices for data collection by discussing devices that leak data versus devices that are inserted into computers or networks in

Stochastic actor-oriented models for network change

NARCIS (Netherlands)

Snijders, T.A.B.

1996-01-01

A class of models is proposed for longitudinal network data. These models are along the lines of methodological individualism: actors use heuristics to try to achieve their individual goals, subject to constraints. The current network structure is among these constraints. The models are continuous

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

Science.gov (United States)

Sun, Xiangqing; Elston, Robert C; Barnholtz-Sloan, Jill S; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Tian, Ye D; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford D; Chandar, Apoorva; Warfe, James M; Brock, Wendy; Chak, Amitabh

2016-05-01

Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR. ©2016 American Association for

Hybrid neural network bushing model for vehicle dynamics simulation

International Nuclear Information System (INIS)

Sohn, Jeong Hyun; Lee, Seung Kyu; Yoo, Wan Suk

2008-01-01

Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers

Who is Surveilling Whom?

DEFF Research Database (Denmark)

Mortensen, Mette

2014-01-01

This article concerns the particular form of counter-surveillance termed “sousveillance”, which aims to turn surveillance at the institutions responsible for surveillance. Drawing on the theoretical perspectives “mediatization” and “aerial surveillance,” the article studies WikiLeaks’ publication...

Mathematical model of highways network optimization

Science.gov (United States)

Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.

2017-12-01

The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.

Kestrel: force protection and Intelligence, Surveillance, and Reconnaissance (ISR) persistent surveillance on aerostats

Science.gov (United States)

Luber, David R.; Marion, John E.; Fields, David

2012-05-01

Logos Technologies has developed and fielded the Kestrel system, an aerostat-based, wide area persistent surveillance system dedicated to force protection and ISR mission execution operating over forward operating bases. Its development included novel imaging and stabilization capability for day/night operations on military aerostat systems. The Kestrel system's contribution is a substantial enhancement to aerostat-based, force protection systems which to date have relied on narrow field of view ball gimbal sensors to identify targets of interest. This inefficient mechanism to conduct wide area field of view surveillance is greatly enhanced by Kestrel's ability to maintain a constant motion imagery stare of the entire forward operating base (FOB) area. The Kestrel airborne sensor enables 360° coverage out to extended ranges which covers a city sized area at moderate resolution, while cueing a narrow field of view sensor to provide high resolution imagery of targets of interest. The ground station exploitation system enables operators to autonomously monitor multiple regions of interest in real time, and allows for backtracking through the recorded imagery, while continuing to monitor ongoing activity. Backtracking capability allows operators to detect threat networks, their CONOPS, and locations of interest. Kestrel's unique advancement has already been utilized successfully in OEF operations.

Neural network modeling of associative memory: Beyond the Hopfield model

Science.gov (United States)

Dasgupta, Chandan

1992-07-01

A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

SOA-surveillance Nederland

NARCIS (Netherlands)

Rijlaarsdam J; Bosman A; Laar MJW van de; CIE

2000-01-01

In May 1999 a working group was started to evaluate the current surveillance systems for sexually transmitted diseases (STD) and to make suggestions for a renewed effective and efficient STD surveillance system in the Netherlands. The surveillance system has to provide insight into the prevalence

Future Expansion of the Lightning Surveillance System at the Kennedy Space Center and the Cape Canaveral Air Force Station, Florida, USA

Science.gov (United States)

Mata, C. T.; Wilson, J. G.

2012-01-01

The NASA Kennedy Space Center (KSC) and the Air Force Eastern Range (ER) use data from two cloud-to-ground (CG) lightning detection networks, the Cloud-to-Ground Lightning Surveillance System (CGLSS) and the U.S. National Lightning Detection Network (NLDN), and a volumetric mapping array, the lightning detection and ranging II (LDAR II) system: These systems are used to monitor and characterize lightning that is potentially hazardous to launch or ground operations and hardware. These systems are not perfect and both have documented missed lightning events when compared to the existing lightning surveillance system at Launch Complex 39B (LC39B). Because of this finding it is NASA's plan to install a lightning surveillance system around each of the active launch pads sharing site locations and triggering capabilities when possible. This paper shows how the existing lightning surveillance system at LC39B has performed in 2011 as well as the plan for the expansion around all active pads.

Sparsity in Model Gene Regulatory Networks

International Nuclear Information System (INIS)

Zagorski, M.

2011-01-01

We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

OpenAIRE

Orts-Escolano, Sergio; Garcia-Rodriguez, Jose; Morell, Vicente; Cazorla, Miguel; Azorin-Lopez, Jorge; García-Chamizo, Juan Manuel

2014-01-01

In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mob...

Evaluating surveillance indicators supporting the Global Polio Eradication Initiative, 2011-2012.

Science.gov (United States)

2013-04-12

The Global Polio Eradication Initiative (GPEI) was established in 1988 by the World Health Assembly to interrupt transmission of wild poliovirus (WPV); completion of this initiative was declared a programmatic emergency of public health in January 2012. Polio cases are detected through surveillance for acute flaccid paralysis (AFP) with linked stool specimens tested for polioviruses (PVs) at accredited laboratories within the Global Polio Laboratory Network (GPLN). AFP surveillance findings are supplemented by testing sewage samples (environmental surveillance) collected at selected sites. Virologic data guide where targeted immunization activities should be conducted or improved. Key performance indicators are used to 1) monitor AFP surveillance quality at national and subnational levels to identify gaps where PV transmission could occur undetected; 2) provide evidence of where PV circulation has been interrupted; and 3) allow timely detection of an outbreak. Standardized surveillance indicators allow progress to be monitored over time and compared among countries. This report presents AFP surveillance performance indicators at national and subnational levels for countries affected by polio during 2011-2012, and trends in environmental surveillance, updating previous reports. In the 19 countries with transmission of PV (WPV and/or circulating vaccine-derived poliovirus [cVDPV]) during 2011-2012, national performance indicator targets were met in 12 (63%) countries in 2011 and 13 (68%) countries in 2012. Seven countries (37%) in 2011 had ≥80% of the population living in areas meeting performance indicators, increasing to nine countries (47%) in 2012. Performance indicators for timely reporting of PV isolation and characterization were met in four of six World Health Organization (WHO) regions in 2011 and five regions in 2012. To achieve global polio eradication, efforts are needed to improve and maintain AFP surveillance and laboratory performance.

Identifying optimal postmarket surveillance strategies for medical and surgical devices: implications for policy, practice and research.

Science.gov (United States)

Gagliardi, Anna R; Umoquit, Muriah; Lehoux, Pascale; Ross, Sue; Ducey, Ariel; Urbach, David R

2013-03-01

Non-drug technologies offer many benefits, but have been associated with adverse events, prompting calls for improved postmarket surveillance. There is little empirical research to guide the development of such a system. The purpose of this study was to identify optimal postmarket surveillance strategies for medical and surgical devices. Qualitative methods were used for sampling, data collection and analysis. Stakeholders from Canada and the USA representing different roles and perspectives were first interviewed to identify examples and characteristics of different surveillance strategies. These stakeholders and others they recommended were then assembled at a 1-day nominal group meeting to discuss and prioritise the components of a postmarket device surveillance system, and research needed to achieve such a system. Consultations were held with 37 participants, and 47 participants attended the 1-day meeting. They recommended a multicomponent system including reporting by facilities, clinicians and patients, supported with some external surveillance for validation and real-time trials for high-risk devices. Many considerations were identified that constitute desirable characteristics of, and means by which to implement such a system. An overarching network was envisioned to broker linkages, establish a shared minimum dataset, and support communication and decision making. Numerous research questions were identified, which could be pursued in tandem with phased implementation of the system. These findings provide unique guidance for establishing a device safety network that is based on existing initiatives, and could be expanded and evaluated in a prospective, phased fashion as it was developed.

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.

Antimicrobial resistance in leprosy: results of the first prospective open survey conducted by a WHO surveillance network for the period 2009-15.

Science.gov (United States)

Cambau, E; Saunderson, P; Matsuoka, M; Cole, S T; Kai, M; Suffys, P; Rosa, P S; Williams, D; Gupta, U D; Lavania, M; Cardona-Castro, N; Miyamoto, Y; Hagge, D; Srikantam, A; Hongseng, W; Indropo, A; Vissa, V; Johnson, R C; Cauchoix, B; Pannikar, V K; Cooreman, E A W D; Pemmaraju, V R R; Gillini, L

2018-03-01

Antimicrobial resistance (AMR) is a priority for surveillance in bacterial infections. For leprosy, AMR has not been assessed because Mycobacterium leprae does not grow in vitro. We aim to obtain AMR data using molecular detection of resistance genes and to conduct a prospective open survey of resistance to antileprosy drugs in countries where leprosy is endemic through a WHO surveillance network. From 2009 to 2015, multi-bacillary leprosy cases at sentinel sites of 19 countries were studied for resistance to rifampicin, dapsone and ofloxacin by PCR sequencing of the drug-resistance-determining regions of the genes rpoB, folP1 and gyrA. Among 1932 (1143 relapse and 789 new) cases studied, 154 (8.0%) M. leprae strains were found with mutations conferring resistance showing 182 resistance traits (74 for rifampicin, 87 for dapsone and 21 for ofloxacin). Twenty cases showed rifampicin and dapsone resistance, four showed ofloxacin and dapsone resistance, but no cases were resistant to rifampicin and ofloxacin. Rifampicin resistance was observed among relapse (58/1143, 5.1%) and new (16/789, 2.0%) cases in 12 countries. India, Brazil and Colombia reported more than five rifampicin-resistant cases. This is the first study reporting global data on AMR in leprosy. Rifampicin resistance emerged, stressing the need for expansion of surveillance. This is also a call for vigilance on the global use of antimicrobial agents, because ofloxacin resistance probably developed in relation to the general intake of antibiotics for other infections as it is not part of the multidrug combination used to treat leprosy. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

An airport surface surveillance solution based on fusion algorithm

Science.gov (United States)

Liu, Jianliang; Xu, Yang; Liang, Xuelin; Yang, Yihuang

2017-01-01

In this paper, we propose an airport surface surveillance solution combined with Multilateration (MLAT) and Automatic Dependent Surveillance Broadcast (ADS-B). The moving target to be monitored is regarded as a linear stochastic hybrid system moving freely and each surveillance technology is simplified as a sensor with white Gaussian noise. The dynamic model of target and the observation model of sensor are established in this paper. The measurements of sensors are filtered properly by estimators to get the estimation results for current time. Then, we analysis the characteristics of two fusion solutions proposed, and decide to use the scheme based on sensor estimation fusion for our surveillance solution. In the proposed fusion algorithm, according to the output of estimators, the estimation error is quantified, and the fusion weight of each sensor is calculated. The two estimation results are fused with weights, and the position estimation of target is computed accurately. Finally the proposed solution and algorithm are validated by an illustrative target tracking simulation.

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.

Northern emporia and maritime networks. Modelling past communication using archaeological network analysis

DEFF Research Database (Denmark)

Sindbæk, Søren Michael

2015-01-01

preserve patterns of thisinteraction. Formal network analysis and modelling holds the potential to identify anddemonstrate such patterns, where traditional methods often prove inadequate. Thearchaeological study of communication networks in the past, however, calls for radically different analytical...... this is not a problem of network analysis, but network synthesis: theclassic problem of cracking codes or reconstructing black-box circuits. It is proposedthat archaeological approaches to network synthesis must involve a contextualreading of network data: observations arising from individual contexts, morphologies...

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. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Irradiation temperature measurements in the surveillance program

International Nuclear Information System (INIS)

Pav, T.; Krhounek, V.

1991-01-01

Evaluation of the diamond monitor method for the determination of the irradiation temperature in the surveillance programme of WWER-440 reactors is discussed. One of the difficulties with the practical application of the method is that the measured values of irradiation temperature are unlikely high. Using a thermodynamical model of the processes in the annealing of the irradiated diamond crystals, it was shown that experimental difficulties came from the principles of the method used. An analysis was performed of the thermal field inside the capsule of the surveillance chain in operational conditions, using the finite element method. The diamond monitor method was suggested to be eliminated from the surveillance programme and the use was proposed of the value of 273+-3 degC (as the most likely value) for the irradiation temperature of surveillance samples in WWER-440 reactors. (Z.S.). 3 tabs., 6 figs., 4 refs

Conversion to use of digital chest images for surveillance of coal workers' pneumoconiosis (black lung).

Science.gov (United States)

Levine, Betty A; Ingeholm, Mary Lou; Prior, Fred; Mun, Seong K; Freedman, Matthew; Weissman, David; Attfield, Michael; Wolfe, Anita; Petsonk, Edward

2009-01-01

To protect the health of active U.S. underground coal miners, the National Institute for Occupational Safety and Health (NIOSH) has a mandate to carry out surveillance for coal workers' pneumoconiosis, commonly known as Black Lung (PHS 2001). This is accomplished by reviewing chest x-ray films obtained from miners at approximately 5-year intervals in approved x-ray acquisition facilities around the country. Currently, digital chest images are not accepted. Because most chest x-rays are now obtained in digital format, NIOSH is redesigning the surveillance program to accept and manage digital x-rays. This paper highlights the functional and security requirements for a digital image management system for a surveillance program. It also identifies the operational differences between a digital imaging surveillance network and a clinical Picture Archiving Communication Systems (PACS) or teleradiology system.

Modeling online social networks based on preferential linking

International Nuclear Information System (INIS)

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

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

Stabilization of model-based networked control systems

Energy Technology Data Exchange (ETDEWEB)

Miranda, Francisco [CIDMA, Universidade de Aveiro, Aveiro (Portugal); Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); Abreu, Carlos [Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); CMEMS-UMINHO, Universidade do Minho, Braga (Portugal); Mendes, Paulo M. [CMEMS-UMINHO, Universidade do Minho, Braga (Portugal)

2016-06-08

A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtain an optimal feedback control is also presented.

Discrete dynamic modeling of cellular signaling networks.

Science.gov (United States)

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Neural network tagging in a toy model

International Nuclear Information System (INIS)

Milek, Marko; Patel, Popat

1999-01-01

The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of 'events' was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed

Relevance of indirect transmission for wildlife disease surveillance

Directory of Open Access Journals (Sweden)

Martin Lange

2016-11-01

Full Text Available Epidemiological models of infectious diseases are essential tools in support of risk assessment, surveillance design and contingency planning in public and animal health. Direct pathogen transmission from host to host is an essential process of each host-pathogen system and respective epidemiological modelling concepts. It is widely accepted that numerous diseases involve indirect transmission through pathogens shed by infectious hosts to their environment. However, epidemiological models largely do not represent pathogen persistence outside the host explicitly. We hypothesize that this simplification might bias management-related model predictions for disease agents that can persist outside their host for a certain time span. We adapted an individual-based, spatially explicit epidemiological model that can mimic both transmission processes. One version explicitly simulated indirect pathogen transmission through a contaminated environment. A second version simulated direct host-to-host transmission only. We aligned the model variants by the transmission potential per infectious host (i.e. basic reproductive number R0 and the spatial transmission kernel of the infection to allow unbiased comparison of predictions. The quantitative model results are provided for the example of surveillance plans for early detection of foot-and-mouth disease in wild boar, a social host.We applied systematic sampling strategies on the serological status of randomly selected host individuals in both models. We compared between the model variants the time to detection and the area affected prior to detection, measures that strongly influence mitigation costs. Moreover, the ideal sampling strategy to detect the infection in a given time frame was compared between both models.We found the simplified, direct transmission model to underestimate necessary sample size by up to one order of magnitude, but to overestimate the area put under control measures. Thus, the model

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

Energy Technology Data Exchange (ETDEWEB)

Rossi, R; Gallagher, B; Neville, J; Henderson, K

2011-11-11

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

Advanced digital video surveillance for safeguard and physical protection

International Nuclear Information System (INIS)

Kumar, R.

2002-01-01

. These advanced surveillance systems aided with highly optimized video compression technologies over wireless and other communicating network media to provide security personnel real time, relevant only, timely information is going to be a great boon for physical security applications. This paper discusses some recent advances in digital video surveillance and its application in safeguard and physical protection. Refs. 5 (author)

Improved Maximum Parsimony Models for Phylogenetic Networks.

Science.gov (United States)

Van Iersel, Leo; Jones, Mark; Scornavacca, Celine

2018-05-01

Phylogenetic networks are well suited to represent evolutionary histories comprising reticulate evolution. Several methods aiming at reconstructing explicit phylogenetic networks have been developed in the last two decades. In this article, we propose a new definition of maximum parsimony for phylogenetic networks that permits to model biological scenarios that cannot be modeled by the definitions currently present in the literature (namely, the "hardwired" and "softwired" parsimony). Building on this new definition, we provide several algorithmic results that lay the foundations for new parsimony-based methods for phylogenetic network reconstruction.

Equity venture capital platform model based on complex network

Science.gov (United States)

Guo, Dongwei; Zhang, Lanshu; Liu, Miao

2018-05-01

This paper uses the small-world network and the random-network to simulate the relationship among the investors, construct the network model of the equity venture capital platform to explore the impact of the fraud rate and the bankruptcy rate on the robustness of the network model while observing the impact of the average path length and the average agglomeration coefficient of the investor relationship network on the income of the network model. The study found that the fraud rate and bankruptcy rate exceeded a certain threshold will lead to network collapse; The bankruptcy rate has a great influence on the income of the platform; The risk premium exists, and the average return is better under a certain range of bankruptcy risk; The structure of the investor relationship network has no effect on the income of the investment model.

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

Human behavior understanding in networked sensing theory and applications of networks of sensors

CERN Document Server

Spagnolo, Paolo; Distante, Cosimo

2014-01-01

This unique text/reference provides a broad overview of both the technical challenges in sensor network development, and the real-world applications of distributed sensing. Important aspects of distributed computing in large-scale networked sensor systems are analyzed in the context of human behavior understanding, including such topics as systems design tools and techniques, in-network signals, and information processing. Additionally, the book examines a varied range of application scenarios, covering surveillance, indexing and retrieval, patient care, industrial safety, social and ambient

A Network Contention Model for the Extreme-scale Simulator

Energy Technology Data Exchange (ETDEWEB)

Engelmann, Christian [ORNL; Naughton III, Thomas J [ORNL

2015-01-01

The Extreme-scale Simulator (xSim) is a performance investigation toolkit for high-performance computing (HPC) hardware/software co-design. It permits running a HPC application with millions of concurrent execution threads, while observing its performance in a simulated extreme-scale system. This paper details a newly developed network modeling feature for xSim, eliminating the shortcomings of the existing network modeling capabilities. The approach takes a different path for implementing network contention and bandwidth capacity modeling using a less synchronous and accurate enough model design. With the new network modeling feature, xSim is able to simulate on-chip and on-node networks with reasonable accuracy and overheads.

Innovative research of AD HOC network mobility model

Science.gov (United States)

Chen, Xin

2017-08-01

It is difficult for researchers of AD HOC network to conduct actual deployment during experimental stage as the network topology is changeable and location of nodes is unfixed. Thus simulation still remains the main research method of the network. Mobility model is an important component of AD HOC network simulation. It is used to describe the movement pattern of nodes in AD HOC network (including location and velocity, etc.) and decides the movement trail of nodes, playing as the abstraction of the movement modes of nodes. Therefore, mobility model which simulates node movement is an important foundation for simulation research. In AD HOC network research, mobility model shall reflect the movement law of nodes as truly as possible. In this paper, node generally refers to the wireless equipment people carry. The main research contents include how nodes avoid obstacles during movement process and the impacts of obstacles on the mutual relation among nodes, based on which a Node Self Avoiding Obstacle, i.e. NASO model is established in AD HOC network.

Using Google Trends for influenza surveillance in South China.

Science.gov (United States)

Kang, Min; Zhong, Haojie; He, Jianfeng; Rutherford, Shannon; Yang, Fen

2013-01-01

Google Flu Trends was developed to estimate influenza activity in many countries; however there is currently no Google Flu Trends or other Internet search data used for influenza surveillance in China. Influenza surveillance data from 2008 through 2011 were obtained from provincial CDC influenza-like illness and virological surveillance systems of Guangdong, a province in south China. Internet search data were downloaded from the website of Google Trends. Pearson's correlation coefficients with 95% confidence intervals (95% CI) were calculated to compare surveillance data and internet search trends. The correlation between CDC ILI surveillance and CDC virus surveillance was 0.56 (95% CI: 0.43, 0.66). The strongest correlation was between the Google Trends term of Fever and ILI surveillance with a correlation coefficient of 0.73 (95% CI: 0.66, 0.79). When compared with influenza virological surveillance, the Google Trends term of Influenza A had the strongest correlation with a correlation coefficient of 0.64 (95% CI: 0.43, 0.79) in the 2009 H1N1 influenza pandemic period. This study shows that Google Trends in Chinese can be used as a complementary source of data for influenza surveillance in south China. More research in the future should develop new models using search trends in Chinese language to estimate local disease activity and detect early signals of outbreaks.

Building functional networks of spiking model neurons.

Science.gov (United States)

Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin

2016-03-01

Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

SPLAI: Computational Finite Element Model for Sensor Networks

Directory of Open Access Journals (Sweden)

Ruzana Ishak

2006-01-01

Full Text Available Wireless sensor network refers to a group of sensors, linked by a wireless medium to perform distributed sensing task. The primary interest is their capability in monitoring the physical environment through the deployment of numerous tiny, intelligent, wireless networked sensor nodes. Our interest consists of a sensor network, which includes a few specialized nodes called processing elements that can perform some limited computational capabilities. In this paper, we propose a model called SPLAI that allows the network to compute a finite element problem where the processing elements are modeled as the nodes in the linear triangular approximation problem. Our model also considers the case of some failures of the sensors. A simulation model to visualize this network has been developed using C++ on the Windows environment.

Privacy Sensitive Surveillance for Assisted Living - A Smart Camera Approach