Tracking and vertex finding with drift chambers and neural networks

International Nuclear Information System (INIS)

Lindsey, C.

1991-09-01

Finding tracks, track vertices and event vertices with neural networks from drift chamber signals is discussed. Simulated feed-forward neural networks have been trained with back-propagation to give track parameters using Monte Carlo simulated tracks in one case and actual experimental data in another. Effects on network performance of limited weight resolution, noise and drift chamber resolution are given. Possible implementations in hardware are discussed. 7 refs., 10 figs

Collaborative In-Network Processing for Target Tracking

Directory of Open Access Journals (Sweden)

Juan Liu

2003-03-01

Full Text Available This paper presents a class of signal processing techniques for collaborative signal processing in ad hoc sensor networks, focusing on a vehicle tracking application. In particular, we study two types of commonly used sensors—acoustic-amplitude sensors for target distance estimation and direction-of-arrival sensors for bearing estimation—and investigate how networks of such sensors can collaborate to extract useful information with minimal resource usage. The information-driven sensor collaboration has several advantages: tracking is distributed, and the network is energy-efficient, activated only on a when-needed basis. We demonstrate the effectiveness of the approach to target tracking using both simulation and field data.

Robust visual tracking via multiscale deep sparse networks

Science.gov (United States)

Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

2017-04-01

In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

Identifying Tracks Duplicates via Neural Network

CERN Document Server

Sunjerga, Antonio; CERN. Geneva. EP Department

2017-01-01

The goal of the project is to study feasibility of state of the art machine learning techniques in track reconstruction. Machine learning techniques provide promising ways to speed up the pattern recognition of tracks by adding more intelligence in the algorithms. Implementation of neural network to process of track duplicates identifying will be discussed. Different approaches are shown and results are compared to method that is currently in use.

Track classification within wireless sensor network

Science.gov (United States)

Doumerc, Robin; Pannetier, Benjamin; Moras, Julien; Dezert, Jean; Canevet, Loic

2017-05-01

In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of this system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Development of target-tracking algorithms using neural network

Energy Technology Data Exchange (ETDEWEB)

Park, Dong Sun; Lee, Joon Whaoan; Yoon, Sook; Baek, Seong Hyun; Lee, Myung Jae [Chonbuk National University, Chonjoo (Korea)

1998-04-01

The utilization of remote-control robot system in atomic power plants or nuclear-related facilities grows rapidly, to protect workers form high radiation environments. Such applications require complete stability of the robot system, so that precisely tracking the robot is essential for the whole system. This research is to accomplish the goal by developing appropriate algorithms for remote-control robot systems. A neural network tracking system is designed and experimented to trace a robot Endpoint. This model is aimed to utilized the excellent capabilities of neural networks; nonlinear mapping between inputs and outputs, learning capability, and generalization capability. The neural tracker consists of two networks for position detection and prediction. Tracking algorithms are developed and experimented for the two models. Results of the experiments show that both models are promising as real-time target-tracking systems for remote-control robot systems. (author). 10 refs., 47 figs.

Siamese convolutional networks for tracking the spine motion

Science.gov (United States)

Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong

2017-09-01

Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.

Enhanced online convolutional neural networks for object tracking

Science.gov (United States)

Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen

2018-04-01

In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

BackTrack testing wireless network security

CERN Document Server

Cardwell, Kevin

2013-01-01

Written in an easy-to-follow step-by-step format, you will be able to get started in next to no time with minimal effort and zero fuss.BackTrack: Testing Wireless Network Security is for anyone who has an interest in security and who wants to know more about wireless networks.All you need is some experience with networks and computers and you will be ready to go.

Elastic tracking versus neural network tracking for very high multiplicity problems

International Nuclear Information System (INIS)

Harlander, M.; Gyulassy, M.

1991-04-01

A new Elastic Tracking (ET) algorithm is proposed for finding tracks in very high multiplicity and noisy environments. It is based on a dynamical reinterpretation and generalization of the Radon transform and is related to elastic net algorithms for geometrical optimization. ET performs an adaptive nonlinear fit to noisy data with a variable number of tracks. Its numerics is more efficient than that of the traditional Radon or Hough transform method because it avoids binning of phase space and the costly search for valid minima. Spurious local minima are avoided in ET by introducing a time-dependent effective potential. The method is shown to be very robust to noise and measurement error and extends tracking capabilities to much higher track densities than possible via local road finding or even the novel Denby-Peterson neural network tracking algorithms. 12 refs., 2 figs

Self-tracking as Health promotion

DEFF Research Database (Denmark)

Jelsøe, Erling

Self-tracking has become widespread in many parts of the world and is understood by many of its proponents as a way to obtain bodily control and through that to improve healthy living. As such self-tracking can be understood as a particular approach to practicing individual health promotion (even...... though this is not the only incentive for self-tracking). Even though health promotion is often seen as an activity, which resonates with a focus on individual responsibility, such a conception of health promotion contrasts with a broader critical concept of health promotion that emphasize social...... an analysis of social and community oriented dimensions of self-tracking as a form of health promotion compared to the above mentioned broad critical approach to health promotion in order to identify the contradictions as well as common traits and discuss implications for health promoting initiatives...

The Influence of Academic Tracking on Adolescent Social Networks

Science.gov (United States)

Fisher, Kim W.; Shogren, Karrie A.

2016-01-01

This study examined adolescents' social capital, through social network analyses (i.e., ego network analyses), in two high schools where students were placed into academic tracks adopted by the schools and shaped by disability status (i.e., general education, co-taught, segregated special education classrooms). The impact of academic tracks, as…

GSFC network operations with Tracking and Data Relay Satellites

Science.gov (United States)

Spearing, R.; Perreten, D. E.

The Tracking and Data Relay Satellite System (TDRSS) Network (TN) has been developed to provide services to all NASA User spacecraft in near-earth orbits. Three inter-relating entities will provide these services. The TN has been transformed from a network continuously changing to meet User specific requirements to a network which is flexible to meet future needs without significant changes in operational concepts. Attention is given to the evolution of the TN network, the TN capabilities-space segment, forward link services, tracking services, return link services, the three basic capabilities, single access services, multiple access services, simulation services, the White Sands Ground Terminal, the NASA communications network, and the network control center.

Engaging academia to advance the science and practice of environmental public health tracking.

Science.gov (United States)

Strosnider, Heather; Zhou, Ying; Balluz, Lina; Qualters, Judith

2014-10-01

Public health agencies at the federal, state, and local level are responsible for implementing actions and policies that address health problems related to environmental hazards. These actions and policies can be informed by integrating or linking data on health, exposure, hazards, and population. The mission of the Centers for Disease Control and Prevention׳s National Environmental Public Health Tracking Program (Tracking Program) is to provide information from a nationwide network of integrated health, environmental hazard, and exposure data that drives actions to improve the health of communities. The Tracking Program and federal, state, and local partners collect, integrate, analyze, and disseminate data and information to inform environmental public health actions. However, many challenges exist regarding the availability and quality of data, the application of appropriate methods and tools to link data, and the state of the science needed to link and analyze health and environmental data. The Tracking Program has collaborated with academia to address key challenges in these areas. The collaboration has improved our understanding of the uses and limitations of available data and methods, expanded the use of existing data and methods, and increased our knowledge about the connections between health and environment. Valuable working relationships have been forged in this process, and together we have identified opportunities and improvements for future collaborations to further advance the science and practice of environmental public health tracking. Published by Elsevier Inc.

Identifying and tracking dynamic processes in social networks

Science.gov (United States)

Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George

2006-05-01

The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.

Comparison of home and away-from-home physical activity using accelerometers and cellular network-based tracking devices.

Science.gov (United States)

Ramulu, Pradeep Y; Chan, Emilie S; Loyd, Tara L; Ferrucci, Luigi; Friedman, David S

2012-08-01

Measuring physical at home and away from home is essential for assessing health and well-being, and could help design interventions to increase physical activity. Here, we describe how physical activity at home and away from home can be quantified by combining information from cellular network-based tracking devices and accelerometers. Thirty-five working adults wore a cellular network-based tracking device and an accelerometer for 6 consecutive days and logged their travel away from home. Performance of the tracking device was determined using the travel log for reference. Tracking device and accelerometer data were merged to compare physical activity at home and away from home. The tracking device detected 98.6% of all away-from-home excursions, accurately measured time away from home and demonstrated few prolonged signal drop-out periods. Most physical activity took place away from home on weekdays, but not on weekends. Subjects were more physically active per unit of time while away from home, particularly on weekends. Cellular network-based tracking devices represent an alternative to global positioning systems for tracking location, and provide information easily integrated with accelerometers to determine where physical activity takes place. Promoting greater time spent away from home may increase physical activity.

Tests of track segment and vertex finding with neural networks

International Nuclear Information System (INIS)

Denby, B.; Lessner, E.; Lindsey, C.S.

1990-04-01

Feed forward neural networks have been trained, using back-propagation, to find the slopes of simulated track segments in a straw chamber and to find the vertex of tracks from both simulated and real events in a more conventional drift chamber geometry. Network architectures, training, and performance are presented. 12 refs., 7 figs

A framework for multi-object tracking over distributed wireless camera networks

Science.gov (United States)

Gau, Victor; Hwang, Jenq-Neng

2010-07-01

In this paper, we propose a unified framework targeting at two important issues in a distributed wireless camera network, i.e., object tracking and network communication, to achieve reliable multi-object tracking over distributed wireless camera networks. In the object tracking part, we propose a fully automated approach for tracking of multiple objects across multiple cameras with overlapping and non-overlapping field of views without initial training. To effectively exchange the tracking information among the distributed cameras, we proposed an idle probability based broadcasting method, iPro, which adaptively adjusts the broadcast probability to improve the broadcast effectiveness in a dense saturated camera network. Experimental results for the multi-object tracking demonstrate the promising performance of our approach on real video sequences for cameras with overlapping and non-overlapping views. The modeling and ns-2 simulation results show that iPro almost approaches the theoretical performance upper bound if cameras are within each other's transmission range. In more general scenarios, e.g., in case of hidden node problems, the simulation results show that iPro significantly outperforms standard IEEE 802.11, especially when the number of competing nodes increases.

Track reconstruction in discrete detectors by neutral networks

Energy Technology Data Exchange (ETDEWEB)

Glazov, A A; Kisel` , I V; Konotopskaya, E V; Neskoromnyj, V N; Ososkov, G A

1993-12-31

On the basis of applying neutral networks to the track recognition problem the investigations are made according to the specific properties of such discrete detectors as multiwire proportional chambers. These investigations result in the modification of the so-called rotor model in a neutral neural network. The energy function of a network in this modification contains only one cost term. This speeds up calculations considerably. The reduction of the energy function is done by the neuron selection with the help of simplegeometrical and energetical criteria. Besides, the cellular automata were applied to preliminary selection of data that made it possible to create an initial network configuration with the energy closer to its global minimum. The algorithm was tested on 10{sup 4} real three-prong events obtained from the ARES-spectrometer. The results are satisfactory including the noise robustness and good resolution of nearby going tracks. 12 refs.; 10 figs.

Track reconstruction in discrete detectors by neutral networks

International Nuclear Information System (INIS)

Glazov, A.A.; Kisel', I.V.; Konotopskaya, E.V.; Neskoromnyj, V.N.; Ososkov, G.A.

1992-01-01

On the basis of applying neutral networks to the track recognition problem the investigations are made according to the specific properties of such discrete detectors as multiwire proportional chambers. These investigations result in the modification of the so-called rotor model in a neutral neural network. The energy function of a network in this modification contains only one cost term. This speeds up calculations considerably. The reduction of the energy function is done by the neuron selection with the help of simplegeometrical and energetical criteria. Besides, the cellular automata were applied to preliminary selection of data that made it possible to create an initial network configuration with the energy closer to its global minimum. The algorithm was tested on 10 4 real three-prong events obtained from the ARES-spectrometer. The results are satisfactory including the noise robustness and good resolution of nearby going tracks. 12 refs.; 10 figs

Track finding with neural networks in ALICE ITS

International Nuclear Information System (INIS)

Batyunya, B.V.; Belikov, Yu.A.; Fedunov, A.G.; Zinchenko, A.I.

1996-01-01

A program based on Neural Networks technique was developed for track recognition in ALICE ITS. The efficiency of this program was estimated on Monte Carlo simulated events for particles with p T ≤ 100 MeV/c. The advantages of this program are the high performance, the capability of track finding in conditions of the high particle multiplicity and the ability to find tracks distorted by the multiple Coulomb scattering and the energy losses. 7 refs., 1 tab

Adaptive Moving Object Tracking Integrating Neural Networks And Intelligent Processing

Science.gov (United States)

Lee, James S. J.; Nguyen, Dziem D.; Lin, C.

1989-03-01

A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.

Hopfield neural network in HEP track reconstruction

International Nuclear Information System (INIS)

Muresan, Raluca; Pentia, Mircea

1996-01-01

This work uses neural network technique (Hopfield method) to reconstruct particle tracks starting from a data set obtained with a coordinate detector system placed around a high energy accelerated particle interaction region. A learning algorithm for finding the optimal connection of the signal points have been elaborated and tested. We used a single layer neutral network with constraints in order to obtain the particle tracks drawn through the detected signal points. The dynamics of the systems is given by the MFT equations which determine the system evolution to a minimum energy function. We carried out a computing program that has been tested on a lot of Monte Carlo simulated data. With this program we obtained good results even for noise/signal ratio 200. (authors)

Real time track finding in a drift chamber with a VLSI neural network

International Nuclear Information System (INIS)

Lindsey, C.S.; Denby, B.; Haggerty, H.; Johns, K.

1992-01-01

In a test setup, a hardware neural network determined track parameters of charged particles traversing a drift chamber. Voltages proportional to the drift times in 6 cells of the 3-layer chamber were inputs to the Intel ETANN neural network chip which had been trained to give the slope and intercept of tracks. We compare network track parameters to those obtained from off-line track fits. To our knowledge this is the first on-line application of a VLSI neural network to a high energy physics detector. This test explored the potential of the chip and the practical problems of using it in a real world setting. We compare the chip performance to a neural network simulation on a conventional computer. We discuss possible applications of the chip in high energy physics detector triggers. (orig.)

Virtual target tracking (VTT) as applied to mobile satellite communication networks

Science.gov (United States)

Amoozegar, Farid

1999-08-01

Traditionally, target tracking has been used for aerospace applications, such as, tracking highly maneuvering targets in a cluttered environment for missile-to-target intercept scenarios. Although the speed and maneuvering capability of current aerospace targets demand more efficient algorithms, many complex techniques have already been proposed in the literature, which primarily cover the defense applications of tracking methods. On the other hand, the rapid growth of Global Communication Systems, Global Information Systems (GIS), and Global Positioning Systems (GPS) is creating new and more diverse challenges for multi-target tracking applications. Mobile communication and computing can very well appreciate a huge market for Cellular Communication and Tracking Devices (CCTD), which will be tracking networked devices at the cellular level. The objective of this paper is to introduce a new concept, i.e., Virtual Target Tracking (VTT) for commercial applications of multi-target tracking algorithms and techniques as applied to mobile satellite communication networks. It would be discussed how Virtual Target Tracking would bring more diversity to target tracking research.

PEAK TRACKING WITH A NEURAL NETWORK FOR SPECTRAL RECOGNITION

NARCIS (Netherlands)

COENEGRACHT, PMJ; METTING, HJ; VANLOO, EM; SNOEIJER, GJ; DOORNBOS, DA

1993-01-01

A peak tracking method based on a simulated feed-forward neural network with back-propagation is presented. The network uses the normalized UV spectra and peak areas measured in one chromatogram for peak recognition. It suffices to train the network with only one set of spectra recorded in one

Tracks: EPHT Massachusetts Case Study

Centers for Disease Control (CDC) Podcasts

This podcast highlights the Massachusetts Environmental Public Health Tracking Network and features commentary from Massachusetts Department of Public Health Associate Health Commissioner Suzanne Condon.

Radiology film tracking in a distributed clinical network

International Nuclear Information System (INIS)

Marquette, D.D.; Arrildt, W.

1985-01-01

This paper describes a system for tracking radiology films designed and installed at the Johns Hopkins Hospital. The installation of database and network capabilities in a large hospital environment provide the opportunity to extend to all nursing units and clinics access to the hospital's film tracking system. Ethernet communication technology allows communication to remote libraries. The integration of film tracking with scheduling and order entry systems in radiology make it possible to attain a high level of automated database interface and film jacket label production

Data fusion for target tracking and classification with wireless sensor network

Science.gov (United States)

Pannetier, Benjamin; Doumerc, Robin; Moras, Julien; Dezert, Jean; Canevet, Loic

2016-10-01

In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Conducting swift heavy ion track networks

Czech Academy of Sciences Publication Activity Database

Fink, Dietmar; Kiv, A.; Fuks, D.; Vacík, Jiří; Hnatowicz, Vladimír; Chandra, A.; Saad, A.

2010-01-01

Roč. 165, č. 3 (2010), s. 227-244 ISSN 1042-0150 R&D Projects: GA AV ČR(CZ) KAN400480701 Institutional research plan: CEZ:AV0Z10480505 Keywords : ion tracks * negative differential resistance * neural networks Subject RIV: JJ - Other Materials Impact factor: 0.660, year: 2010

Charged particle track reconstruction using artificial neural networks

International Nuclear Information System (INIS)

Glover, C.; Fu, P.; Gabriel, T.; Handler, T.

1992-01-01

This paper summarizes the current state of our research in developing and applying artificial neural network (ANN) algorithm described here is based on a crude model of the retina. It takes as input the coordinates of each charged particle's interaction point (''hit'') in the tracking chamber. The algorithm's output is a set of vectors pointing to other hits that most likely to form a track

Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

Directory of Open Access Journals (Sweden)

Juan Feng

2016-01-01

Full Text Available In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy.

Hierarchically Coordinated Power Management for Target Tracking in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Feng Juan

2013-10-01

Full Text Available Energy efficiency is very important for wireless sensor networks (WSNs since sensor nodes have a limited energy supply from a battery. So far, a lot research has focused on this issue, while less emphasis has been placed on the adaptive sleep time for each node with a consideration for the application constraints. In this paper, we propose a hierarchically coordinated power management (HCPM approach, which both addresses the energy conservation problem and reduces the packet forwarding delay for target tracking WSNs based on a virtual-grid-based network structure. We extend the network lifetime by adopting an adaptive sleep scheduling scheme that combines the local power management (PM and the adaptive coordinate PM strategies to schedule the activities of the sensor nodes at the surveillance stage. Furthermore, we propose a hierarchical structure for the tracking stage. Experimental results show that the proposed approach has a greater capability of extending the network lifetime while maintaining a short transmission delay when compared with the protocol which does not consider the application constraints in target tracking sensor networks.

Electronic health record case studies to advance environmental public health tracking.

Science.gov (United States)

Namulanda, Gonza; Qualters, Judith; Vaidyanathan, Ambarish; Roberts, Eric; Richardson, Max; Fraser, Alicia; McVeigh, Katharine H; Patterson, Scott

2018-03-01

Data from traditional public health surveillance systems can have some limitations, e.g., timeliness, geographic level, and amount of data accessible. Electronic health records (EHRs) could present an opportunity to supplement current sources of routinely collected surveillance data. The National Environmental Public Health Tracking Program (Tracking Program) sought to explore the use of EHRs for advancing environmental public health surveillance practices. The Tracking Program funded four state/local health departments to obtain and pilot the use of EHR data to address several issues including the challenges and technical requirements for accessing EHR data, and the core data elements required to integrate EHR data within their departments' Tracking Programs. The results of these pilot projects highlighted the potential of EHR data for public health surveillance of rare diseases that may lack comprehensive registries, and surveillance of prevalent health conditions or risk factors for health outcomes at a finer geographic level. EHRs therefore, may have potential to supplement traditional sources of public health surveillance data. Published by Elsevier Inc.

Railway track circuit fault diagnosis using recurrent neural networks

NARCIS (Netherlands)

de Bruin, T.D.; Verbert, K.A.J.; Babuska, R.

2017-01-01

Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available

Moving target tracking through distributed clustering in directional sensor networks.

Science.gov (United States)

Enayet, Asma; Razzaque, Md Abdur; Hassan, Mohammad Mehedi; Almogren, Ahmad; Alamri, Atif

2014-12-18

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works.

Moving Target Tracking through Distributed Clustering in Directional Sensor Networks

Directory of Open Access Journals (Sweden)

Asma Enayet

2014-12-01

Full Text Available The problem of moving target tracking in directional sensor networks (DSNs introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target’s location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works.

The scheduling of tracking times for interplanetary spacecraft on the Deep Space Network

Science.gov (United States)

Webb, W. A.

1978-01-01

The Deep Space Network (DSN) is a network of tracking stations, located throughout the globe, used to track spacecraft for NASA's interplanetary missions. This paper describes a computer program, DSNTRAK, which provides an optimum daily tracking schedule for the DSN given the view periods at each station for a mission set of n spacecraft, where n is between 2 and 6. The objective function is specified in terms of relative total daily tracking time requirements between the n spacecraft. Linear programming is used to maximize the total daily tracking time and determine an optimal daily tracking schedule consistent with DSN station capabilities. DSNTRAK is used as part of a procedure to provide DSN load forecasting information for proposed future NASA mission sets.

Implications of network structure on public health collaboratives.

Science.gov (United States)

Retrum, Jessica H; Chapman, Carrie L; Varda, Danielle M

2013-10-01

Interorganizational collaboration is an essential function of public health agencies. These partnerships form social networks that involve diverse types of partners and varying levels of interaction. Such collaborations are widely accepted and encouraged, yet very little comparative research exists on how public health partnerships develop and evolve, specifically in terms of how subsequent network structures are linked to outcomes. A systems science approach, that is, one that considers the interdependencies and nested features of networks, provides the appropriate methods to examine the complex nature of these networks. Applying Mays and Scutchfields's categorization of "structural signatures" (breadth, density, and centralization), this research examines how network structure influences the outcomes of public health collaboratives. Secondary data from the Program to Analyze, Record, and Track Networks to Enhance Relationships (www.partnertool.net) data set are analyzed. This data set consists of dyadic (N = 12,355), organizational (N = 2,486), and whole network (N = 99) data from public health collaborations around the United States. Network data are used to calculate structural signatures and weighted least squares regression is used to examine how network structures can predict selected intermediary outcomes (resource contributions, overall value and trust rankings, and outcomes) in public health collaboratives. Our findings suggest that network structure may have an influence on collaborative-related outcomes. The structural signature that had the most significant relationship to outcomes was density, with higher density indicating more positive outcomes. Also significant was the finding that more breadth creates new challenges such as difficulty in reaching consensus and creating ties with other members. However, assumptions that these structural components lead to improved outcomes for public health collaboratives may be slightly premature. Implications of

Research on Key Technologies of Network Centric System Distributed Target Track Fusion

Directory of Open Access Journals (Sweden)

Yi Mao

2017-01-01

Full Text Available To realize common tactical picture in network-centered system, this paper proposes a layered architecture for distributed information processing and a method for distributed track fusion on the basis of analyzing the characteristics of network-centered systems. Basing on the noncorrelation of three-dimensional measurement of surveillance and reconnaissance sensors under polar coordinates, it also puts forward an algorithm for evaluating track quality (TQ using statistical decision theory. According to simulation results, the TQ value is associated with the measurement accuracy of sensors and the motion state of targets, which is well matched with the convergence process of tracking filters. Besides, the proposed algorithm has good reliability and timeliness in track quality evaluation.

Moving Target Detection and Active Tracking with a Multicamera Network

Directory of Open Access Journals (Sweden)

Long Zhao

2014-01-01

Full Text Available We propose a systematic framework for Intelligence Video Surveillance System (IVSS with a multicamera network. The proposed framework consists of low-cost static and PTZ cameras, target detection and tracking algorithms, and a low-cost PTZ camera feedback control algorithm based on target information. The target detection and tracking is realized by fixed cameras using a moving target detection and tracking algorithm; the PTZ camera is manoeuvred to actively track the target from the tracking results of the static camera. The experiments are carried out using practical surveillance system data, and the experimental results show that the systematic framework and algorithms presented in this paper are efficient.

Tracks FAQs: What Chemicals Are In My Drinking Water?

Centers for Disease Control (CDC) Podcasts

2011-08-10

In this podcast, CDC Tracking experts discuss how you can use the Tracking Network to determine what chemicals are in your drinking water. Do you have a question for our Tracking experts? Please e-mail questions to trackingsupport@cdc.gov.  Created: 8/10/2011 by National Center for Environmental Health, Division of Environmental Hazards and Health Effects, Environmental Health Tracking Branch.   Date Released: 8/10/2011.

Progress along developmental tracks for electronic health records implementation in the United States

Directory of Open Access Journals (Sweden)

Hollar David W

2009-03-01

Full Text Available Abstract The development and implementation of electronic health records (EHR have occurred slowly in the United States. To date, these approaches have, for the most part, followed four developmental tracks: (a Enhancement of immunization registries and linkage with other health records to produce Child Health Profiles (CHP, (b Regional Health Information Organization (RHIO demonstration projects to link together patient medical records, (c Insurance company projects linked to ICD-9 codes and patient records for cost-benefit assessments, and (d Consortia of EHR developers collaborating to model systems requirements and standards for data linkage. Until recently, these separate efforts have been conducted in the very silos that they had intended to eliminate, and there is still considerable debate concerning health professionals access to as well as commitment to using EHR if these systems are provided. This paper will describe these four developmental tracks, patient rights and the legal environment for EHR, international comparisons, and future projections for EHR expansion across health networks in the United States.

Optical track width measurements below 100 nm using artificial neural networks

Science.gov (United States)

Smith, R. J.; See, C. W.; Somekh, M. G.; Yacoot, A.; Choi, E.

2005-12-01

This paper discusses the feasibility of using artificial neural networks (ANNs), together with a high precision scanning optical profiler, to measure very fine track widths that are considerably below the conventional diffraction limit of a conventional optical microscope. The ANN is trained using optical profiles obtained from tracks of known widths, the network is then assessed by applying it to test profiles. The optical profiler is an ultra-stable common path scanning interferometer, which provides extremely precise surface measurements. Preliminary results, obtained with a 0.3 NA objective lens and a laser wavelength of 633 nm, show that the system is capable of measuring a 50 nm track width, with a standard deviation less than 4 nm.

On the Impact of Localization and Density Control Algorithms in Target Tracking Applications for Wireless Sensor Networks

Science.gov (United States)

Campos, Andre N.; Souza, Efren L.; Nakamura, Fabiola G.; Nakamura, Eduardo F.; Rodrigues, Joel J. P. C.

2012-01-01

Target tracking is an important application of wireless sensor networks. The networks' ability to locate and track an object is directed linked to the nodes' ability to locate themselves. Consequently, localization systems are essential for target tracking applications. In addition, sensor networks are often deployed in remote or hostile environments. Therefore, density control algorithms are used to increase network lifetime while maintaining its sensing capabilities. In this work, we analyze the impact of localization algorithms (RPE and DPE) and density control algorithms (GAF, A3 and OGDC) on target tracking applications. We adapt the density control algorithms to address the k-coverage problem. In addition, we analyze the impact of network density, residual integration with density control, and k-coverage on both target tracking accuracy and network lifetime. Our results show that DPE is a better choice for target tracking applications than RPE. Moreover, among the evaluated density control algorithms, OGDC is the best option among the three. Although the choice of the density control algorithm has little impact on the tracking precision, OGDC outperforms GAF and A3 in terms of tracking time. PMID:22969329

Your Health Buddies Matter: Preferential Selection and Social Influence on Weight Management in an Online Health Social Network.

Science.gov (United States)

Meng, Jingbo

2016-12-01

A growing number of online social networks are designed with the intention to promote health by providing virtual space wherein individuals can seek and share information and support with similar others. Research has shown that real-world social networks have a significant influence on one's health behavior and outcomes. However, there is a dearth of studies on how individuals form social networks in virtual space and whether such online social networks exert any impact on individuals' health outcomes. Built on the Multi-Theoretical Multilevel (MTML) framework and drawing from literature on social influence, this study examined the mechanisms underlying the formation of an online health social network and empirically tested social influence on individual health outcomes through the network. Situated in a weight management social networking site, the study tracked a health buddy network of 709 users and their weight management activities and outcomes for 4 months. Actor-based modeling was used to test the joint dynamics of preferential selection and social influence among health buddies. The results showed that baseline, inbreeding, and health status homophily significantly predicted preferential selection of health buddies in the weight management social networking site, whereas self-interest in seeking experiential health information did not. The study also found peer influence of online health buddy networks on individual weight outcomes, such that an individual's odds of losing weight increased if, on average, the individual's health buddies were losing weight.

Tracking Mobile Robot in Indoor Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Liping Zhang

2014-01-01

Full Text Available This work addresses the problem of tracking mobile robots in indoor wireless sensor networks (WSNs. Our approach is based on a localization scheme with RSSI (received signal strength indication which is used widely in WSN. The developed tracking system is designed for continuous estimation of the robot’s trajectory. A WSN, which is composed of many very simple and cheap wireless sensor nodes, is deployed at a specific region of interest. The wireless sensor nodes collect RSSI information sent by mobile robots. A range-based data fusion scheme is used to estimate the robot’s trajectory. Moreover, a Kalman filter is designed to improve tracking accuracy. Experiments are provided to assess the performance of the proposed scheme.

Controlled neural network application in track-match problem

International Nuclear Information System (INIS)

Baginyan, S.A.; Ososkov, G.A.

1993-01-01

Track-match problem of high energy physics (HEP) data handling is formulated in terms of incidence matrices. The corresponding Hopfield neural network is developed to solve this type of constraint satisfaction problems (CSP). A special concept of the controlled neural network is proposed as a basis of an algorithm for the effective CSP solution. Results of comparable calculations show the very high performance of this algorithm against conventional search procedures. 8 refs.; 1 fig.; 1 tab

Recognition of decays of charged tracks with neural network techniques

International Nuclear Information System (INIS)

Stimpfl-Abele, G.

1991-01-01

We developed neural-network learning techniques for the recognition of decays of charged tracks using a feed-forward network with error back-propagation. Two completely different methods are described in detail and their efficiencies for several NN architectures are compared with conventional methods. Excellent results are obtained. (orig.)

Neural network fusion capabilities for efficient implementation of tracking algorithms

Science.gov (United States)

Sundareshan, Malur K.; Amoozegar, Farid

1997-03-01

The ability to efficiently fuse information of different forms to facilitate intelligent decision making is one of the major capabilities of trained multilayer neural networks that is now being recognized. While development of innovative adaptive control algorithms for nonlinear dynamical plants that attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. We describe the capabilities and functionality of neural network algorithms for data fusion and implementation of tracking filters. To discuss details and to serve as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target- tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The innovation lies in the way the fusion of multisensor data is accomplished to facilitate improved estimation without increasing the computational complexity of the dynamical state estimator itself.

A lightweight target-tracking scheme using wireless sensor network

International Nuclear Information System (INIS)

Kuang, Xing-hong; Shao, Hui-he; Feng, Rui

2008-01-01

This paper describes a lightweight target-tracking scheme using wireless sensor network, where randomly distributed sensor nodes take responsibility for tracking the moving target based on the acoustic sensing signal. At every localization interval, a backoff timer algorithm is performed to elect the leader node and determine the transmission order of the localization nodes. An adaptive active region size algorithm based on the node density is proposed to select the optimal nodes taking part in localization. An improved particle filter algorithm performed by the leader node estimates the target state based on the selected nodes' acoustic energy measurements. Some refinements such as optimal linear combination algorithm, residual resampling algorithm, Markov chain Monte Carlo method are introduced in the scheme to improve the tracking performance. Simulation results validate the efficiency of the proposed tracking scheme

Tracks: EPHT Massachusetts Case Study

Centers for Disease Control (CDC) Podcasts

2009-07-20

This podcast highlights the Massachusetts Environmental Public Health Tracking Network and features commentary from Massachusetts Department of Public Health Associate Health Commissioner Suzanne Condon.  Created: 7/20/2009 by Centers for Disease Control and Prevention (CDC).   Date Released: 7/20/2009.

VT - Environmental Public Health Tracking Data Explorer

Data.gov (United States)

Vermont Center for Geographic Information — What is Environmental Public Health Tracking?Tracking is an ongoing national effort to better understand how environmental hazards can contribute to certain...

Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Zenghai Chen

2018-01-01

Full Text Available Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject’s eye movements. A gaze deviation (GaDe image is then proposed to characterize the subject’s eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image’s features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.

Use of artificial neural networks on optical track width measurements

Science.gov (United States)

Smith, Richard J.; See, Chung W.; Somekh, Mike G.; Yacoot, Andrew

2007-08-01

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

Track finding in a Time Projection Chamber with a neural network

International Nuclear Information System (INIS)

Espe, I.; Engeland, T.

1994-03-01

A Hopfield neutral network has been designed in order to reproduce particle tracks in electric and magnetic fields. The main goal is to reproduce tracks from particles in a relativistic heavy ion CERN experiment. In the present work properties of the system are investigated on simulated data. 6 refs., 14 figs., 9 tabs

Exploring mobile health in a private online social network.

Science.gov (United States)

Memon, Qurban A; Mustafa, Asma Fayes

2015-01-01

Health information is very vulnerable. Certain individuals or corporate organisations will continue to steal it similar to bank account data once data is on wireless channels. Once health information is part of a social network, corresponding privacy issues also surface. Insufficiently trained employees at hospitals that pay less attention to creating a privacy-aware culture will suffer loss when mobile devices containing health information are lost, stolen or sniffed. In this work, a social network system is explored as a m-health system from a privacy perspective. A model is developed within a framework of data-driven privacy and implemented on Android operating system. In order to check feasibility of the proposed model, a prototype application is developed on Facebook for different services, including: i) sharing user location; ii) showing nearby friends; iii) calculating and sharing distance moved, and calories burned; iv) calculating, tracking and sharing user heart rate; etc.

VA Suicide Prevention Applications Network: A National Health Care System-Based Suicide Event Tracking System.

Science.gov (United States)

Hoffmire, Claire; Stephens, Brady; Morley, Sybil; Thompson, Caitlin; Kemp, Janet; Bossarte, Robert M

2016-11-01

The US Department of Veterans Affairs' Suicide Prevention Applications Network (SPAN) is a national system for suicide event tracking and case management. The objective of this study was to assess data on suicide attempts among people using Veterans Health Administration (VHA) services. We assessed the degree of data overlap on suicide attempters reported in SPAN and the VHA's medical records from October 1, 2010, to September 30, 2014-overall, by year, and by region. Data on suicide attempters in the VHA's medical records consisted of diagnoses documented with E95 codes from the International Classification of Diseases, Ninth Revision . Of 50 518 VHA patients who attempted suicide during the 4-year study period, data on fewer than half (41%) were reported in both SPAN and the medical records; nearly 65% of patients whose suicide attempt was recorded in SPAN had no data on attempted suicide in the VHA's medical records. Evaluation of administrative data suggests that use of SPAN substantially increases the collection of data on suicide attempters as compared with the use of medical records alone, but neither SPAN nor the VHA's medical records identify all suicide attempters. Further research is needed to better understand the strengths and limitations of both systems and how to best combine information across systems.

Neural network based satellite tracking for deep space applications

Science.gov (United States)

Amoozegar, F.; Ruggier, C.

2003-01-01

The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions and examine the trade-off between tracing accuracy and communication link performance.

Track filtering by robust neural network

International Nuclear Information System (INIS)

Baginyan, S.A.; Kisel', I.V.; Konotopskaya, E.V.; Ososkov, G.A.

1993-01-01

In the present paper we study the following problems of track information extraction by the artificial neural network (ANN) rotor model: providing initial ANN configuration by an algorithm general enough to be applicable for any discrete detector in- or out of a magnetic field; robustness to heavy contaminated raw data (up to 100% signal-to-noise ratio); stability to the growing event multiplicity. These problems were carried out by corresponding innovations of our model, namely: by a special one-dimensional histogramming, by multiplying weights by a specially designed robust multiplier, and by replacing the simulated annealing schedule by ANN dynamics with an optimally fixed temperature. Our approach is valid for both circular and straight (non-magnetic) tracks and tested on 2D simulated data contaminated by 100% noise points distributed uniformly. To be closer to some reality in our simulation, we keep parameters of the cylindrical spectrometer ARES. 12 refs.; 9 figs

The effect of tracking network configuration on GPS baseline estimates for the CASA Uno experiment

Science.gov (United States)

Wolf, S. Kornreich; Dixon, T. H.; Freymueller, J. T.

1990-01-01

The effect of the tracking network on long (greater than 100 km) GPS baseline estimates was estimated using various subsets of the global tracking network initiated by the first Central and South America (CASA Uno) experiment. It was found that best results could be obtained with a global tacking network consisting of three U.S. stations, two sites in the southwestern Pacific, and two sites in Europe. In comparison with smaller subsets, this global network improved the baseline repeatability, the resolution of carrier phase cycle ambiguities, and formal errors of the orbit estimates.

Web-Enabling an Early Warning and Tracking System for Network Vulnerabilities

National Research Council Canada - National Science Library

Coffman, James

2001-01-01

.... Additionally, it lacked support for automated data validation, resulting in unreliable vulnerability tracking information As a result, the process was ineffective, and Navy networks remained highly...

Experimental validation of concept for real-time wavelength monitoring and tracking in densely populated WDM networks

Science.gov (United States)

Vukovic, Alex; Savoie, Michel; Hua, Heng; Campbell, Scott; Nguyen, Thao

2005-10-01

As the telecom industry responds with technological innovations to requests for higher data rates, increased number of wavelengths at higher densities, longer transmission distances and more intelligence for next generation optical networks, new monitoring schemes based on monitoring and tracking of each wavelength need to be developed and deployed. An optical layer monitoring scheme, based on tracking key optical parameters per each wavelength, is considered to be one of enablers for the transformation of today's opaque networks to dynamic, agile future networks. Ever-tighter network monitoring and control will be required to fulfill customer Service Level Agreements (SLAs). A wavelength monitoring and tracking concept was developed as a three-step approach. It started with the identification of all critical parameters required to obtain sufficient information about each wavelength; followed by the deployment of a cost-efficient device to provide simultaneous, accurate measurements in real-time of all critical parameters; and finally, the formulation of a specification for wavelength monitoring and tracking devices for real-time, simultaneous measurements and processing the data. A prototype solution based on a commercially available integrated modular spectrometer within a testbed environment associated with the all-optical network (AON) demonstrator program was used to verify and validate the wavelength monitoring and tracking concept. The developed concept verified that it can manage tracking of 32 wavelengths within a wavelength division multiplexing network. The developed concept presented in this paper can be used inside the transparent domains of networks to detect, identify and locate signal degradations in real-time, even sometimes to recognize the cause of the failure. Aside from the reduction of operational expenses due to the elimination of the need for operators at every site and skilled field technicians to isolate and repair faults, the developed

An Energy-Efficient Sleep Strategy for Target Tracking Sensor Networks

Directory of Open Access Journals (Sweden)

Juan FENG

2014-02-01

Full Text Available Energy efficiency is very important for sensor networks since sensor nodes have limited energy supply from battery. So far, many researches have been focused on this issue, while less emphasis was placed on the optimal sleep time of each node. This paper proposed an adaptive energy conservation strategy for target tracking based on a grid network structure, where each node autonomously determines when and if to sleep. It allows sensor nodes far away from targets to sleep to save energy and guarantee the tracking accuracy. The proposed approach extend network lifetime by adopting an adaptive sleep scheduling scheme that combines the local power management (PM and the adaptive coordinate PM strategies to schedule the activities of sensor nodes. And each node can choose an optimal sleep time so as to make system adaptive and energy-efficient. We show the performance of our approach in terms of energy drop, comparing it to a naive approach, dynamic PM with fixed sleep time and the coordinate PM strategies. From the experimental results, it is readily seen that the efficiency of the proposed approach.

Self-Tracking, Social Media and Personal Health Records for Patient Empowered Self-Care. Contribution of the IMIA Social Media Working Group.

Science.gov (United States)

Paton, C; Hansen, M; Fernandez-Luque, L; Lau, A Y S

2012-01-01

This paper explores the range of self-tracking devices and social media platforms used by the self-tracking community, and examines the implications of widespread adoption of these tools for scientific progress in health informatics. A literature review was performed to investigate the use of social media and self-tracking technologies in the health sector. An environmental scan identified a range of products and services which were used to exemplify three levels of self-tracking: self-experimentation, social sharing of data and patient controlled electronic health records. There appears to be an increase in the use of self-tracking tools, particularly in the health and fitness sector, but also used in the management of chronic diseases. Evidence of efficacy and effectiveness is limited to date, primarily due to the health and fitness focus of current solutions as opposed to their use in disease management. Several key technologies are converging to produce a trend of increased personal health surveillance and monitoring, social connectedness and sharing, and integration of regional and national health information systems. These trends are enabling new applications of scientific techniques, from personal experimentation to e-epidemiology, as data gathered by individuals are aggregated and shared across increasingly connected healthcare networks. These trends also raise significant new ethical and scientific issues that will need to be addressed, both by health informatics researchers and the communities of self-trackers themselves.

Techniques for Efficient Tracking of Road-Network-Based Moving Objects

DEFF Research Database (Denmark)

Civilis, Alminas; Jensen, Christian Søndergaard; Saltenis, Simonas

With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects....... The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents...... algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and it proposes to use acceleration profiles together with the routes. Using real GPS...

Techniques for efficient road-network-based tracking of moving objects

DEFF Research Database (Denmark)

Civilis, A.; Jensen, Christian Søndergaard; Pakalnis, Stardas

2005-01-01

With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects....... The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents...... algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and it proposes to use acceleration profiles together with the routes. Using real GPS...

Tracking and Treating Mobile Populations. The TB Net System. Migrant Clinicians Network Monograph Series. = El Sistema de Red para la TB.

Science.gov (United States)

Migrant Clinicians Network, Inc., Austin, TX.

A comprehensive tracking and referral network that helps provide continuity of care for mobile populations with active tuberculosis (TB) or TB infection is considered essential for effective treatment of TB. However, the interstate referral system that exists between state health departments has been highly inefficient for serving migrant…

Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures.

Science.gov (United States)

Cañete, Eduardo; Chen, Jaime; Díaz, Manuel; Llopis, Luis; Reyna, Ana; Rubio, Bartolomé

2015-06-26

Recently, slab track systems have arisen as a safer and more sustainable option for high speed railway infrastructures, compared to traditional ballasted tracks. Integrating Wireless Sensor Networks within these infrastructures can provide structural health related data that can be used to evaluate their degradation and to not only detect failures but also to predict them. The design of such systems has to deal with a scenario of large areas with inaccessible zones, where neither Internet coverage nor electricity supply is guaranteed. In this paper we propose a monitoring system for slab track systems that measures vibrations and displacements in the track. Collected data is transmitted to passing trains, which are used as data mules to upload the information to a remote control center. On arrival at the station, the data is stored in a database, which is queried by an application in order to detect and predict failures. In this paper, different communication architectures are designed and tested to select the most suitable system meeting such requirements as efficiency, low cost and data accuracy. In addition, to ensure communication between the sensing devices and the train, the communication system must take into account parameters such as train speed, antenna coverage, band and frequency.

Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures

Directory of Open Access Journals (Sweden)

Eduardo Cañete

2015-06-01

Full Text Available Recently, slab track systems have arisen as a safer and more sustainable option for high speed railway infrastructures, compared to traditional ballasted tracks. Integrating Wireless Sensor Networks within these infrastructures can provide structural health related data that can be used to evaluate their degradation and to not only detect failures but also to predict them. The design of such systems has to deal with a scenario of large areas with inaccessible zones, where neither Internet coverage nor electricity supply is guaranteed. In this paper we propose a monitoring system for slab track systems that measures vibrations and displacements in the track. Collected data is transmitted to passing trains, which are used as data mules to upload the information to a remote control center. On arrival at the station, the data is stored in a database, which is queried by an application in order to detect and predict failures. In this paper, different communication architectures are designed and tested to select the most suitable system meeting such requirements as efficiency, low cost and data accuracy. In addition, to ensure communication between the sensing devices and the train, the communication system must take into account parameters such as train speed, antenna coverage, band and frequency.

Walking- and cycling track networks in Norwegian cities : cost-benefit analyses including health effects and external costs of road traffic : summary

Science.gov (United States)

2002-04-01

Cost- benefit analyses of walking- and cycling track net-works in three Norwegian cities are presented in this study. A project group working with a National Cycling Strategy in Norway initialised the study. Motivation for starting the study is the P...

Artificial Neural Network In Maximum Power Point Tracking Algorithm Of Photovoltaic Systems

Directory of Open Access Journals (Sweden)

Modestas Pikutis

2014-05-01

Full Text Available Scientists are looking for ways to improve the efficiency of solar cells all the time. The efficiency of solar cells which are available to the general public is up to 20%. Part of the solar energy is unused and a capacity of solar power plant is significantly reduced – if slow controller or controller which cannot stay at maximum power point of solar modules is used. Various algorithms of maximum power point tracking were created, but mostly algorithms are slow or make mistakes. In the literature more and more oftenartificial neural networks (ANN in maximum power point tracking process are mentioned, in order to improve performance of the controller. Self-learner artificial neural network and IncCond algorithm were used for maximum power point tracking in created solar power plant model. The algorithm for control was created. Solar power plant model is implemented in Matlab/Simulink environment.

Wireless Sensor Networks for Heritage Object Deformation Detection and Tracking Algorithm

Directory of Open Access Journals (Sweden)

Zhijun Xie

2014-10-01

Full Text Available Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT. In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection.

Design and optimization of wireless sensor networks for localization and tracking

OpenAIRE

Pino Povedano, Sara

2016-01-01

Knowledge of the position of nodes in a WSN is crucial in most wireless sensor network (WSN) applications. The gathered information needs to be associated with a particular location in a specific time instant in order to appropiately control de surveillance area. Moreover, WSNs may be used for tracking certain objects in monitoring applications, which also requires the incorporation of location information of the sensor nodes into the tracking algorithms. These requisites ma...

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.

Automated target recognition and tracking using an optical pattern recognition neural network

Science.gov (United States)

Chao, Tien-Hsin

1991-01-01

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Environmental Public Health Tracking: Health and Environment Linked for Information Exchange-Atlanta (HEXIX-Atlanta: A cooperative Program Between CDC and NASA for Development of an Environmental Public Health Tracking Network in the Atlanta Metropolitan Area

Science.gov (United States)

Quattrochi, Dale A.; Niskar, Amanda Sue

2005-01-01

The Centers for Disease Control and Prevention (CDC) is coordinating HELIX- Atlanta to provide information regarding the five-county Metropolitan Atlanta Area (Clayton, Cobb, DeKalb, Fulton, and Gwinett) via a network of integrated environmental monitoring and public health data systems so that all sectors can take action to prevent and control environmentally related health effects. The HELIX-Atlanta Network is a tool to access interoperable information systems with optional information technology linkage functionality driven by scientific rationale. HELIX-Atlanta is a collaborative effort with local, state, federal, and academic partners, including the NASA Marshall Space Flight Center. The HELIX-Atlanta Partners identified the following HELIX-Atlanta initial focus areas: childhood lead poisoning, short-latency cancers, developmental disabilities, birth defects, vital records, respiratory health, age of housing, remote sensing data, and environmental monitoring, HELIX-Atlanta Partners identified and evaluated information systems containing information on the above focus areas. The information system evaluations resulted in recommendations for what resources would be needed to interoperate selected information systems in compliance with the CDC Public Health Information Network (PHIN). This presentation will discuss the collaborative process of building a network that links health and environment data for information exchange, including NASA remote sensing data, for use in HELIX-Atlanta.

Region stability analysis and tracking control of memristive recurrent neural network.

Science.gov (United States)

Bao, Gang; Zeng, Zhigang; Shen, Yanjun

2018-02-01

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive filtering for hidden node detection and tracking in networks.

Science.gov (United States)

Hamilton, Franz; Setzer, Beverly; Chavez, Sergio; Tran, Hien; Lloyd, Alun L

2017-07-01

The identification of network connectivity from noisy time series is of great interest in the study of network dynamics. This connectivity estimation problem becomes more complicated when we consider the possibility of hidden nodes within the network. These hidden nodes act as unknown drivers on our network and their presence can lead to the identification of false connections, resulting in incorrect network inference. Detecting the parts of the network they are acting on is thus critical. Here, we propose a novel method for hidden node detection based on an adaptive filtering framework with specific application to neuronal networks. We consider the hidden node as a problem of missing variables when model fitting and show that the estimated system noise covariance provided by the adaptive filter can be used to localize the influence of the hidden nodes and distinguish the effects of different hidden nodes. Additionally, we show that the sequential nature of our algorithm allows for tracking changes in the hidden node influence over time.

Finding the Right Fit: Understanding Health Tracking in Workplace Wellness Programs

DEFF Research Database (Denmark)

Chung, Chia-Fang; Jensen, Nanna Gorm; Shklovski, Irina

2017-01-01

Workplace health and wellness programs are increasingly integrating personal health tracking technologies, such as Fitbit and Apple Watch. Many question whether these technologies truly support employees in their pursuit of better wellness levels, raising objections about workplace surveillance...... and further blurring of boundaries between work and personal life. We conducted a study to understand how tracking tools are adopted in wellness programs and employees' opinions about these programs. We find that employees are generally positive about incentivized health tracking in the workplace, as it helps...... raise awareness of activity levels. However, there is a gap between the intentions of the programs and individual experiences and health goals. This sometimes results in confusion and creates barriers to participation. Even if this gap can be addressed, health tracking in the workplace...

Dynamical networks: Finding, measuring, and tracking neural population activity using network science

Directory of Open Access Journals (Sweden)

Mark D. Humphries

2017-12-01

Full Text Available Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.

Event-triggered cooperative target tracking in wireless sensor networks

Directory of Open Access Journals (Sweden)

Lu Kelin

2016-10-01

Full Text Available Since the issues of low communication bandwidth supply and limited battery capacity are very crucial for wireless sensor networks, this paper focuses on the problem of event-triggered cooperative target tracking based on set-membership information filtering. We study some fundamental properties of the set-membership information filter with multiple sensor measurements. First, a sufficient condition is derived for the set-membership information filter, under which the boundedness of the outer ellipsoidal approximation set of the estimation means is guaranteed. Second, the equivalence property between the parallel and sequential versions of the set-membership information filter is presented. Finally, the results are applied to a 1D event-triggered target tracking scenario in which the negative information is exploited in the sense that the measurements that do not satisfy the triggering conditions are modelled as set-membership measurements. The tracking performance of the proposed method is validated with extensive Monte Carlo simulations.

Online variational Bayesian filtering-based mobile target tracking in wireless sensor networks.

Science.gov (United States)

Zhou, Bingpeng; Chen, Qingchun; Li, Tiffany Jing; Xiao, Pei

2014-11-11

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer-Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying.

Studies on the Belle II L1 CDC track trigger's z-vertex resolution with neural networks

Energy Technology Data Exchange (ETDEWEB)

Skambraks, Sebastian; Neuhaus, Sara; Chen, Yang [Technische Universitaet Muenchen (Germany); Abudinen, Fernando; Kiesling, Christian [Max-Planck-Institut fuer Physik, Muenchen (Germany)

2015-07-01

We present the use of a neural network ensemble for the first level (L1) track trigger subsystem of Belle II. Our method employs hit and drift time information from the Central Drift Chamber (CDC). Estimating the z-coordinates of the vertex positions improves the signal to background ratio in the recorded data. Especially beam induced background can clearly be rejected, allowing to relax the 2D trigger conditions and thus enhancing the physics gain for low multiplicity events (e.g. tau pair production). Neural networks enable an improvement of the z-vertex resolution compared to linear least squares track fitting. As general function approximators, they are capable of learning nonlinearities solely from a training dataset. We propose a combined setup, integrating the benefits of the linear fit and enriching it with the nonlinear prediction capabilities of the neural networks. The precise z-vertices of single tracks are estimated by an ensemble of local expert neural networks, specialized to sectors in the track parameter phase space. A comparison is presented, demonstrating the differences of the linear fit and the neural network approach.

Tracking health-related Sustainable Development Goals (SDGs in Nepal

Directory of Open Access Journals (Sweden)

Meghnath Dhimal

2017-07-01

Full Text Available Sustainable Development Goals (SDGs comprise of 17 goals and 169 targets. All SDGs are interlinked to produce synergetic eff ects and emphasize health in all policies. Among the 17 Goals, Goal 3 has a central focus on health, which is underpinned by 13 targets. The other 16 goals are also directly or indirectly related to health and will contribute to achieving the associated targets for Goal 3. The ambitious SDG agenda and their progress can be tracked by measuring numerous goals, targets, and indicators. The main objective of this paper is to provide an overview about how health- related SDGs and their targets and indicators are being tracked in the national context of Nepal. Adequate investment in research for knowledge generation, capacity building and innovation, and continous research communication among policy makers, researchers and external development partners will contribute to tracking the progress of SDGs in Nepal.

Digital divide 2.0: the role of social networking sites in seeking health information online from a longitudinal perspective.

Science.gov (United States)

Feng, Yang; Xie, Wenjing

2015-01-01

Adopting a longitudinal angle, this study analyzed data from the Pew Internet's Health Tracking Survey in 2006, 2008, and 2010 to identify potential communication inequalities in social networking site use. Results showed that with the growing role of social networking site use in predicting people's likelihood of seeking health information online, the socioeconomic and demographic factors that contributed to the disparities in social networking site use could also lead to disparities in seeking health information online. Also, results indicated that people are more likely to seek heath-related information online if they or their close family or friends have a chronic disease situation.

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

Drift chamber tracking with a VLSI neural network

International Nuclear Information System (INIS)

Lindsey, C.S.; Denby, B.; Haggerty, H.; Johns, K.

1992-10-01

We have tested a commercial analog VLSI neural network chip for finding in real time the intercept and slope of charged particles traversing a drift chamber. Voltages proportional to the drift times were input to the Intel ETANN chip and the outputs were recorded and later compared off line to conventional track fits. We will discuss the chamber and test setup, the chip specifications, and results of recent tests. We'll briefly discuss possible applications in high energy physics detector triggers

Tracking the dynamic variations in a social network formed through shared interests

Directory of Open Access Journals (Sweden)

Gerold Pedemonte

2009-06-01

Full Text Available We tracked the dynamics of a social network formed by a shared interest in movies. Users-, movie ratings-, and rental date-data from the Netflix Prize dataset were used to construct a series of date-filtered social networks, wherein viewers were linked when they rented the same movie and gave the same rating. We obtained a nearly constant high clustering coefficient (0.60 – 0.85, and a low average path length (1.4 – 2.3 indicating a static 'small-world' network despite the dynamic behavior of the borrowers.

Clustering and fault tolerance for target tracking using wireless sensor networks

International Nuclear Information System (INIS)

Bhatti, S.; Khanzada, S.; Memon, S.

2012-01-01

Over the last few years, the deployment of WSNs (Wireless Sensor Networks) has been fostered in diverse applications. WSN has great potential for a variety of domains ranging from scientific experiments to commercial applications. Due to the deployment of WSNs in dynamic and unpredictable environments. They have potential to cope with variety of faults. This paper proposes an energy-aware fault-tolerant clustering protocol for target tracking applications termed as the FITf (Fault Tolerant Target Tracking) protocol The identification of RNs (Redundant Nodes) makes SN (Sensor Node) fault tolerance plausible and the clustering endorsed recovery of sensors supervised by a faulty CH (Cluster Head). The FfTT protocol intends two steps of reducing energy consumption: first, by identifying RNs in the network; secondly, by restricting the numbers of SNs sending data to the CH. Simulations validate the scalability and low power consumption of the FITf protocol in comparison with LEACH protocol. (author)

Neural network consistent empirical physical formula construction for neutron–gamma discrimination in gamma ray tracking

International Nuclear Information System (INIS)

Yildiz, Nihat; Akkoyun, Serkan

2013-01-01

Highlights: ► Detector responses in neutron–gamma discrimination were estimated by neural networks. ► Novel consistent neural network empirical physical formulas (EPFs) were constructed for detector responses. ► The EPFs are of explicit mathematical functional form. ► The EPFs can be used to derive various physical functions relevant to neutron–gamma discrimination in gamma ray tracking. -- Abstract: Gamma ray tracking is an efficient detection technique in studying exotic nuclei which lies far from beta stability line. To achieve very powerful and extraordinary resolution ability, new detectors based on gamma ray tracking are currently being developed. To reach this achievement, the neutron–gamma discrimination in these detectors is also an important task. In this paper, by suitable layered feedforward neural networks (LFNNs), we have constructed novel and consistent empirical physical formulas (EPFs) for some highly nonlinear detector counts measured in neutron–gamma discrimination. The detector counts data used in the discrimination was actually borrowed from our previous paper. The counts used here had been originally measured versus the following parameters: energy deposited in the first interaction points, difference in the incoming direction of initial gamma rays, and finally figure of merit values of the clusters determined by tracking. The LFNN–EPFs are of explicit mathematical functional form. Therefore, by various suitable operations of mathematical analysis, these LFNN–EPFs can be used to derivate further physical functions which might be potentially relevant to neutron–gamma discrimination performance of gamma ray tracking.

Neural mechanisms tracking popularity in real-world social networks.

Science.gov (United States)

Zerubavel, Noam; Bearman, Peter S; Weber, Jochen; Ochsner, Kevin N

2015-12-08

Differences in popularity are a key aspect of status in virtually all human groups and shape social interactions within them. Little is known, however, about how we track and neurally represent others' popularity. We addressed this question in two real-world social networks using sociometric methods to quantify popularity. Each group member (perceiver) viewed faces of every other group member (target) while whole-brain functional MRI data were collected. Independent functional localizer tasks were used to identify brain systems supporting affective valuation (ventromedial prefrontal cortex, ventral striatum, amygdala) and social cognition (dorsomedial prefrontal cortex, precuneus, temporoparietal junction), respectively. During the face-viewing task, activity in both types of neural systems tracked targets' sociometric popularity, even when controlling for potential confounds. The target popularity-social cognition system relationship was mediated by valuation system activity, suggesting that observing popular individuals elicits value signals that facilitate understanding their mental states. The target popularity-valuation system relationship was strongest for popular perceivers, suggesting enhanced sensitivity to differences among other group members' popularity. Popular group members also demonstrated greater interpersonal sensitivity by more accurately predicting how their own personalities were perceived by other individuals in the social network. These data offer insights into the mechanisms by which status guides social behavior.

Indoor Trajectory Tracking Scheme Based on Delaunay Triangulation and Heuristic Information in Wireless Sensor Networks.

Science.gov (United States)

Qin, Junping; Sun, Shiwen; Deng, Qingxu; Liu, Limin; Tian, Yonghong

2017-06-02

Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal strength indicator ( RSSI ) has received increased attention because it has low cost and requires no special infrastructure. However, RSSI tracking introduces uncertainty because of the inaccuracies of measurement instruments and the irregularities (unstable, multipath, diffraction) of wireless signal transmissions in indoor environments. Heuristic information includes some key factors for trajectory tracking procedures. This paper proposes a novel trajectory tracking scheme based on Delaunay triangulation and heuristic information (TTDH). In this scheme, the entire field is divided into a series of triangular regions. The common side of adjacent triangular regions is regarded as a regional boundary. Our scheme detects heuristic information related to a moving object's trajectory, including boundaries and triangular regions. Then, the trajectory is formed by means of a dynamic time-warping position-fingerprint-matching algorithm with heuristic information constraints. Field experiments show that the average error distance of our scheme is less than 1.5 m, and that error does not accumulate among the regions.

An Efficient Approach for Node Localisation and Tracking in Wireless Sensor Networks

CSIR Research Space (South Africa)

Mwila, Martin

2014-08-01

Full Text Available -1 An Efficient Approach for Node Localisation and Tracking in Wireless Sensor Networks Martin K. Mwila Submitted in partial fulfilment of the requirements for the degree Magister Technologiae: Electrical Engineering in the Department of Electrical Engineering...

Could we do better? Behavioural tracking on recommended consumer health websites.

Science.gov (United States)

Burkell, Jacquelyn; Fortier, Alexandre

2015-09-01

This study examines behavioural tracking practices on consumer health websites, contrasting tracking on sites recommended by information professionals with tracking on sites returned by Google. Two lists of consumer health websites were constructed: sites recommended by information professionals and sites returned by Google searches. Sites were divided into three groups according to source (Recommended-Only, Google-Only or both) and type (Government, Not-for-Profit or Commercial). Behavioural tracking practices on each website were documented using a protocol that detected cookies, Web beacons and Flash cookies. The presence and the number of trackers that collect personal information were contrasted across source and type of site; a second set of analyses specifically examined Advertising trackers. Recommended-Only sites show lower levels of tracking - especially tracking by advertisers - than do Google-Only sites or sites found through both sources. Government and Not-for-Profit sites have fewer trackers, particularly from advertisers, than do Commercial sites. Recommended sites, especially those from Government or Not-for-Profit organisations, present a lower privacy threat than sites returned by Google searches. Nonetheless, most recommended websites include some trackers, and half include at least one Advertising tracker. To protect patron privacy, information professionals should examine the tracking practices of the websites they recommend. © 2015 Health Libraries Group.

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

Science.gov (United States)

Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert

2018-05-01

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

An Energy-Efficient Target Tracking Framework in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Zhijun Yu

2009-01-01

Full Text Available This study devises and evaluates an energy-efficient distributed collaborative signal and information processing framework for acoustic target tracking in wireless sensor networks. The distributed processing algorithm is based on mobile agent computing paradigm and sequential Bayesian estimation. At each time step, the short detection reports of cluster members will be collected by cluster head, and a sensor node with the highest signal-to-noise ratio (SNR is chosen there as reference node for time difference of arrive (TDOA calculation. During the mobile agent migration, the target state belief is transmitted among nodes and updated using the TDOA measurement of these fusion nodes one by one. The computing and processing burden is evenly distributed in the sensor network. To decrease the wireless communications, we propose to represent the belief by parameterized methods such as Gaussian approximation or Gaussian mixture model approximation. Furthermore, we present an attraction force function to handle the mobile agent migration planning problem, which is a combination of the node residual energy, useful information, and communication cost. Simulation examples demonstrate the estimation effectiveness and energy efficiency of the proposed distributed collaborative target tracking framework.

Tracks: Working with Partners to Achieve Success

Centers for Disease Control (CDC) Podcasts

2009-07-20

This podcast explores the partnerships that played a crucial role in building the National Environmental Public Health Tracking Network.  Created: 7/20/2009 by Centers for Disease Control and Prevention (CDC).   Date Released: 7/20/2009.

Real-time object tracking system based on field-programmable gate array and convolution neural network

Directory of Open Access Journals (Sweden)

Congyi Lyu

2016-12-01

Full Text Available Vision-based object tracking has lots of applications in robotics, like surveillance, navigation, motion capturing, and so on. However, the existing object tracking systems still suffer from the challenging problem of high computation consumption in the image processing algorithms. The problem can prevent current systems from being used in many robotic applications which have limitations of payload and power, for example, micro air vehicles. In these applications, the central processing unit- or graphics processing unit-based computers are not good choices due to the high weight and power consumption. To address the problem, this article proposed a real-time object tracking system based on field-programmable gate array, convolution neural network, and visual servo technology. The time-consuming image processing algorithms, such as distortion correction, color space convertor, and Sobel edge, Harris corner features detector, and convolution neural network were redesigned using the programmable gates in field-programmable gate array. Based on the field-programmable gate array-based image processing, an image-based visual servo controller was designed to drive a two degree of freedom manipulator to track the target in real time. Finally, experiments on the proposed system were performed to illustrate the effectiveness of the real-time object tracking system.

A game theory approach to target tracking in sensor networks.

Science.gov (United States)

Gu, Dongbing

2011-02-01

In this paper, we investigate a moving-target tracking problem with sensor networks. Each sensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is "smart" enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore, we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result.

[Health care networks].

Science.gov (United States)

Mendes, Eugênio Vilaça

2010-08-01

The demographic and epidemiologic transition resulting from aging and the increase of life expectation means an increment related to chronic conditions. The healthcare systems contemporary crisis is characterized by the organization of the focus on fragmented systems turned to the acute conditions care, in spite of the chronic conditions prevalence, and by the hierarchical structure without communication flow among the different health care levels. Brazil health care situation profile is now presenting a triple burden of diseases, due to the concomitant presence of infectious diseases, external causes and chronic diseases. The solution is to restore the consistence between the triple burden of diseases on the health situation and the current system of healthcare practice, with the implantation of health care networks. The conclusion is that there are evidences in the international literature on health care networks that these networks may improve the clinical quality, the sanitation results and the user's satisfaction and the reduction of healthcare systems costs.

A new paradigm for particle tracking velocimetry, based on graph-theory and pulsed neural network

International Nuclear Information System (INIS)

Derou, D.; Herault, L.

1994-01-01

The Particle Tracking Velocimetry (PTV) technique works by recording, at different instances in time, positions of small tracers particles following a flow and illuminated by a sheet, or pseudo sheet, of light. It aims to recognize each particle trajectory, constituted of n different spots and determine thus each particle velocity vector. In this paper, we devise a new method, taking into account a global consistency of the trajectories to be extracted, in terms of visual perception and physical properties. It is based on a graph-theoretic formulation of the particle tracking problem and the use of an original neural network, called pulsed neural network. (authors). 4 figs

TRUSTWORTHY OPTIMIZED CLUSTERING BASED TARGET DETECTION AND TRACKING FOR WIRELESS SENSOR NETWORK

Directory of Open Access Journals (Sweden)

C. Jehan

2016-06-01

Full Text Available In this paper, an efficient approach is proposed to address the problem of target tracking in wireless sensor network (WSN. The problem being tackled here uses adaptive dynamic clustering scheme for tracking the target. It is a specific problem in object tracking. The proposed adaptive dynamic clustering target tracking scheme uses three steps for target tracking. The first step deals with the identification of clusters and cluster heads using OGSAFCM. Here, kernel fuzzy c-means (KFCM and gravitational search algorithm (GSA are combined to create clusters. At first, oppositional gravitational search algorithm (OGSA is used to optimize the initial clustering center and then the KFCM algorithm is availed to guide the classification and the cluster formation process. In the OGSA, the concept of the opposition based population initialization in the basic GSA to improve the convergence profile. The identified clusters are changed dynamically. The second step deals with the data transmission to the cluster heads. The third step deals with the transmission of aggregated data to the base station as well as the detection of target. From the experimental results, the proposed scheme efficiently and efficiently identifies the target. As a result the tracking error is minimized.

An Energy-Efficient Target-Tracking Strategy for Mobile Sensor Networks.

Science.gov (United States)

Mahboubi, Hamid; Masoudimansour, Walid; Aghdam, Amir G; Sayrafian-Pour, Kamran

2017-02-01

In this paper, an energy-efficient strategy is proposed for tracking a moving target in an environment with obstacles, using a network of mobile sensors. Typically, the most dominant sources of energy consumption in a mobile sensor network are sensing, communication, and movement. The proposed algorithm first divides the field into a grid of sufficiently small cells. The grid is then represented by a graph whose edges are properly weighted to reflect the energy consumption of sensors. The proposed technique searches for near-optimal locations for the sensors in different time instants to route information from the target to destination, using a shortest path algorithm. Simulations confirm the efficacy of the proposed algorithm.

To track or not to track: user reactions to concepts in longitudinal health monitoring.

Science.gov (United States)

Beaudin, Jennifer S; Intille, Stephen S; Morris, Margaret E

2006-01-01

Advances in ubiquitous computing, smart homes, and sensor technologies enable novel, longitudinal health monitoring applications in the home. Many home monitoring technologies have been proposed to detect health crises, support aging-in-place, and improve medical care. Health professionals and potential end users in the lay public, however, sometimes question whether home health monitoring is justified given the cost and potential invasion of privacy. The aim of the study was to elicit specific feedback from health professionals and laypeople about how they might use longitudinal health monitoring data for proactive health and well-being. Interviews were conducted with 8 health professionals and 26 laypeople. Participants were asked to evaluate mock data visualization displays that could be generated by novel home monitoring systems. The mock displays were used to elicit reactions to longitudinal monitoring in the home setting as well as what behaviors, events, and physiological indicators people were interested in tracking. Based on the qualitative data provided by the interviews, lists of benefits of and concerns about health tracking from the perspectives of the practitioners and laypeople were compiled. Variables of particular interest to the interviewees, as well as their specific ideas for applications of collected data, were documented. Based upon these interviews, we recommend that ubiquitous "monitoring" systems may be more readily adopted if they are developed as tools for personalized, longitudinal self-investigation that help end users learn about the conditions and variables that impact their social, cognitive, and physical health.

Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Dao-Wei Bi

2007-07-01

Full Text Available A primary criterion of wireless sensor network is energy efficiency. Focused onthe energy problem of target tracking in wireless sensor networks, this paper proposes acluster-based dynamic energy management mechanism. Target tracking problem isformulated by the multi-sensor detection model as well as energy consumption model. Adistributed adaptive clustering approach is investigated to form a reasonable routingframework which has uniform cluster head distribution. Dijkstra’s algorithm is utilized toobtain optimal intra-cluster routing. Target position is predicted by particle filter. Thepredicted target position is adopted to estimate the idle interval of sensor nodes. Hence,dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that theoperation energy consumption of wireless sensor network can be reduced. The sensornodes around the target wake up on time and act as sensing candidates. With the candidatesensor nodes and predicted target position, the optimal sensor node selection is considered.Binary particle swarm optimization is proposed to minimize the total energy consumptionduring collaborative sensing and data reporting. Experimental results verify that theproposed clustering approach establishes a low-energy communication structure while theenergy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energymanagement.

Track: A randomized controlled trial of a digital health obesity treatment intervention for medically vulnerable primary care patients.

Science.gov (United States)

Foley, Perry; Steinberg, Dori; Levine, Erica; Askew, Sandy; Batch, Bryan C; Puleo, Elaine M; Svetkey, Laura P; Bosworth, Hayden B; DeVries, Abigail; Miranda, Heather; Bennett, Gary G

2016-05-01

Obesity continues to disproportionately affect medically vulnerable populations. Digital health interventions may be effective for delivering obesity treatment in low-resource primary care settings. Track is a 12-month randomized controlled trial of a digital health weight loss intervention in a community health center system. Participants are 351 obese men and women aged 21 to 65years with an obesity-related comorbidity. Track participants are randomized to usual primary care or to a 12-month intervention consisting of algorithm-generated tailored behavior change goals, self-monitoring via mobile technologies, daily self-weighing using a network-connected scale, skills training materials, 18 counseling phone calls with a Track coach, and primary care provider counseling. Participants are followed over 12months, with study visits at baseline, 6, and 12months. Anthropometric data, blood pressure, fasting lipids, glucose and HbA1C and self-administered surveys are collected. Follow-up data will be collected from the medical record at 24months. Participants are 68% female and on average 50.7years old with a mean BMI of 35.9kg/m(2). Participants are mainly black (54%) or white (33%); 12.5% are Hispanic. Participants are mostly employed and low-income. Over 20% of the sample has hypertension, diabetes and hyperlipidemia. Almost 27% of participants currently smoke and almost 20% score above the clinical threshold for depression. Track utilizes an innovative, digital health approach to reduce obesity and chronic disease risk among medically vulnerable adults in the primary care setting. Baseline characteristics reflect a socioeconomically disadvantaged, high-risk patient population in need of evidence-based obesity treatment. Copyright © 2016 Elsevier Inc. All rights reserved.

Event-triggered Kalman-consensus filter for two-target tracking sensor networks.

Science.gov (United States)

Su, Housheng; Li, Zhenghao; Ye, Yanyan

2017-11-01

This paper is concerned with the problem of event-triggered Kalman-consensus filter for two-target tracking sensor networks. According to the event-triggered protocol and the mean-square analysis, a suboptimal Kalman gain matrix is derived and a suboptimal event-triggered distributed filter is obtained. Based on the Kalman-consensus filter protocol, all sensors which only depend on its neighbors' information can track their corresponding targets. Furthermore, utilizing Lyapunov method and matrix theory, some sufficient conditions are presented for ensuring the stability of the system. Finally, a simulation example is presented to verify the effectiveness of the proposed event-triggered protocol. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Sensor Network-Based Localization for Continuous Tracking Applications: Implementation and Performance Evaluation

Directory of Open Access Journals (Sweden)

Franco Davoli

2008-10-01

Full Text Available The increasing interest in systems able to provide users with immersive services (e.g., domotics, context-aware applications, and immersive distance learning tools has encouraged the development of cheap and effective platforms aimed at tracking objects and people within a certain space. In this context, wireless sensor networks (WSNs can play a very important role, since specialized sensors can be fruitfully exploited in order to generate/receive signals by means of which the WSN can derive the position of nodes joined to the objects to be tracked. The paper presents an original localization platform that exploits a single-hop WSN, based on a Microchip MCU and a Cypress RF device, to track its moving nodes. Specifically, the nodes of the network are divided into three sets: the first set consists of anchor nodes that, according to the commands from the sink (the central node of the WSN, generate ultrasonic pulses. These pulses are received by the second set of (moving nodes, which estimate the pulse time trip and communicate it to the sink. Finally, the last set is constituted by general purpose nodes that collect any kind of data from the surrounding field. The sink gathers all the data, computes the position of moving nodes, and transfers information to external users on the Internet. The algorithms adopted to manage the network and to localize moving nodes are discussed. A working prototype based upon the hardware platform, software, and protocol described in this paper has been deployed and tested, and some results are shown. Simulation results of the localization system are presented to show system scalability.

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.

Efficient Dynamic Adaptation Strategies for Object Tracking Tree in Wireless Sensor Network

Directory of Open Access Journals (Sweden)

CHEN, M.

2012-12-01

Full Text Available Most object tracking trees are established using the predefined mobility profile. However, when the real object's movement behaviors and query rates are different from the predefined mobility profile and query rates, the update cost and query cost of object tracking tree may increase. To upgrade the object tracking tree, the sink needs to send very large messages to collect the real movement information from the network, introducing a very large message overhead, which is referred to as adaptation cost. The Sub Root Message-Tree Adaptive procedure was proposed to dynamically collect the real movement information under the sub-tree and reconstruct the sub-tree to provide good performance based on the collected information. The simulation results indicates that the Sub Root Message-Tree Adaptive procedure is sufficient to achieve good total cost and lower adaptation cost.

Positioning and tracking control system analysis for mobile free space optical network

Science.gov (United States)

Li, Yushan; Refai, Hazem; Sluss, , James J., Jr.; Verma, Pramode; LoPresti, Peter

2005-08-01

Free Space Optical (FSO) communication has evolved to be applied to the mobile network, because it can provide up to 2.5Gbps or higher data rate wireless communication. One of the key challenges with FSO systems is to maintain the Line of Sight (LOS) between transmitter and receiver. In this paper, the feasibility and performance of applying the FSO technology to the mobile network is explored, and the design plan of the attitude positioning and tracking control system of the FSO transceiver is investigated. First, the system architecture is introduced, the requirements for the control system are analyzed, the involved reference frames and frame transformation are presented. Second, the control system bandwidth is used to evaluate the system performance in controlling a positioning system consisting of a gimbal and a steering mirror, some definitions to describe the positioning accuracy and tracking capacity are given. The attitude control of a FSO transceiver is split into 2 similar channels: pitch and yaw. Using an equivalent linear control system model, the simulations are carried out, with and without the presence of uncertainties that includes GPS data errors and sensor measurement errors. Finally, based on the simulation results in the pitch channel, the quantitative evaluation on the performance of the control system is given, including positioning accuracy, tracking capability and uncertainty tolerance.

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.

Fuel economy and torque tracking in camless engines through optimization of neural networks

International Nuclear Information System (INIS)

Ashhab, Moh'd Sami S.

2008-01-01

The feed forward controller of a camless internal combustion engine is modeled by inverting a multi-input multi-output feed forward artificial neural network (ANN) model of the engine. The engine outputs, pumping loss and cylinder air charge, are related to the inputs, intake valve lift and closing timing, by the artificial neural network model, which is trained with historical input-output data. The controller selects the intake valve lift and closing timing that will mimimize the pumping loss and achieve engine torque tracking. Lower pumping loss means better fuel economy, whereas engine torque tracking gurantees the driver's torque demand. The inversion of the ANN is performed with the complex method constrained optimization. How the camless engine inverse controller can be augmented with adaptive techniques to maintain accuracy even when the engine parts degrade is discussed. The simulation results demonstrate the effectiveness of the developed camless engine controller

Extended neural network-based scheme for real-time force tracking with magnetorheological dampers

DEFF Research Database (Denmark)

Weber, Felix; Bhowmik, Subrata; Høgsberg, Jan Becker

2014-01-01

This paper validates numerically and experimentally a new neural network-based real-time force tracking scheme for magnetorheological (MR) dampers on a five-storey shear frame with MR damper. The inverse model is trained with absolute values of measured velocity and force because the targeted...... the pre-yield to the post-yield region. A control-oriented approach is presented to compensate for these drawbacks. The resulting control force tracking scheme is validated for the emulation of viscous damping, clipped viscous damping with negative stiffness, and friction damping with negative stiffness...

Compression of Video Tracking and Bandwidth Balancing Routing in Wireless Multimedia Sensor Networks

Directory of Open Access Journals (Sweden)

Yin Wang

2015-12-01

Full Text Available There has been a tremendous growth in multimedia applications over wireless networks. Wireless Multimedia Sensor Networks(WMSNs have become the premier choice in many research communities and industry. Many state-of-art applications, such as surveillance, traffic monitoring, and remote heath care are essentially video tracking and transmission in WMSNs. The transmission speed is constrained by the big file size of video data and fixed bandwidth allocation in constant routing paths. In this paper, we present a CamShift based algorithm to compress the tracking of videos. Then we propose a bandwidth balancing strategy in which each sensor node is able to dynamically select the node for the next hop with the highest potential bandwidth capacity to resume communication. Key to this strategy is that each node merely maintains two parameters that contain its historical bandwidth varying trend and then predict its near future bandwidth capacity. Then, the forwarding node selects the next hop with the highest potential bandwidth capacity. Simulations demonstrate that our approach significantly increases the data received by the sink node and decreases the delay on video transmission in Wireless Multimedia Sensor Network environments.

Do governance choices matter in health care networks?: an exploratory configuration study of health care networks

Science.gov (United States)

2013-01-01

Background Health care networks are widely used and accepted as an organizational form that enables integrated care as well as dealing with complex matters in health care. However, research on the governance of health care networks lags behind. The research aim of our study is to explore the type and importance of governance structure and governance mechanisms for network effectiveness. Methods The study has a multiple case study design and covers 22 health care networks. Using a configuration view, combinations of network governance and other network characteristics were studied on the level of the network. Based on interview and questionnaire data, network characteristics were identified and patterns in the data looked for. Results Neither a dominant (or optimal) governance structure or mechanism nor a perfect fit among governance and other characteristics were revealed, but a number of characteristics that need further study might be related to effective networks such as the role of governmental agencies, legitimacy, and relational, hierarchical, and contractual governance mechanisms as complementary factors. Conclusions Although the results emphasize the situational character of network governance and effectiveness, they give practitioners in the health care sector indications of which factors might be more or less crucial for network effectiveness. PMID:23800334

Eye tracking to explore attendance in health-state descriptions.

Directory of Open Access Journals (Sweden)

Anna Selivanova

Full Text Available A crucial assumption in health valuation methods is that respondents pay equal attention to all information components presented in the response task. So far, there is no solid evidence that respondents are fulfilling this condition. The aim of our study is to explore the attendance to various information cues presented in the discrete choice (DC response tasks.Eye tracking was used to study the eye movements and fixations on specific information areas. This was done for seven DC response tasks comprising health-state descriptions. A sample of 10 respondents participated in the study. Videos of their eye movements were recorded and are presented graphically. Frequencies were computed for length of fixation and number of fixations, so differences in attendance were demonstrated for particular attributes in the tasks.All respondents completed the survey. Respondents were fixating on the left-sided health-state descriptions slightly longer than on the right-sided. Fatigue was not observed, as the time spent did not decrease in the final response tasks. The time spent on the tasks depended on the difficulty of the task and the amount of information presented.Eye tracking proved to be a feasible method to study the process of paying attention and fixating on health-state descriptions in the DC response tasks. Eye tracking facilitates the investigation of whether respondents fully read the information in health descriptions or whether they ignore particular elements.

Distributed Tracking in Distributed Sensor Networks

Science.gov (United States)

1988-05-26

Glocal Track 6-17 6-12: Case II: Initial Glocal Track 6-18 6-13: Local Tracking Results with Multiple Model Approach 6-19 6-14: Model Probability History...3480.0- 2290.0e iee. onee -5800 -4600.8 -3400.8 -2208.8 -1886 X (Mi) Figure 6-11: Case 1: Initial Glocal Track 6-17 460. 420. 38 . 3488.9 1st 3498.9

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.

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications.

Science.gov (United States)

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

2016-08-06

In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

On the track of fish batches in three distribution networks

DEFF Research Database (Denmark)

Randrup, Maria; Wu, Haiping; Jørgensen, Bo M.

2012-01-01

Three fish products sampled in retail shops were traced back to their origin and fish from the same batch were tracked forward towards the retailer, thereby simulating a recall situation. The resulting distribution networks were very complex, but to the extent that companies were willing to provi...... of discovering a fault as early as possible in order to minimise the costs of a recall. The localisation of distributed products during a recall operation can be facilitated by a well-constructed traceability system....

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

OpenAIRE

Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

2016-01-01

In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of tra...

Partnership capacity for community health improvement plan implementation: findings from a social network analysis.

Science.gov (United States)

McCullough, J Mac; Eisen-Cohen, Eileen; Salas, S Bianca

2016-07-13

Many health departments collaborate with community organizations on community health improvement processes. While a number of resources exist to plan and implement a community health improvement plan (CHIP), little empirical evidence exists on how to leverage and expand partnerships when implementing a CHIP. The purpose of this study was to identify characteristics of the network involved in implementing the CHIP in one large community. The aims of this analysis are to: 1) identify essential network partners (and thereby highlight potential network gaps), 2) gauge current levels of partner involvement, 3) understand and effectively leverage network resources, and 4) enable a data-driven approach for future collaborative network improvements. We collected primary data via survey from n = 41 organizations involved in the Health Improvement Partnership of Maricopa County (HIPMC), in Arizona. Using the previously validated Program to Analyze, Record, and Track Networks to Enhance Relationships (PARTNER) tool, organizations provided information on existing ties with other coalition members, including frequency and depth of partnership and eight categories of perceived value/trust of each current partner organization. The coalition's overall network had a density score of 30 %, degree centralization score of 73 %, and trust score of 81 %. Network maps are presented to identify existing relationships between HIPMC members according to partnership frequency and intensity, duration of involvement in the coalition, and self-reported contributions to the coalition. Overall, number of ties and other partnership measures were positively correlated with an organization's perceived value and trustworthiness as rated by other coalition members. Our study presents a novel use of social network analysis methods to evaluate the coalition of organizations involved in implementing a CHIP in an urban community. The large coalition had relatively low network density but high

The Motus Wildlife Tracking System: a collaborative research network to enhance the understanding of wildlife movement

Directory of Open Access Journals (Sweden)

Philip D. Taylor

2017-06-01

Full Text Available We describe a new collaborative network, the Motus Wildlife Tracking System (Motus; https://motus.org, which is an international network of researchers using coordinated automated radio-telemetry arrays to study movements of small flying organisms including birds, bats, and insects, at local, regional, and hemispheric scales. Radio-telemetry has been a cornerstone of tracking studies for over 50 years, and because of current limitations of geographic positioning systems (GPS and satellite transmitters, has remained the primary means to track movements of small animals with high temporal and spatial precision. Automated receivers, along with recent miniaturization and digital coding of tags, have further improved the utility of radio-telemetry by allowing many individuals to be tracked continuously and simultaneously across broad landscapes. Motus is novel among automated arrays in that collaborators employ a single radio frequency across receiving stations over a broad geographic scale, allowing individuals to be detected at sites maintained by others. Motus also coordinates, disseminates, and archives detections and associated metadata in a central repository. Combined with the ability to track many individuals simultaneously, Motus has expanded the scope and spatial scale of research questions that can be addressed using radio-telemetry from local to regional and even hemispheric scales. Since its inception in 2012, more than 9000 individuals of over 87 species of birds, bats, and insects have been tracked, resulting in more than 250 million detections. This rich and comprehensive dataset includes detections of individuals during all phases of the annual cycle (breeding, migration, and nonbreeding, and at a variety of spatial scales, resulting in novel insights into the movement behavior of small flying animals. The value of the Motus network will grow as spatial coverage of stations and number of partners and collaborators increases. With

42 CFR 485.603 - Rural health network.

Science.gov (United States)

2010-10-01

... 42 Public Health 5 2010-10-01 2010-10-01 false Rural health network. 485.603 Section 485.603... Participation: Critical Access Hospitals (CAHs) § 485.603 Rural health network. A rural health network is an... quality assurance with at least— (1) One hospital that is a member of the network when applicable; (2) One...

Tracking socioeconomic vulnerability using network analysis: insights from an avian influenza outbreak in an ostrich production network.

Directory of Open Access Journals (Sweden)

Christine Moore

Full Text Available BACKGROUND: The focus of management in many complex systems is shifting towards facilitation, adaptation, building resilience, and reducing vulnerability. Resilience management requires the development and application of general heuristics and methods for tracking changes in both resilience and vulnerability. We explored the emergence of vulnerability in the South African domestic ostrich industry, an animal production system which typically involves 3-4 movements of each bird during its lifetime. This system has experienced several disease outbreaks, and the aim of this study was to investigate whether these movements have contributed to the vulnerability of this system to large disease outbreaks. METHODOLOGY/PRINCIPAL FINDINGS: The ostrich production system requires numerous movements of birds between different farm types associated with growth (i.e. Hatchery to juvenile rearing farm to adult rearing farm. We used 5 years of movement records between 2005 and 2011 prior to an outbreak of Highly Pathogenic Avian Influenza (H5N2. These data were analyzed using a network analysis in which the farms were represented as nodes and the movements of birds as links. We tested the hypothesis that increasing economic efficiency in the domestic ostrich industry in South Africa made the system more vulnerable to outbreak of Highly Pathogenic Avian Influenza (H5N2. Our results indicated that as time progressed, the network became increasingly vulnerable to pathogen outbreaks. The farms that became infected during the outbreak displayed network qualities, such as significantly higher connectivity and centrality, which predisposed them to be more vulnerable to disease outbreak. CONCLUSIONS/SIGNIFICANCE: Taken in the context of previous research, our results provide strong support for the application of network analysis to track vulnerability, while also providing useful practical implications for system monitoring and management.

Distributed radar network for real-time tracking of bullet trajectory

Science.gov (United States)

Zhang, Yimin; Li, Xin; Jin, Yuanwei; Amin, Moeness G.; Eydgahi, Ali

2009-05-01

Gunshot detection, sniper localization, and bullet trajectory prediction are of significant importance in military and homeland security applications. While the majority of existing work is based on acoustic and electro-optical sensors, this paper develops a framework of networked radar systems that uses distributed radar sensor networks to achieve the aforementioned objectives. The use of radio frequency radar systems allows the achievement of subtime- of-flight tracking response, enabling to response before the bullet reaches its target and, as such, effectively leading to the reduction of injuries and casualties in military and homeland security operations. The focus of this paper is to examine the MIMO radar concept with concurrent transmission of low-correlation waveforms from multiple radar sets to ensure wide surveillance coverage and maintain a high waveform repetition frequency for long coherent time interval required to achieve return signal concentration.

Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.

Science.gov (United States)

Aftab, Muhammad Saleheen; Shafiq, Muhammad

2015-11-01

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

On-Line Tracking Controller for Brushless DC Motor Drives Using Artificial Neural Networks

Science.gov (United States)

Rubaai, Ahmed

1996-01-01

A real-time control architecture is developed for time-varying nonlinear brushless dc motors operating in a high performance drives environment. The developed control architecture possesses the capabilities of simultaneous on-line identification and control. The dynamics of the motor are modeled on-line and controlled using an artificial neural network, as the system runs. The control architecture combines the experience and dependability of adaptive tracking systems with potential and promise of the neural computing technology. The sensitivity of real-time controller to parametric changes that occur during training is investigated. Such changes are usually manifested by rapid changes in the load of the brushless motor drives. This sudden change in the external load is simulated for the sigmoidal and sinusoidal reference tracks. The ability of the neuro-controller to maintain reasonable tracking accuracy in the presence of external noise is also verified for a number of desired reference trajectories.

Environmental Public Health Tracking: a cost-effective system for characterizing the sources, distribution and public health impacts of environmental hazards.

Science.gov (United States)

Saunders, P J; Middleton, J D; Rudge, G

2017-09-01

The contemporary environment is a complex of interactions between physical, biological, socio-economic systems with major impacts on public health. However, gaps in our understanding of the causes, extent and distribution of these effects remain. The public health community in Sandwell West Midlands has collaborated to successfully develop, pilot and establish the first Environmental Public Health Tracking (EPHT) programme in Europe to address this 'environmental health gap' through systematically linking data on environmental hazards, exposures and diseases. Existing networks of environmental, health and regulatory agencies developed a suite of innovative methods to routinely share, integrate and analyse data on hazards, exposures and health outcomes to inform interventions. Effective data sharing and horizon scanning systems have been established, novel statistical methods piloted, plausible associations framed and tested, and targeted interventions informed by local concerns applied. These have influenced changes in public health practice. EPHT is a powerful tool for identifying and addressing the key environmental public health impacts at a local level. Sandwell's experience demonstrates that it can be established and operated at virtually no cost. The transfer of National Health Service epidemiological skills to local authorities in 2013 provides an opportunity to expand the programme to fully exploit its potential. © The Author 2016. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision

Directory of Open Access Journals (Sweden)

Bineng Zhong

2016-01-01

Full Text Available Tracking individual-cell/object over time is important in understanding drug treatment effects on cancer cells and video surveillance. A fundamental problem of individual-cell/object tracking is to simultaneously address the cell/object appearance variations caused by intrinsic and extrinsic factors. In this paper, inspired by the architecture of deep learning, we propose a robust feature learning method for constructing discriminative appearance models without large-scale pretraining. Specifically, in the initial frames, an unsupervised method is firstly used to learn the abstract feature of a target by exploiting both classic principal component analysis (PCA algorithms with recent deep learning representation architectures. We use learned PCA eigenvectors as filters and develop a novel algorithm to represent a target by composing of a PCA-based filter bank layer, a nonlinear layer, and a patch-based pooling layer, respectively. Then, based on the feature representation, a neural network with one hidden layer is trained in a supervised mode to construct a discriminative appearance model. Finally, to alleviate the tracker drifting problem, a sample update scheme is carefully designed to keep track of the most representative and diverse samples during tracking. We test the proposed tracking method on two standard individual cell/object tracking benchmarks to show our tracker's state-of-the-art performance.

Perceptions of Smartphone User-Centered Mobile Health Tracking Apps Across Various Chronic Illness Populations: An Integrative Review.

Science.gov (United States)

Birkhoff, Susan D; Smeltzer, Suzanne C

2017-07-01

This integrative review presents a synthesis of the current qualitative research addressing the motivating factors, usability, and experiences of mobile health tracking applications (apps) across various chronic disease populations. Integrative review of the literature. Databases used to conduct this integrative review included: PubMed Plus, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Google Scholar, Science Direct, and EBSCO megafile. The following search terms were used in all five databases: smartphone apps, apps, mHealth, eHealth, mobile health apps, health tracking apps, user-centered apps, wireless technology, engagement, qualitative, and usability. The initial literature review yielded 689 results. Once inclusion and exclusion criteria were employed, 11 studies met the criteria set forth for this review. The reviewed studies provided insight into users' perceptions, experiences, and motivations to incorporate smartphone mobile health apps into their daily lives when living with chronic illnesses. This review indicates the growing interest in user-centered mobile health tracking apps, but with little understanding of motivating factors that foster sustained app use. Mobile health tracking apps targeted to users with chronic conditions need to have a high level of usability in order to motivate users to sustain engagement with their mobile health tracking app. User-centered mobile health tracking app technology is being used with increasing frequency to potentially provide individualized support to chronic illness populations. © 2017 Sigma Theta Tau International.

An innovative strategy to increase a professional workforce: the fast track initiative for health visitors in England.

Science.gov (United States)

Sayer, Lynn; Barriball, K Louise; Bliss, Julie; Bakhshi, Savita; While, Alison E

2016-04-01

Fast tracking is one strategy that organizations use to ameliorate workforce shortfalls by attracting new recruits, and accelerating their skills development and experience. In response to the Government's target of rapidly expanding the number of health visitors in England's National Health Service, the fast track initiative was launched to recruit newly qualified (fast track) as well as experienced (standard entry) nurses and midwives onto health visiting programmes. This paper evaluates the fast track initiative, from the perspective of fast track and standard entry students, practice teachers and health visitor managers. A mixed methods design was used comprising a questionnaire survey (n = 71 students), semi-structured interviews (n = 37 students), telephone interviews (n = 13 managers) and six focus groups (n = 24 practice teachers). Data were collected between April 2012 and July 2013. Descriptive statistics, t-tests and the Pearson Chi-square test were used to analyse the quantitative data. The qualitative data were analysed thematically. Motivations for health visiting as a career choice were similar for fast track and standard entry students, with career progression and interest in health promotion being key motivators. There was consensus that personal qualities and characteristics were more important than experience or qualifications. However, fast track students were significantly less confident about their public health competencies in leadership and management (p communication (p  0.5). Fast tracking offers a useful recruitment strategy in order to expand the health visitor workforce, but longitudinal research is needed to confirm benefits such as retention and career trajectories. © The Author(s) 2015.

Advanced Tracking of Vehicles

DEFF Research Database (Denmark)

Jensen, Christian Søndergaard; Li, K.-J.; Pakalnis, Stardas

2005-01-01

efficient tracking techniques. More specifically, while almost all commercially available tracking solutions simply offer time-based sampling of positions, this paper's techniques aim to offer a guaranteed tracking accuracy for each vehicle at the lowest possible costs, in terms of network traffic...

Nonlinear Autoregressive Network with the Use of a Moving Average Method for Forecasting Typhoon Tracks

OpenAIRE

Tienfuan Kerh; Shin-Hung Wu

2017-01-01

Forecasting of a typhoon moving path may help to evaluate the potential negative impacts in the neighbourhood areas along the moving path. This study proposed a work of using both static and dynamic neural network models to link a time series of typhoon track parameters including longitude and latitude of the typhoon central location, cyclonic radius, central wind speed, and typhoon moving speed. Based on the historical records of 100 typhoons, the performances of neural network models are ev...

Social network fragmentation and community health.

Science.gov (United States)

Chami, Goylette F; Ahnert, Sebastian E; Kabatereine, Narcis B; Tukahebwa, Edridah M

2017-09-05

Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

Using tracking infrastructure to support public health programs, policies, and emergency response in New York City.

Science.gov (United States)

Jeffery, Nancy Loder; McKelvey, Wendy; Matte, Thomas

2015-01-01

To describe how the New York City (NYC) Tracking Program has used nationally mandated Secure Portal infrastructure and staff analytical expertise to support programs and inform policy. The NYC Health Department assesses, investigates, and acts on a wide range of environmental concerns to protect the health of New Yorkers. Specific examples of highly effective policies or initiatives that relied on the NYC Tracking Program are described, including restaurant sanitary grade posting, rat indexing, converting boilers to cleaner-burning fuels, reducing exposure to mercury from fish and contaminated products, and responding to Superstorm Sandy. The NYC Tracking Program supports the Health Department in using inspectional, administrative, and health data to guide operations. Tracking has also allowed internal and external partners to use these data to guide policy development.

Distributed Data Networks That Support Public Health Information Needs.

Science.gov (United States)

Tabano, David C; Cole, Elizabeth; Holve, Erin; Davidson, Arthur J

Data networks, consisting of pooled electronic health data assets from health care providers serving different patient populations, promote data sharing, population and disease monitoring, and methods to assess interventions. Better understanding of data networks, and their capacity to support public health objectives, will help foster partnerships, expand resources, and grow learning health systems. We conducted semistructured interviews with 16 key informants across the United States, identified as network stakeholders based on their respective experience in advancing health information technology and network functionality. Key informants were asked about their experience with and infrastructure used to develop data networks, including each network's utility to identify and characterize populations, usage, and sustainability. Among 11 identified data networks representing hundreds of thousands of patients, key informants described aggregated health care clinical data contributing to population health measures. Key informant interview responses were thematically grouped to illustrate how networks support public health, including (1) infrastructure and information sharing; (2) population health measures; and (3) network sustainability. Collaboration between clinical data networks and public health entities presents an opportunity to leverage infrastructure investments to support public health. Data networks can provide resources to enhance population health information and infrastructure.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

Science.gov (United States)

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-05-30

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

Directory of Open Access Journals (Sweden)

Gaining Han

2017-05-01

Full Text Available The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS, the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

Impact of a workplace physical activity tracking program on biometric health outcomes.

Science.gov (United States)

Yu, Jiani; Abraham, Jean M; Dowd, Bryan; Higuera, Lucas F; Nyman, John A

2017-12-01

Wellness programs are a popular strategy utilized by large U.S. employers. As mobile health applications and wearable tracking devices increase in prevalence, many employers now offer physical activity tracking applications. This longitudinal study evaluates the impact of engagement with a web-based, physical activity tracking program on changes in individuals' biometric outcomes in an employer population. The study population includes active employees and adult dependents continuously enrolled in an eligible health plan and who have completed at least two biometric screenings (n=36,882 person-years with 11,436 unique persons) between 2011 and 2014. Using difference-in-differences (DID) regression, we estimate the effect of participation in the physical activity tracking application on BMI, total cholesterol, and blood pressure. Participation was significantly associated with a reduction of 0.275 in BMI in the post-period, relative to the comparison group, representing a 1% change from baseline BMI. The program did not have a statistically significant impact on cholesterol or blood pressure. Sensitivity checks revealed slightly larger BMI reductions among participants with higher intensity of tracking activity and in the period following the employer's shift to an outcomes-based incentive design. Results are broadly consistent with the existing literature on changes in biometric outcomes from workplace initiatives promoting increased physical activity. Employers should have modest expectations about the potential health benefits of such programs, given current designs and implementation in real-world settings. Copyright © 2017 Elsevier Inc. All rights reserved.

A Novel Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking in an LPI Radar Network

Directory of Open Access Journals (Sweden)

Ji She

2016-12-01

Full Text Available Radar networks are proven to have numerous advantages over traditional monostatic and bistatic radar. With recent developments, radar networks have become an attractive platform due to their low probability of intercept (LPI performance for target tracking. In this paper, a joint sensor selection and power allocation algorithm for multiple-target tracking in a radar network based on LPI is proposed. It is found that this algorithm can minimize the total transmitted power of a radar network on the basis of a predetermined mutual information (MI threshold between the target impulse response and the reflected signal. The MI is required by the radar network system to estimate target parameters, and it can be calculated predictively with the estimation of target state. The optimization problem of sensor selection and power allocation, which contains two variables, is non-convex and it can be solved by separating power allocation problem from sensor selection problem. To be specific, the optimization problem of power allocation can be solved by using the bisection method for each sensor selection scheme. Also, the optimization problem of sensor selection can be solved by a lower complexity algorithm based on the allocated powers. According to the simulation results, it can be found that the proposed algorithm can effectively reduce the total transmitted power of a radar network, which can be conducive to improving LPI performance.

State Support: A Prerequisite for Global Health Network Effectiveness Comment on "Four Challenges that Global Health Networks Face".

Science.gov (United States)

Marten, Robert; Smith, Richard D

2017-07-24

Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks' success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks' effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research. © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Railway Track Allocation: Models and Methods

DEFF Research Database (Denmark)

Lusby, Richard Martin; Larsen, Jesper; Ehrgott, Matthias

2011-01-01

Efficiently coordinating the movement of trains on a railway network is a central part of the planning process for a railway company. This paper reviews models and methods that have been proposed in the literature to assist planners in finding train routes. Since the problem of routing trains......, and train routing problems, group them by railway network type, and discuss track allocation from a strategic, tactical, and operational level....... on a railway network entails allocating the track capacity of the network (or part thereof) over time in a conflict-free manner, all studies that model railway track allocation in some capacity are considered relevant. We hence survey work on the train timetabling, train dispatching, train platforming...

Railway Track Allocation: Models and Methods

DEFF Research Database (Denmark)

Lusby, Richard Martin; Larsen, Jesper; Ehrgott, Matthias

Eciently coordinating the movement of trains on a railway network is a central part of the planning process for a railway company. This paper reviews models and methods that have been proposed in the literature to assist planners in nding train routes. Since the problem of routing trains......, and train routing problems, group them by railway network type, and discuss track allocation from a strategic, tactical, and operational level....... on a railway network entails allocating the track capacity of the network (or part thereof) over time in a con ict-free manner, all studies that model railway track allocation in some capacity are considered relevant. We hence survey work on the train timetabling, train dispatching, train platforming...

The tracking and locating system of the mining personnel based on the Zigbee wireless network platform

Energy Technology Data Exchange (ETDEWEB)

Xiao-jia, Xie; Li-jun, Cheng; Yong, Wang [Chongqing University, Chongqing (China). College of Resources and Environmental Science

2007-08-15

By comparing the advanced Zigbee wireless network system with active RFID, the Zigbee wireless network technology was introduced into the system for tracking and locating of coal miners. The system structure and function characteristics were also introduced. This system makes it convenient to mine administrators, control room personnel and security inspectors to obtain information on the working hours and location of mine workers in the mine. 6 refs., 2 figs., 3 tabs.

Scalable Track Detection in SAR CCD Images

Energy Technology Data Exchange (ETDEWEB)

Chow, James G [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Quach, Tu-Thach [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-03-01

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988, up fr om 0.907 obtained by the current state-of-the-art method.

The Nordic Health Promotion Research Network (NHPRN).

Science.gov (United States)

Ringsberg, Karin C

2015-08-01

The Nordic Health Promotion Research Network (NHPRN) was established in 2007 at the Nordic School of Public Health (NHV). This article aims to describe the foundation of the NHPRN, the development and the present status of the work of NHPRN. The NHPRN consists of about 50 senior and junior researchers from all Nordic countries. It is a working network that aims to develop the theoretical understanding of health promotion, to create research cooperation in health promotion from a Nordic perspective and to extend the scope of health promotion through education. Network members meet biannually to discuss and further develop research within the field and are also responsible for the Nordic conference on Health Promotion, organized every 3 years. The NHV hosted the network between 2007 and 2014; and the World Health Organisation (WHO) will assume this role in 2015. © 2015 the Nordic Societies of Public Health.

Digital Networked Information Society and Public Health: Problems and Promises of Networked Health Communication of Lay Publics.

Science.gov (United States)

Kim, Jeong-Nam

2018-01-01

This special issue of Health Communication compiles 10 articles to laud the promise and yet confront the problems in the digital networked information society related to public health. We present this anthology of symphony and cacophony of lay individuals' communicative actions in a digital networked information society. The collection of problems and promise of the new digital world may be a cornerstone joining two worlds-pre- and postdigital network society-and we hope this special issue will help better shape our future states of public health.

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Science.gov (United States)

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo; Cerati, Giuseppe; Gray, Lindsey; Kowalkowski, Jim; Mudigonda, Mayur; Prabhat; Spentzouris, Panagiotis; Spiropoulou, Maria; Tsaris, Aristeidis; Vlimant, Jean-Roch; Zheng, Stephan

2017-08-01

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Directory of Open Access Journals (Sweden)

Farrell Steven

2017-01-01

Full Text Available Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM and convolutional neural networks to find and fit tracks in toy detector data.

Social Networks and Health Knowledge in India

DEFF Research Database (Denmark)

Blunch, Niels-Hugo; Datta Gupta, Nabanita

such as education and access to social networks explain part of the gap, a substantial part of the health knowledge gap is left unexplained. All groups have greater health knowledge in urban than in rural areas, but the gap is even wider in urban than in rural areas. Additionally, high caste women benefit more...... in terms of health knowledge from having health networks than women from other groups; except if the health person is of the same caste/religion, in which case low caste and Muslim women sometimes benefit by as much as double that of high caste women, or even more. It may therefore not be enough to give...... individuals access to high quality networks if caste and religion-related gaps in health knowledge are to be reduced; such networks also have to be homophilous, to have the maximum effect. Improved treatment from and confidence in the medical profession is found to be part of the mechanism linking health...

Privacy Practices of Health Social Networking Sites: Implications for Privacy and Data Security in Online Cancer Communities.

Science.gov (United States)

Charbonneau, Deborah H

2016-08-01

While online communities for social support continue to grow, little is known about the state of privacy practices of health social networking sites. This article reports on a structured content analysis of privacy policies and disclosure practices for 25 online ovarian cancer communities. All of the health social networking sites in the study sample provided privacy statements to users, yet privacy practices varied considerably across the sites. The majority of sites informed users that personal information was collected about participants and shared with third parties (96%, n = 24). Furthermore, more than half of the sites (56%, n = 14) stated that cookies technology was used to track user behaviors. Despite these disclosures, only 36% (n = 9) offered opt-out choices for sharing data with third parties. In addition, very few of the sites (28%, n = 7) allowed individuals to delete their personal information. Discussions about specific security measures used to protect personal information were largely missing. Implications for privacy, confidentiality, consumer choice, and data safety in online environments are discussed. Overall, nurses and other health professionals can utilize these findings to encourage individuals seeking online support and participating in social networking sites to build awareness of privacy risks to better protect their personal health information in the digital age.

A Velocity-Level Bi-Criteria Optimization Scheme for Coordinated Path Tracking of Dual Robot Manipulators Using Recurrent Neural Network.

Science.gov (United States)

Xiao, Lin; Zhang, Yongsheng; Liao, Bolin; Zhang, Zhijun; Ding, Lei; Jin, Long

2017-01-01

A dual-robot system is a robotic device composed of two robot arms. To eliminate the joint-angle drift and prevent the occurrence of high joint velocity, a velocity-level bi-criteria optimization scheme, which includes two criteria (i.e., the minimum velocity norm and the repetitive motion), is proposed and investigated for coordinated path tracking of dual robot manipulators. Specifically, to realize the coordinated path tracking of dual robot manipulators, two subschemes are first presented for the left and right robot manipulators. After that, such two subschemes are reformulated as two general quadratic programs (QPs), which can be formulated as one unified QP. A recurrent neural network (RNN) is thus presented to solve effectively the unified QP problem. At last, computer simulation results based on a dual three-link planar manipulator further validate the feasibility and the efficacy of the velocity-level optimization scheme for coordinated path tracking using the recurrent neural network.

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

Science.gov (United States)

Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

2016-01-01

In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

State Support: A Prerequisite for Global Health Network Effectiveness; Comment on “Four Challenges that Global Health Networks Face”

Directory of Open Access Journals (Sweden)

Robert Marten

2018-03-01

Full Text Available Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks’ success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks’ effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research.

Probabilistic Localization and Tracking of Malicious Insiders Using Hyperbolic Position Bounding in Vehicular Networks

Directory of Open Access Journals (Sweden)

Laurendeau Christine

2009-01-01

Full Text Available A malicious insider in a wireless network may carry out a number of devastating attacks without fear of retribution, since the messages it broadcasts are authenticated with valid credentials such as a digital signature. In attributing an attack message to its perpetrator by localizing the signal source, we can make no presumptions regarding the type of radio equipment used by a malicious transmitter, including the transmitting power utilized to carry out an exploit. Hyperbolic position bounding (HPB provides a mechanism to probabilistically estimate the candidate location of an attack message's originator using received signal strength (RSS reports, without assuming knowledge of the transmitting power. We specialize the applicability of HPB into the realm of vehicular networks and provide alternate HPB algorithms to improve localization precision and computational efficiency. We extend HPB for tracking the consecutive locations of a mobile attacker. We evaluate the localization and tracking performance of HPB in a vehicular scenario featuring a variable number of receivers and a known navigational layout. We find that HPB can position a transmitting device within stipulated guidelines for emergency services localization accuracy.

Advancing Health Professions Education Research by Creating a Network of Networks.

Science.gov (United States)

Carney, Patricia A; Brandt, Barbara; Dekhtyar, Michael; Holmboe, Eric S

2018-02-27

Producing the best evidence to show educational outcomes, such as competency achievement and credentialing effectiveness, across the health professions education continuum will require large multisite research projects and longitudinal studies. Current limitations that must be overcome to reach this goal include the prevalence of single-institution study designs, assessments of a single curricular component, and cross-sectional study designs that provide only a snapshot in time of a program or initiative rather than a longitudinal perspective.One solution to overcoming these limitations is to develop a network of networks that collaborates, using longitudinal approaches, across health professions and regions of the United States. Currently, individual networks are advancing educational innovation toward understanding the effectiveness of educational and credentialing programs. Examples of such networks include: (1) the American Medical Association's Accelerating Change in Medical Education initiative, (2) the National Center for Interprofessional Practice and Education, and (3) the Accreditation Council for Graduate Medical Education's Accreditation System. In this Invited Commentary, the authors briefly profile these existing networks, identify their progress and the challenges they have encountered, and propose a vigorous way forward toward creating a national network of networks designed to determine the effectiveness of health professions education and credentialing.

Service network analysis for agricultural mental health

Directory of Open Access Journals (Sweden)

Fuller Jeffrey D

2009-05-01

Full Text Available Abstract Background Farmers represent a subgroup of rural and remote communities at higher risk of suicide attributed to insecure economic futures, self-reliant cultures and poor access to health services. Early intervention models are required that tap into existing farming networks. This study describes service networks in rural shires that relate to the mental health needs of farming families. This serves as a baseline to inform service network improvements. Methods A network survey of mental health related links between agricultural support, health and other human services in four drought declared shires in comparable districts in rural New South Wales, Australia. Mental health links covered information exchange, referral recommendations and program development. Results 87 agencies from 111 (78% completed a survey. 79% indicated that two thirds of their clients needed assistance for mental health related problems. The highest mean number of interagency links concerned information exchange and the frequency of these links between sectors was monthly to three monthly. The effectiveness of agricultural support and health sector links were rated as less effective by the agricultural support sector than by the health sector (p Conclusion Aligning with agricultural agencies is important to build effective mental health service pathways to address the needs of farming populations. Work is required to ensure that these agricultural support agencies have operational and effective links to primary mental health care services. Network analysis provides a baseline to inform this work. With interventions such as local mental health training and joint service planning to promote network development we would expect to see over time an increase in the mean number of links, the frequency in which these links are used and the rated effectiveness of these links.

Hardware realization of a fast neural network algorithm for real-time tracking in HEP experiments

International Nuclear Information System (INIS)

Leimgruber, F.R.; Pavlopoulos, P.; Steinacher, M.; Tauscher, L.; Vlachos, S.; Wendler, H.

1995-01-01

A fast pattern recognition system for HEP experiments, based on artificial neural network algorithms (ANN), has been realized with standard electronics. The multiplicity and location of tracks in an event are determined in less than 75 ns. Hardware modules of this first level trigger were extensively tested for performance and reliability with data from the CPLEAR experiment. (orig.)

Quantized Iterative Learning Consensus Tracking of Digital Networks With Limited Information Communication.

Science.gov (United States)

Xiong, Wenjun; Yu, Xinghuo; Chen, Yao; Gao, Jie

2017-06-01

This brief investigates the quantized iterative learning problem for digital networks with time-varying topologies. The information is first encoded as symbolic data and then transmitted. After the data are received, a decoder is used by the receiver to get an estimate of the sender's state. Iterative learning quantized communication is considered in the process of encoding and decoding. A sufficient condition is then presented to achieve the consensus tracking problem in a finite interval using the quantized iterative learning controllers. Finally, simulation results are given to illustrate the usefulness of the developed criterion.

Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems.

Science.gov (United States)

Han, Seong-Ik; Lee, Jang-Myung

2014-01-01

This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

A Case for Open Network Health Systems: Systems as Networks in Public Mental Health

Directory of Open Access Journals (Sweden)

Michael Grant Rhodes

2017-03-01

Full Text Available Increases in incidents involving so-called confused persons have brought attention to the potential costs of recent changes to public mental health (PMH services in the Netherlands. Decentralized under the (Community Participation Act (2014, local governments must find resources to compensate for reduced central funding to such services or “innovate.” But innovation, even when pressure for change is intense, is difficult. This perspective paper describes experience during and after an investigation into a particularly violent incident and murder. The aim was to provide recommendations to improve the functioning of local PMH services. The investigation concluded that no specific failure by an individual professional or service provider facility led to the murder. Instead, also as a result of the Participation Act that severed communication lines between individuals and organizations, information sharing failures were likely to have reduced system level capacity to identify risks. The methods and analytical frameworks employed to reach this conclusion, also lead to discussion as to the plausibility of an unconventional solution. If improving communication is the primary problem, non-hierarchical information, and organizational networks arise as possible and innovative system solutions. The proposal for debate is that traditional “health system” definitions, literature and narratives, and operating assumptions in public (mental health are ‘locked in’ constraining technical and organization innovations. If we view a “health system” as an adaptive system of economic and social “networks,” it becomes clear that the current orthodox solution, the so-called integrated health system, typically results in a “centralized hierarchical” or “tree” network. An overlooked alternative that breaks out of the established policy narratives is the view of a ‘health systems’ as a non-hierarchical organizational structure or �

Health 2.0-Lessons Learned: Social Networking With Patients for Health Promotion.

Science.gov (United States)

Sharma, Suparna; Kilian, Reena; Leung, Fok-Han

2014-07-01

The advent of social networking as a major platform for human interaction has introduced a new dimension into the physician-patient relationship, known as Health 2.0. The concept of Health 2.0 is young and evolving; so far, it has meant the use of social media by health professionals and patients to personalize health care and promote health education. Social networking sites like Facebook and Twitter offer promising platforms for health care providers to engage patients. Despite the vast potential of Health 2.0, usage by health providers remains relatively low. Using a pilot study as an example, this commentary reviews the ways in which physicians can effectively harness the power of social networking to meaningfully engage their patients in primary prevention. © The Author(s) 2014.

Tufts academic health information network: concept and scenario.

Science.gov (United States)

Stearns, N S

1986-04-01

Tufts University School of Medicine's new health sciences education building, the Arthur M. Sackler Center for Health Communications, will house a modern medical library and computer center, classrooms, auditoria, and media facilities. The building will also serve as the center for an information and communication network linking the medical school and adjacent New England Medical Center, Tufts' primary teaching hospital, with Tufts Associated Teaching Hospitals throughout New England. Ultimately, the Tufts network will join other gateway networks, information resource facilities, health care institutions, and medical schools throughout the world. The center and the network are intended to facilitate and improve the education of health professionals, the delivery of health care to patients, the conduct of research, and the implementation of administrative management approaches that should provide more efficient utilization of resources and save dollars. A model and scenario show how health care delivery and health care education are integrated through better use of information transfer technologies by health information specialists, practitioners, and educators.

Rural Health Networks: How Network Analysis Can Inform Patient Care and Organizational Collaboration in a Rural Breast Cancer Screening Network.

Science.gov (United States)

Prusaczyk, Beth; Maki, Julia; Luke, Douglas A; Lobb, Rebecca

2018-04-15

Rural health networks have the potential to improve health care quality and access. Despite this, the use of network analysis to study rural health networks is limited. The purpose of this study was to use network analysis to understand how a network of rural breast cancer care providers deliver services and to demonstrate the value of this methodology in this research area. Leaders at 47 Federally Qualified Health Centers and Rural Health Clinics across 10 adjacent rural counties were asked where they refer patients for mammograms or breast biopsies. These clinics and the 22 referral providers that respondents named comprised the network. The network was analyzed graphically and statistically with exponential random graph modeling. Most (96%, n = 45) of the clinics and referral sites (95%, n = 21) are connected to each other. Two clinics of the same type were 62% less likely to refer patients to the same providers as 2 clinics of different types (OR = 0.38, 95% CI = 0.29-0.50). Clinics in the same county have approximately 8 times higher odds of referring patients to the same providers compared to clinics in different counties (OR = 7.80, CI = 4.57-13.31). This study found that geographic location of resources is an important factor in rural health care providers' referral decisions and demonstrated the usefulness of network analysis for understanding rural health networks. These results can be used to guide delivery of patient care and strengthen the network by building resources that take location into account. © 2018 National Rural Health Association.

Multisector Health Policy Networks in 15 Large US Cities

Science.gov (United States)

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

2016-01-01

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

Tweet for health: using an online social network to examine temporal trends in weight loss-related posts.

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Turner-McGrievy, Gabrielle M; Beets, Michael W

2015-06-01

Few studies have used social networking sites to track temporal trends in health-related posts, particularly around weight loss. To examine the temporal relationship of Twitter messages about weight loss over 1 year (2012). Temporal trends in #weightloss mentions and #fitness, #diet, and #health tweets which also had the word "weight" in them were examined using three a priori time periods: (1) holidays: pre-winter holidays, holidays, and post-holidays; (2) Season: winter and summer; and (3) New Year's: pre-New Year's and post-New Year's. Regarding #weightloss, there were 145 (95 % CI 79, 211) more posts/day during holidays and 143 (95 % CI 76, 209) more posts/day after holidays as compared to 480 pre-holiday posts/day; 232 (95 % CI 178, 286) more posts/day during the winter versus summer (441 posts/day); there was no difference in posts around New Year's. Examining social networks for trends in health-related posts may aid in timing interventions when individuals are more likely to be discussing weight loss.

A tripartite regulation of health networks.

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Weil, T P; Jorgensen, N E

1996-01-01

With the Republicans in power, market-driven forces of managed care plans, capitated payment, and the regional networks (alliances) are likely to serve as centerpieces for improving the organization, financing, and delivery of our nation's health services. These "voluntary" alliances of health providers and health insurance underwriters foreshadow the powerful, geographically linked regional health networks that are now becoming oligopolies. As a result of these providers developing monopolistic practices, state health services commissions will be formed to regulate market share, the scope of health services, reimbursement rates, and profits. State departments of public health will continue to focus on broader community health initiatives such as access and quality. Complexities of relationships among those regulated by these responsible agencies, and the interfacing of these state health services commissions and state departments of public health and insurance, with their potentially conflicting goals and political forces, are expected.

A review of influenza detection and prediction through social networking sites.

Science.gov (United States)

Alessa, Ali; Faezipour, Miad

2018-02-01

Early prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions.

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.

Empowering health personnel for decentralized health planning in India: The Public Health Resource Network

Directory of Open Access Journals (Sweden)

Prasad Vandana

2009-07-01

Full Text Available Abstract The Public Health Resource Network is an innovative distance-learning course in training, motivating, empowering and building a network of health personnel from government and civil society groups. Its aim is to build human resource capacity for strengthening decentralized health planning, especially at the district level, to improve accountability of health systems, elicit community participation for health, ensure equitable and accessible health facilities and to bring about convergence in programmes and services. The question confronting health systems in India is how best to reform, revitalize and resource primary health systems to deliver different levels of service aligned to local realities, ensuring universal coverage, equitable access, efficiency and effectiveness, through an empowered cadre of health personnel. To achieve these outcomes it is essential that health planning be decentralized. Districts vary widely according to the specific needs of their population, and even more so in terms of existing interventions and available resources. Strategies, therefore, have to be district-specific, not only because health needs vary, but also because people's perceptions and capacities to intervene and implement programmes vary. In centrally designed plans there is little scope for such adaptation and contextualization, and hence decentralized planning becomes crucial. To undertake these initiatives, there is a strong need for trained, motivated, empowered and networked health personnel. It is precisely at this level that a lack of technical knowledge and skills and the absence of a supportive network or adequate educational opportunities impede personnel from making improvements. The absence of in-service training and of training curricula that reflect field realities also adds to this, discouraging health workers from pursuing effective strategies. The Public Health Resource Network is thus an attempt to reach out to motivated

Empowering health personnel for decentralized health planning in India: The Public Health Resource Network.

Science.gov (United States)

Kalita, Anuska; Zaidi, Sarover; Prasad, Vandana; Raman, V R

2009-07-20

The Public Health Resource Network is an innovative distance-learning course in training, motivating, empowering and building a network of health personnel from government and civil society groups. Its aim is to build human resource capacity for strengthening decentralized health planning, especially at the district level, to improve accountability of health systems, elicit community participation for health, ensure equitable and accessible health facilities and to bring about convergence in programmes and services. The question confronting health systems in India is how best to reform, revitalize and resource primary health systems to deliver different levels of service aligned to local realities, ensuring universal coverage, equitable access, efficiency and effectiveness, through an empowered cadre of health personnel. To achieve these outcomes it is essential that health planning be decentralized. Districts vary widely according to the specific needs of their population, and even more so in terms of existing interventions and available resources. Strategies, therefore, have to be district-specific, not only because health needs vary, but also because people's perceptions and capacities to intervene and implement programmes vary. In centrally designed plans there is little scope for such adaptation and contextualization, and hence decentralized planning becomes crucial. To undertake these initiatives, there is a strong need for trained, motivated, empowered and networked health personnel. It is precisely at this level that a lack of technical knowledge and skills and the absence of a supportive network or adequate educational opportunities impede personnel from making improvements. The absence of in-service training and of training curricula that reflect field realities also adds to this, discouraging health workers from pursuing effective strategies. The Public Health Resource Network is thus an attempt to reach out to motivated though often isolated health

Interactive navigation and bronchial tube tracking in virtual bronchoscopy.

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Heng, P A; Fung, P F; Wong, T T; Siu, Y H; Sun, H

1999-01-01

An interactive virtual environment for simulation of bronchoscopy is developed. Medical doctor can safely plan their surgical bronchoscopy using the virtual environment without any invasive diagnosis which may risk the patient's health. The 3D pen input device of the system allows the doctor to navigate and visualize the bronchial tree of the patient naturally and interactively. To navigate the patient's bronchial tree, a vessel tracking process is required. While manual tracking is tedious and labor-intensive, fully automatic tracking may not be reliable. We propose a semi-automatic tracking technique called Intelligent Path Tracker which provides automation and enough user control during the vessel tracking. To support an interactive frame rate, we also introduce a new volume rendering acceleration technique, named as IsoRegion Leaping. The volume rendering is further accelerated by distributed rendering on a TCP/IP-based network of low-cost PCs. With these approaches, a 256 x 256 x 256 volume data of human lung, can be navigated and visualized at a frame rate of over 10 Hz in our virtual bronchoscopy system.

Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori.

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Chen, Weisheng; Ge, Shuzhi Sam; Wu, Jian; Gong, Maoguo

2015-09-01

This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.

Future Earth Health Knowledge-Action Network.

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Shrivastava, Paul; Raivio, Kari; Kasuga, Fumiko; Tewksbury, Joshua; Haines, Andy; Daszak, Peter

Future Earth is an international research platform providing the knowledge and support to accelerate our transformations to a sustainable world. Future Earth 2025 Vision identified eight key focal challenges, and challenge #6 is to "Improve human health by elucidating, and finding responses to, the complex interactions amongst environmental change, pollution, pathogens, disease vectors, ecosystem services, and people's livelihoods, nutrition and well-being." Several studies, including the Rockefeller Foundation/Lancet Planetary Health Commission Report of 2015, the World Health Organization/Convention on Biological Diversity report and those by oneHEALTH (former ecoHEALTH), have been conducted over the last 30 years. Knowledge-Action Networks (KANs) are the frameworks to apply Future Earth principles of research to related activities that respond to societal challenges. Future Earth Health Knowledge-Action Network will connect health researchers with other natural and social scientists, health and environmental policy professionals and leaders in government, the private sector and civil society to provide research-based solutions based on better, integrated understanding of the complex interactions between a changing global environment and human health. It will build regional capacity to enhance resilience, protect the environment and avert serious threats to health and will also contribute to achieving Sustainable Development Goals. In addition to the initial partners, Future Earth Health Knowledge-Action Network will further nourish collaboration with other on-going, leading research programmes outside Future Earth, by encouraging them in active participation.

[Linking: relationships between health professionals in the informal health networks].

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Sarradon-Eck, A; Vega, A; Faure, M; Humbert-Gaudart, A; Lustman, M

2008-07-01

During the last years, the french health system has been developing formal health networks. So, it was necessary to study informal health networks as networks. More precisely, we studied the nature of relationships between various stakeholders around general practionners wich are commonly considering as the stakeholder of the health system private sector. Fieldwork (ethnography based on direct observations and interviews) was conducted between October 2002 and april 2004, in the South-East of France. Ten monographs of general practioner's offices were achieved in a rural area; then, we achieved fieldwork of the informal health networks identified. There is a cultural frame wich is common to all private professionals. This frame includes a triple ideal (teamwork built up the hospital model, independance, and an relational approach with patients). This frame does not square with the real practices. In fact, regulation mechanisms preserve the balance of relashionships between professionnal groups, by restricting/promoting exchanges and complex alliance strategies. These mecanisms include: (1) a few professionnal's rule as disponibility (to the patients and to the professionnals), as communication about patient, as patient's reference, as obligation to communicate between professionals; (2) some constraints such as territory superposition and competition with other professional groups; (3) some needs for: rileiving (of emotions and worries connected to work), sharing (decisions, responsabilities), of delegation (medical treatment, practices), protection against social and legal risk through the creation of trust relationships. These trust relationships are based on several logics (affinity, solidarity, similarity). The study shows the major place of the patient who is often the main organizer of his network, and even though he makes an important structuring work between medical staff, and an information transfer (on his diagnosis, on his treatment, and professionals

Route planning with transportation network maps: an eye-tracking study.

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Grison, Elise; Gyselinck, Valérie; Burkhardt, Jean-Marie; Wiener, Jan Malte

2017-09-01

Planning routes using transportation network maps is a common task that has received little attention in the literature. Here, we present a novel eye-tracking paradigm to investigate psychological processes and mechanisms involved in such a route planning. In the experiment, participants were first presented with an origin and destination pair before we presented them with fictitious public transportation maps. Their task was to find the connecting route that required the minimum number of transfers. Based on participants' gaze behaviour, each trial was split into two phases: (1) the search for origin and destination phase, i.e., the initial phase of the trial until participants gazed at both origin and destination at least once and (2) the route planning and selection phase. Comparisons of other eye-tracking measures between these phases and the time to complete them, which depended on the complexity of the planning task, suggest that these two phases are indeed distinct and supported by different cognitive processes. For example, participants spent more time attending the centre of the map during the initial search phase, before directing their attention to connecting stations, where transitions between lines were possible. Our results provide novel insights into the psychological processes involved in route planning from maps. The findings are discussed in relation to the current theories of route planning.

The effect of narrow provider networks on health care use.

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Atwood, Alicia; Lo Sasso, Anthony T

2016-12-01

Network design is an often overlooked aspect of health insurance contracts. Recent policy factors have resulted in narrower provider networks. We provide plausibly causal evidence on the effect of narrow network plans offered by a large national health insurance carrier in a major metropolitan market. Our econometric design exploits the fact that some firms offer a narrow network plan to their employees and some do not. Our results show that narrow network health plans lead to reductions in health care utilization and spending. We find evidence that narrow networks save money by selecting lower cost providers into the network. Copyright © 2016 Elsevier B.V. All rights reserved.

A Case for Open Network Health Systems: Systems as Networks in Public Mental Health.

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Rhodes, Michael Grant; de Vries, Marten W

2017-01-08

Increases in incidents involving so-called confused persons have brought attention to the potential costs of recent changes to public mental health (PMH) services in the Netherlands. Decentralized under the (Community) Participation Act (2014), local governments must find resources to compensate for reduced central funding to such services or "innovate." But innovation, even when pressure for change is intense, is difficult. This perspective paper describes experience during and after an investigation into a particularly violent incident and murder. The aim was to provide recommendations to improve the functioning of local PMH services. The investigation concluded that no specific failure by an individual professional or service provider facility led to the murder. Instead, also as a result of the Participation Act that severed communication lines between individuals and organizations, information sharing failures were likely to have reduced system level capacity to identify risks. The methods and analytical frameworks employed to reach this conclusion, also lead to discussion as to the plausibility of an unconventional solution. If improving communication is the primary problem, non-hierarchical information, and organizational networks arise as possible and innovative system solutions. The proposal for debate is that traditional "health system" definitions, literature and narratives, and operating assumptions in public (mental) health are 'locked in' constraining technical and organization innovations. If we view a "health system" as an adaptive system of economic and social "networks," it becomes clear that the current orthodox solution, the so-called integrated health system, typically results in a "centralized hierarchical" or "tree" network. An overlooked alternative that breaks out of the established policy narratives is the view of a 'health systems' as a non-hierarchical organizational structure or 'Open Network.' In turn, this opens new technological and

Neural-Network-Based Robust Optimal Tracking Control for MIMO Discrete-Time Systems With Unknown Uncertainty Using Adaptive Critic Design.

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Liu, Lei; Wang, Zhanshan; Zhang, Huaguang

2018-04-01

This paper is concerned with the robust optimal tracking control strategy for a class of nonlinear multi-input multi-output discrete-time systems with unknown uncertainty via adaptive critic design (ACD) scheme. The main purpose is to establish an adaptive actor-critic control method, so that the cost function in the procedure of dealing with uncertainty is minimum and the closed-loop system is stable. Based on the neural network approximator, an action network is applied to generate the optimal control signal and a critic network is used to approximate the cost function, respectively. In contrast to the previous methods, the main features of this paper are: 1) the ACD scheme is integrated into the controllers to cope with the uncertainty and 2) a novel cost function, which is not in quadric form, is proposed so that the total cost in the design procedure is reduced. It is proved that the optimal control signals and the tracking errors are uniformly ultimately bounded even when the uncertainty exists. Finally, a numerical simulation is developed to show the effectiveness of the present approach.

Infrared dim moving target tracking via sparsity-based discriminative classifier and convolutional network

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Qian, Kun; Zhou, Huixin; Wang, Bingjian; Song, Shangzhen; Zhao, Dong

2017-11-01

Infrared dim and small target tracking is a great challenging task. The main challenge for target tracking is to account for appearance change of an object, which submerges in the cluttered background. An efficient appearance model that exploits both the global template and local representation over infrared image sequences is constructed for dim moving target tracking. A Sparsity-based Discriminative Classifier (SDC) and a Convolutional Network-based Generative Model (CNGM) are combined with a prior model. In the SDC model, a sparse representation-based algorithm is adopted to calculate the confidence value that assigns more weights to target templates than negative background templates. In the CNGM model, simple cell feature maps are obtained by calculating the convolution between target templates and fixed filters, which are extracted from the target region at the first frame. These maps measure similarities between each filter and local intensity patterns across the target template, therefore encoding its local structural information. Then, all the maps form a representation, preserving the inner geometric layout of a candidate template. Furthermore, the fixed target template set is processed via an efficient prior model. The same operation is applied to candidate templates in the CNGM model. The online update scheme not only accounts for appearance variations but also alleviates the migration problem. At last, collaborative confidence values of particles are utilized to generate particles' importance weights. Experiments on various infrared sequences have validated the tracking capability of the presented algorithm. Experimental results show that this algorithm runs in real-time and provides a higher accuracy than state of the art algorithms.

Data for decision making in networked health

Directory of Open Access Journals (Sweden)

Christian Bourret

2006-06-01

Full Text Available In developed countries, nowadays we live in a networked society: a society of information, knowledge and services (Castells, 1996, with strong specificities in the Health field (Bourret, 2003, Silber, 2003. The World Health Organization (WHO has outlined the importance of information for improving health for all. However, financial resources remain limited. Health costs represent 11% of GNP in France, Germany, Switzerland and Canada, 14% in the USA, and 7.5% in Spain and the United Kingdom. Governments, local powers, health or insurance organizations therefore face difficult choices in terms of opportunities and priorities, and for that they need specific and valuable data. Firstly, this paper provide a comprehensive overview of our networked society and the appointment of ICT (Information and Communication Technologies and Health (in other words e-Health in a perspective of needs and uses at the micro, meso, and macro levels. We point out the main challenges of development of Nationwide Health Information Network both in the US, UK and France. Then we analyze the main issues about data for Decision Making in Networked Health: coordination and evaluation. In the last sections, we use an Information System perspective to investigate the three interoperability layers (micro, meso and macro. We analyze the requirements and challenges to design an interoperability global architecture which supports different kinds of interactions; then we focus on the harmonization efforts provided at several levels. Finally, we identify common methodological and engineering issues.

Program Spotlight: National Outreach Network's Community Health Educators

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National Outreach Network of Community Health Educators located at Community Network Program Centers, Partnerships to Advance Cancer Health Equity, and NCI-designated cancer centers help patients and their families receive survivorship support.

Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks.

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Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang

2017-11-01

The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

Simulation studies of a wide area health care network.

Science.gov (United States)

McDaniel, J. G.

1994-01-01

There is an increasing number of efforts to install wide area health care networks. Some of these networks are being built to support several applications over a wide user base consisting primarily of medical practices, hospitals, pharmacies, medical laboratories, payors, and suppliers. Although on-line, multi-media telecommunication is desirable for some purposes such as cardiac monitoring, store-and-forward messaging is adequate for many common, high-volume applications. Laboratory test results and payment claims, for example, can be distributed using electronic messaging networks. Several network prototypes have been constructed to determine the technical problems and to assess the effectiveness of electronic messaging in wide area health care networks. Our project, Health Link, developed prototype software that was able to use the public switched telephone network to exchange messages automatically, reliably and securely. The network could be configured to accommodate the many different traffic patterns and cost constraints of its users. Discrete event simulations were performed on several network models. Canonical star and mesh networks, that were composed of nodes operating at steady state under equal loads, were modeled. Both topologies were found to support the throughput of a generic wide area health care network. The mean message delivery time of the mesh network was found to be less than that of the star network. Further simulations were conducted for a realistic large-scale health care network consisting of 1,553 doctors, 26 hospitals, four medical labs, one provincial lab and one insurer. Two network topologies were investigated: one using predominantly peer-to-peer communication, the other using client-server communication.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:7949966

78 FR 17418 - Rural Health Information Technology Network Development Grant

Science.gov (United States)

2013-03-21

... Information Technology Network Development Grant AGENCY: Health Resources and Services Administration (HRSA...-competitive replacement award under the Rural Health Information Technology Network Development Grant (RHITND... relinquishing its fiduciary responsibilities for the Rural Health Information Technology Network Development...

Diffusion of the Digital Health Self-Tracking Movement in Canada: Results of a National Survey

Science.gov (United States)

Leaver, Chad; Bourget, Claire

2018-01-01

Background With the ever-increasing availability of mobile apps, consumer wearables, and smart medical devices, more and more individuals are self-tracking and managing their personal health data. Objective The aim of this study was to investigate the diffusion of the digital self-tracking movement in Canada. It provides a comprehensive, yet detailed account of this phenomenon. It examines the profile of digital self-trackers, traditional self-trackers, and nontrackers, further investigating the primary motivations for self-tracking and reasons for nontracking; barriers to adoption of connected care technologies; users’ appreciation of their self-tracking devices, including what they perceive to be the main benefits; factors that influence people’s intention to continue using connected care technologies in the future; and the reasons for usage discontinuance. Methods We conducted an online survey with a sample of 4109 Canadian adults, one of the largest ever. To ensure a representative sample, quota method was used (gender, age), following stratification by region. The maximum margin of error is estimated at 1.6%, 19 times out of 20. Results Our findings reveal that 66.20% (2720/4109) of our respondents regularly self-track one or more aspects of their health. About one in 4 respondents (1014/4109, 24.68%) currently owns a wearable or smart medical device, and 57.20% (580/1014) use their devices on a regular basis for self-tracking purposes. Digital self-trackers are typically young or mature adults, healthy, employed, university educated, with an annual family income of over $80,000 CAD. The most popular reported device is the fitness tracker or smartwatch that can capture a range of parameters. Currently, mobile apps and digital self-tracking devices are mainly used to monitor physical activity (856/1669, 51.13%), nutrition (545/1669, 32.65%), sleep patterns (482/1669, 28.88%) and, to a much lesser extent, cardiovascular and pulmonary biomarkers (215/1669, 12

Multitarget tracking in cluttered environment for a multistatic passive radar system under the DAB/DVB network

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Shi, Yi Fang; Park, Seung Hyo; Song, Taek Lyul

2017-12-01

The target tracking using multistatic passive radar in a digital audio/video broadcast (DAB/DVB) network with illuminators of opportunity faces two main challenges: the first challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional association ambiguity between the measurements and targets, which introduces a significantly complex three-dimensional (3-D) data association problem among the target-measurement illuminator, this is because all the illuminators transmit the same carrier frequency signals and signals transmitted by different illuminators but reflected via the same target become indistinguishable; the other challenge is that only the bistatic range and range-rate measurements are available while the angle information is unavailable or of very poor quality. In this paper, the authors propose a new target tracking algorithm directly in three-dimensional (3-D) Cartesian coordinates with the capability of track management using the probability of target existence as a track quality measure. The proposed algorithm is termed sequential processing-joint integrated probabilistic data association (SP-JIPDA), which applies the modified sequential processing technique to resolve the additional association ambiguity between measurements and illuminators. The SP-JIPDA algorithm sequentially operates the JIPDA tracker to update each track for each illuminator with all the measurements in the common measurement set at each time. For reasons of fair comparison, the existing modified joint probabilistic data association (MJPDA) algorithm that addresses the 3-D data association problem via "supertargets" using gate grouping and provides tracks directly in 3-D Cartesian coordinates, is enhanced by incorporating the probability of target existence as an effective track quality measure for track management. Both algorithms deal with nonlinear observations using the extended Kalman filtering. A simulation study is

Client perceptions of the mental health engagement network: a qualitative analysis of an electronic personal health record.

Science.gov (United States)

Forchuk, Cheryl; Reiss, Jeffrey P; O'Regan, Tony; Ethridge, Paige; Donelle, Lorie; Rudnick, Abraham

2015-10-14

Information technologies such as websites, mobile phone applications, and virtual reality programs have been shown to deliver innovative and effective treatments for mental illness. Much of the research studying electronic mental health interventions focuses on symptom reduction; however, to facilitate the implementation of electronic interventions in usual mental health care, it is also important to investigate the perceptions of clients who will be using the technologies. To this end, a qualitative analysis of focus group discussions regarding the Mental Health Engagement Network, a web-based personal health record and smartphone intervention, is presented here. Individuals living in the community with a mood or psychotic disorder (n = 394) were provided with a smartphone and access to an electronic personal health record, the Lawson SMART Record, for 12 to 18 months to manage their mental health. This study employed a delayed-implementation design and obtained both quantitative and qualitative data through individual interviews and focus group sessions. Participants had the opportunity to participate in voluntary focus group sessions at three points throughout the study to discuss their perceptions of the technologies. Qualitative data from 95 focus group participants were analysed using a thematic analysis. Four overarching themes emerged from focus group discussions: 1) Versatile functionality of the Lawson SMART Record and smartphone facilitated use; 2) Aspects of the technologies as barriers to use; 3) Use of the Mental health Engagement Network technologies resulted in perceived positive outcomes; 4) Future enhancement of the Lawson SMART Record and intervention is recommended. These qualitative data provide a valuable contribution to the understanding of how smarttechnologies can be integrated into usual mental health care. Smartphones are extremely portable andcommonplace in society. Therefore, clients can use these devices to manage and track mental

Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System

Directory of Open Access Journals (Sweden)

Ramji Tiwari

2018-02-01

Full Text Available This paper proposes an artificial neural network (ANN based maximum power point tracking (MPPT control strategy for wind energy conversion system (WECS implemented with a DC/DC converter. The proposed topology utilizes a radial basis function network (RBFN based neural network control strategy to extract the maximum available power from the wind velocity. The results are compared with a classical Perturb and Observe (P&O method and Back propagation network (BPN method. In order to achieve a high voltage rating, the system is implemented with a quadratic boost converter and the performance of the converter is validated with a boost and single ended primary inductance converter (SEPIC. The performance of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink.

Hospital Medicine Resident Training Tracks: Developing the Hospital Medicine Pipeline.

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Sweigart, Joseph R; Tad-Y, Darlene; Kneeland, Patrick; Williams, Mark V; Glasheen, Jeffrey J

2017-03-01

Hospital medicine (HM) is rapidly evolving into new clinical and nonclinical roles. Traditional internal medicine (IM) residency training likely does not optimally prepare residents for success in HM. Hospital medicine residency training tracks may offer a preferred method for specialized HM education. Internet searches and professional networks were used to identify HM training tracks. Information was gathered from program websites and discussions with track directors. The 11 HM tracks at academic medical centers across the United States focus mostly on senior residents. Track structure and curricular content are determined largely by the structure and curricula of the IM residency programs in which they exist. Almost all tracks feature experiential quality improvement projects. Content on healthcare economics and value is common, and numerous track leaders report this content is expanding from HM tracks into entire residency programs. Tracks also provide opportunities for scholarship and professional development, such as workshops on abstract creation and job procurement skills. Almost all tracks include HM preceptorships as well as rotations within various disciplines of HM. HM residency training tracks focus largely on quality improvement, health care economics, and professional development. The structures and curricula of these tracks are tightly linked to opportunities within IM residency programs. As HM continues to evolve, these tracks likely will expand to bridge clinical and extra-clinical gaps between traditional IM training and contemporary HM practice. Journal of Hospital Medicine 2017;12:173-176. © 2017 Society of Hospital Medicine

Social Network Types and Mental Health Among LGBT Older Adults

Science.gov (United States)

Kim, Hyun-Jun; Fredriksen-Goldsen, Karen I.; Bryan, Amanda E. B.; Muraco, Anna

2017-01-01

Purpose of the Study: This study was designed to identify social network types among lesbian, gay, bisexual, and transgender (LGBT) older adults and examine the relationship between social network type and mental health. Design and Methods: We analyzed the 2014 survey data of LGBT adults aged 50 and older (N = 2,450) from Aging with Pride: National Health, Aging, and Sexuality/Gender Study. Latent profile analyses were conducted to identify clusters of social network ties based on 11 indicators. Multiple regression analysis was performed to examine the association between social network types and mental health. Results: We found five social network types. Ordered from greatest to least access to family, friend, and other non-family network ties, they were diverse, diverse/no children, immediate family-focused, friend-centered/restricted, and fully restricted. The friend-centered/restricted (33%) and diverse/no children network types (31%) were the most prevalent. Among individuals with the friend-centered/restricted type, access to social networks was limited to friends, and across both types children were not present. The least prevalent type was the fully restricted network type (6%). Social network type was significantly associated with mental health, after controlling for background characteristics and total social network size; those with the fully restricted type showed the poorest mental health. Implications: Unique social network types (diverse/no children and friend-centered/restricted) emerge among LGBT older adults. Moreover, individuals with fully restricted social networks are at particular risk due to heightened health needs and limited social resources. This study highlights the importance of understanding heterogeneous social relations and developing tailored interventions to promote social connectedness and mental health in LGBT older adults. PMID:28087798

Solid state nuclear track detectors and their application in industrial health, radiological and environmental protection

International Nuclear Information System (INIS)

Urban, M.

1993-09-01

Passive Solid State Nuclear Track Detectors are electrically non conductive solids, mainly used for the registration of α-particles and neutron induced recoils. The stability of the particle tracks in the solid allow longer integration periods, what is essential for the measurement of small, time variant radiation exposures. This report gives an overview on non-photographic track detectors, their processing, dosimetric properties and examples for their application in industrial health, radiological and environmental protection. (orig.) [de

Orbit and clock determination of BDS regional navigation satellite system based on IGS M-GEX and WHU BETS tracking network

Science.gov (United States)

GENG, T.; Zhao, Q.; Shi, C.; Shum, C.; Guo, J.; Su, X.

2013-12-01

BeiDou Navigation Satellite System (BDS) began to provide the regional open service on December 27th 2012 and will provide the global open service by the end of 2020. Compared to GPS, the space segment of BDS Regional System consists of 5 Geostationary Earth Orbit satellites (GEO), 5 Inclined Geosynchronous Orbit satellites (IGSO) and 4 Medium Earth orbit (MEO) satellites. Since 2011, IGS Multiple-GNSS Experiment (M-GEX) focuses on tracking the newly available GNSS signals. This includes all signals from the modernized satellites of the GPS and GLONASS systems, as well as signals of the BDS, Galileo and QZSS systems. Up to now, BDS satellites are tracked by around 25 stations with a variety of different antennas and receivers from different GNSS manufacture communities in M-GEX network. Meanwhile, there are 17 stations with Unicore Communications Incorporation's GPS/BDS receivers in BeiDou Experimental Tracking Stations (BETS) network by Wuhan University. In addition, 5 BDS satellites have been tracking by the International Laser Ranging Service (ILRS). BDS performance is expected to be further studied by the GNSS communities. Following an introduction of the BDS system and above different tracking network, this paper discusses the achieved BDS characterization and performance assessment. Firstly, the BDS signal and measurement quality are analyzed with different antennas and receivers in detail compared to GPS. This includes depth of coverage for satellite observation, carrier-to-noise-density ratios, code noise and multipath, carrier phase errors. Secondly, BDS Precise Orbit Determination (POD) is processed. Different arc lengths and sets of orbit parameters are tested using Position And Navigation Data Analysis software (PANDA) which is developed at the Wuhan University. GEO, IGSO and MEO satellites orbit quality will be assessed using overlap comparison, 2-day orbit fit and external validations with Satellite Laser Range (SLR). Then BDS satellites are equipped

Implementation and integration of regional health care data networks in the Hellenic National Health Service.

Science.gov (United States)

Lampsas, Petros; Vidalis, Ioannis; Papanikolaou, Christos; Vagelatos, Aristides

2002-12-01

Modern health care is provided with close cooperation among many different institutions and professionals, using their specialized expertise in a common effort to deliver best-quality and, at the same time, cost-effective services. Within this context of the growing need for information exchange, the demand for realization of data networks interconnecting various health care institutions at a regional level, as well as a national level, has become a practical necessity. To present the technical solution that is under consideration for implementing and interconnecting regional health care data networks in the Hellenic National Health System. The most critical requirements for deploying such a regional health care data network were identified as: fast implementation, security, quality of service, availability, performance, and technical support. The solution proposed is the use of proper virtual private network technologies for implementing functionally-interconnected regional health care data networks. The regional health care data network is considered to be a critical infrastructure for further development and penetration of information and communication technologies in the Hellenic National Health System. Therefore, a technical approach was planned, in order to have a fast cost-effective implementation, conforming to certain specifications.

Work-based social networks and health status among Japanese employees.

Science.gov (United States)

Suzuki, E; Takao, S; Subramanian, S V; Doi, H; Kawachi, I

2009-09-01

Despite the worldwide trend towards more time being spent at work by employed people, few studies have examined the independent influences of work-based versus home-based social networks on employees' health. We examined the association between work-based social networks and health status by controlling for home-based social networks in a cross-sectional study. By employing a two-stage stratified random sampling procedure, 1105 employees were identified from 46 companies in Okayama, Japan, in 2007. Work-based social networks were assessed by asking the number of co-workers whom they consult with ease on personal issues. The outcome was self-rated health; the adjusted OR for poor health compared employees with no network with those who have larger networks. Although a clear (and inverse) dose-response relationship was found between the size of work-based social networks and poor health (OR 1.53, 95% CI 1.03 to 2.27, comparing those with the lowest versus highest level of social network), the association was attenuated to statistical non-significance after we controlled for the size of home-based social networks. In further analyses stratified on age groups, in older workers (> or =50 years) work-based social networks were apparently associated with better health status, whereas home-based networks were not. The reverse was true among middle-aged workers (30-49 years). No associations were found among younger workers (social support on health according to age groups. We hypothesise that these patterns reflect generational differences in workers' commitment to their workplace.

Four Challenges That Global Health Networks Face

Directory of Open Access Journals (Sweden)

Jeremy Shiffman

2017-04-01

Full Text Available Global health networks, webs of individuals and organizations with a shared concern for a particular condition, have proliferated over the past quarter century. They differ in their effectiveness, a factor that may help explain why resource allocations vary across health conditions and do not correspond closely with disease burden. Drawing on findings from recently concluded studies of eight global health networks—addressing alcohol harm, early childhood development (ECD, maternal mortality, neonatal mortality, pneumonia, surgically-treatable conditions, tobacco use, and tuberculosis—I identify four challenges that networks face in generating attention and resources for the conditions that concern them. The first is problem definition: generating consensus on what the problem is and how it should be addressed. The second is positioning: portraying the issue in ways that inspire external audiences to act. The third is coalition-building: forging alliances with these external actors, particularly ones outside the health sector. The fourth is governance: establishing institutions to facilitate collective action. Research indicates that global health networks that effectively tackle these challenges are more likely to garner support to address the conditions that concern them. In addition to the effectiveness of networks, I also consider their legitimacy, identifying reasons both to affirm and to question their right to exert power.

Social Network Types and Mental Health Among LGBT Older Adults.

Science.gov (United States)

Kim, Hyun-Jun; Fredriksen-Goldsen, Karen I; Bryan, Amanda E B; Muraco, Anna

2017-02-01

This study was designed to identify social network types among lesbian, gay, bisexual, and transgender (LGBT) older adults and examine the relationship between social network type and mental health. We analyzed the 2014 survey data of LGBT adults aged 50 and older (N = 2,450) from Aging with Pride: National Health, Aging, and Sexuality/Gender Study. Latent profile analyses were conducted to identify clusters of social network ties based on 11 indicators. Multiple regression analysis was performed to examine the association between social network types and mental health. We found five social network types. Ordered from greatest to least access to family, friend, and other non-family network ties, they were diverse, diverse/no children, immediate family-focused, friend-centered/restricted, and fully restricted. The friend-centered/restricted (33%) and diverse/no children network types (31%) were the most prevalent. Among individuals with the friend-centered/restricted type, access to social networks was limited to friends, and across both types children were not present. The least prevalent type was the fully restricted network type (6%). Social network type was significantly associated with mental health, after controlling for background characteristics and total social network size; those with the fully restricted type showed the poorest mental health. Unique social network types (diverse/no children and friend-centered/restricted) emerge among LGBT older adults. Moreover, individuals with fully restricted social networks are at particular risk due to heightened health needs and limited social resources. This study highlights the importance of understanding heterogeneous social relations and developing tailored interventions to promote social connectedness and mental health in LGBT older adults. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Social Networks, Interpersonal Social Support, and Health Outcomes: A Health Communication Perspective

OpenAIRE

Wright, Kevin

2016-01-01

This manuscript discusses the development, impact, and several major research findings of studies in the area of social network support and health outcomes. The review focuses largely on the development of online social support networks and the ways in which they may interact with face-to-face support networks to influence physical and psychological health outcomes. The manuscript discusses this area, and it presents a research agenda for future work in this area from an Associate Editor’s pe...

Developing a smartphone interface for the Florida Environmental Public Health Tracking Web portal.

Science.gov (United States)

Jordan, Melissa; DuClos, Chris; Folsom, John; Thomas, Rebecca

2015-01-01

As smartphone and tablet devices continue to proliferate, it is becoming increasingly important to tailor information delivery to the mobile device. The Florida Environmental Public Health Tracking Program recognized that the mobile device user needs Web content formatted to smaller screen sizes, simplified data displays, and reduced textual information. The Florida Environmental Public Health Tracking Program developed a smartphone-friendly version of the state Web portal for easier access by mobile device users. The resulting smartphone-friendly portal combines calculated data measures such as inpatient hospitalizations and emergency department visits and presents them grouped by county, along with temporal trend graphs. An abbreviated version of the public health messaging provided on the traditional Web portal is also provided, along with social media connections. As a result of these efforts, the percentage of Web site visitors using an iPhone tripled in just 1 year.

State Support: A Prerequisite for Global Health Network Effectiveness

Science.gov (United States)

Marten, Robert; Smith, Richard D.

2018-01-01

Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks’ success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks’ effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research. PMID:29524958

PRiFi Networking for Tracking-Resistant Mobile Computing

Science.gov (United States)

2017-11-01

conjunction with other relevant services, including Tor for tracking-resistant inter-organizational communication, distributed protocols for the...is a hardened, fully deployable PriFi implementation that can be used in conjunction with other relevant services, including Tor for tracking

Diffusion of the Digital Health Self-Tracking Movement in Canada: Results of a National Survey.

Science.gov (United States)

Paré, Guy; Leaver, Chad; Bourget, Claire

2018-05-02

With the ever-increasing availability of mobile apps, consumer wearables, and smart medical devices, more and more individuals are self-tracking and managing their personal health data. The aim of this study was to investigate the diffusion of the digital self-tracking movement in Canada. It provides a comprehensive, yet detailed account of this phenomenon. It examines the profile of digital self-trackers, traditional self-trackers, and nontrackers, further investigating the primary motivations for self-tracking and reasons for nontracking; barriers to adoption of connected care technologies; users' appreciation of their self-tracking devices, including what they perceive to be the main benefits; factors that influence people's intention to continue using connected care technologies in the future; and the reasons for usage discontinuance. We conducted an online survey with a sample of 4109 Canadian adults, one of the largest ever. To ensure a representative sample, quota method was used (gender, age), following stratification by region. The maximum margin of error is estimated at 1.6%, 19 times out of 20. Our findings reveal that 66.20% (2720/4109) of our respondents regularly self-track one or more aspects of their health. About one in 4 respondents (1014/4109, 24.68%) currently owns a wearable or smart medical device, and 57.20% (580/1014) use their devices on a regular basis for self-tracking purposes. Digital self-trackers are typically young or mature adults, healthy, employed, university educated, with an annual family income of over $80,000 CAD. The most popular reported device is the fitness tracker or smartwatch that can capture a range of parameters. Currently, mobile apps and digital self-tracking devices are mainly used to monitor physical activity (856/1669, 51.13%), nutrition (545/1669, 32.65%), sleep patterns (482/1669, 28.88%) and, to a much lesser extent, cardiovascular and pulmonary biomarkers (215/1669, 12.88%), medication intake (126/1669, 7

Growing a professional network to over 3000 members in less than 4 years: evaluation of InspireNet, British Columbia's virtual nursing health services research network.

Science.gov (United States)

Frisch, Noreen; Atherton, Pat; Borycki, Elizabeth; Mickelson, Grace; Cordeiro, Jennifer; Novak Lauscher, Helen; Black, Agnes

2014-02-21

Use of Web 2.0 and social media technologies has become a new area of research among health professionals. Much of this work has focused on the use of technologies for health self-management and the ways technologies support communication between care providers and consumers. This paper addresses a new use of technology in providing a platform for health professionals to support professional development, increase knowledge utilization, and promote formal/informal professional communication. Specifically, we report on factors necessary to attract and sustain health professionals' use of a network designed to increase nurses' interest in and use of health services research and to support knowledge utilization activities in British Columbia, Canada. "InspireNet", a virtual professional network for health professionals, is a living laboratory permitting documentation of when and how professionals take up Web 2.0 and social media. Ongoing evaluation documents our experiences in establishing, operating, and evaluating this network. Overall evaluation methods included (1) tracking website use, (2) conducting two member surveys, and (3) soliciting member feedback through focus groups and interviews with those who participated in electronic communities of practice (eCoPs) and other stakeholders. These data have been used to learn about the types of support that seem relevant to network growth. Network growth exceeded all expectations. Members engaged with varying aspects of the network's virtual technologies, such as teams of professionals sharing a common interest, research teams conducting their work, and instructional webinars open to network members. Members used wikis, blogs, and discussion groups to support professional work, as well as a members' database with contact information and areas of interest. The database is accessed approximately 10 times per day. InspireNet public blog posts are accessed roughly 500 times each. At the time of writing, 21 research teams

Ion track based tunable device as humidity sensor: a neural network approach

Science.gov (United States)

Sharma, Mamta; Sharma, Anuradha; Bhattacherjee, Vandana

2013-01-01

Artificial Neural Network (ANN) has been applied in statistical model development, adaptive control system, pattern recognition in data mining, and decision making under uncertainty. The nonlinear dependence of any sensor output on the input physical variable has been the motivation for many researchers to attempt unconventional modeling techniques such as neural networks and other machine learning approaches. Artificial neural network (ANN) is a computational tool inspired by the network of neurons in biological nervous system. It is a network consisting of arrays of artificial neurons linked together with different weights of connection. The states of the neurons as well as the weights of connections among them evolve according to certain learning rules.. In the present work we focus on the category of sensors which respond to electrical property changes such as impedance or capacitance. Recently, sensor materials have been embedded in etched tracks due to their nanometric dimensions and high aspect ratio which give high surface area available for exposure to sensing material. Various materials can be used for this purpose to probe physical (light intensity, temperature etc.), chemical (humidity, ammonia gas, alcohol etc.) or biological (germs, hormones etc.) parameters. The present work involves the application of TEMPOS structures as humidity sensors. The sample to be studied was prepared using the polymer electrolyte (PEO/NH4ClO4) with CdS nano-particles dispersed in the polymer electrolyte. In the present research we have attempted to correlate the combined effects of voltage and frequency on impedance of humidity sensors using a neural network model and results have indicated that the mean absolute error of the ANN Model for the training data was 3.95% while for the validation data it was 4.65%. The corresponding values for the LR model were 8.28% and 8.35% respectively. It was also demonstrated the percentage improvement of the ANN Model with respect to the

Web-based geo-visualisation of spatial information to support evidence-based health policy: a case study of the development process of HealthTracks.

Science.gov (United States)

Jardine, Andrew; Mullan, Narelle; Gudes, Ori; Cosford, James; Moncrieff, Simon; West, Geoff; Xiao, Jianguo; Yun, Grace; Someford, Peter

Place is of critical importance to health as it can reveal patterns of disease spread and clustering, associations with risk factors, and areas with greatest need for, or least access to healthcare services and promotion activities. Furthermore, in order to get a good understanding of the health status and needs of a particular area a broad range of data are required which can often be difficult and time consuming to obtain and collate. This process has been expedited by bringing together multiple data sources and making them available in an online geo-visualisation, HealthTracks, which consists of a mapping and reporting component. The overall aim of the HealthTracks project is to make spatial health information more accessible to policymakers, analysts, planners and program managers to inform decision-making across the Department of Health Western Australia. Preliminary mapping and reporting applications that have been utilised to inform service planning, increased awareness of the utility of spatial information and improved efficiency in data access were developed. The future for HealthTracks involves expanding the range of data available and developing new analytical capabilities in order to work towards providing external agencies, researchers and eventually the general public access to rich local area spatial data.

Tracking Objects with Networked Scattered Directional Sensors

Science.gov (United States)

Plarre, Kurt; Kumar, P. R.

2007-12-01

We study the problem of object tracking using highly directional sensors—sensors whose field of vision is a line or a line segment. A network of such sensors monitors a certain region of the plane. Sporadically, objects moving in straight lines and at a constant speed cross the region. A sensor detects an object when it crosses its line of sight, and records the time of the detection. No distance or angle measurements are available. The task of the sensors is to estimate the directions and speeds of the objects, and the sensor lines, which are unknown a priori. This estimation problem involves the minimization of a highly nonconvex cost function. To overcome this difficulty, we introduce an algorithm, which we call "adaptive basis algorithm." This algorithm is divided into three phases: in the first phase, the algorithm is initialized using data from six sensors and four objects; in the second phase, the estimates are updated as data from more sensors and objects are incorporated. The third phase is an optional coordinated transformation. The estimation is done in an "ad-hoc" coordinate system, which we call "adaptive coordinate system." When more information is available, for example, the location of six sensors, the estimates can be transformed to the "real-world" coordinate system. This constitutes the third phase.

Development of an online tool for public health: the European Public Health Law Network.

Science.gov (United States)

Basak, P

2011-09-01

The European Public Health Law Network was established in 2007 as part of the European Union (EU) co-funded Public Health Law Flu project. The aims of the website consisted of designing an interactive network of specialist information and encouraging an exchange of expertise amongst members. The website sought to appeal to academics, public health professionals and lawyers. The Public Health Law Flu project team designed and managed the website. Registered network members were recruited through publicity, advertising and word of mouth. Details of the network were sent to health organizations and universities throughout Europe. Corresponding website links attracted many new visitors. Publications, news, events and a pandemic glossary became popular features on the site. Although the website initially focused only on pandemic diseases it has grown into a multidisciplinary website covering a range of public health law topics. The network contains over 700 publications divided into 28 public health law categories. News, events, front page content, legislation and the francophone section are updated on a regular basis. Since 2007 the website has received over 15,000 views from 156 countries. Newsletter subscribers have risen to 304. There are now 723 followers on the associated Twitter site. The European Public Health Law Network has been a successful and innovative site in the area of public health law. Interest in the site continues to grow. Future funding can contribute to a bigger site with interactive features and pages in a wider variety of languages to attract a wider global audience. Copyright © 2011 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Health implications of social networks for children living in public housing.

Science.gov (United States)

Kennedy-Hendricks, Alene; Schwartz, Heather L; Griffin, Beth Ann; Burkhauser, Susan; Green, Harold D; Kennedy, David P; Pollack, Craig Evan

2015-11-01

This study sought to examine whether: (1) the health composition of the social networks of children living in subsidized housing within market rate developments (among higher-income neighbors) differs from the social network composition of children living in public housing developments (among lower-income neighbors); and (2) children's social network composition is associated with children's own health. We found no significant differences in the health characteristics of the social networks of children living in these different types of public housing. However, social network composition was significantly associated with several aspects of children's own health, suggesting the potential importance of social networks for the health of vulnerable populations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Artificial Neural Network based control for PV/T panel to track optimum thermal and electrical power

International Nuclear Information System (INIS)

Ben Ammar, Majed; Chaabene, Maher; Chtourou, Zied

2013-01-01

Highlights: ► We establish a state model of PV/T panel. ► We study the effect of mass flow rate on PV/T efficiency. ► A real time PV/T control algorithm is proposed. ► A model based optimal thermal and electrical power operation point is tracked. - Abstract: As solar energy is intermittent, many algorithms and electronics have been developed to track the maximum power generation from photovoltaic and thermal panels. Following technological advances, these panels are gathered into one unit: PV/T system. PV/T delivers simultaneously two kinds of power: electrical power and thermal power. Nevertheless, no control systems have been developed in order to track maximum power generation from PV/T system. This paper suggests a PV/T control algorithm based on Artificial Neural Network (ANN) to detect the optimal power operating point (OPOP) by considering PV/T model behavior. The OPOP computes the optimum mass flow rate of PV/T for a considered irradiation and ambient temperature. Simulation results demonstrate great concordance between OPOP model based calculation and ANN outputs.

Proposed health state awareness of helicopter blades using an artificial neural network strategy

Science.gov (United States)

Lee, Andrew; Habtour, Ed; Gadsden, S. A.

2016-05-01

Structural health prognostics and diagnosis strategies can be classified as either model or signal-based. Artificial neural network strategies are popular signal-based techniques. This paper proposes the use of helicopter blades in order to study the sensitivity of an artificial neural network to structural fatigue. The experimental setup consists of a scale aluminum helicopter blade exposed to transverse vibratory excitation at the hub using single axis electrodynamic shaker. The intent of this study is to optimize an algorithm for processing high-dimensional data while retaining important information content in an effort to select input features and weights, as well as health parameters, for training a neural network. Data from accelerometers and piezoelectric transducers is collected from a known system designated as healthy. Structural damage will be introduced to different blades, which they will be designated as unhealthy. A variety of different tests will be performed to track the evolution and severity of the damage. A number of damage detection and diagnosis strategies will be implemented. A preliminary experiment was performed on aluminum cantilever beams providing a simpler model for implementation and proof of concept. Future work will look at utilizing the detection information as part of a hierarchical control system in order to mitigate structural damage and fatigue. The proposed approach may eliminate massive data storage on board of an aircraft through retaining relevant information only. The control system can then employ the relevant information to intelligently reconfigure adaptive maneuvers to avoid harmful regimes, thus, extending the life of the aircraft.

The Impact of Online Social Networks on Health and Health Systems: A Scoping Review and Case Studies.

Science.gov (United States)

Griffiths, Frances; Dobermann, Tim; Cave, Jonathan A K; Thorogood, Margaret; Johnson, Samantha; Salamatian, Kavé; Gomez Olive, Francis X; Goudge, Jane

2015-12-01

Interaction through online social networks potentially results in the contestation of prevailing ideas about health and health care, and to mass protest where health is put at risk or health care provision is wanting. Through a review of the academic literature and case studies of four social networking health sites (PatientsLikeMe, Mumsnet, Treatment Action Campaign, and My Pro Ana), we establish the extent to which this phenomenon is documented, seek evidence of the prevalence and character of health-related networks, and explore their structure, function, participants, and impact, seeking to understand how they came into being and how they sustain themselves. Results indicate mass protest is not arising from these established health-related networking platforms. There is evidence of changes in policy following campaigning activity prompted by experiences shared through social networking such as improved National Health Service care for miscarriage (a Mumsnet campaign). Platform owners and managers have considerable power to shape these campaigns. Social networking is also influencing health policy indirectly through increasing awareness and so demand for health care. Transient social networking about health on platforms such as Twitter were not included as case studies but may be where the most radical or destabilizing influence on health care policy might arise.

Using online social networks to track a pandemic: A systematic review.

Science.gov (United States)

Al-Garadi, Mohammed Ali; Khan, Muhammad Sadiq; Varathan, Kasturi Dewi; Mujtaba, Ghulam; Al-Kabsi, Abdelkodose M

2016-08-01

The popularity and proliferation of online social networks (OSNs) have created massive social interaction among users that generate an extensive amount of data. An OSN offers a unique opportunity for studying and understanding social interaction and communication among far larger populations now more than ever before. Recently, OSNs have received considerable attention as a possible tool to track a pandemic because they can provide an almost real-time surveillance system at a less costly rate than traditional surveillance systems. A systematic literature search for studies with the primary aim of using OSN to detect and track a pandemic was conducted. We conducted an electronic literature search for eligible English articles published between 2004 and 2015 using PUBMED, IEEExplore, ACM Digital Library, Google Scholar, and Web of Science. First, the articles were screened on the basis of titles and abstracts. Second, the full texts were reviewed. All included studies were subjected to quality assessment. OSNs have rich information that can be utilized to develop an almost real-time pandemic surveillance system. The outcomes of OSN surveillance systems have demonstrated high correlations with the findings of official surveillance systems. However, the limitation in using OSN to track pandemic is in collecting representative data with sufficient population coverage. This challenge is related to the characteristics of OSN data. The data are dynamic, large-sized, and unstructured, thus requiring advanced algorithms and computational linguistics. OSN data contain significant information that can be used to track a pandemic. Different from traditional surveys and clinical reports, in which the data collection process is time consuming at costly rates, OSN data can be collected almost in real time at a cheaper cost. Additionally, the geographical and temporal information can provide exploratory analysis of spatiotemporal dynamics of infectious disease spread. However, on

Social networks of professionals in health care organizations: a review.

Science.gov (United States)

Tasselli, Stefano

2014-12-01

In this article, we provide an overview of social network research in health care, with a focus on social interactions between professionals in organizations. We begin by introducing key concepts defining the social network approach, including network density, centrality, and brokerage. We then review past and current research on the antecedents of health care professionals' social networks-including demographic attributes, professional groups, and organizational arrangements-and their consequences-including satisfaction at work, leadership, behaviors, knowledge transfer, diffusion of innovation, and performance. Finally, we examine future directions for social network research in health care, focusing on micro-macro linkages and network dynamics. © The Author(s) 2014.

Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries.

Science.gov (United States)

Perkins, Jessica M; Subramanian, S V; Christakis, Nicholas A

2015-01-01

In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex

Social Networks and Health: A Systematic Review of Sociocentric Network Studies in Low- and Middle-Income Countries

Science.gov (United States)

Perkins, Jessica M; Subramanian, S V; Christakis, Nicholas A

2015-01-01

In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex

Quebec mental health services networks: models and implementation

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Marie-Josée Fleury

2005-06-01

Full Text Available Purpose: In the transformation of health care systems, the introduction of integrated service networks is considered to be one of the main solutions for enhancing efficiency. In the last few years, a wealth of literature has emerged on the topic of services integration. However, the question of how integrated service networks should be modelled to suit different implementation contexts has barely been touched. To fill that gap, this article presents four models for the organization of mental health integrated networks. Data sources: The proposed models are drawn from three recently published studies on mental health integrated services in the province of Quebec (Canada with the author as principal investigator. Description: Following an explanation of the concept of integrated service network and a description of the Quebec context for mental health networks, the models, applicable in all settings: rural, urban or semi-urban, and metropolitan, and summarized in four figures, are presented. Discussion and conclusion: To apply the models successfully, the necessity of rallying all the actors of a system, from the strategic, tactical and operational levels, according to the type of integration involved: functional/administrative, clinical and physician-system is highlighted. The importance of formalizing activities among organizations and actors in a network and reinforcing the governing mechanisms at the local level is also underlined. Finally, a number of integration strategies and key conditions of success to operationalize integrated service networks are suggested.

Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

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Suliang Ma

2016-11-01

Full Text Available Photovoltaic (PV systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP. Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL non-linear controller combined with an artificial neural network (ANN is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate the effectiveness and superiority of the proposed method. The AFL with ANN controller can provide good dynamic operation, faster convergence speed, and fewer operating-point oscillations around the MPP. It also tracks the global maxima under different conditions, especially irradiance-mutating situations, more effectively than the conventional methods. Detailed mathematical models and a control approach for a three-phase grid-connected intelligent hybrid system are proposed using MATLAB/Simulink.

Nonlinear Motion Tracking by Deep Learning Architecture

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Verma, Arnav; Samaiya, Devesh; Gupta, Karunesh K.

2018-03-01

In the world of Artificial Intelligence, object motion tracking is one of the major problems. The extensive research is being carried out to track people in crowd. This paper presents a unique technique for nonlinear motion tracking in the absence of prior knowledge of nature of nonlinear path that the object being tracked may follow. We achieve this by first obtaining the centroid of the object and then using the centroid as the current example for a recurrent neural network trained using real-time recurrent learning. We have tweaked the standard algorithm slightly and have accumulated the gradient for few previous iterations instead of using just the current iteration as is the norm. We show that for a single object, such a recurrent neural network is highly capable of approximating the nonlinearity of its path.

Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and Unknown Deadzone

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J. Humberto Pérez-Cruz

2012-01-01

Full Text Available This paper deals with the problem of trajectory tracking for a broad class of uncertain nonlinear systems with multiple inputs each one subject to an unknown symmetric deadzone. On the basis of a model of the deadzone as a combination of a linear term and a disturbance-like term, a continuous-time recurrent neural network is directly employed in order to identify the uncertain dynamics. By using a Lyapunov analysis, the exponential convergence of the identification error to a bounded zone is demonstrated. Subsequently, by a proper control law, the state of the neural network is compelled to follow a bounded reference trajectory. This control law is designed in such a way that the singularity problem is conveniently avoided and the exponential convergence to a bounded zone of the difference between the state of the neural identifier and the reference trajectory can be proven. Thus, the exponential convergence of the tracking error to a bounded zone and the boundedness of all closed-loop signals can be guaranteed. One of the main advantages of the proposed strategy is that the controller can work satisfactorily without any specific knowledge of an upper bound for the unmodeled dynamics and/or the disturbance term.

Social networks, health promoting-behavior, and health-related quality of life in older Korean adults.

Science.gov (United States)

Hong, Minjoo; De Gagne, Jennie C; Shin, Hyewon

2018-03-01

In this cross-sectional, descriptive study, we compared the sociodemographic characteristics, social networks, health-promoting behavior, and the health-related quality of life of older Korean adults living in South Korea to those of older Korean adult immigrants living in the USA. A total of 354 older adults, aged 65 years or older, participated. Data were collected through self-directed questionnaires, and analyzed using a two way analysis of variance, t-tests, χ 2 -tests, and Pearson's correlation coefficient. The association between four sociodemographic characteristics and health-related quality of life was significantly different between the two groups. For the older Korean adults living in South Korea, positive correlations existed between a measure of their social networks and both health-promoting behavior and health-related quality of life. For the older Korean immigrants, the findings revealed a positive correlation only between social networks and health-promoting behavior. The study findings support the important association social networks can have with health-related quality of life, and their possible relationship to health-promoting behaviors of older Korean adults. We suggest that health policy-makers and healthcare providers develop comprehensive programs that are designed to improve older adults' social networks. © 2017 John Wiley & Sons Australia, Ltd.

Autonomous Multicamera Tracking on Embedded Smart Cameras

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Bischof Horst

2007-01-01

Full Text Available There is currently a strong trend towards the deployment of advanced computer vision methods on embedded systems. This deployment is very challenging since embedded platforms often provide limited resources such as computing performance, memory, and power. In this paper we present a multicamera tracking method on distributed, embedded smart cameras. Smart cameras combine video sensing, processing, and communication on a single embedded device which is equipped with a multiprocessor computation and communication infrastructure. Our multicamera tracking approach focuses on a fully decentralized handover procedure between adjacent cameras. The basic idea is to initiate a single tracking instance in the multicamera system for each object of interest. The tracker follows the supervised object over the camera network, migrating to the camera which observes the object. Thus, no central coordination is required resulting in an autonomous and scalable tracking approach. We have fully implemented this novel multicamera tracking approach on our embedded smart cameras. Tracking is achieved by the well-known CamShift algorithm; the handover procedure is realized using a mobile agent system available on the smart camera network. Our approach has been successfully evaluated on tracking persons at our campus.

Social networks and mental health in post-conflict Mitrovica, Kosova.

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Nakayama, Risa; Koyanagi, Ai; Stickley, Andrew; Kondo, Tetsuo; Gilmour, Stuart; Arenliu, Aliriza; Shibuya, Kenji

2014-11-17

To investigate the relation between social networks and mental health in the post-conflict municipality of Mitrovica, Kosovo. Using a three-stage stratified sampling method, 1239 respondents aged 16 years or above were recruited in the Greater Mitrovica region. Social network depth was measured by the frequency of contacts with friends, relatives and strangers. Depression and anxiety were measured using the Hospital Anxiety and Depression Scale (HADS). Multivariate logistic regression was used to examine the association between social network depth and mental health. The analytical sample consisted of 993 respondents. The prevalence of depression (54.3%) and anxiety (64.4%) were extremely high. In multiple regression analysis, a lower depth of social network (contact with friends) was associated with higher levels of both depression and anxiety. This study has shown that only one variety of social network--contact with friends--was important in terms of mental health outcomes in a population living in an area heavily affected by conflict. This suggests that the relation between social networks and mental health may be complex in that the effects of different forms of social network on mental health are not uniform and may depend on the way social networks are operationalised and the particular context in which the relationship is examined.

One Health in social networks and social media.

Science.gov (United States)

Mekaru, S R; Brownstein, J S

2014-08-01

In the rapidly evolving world of social media, social networks, mobile applications and citizen science, online communities can develop organically and separately from larger or more established organisations. The One Health online community is experiencing expansion from both the bottom up and the top down. In this paper, the authors review social media's strengths and weaknesses, earlier work examining Internet resources for One Health, the current state of One Health in social media (e.g. Facebook, Twitter, YouTube) and online social networking sites (e.g. LinkedIn and ResearchGate), as well as social media in One Health-related citizen science projects. While One Health has a fairly strong presence on websites, its social media presence is more limited and has an uneven geographic distribution. In work following the Stone Mountain Meeting,the One Health Global Network Task Force Report recommended the creation of an online community of practice. Professional social networks as well as the strategic use of social media should be employed in this effort. Finally, One Health-related research projects using volunteers (citizen science) often use social media to enhance their recruitment. Including these researchers in a community of practitioners would take full advantage of their existing social media presence. In conclusion, the interactive nature of social media, combined with increasing global Internet access, provides the One Health community with opportunities to meaningfully expand their community and promote their message.

Social network types among older Korean adults: Associations with subjective health.

Science.gov (United States)

Sohn, Sung Yun; Joo, Won-Tak; Kim, Woo Jung; Kim, Se Joo; Youm, Yoosik; Kim, Hyeon Chang; Park, Yeong-Ran; Lee, Eun

2017-01-01

With population aging now a global phenomenon, the health of older adults is becoming an increasingly important issue. Because the Korean population is aging at an unprecedented rate, preparing for public health problems associated with old age is particularly salient in this country. As the physical and mental health of older adults is related to their social relationships, investigating the social networks of older adults and their relationship to health status is important for establishing public health policies. The aims of this study were to identify social network types among older adults in South Korea and to examine the relationship of these social network types with self-rated health and depression. Data from the Korean Social Life, Health, and Aging Project were analyzed. Model-based clustering using finite normal mixture modeling was conducted to identify the social network types based on ten criterion variables of social relationships and activities: marital status, number of children, number of close relatives, number of friends, frequency of attendance at religious services, attendance at organized group meetings, in-degree centrality, out-degree centrality, closeness centrality, and betweenness centrality. Multivariate regression analysis was conducted to examine associations between the identified social network types and self-rated health and depression. The model-based clustering analysis revealed that social networks clustered into five types: diverse, family, congregant, congregant-restricted, and restricted. Diverse or family social network types were significantly associated with more favorable subjective mental health, whereas the restricted network type was significantly associated with poorer ratings of mental and physical health. In addition, our analysis identified unique social network types related to religious activities. In summary, we developed a comprehensive social network typology for older Korean adults. Copyright © 2016

Quantization-Based Adaptive Actor-Critic Tracking Control With Tracking Error Constraints.

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Fan, Quan-Yong; Yang, Guang-Hong; Ye, Dan

2018-04-01

In this paper, the problem of adaptive actor-critic (AC) tracking control is investigated for a class of continuous-time nonlinear systems with unknown nonlinearities and quantized inputs. Different from the existing results based on reinforcement learning, the tracking error constraints are considered and new critic functions are constructed to improve the performance further. To ensure that the tracking errors keep within the predefined time-varying boundaries, a tracking error transformation technique is used to constitute an augmented error system. Specific critic functions, rather than the long-term cost function, are introduced to supervise the tracking performance and tune the weights of the AC neural networks (NNs). A novel adaptive controller with a special structure is designed to reduce the effect of the NN reconstruction errors, input quantization, and disturbances. Based on the Lyapunov stability theory, the boundedness of the closed-loop signals and the desired tracking performance can be guaranteed. Finally, simulations on two connected inverted pendulums are given to illustrate the effectiveness of the proposed method.

Social network characteristics associated with health promoting behaviors among Latinos.

Science.gov (United States)

Marquez, Becky; Elder, John P; Arredondo, Elva M; Madanat, Hala; Ji, Ming; Ayala, Guadalupe X

2014-06-01

This study examined the relationship between social network characteristics and health promoting behaviors (having a routine medical check-up, consuming no alcohol, consuming no fast food, and meeting recommendations for leisure-time physical activity and sleep duration) among Latinos to identify potential targets for behavioral interventions. Personal network characteristics and health behavior data were collected from a community sample of 393 adult Latinos (73% women) in San Diego County, California. Network characteristics consisted of size and composition. Network size was calculated by the number of alters listed on a name generator questionnaire eliciting people with whom respondents discussed personal issues. Network composition variables were the proportion of Latinos, Spanish-speakers, females, family, and friends listed in the name generator. Additional network composition variables included marital status and the number of adults or children in the household. Network members were predominately Latinos (95%), Spanish-speakers (80%), females (64%), and family (55%). In multivariate logistic regression analyses, gender moderated the relationship between network composition, but not size, and a health behavior. Married women were more likely to have had a routine medical check-up than married men. For both men and women, having a larger network was associated with meeting the recommendation for leisure-time physical activity. Few social network characteristics were significantly associated with health promoting behaviors, suggesting a need to examine other aspects of social relationships that may influence health behaviors. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Constructing a generalized network design model to study air distribution in ventilation networks in subway with a single-track tunnel

Science.gov (United States)

Lugin, IV

2018-03-01

In focus are the features of construction of the generalized design model for the network method to study air distribution in ventilation system in subway with the single-track tunnel. The generalizations, assumptions and simplifications included in the model are specified. The air distribution is calculated with regard to the influence of topology and air resistances of the ventilation network sections. The author studies two variants of the subway line: half-open and closed with dead end on the both sides. It is found that the total air exchange at a subway station depends on the station location within the line. The operating mode of fans remains unaltered in this case. The article shows that elimination of air leakage in the station ventilation room allows an increase in the air flow rate by 7–8% at the same energy consumption by fans. The influence of the stop of a train in the tunnel on the air distribution is illustrated.

NETWORKS OF HEALTH CARE: A CHALLENGE TO SUS MANAGEMENT

Directory of Open Access Journals (Sweden)

Camila Dubow

2013-09-01

Full Text Available The article proposes a critical reflection, based on national law, scholarly, scientific, on the current development of Networks of Health Care, as a strategy for strengthening the Single Health System (SUS. Are weighted inefficiency of traditional ways of organizing care and management, the challenge of Network Health Care for comprehensive care and management mechanisms used in this process. The work provides subsidies for the care practices and health management are reflected, pointing strategies that result in disruptions of paradigms through a refocusing of attention in existing models. For networks of health care can be consolidated, is fundamental to political sensitivity of health managers with a commitment to build a new model of care, through the struggle to consolidate the SUS and the realization of the principles of universality, comprehensiveness and equity.

Odors pulsed at wing beat frequencies are tracked by primary olfactory networks and enhance odor detection

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Shreejoy Tripathy

2010-03-01

Full Text Available Each down stroke of an insect’s wings accelerates axial airflow over the antennae. Modeling studies suggest that this can greatly enhance penetration of air and air-born odorants through the antennal sensilla thereby periodically increasing odorant-receptor interactions. Do these periodic changes result in entrainment of neural responses in the antenna and antennal lobe (AL? Does this entrainment affect olfactory acuity? To address these questions, we monitored antennal and AL responses in the moth Manduca sexta while odorants were pulsed at frequencies from 10-72 Hz, encompassing the natural wingbeat frequency. Power spectral density (PSD analysis was used to identify entrainment of neural activity. Statistical analysis of PSDs indicates that the antennal nerve tracked pulsed odor up to 30 Hz. Furthermore, at least 50% of AL local field potentials (LFPs and between 7-25% of unitary spiking responses also tracked pulsed odor up to 30 Hz in a frequency-locked manner. Application of bicuculline (200µM abolished pulse tracking in both LFP and unitary responses suggesting that GABAA receptor activation is necessary for pulse tracking within the AL. Finally, psychophysical measures of odor detection establish that detection thresholds are lowered when odor is pulsed at 20 Hz. These results suggest that AL networks can respond to the oscillatory dynamics of stimuli such as those imposed by the wing beat in a manner analogous to mammalian sniffing.

Challenges of Health Games in the Social Network Environment.

Science.gov (United States)

Paredes, Hugo; Pinho, Anabela; Zagalo, Nelson

2012-04-01

Virtual communities and their benefits have been widely exploited to support patients, caregivers, families, and healthcare providers. The complexity of the social organization evolved the concept of virtual community to social networks, exploring the establishment of ties and relations between people. These technological platforms provide a way to keep up with one's connections network, through a set of communication and interaction tools. Games, as social interactive technologies, have great potential, ensuring a supportive community and thereby reducing social isolation. Serious social health games bring forward several research challenges. This article examines the potential benefits of the triad "health-serious games-social networks" and discusses some research challenges and opportunities of the liaison of serious health games and social networks.

Privacy policies for health social networking sites

Science.gov (United States)

Li, Jingquan

2013-01-01

Health social networking sites (HSNS), virtual communities where users connect with each other around common problems and share relevant health data, have been increasingly adopted by medical professionals and patients. The growing use of HSNS like Sermo and PatientsLikeMe has prompted public concerns about the risks that such online data-sharing platforms pose to the privacy and security of personal health data. This paper articulates a set of privacy risks introduced by social networking in health care and presents a practical example that demonstrates how the risks might be intrinsic to some HSNS. The aim of this study is to identify and sketch the policy implications of using HSNS and how policy makers and stakeholders should elaborate upon them to protect the privacy of online health data. PMID:23599228

Privacy policies for health social networking sites.

Science.gov (United States)

Li, Jingquan

2013-01-01

Health social networking sites (HSNS), virtual communities where users connect with each other around common problems and share relevant health data, have been increasingly adopted by medical professionals and patients. The growing use of HSNS like Sermo and PatientsLikeMe has prompted public concerns about the risks that such online data-sharing platforms pose to the privacy and security of personal health data. This paper articulates a set of privacy risks introduced by social networking in health care and presents a practical example that demonstrates how the risks might be intrinsic to some HSNS. The aim of this study is to identify and sketch the policy implications of using HSNS and how policy makers and stakeholders should elaborate upon them to protect the privacy of online health data.

TRACKING VEHICLE IN GSM NETWORK TO SUPPORT INTELLIGENT TRANSPORTATION SYSTEMS

Directory of Open Access Journals (Sweden)

Z. Koppanyi

2012-07-01

Full Text Available The penetration of GSM capable devices is very high, especially in Europe. To exploit the potential of turning these mobile devices into dynamic data acquisition nodes that provides valuable data for Intelligent Transportation Systems (ITS, position information is needed. The paper describes the basic operation principles of the GSM system and provides an overview on the existing methods for deriving location data in the network. A novel positioning solution is presented that rely on handover (HO zone measurements; the zone geometry properties are also discussed. A new concept of HO zone sequence recognition is introduced that involves application of Probabilistic Deterministic Finite State Automata (PDFA. Both the potential commercial applications and the use of the derived position data in ITS is discussed for tracking vehicles and monitoring traffic flow. As a practical cutting edge example, the integration possibility of the technology in the SafeTRIP platform (developed in an EC FP7 project is presented.

Tracks FAQs: What Chemicals Are In My Drinking Water?

Centers for Disease Control (CDC) Podcasts

In this podcast, CDC Tracking experts discuss how you can use the Tracking Network to determine what chemicals are in your drinking water. Do you have a question for our Tracking experts? Please e-mail questions to trackingsupport@cdc.gov.

Software Health Management with Bayesian Networks

Science.gov (United States)

Mengshoel, Ole; Schumann, JOhann

2011-01-01

Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.

Management continuity in local health networks

Directory of Open Access Journals (Sweden)

Mylaine Breton

2012-04-01

Full Text Available Introduction: Patients increasingly receive care from multiple providers in a variety of settings. They expect management continuity that crosses boundaries and bridges gaps in the healthcare system. To our knowledge, little research has been done to assess coordination across organizational and professional boundaries from the patients' perspective. Our objective was to assess whether greater local health network integration is associated with management continuity as perceived by patients. Method: We used the data from a research project on the development and validation of a generic and comprehensive continuity measurement instrument that can be applied to a variety of patient conditions and settings. We used the results of a cross-sectional survey conducted in 2009 with 256 patients in two local health networks in Quebec, Canada. We compared four aspects of management continuity between two contrasting network types (highly integrated vs. poorly integrated. Results: The scores obtained in the highly integrated network are better than those of the poorly integrated network on all dimensions of management continuity (coordinator role, role clarity and coordination between clinics, and information gaps between providers except for experience of care plan. Conclusion: Some aspects of care coordination among professionals and organizations are noticed by patients and may be valid indicators to assess care coordination.

Tracking mobile users in wireless networks via semi-supervised colocalization.

Science.gov (United States)

Pan, Jeffrey Junfeng; Pan, Sinno Jialin; Yin, Jie; Ni, Lionel M; Yang, Qiang

2012-03-01

Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users' locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds.

Bit of History and Some Lessons Learned in Using NASA Remote Sensing Data in Public Health Applications

Science.gov (United States)

Quattrochi, Dale A.; Estes, Sue

2011-01-01

The NASA Applied Sciences Program's public health initiative began in 2004 to illustratethe potential benefits for using remote sensing in public health applications. Objectives/Purpose: The CDC initiated a st udy with NASA through the National Center for Environmental Health (NCEH) to establish a pilot effort to use remote sensing data as part of its Environmental Public Health Tracking Network (EPHTN). As a consequence, the NCEH and NASA developed a project called HELIX-Atlanta (Health and Environment Linkage for Information Exchange) to demonstrate a process for developing a local environmental public health tracking and surveillance network that integrates non-infectious health and environment systems for the Atlanta metropolitan area. Methods: As an ongo ing, systematic integration, analysis and interpretation of data, an EPHTN focuses on: 1 -- environmental hazards; 2 -- human exposure to environmental hazards; and 3 -- health effects potentially related to exposure to environmental hazards. To satisfy the definition of a surveillance system the data must be disseminated to plan, implement, and evaluate environmental public health action. Results: A close working r elationship developed with NCEH where information was exchanged to assist in the development of an EPHTN that incorporated NASA remote sensing data into a surveillance network for disseminating public health tracking information to users. This project?s success provided NASA with the opportunity to work with other public health entities such as the University of Mississippi Medical Center, the University of New Mexico and the University of Arizona. Conclusions: HELIX-Atlanta became a functioning part of the national EPHTN for tracking environmental hazards and exposure, particularly as related to air quality over Atlanta. Learning Objectives: 1 -- remote sensing data can be integral to an EPHTN; 2 -- public tracking objectives can be enhanced through remote sensing data; 3 -- NASA's involvement in

Networked Learning and Network Science: Potential Applications to Health Professionals' Continuing Education and Development.

Science.gov (United States)

Margolis, Alvaro; Parboosingh, John

2015-01-01

Prior interpersonal relationships and interactivity among members of professional associations may impact the learning process in continuing medical education (CME). On the other hand, CME programs that encourage interactivity between participants may impact structures and behaviors in these professional associations. With the advent of information and communication technologies, new communication spaces have emerged that have the potential to enhance networked learning in national and international professional associations and increase the effectiveness of CME for health professionals. In this article, network science, based on the application of network theory and other theories, is proposed as an approach to better understand the contribution networking and interactivity between health professionals in professional communities make to their learning and adoption of new practices over time. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.

Science.gov (United States)

Marsolo, Keith; Margolis, Peter A; Forrest, Christopher B; Colletti, Richard B; Hutton, John J

2015-01-01

We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research. We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests. The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however. We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.

An improved multi-domain convolution tracking algorithm

Science.gov (United States)

Sun, Xin; Wang, Haiying; Zeng, Yingsen

2018-04-01

Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multidomain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn. which illustrates outstanding performance of the tracking algorithm in this paper.

Mobile phone tracking: in support of modelling traffic-related air pollution contribution to individual exposure and its implications for public health impact assessment

Science.gov (United States)

2013-01-01

We propose a new approach to assess the impact of traffic-related air pollution on public health by mapping personal trajectories using mobile phone tracking technology in an urban environment. Although this approach is not based on any empirical studies, we believe that this method has great potential and deserves serious attention. Mobile phone tracking technology makes it feasible to generate millions of personal trajectories and thereby cover a large fraction of an urban population. Through analysis, personal trajectories are not only associated to persons, but it can also be associated with vehicles, vehicle type, vehicle speed, vehicle emission rates, and sources of vehicle emissions. Pollution levels can be estimated by dispersion models from calculated traffic emissions. Traffic pollution exposure to individuals can be estimated based on the exposure along the individual human trajectories in the estimated pollution concentration fields by utilizing modelling tools. By data integration, one may identify trajectory patterns of particularly exposed human groups. The approach of personal trajectories may open a new paradigm in understanding urban dynamics and new perspectives in population-wide empirical public health research. This new approach can be further applied to individual commuter route planning, land use planning, urban traffic network planning, and used by authorities to formulate air pollution mitigation policies and regulations. PMID:24188173

Tracks FAQs: What is Modeled Air Data?

Centers for Disease Control (CDC) Podcasts

2011-04-25

In this podcast, CDC Tracking experts discuss modeled air data. Do you have a question for our Tracking experts? Please e-mail questions to trackingsupport@cdc.gov.  Created: 4/25/2011 by National Center for Environmental Health, Division of Environmental Hazards and Health Effects, Environmental Health Tracking Branch.   Date Released: 4/25/2011.

A real-time in-memory discovery service leveraging hierarchical packaging information in a unique identifier network to retrieve track and trace information

CERN Document Server

Müller, Jürgen

2014-01-01

This book examines how to efficiently retrieve track and trace information for an item that took a certain path through a complex network of manufacturers, wholesalers, retailers and consumers. It includes valuable tips on in-memory data management.

Chaotic system optimal tracking using data-based synchronous method with unknown dynamics and disturbances

International Nuclear Information System (INIS)

Song Ruizhuo; Wei Qinglai

2017-01-01

We develop an optimal tracking control method for chaotic system with unknown dynamics and disturbances. The method allows the optimal cost function and the corresponding tracking control to update synchronously. According to the tracking error and the reference dynamics, the augmented system is constructed. Then the optimal tracking control problem is defined. The policy iteration (PI) is introduced to solve the min-max optimization problem. The off-policy adaptive dynamic programming (ADP) algorithm is then proposed to find the solution of the tracking Hamilton–Jacobi–Isaacs (HJI) equation online only using measured data and without any knowledge about the system dynamics. Critic neural network (CNN), action neural network (ANN), and disturbance neural network (DNN) are used to approximate the cost function, control, and disturbance. The weights of these networks compose the augmented weight matrix, and the uniformly ultimately bounded (UUB) of which is proven. The convergence of the tracking error system is also proven. Two examples are given to show the effectiveness of the proposed synchronous solution method for the chaotic system tracking problem. (paper)

Data Quality in Online Health Social Networks for Chronic Diseases

Science.gov (United States)

Venkatesan, Srikanth

2017-01-01

Can medical advice from other participants in online health social networks impact patient safety? What can we do alleviate this problem? How does the accuracy of information on such networks affect the patients?. There has been a significant increase , in recent years, in the use of online health social network sites as more patients seek to…

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

Science.gov (United States)

Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

2016-01-01

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

Networks as a Type of Social Entrepreneurship to Advance Population Health

OpenAIRE

Wei-Skillern, Jane

2010-01-01

A detailed case study from the field of social entrepreneurship is used to illustrate the network approach, which does not require more resources but rather makes better use of existing resources. Leaders in public health can use networks to overcome some of the barriers that inhibit the widespread adoption of a population health approach to community health. Public health leaders who embrace social entrepreneurship may be better able to accomplish their missions by building their networks ra...

Robust Target Tracking with Multi-Static Sensors under Insufficient TDOA Information.

Science.gov (United States)

Shin, Hyunhak; Ku, Bonhwa; Nelson, Jill K; Ko, Hanseok

2018-05-08

This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations.

Father Absence, Social Networks, and Maternal Ratings of Child Health: Evidence from the 2013 Social Networks and Health Information Survey in Mexico.

Science.gov (United States)

Edelblute, Heather B; Altman, Claire E

2018-04-01

Objectives To bridge the literature on the effect of father absence, international migration, and social networks on child health, we assess the association between father absence and maternal ratings of child poor health (MCPH). Next we test whether social networks of immediate and extended kin mediate the relationship between fathers' absence and MCPH. Methods Nested logistic regression models predicting MCPH are estimated using the 2013 Social Networks and Health Information Survey, collected in a migrant-sending community in Guanajuato, Mexico. These unique data distinguish among father absence due to migration versus other reasons and between immediate and extended kin ties. Results Descriptive results indicate that 25% of children with migrant fathers are assessed as having poor health, more often than children with present (15.5%) or otherwise absent fathers (17.5%). In the multivariate models, fathers' absence is not predictive of MCPH. However, the presence of extended kin ties for the mother was associated with approximately a 50% reduction in the odds of MCPH. Additionally, mother's poor self-assessed health was associated with increased odds of MCPH while the presence of a co-resident adult lowered the odds of MCPH. In sensitivity analysis among children with migrant fathers, the receipt of paternal remittances lowered the odds of MCPH. Conclusions for Practice Social networks have a direct and positive association with MCPH rather than mediating the father absence-MCPH relationship. The presence of extended kin ties in the local community is salient for more favorable child health and should be considered in public health interventions aimed at improving child health.

Tracking of overweight from mid-adolescence into adulthood

DEFF Research Database (Denmark)

Rasmussen, Mette; Holstein, Bjørn E; Due, Pernille

2012-01-01

Socially differentiated tracking of health and health behaviours may contribute to health inequalities in adulthood. The modifying effect of socio-economic position on the tracking of overweight from mid-adolescence (age 15 years) into adulthood (age 27 years) was assessed in a randomly sampled D...... Danish cohort (n = 561). The tracking was studied by prediction analyses conducted by logistic regression analyses. Strong tracking patterns were found to be independent of socio-economic background....

Networks as a type of social entrepreneurship to advance population health.

Science.gov (United States)

Wei-Skillern, Jane

2010-11-01

A detailed case study from the field of social entrepreneurship is used to illustrate the network approach, which does not require more resources but rather makes better use of existing resources. Leaders in public health can use networks to overcome some of the barriers that inhibit the widespread adoption of a population health approach to community health. Public health leaders who embrace social entrepreneurship may be better able to accomplish their missions by building their networks rather than just their organizations.

Social Network Assessments and Interventions for Health Behavior Change: A Critical Review.

Science.gov (United States)

Latkin, Carl A; Knowlton, Amy R

2015-01-01

Social networks provide a powerful approach for health behavior change. This article documents how social network interventions have been successfully used for a range of health behaviors, including HIV risk practices, smoking, exercise, dieting, family planning, bullying, and mental health. We review the literature that suggests the relationship between health behaviors and social network attributes demonstrates a high degree of specificity. The article then examines hypothesized social influence mechanisms including social norms, modeling, and social rewards and the factors of social identity and social rewards that can be employed to sustain social network interventions. Areas of future research avenues are highlighted, including the need to examine and to adjust analytically for contamination and social diffusion, social influence versus differential affiliation, and network change. Use and integration of mhealth and face-to-face networks for promoting health behavior change are also critical research areas.

Frequent Surfing on Social Health Networks is Associated With Increased Knowledge and Patient Health Activation.

Science.gov (United States)

Grosberg, Dafna; Grinvald, Haya; Reuveni, Haim; Magnezi, Racheli

2016-08-10

The advent of the Internet has driven a technological revolution that has changed our lives. As part of this phenomenon, social networks have attained a prominent role in health care. A variety of medical services is provided over the Internet, including home monitoring, interactive communications between the patient and service providers, and social support, among others. This study emphasizes some of the practical implications of Web-based health social networks for patients and for health care systems. The objective of this study was to assess how participation in a social network among individuals with a chronic condition contributed to patient activation, based on the Patient Activation Measure (PAM). A prospective, cross-sectional survey with a retrospective component was conducted. Data were collected from Camoni, a Hebrew-language Web-based social health network, participants in the diabetes mellitus, pain, hypertension, and depression/anxiety forums, during November 2012 to 2013. Experienced users (enrolled at least 6 months) and newly enrolled received similar versions of the same questionnaire including sociodemographics and PAM. Among 686 participants, 154 of 337 experienced and 123 of 349 newly enrolled completed the questionnaire. Positive correlations (Psocial relationships, and chronic disease knowledge. Men surfed longer than women (χ²3=10.104, Psocial health network use were correlated with increased knowledge about a chronic disease. Experienced surfers had higher PAM than newly enrolled, suggesting that continued site use may contribute to increased activation. Web-based social health networks offer an opportunity to expand patient knowledge and increase involvement in personal health, thereby increasing patient activation. Further studies are needed to examine these changes on other aspects of chronic illnesses such as quality of life and costs.

Precise orbit determination for BDS3 experimental satellites using iGMAS and MGEX tracking networks

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Li, Xingxing; Yuan, Yongqiang; Zhu, Yiting; Huang, Jiande; Wu, Jiaqi; Xiong, Yun; Zhang, Xiaohong; Li, Xin

2018-04-01

In this contribution, we focus on the precise orbit determination (POD) for BDS3 experimental satellites with the international GNSS Monitoring and Assessment System (iGMAS) and Multi-GNSS Experiment (MGEX) tracking networks. The datasets of DOY (day of year) 001-230 in 2017 are analyzed with different processing strategies. By comparing receiver clock biases and receiver B1I-B3I DCBs, it is confirmed that there is no obvious systematic bias between experimental BDS3 and BDS2 in the common B1I and B3I signals, which indicates that experimental BDS3 and BDS2 can be treated as one system when performing combined POD. With iGMAS-only BDS3 stations, the 24-h overlap RMS of BDS3 + BDS2 + GPS combined POD is 24.3, 16.1 and 8.4 cm in along-track, cross-track and radial components, which is better than BDS3-only POD by 80-90% and better than BDS3+BDS2 combined POD by about 10%. With more stations (totally 20 stations from both iGMAS and MGEX) and the proper ambiguity resolution strategy (GEO ambiguities are float and BDS3 ambiguities are fixed), the performance of BDS3 POD can be further improved to 14.6, 7.9 and 3.7 cm, respectively, in along-track, cross-track and radial components, which is comparable to the performance of BDS2 POD. The 230-day SLR validations of C32, C33 and C34 show that the mean differences of - 3.48 , 7.81 and 8.19 cm can be achieved, while the STD is 13.35, 13.46 and 13.11 cm, respectively. Furthermore, the 230-day overlap comparisons reveal that C31 most likely still uses an orbit-normal mode and exhibits similar orbit modeling problems in orbit-normal periods as found in most of the BDS2 satellites.

A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller

Directory of Open Access Journals (Sweden)

Carlos Robles Algarín

2018-01-01

Full Text Available This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV module. A nonlinear autoregressive network with exogenous inputs (NARX was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA, and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W.

Loneliness, Social Networks, and Health: A Cross-Sectional Study in Three Countries.

Science.gov (United States)

Rico-Uribe, Laura Alejandra; Caballero, Francisco Félix; Olaya, Beatriz; Tobiasz-Adamczyk, Beata; Koskinen, Seppo; Leonardi, Matilde; Haro, Josep Maria; Chatterji, Somnath; Ayuso-Mateos, José Luis; Miret, Marta

2016-01-01

It is widely recognized that social networks and loneliness have effects on health. The present study assesses the differential association that the components of the social network and the subjective perception of loneliness have with health, and analyzes whether this association is different across different countries. A total of 10 800 adults were interviewed in Finland, Poland and Spain. Loneliness was assessed by means of the 3-item UCLA Loneliness Scale. Individuals' social networks were measured by asking about the number of members in the network, how often they had contacts with these members, and whether they had a close relationship. The differential association of loneliness and the components of the social network with health was assessed by means of hierarchical linear regression models, controlling for relevant covariates. In all three countries, loneliness was the variable most strongly correlated with health after controlling for depression, age, and other covariates. Loneliness contributed more strongly to health than any component of the social network. The relationship between loneliness and health was stronger in Finland (|β| = 0.25) than in Poland (|β| = 0.16) and Spain (|β| = 0.18). Frequency of contact was the only component of the social network that was moderately correlated with health. Loneliness has a stronger association with health than the components of the social network. This association is similar in three different European countries with different socio-economic and health characteristics and welfare systems. The importance of evaluating and screening feelings of loneliness in individuals with health problems should be taken into account. Further studies are needed in order to be able to confirm the associations found in the present study and infer causality.

Loneliness, Social Networks, and Health: A Cross-Sectional Study in Three Countries.

Directory of Open Access Journals (Sweden)

Laura Alejandra Rico-Uribe

Full Text Available It is widely recognized that social networks and loneliness have effects on health. The present study assesses the differential association that the components of the social network and the subjective perception of loneliness have with health, and analyzes whether this association is different across different countries.A total of 10 800 adults were interviewed in Finland, Poland and Spain. Loneliness was assessed by means of the 3-item UCLA Loneliness Scale. Individuals' social networks were measured by asking about the number of members in the network, how often they had contacts with these members, and whether they had a close relationship. The differential association of loneliness and the components of the social network with health was assessed by means of hierarchical linear regression models, controlling for relevant covariates.In all three countries, loneliness was the variable most strongly correlated with health after controlling for depression, age, and other covariates. Loneliness contributed more strongly to health than any component of the social network. The relationship between loneliness and health was stronger in Finland (|β| = 0.25 than in Poland (|β| = 0.16 and Spain (|β| = 0.18. Frequency of contact was the only component of the social network that was moderately correlated with health.Loneliness has a stronger association with health than the components of the social network. This association is similar in three different European countries with different socio-economic and health characteristics and welfare systems. The importance of evaluating and screening feelings of loneliness in individuals with health problems should be taken into account. Further studies are needed in order to be able to confirm the associations found in the present study and infer causality.

[Emergency response management near the tracks of the public railway network: special aspects of missions connected with the German national railway system].

Science.gov (United States)

Krämer, P; Aul, A; Vock, B; Frank, C

2010-11-01

Emergency response management and rescue operations concerning the railway network in Germany need special attention and implementation in several ways. The emergency response concerning the German national railway network managed by Deutsche Bahn AG is subject to various rules and regulations which have to be followed precisely. Only by following these rules and procedures is the safety of all emergency staff at the scene ensured. The German national railway network (Deutsche Bahn AG) provides its own emergency response control center, which specializes in managing its response to emergencies and dispatches an emergency response manager to the scene. This person serves as the primary Deutsche Bahn AG representative at the scene and is the only person who is allowed to earth the railway electrical power lines. This article will discuss different emergency situations concerning railway accidents and the emergency medical response to them based on a near collision with a high speed train during a rescue mission close to the railway track. Injury to personnel could only be avoided by chance and luck. The dangers and risks for rescue staff are specified. Furthermore, the article details practical guidelines for rescue operations around the German national railway track system.

Enhanced compressed sensing for visual target tracking in wireless visual sensor networks

Science.gov (United States)

Qiang, Guo

2017-11-01

Moving object tracking in wireless sensor networks (WSNs) has been widely applied in various fields. Designing low-power WSNs for the limited resources of the sensor, such as energy limitation, energy restriction, and bandwidth constraints, is of high priority. However, most existing works focus on only single conflicting optimization criteria. An efficient compressive sensing technique based on a customized memory gradient pursuit algorithm with early termination in WSNs is presented, which strikes compelling trade-offs among energy dissipation for wireless transmission, certain types of bandwidth, and minimum storage. Then, the proposed approach adopts an unscented particle filter to predict the location of the target. The experimental results with a theoretical analysis demonstrate the substantially superior effectiveness of the proposed model and framework in regard to the energy and speed under the resource limitation of a visual sensor node.

Social network analysis of public health programs to measure partnership.

Science.gov (United States)

Schoen, Martin W; Moreland-Russell, Sarah; Prewitt, Kim; Carothers, Bobbi J

2014-12-01

In order to prevent chronic diseases, community-based programs are encouraged to take an ecological approach to public health promotion and involve many diverse partners. Little is known about measuring partnership in implementing public health strategies. We collected data from 23 Missouri communities in early 2012 that received funding from three separate programs to prevent obesity and/or reduce tobacco use. While all of these funding programs encourage partnership, only the Social Innovation for Missouri (SIM) program included a focus on building community capacity and enhancing collaboration. Social network analysis techniques were used to understand contact and collaboration networks in community organizations. Measurements of average degree, density, degree centralization, and betweenness centralization were calculated for each network. Because of the various sizes of the networks, we conducted comparative analyses with and without adjustment for network size. SIM programs had increased measurements of average degree for partner collaboration and larger networks. When controlling for network size, SIM groups had higher measures of network density and lower measures of degree centralization and betweenness centralization. SIM collaboration networks were more dense and less centralized, indicating increased partnership. The methods described in this paper can be used to compare partnership in community networks of various sizes. Further research is necessary to define causal mechanisms of partnership development and their relationship to public health outcomes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Narrow Networks On The Health Insurance Marketplaces: Prevalence, Pricing, And The Cost Of Network Breadth.

Science.gov (United States)

Dafny, Leemore S; Hendel, Igal; Marone, Victoria; Ody, Christopher

2017-09-01

Anecdotal reports and systematic research highlight the prevalence of narrow-network plans on the Affordable Care Act's health insurance Marketplaces. At the same time, Marketplace premiums in the period 2014-16 were much lower than projected by the Congressional Budget Office in 2009. Using detailed data on the breadth of both hospital and physician networks, we studied the prevalence of narrow networks and quantified the association between network breadth and premiums. Controlling for many potentially confounding factors, we found that a plan with narrow physician and hospital networks was 16 percent cheaper than a plan with broad networks for both, and that narrowing the breadth of just one type of network was associated with a 6-9 percent decrease in premiums. Narrow-network plans also have a sizable impact on federal outlays, as they depress the premium of the second-lowest-price silver plan, to which subsidy amounts are linked. Holding all else constant, we estimate that federal subsidies would have been 10.8 percent higher in 2014 had Marketplaces required all plans to offer broad provider networks. Narrow networks are a promising source of potential savings for other segments of the commercial insurance market. Project HOPE—The People-to-People Health Foundation, Inc.

A Model for Nationwide Patient Tracking

Science.gov (United States)

2009-09-01

SUBJECT TERMS Patient Tracking, Public Health, Emergency Medical Services, Patient Movement, Evacuation, Public Health Preparedness 16. PRICE CODE 17...Emergency Medical Services................................................................................................19 2. Ideal Patient Tracking... Medical Services ............................................................36 a. Patient Flow in Field-Based Casualty Care—Current Process

Coordinators for health science libraries in the Midwest Health Science Library Network.

Science.gov (United States)

Holtum, E A; McKloskey, J; Mahan, R

1977-04-01

In the summer of 1973 one resource library in each of the six states of the Midwest Health Science Library Network received funding from the National Library of Medicine to hire a coordinator for health science libraries. The development of the role of coordinator is examined and evaluated. The coordinators have proved valuable in the areas of consortium formation, basic unit development, communication facilitation, and program initiation. The function of the coordinators in the extensive planning effort now being undertaken by the network and the future need for the coordinator positions are discussed.

Health Participatory Sensing Networks

Directory of Open Access Journals (Sweden)

Andrew Clarke

2014-01-01

Full Text Available The use of participatory sensing in relation to the capture of health-related data is rapidly becoming a possibility due to the widespread consumer adoption of emerging mobile computing technologies and sensing platforms. This has the potential to revolutionize data collection for population health, aspects of epidemiology, and health-related e-Science applications and as we will describe, provide new public health intervention capabilities, with the classifications and capabilities of such participatory sensing platforms only just beginning to be explored. Such a development will have important benefits for access to near real-time, large-scale, up to population-scale data collection. However, there are also numerous issues to be addressed first: provision of stringent anonymity and privacy within these methodologies, user interface issues, and the related issue of how to incentivize participants and address barriers/concerns over participation. To provide a step towards describing these aspects, in this paper we present a first classification of health participatory sensing models, a novel contribution to the literature, and provide a conceptual reference architecture for health participatory sensing networks (HPSNs and user interaction example case study.

[Networks of experiences on community health as an information system in health promotion: lessons learned in Aragon (Spain)].

Science.gov (United States)

Gállego-Diéguez, Javier; Aliaga Traín, Pilar; Benedé Azagra, Carmen Belén; Bueno Franco, Manuel; Ferrer Gracia, Elisa; Ipiéns Sarrate, José Ramón; Muñoz Nadal, Pilar; Plumed Parrilla, Manuela; Vilches Urrutia, Begoña

2016-11-01

Networks of community health experiences promote interaction and knowledge management in health promotion among their participants. These networks integrate both professionals and social agents who work directly on the ground in small environments, with defined objectives and inclusion criteria and voluntary participation. In this article, networks in Aragon (Spain) are reviewed in order to analyse their role as an information system. The Health Promotion Projects Network of Aragon (Red Aragonesa de Proyectos de Promoción de la Salud, RAPPS) was launched in 1996 and currently includes 73 projects. The average duration of projects is 12.7 years. RAPPS interdisciplinary teams involve 701 people, of which 89.6% are professionals and 10.6% are social agents. The Aragon Health Promoting Schools Network (Red Aragonesa de Escuelas Promotoras de Salud, RAEPS) integrates 134 schools (24.9% of Aragon). The schools teams involve 829 teachers and members of the school community, students (35.2%), families (26.2%) and primary care health professionals (9.8%). Experiences Networks boost citizen participation, have an influence in changing social determinants and contribute to the formulation of plans and regional strategies. Networks can provide indicators for a health promotion information and monitoring system on: capacity building services in the territory, identifying assets and models of good practice, cross-sectoral and equity initiatives. Experiences Networks represent an opportunity to create a health promotion information system, systematising available information and establishing quality criteria for initiatives. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Rwanda Health and Education Information Network (OASIS-RHEIN ...

International Development Research Centre (IDRC) Digital Library (Canada)

Rwanda Health and Education Information Network (OASIS-RHEIN). Partners in Health (PIH), an international nongovernmental organization, has demonstrated the effectiveness of its open source electronic medical record system (OpenMRS) in eight clinics in Rwanda. As a result, the Ministry of Health has decided to roll ...

Communication security in open health care networks.

Science.gov (United States)

Blobel, B; Pharow, P; Engel, K; Spiegel, V; Krohn, R

1999-01-01

Fulfilling the shared care paradigm, health care networks providing open systems' interoperability in health care are needed. Such communicating and co-operating health information systems, dealing with sensitive personal medical information across organisational, regional, national or even international boundaries, require appropriate security solutions. Based on the generic security model, within the European MEDSEC project an open approach for secure EDI like HL7, EDIFACT, XDT or XML has been developed. The consideration includes both securing the message in an unsecure network and the transport of the unprotected information via secure channels (SSL, TLS etc.). Regarding EDI, an open and widely usable security solution has been specified and practically implemented for the examples of secure mailing and secure file transfer (FTP) via wrapping the sensitive information expressed by the corresponding protocols. The results are currently prepared for standardisation.

Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method.

Science.gov (United States)

Zhao, Zijian; Voros, Sandrine; Weng, Ying; Chang, Faliang; Li, Ruijian

2017-12-01

Worldwide propagation of minimally invasive surgeries (MIS) is hindered by their drawback of indirect observation and manipulation, while monitoring of surgical instruments moving in the operated body required by surgeons is a challenging problem. Tracking of surgical instruments by vision-based methods is quite lucrative, due to its flexible implementation via software-based control with no need to modify instruments or surgical workflow. A MIS instrument is conventionally split into a shaft and end-effector portions, while a 2D/3D tracking-by-detection framework is proposed, which performs the shaft tracking followed by the end-effector one. The former portion is described by line features via the RANSAC scheme, while the latter is depicted by special image features based on deep learning through a well-trained convolutional neural network. The method verification in 2D and 3D formulation is performed through the experiments on ex-vivo video sequences, while qualitative validation on in-vivo video sequences is obtained. The proposed method provides robust and accurate tracking, which is confirmed by the experimental results: its 3D performance in ex-vivo video sequences exceeds those of the available state-of -the-art methods. Moreover, the experiments on in-vivo sequences demonstrate that the proposed method can tackle the difficult condition of tracking with unknown camera parameters. Further refinements of the method will refer to the occlusion and multi-instrumental MIS applications.

Building research infrastructure in community health centers: a Community Health Applied Research Network (CHARN) report.

Science.gov (United States)

Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E

2013-01-01

This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and "matchmaking" between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings.

Structural and functional social network attributes moderate the association of self-rated health with mental health in midlife and older adults.

Science.gov (United States)

Windsor, Tim D; Rioseco, Pilar; Fiori, Katherine L; Curtis, Rachel G; Booth, Heather

2016-01-01

Social relationships are multifaceted, and different social network components can operate via different processes to influence well-being. This study examined associations of social network structure and relationship quality (positive and negative social exchanges) with mental health in midlife and older adults. The focus was on both direct associations of network structure and relationship quality with mental health, and whether these social network attributes moderated the association of self-rated health (SRH) with mental health. Analyses were based on survey data provided by 2001 (Mean age = 65, SD = 8.07) midlife and older adults. We used Latent Class Analysis (LCA) to classify participants into network types based on network structure (partner status, network size, contact frequency, and activity engagement), and used continuous measures of positive and negative social exchanges to operationalize relationship quality. Regression analysis was used to test moderation. LCA revealed network types generally consistent with those reported in previous studies. Participants in more diverse networks reported better mental health than those categorized into a restricted network type after adjustment for age, sex, education, and employment status. Analysis of moderation indicated that those with poorer SRH were less likely to report poorer mental health if they were classified into more diverse networks. A similar moderation effect was also evident for positive exchanges. The findings suggest that both quantity and quality of social relationships can play a role in buffering against the negative implications of physical health decline for mental health.

Evaluation of a Pilot Surveillance System: Health and Environment Linked for Information Exchange in Atlanta (HELIX-Atlanta)

Science.gov (United States)

Meyer, P.; Shire, J.; Qualters, Judy; Daley, Randolph; Fiero, Leslie Todorov; Autry, Andy; Avchen, Rachel; Stock, Allison; Correa, Adolofo; Siffel, Csaba;

Does attention to health labels predict a healthy food choice? An eye-tracking study

NARCIS (Netherlands)

Fenko, Anna; Nicolaas, Iris; Galetzka, Mirjam

2018-01-01

Visual attention to health labels can indicate a subsequent healthy food choice. This study looked into the relative effects of Choices logos and traffic light labels on consumers’ visual attention and food choice. A field experiment using mobile eye-tracking was conducted in a Dutch university

Social Network resources and self-rated health in a deprived Danish neighborhood

DEFF Research Database (Denmark)

Tanggaard Andersen, Pernille; Holst Algren, Maria; Fromsejer Heiberg, Regina

2017-01-01

Research has demonstrated that living in a deprived neighborhood contributes to the occurrence and development of poor health. Furthermore evidence shows that social networks are fundamental resources in preventing poor mental health. Neighborhood relationships and networks are vital for sustaining...... and improving quality of life. However, to determine potentials for public health action, the health impact of various types of network resources need to be explored and the association between socioeconomic position and self-rated health needs to be analysed to determine whether it is partially explained...... by social network resources. This is the main aim of this article. Cross-sectional data from one deprived neighborhood located in Denmark were collected in 2008 and 2013 using a postal health survey. The target group was defined as adults older than 16 years. In 2008, 408 residents participated...

Health disparities in Europe's ageing population: the role of social network.

Science.gov (United States)

Olofsson, Jenny; Padyab, Mojgan; Malmberg, Gunnar

2018-01-01

Previous research suggests that the social network may play very different roles in relation to health in countries with differing welfare regimes. The study aimed to assess the interplay between social network, socioeconomic position, and self-rated health (SRH) in European countries. The study used cross-sectional data on individuals aged 50+ from the fourth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE) and includes data from 16 countries. The outcome is poor SRH. All analyses are adjusted for age and stratified by gender. Low satisfaction with the social network was associated with poor SRH among women in all country groups, but predicted poor SRH among males in West/Central and Eastern Europe only. The results from the multivariable analysis showed an increased likelihood of poor SRH among those with relatively lower education, as well as among those with low satisfaction with the social network (women from all country groups and men from Western/Central and Eastern Europe). However, the results from interaction analysis show that poor SRH for those with lower relative position in educational level was greater among those with higher satisfaction with the social network among male and female participants from Northern Europe. The health of individuals who are highly satisfied with their social network is more associated with socioeconomic status in Northern Europe. This study highlights the significance of social network and socioeconomic gradients in health among the elderly in Europe.

Health Promoting Hospitals – Assessing developments in the network

Directory of Open Access Journals (Sweden)

Jürgen M. Pelikan

2007-12-01

Full Text Available Hospitals are specific organizational settings for health promotion efforts. As health care institutions they are already oriented at health, or better at ill health, but with a rather limited focus on health outcomes for patients. Therefore, the Ottawa Charter explicitly asks for the reorientation of health services. And, hospitals also have considerable health effects for other stakeholder populations. This specific potential and challenge has been taken up by the WHO network of Health Promoting Hospitals (HPH, in the last two decades. Based on available literature the article relates the HPH concept to a more general paradigm of health promoting organizational settings; reconstructs the developmental phases of the international WHO HPH Network; elaborates on its concept development and implementation experiences, and discusses its rather limited investments in evaluation studies and the few assessments from outside. HPH has developed a convincing comprehensive concept by demonstration projects, using systematically action and evaluation research. To a lesser degree, the same holds true for its developments of health promotion policies for selected vulnerable groups and linking HPH to quality methodology. But there is no systematic evaluation of health promotion in and by hospitals, especially for the networks and member hospitals of HPH. Even if much of the relevant evidence for HPH comes and will have to come from more general clinical epidemiological, health promotion, quality, organizational and management research, there is need for specific HPH evaluation research, to better utilize, what can be learned from the social experiment of HPH.

Environmental Public Health Surveillance for Exposure to Respiratory Health Hazards: A Joint NASA/CDC Project to Use Remote Sensing Data for Estimating Airborne Particulate Matter Over the Atlanta, Georgia Metropolitan Area

Science.gov (United States)

Quattrochi, Dale A.; Al-Hamdan, Mohammad; Estes, Maurice; Crosson, William

2007-01-01

As part of the National Environmental Public Health Tracking Network (EPHTN) the National Center for Environmental Health (NCEH) at the Centers for Disease Control and Prevention (CDC) is leading a project called Health and Environment Linked for Information Exchange (HELiX-Atlanta). The goal of developing the National Environmental Public Health Tracking Network is to improve the health of communities. Currently, few systems exist at the state or national level to concurrently track many of the exposures and health effects that might be associated with environmental hazards. An additional challenge is estimating exposure to environmental hazards such as particulate matter whose aerodynamic diameter is less than or equal to 2.5 micrometers (PM2.5). HELIX-Atlanta's goal is to examine the feasibility of building an integrated electronic health and environmental data network in five counties of Metropolitan Atlanta, GA. NASA Marshall Space Flight Center (NASA/MSFC) is collaborating with CDC to combine NASA earth science satellite observations related to air quality and environmental monitoring data to model surface estimates of PM2.5 concentrations that can be linked with clinic visits for asthma. While use of the Air Quality System (AQS) PM2.5 data alone could meet HELIX-Atlanta specifications, there are only five AQS sites in the Atlanta area, thus the spatial coverage is not ideal. We are using NASA Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Aerosol Optical Depth (AOD) data for estimating daily ground level PM2.5 at 10 km resolution over the metropolitan Atlanta area supplementing the AQS ground observations and filling their spatial and temporal gaps.

Applying a Health Network approach to translate evidence-informed policy into practice: a review and case study on musculoskeletal health.

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Briggs, Andrew M; Bragge, Peter; Slater, Helen; Chan, Madelynn; Towler, Simon C B

2012-11-14

While translation of evidence into health policy and practice is recognised as critical to optimising health system performance and health-related outcomes for consumers, mechanisms to effectively achieve these goals are neither well understood, nor widely communicated. Health Networks represent a framework which offers a possible solution to this dilemma, particularly in light of emerging evidence regarding the importance of establishing relationships between stakeholders and identifying clinical leaders to drive evidence integration and translation into policy. This is particularly important for service delivery related to chronic diseases. In Western Australia (WA), disease and population-specific Health Networks are comprised of cross-discipline stakeholders who work collaboratively to develop evidence-informed policies and drive their implementation. Since establishment of the Health Networks in WA, over 50 evidence-informed Models of Care (MoCs) have been produced across 18 condition or population-focused Networks. The aim of this paper is to provide an overview of the Health Network framework in facilitating the translation of evidence into policy and practice with a particular focus on musculoskeletal health. A review of activities of the WA Musculoskeletal Health Network was undertaken, focussing on outcomes and the processes used to achieve them in the context of: development of policy, procurement of funding, stakeholder engagement, publications, and projects undertaken by the Network which aligned to implementation of MoCs.The Musculoskeletal Health Network has developed four MoCs which reflect Australian National Health Priority Areas. Establishment of community-based services for consumers with musculoskeletal health conditions is a key recommendation from these MoCs. Through mapping barriers and enablers to policy implementation, working groups, led by local clinical leaders and supported by the broader Network and government officers, have

Applying a Health Network approach to translate evidence-informed policy into practice: A review and case study on musculoskeletal health

Directory of Open Access Journals (Sweden)

Briggs Andrew M

2012-11-01

Full Text Available Abstract Background While translation of evidence into health policy and practice is recognised as critical to optimising health system performance and health-related outcomes for consumers, mechanisms to effectively achieve these goals are neither well understood, nor widely communicated. Health Networks represent a framework which offers a possible solution to this dilemma, particularly in light of emerging evidence regarding the importance of establishing relationships between stakeholders and identifying clinical leaders to drive evidence integration and translation into policy. This is particularly important for service delivery related to chronic diseases. In Western Australia (WA, disease and population-specific Health Networks are comprised of cross-discipline stakeholders who work collaboratively to develop evidence-informed policies and drive their implementation. Since establishment of the Health Networks in WA, over 50 evidence-informed Models of Care (MoCs have been produced across 18 condition or population-focused Networks. The aim of this paper is to provide an overview of the Health Network framework in facilitating the translation of evidence into policy and practice with a particular focus on musculoskeletal health. Case presentation A review of activities of the WA Musculoskeletal Health Network was undertaken, focussing on outcomes and the processes used to achieve them in the context of: development of policy, procurement of funding, stakeholder engagement, publications, and projects undertaken by the Network which aligned to implementation of MoCs. The Musculoskeletal Health Network has developed four MoCs which reflect Australian National Health Priority Areas. Establishment of community-based services for consumers with musculoskeletal health conditions is a key recommendation from these MoCs. Through mapping barriers and enablers to policy implementation, working groups, led by local clinical leaders and supported by

Optimizing network connectivity for mobile health technologies in sub-Saharan Africa.

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Siedner, Mark J; Lankowski, Alexander; Musinga, Derrick; Jackson, Jonathon; Muzoora, Conrad; Hunt, Peter W; Martin, Jeffrey N; Bangsberg, David R; Haberer, Jessica E

2012-01-01

Mobile health (mHealth) technologies hold incredible promise to improve healthcare delivery in resource-limited settings. Network reliability across large catchment areas can be a major challenge. We performed an analysis of network failure frequency as part of a study of real-time adherence monitoring in rural Uganda. We hypothesized that the addition of short messaging service (SMS+GPRS) to the standard cellular network modality (GPRS) would reduce network disruptions and improve transmission of data. Participants were enrolled in a study of real-time adherence monitoring in southwest Uganda. In June 2011, we began using Wisepill devices that transmit data each time the pill bottle is opened. We defined network failures as medication interruptions of >48 hours duration that were transmitted when network connectivity was re-established. During the course of the study, we upgraded devices from GPRS to GPRS+SMS compatibility. We compared network failure rates between GPRS and GPRS+SMS periods and created geospatial maps to graphically demonstrate patterns of connectivity. One hundred fifty-seven participants met inclusion criteria of seven days of SMS and seven days of SMS+GPRS observation time. Seventy-three percent were female, median age was 40 years (IQR 33-46), 39% reported >1-hour travel time to clinic and 17% had home electricity. One hundred one had GPS coordinates recorded and were included in the geospatial maps. The median number of network failures per person-month for the GPRS and GPRS+SMS modalities were 1.5 (IQR 1.0-2.2) and 0.3 (IQR 0-0.9) respectively, (mean difference 1.2, 95%CI 1.0-1.3, p-valueImprovements in network connectivity were notable throughout the region. Study costs increased by approximately $1USD per person-month. Addition of SMS to standard GPRS cellular network connectivity can significantly reduce network connection failures for mobile health applications in remote areas. Projects depending on mobile health data in resource

German cooperation-network 'equity in health'-health promotion in settings.

Science.gov (United States)

Mielck, Andreas; Kilian, Holger; Lehmann, Frank; Richter-Kornweitz, Antje; Kaba-Schönstein, Lotte

2018-04-01

In 2003, the German Federal Centre for Health Education (BZgA) initiated the national Cooperation-Network (CN) 'Equity in Health'. The CN is constantly increasing in size and scope, supporting setting approaches aimed at reducing health inequalities. A detailed description of the CN has not yet been available in English. The CN comprises a total of 66 institutional cooperation partners. Information concerning the structure and activities can be found on a special website. Coordination Centres (CC) have been established in the 16 federal states, for the coordination of all state-specific activities. Funding for the CN and CC is provided by the BZgA, the German statutory sickness funds and by the state-specific ministries of health. These partners also support the continuous quality improvement, which is based on the good-practice criteria developed by the Advisory Committee of the CN. In 2011, the 'Municipal Partner Process (MPP)' has been launched, specifically supporting local partners and integrated life-course approaches focussing on children. In 2015, the focus has been widened to include all age-groups. In July 2015, a new national health law concerning health promotion and prevention has been ratified by the federal Parliament, with a focus on reducing health inequalities. Currently, the details of its implementation are discussed on a nationwide basis. The CN has long advocated for such a law, and today the CN is a well-accepted partner providing concepts, methods and a strong and long-standing network. The article closes with future challenges faced by the CN.

Using Sunlight and Cell Networks to Bring Fleeting Tracking to Small Scale Fisheries

Science.gov (United States)

Garren, M.; Selbie, H.; Suchomel, D.; McDonald, W.; Solomon, D.

2016-12-01

Traditionally, the efforts of small scale fisheries have not been easily incorporated into the global picture of fishing effort and activity. That means that the activities of the vast majority ( 90%) of fishing vessels in the world have remained unquantified and largely opaque. With newly developed technology that harnesses solar power and cost-effective cellular networks to transmit data, it is becoming possible to provide vessel tracking systems on a large scale for vessels of all sizes. Furthermore, capitalizing on the relatively inexpensive cellular networks to transfer the data enables data of much higher granularity to be captured. By recording a vessel's position every few seconds, instead of minutes to hours as is typical of most satellite-based systems, we are able to resolve a diverse array of behaviors happening at sea including when and where fishing occurred and what type of fishing gear was used. This high granularity data is both incredibly useful and also a challenge to manage and mine. New approaches for handling and processing this continuous data stream of vessel positions are being developed to extract the most informative and actionable pieces of information for a variety of audiences including governing agencies, industry supply chains seeking transparency, non-profit organizations supporting conservation efforts, academic researchers and the fishers and boat owners.

Implementing multiple intervention strategies in Dutch public health-related policy networks

NARCIS (Netherlands)

Harting, Janneke; Peters, Dorothee; Grêaux, Kimberly; van Assema, Patricia; Verweij, Stefan; Stronks, Karien; Klijn, Erik-Hans

2017-01-01

Improving public health requires multiple intervention strategies. Implementing such an intervention mix is supposed to require a multisectoral policy network. As evidence to support this assumption is scarce, we examined under which conditions public health-related policy networks were able to

Integrating wireless sensor networks with CE devices for health care activity tracking in the home environment

NARCIS (Netherlands)

Bosman, R.P.; Lukkien, J.J.; Verhoeven, R.

2009-01-01

Wireless sensing devices containing limited processing and communication capabilities are becoming available for all sorts of purposes. An important problem is to integrate networks of these sensors with the existing CE en IT infrastructure such that a) data coming out of the sensor network can be

Visual Vehicle Tracking Based on Deep Representation and Semisupervised Learning

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Yingfeng Cai

2017-01-01

Full Text Available Discriminative tracking methods use binary classification to discriminate between the foreground and background and have achieved some useful results. However, the use of labeled training samples is insufficient for them to achieve accurate tracking. Hence, discriminative classifiers must use their own classification results to update themselves, which may lead to feedback-induced tracking drift. To overcome these problems, we propose a semisupervised tracking algorithm that uses deep representation and transfer learning. Firstly, a 2D multilayer deep belief network is trained with a large amount of unlabeled samples. The nonlinear mapping point at the top of this network is subtracted as the feature dictionary. Then, this feature dictionary is utilized to transfer train and update a deep tracker. The positive samples for training are the tracked vehicles, and the negative samples are the background images. Finally, a particle filter is used to estimate vehicle position. We demonstrate experimentally that our proposed vehicle tracking algorithm can effectively restrain drift while also maintaining the adaption of vehicle appearance. Compared with similar algorithms, our method achieves a better tracking success rate and fewer average central-pixel errors.

Real-Time Cloud-Based Health Tracking and Monitoring System in Designed Boundary for Cardiology Patients

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Aamir Shahzad

2018-01-01

Full Text Available Telemonitoring is not a new term, in information technology (IT, which has been employed to remotely monitor the health of patients that are located not in common places, such hospitals or medical centers. For that, wearable medical sensors, such as electrocardiography sensors, blood pressure sensors, and glucometer, have commonly been used to make possible to acquire the real-time information from the remotely located patients; therefore, the medical information is further carried, via the Internet, to perform medical diagnosis and the corresponding treatments. Like in other IT sectors, there has been tremendous progress accounted in medical sectors (and in telemonitoring systems that changes the human life protection against several chronic diseases, and the patient’s medical information can be accessed wirelessly via Wi-Fi and cellular systems. Further, with the advents of cloud computing technology, medical systems are now more efficient and scalable in processing, such as storage and access, the medical information with minimal development costs. This study is also a piece of enhancement made to track and monitor the real-time medical information, bounded in authorized area, through the modeling of private cloud computing. The private cloud-based environment is designed, for patient health monitoring called bounded telemonitoring system, to acquire the real-time medical information of patients that resided in the boundary, inside medical wards and outside medical wards, of the medical center. A new wireless sensor network scenario is designed and modeled to keep or monitor the patients’ health information whole day, 24 hours. This research is a new secured sight towards medical information access and gives directions for future developments in the medical systems.

European network for health technology assessment, EUnetHTA: planning, development, and implementation of a sustainable European network for health technology assessment

DEFF Research Database (Denmark)

Kristensen, Finn Børlum; Mäkelä, Marjukka; Neikter, Susanna Allgurin

2009-01-01

OBJECTIVES: The European network on Health Technology Assessment (EUnetHTA) aimed to produce tangible and practical results to be used in the various phases of health technology assessment and to establish a framework and processes to support this. This article presents the background, objectives......, and organization of EUnetHTA, which involved a total of sixty-four partner organizations. METHODS: Establishing an effective and sustainable structure for a transnational network involved many managerial, policy, and methodological tools, according to the objective of each task or Work Package. Transparency...... the use of HTA at national and regional levels. Responsiveness to political developments in Europe should be balanced with maintaining a high level of ambition to promote independent, evidence-based information and well-tested tools for best practice based on a strong network of HTA institutions....

The Ocean Tracking Network and its contribution to ocean biological observation

Science.gov (United States)

Whoriskey, F. G.

2016-02-01

Animals move to meet their needs for food, shelter, reproduction and to avoid unfavorable environments. In aquatic systems, it is essential that we understand these movements if we are to sustainably manage populations and maintain healthy ecosystems. Thus the ability to document and monitor changes in aquatic animal movements is a biological observing system need. The Ocean Tracking Network (OTN) is a global research, technology development, and data management platform headquartered at Dalhousie University, in Halifax, Nova Scotia working to fill this need. OTN uses electronic telemetry to document the local-to-global movements and survival of aquatic animals, and to correlate them to oceanographic or limnological variables that are influencing movements. Such knowledge can assist with planning for and managing of anthropogenic impacts on present and future animal distributions, including those due to climate change. OTN works with various tracking methods including satellite and data storage tag systems, but its dominant focus is acoustic telemetry. OTN is built on global partnerships for the sharing of equipment and data, and has stimulated technological development in telemetry by bringing researchers with needs for new capabilities together with manufacturers to generate, test, and operationalize new technologies. This has included pioneering work into the use of marine autonomous vehicles (Slocum electric gliders; Liquid Robotics Wave Glider) in animal telemetry research. Similarly, OTN scientists worked with the Sea Mammal Research Unit to develop mobile acoustic receiver that have been placed on grey seals and linked via Bluetooth to a satellite transmitter/receiver. This provided receiver coverage in areas occupied by the seals during their typically extensive migrations and allowed for the examination of ecosystem linkages by documenting behavioral interactions the seals had with the physical environment, conspecifics, and other tagged species.

ESAM: Endocrine inspired Sensor Activation Mechanism for multi-target tracking in WSNs

Science.gov (United States)

Adil Mahdi, Omar; Wahab, Ainuddin Wahid Abdul; Idris, Mohd Yamani Idna; Znaid, Ammar Abu; Khan, Suleman; Al-Mayouf, Yusor Rafid Bahar

2016-10-01

Target tracking is a significant application of wireless sensor networks (WSNs) in which deployment of self-organizing and energy efficient algorithms is required. The tracking accuracy increases as more sensor nodes are activated around the target but more energy is consumed. Thus, in this study, we focus on limiting the number of sensors by forming an ad-hoc network that operates autonomously. This will reduce the energy consumption and prolong the sensor network lifetime. In this paper, we propose a fully distributed algorithm, an Endocrine inspired Sensor Activation Mechanism for multi target-tracking (ESAM) which reflecting the properties of real life sensor activation system based on the information circulating principle in the endocrine system of the human body. Sensor nodes in our network are secreting different hormones according to certain rules. The hormone level enables the nodes to regulate an efficient sleep and wake up cycle of nodes to reduce the energy consumption. It is evident from the simulation results that the proposed ESAM in autonomous sensor network exhibits a stable performance without the need of commands from a central controller. Moreover, the proposed ESAM generates more efficient and persistent results as compared to other algorithms for tracking an invading object.

Self-Tracking: Reflections from the BodyTrack Project.

Science.gov (United States)

Wright, Anne

2016-07-06

Based on the author's experiences the practice of self-tracking can empower individuals to explore and address issues in their lives. This work is inspired by examples of people who have reclaimed their wellness through an iterative process of noticing patterns of ups and downs, trying out new ideas and strategies, and observing the results. In some cases, individuals have realized that certain foods, environmental exposures, or practices have unexpected effects for them, and that adopting custom strategies can greatly improve quality of life, overcoming chronic problems. Importantly, adopting the role of investigator of their own situation appears to be transformative: people who embarked on this path changed their relationship to their health situation even before making discoveries that helped lead to symptom improvement. The author co-founded the BodyTrack project in 2010 with the goal of empowering a broader set of people to embrace this investigator role in their own lives and better address their health and wellness concerns, particularly those with complex environmental or behavioral components. The core of the BodyTrack system is an open source web service called Fluxtream ( https://fluxtream.org ) that allows users to aggregate, visualize, and reflect on data from myriad sources on a common timeline. The project is also working to develop and spread peer coaching practices to help transfer the culture and skills of self-tracking while mentoring individuals in how to self-assess their own situation and guide the process for themselves.

Effects of social networks on physical health among people with serious mental illness.

Science.gov (United States)

Lee, Sungkyu; Wong, Yin-Ling Irene; Rothbard, Aileen

2014-12-01

This study examined the effects of social network characteristics on physical health among people with serious mental illness using social transactions that are reciprocal, and the combination of objective and subjective health measures. The sample consisted of a probability sample of 231 adults with serious mental illness who resided in permanent supportive housing in Philadelphia, Pennsylvania. Path analyses were conducted to examine the relationships between social network characteristics and two aspects of medical comorbidity, objective health and subjective health. Bivariate statistics showed that individuals with medical comorbidity were more likely to have contact with their network members and had a higher level of reciprocal positive tangible support when compared to those who did not have medical comorbidity. The results of the path analyses revealed that none of the social network characteristics were associated with better physical health. The lack of a significant relationship between social networks and better physical health is contrary to prior research findings. However, this is the first study to include both types of social transactions simultaneously as predictors of better physical health for individuals with serious mental illness. A longitudinal study would provide more insight into the temporal relationship of social networks and physical health conditions of people with serious mental illness. Furthermore, the transactional nature of social relationships, particularly for those with mental health issues, requires greater exploration.

An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances.

Science.gov (United States)

Fairbank, Michael; Li, Shuhui; Fu, Xingang; Alonso, Eduardo; Wunsch, Donald

2014-01-01

We present a recurrent neural-network (RNN) controller designed to solve the tracking problem for control systems. We demonstrate that a major difficulty in training any RNN is the problem of exploding gradients, and we propose a solution to this in the case of tracking problems, by introducing a stabilization matrix and by using carefully constrained context units. This solution allows us to achieve consistently lower training errors, and hence allows us to more easily introduce adaptive capabilities. The resulting RNN is one that has been trained off-line to be rapidly adaptive to changing plant conditions and changing tracking targets. The case study we use is a renewable-energy generator application; that of producing an efficient controller for a three-phase grid-connected converter. The controller we produce can cope with the random variation of system parameters and fluctuating grid voltages. It produces tracking control with almost instantaneous response to changing reference states, and virtually zero oscillation. This compares very favorably to the classical proportional integrator (PI) controllers, which we show produce a much slower response and settling time. In addition, the RNN we propose exhibits better learning stability and convergence properties, and can exhibit faster adaptation, than has been achieved with adaptive critic designs. Copyright © 2013 Elsevier Ltd. All rights reserved.

Social pathways to health: On the mediating role of the social network in the relation between socio-economic position and health.

Science.gov (United States)

Aartsen, Marja; Veenstra, Marijke; Hansen, Thomas

2017-12-01

Good health is one of the key qualities of life, but opportunities to be and remain healthy are unequally distributed across socio-economic groups. The beneficial health effects of the social network are well known. However, research on the social network as potential mediator in the pathway from socio-economic position (SEP) to health is scarce, while there are good reasons to expect a socio-economical patterning of networks. We aim to contribute to our understanding of socio-economic inequalities in health by examining the mediating role of structural and functional characteristics of the social network in the SEP-health relationship. Data were from the second wave of the Norwegian study on the life course, aging and generation study (NorLAG) and comprised 4534 men and 4690 women aged between 40 and 81. We applied multiple mediation models to evaluate the relative importance of each network characteristic, and multiple group analysis to examine differences between middle-aged and older men and women. Our results indicated a clear socio-economical patterning of the social network for men and women. People with higher SEP had social networks that better protect against loneliness, which in turn lead to better health outcomes. The explained variance in health in older people by the social network and SEP was only half of the explained variance observed in middle-aged people, suggesting that other factors than SEP were more important for health when people age. We conclude that it is the function of the network, rather than the structure, that counts for health.

Social pathways to health: On the mediating role of the social network in the relation between socio-economic position and health

Directory of Open Access Journals (Sweden)

Marja Aartsen

2017-12-01

Full Text Available Good health is one of the key qualities of life, but opportunities to be and remain healthy are unequally distributed across socio-economic groups. The beneficial health effects of the social network are well known. However, research on the social network as potential mediator in the pathway from socio-economic position (SEP to health is scarce, while there are good reasons to expect a socio-economical patterning of networks. We aim to contribute to our understanding of socio-economic inequalities in health by examining the mediating role of structural and functional characteristics of the social network in the SEP-health relationship. Data were from the second wave of the Norwegian study on the life course, aging and generation study (NorLAG and comprised 4534 men and 4690 women aged between 40 and 81. We applied multiple mediation models to evaluate the relative importance of each network characteristic, and multiple group analysis to examine differences between middle-aged and older men and women. Our results indicated a clear socio-economical patterning of the social network for men and women. People with higher SEP had social networks that better protect against loneliness, which in turn lead to better health outcomes. The explained variance in health in older people by the social network and SEP was only half of the explained variance observed in middle-aged people, suggesting that other factors than SEP were more important for health when people age. We conclude that it is the function of the network, rather than the structure, that counts for health.

Implementing multiple intervention strategies in Dutch public health-related policy networks.

Science.gov (United States)

Harting, Janneke; Peters, Dorothee; Grêaux, Kimberly; van Assema, Patricia; Verweij, Stefan; Stronks, Karien; Klijn, Erik-Hans

2017-10-13

Improving public health requires multiple intervention strategies. Implementing such an intervention mix is supposed to require a multisectoral policy network. As evidence to support this assumption is scarce, we examined under which conditions public health-related policy networks were able to implement an intervention mix. Data were collected (2009-14) from 29 Dutch public health policy networks. Surveys were used to identify the number of policy sectors, participation of actors, level of trust, networking by the project leader, and intervention strategies implemented. Conditions sufficient for an intervention mix (≥3 of 4 non-educational strategies present) were determined in a fuzzy-set qualitative comparative analysis. A multisectoral policy network (≥7 of 14 sectors present) was neither a necessary nor a sufficient condition. In multisectoral networks, additionally required was either the active participation of network actors (≥50% actively involved) or active networking by the project leader (≥monthly contacts with network actors). In policy networks that included few sectors, a high level of trust (positive perceptions of each other's intentions) was needed-in the absence though of any of the other conditions. If the network actors were also actively involved, an extra requirement was active networking by the project leader. We conclude that the multisectoral composition of policy networks can contribute to the implementation of a variety of intervention strategies, but not without additional efforts. However, policy networks that include only few sectors are also able to implement an intervention mix. Here, trust seems to be the most important condition. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Automated tracking for advanced satellite laser ranging systems

Science.gov (United States)

McGarry, Jan F.; Degnan, John J.; Titterton, Paul J., Sr.; Sweeney, Harold E.; Conklin, Brion P.; Dunn, Peter J.

1996-06-01

NASA's Satellite Laser Ranging Network was originally developed during the 1970's to track satellites carrying corner cube reflectors. Today eight NASA systems, achieving millimeter ranging precision, are part of a global network of more than 40 stations that track 17 international satellites. To meet the tracking demands of a steadily growing satellite constellation within existing resources, NASA is embarking on a major automation program. While manpower on the current systems will be reduced to a single operator, the fully automated SLR2000 system is being designed to operate for months without human intervention. Because SLR2000 must be eyesafe and operate in daylight, tracking is often performed in a low probability of detection and high noise environment. The goal is to automatically select the satellite, setup the tracking and ranging hardware, verify acquisition, and close the tracking loop to optimize data yield. TO accomplish the autotracking tasks, we are investigating (1) improved satellite force models, (2) more frequent updates of orbital ephemerides, (3) lunar laser ranging data processing techniques to distinguish satellite returns from noise, and (4) angular detection and search techniques to acquire the satellite. A Monte Carlo simulator has been developed to allow optimization of the autotracking algorithms by modeling the relevant system errors and then checking performance against system truth. A combination of simulator and preliminary field results will be presented.

Health in All Policies (HiAP) governance: lessons from network governance.

Science.gov (United States)

Khayatzadeh-Mahani, Akram; Ruckert, Arne; Labonté, Ronald; Kenis, Patrick; Akbari-Javar, Mohammad Reza

2018-05-25

The Health in All Policies (HiAP) approach requires formal and sustained governance structures and mechanisms to ensure that the policies of various non-health sectors maximize positive and minimize negative impacts on population health. In this paper, we demonstrate the usefulness of a network perspective in understanding and contributing to the effectiveness of HiAP. We undertook an exploratory, qualitative case study of a HiAP structure in Iran, the Kerman province Council of Health and Food Security (CHFS) with diverse members from health and non-health sectors. We analyzed relevant policy texts and interviewed 32 policy actors involved in the CHFS. Data were analyzed using within-case analysis and constant comparative methodology. Our findings suggest that CHFS governance from a network perspective drew in practice on elements of two competing network governance modes: the network administrative organization (NAO) and the lead organization mode. Our results also show that a shift from a hierarchical and market-based mode of interaction to a network logic within CHFS has not yet taken place. In addition, CHFS suffers from large membership and an inability to address complex 'wicked problems', as well as low trust, legitimacy and goal consensus among its members. Drawing on other HiAP studies and commentaries, insights from organization network theory, and in-depth findings from our case study, we conclude that a NAO may be the most effective mode of governance for tackling complex social problems in HiAP structures. Since similar studies are limited, and our single case study may not be transferable across all contexts, we suggest that further research be undertaken to explore HiAP structures from a network perspective in different institutional and cultural settings. With increasing emphasis given to HiAP approaches in national and international health policy discourse, it is important that comparative knowledge about the effectiveness of HiAP governance

Neuro-PID tracking control of a discharge air temperature system

International Nuclear Information System (INIS)

Zaheer-uddin, M.; Tudoroiu, N.

2004-01-01

In this paper, the problem of improving the performance of a discharge air temperature (DAT) system using a PID controller and augmenting it with neural network based tuning and tracking functions is explored. The DAT system is modeled as a SISO (single input single output) system. The architecture of the real time neuro-PID controller and simulation results obtained under realistic operating conditions are presented. The neural network assisted PID tuning method is simple to implement. Results show that the network assisted PID controller is able to track both constant and variable set point trajectories efficiently in the presence of disturbances acting on the DAT system

Application of Intelligent Dynamic Bayesian Network with Wavelet Analysis for Probabilistic Prediction of Storm Track Intensity Index

Directory of Open Access Journals (Sweden)

Ming Li

2018-06-01

Full Text Available The effective prediction of storm track (ST is greatly beneficial for analyzing the development and anomalies of mid-latitude weather systems. For the non-stationarity, nonlinearity, and uncertainty of ST intensity index (STII, a new probabilistic prediction model was proposed based on dynamic Bayesian network (DBN and wavelet analysis (WA. We introduced probability theory and graph theory for the first time to quantitatively describe the nonlinear relationship and uncertain interaction of the ST system. Then a casual prediction network (i.e., DBN was constructed through wavelet decomposition, structural learning, parameter learning, and probabilistic inference, which was used for expression of relation among predictors and probabilistic prediction of STII. The intensity prediction of the North Pacific ST with data from 1961–2010 showed that the new model was able to give more comprehensive prediction information and higher prediction accuracy and had strong generalization ability and good stability.

distance tracking scheme for seamless handover in ims-based

African Journals Online (AJOL)

userpc

Mobile Node (MN) in a network cell are proactively tracked in order to determine the exact .... The parameter εi is a correction factor for effective mobile antenna height, which is a function of .... Wireless Networks: Ad Hoc, Vehicular and.

Low-momentum track finding in Belle II

International Nuclear Information System (INIS)

Lettenbichler, J; Frühwirth, R; Nadler, M; Glattauer, R

2012-01-01

The Silicon Vertex Detector (SVD) of the Belle II experiment is a newly developed device with four measurement layers. Track finding in the SVD will be done both in conjunction with the Central Drift Chamber and in stand-alone mode. The reconstruction of very-low-momentum tracks in stand-alone mode is a big challenge, especially in view of the low redundancy and the large expected background. We describe an approach for track finding in this domain, where a cellular automaton and a Kalman filter is combined with a Hopfield network which finds an optimal subset of non-overlapping tracks. We present results on simulated data and evaluate them in terms of efficiency and purity.

Transforming public health education in India through networking and collaborations: opportunities and challenges.

Science.gov (United States)

Sharma, Anjali; Zodpey, Sanjay P

2013-01-01

A competent and motivated health workforce is indispensable to achieve the best health outcomes possible through given available resources and circumstances. However, apart from the shortages and unequal distribution, the workforce has fallen short of responding to the public health challenges of 21 st century also because of primarily the traditional training of health professionals. Although, health professionals have made enormous contributions to health and development over the past century, the 20 th century educational strategies are unfit to tackle 21 st century challenges. One of the key recommendations of the Lancet Commission on Education of Health Professionals is to improve health through reforms of professional education by establishing networks and partnerships which takes advantage of information and communication linkages. The primary goal of this manuscript is to highlight the potential of networks and partnerships in advancing the agenda of educational reforms to revitalize public health education in India. It outlines the current status and expanding scope of public health education in India, existing networks of public health professionals and public health education institutions in the country, and opportunities, advantages and challenges for such networks. Although, we have networks of individuals and institutions in the country, there potential to bring about change has still not being utilized fully and effectively. Immediate collaborative efforts could be directed towards designing and adaptation of competency driven curriculum frameworks suitable of addressing public health challenges of 21 st century, shifting the current focus of curriculum to multidisciplinary public health outlook, developing accreditation mechanisms for both the programs and institutions, engaging in creating job opportunities and designing career pathways for public health professionals in public and private sector. These efforts could certainly be facilitated

A comparative study of 11 local health department organizational networks.

Science.gov (United States)

Merrill, Jacqueline; Keeling, Jonathan W; Carley, Kathleen M

2010-01-01

Although the nation's local health departments (LHDs) share a common mission, variability in administrative structures is a barrier to identifying common, optimal management strategies. There is a gap in understanding what unifying features LHDs share as organizations that could be leveraged systematically for achieving high performance. To explore sources of commonality and variability in a range of LHDs by comparing intraorganizational networks. We used organizational network analysis to document relationships between employees, tasks, knowledge, and resources within LHDs, which may exist regardless of formal administrative structure. A national sample of 11 LHDs from seven states that differed in size, geographic location, and governance. Relational network data were collected via an on-line survey of all employees in 11 LHDs. A total of 1062 out of 1239 employees responded (84% response rate). Network measurements were compared using coefficient of variation. Measurements were correlated with scores from the National Public Health Performance Assessment and with LHD demographics. Rankings of tasks, knowledge, and resources were correlated across pairs of LHDs. We found that 11 LHDs exhibited compound organizational structures in which centralized hierarchies were coupled with distributed networks at the point of service. Local health departments were distinguished from random networks by a pattern of high centralization and clustering. Network measurements were positively associated with performance for 3 of 10 essential services (r > 0.65). Patterns in the measurements suggest how LHDs adapt to the population served. Shared network patterns across LHDs suggest where common organizational management strategies are feasible. This evidence supports national efforts to promote uniform standards for service delivery to diverse populations.

Mental Health and Social Networks After Disaster.

Science.gov (United States)

Bryant, Richard A; Gallagher, H Colin; Gibbs, Lisa; Pattison, Philippa; MacDougall, Colin; Harms, Louise; Block, Karen; Baker, Elyse; Sinnott, Vikki; Ireton, Greg; Richardson, John; Forbes, David; Lusher, Dean

2017-03-01

Although disasters are a major cause of mental health problems and typically affect large numbers of people and communities, little is known about how social structures affect mental health after a disaster. The authors assessed the extent to which mental health outcomes after disaster are associated with social network structures. In a community-based cohort study of survivors of a major bushfire disaster, participants (N=558) were assessed for probable posttraumatic stress disorder (PTSD) and probable depression. Social networks were assessed by asking participants to nominate people with whom they felt personally close. These nominations were used to construct a social network map that showed each participant's ties to other participants they nominated and also to other participants who nominated them. This map was then analyzed for prevailing patterns of mental health outcomes. Depression risk was higher for participants who reported fewer social connections, were connected to other depressed people, or were connected to people who had left their community. PTSD risk was higher if fewer people reported being connected with the participant, if those who felt close to the participant had higher levels of property loss, or if the participant was linked to others who were themselves not interconnected. Interestingly, being connected to other people who in turn were reciprocally close to each other was associated with a lower risk of PTSD. These findings provide the first evidence of disorder-specific patterns in relation to one's social connections after disaster. Depression appears to co-occur in linked individuals, whereas PTSD risk is increased with social fragmentation. These patterns underscore the need to adopt a sociocentric perspective of postdisaster mental health in order to better understand the potential for societal interventions in the wake of disaster.

US adolescents' friendship networks and health risk behaviors: a systematic review of studies using social network analysis and Add Health data.

Science.gov (United States)

Jeon, Kwon Chan; Goodson, Patricia

2015-01-01

Background. Documented trends in health-related risk behaviors among US adolescents have remained high over time. Studies indicate relationships among mutual friends are a major influence on adolescents' risky behaviors. Social Network Analysis (SNA) can help understand friendship ties affecting individual adolescents' engagement in these behaviors. Moreover, a systematic literature review can synthesize findings from a range of studies using SNA, as well as assess these studies' methodological quality. Review findings also can help health educators and promoters develop more effective programs. Objective. This review systematically examined studies of the influence of friendship networks on adolescents' risk behaviors, which utilized SNA and the Add Health data (a nationally representative sample). Methods. We employed the Matrix Method to synthesize and evaluate 15 published studies that met our inclusion and exclusion criteria, retrieved from the Add Health website and 3 major databases (Medline, Eric, and PsycINFO). Moreover, we assigned each study a methodological quality score (MQS). Results. In all studies, friendship networks among adolescents promoted their risky behaviors, including drinking alcohol, smoking, sexual intercourse, and marijuana use. The average MQS was 4.6, an indicator of methodological rigor (scale: 1-9). Conclusion. Better understanding of risky behaviors influenced by friends can be useful for health educators and promoters, as programs targeting friendships might be more effective. Additionally, the overall MQ of these reviewed studies was good, as average scores fell above the scale's mid-point.

US adolescents’ friendship networks and health risk behaviors: a systematic review of studies using social network analysis and Add Health data

Directory of Open Access Journals (Sweden)

Kwon Chan Jeon

2015-06-01

Full Text Available Background. Documented trends in health-related risk behaviors among US adolescents have remained high over time. Studies indicate relationships among mutual friends are a major influence on adolescents’ risky behaviors. Social Network Analysis (SNA can help understand friendship ties affecting individual adolescents’ engagement in these behaviors. Moreover, a systematic literature review can synthesize findings from a range of studies using SNA, as well as assess these studies’ methodological quality. Review findings also can help health educators and promoters develop more effective programs.Objective. This review systematically examined studies of the influence of friendship networks on adolescents’ risk behaviors, which utilized SNA and the Add Health data (a nationally representative sample.Methods. We employed the Matrix Method to synthesize and evaluate 15 published studies that met our inclusion and exclusion criteria, retrieved from the Add Health website and 3 major databases (Medline, Eric, and PsycINFO. Moreover, we assigned each study a methodological quality score (MQS.Results. In all studies, friendship networks among adolescents promoted their risky behaviors, including drinking alcohol, smoking, sexual intercourse, and marijuana use. The average MQS was 4.6, an indicator of methodological rigor (scale: 1–9.Conclusion. Better understanding of risky behaviors influenced by friends can be useful for health educators and promoters, as programs targeting friendships might be more effective. Additionally, the overall MQ of these reviewed studies was good, as average scores fell above the scale’s mid-point.

Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input.

Science.gov (United States)

Hua, Changchun; Zhang, Liuliu; Guan, Xinping

2017-01-01

This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

BP neural network tuned PID controller for position tracking of a pneumatic artificial muscle.

Science.gov (United States)

Fan, Jizhuang; Zhong, Jun; Zhao, Jie; Zhu, Yanhe

2015-01-01

Although Pneumatic Artificial Muscle (PAM) has a promising future in rehabilitation robots, it's difficult to realize accurate position control due to its highly nonlinear properties. This paper deals with position control of PAM. To describe the hysteresis inside PAM, a polynomial based phenomenological function is developed. Based on the phenomenological model for PAM and analysis of pressure dynamics within PAM, an adaptive cascade controller is proposed. Both outer loop and inner loop employ BP Neural Network tuned PID algorithm. The outer loop is to handle high nonlinearities and unmodeled dynamics of PAM, while the inner loop is responsible for nonlinearities caused by pressure dynamics. Experimental results show high tracking accuracy as compared with a convention PID controller. The proposed controller is effective in improving performance of PAM and will be implemented in a rehabilitation robot.

Methods for Inferring Health-Related Social Networks among Coworkers from Online Communication Patterns

Science.gov (United States)

Matthews, Luke J.; DeWan, Peter; Rula, Elizabeth Y.

2013-01-01

Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network. PMID

Methods for inferring health-related social networks among coworkers from online communication patterns.

Directory of Open Access Journals (Sweden)

Luke J Matthews

Full Text Available Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1 the absolute number of emails exchanged, (2 logistic regression probability of an offline relationship, and (3 the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a

Methods for inferring health-related social networks among coworkers from online communication patterns.

Science.gov (United States)

Matthews, Luke J; DeWan, Peter; Rula, Elizabeth Y

2013-01-01

Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network.

A pervasive health monitoring service system based on ubiquitous network technology.

Science.gov (United States)

Lin, Chung-Chih; Lee, Ren-Guey; Hsiao, Chun-Chieh

2008-07-01

The phenomenon of aging society has derived problems such as shortage of medical resources and reduction of quality in healthcare services. This paper presents a system infrastructure for pervasive and long-term healthcare applications, i.e. a ubiquitous network composed of wireless local area network (WLAN) and cable television (CATV) network serving as a platform for monitoring physiological signals. Users can record vital signs including heart rate, blood pressure, and body temperature anytime either at home or at frequently visited public places in order to create a personal health file. The whole system was formally implemented in December 2004. Analysis of 2000 questionnaires indicates that 85% of users were satisfied with the provided community-wide healthcare services. Among the services provided by our system, health consultation services offered by family doctors was rated the most important service by 17.9% of respondents, and was followed by control of one's own health condition (16.4% of respondents). Convenience of data access was rated most important by roughly 14.3% of respondents. We proposed and implemented a long-term healthcare system integrating WLAN and CATV networks in the form of a ubiquitous network providing a service platform for physiological monitoring. This system can classify the health levels of the resident according to the variation tendency of his or her physiological signal for important reference of health management.

Understanding Online Health Groups for Depression: Social Network and Linguistic Perspectives.

Science.gov (United States)

Xu, Ronghua; Zhang, Qingpeng

2016-03-10

Mental health problems have become increasingly prevalent in the past decade. With the advance of Web 2.0 technologies, social media present a novel platform for Web users to form online health groups. Members of online health groups discuss health-related issues and mutually help one another by anonymously revealing their mental conditions, sharing personal experiences, exchanging health information, and providing suggestions and support. The conversations in online health groups contain valuable information to facilitate the understanding of their mutual help behaviors and their mental health problems. We aimed to characterize the conversations in a major online health group for major depressive disorder (MDD) patients in a popular Chinese social media platform. In particular, we intended to explain how Web users discuss depression-related issues from the perspective of the social networks and linguistic patterns revealed by the members' conversations. Social network analysis and linguistic analysis were employed to characterize the social structure and linguistic patterns, respectively. Furthermore, we integrated both perspectives to exploit the hidden relations between them. We found an intensive use of self-focus words and negative affect words. In general, group members used a higher proportion of negative affect words than positive affect words. The social network of the MDD group for depression possessed small-world and scale-free properties, with a much higher reciprocity ratio and clustering coefficient value as compared to the networks of other social media platforms and classic network models. We observed a number of interesting relationships, either strong correlations or convergent trends, between the topological properties and linguistic properties of the MDD group members. (1) The MDD group members have the characteristics of self-preoccupation and negative thought content, according to Beck's cognitive theory of depression; (2) the social structure

iTAG: Integrating a Cloud Based, Collaborative Animal Tracking Network into the GCOOS data portal in the Gulf of Mexico

Science.gov (United States)

Kirkpatrick, B. A.; Currier, R. D.; Simoniello, C.

2016-02-01

The tagging and tracking of aquatic animals using acoustic telemetry hardware has traditionally been the purview of individual researchers that specialize in single species. Concerns over data privacy and unauthorized use of receiver arrays have prevented the construction of large-scale, multi-species, multi-institution, multi-researcher collaborative acoustic arrays. We have developed a toolset to build the new portal using the Flask microframework, Python language, and Twitter bootstrap. Initial feedback has been overwhelmingly positive. The privacy policy has been praised for its granularity: principal investigators can choose between three levels of privacy for all data and hardware: Completely private - viewable only by the PI Visible to iTAG members Visible to the general public At the time of this writing iTAG is still in the beta stage, but the feedback received to date indicates that with the proper design and security features, and an iterative cycle of feedback from potential members, constructing a collaborative acoustic tracking network system is possible. Initial usage will be limited to the entry and searching for `orphan/mystery' tags, with the integration of historical array deployments and data following shortly thereafter. We have also been working with staff from the Ocean Tracking Network to allow for integration of the two systems. The database schema of iTAG is based on the marine metadata convention for acoustic telemetry. This should permit machine-to-machine data exchange between iTAG and OTN. The integration of animal telemetry data into the GCOOS portal will allow researchers to easily access the physiochemical oceanography data, thus allowing for a more in depth understanding of animal response and usage patterns.

A Privacy Preservation Model for Health-Related Social Networking Sites

Science.gov (United States)

2015-01-01

The increasing use of social networking sites (SNS) in health care has resulted in a growing number of individuals posting personal health information online. These sites may disclose users' health information to many different individuals and organizations and mine it for a variety of commercial and research purposes, yet the revelation of personal health information to unauthorized individuals or entities brings a concomitant concern of greater risk for loss of privacy among users. Many users join multiple social networks for different purposes and enter personal and other specific information covering social, professional, and health domains into other websites. Integration of multiple online and real social networks makes the users vulnerable to unintentional and intentional security threats and misuse. This paper analyzes the privacy and security characteristics of leading health-related SNS. It presents a threat model and identifies the most important threats to users and SNS providers. Building on threat analysis and modeling, this paper presents a privacy preservation model that incorporates individual self-protection and privacy-by-design approaches and uses the model to develop principles and countermeasures to protect user privacy. This study paves the way for analysis and design of privacy-preserving mechanisms on health-related SNS. PMID:26155953

A Privacy Preservation Model for Health-Related Social Networking Sites.

Science.gov (United States)

Li, Jingquan

2015-07-08

The increasing use of social networking sites (SNS) in health care has resulted in a growing number of individuals posting personal health information online. These sites may disclose users' health information to many different individuals and organizations and mine it for a variety of commercial and research purposes, yet the revelation of personal health information to unauthorized individuals or entities brings a concomitant concern of greater risk for loss of privacy among users. Many users join multiple social networks for different purposes and enter personal and other specific information covering social, professional, and health domains into other websites. Integration of multiple online and real social networks makes the users vulnerable to unintentional and intentional security threats and misuse. This paper analyzes the privacy and security characteristics of leading health-related SNS. It presents a threat model and identifies the most important threats to users and SNS providers. Building on threat analysis and modeling, this paper presents a privacy preservation model that incorporates individual self-protection and privacy-by-design approaches and uses the model to develop principles and countermeasures to protect user privacy. This study paves the way for analysis and design of privacy-preserving mechanisms on health-related SNS.

Cooperation in health: mapping collaborative networks on the web.

Science.gov (United States)

Lang, Pamela Barreto; Gouveia, Fábio Castro; Leta, Jacqueline

2013-01-01

To map and investigate the relationships established on the web between leading health-research institutions around the world. Sample selection was based on the World Health Organization (WHO) Collaborating Centres (CCs). Data on the 768 active CCs in 89 countries were retrieved from the WHO's database. The final sample consisted of 190 institutions devoted to health sciences in 42 countries. Data on each institution's website were retrieved using webometric techniques (interlinking), and an asymmetric matrix was generated for social network analysis. The results showed that American and European institutions, such as the Centers for Disease Control and Prevention (CDC), the National Institutes of Health (NIH) and the National Institute of Health and Medical Research (INSERM), are the most highly connected on the web and have a higher capacity to attract hyperlinks. The Karolinska Institute (KI-SE) in Sweden is well placed as an articulation point between several integrants of the network and the component's core but lacks general recognition on the web by hyperlinks. Regarding the north-south divide, Mexico and Brazil appear to be key southern players on the web. The results showed that the hyperlinks exchanged between northern and southern countries present an abysmal gap: 99.49% of the hyperlinks provided by the North are directed toward the North itself, in contrast to 0.51% that are directed toward the South. Regarding the South, its institutions are more connected to its northern partners, with 98.46% of its hyperlinks directed toward the North, and mainly toward the United States, compared with 1.54% toward southern neighbors. It is advisable to strengthen integration policies on the web and to increase web networking through hyperlink exchange. In this way, the web could actually reflect international cooperation in health and help to legitimize and enhance the visibility of the many existing south-south collaboration networks.

Cooperation in health: mapping collaborative networks on the web.

Directory of Open Access Journals (Sweden)

Pamela Barreto Lang

Full Text Available OBJECTIVE: To map and investigate the relationships established on the web between leading health-research institutions around the world. METHODS: Sample selection was based on the World Health Organization (WHO Collaborating Centres (CCs. Data on the 768 active CCs in 89 countries were retrieved from the WHO's database. The final sample consisted of 190 institutions devoted to health sciences in 42 countries. Data on each institution's website were retrieved using webometric techniques (interlinking, and an asymmetric matrix was generated for social network analysis. FINDINGS: The results showed that American and European institutions, such as the Centers for Disease Control and Prevention (CDC, the National Institutes of Health (NIH and the National Institute of Health and Medical Research (INSERM, are the most highly connected on the web and have a higher capacity to attract hyperlinks. The Karolinska Institute (KI-SE in Sweden is well placed as an articulation point between several integrants of the network and the component's core but lacks general recognition on the web by hyperlinks. Regarding the north-south divide, Mexico and Brazil appear to be key southern players on the web. The results showed that the hyperlinks exchanged between northern and southern countries present an abysmal gap: 99.49% of the hyperlinks provided by the North are directed toward the North itself, in contrast to 0.51% that are directed toward the South. Regarding the South, its institutions are more connected to its northern partners, with 98.46% of its hyperlinks directed toward the North, and mainly toward the United States, compared with 1.54% toward southern neighbors. CONCLUSION: It is advisable to strengthen integration policies on the web and to increase web networking through hyperlink exchange. In this way, the web could actually reflect international cooperation in health and help to legitimize and enhance the visibility of the many existing south

The prevalence and usage of mobile health applications among mental health patients in Saudi Arabia.

Science.gov (United States)

Atallah, Nora; Khalifa, Mohamed; El Metwally, Ashraf; Househ, Mowafa

2018-03-01

Mobile health (mHealth) applications provide new methods of engagement with patients and can help patients manage their mental health condition. The main objective of this study is to explore the prevalence of the use of mobile health applications for mental health patients in Saudi Arabia. A total of 376 participants with depression and/or anxiety completed an online survey distributed by social networks which asked questions relating to mobile phone ownership, uses of health applications, and utilization patterns to track mental health related issues. Approximately, 46% of the participants reported running one or two healthcare related applications on their mobile phones. In all age groups, 64% of the participants used their mobile phones to access information related to their own health. Also, 64% of the participants expressed interest in using their own mobile phones to track and follow the progression of their depression and/or anxiety. Developing mobile health applications for Saudi mental health patients is needed since it can offer opportunities for patients, researchers, caregivers, and legislators to work together to improve the state of mental health care in Saudi Arabia. Copyright © 2017 Elsevier B.V. All rights reserved.

Sharing for Health: A Study of Chinese Adolescents' Experiences and Perspectives on Using Social Network Sites to Share Health Information.

Science.gov (United States)

Zhang, Ni; Teti, Michele; Stanfield, Kellie; Campo, Shelly

2017-07-01

This exploratory qualitative study examines Chinese adolescents' health information sharing habits on social network sites. Ten focus group meetings with 76 adolescents, ages 12 to 17 years, were conducted at community-based organizations in Chicago's Chinatown. The research team transcribed the recording and analyzed the transcripts using ATLAS.ti. Chinese adolescents are using different social network sites for various topics of health information including food, physical activity, and so on. Adolescents would share useful and/or interesting health information. Many adolescents raised credibility concerns regarding health information and suggested evaluating the information based on self-experience or intuition, word-of-mouth, or information online. The findings shed lights on future intervention using social network sites to promote health among Chinese adolescents in the United States. Future interventions should provide adolescents with interesting and culturally sensitive health information and educate them to critically evaluate health information on social network sites.

Effective Multi-Model Motion Tracking Under Multiple Team Member Actuators

OpenAIRE

Gu, Yang; Veloso, Manuela

2009-01-01

Motivated by the interactions between a team and the tracked target, we contribute a method to achieve efficient tracking through using a play-based motion model and combined vision and infrared sensory information. This method gives the robot a more exact task-specific motion model when executing different tactics over the tracked target (e.g. the ball) or collaborating with the tracked target (e.g. the team member). Then we represent the system in a compact dynamic Bayesian network and use ...

Selected aspects of the logistics network of public hospitals in the competitive market of health services

Directory of Open Access Journals (Sweden)

Justyna Majchrzak-Lepczyk

2016-12-01

Full Text Available Background: The below considerations provide an overview of the issues of sustainable development, logistics, to financial engineering instruments and the role of intellectual capital in the process of transformation of public hospitals. The aim of this research was to assess the competitiveness of the network of public hospitals in the market of health services based on literature studies, as well as empirical research. Methods: Empirical study using a questionnaire survey was conducted in the period from January 2007 to December 2011, in the area of Warmia and Mazury, Pomerania and Wielkopolska. The goal of this questionnaire survey was to know the medical staff reviews issues related to adaptation to the nature of the network of public hospitals methods and logistics tools, sustainable development, corporate social responsibility - CSR. The study was carried out in 104 public hospitals, on a sample of 8975 respondents. Results and conclusions: Analysis of the completed study showed that the logistic processes and their improvement in the health sector play a significant role. The surveyed entities explicitly draw attention to the need for information systems,  pro-environment activities, access to information, or the use of GS1 global standards. These tools allow you to increase the efficiency of supply chains, ensuring not only tracking and tracing of products from the manufacturer to the patient, but also enabling better protection against making a mistake or counterfeit products.

The Issues of Track Maintenance Management in Indonesia (Based on Study of the British Railways

Directory of Open Access Journals (Sweden)

Tetria Yunik Dewi Pamungkas

2017-01-01

Full Text Available It is believed that there is merit in researching how the management of track maintenance is organized in the United Kingdom, in particular by Network Rail, and then evaluating how this can be applied to the benefit of Indonesian Railways. The purpose of this research was expected to provide recommendations for management improvement of track maintenance by researching the track maintenance management system in Network Rail. This research study is based on secondary data. The analysis was conducted by using comparison method which compares the British Railways management and Indonesia railway management related to track maintenance management. The track maintenance management system was studied by using Network Rail. The results were compared with track maintenance management system in Indonesia. The comparison will provide the data differences that evaluated so the problem can be identified. The final result of this analysis was the identification of problems and improvisation that can be done for the development of track maintenance management in Indonesia. Several management issues have been mapped to several groups: finance management, structure organization, and asset management. From the problems that have been mapped, some improvements are recommended as monopolist authority restriction, maintenance system contract extension, re-structuring organization hierarchy, business plan consideration, track monitoring strategy arrangement, and condition based maintenance strategy adoption.

A telecommunications journey rural health network.

Science.gov (United States)

Moore, Joe

2012-01-01

Utilizing a multi-gigabit statewide fiber healthcare network, Radiology Consultants of Iowa (RCI) set out to provide instantaneous service to their rural, critical access, hospital partners. RCIs idea was to assemble a collection of technologies and services that would even out workflow, reduce time on the road, and provide superior service. These technologies included PACS, voice recognition enabled dictation, HL7 interface technology, an imaging system for digitizing paper and prior films, and modern communication networks. The Iowa Rural Health Telecommunication Project was undertaken to form a system that all critical access hospitals would participate in, allowing RCI radiologists the efficiency of "any image, anywhere, anytime".

The Relationship of Policymaking and Networking Characteristics among Leaders of Large Urban Health Departments.

Science.gov (United States)

Leider, Jonathon P; Castrucci, Brian C; Harris, Jenine K; Hearne, Shelley

2015-08-06

The relationship between policy networks and policy development among local health departments (LHDs) is a growing area of interest to public health practitioners and researchers alike. In this study, we examine policy activity and ties between public health leadership across large urban health departments. This study uses data from a national profile of local health departments as well as responses from a survey sent to three staff members (local health official, chief of policy, chief science officer) in each of 16 urban health departments in the United States. Network questions related to frequency of contact with health department personnel in other cities. Using exponential random graph models, network density and centrality were examined, as were patterns of communication among those working on several policy areas using exponential random graph models. All 16 LHDs were active in communicating about chronic disease as well as about use of alcohol, tobacco, and other drugs (ATOD). Connectedness was highest among local health officials (density = .55), and slightly lower for chief science officers (d = .33) and chiefs of policy (d = .29). After accounting for organizational characteristics, policy homophily (i.e., when two network members match on a single characteristic) and tenure were the most significant predictors of formation of network ties. Networking across health departments has the potential for accelerating the adoption of public health policies. This study suggests similar policy interests and formation of connections among senior leadership can potentially drive greater connectedness among other staff.

Tracking and Recognition of Multiple Human Targets Moving in a Wireless Pyroelectric Infrared Sensor Network

Directory of Open Access Journals (Sweden)

Ji Xiong

2014-04-01

Full Text Available With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%.

The British Columbia Emergency Medicine Network: A Paradigm Shift in a Provincial System of Emergency Care.

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Abu-Laban, Riyad B; Drebit, Sharla; Lindstrom, Ronald R; Archibald, Chantel; Eggers, Kim; Ho, Kendall; Khazei, Afshin; Lund, Adam; MacKinnon, Carolyn; Markham, Ray; Marsden, Julian; Martin, Ed; Christenson, Jim

2018-01-04

As generalists, emergency practitioners face challenges in providing state-of-the-art care owing to the broad spectrum of practice and the rapid rate of new knowledge generation. Networks have become increasingly prevalent in health care, and it was in this backdrop, and the resulting opportunity to advance evidence-informed emergency care in the Canadian province of British Columbia (BC), that a new "Emergency Medicine Network" (EM Network) was launched in 2017. The EM Network consists of four programs, each led by a physician with expertise and a track record in the domain: (1) Clinical Resources; (2) Innovation; (3) Continuing Professional Development; and (4) Real-time Support. This paper provides an overview of the EM Network, including its background, purpose, programs, anticipated evolution, and impact on the BC health care system.

Social network activation: the role of health discussion partners in recovery from mental illness.

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Perry, Brea L; Pescosolido, Bernice A

2015-01-01

In response to health problems, individuals may strategically activate their social network ties to help manage crisis and uncertainty. While it is well-established that social relationships provide a crucial safety net, little is known about who is chosen to help during an episode of illness. Guided by the Network Episode Model, two aspects of consulting others in the face of mental illness are considered. First, we ask who activates ties, and what kinds of ties and networks they attempt to leverage for discussing health matters. Second, we ask about the utility of activating health-focused network ties. Specifically, we examine the consequences of network activation at time of entry into treatment for individuals' quality of life, social satisfaction, ability to perform social roles, and mental health functioning nearly one year later. Using interview data from the longitudinal Indianapolis Network Mental Health Study (INMHS, N = 171), we focus on a sample of new patients with serious mental illness and a group with less severe disorders who are experiencing their first contact with the mental health treatment system. Three findings stand out. First, our results reveal the nature of agency in illness response. Whether under a rational choice or habitus logic, individuals appear to evaluate support needs, identifying the best possible matches among a larger group of potential health discussants. These include members of the core network and those with prior mental health experiences. Second, selective activation processes have implications for recovery. Those who secure adequate network resources report better outcomes than those who injudiciously activate network ties. Individuals who activate weaker relationships and those who are unsupportive of medical care experience poorer functioning, limited success in fulfilling social roles, and lower social satisfaction and quality of life later on. Third, the evidence suggests that social networks matter above and

Health preemption behind closed doors: trade agreements and fast-track authority.

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Crosbie, Eric; Gonzalez, Mariaelena; Glantz, Stanton A

2014-09-01

Noncommunicable diseases result from consuming unhealthy products, including tobacco, which are promoted by transnational corporations. The tobacco industry uses preemption to block or reverse tobacco control policies. Preemption removes authority from jurisdictions where tobacco companies' influence is weak and transfers it to jurisdictions where they have an advantage. International trade agreements relocate decisions about tobacco control policy to venues where there is little opportunity for public scrutiny, participation, and debate. Tobacco companies are using these agreements to preempt domestic authority over tobacco policy. Other transnational corporations that profit by promoting unhealthy foods could do the same. "Fast-track authority," in which Congress cedes ongoing oversight authority to the President, further distances the public from the debate. With international agreements binding governments to prioritize trade over health, transparency and public oversight of the trade negotiation process is necessary to safeguard public health interests.

Why common carrier and network neutrality principles apply to the Nationwide Health Information Network (NWHIN).

Science.gov (United States)

Gaynor, Mark; Lenert, Leslie; Wilson, Kristin D; Bradner, Scott

2014-01-01

The Office of the National Coordinator will be defining the architecture of the Nationwide Health Information Network (NWHIN) together with the proposed HealtheWay public/private partnership as a development and funding strategy. There are a number of open questions--for example, what is the best way to realize the benefits of health information exchange? How valuable are regional health information organizations in comparison with a more direct approach? What is the role of the carriers in delivering this service? The NWHIN is to exist for the public good, and thus shares many traits of the common law notion of 'common carriage' or 'public calling,' the modern term for which is network neutrality. Recent policy debates in Congress and resulting potential regulation have implications for key stakeholders within healthcare that use or provide services, and for those who exchange information. To date, there has been little policy debate or discussion about the implications of a neutral NWHIN. This paper frames the discussion for future policy debate in healthcare by providing a brief education and summary of the modern version of common carriage, of the key stakeholder positions in healthcare, and of the potential implications of the network neutrality debate within healthcare.

Social network properties and self-rated health in later life: comparisons from the Korean social life, health, and aging project and the national social life, health and aging project.

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Youm, Yoosik; Laumann, Edward O; Ferraro, Kenneth F; Waite, Linda J; Kim, Hyeon Chang; Park, Yeong-Ran; Chu, Sang Hui; Joo, Won-Tak; Lee, Jin A

2014-09-14

This paper has two objectives. Firstly, it provides an overview of the social network module, data collection procedures, and measurement of ego-centric and complete-network properties in the Korean Social Life, Health, and Aging Project (KSHAP). Secondly, it directly compares the KSHAP structure and results to the ego-centric network structure and results of the National Social Life, Health, and Aging Project (NSHAP), which conducted in-home interviews with 3,005 persons 57 to 85 years of age in the United States. The structure of the complete social network of 814 KSHAP respondents living in Township K was measured and examined at two levels of networks. Ego-centric network properties include network size, composition, volume of contact with network members, density, and bridging potential. Complete-network properties are degree centrality, closeness centrality, betweenness centrality, and brokerage role. We found that KSHAP respondents with a smaller number of social network members were more likely to be older and tended to have poorer self-rated health. Compared to the NSHAP, the KSHAP respondents maintained a smaller network size with a greater network density among their members and lower bridging potential. Further analysis of the complete network properties of KSHAP respondents revealed that more brokerage roles inside the same neighborhood (Ri) were significantly associated with better self-rated health. Socially isolated respondents identified by network components had the worst self-rated health. The findings demonstrate the importance of social network analysis for the study of older adults' health status in Korea. The study also highlights the importance of complete-network data and its ability to reveal mechanisms beyond ego-centric network data.

The link between workforce health and safety and the health of the bottom line: tracking market performance of companies that nurture a "culture of health".

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Fabius, Raymond; Thayer, R Dixon; Konicki, Doris L; Yarborough, Charles M; Peterson, Kent W; Isaac, Fikry; Loeppke, Ronald R; Eisenberg, Barry S; Dreger, Marianne

2013-09-01

To test the hypothesis that comprehensive efforts to reduce a workforce's health and safety risks can be associated with a company's stock market performance. Stock market performance of Corporate Health Achievement Award winners was tracked under four different scenarios using simulation and past market performance. A portfolio of companies recognized as award winning for their approach to the health and safety of their workforce outperformed the market. Evidence seems to support that building cultures of health and safety provides a competitive advantage in the marketplace. This research may have also identified an association between companies that focus on health and safety and companies that manage other aspects of their business equally well. Companies that build a culture of health by focusing on the well-being and safety of their workforce yield greater value for their investors.

Social Network Analysis as a Methodological Approach to Explore Health Systems: A Case Study Exploring Support among Senior Managers/Executives in a Hospital Network.

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De Brún, Aoife; McAuliffe, Eilish

2018-03-13

Health systems research recognizes the complexity of healthcare, and the interacting and interdependent nature of components of a health system. To better understand such systems, innovative methods are required to depict and analyze their structures. This paper describes social network analysis as a methodology to depict, diagnose, and evaluate health systems and networks therein. Social network analysis is a set of techniques to map, measure, and analyze social relationships between people, teams, and organizations. Through use of a case study exploring support relationships among senior managers in a newly established hospital group, this paper illustrates some of the commonly used network- and node-level metrics in social network analysis, and demonstrates the value of these maps and metrics to understand systems. Network analysis offers a valuable approach to health systems and services researchers as it offers a means to depict activity relevant to network questions of interest, to identify opinion leaders, influencers, clusters in the network, and those individuals serving as bridgers across clusters. The strengths and limitations inherent in the method are discussed, and the applications of social network analysis in health services research are explored.

Social Network Analysis as a Methodological Approach to Explore Health Systems: A Case Study Exploring Support among Senior Managers/Executives in a Hospital Network

Directory of Open Access Journals (Sweden)

Aoife De Brún

2018-03-01

Full Text Available Health systems research recognizes the complexity of healthcare, and the interacting and interdependent nature of components of a health system. To better understand such systems, innovative methods are required to depict and analyze their structures. This paper describes social network analysis as a methodology to depict, diagnose, and evaluate health systems and networks therein. Social network analysis is a set of techniques to map, measure, and analyze social relationships between people, teams, and organizations. Through use of a case study exploring support relationships among senior managers in a newly established hospital group, this paper illustrates some of the commonly used network- and node-level metrics in social network analysis, and demonstrates the value of these maps and metrics to understand systems. Network analysis offers a valuable approach to health systems and services researchers as it offers a means to depict activity relevant to network questions of interest, to identify opinion leaders, influencers, clusters in the network, and those individuals serving as bridgers across clusters. The strengths and limitations inherent in the method are discussed, and the applications of social network analysis in health services research are explored.

Childhood Trauma, Social Networks, and the Mental Health of Adult Survivors.

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Schneider, F David; Loveland Cook, Cynthia A; Salas, Joanne; Scherrer, Jeffrey; Cleveland, Ivy N; Burge, Sandra K

2017-03-01

The purpose of this study was to investigate the relationship of childhood trauma to the quality of social networks and health outcomes later in adulthood. Data were obtained from a convenience sample of 254 adults seen in one of 10 primary care clinics in the state of Texas. Standardized measures of adverse childhood experiences (ACEs), stressful and supportive social relationships, medical conditions, anxiety, depression, and health-related quality of life were administered. Using latent class analysis, subjects were assigned to one of four ACE classes: (a) minimal childhood abuse (56%), (b) physical/verbal abuse of both child and mother with household alcohol abuse (13%), (c) verbal and physical abuse of child with household mental illness (12%), and (d) verbal abuse only (19%). Statistically significant differences across the four ACE classes were found for mental health outcomes in adulthood. Although respondents who were physically and verbally abused as children reported compromised mental health, this was particularly true for those who witnessed physical abuse of their mother. A similar relationship between ACE class and physical health was not found. The quality of adult social networks partly accounted for the relationship between ACE classes and mental health outcomes. Respondents exposed to ACEs with more supportive social networks as adults had diminished odds of reporting poor mental health. Conversely, increasing numbers of stressful social relationships contributed to adverse mental health outcomes. Although efforts to prevent childhood trauma remain a critical priority, the treatment of adult survivors needs to expand its focus on both strengthening social networks and decreasing the negative effects of stressful ones.

Invited Commentary: Evolution of Social Networks, Health, and the Role of Epidemiology.

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Aiello, Allison E

2017-06-01

Almost 40 years ago, Berkman and Syme demonstrated that social networks were related to the risk of early mortality (Am J Epidemiol. 1979;109(2):186-204). Their study was highly innovative because they directly measured and quantified social networks in a large prospective population-based survey with mortality follow-up. The results of the study showed robust network gradients, whereby those with fewer networks and weaker social ties had significantly higher mortality rates. The important influence of social networks that Berkman and Syme noted many years ago is likely to heighten in the future, as demographic characteristics shift and individuals become more inclined to socialize through online platforms instead of real-world interactions. Berkman and Syme's research in 1979 continues to play a key role in shaping recent efforts to uncover the influence of social networks on health. Looking back on their findings may help epidemiologists better understand the importance of both online and offline networks for population health today. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Thermosetting resins for nuclear track detection

International Nuclear Information System (INIS)

Fujii, M.

1985-01-01

Several new thermosetting resins with a three dimensional network structure like CR-39 were polymerized to study their characteristics for use as nuclear track detectors. During the course of this study, thermosetting resins with good etching properties and various sensitivities have been obtained. The comparison of the molecular structures of these resins gives up an important clue for the development of highly sensitive polymeric track detectors. They will also be useful for observations of ultra-heavy cosmic rays and heavily ionizing particles at low energies. (orig.)

Thermosetting resins for nuclear track detection

International Nuclear Information System (INIS)

Fujii, Masami; Yokota, Rikio

1985-01-01

Several new thermosetting resins with a three dimensional network structure like CR-39 were polymerized to study their characteristics for use as nuclear track detectors. During the course of this study, thermosetting resins with good etching properties and various sensitivities have been obtained. The comparison of the molecular structures of these resins gives us an important clue for the development of highly sensitive polymeric track detectors. They will also be useful for observations of ultra-heavy cosmic rays and heavily ionizing particles at low energies. (author)

Health impact assessment of cycling network expansions in European cities.

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Mueller, Natalie; Rojas-Rueda, David; Salmon, Maëlle; Martinez, David; Ambros, Albert; Brand, Christian; de Nazelle, Audrey; Dons, Evi; Gaupp-Berghausen, Mailin; Gerike, Regine; Götschi, Thomas; Iacorossi, Francesco; Int Panis, Luc; Kahlmeier, Sonja; Raser, Elisabeth; Nieuwenhuijsen, Mark

2018-04-01

We conducted a health impact assessment (HIA) of cycling network expansions in seven European cities. We modeled the association between cycling network length and cycling mode share and estimated health impacts of the expansion of cycling networks. First, we performed a non-linear least square regression to assess the relationship between cycling network length and cycling mode share for 167 European cities. Second, we conducted a quantitative HIA for the seven cities of different scenarios (S) assessing how an expansion of the cycling network [i.e. 10% (S1); 50% (S2); 100% (S3), and all-streets (S4)] would lead to an increase in cycling mode share and estimated mortality impacts thereof. We quantified mortality impacts for changes in physical activity, air pollution and traffic incidents. Third, we conducted a cost-benefit analysis. The cycling network length was associated with a cycling mode share of up to 24.7% in European cities. The all-streets scenario (S4) produced greatest benefits through increases in cycling for London with 1,210 premature deaths (95% CI: 447-1,972) avoidable annually, followed by Rome (433; 95% CI: 170-695), Barcelona (248; 95% CI: 86-410), Vienna (146; 95% CI: 40-252), Zurich (58; 95% CI: 16-100) and Antwerp (7; 95% CI: 3-11). The largest cost-benefit ratios were found for the 10% increase in cycling networks (S1). If all 167 European cities achieved a cycling mode share of 24.7% over 10,000 premature deaths could be avoided annually. In European cities, expansions of cycling networks were associated with increases in cycling and estimated to provide health and economic benefits. Copyright © 2018 Elsevier Inc. All rights reserved.

[Benefit and Sustainability of Networks for workplace Health Promotion in SME Examined at the SME Networks "Bewegte Unternehmen" and "Vitale Unternehmen"].

Science.gov (United States)

Müller, Eva; Fischmann, Wolfgang; Kötter, Rudolf; Drexler, Hans; Kiesel, Johannes

2018-05-01

The purpose of the study was to analyze if 2 regional networks of small and medium enterprises (SME) for workplace health promotion are sustainable, and to find out the motivation of the enterprises to join the network. It was also examined if there is a stable culture of cooperation 6 -10 years after the founding of the network. Additionally, the study checked the current work and suggestions for improvement to the network structure, so that in the future, promotion of workplace health can be further improved. 2 regional networks, founded in 2005 and 2009, were studied. Standardized telephone interviews carried out between September 2013 and January 2014 enabled data collection for this cross-sectional study. 42 interviews with 6 open questions were organized with the managers of the companies or the person responsible for workplace health promotion. The results of the study show that 88.1% (n=37) of the network company members profited from the exchange of experiences. 50.0% (n=21) benefited from shared activities and 28.6% (n=12) from making new contacts. 9.5% (n=4) of the respondents expressed concerns about excessive bureaucracy resulting in too much effort for too little benefit and 7.1% (n=3) were also missing comprehensive structural measures. Suggestions for improvement were enhancement of practical work (26.2%, n=11) and the wish for stronger commitment (11.9%, n=5). 90.5% (n=38) considered their expectations as fulfilled and 66.7% (n=28) evaluated the current work as being quite positive. The networks have turned out to be sustainable, proven by the fact that the companies still are members of the networks for 6 and 10 years, respectively and are still satisfied with the network. The study shows that the majority of the members profits from the membership of these regional networks. Networks can help them to implement permanent workplace health promotion. To further improve the work of the network, a systematic and scientific workplace health promotion

Tracking official development assistance for reproductive health in conflict-affected countries.

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Patel, Preeti; Roberts, Bayard; Guy, Samantha; Lee-Jones, Louise; Conteh, Lesong

2009-06-09

Reproductive health needs are particularly acute in countries affected by armed conflict. Reliable information on aid investment for reproductive health in these countries is essential for improving the efficiency and effectiveness of aid. The purpose of this study was to analyse official development assistance (ODA) for reproductive health activities in conflict-affected countries from 2003 to 2006. The Creditor Reporting System and the Financial Tracking System databases were the chosen data sources for the study. ODA disbursement for reproductive health activities to 18 conflict-affected countries was analysed for 2003, 2004, 2005, and 2006. An average of US $20.8 billion in total ODA was disbursed annually to the 18 conflict-affected countries between 2003 and 2006, of which US $509.3 million (2.4%) was allocated to reproductive health. This represents an annual average of US $1.30 disbursed per capita in the 18 sampled countries for reproductive health activities. Non-conflict-affected least-developed countries received 53.3% more ODA for reproductive health activities than conflict-affected least-developed countries, despite the latter generally having greater reproductive health needs. ODA disbursed for HIV/AIDS prevention and treatment increased by 119.4% from 2003 to 2006. The ODA disbursed for other direct reproductive health activities declined by 35.9% over the same period. This study provides evidence of inequity in disbursement of reproductive health ODA between conflict-affected countries and non-conflict-affected countries, and between different reproductive health activities. These findings and the study's recommendations seek to support initiatives to make aid financing more responsive to need in the context of armed conflict.

Tracking official development assistance for reproductive health in conflict-affected countries.

Directory of Open Access Journals (Sweden)

Preeti Patel

2009-06-01

Full Text Available BACKGROUND: Reproductive health needs are particularly acute in countries affected by armed conflict. Reliable information on aid investment for reproductive health in these countries is essential for improving the efficiency and effectiveness of aid. The purpose of this study was to analyse official development assistance (ODA for reproductive health activities in conflict-affected countries from 2003 to 2006. METHODS AND FINDINGS: The Creditor Reporting System and the Financial Tracking System databases were the chosen data sources for the study. ODA disbursement for reproductive health activities to 18 conflict-affected countries was analysed for 2003, 2004, 2005, and 2006. An average of US $20.8 billion in total ODA was disbursed annually to the 18 conflict-affected countries between 2003 and 2006, of which US $509.3 million (2.4% was allocated to reproductive health. This represents an annual average of US $1.30 disbursed per capita in the 18 sampled countries for reproductive health activities. Non-conflict-affected least-developed countries received 53.3% more ODA for reproductive health activities than conflict-affected least-developed countries, despite the latter generally having greater reproductive health needs. ODA disbursed for HIV/AIDS prevention and treatment increased by 119.4% from 2003 to 2006. The ODA disbursed for other direct reproductive health activities declined by 35.9% over the same period. CONCLUSIONS: This study provides evidence of inequity in disbursement of reproductive health ODA between conflict-affected countries and non-conflict-affected countries, and between different reproductive health activities. These findings and the study's recommendations seek to support initiatives to make aid financing more responsive to need in the context of armed conflict.

The Relationship of Policymaking and Networking Characteristics among Leaders of Large Urban Health Departments

Directory of Open Access Journals (Sweden)

Jonathon P. Leider

2015-08-01

Full Text Available Background: The relationship between policy networks and policy development among local health departments (LHDs is a growing area of interest to public health practitioners and researchers alike. In this study, we examine policy activity and ties between public health leadership across large urban health departments. Methods: This study uses data from a national profile of local health departments as well as responses from a survey sent to three staff members (local health official, chief of policy, chief science officer in each of 16 urban health departments in the United States. Network questions related to frequency of contact with health department personnel in other cities. Using exponential random graph models, network density and centrality were examined, as were patterns of communication among those working on several policy areas using exponential random graph models. Results: All 16 LHDs were active in communicating about chronic disease as well as about use of alcohol, tobacco, and other drugs (ATOD. Connectedness was highest among local health officials (density = .55, and slightly lower for chief science officers (d = .33 and chiefs of policy (d = .29. After accounting for organizational characteristics, policy homophily (i.e., when two network members match on a single characteristic and tenure were the most significant predictors of formation of network ties. Conclusion: Networking across health departments has the potential for accelerating the adoption of public health policies. This study suggests similar policy interests and formation of connections among senior leadership can potentially drive greater connectedness among other staff.

Using social networking to understand social networks: analysis of a mobile phone closed user group used by a Ghanaian health team.

Science.gov (United States)

Kaonga, Nadi Nina; Labrique, Alain; Mechael, Patricia; Akosah, Eric; Ohemeng-Dapaah, Seth; Sakyi Baah, Joseph; Kodie, Richmond; Kanter, Andrew S; Levine, Orin

2013-04-03

The network structure of an organization influences how well or poorly an organization communicates and manages its resources. In the Millennium Villages Project site in Bonsaaso, Ghana, a mobile phone closed user group has been introduced for use by the Bonsaaso Millennium Villages Project Health Team and other key individuals. No assessment on the benefits or barriers of the use of the closed user group had been carried out. The purpose of this research was to make the case for the use of social network analysis methods to be applied in health systems research--specifically related to mobile health. This study used mobile phone voice records of, conducted interviews with, and reviewed call journals kept by a mobile phone closed user group consisting of the Bonsaaso Millennium Villages Project Health Team. Social network analysis methodology complemented by a qualitative component was used. Monthly voice data of the closed user group from Airtel Bharti Ghana were analyzed using UCINET and visual depictions of the network were created using NetDraw. Interviews and call journals kept by informants were analyzed using NVivo. The methodology was successful in helping identify effective organizational structure. Members of the Health Management Team were the more central players in the network, rather than the Community Health Nurses (who might have been expected to be central). Social network analysis methodology can be used to determine the most productive structure for an organization or team, identify gaps in communication, identify key actors with greatest influence, and more. In conclusion, this methodology can be a useful analytical tool, especially in the context of mobile health, health services, and operational and managerial research.

City networks collaboration and planning for health and sustainability

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Migdalas, Athanasios; Rassia, Stamatina; Pardalos, Panos

2017-01-01

Sustainable development within urban and rural areas, transportation systems, logistics, supply chain management, urban health, social services, and architectural design are taken into consideration in the cohesive network models provided in this book. The ideas, methods, and models presented consider city landscapes and quality of life conditions based on mathematical network models and optimization. Interdisciplinary Works from prominent researchers in mathematical modeling, optimization, architecture, engineering, and physics are featured in this volume to promote health and well-being through design.   Specific topics include: -          Current technology that form the basis of future living in smart cities -          Interdisciplinary design and networking of large-scale urban systems  -          Network communication and route traffic optimization -          Carbon dioxide emission reduction -          Closed-loop logistics chain management and operation ...

Biomarkers in Sports and Exercise: Tracking Health, Performance, and Recovery in Athletes.

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Lee, Elaine C; Fragala, Maren S; Kavouras, Stavros A; Queen, Robin M; Pryor, John Luke; Casa, Douglas J

2017-10-01

Biomarker discovery and validation is a critical aim of the medical and scientific community. Research into exercise and diet-related biomarkers aims to improve health, performance, and recovery in military personnel, athletes, and lay persons. Exercise physiology research has identified individual biomarkers for assessing health, performance, and recovery during exercise training. However, there are few recommendations for biomarker panels for tracking changes in individuals participating in physical activity and exercise training programs. Our approach was to review the current literature and recommend a collection of validated biomarkers in key categories of health, performance, and recovery that could be used for this purpose. We determined that a comprehensive performance set of biomarkers should include key markers of (a) nutrition and metabolic health, (b) hydration status, (c) muscle status, (d) endurance performance, (e) injury status and risk, and (f) inflammation. Our review will help coaches, clinical sport professionals, researchers, and athletes better understand how to comprehensively monitor physiologic changes, as they design training cycles that elicit maximal improvements in performance while minimizing overtraining and injury risk.

Online Social Networking and Mental Health

Science.gov (United States)

2014-01-01

Abstract During the past decade, online social networking has caused profound changes in the way people communicate and interact. It is unclear, however, whether some of these changes may affect certain normal aspects of human behavior and cause psychiatric disorders. Several studies have indicated that the prolonged use of social networking sites (SNS), such as Facebook, may be related to signs and symptoms of depression. In addition, some authors have indicated that certain SNS activities might be associated with low self-esteem, especially in children and adolescents. Other studies have presented opposite results in terms of positive impact of social networking on self-esteem. The relationship between SNS use and mental problems to this day remains controversial, and research on this issue is faced with numerous challenges. This concise review focuses on the recent findings regarding the suggested connection between SNS and mental health issues such as depressive symptoms, changes in self-esteem, and Internet addiction. PMID:25192305

Online social networking and mental health.