Design of neural network model-based controller in a fed-batch microbial electrolysis cell reactor for bio-hydrogen gas production

NASA Astrophysics Data System (ADS)

Azwar; Hussain, M. A.; Abdul-Wahab, A. K.; Zanil, M. F.; Mukhlishien

2018-03-01

One of major challenge in bio-hydrogen production process by using MEC process is nonlinear and highly complex system. This is mainly due to the presence of microbial interactions and highly complex phenomena in the system. Its complexity makes MEC system difficult to operate and control under optimal conditions. Thus, precise control is required for the MEC reactor, so that the amount of current required to produce hydrogen gas can be controlled according to the composition of the substrate in the reactor. In this work, two schemes for controlling the current and voltage of MEC were evaluated. The controllers evaluated are PID and Inverse neural network (NN) controller. The comparative study has been carried out under optimal condition for the production of bio-hydrogen gas wherein the controller output is based on the correlation of optimal current and voltage to the MEC. Various simulation tests involving multiple set-point changes and disturbances rejection have been evaluated and the performances of both controllers are discussed. The neural network-based controller results in fast response time and less overshoots while the offset effects are minimal. In conclusion, the Inverse neural network (NN)-based controllers provide better control performance for the MEC system compared to the PID controller.

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks

PubMed Central

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model. PMID:24634645

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks.

PubMed

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model.

Species-free species distribution models describe macroecological properties of protected area networks.

PubMed

Robinson, Jason L; Fordyce, James A

2017-01-01

Among the greatest challenges facing the conservation of plants and animal species in protected areas are threats from a rapidly changing climate. An altered climate creates both challenges and opportunities for improving the management of protected areas in networks. Increasingly, quantitative tools like species distribution modeling are used to assess the performance of protected areas and predict potential responses to changing climates for groups of species, within a predictive framework. At larger geographic domains and scales, protected area network units have spatial geoclimatic properties that can be described in the gap analysis typically used to measure or aggregate the geographic distributions of species (stacked species distribution models, or S-SDM). We extend the use of species distribution modeling techniques in order to model the climate envelope (or "footprint") of individual protected areas within a network of protected areas distributed across the 48 conterminous United States and managed by the US National Park System. In our approach we treat each protected area as the geographic range of a hypothetical endemic species, then use MaxEnt and 5 uncorrelated BioClim variables to model the geographic distribution of the climatic envelope associated with each protected area unit (modeling the geographic area of park units as the range of a species). We describe the individual and aggregated climate envelopes predicted by a large network of 163 protected areas and briefly illustrate how macroecological measures of geodiversity can be derived from our analysis of the landscape ecological context of protected areas. To estimate trajectories of change in the temporal distribution of climatic features within a protected area network, we projected the climate envelopes of protected areas in current conditions onto a dataset of predicted future climatic conditions. Our results suggest that the climate envelopes of some parks may be locally unique or have narrow geographic distributions, and are thus prone to future shifts away from the climatic conditions in these parks in current climates. In other cases, some parks are broadly similar to large geographic regions surrounding the park or have climatic envelopes that may persist into near-term climate change. Larger parks predict larger climatic envelopes, in current conditions, but on average the predicted area of climate envelopes are smaller in our single future conditions scenario. Individual units in a protected area network may vary in the potential for climate adaptation, and adaptive management strategies for the network should account for the landscape contexts of the geodiversity or climate diversity within individual units. Conservation strategies, including maintaining connectivity, assessing the feasibility of assisted migration and other landscape restoration or enhancements can be optimized using analysis methods to assess the spatial properties of protected area networks in biogeographic and macroecological contexts.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.

PubMed

Mazzoni, Alberto; Lindén, Henrik; Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T

2015-12-01

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

PubMed Central

Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T.

2015-01-01

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo. PMID:26657024

Disentangling the attention network test: behavioral, event related potentials, and neural source analyses

PubMed Central

Galvao-Carmona, Alejandro; González-Rosa, Javier J.; Hidalgo-Muñoz, Antonio R.; Páramo, Dolores; Benítez, María L.; Izquierdo, Guillermo; Vázquez-Marrufo, Manuel

2014-01-01

Background: The study of the attentional system remains a challenge for current neuroscience. The “Attention Network Test” (ANT) was designed to study simultaneously three different attentional networks (alerting, orienting, and executive) based in subtraction of different experimental conditions. However, some studies recommend caution with these calculations due to the interactions between the attentional networks. In particular, it is highly relevant that several interpretations about attentional impairment have arisen from these calculations in diverse pathologies. Event related potentials (ERPs) and neural source analysis can be applied to disentangle the relationships between these attentional networks not specifically shown by behavioral measures. Results: This study shows that there is a basic level of alerting (tonic alerting) in the no cue (NC) condition, represented by a slow negative trend in the ERP trace prior to the onset of the target stimuli. A progressive increase in the CNV amplitude related to the amount of information provided by the cue conditions is also shown. Neural source analysis reveals specific modulations of the CNV related to a task-related expectancy presented in the NC condition; a late modulation triggered by the central cue (CC) condition and probably representing a generic motor preparation; and an early and late modulation for spatial cue (SC) condition suggesting specific motor and sensory preactivation. Finally, the first component in the information processing of the target stimuli modulated by the interaction between orienting network and the executive system can be represented by N1. Conclusions: The ANT is useful as a paradigm to study specific attentional mechanisms and their interactions. However, calculation of network effects is based in subtractions with non-comparable experimental conditions, as evidenced by the present data, which can induce misinterpretations in the study of the attentional capacity in human subjects. PMID:25352800

E-Center: A Collaborative Platform for Wide Area Network Users

NASA Astrophysics Data System (ADS)

Grigoriev, M.; DeMar, P.; Tierney, B.; Lake, A.; Metzger, J.; Frey, M.; Calyam, P.

2012-12-01

The E-Center is a social collaborative web-based platform for assisting network users in understanding network conditions across network paths of interest to them. It is designed to give a user the necessary tools to isolate, identify, and resolve network performance-related problems. E-Center provides network path information on a link-by-link level, as well as from an end-to-end perspective. In addition to providing current and recent network path data, E-Center is intended to provide a social media environment for them to share issues, ideas, concerns, and problems. The product has a modular design that accommodates integration of other network services that make use of the same network path and performance data.

AIS ASM Operational Integration Plan

DTIC Science & Technology

2013-08-01

al. | Public August 2013 This page intentionally left blank. AIS ASM Operational Integration Plan v ...that supply AIS Routers as part of their AIS shoreside network software : Kongsberg C-Scope, Gatehouse AIS, Transas AIS Network, and CNS DataSwitch...commercial systems would be suitable for the current USCG traffic conditions. The ASM Manager is software that adds the required queuing and

Development of Methodologies for IV and V of Neural Networks

NASA Technical Reports Server (NTRS)

Taylor, Brian; Darrah, Marjorie

2003-01-01

Non-deterministic systems often rely upon neural network (NN) technology to "lean" to manage flight systems under controlled conditions using carefully chosen training sets. How can these adaptive systems be certified to ensure that they will become increasingly efficient and behave appropriately in real-time situations? The bulk of Independent Verification and Validation (IV&V) research of non-deterministic software control systems such as Adaptive Flight Controllers (AFC's) addresses NNs in well-behaved and constrained environments such as simulations and strict process control. However, neither substantive research, nor effective IV&V techniques have been found to address AFC's learning in real-time and adapting to live flight conditions. Adaptive flight control systems offer good extensibility into commercial aviation as well as military aviation and transportation. Consequently, this area of IV&V represents an area of growing interest and urgency. ISR proposes to further the current body of knowledge to meet two objectives: Research the current IV&V methods and assess where these methods may be applied toward a methodology for the V&V of Neural Network; and identify effective methods for IV&V of NNs that learn in real-time, including developing a prototype test bed for IV&V of AFC's. Currently. no practical method exists. lSR will meet these objectives through the tasks identified and described below. First, ISR will conduct a literature review of current IV&V technology. TO do this, ISR will collect the existing body of research on IV&V of non-deterministic systems and neural network. ISR will also develop the framework for disseminating this information through specialized training. This effort will focus on developing NASA's capability to conduct IV&V of neural network systems and to provide training to meet the increasing need for IV&V expertise in such systems.

State feedback control design for Boolean networks.

PubMed

Liu, Rongjie; Qian, Chunjiang; Liu, Shuqian; Jin, Yu-Fang

2016-08-26

Driving Boolean networks to desired states is of paramount significance toward our ultimate goal of controlling the progression of biological pathways and regulatory networks. Despite recent computational development of controllability of general complex networks and structural controllability of Boolean networks, there is still a lack of bridging the mathematical condition on controllability to real boolean operations in a network. Further, no realtime control strategy has been proposed to drive a Boolean network. In this study, we applied semi-tensor product to represent boolean functions in a network and explored controllability of a boolean network based on the transition matrix and time transition diagram. We determined the necessary and sufficient condition for a controllable Boolean network and mapped this requirement in transition matrix to real boolean functions and structure property of a network. An efficient tool is offered to assess controllability of an arbitrary Boolean network and to determine all reachable and non-reachable states. We found six simplest forms of controllable 2-node Boolean networks and explored the consistency of transition matrices while extending these six forms to controllable networks with more nodes. Importantly, we proposed the first state feedback control strategy to drive the network based on the status of all nodes in the network. Finally, we applied our reachability condition to the major switch of P53 pathway to predict the progression of the pathway and validate the prediction with published experimental results. This control strategy allowed us to apply realtime control to drive Boolean networks, which could not be achieved by the current control strategy for Boolean networks. Our results enabled a more comprehensive understanding of the evolution of Boolean networks and might be extended to output feedback control design.

Improved equivalent magnetic network modeling for analyzing working points of PMs in interior permanent magnet machine

NASA Astrophysics Data System (ADS)

Guo, Liyan; Xia, Changliang; Wang, Huimin; Wang, Zhiqiang; Shi, Tingna

2018-05-01

As is well known, the armature current will be ahead of the back electromotive force (back-EMF) under load condition of the interior permanent magnet (PM) machine. This kind of advanced armature current will produce a demagnetizing field, which may make irreversible demagnetization appeared in PMs easily. To estimate the working points of PMs more accurately and take demagnetization under consideration in the early design stage of a machine, an improved equivalent magnetic network model is established in this paper. Each PM under each magnetic pole is segmented, and the networks in the rotor pole shoe are refined, which makes a more precise model of the flux path in the rotor pole shoe possible. The working point of each PM under each magnetic pole can be calculated accurately by the established improved equivalent magnetic network model. Meanwhile, the calculated results are compared with those calculated by FEM. And the effects of d-axis component and q-axis component of armature current, air-gap length and flux barrier size on working points of PMs are analyzed by the improved equivalent magnetic network model.

Autapse-Induced Spiral Wave in Network of Neurons under Noise

PubMed Central

Qin, Huixin; Ma, Jun; Wang, Chunni; Wu, Ying

2014-01-01

Autapse plays an important role in regulating the electric activity of neuron by feedbacking time-delayed current on the membrane of neuron. Autapses are considered in a local area of regular network of neurons to investigate the development of spatiotemporal pattern, and emergence of spiral wave is observed while it fails to grow up and occupy the network completely. It is found that spiral wave can be induced to occupy more area in the network under optimized noise on the network with periodical or no-flux boundary condition being used. The developed spiral wave with self-sustained property can regulate the collective behaviors of neurons as a pacemaker. To detect the collective behaviors, a statistical factor of synchronization is calculated to investigate the emergence of ordered state in the network. The network keeps ordered state when self-sustained spiral wave is formed under noise and autapse in local area of network, and it independent of the selection of periodical or no-flux boundary condition. The developed stable spiral wave could be helpful for memory due to the distinct self-sustained property. PMID:24967577

Autapse-induced spiral wave in network of neurons under noise.

PubMed

Qin, Huixin; Ma, Jun; Wang, Chunni; Wu, Ying

2014-01-01

Autapse plays an important role in regulating the electric activity of neuron by feedbacking time-delayed current on the membrane of neuron. Autapses are considered in a local area of regular network of neurons to investigate the development of spatiotemporal pattern, and emergence of spiral wave is observed while it fails to grow up and occupy the network completely. It is found that spiral wave can be induced to occupy more area in the network under optimized noise on the network with periodical or no-flux boundary condition being used. The developed spiral wave with self-sustained property can regulate the collective behaviors of neurons as a pacemaker. To detect the collective behaviors, a statistical factor of synchronization is calculated to investigate the emergence of ordered state in the network. The network keeps ordered state when self-sustained spiral wave is formed under noise and autapse in local area of network, and it independent of the selection of periodical or no-flux boundary condition. The developed stable spiral wave could be helpful for memory due to the distinct self-sustained property.

A comparison of two types of neural network for weld quality prediction in small scale resistance spot welding

NASA Astrophysics Data System (ADS)

Wan, Xiaodong; Wang, Yuanxun; Zhao, Dawei; Huang, YongAn

2017-09-01

Our study aims at developing an effective quality monitoring system in small scale resistance spot welding of titanium alloy. The measured electrical signals were interpreted in combination with the nugget development. Features were extracted from the dynamic resistance and electrode voltage curve. A higher welding current generally indicated a lower overall dynamic resistance level. A larger electrode voltage peak and higher change rate of electrode voltage could be detected under a smaller electrode force or higher welding current condition. Variation of the extracted features and weld quality was found more sensitive to the change of welding current than electrode force. Different neural network model were proposed for weld quality prediction. The back propagation neural network was more proper in failure load estimation. The probabilistic neural network model was more appropriate to be applied in quality level classification. A real-time and on-line weld quality monitoring system may be developed by taking advantages of both methods.

The Network Structure of Symptoms of the Diagnostic and Statistical Manual of Mental Disorders.

PubMed

Boschloo, Lynn; van Borkulo, Claudia D; Rhemtulla, Mijke; Keyes, Katherine M; Borsboom, Denny; Schoevers, Robert A

2015-01-01

Although current classification systems have greatly contributed to the reliability of psychiatric diagnoses, they ignore the unique role of individual symptoms and, consequently, potentially important information is lost. The network approach, in contrast, assumes that psychopathology results from the causal interplay between psychiatric symptoms and focuses specifically on these symptoms and their complex associations. By using a sophisticated network analysis technique, this study constructed an empirically based network structure of 120 psychiatric symptoms of twelve major DSM-IV diagnoses using cross-sectional data of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, second wave; N = 34,653). The resulting network demonstrated that symptoms within the same diagnosis showed differential associations and indicated that the strategy of summing symptoms, as in current classification systems, leads to loss of information. In addition, some symptoms showed strong connections with symptoms of other diagnoses, and these specific symptom pairs, which both concerned overlapping and non-overlapping symptoms, may help to explain the comorbidity across diagnoses. Taken together, our findings indicated that psychopathology is very complex and can be more adequately captured by sophisticated network models than current classification systems. The network approach is, therefore, promising in improving our understanding of psychopathology and moving our field forward.

Planning protected areas network that are relevant today and under future climate change is possible: the case of Atlantic Forest endemic birds.

PubMed

Vale, Mariana M; Souza, Thiago V; Alves, Maria Alice S; Crouzeilles, Renato

2018-01-01

A key strategy in biodiversity conservation is the establishment of protected areas. In the future, however, the redistribution of species in response to ongoing climate change is likely to affect species' representativeness in those areas. Here we quantify the effectiveness of planning protected areas network to represent 151 birds endemic to the Brazilian Atlantic Forest hotspot, under current and future climate change conditions for 2050. We combined environmental niche modeling and systematic conservation planning using both a county and a regional level planning strategy. We recognized the conflict between biodiversity conservation and economic development, including socio-economic targets (as opposed to biological only) and using planning units that are meaningful for policy-makers. We estimated an average contraction of 29,500 km 2 in environmentally suitable areas for birds, representing 52% of currently suitable areas. Still, the most cost-effective solution represented almost all target species, requiring only ca. 10% of the Atlantic Forest counties to achieve that representativeness, independent of strategy. More than 50% of these counties were selected both in the current and future planned networks, representing >83% of the species. Our results indicate that: (i) planning protected areas network currently can be useful to represent species under climate change; (ii) the overlapped planning units in the best solution for both current and future conditions can be considered as "no regret" areas; (iii) priority counties are spread throughout the biome, providing specific guidance wherever the possibility of creating protected area arises; and (iv) decisions can occur at different administrative spheres (Federal, State or County) as we found quite similar numerical solutions using either county or regional level strategies.

Planning protected areas network that are relevant today and under future climate change is possible: the case of Atlantic Forest endemic birds

PubMed Central

Souza, Thiago V.; Alves, Maria Alice S.; Crouzeilles, Renato

2018-01-01

Background A key strategy in biodiversity conservation is the establishment of protected areas. In the future, however, the redistribution of species in response to ongoing climate change is likely to affect species’ representativeness in those areas. Here we quantify the effectiveness of planning protected areas network to represent 151 birds endemic to the Brazilian Atlantic Forest hotspot, under current and future climate change conditions for 2050. Methods We combined environmental niche modeling and systematic conservation planning using both a county and a regional level planning strategy. We recognized the conflict between biodiversity conservation and economic development, including socio-economic targets (as opposed to biological only) and using planning units that are meaningful for policy-makers. Results We estimated an average contraction of 29,500 km2 in environmentally suitable areas for birds, representing 52% of currently suitable areas. Still, the most cost-effective solution represented almost all target species, requiring only ca. 10% of the Atlantic Forest counties to achieve that representativeness, independent of strategy. More than 50% of these counties were selected both in the current and future planned networks, representing >83% of the species. Discussion Our results indicate that: (i) planning protected areas network currently can be useful to represent species under climate change; (ii) the overlapped planning units in the best solution for both current and future conditions can be considered as “no regret” areas; (iii) priority counties are spread throughout the biome, providing specific guidance wherever the possibility of creating protected area arises; and (iv) decisions can occur at different administrative spheres (Federal, State or County) as we found quite similar numerical solutions using either county or regional level strategies. PMID:29844952

A simulated annealing approach for redesigning a warehouse network problem

NASA Astrophysics Data System (ADS)

Khairuddin, Rozieana; Marlizawati Zainuddin, Zaitul; Jiun, Gan Jia

2017-09-01

Now a day, several companies consider downsizing their distribution networks in ways that involve consolidation or phase-out of some of their current warehousing facilities due to the increasing competition, mounting cost pressure and taking advantage on the economies of scale. Consequently, the changes on economic situation after a certain period of time require an adjustment on the network model in order to get the optimal cost under the current economic conditions. This paper aimed to develop a mixed-integer linear programming model for a two-echelon warehouse network redesign problem with capacitated plant and uncapacitated warehouses. The main contribution of this study is considering capacity constraint for existing warehouses. A Simulated Annealing algorithm is proposed to tackle with the proposed model. The numerical solution showed the model and method of solution proposed was practical.

An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources.

PubMed

Doulamis, A D; Doulamis, N D; Kollias, S D

2003-01-01

Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neural-network architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better performance than other examined techniques.

U.S. Geological Survey Ground-Water Climate Response Network

USGS Publications Warehouse

,

2007-01-01

The U.S. Geological Survey serves the Nation by providing reliable hydrologic information used by others to manage the Nation's water resources. The U.S. Geological Survey (USGS) measures more than 20,000 wells each year for a variety of objectives as part of Federal programs and in cooperation with State and local agencies. Water-level data are collected using consistent data-collection and quality-control methods. A small subset of these wells meets the criteria necessary to be included in a 'Climate Response Network' of wells designed to illustrate the response of the ground-water system to climate variations nationwide. The primary purpose of the Climate Response Network is to portray the effect of climate on ground-water levels in unconfined aquifers or near-surface confined aquifers that are minimally affected by pumping or other anthropogenic stresses. The Climate Response Network Web site (http://groundwaterwatch.usgs.gov/) is the official USGS Web site for illustrating current ground-water conditions in the United States and Puerto Rico. The Climate Response Network Web pages provide information on ground-water conditions at a variety of scales. A national map provides a broad overview of water-table conditions across the Nation. State maps provide a more local picture of ground-water conditions. Site pages provide the details about a specific well.

Analysis of non-destructive current simulators of flux compression generators.

PubMed

O'Connor, K A; Curry, R D

2014-06-01

Development and evaluation of power conditioning systems and high power microwave components often used with flux compression generators (FCGs) requires repeated testing and characterization. In an effort to minimize the cost and time required for testing with explosive generators, non-destructive simulators of an FCG's output current have been developed. Flux compression generators and simulators of FCGs are unique pulsed power sources in that the current waveform exhibits a quasi-exponential increasing rate at which the current rises. Accurately reproducing the quasi-exponential current waveform of a FCG can be important in designing electroexplosive opening switches and other power conditioning components that are dependent on the integral of current action and the rate of energy dissipation. Three versions of FCG simulators have been developed that include an inductive network with decreasing impedance in time. A primary difference between these simulators is the voltage source driving them. It is shown that a capacitor-inductor-capacitor network driving a constant or decreasing inductive load can produce the desired high-order derivatives of the load current to replicate a quasi-exponential waveform. The operation of the FCG simulators is reviewed and described mathematically for the first time to aid in the design of new simulators. Experimental and calculated results of two recent simulators are reported with recommendations for future designs.

Design and application of air-conditioning suit based on eddy current cooling principle for distribution network working with power uninterrupted

NASA Astrophysics Data System (ADS)

Xu, Li; Liu, Lanlan; Niu, Jie; Tang, Li; Li, Jinliang; Zhou, Zhanfan; Long, Chenhai; Yang, Qi; Yi, Ziqi; Guo, Hao; Long, Yang; Fu, Yanyi

2017-05-01

As social requirement of power supply reliability keeps rising, distribution network working with power uninterrupted has been widely carried out, while the high - temperature operating environment in summer can easily lead to physical discomfort for the operators, and then lead to safety incidents. Aiming at above problem, air-conditioning suit for distribution network working with power uninterrupted has been putted forward in this paper, and the structure composition and cooling principle of which has been explained, and it has been ultimately put to on-site application. The results showed that, cooling effect of air-conditioning suits was remarkable, and improved the working environment for the operators effectively, which is of great significance to improve Chinese level of working with power uninterrupted, reduce the probability of accidents and enhance the reliability of power supply.

Implementing wireless sensor networks for architectural heritage conservation

NASA Astrophysics Data System (ADS)

Martínez-Garrido, M. I.; Aparicio, S.; Fort, R.; Izquierdo, M. A. G.; Anaya, J. J.

2012-04-01

Preventive conservation in architectural heritage is one of the most important aims for the development and implementation of new techniques to assess decay, lending to reduce damage before it has occurred and reducing costs in the long term. For that purpose, it is necessary to know all aspects influencing in decay evolution depending on the material under study and its internal and external conditions. Wireless sensor networks are an emerging technology and a minimally invasive technique. The use of these networks facilitates data acquisition and monitoring of a large number of variables that could provoke material damages, such as presence of harmful compounds like salts, dampness, etc. The current project presents different wireless sensors networks (WSN) and sensors used to fulfill the requirements for a complete analysis of main decay agents in a Renaissance church of the 16th century in Madrid (Spain). Current typologies and wireless technologies are studied establishing the most suitable system and the convenience of each one. Firstly, it is very important to consider that microclimate is in close correlation with material deterioration. Therefore a temperature(T) and relative humidity (RH)/moisture network has been developed, using ZigBee wireless communications protocols, and monitoring different points along the church surface. These points are recording RH/T differences depending on the height and the sensor location (inside the material or on the surface). On the other hand, T/RH button sensors have been used, minimizing aesthetical interferences, and concluding which is the most advisable way for monitoring these specific parameters. Due to the fact that microclimate is a complex phenomenon, it is necessary to examine spatial distribution and time evolution at the same time. This work shows both studies since the development expects a long term monitoring. A different wireless network has been deployed to study the effects of pollution caused by other active systems such as a forced-air heating system, the parishioners presence or feasts and other ventilation conditions. Finally weather conditions are registered through a weather station. Outside and inside conditions are compared to incorporate data to the network for a later decay modeling.

Summary of Hydrologic Conditions in Georgia, 2008

USGS Publications Warehouse

Knaak, Andrew E.; Joiner, John K.; Peck, Michael F.

2009-01-01

The United States Geological Survey (USGS) Georgia Water Science Center (WSC) maintains a long-term hydrologic monitoring network of more than 290 real-time streamgages, more than 170 groundwater wells, and 10 lake and reservoir monitoring stations. One of the many benefits of data collected from this monitoring network is that analysis of the data provides an overview of the hydrologic conditions of rivers, creeks, reservoirs, and aquifers in Georgia. Hydrologic conditions are determined by statistical analysis of data collected during the current water year (WY) and comparison of the results to historical data collected at long-term stations. During the drought that persisted through 2008, the USGS succeeded in verifying and documenting numerous historic low-flow statistics at many streamgages and current water levels in aquifers, lakes, and reservoirs in Georgia. Streamflow data from the 2008 WY indicate that this drought is one of the most severe on record when compared to drought periods of 1950-1957, 1985-1989, and 1999-2002.

An fMRI investigation of the relationship between future imagination and cognitive flexibility

PubMed Central

Roberts, R.P.; Wiebels, K.; Sumner, R.L.; van Mulukom, V.; Grady, C.L.; Schacter, D.L.; Addis, D.R.

2016-01-01

While future imagination is largely considered to be a cognitive process grounded in default mode network activity, studies have shown that future imagination recruits regions in both default mode and frontoparietal control networks. In addition, it has recently been shown that the ability to imagine the future is associated with cognitive flexibility, and that tasks requiring cognitive flexibility result in increased coupling of the default mode network with frontoparietal control and salience networks. In the current study, we investigated the neural correlates underlying the association between cognitive flexibility and future imagination in two ways. First, we experimentally varied the degree of cognitive flexibility required during future imagination by manipulating the disparateness of episodic details contributing to imagined events. To this end, participants generated episodic details (persons, locations, objects) within three social spheres; during fMRI scanning they were presented with sets of three episodic details all taken from the same social sphere (Congruent condition) or different social spheres (Incongruent condition) and required to imagine a future event involving the three details. We predicted that, relative to the Congruent condition, future simulation in the Incongruent condition would be associated with increased activity in regions of the default mode, frontoparietal and salience networks. Second, we hypothesized that individual differences in cognitive flexibility, as measured by performance on the Alternate Uses Task, would correspond to individual differences in the brain regions recruited during future imagination. A task partial least squares (PLS) analysis showed that the Incongruent condition resulted in an increase in activity in regions in salience networks (e.g. the insula) but, contrary to our prediction, reduced activity in many regions of the default mode network (including the hippocampus). A subsequent functional connectivity (within-subject seed PLS) analysis showed that the insula exhibited increased coupling with default mode regions during the Incongruent condition. Finally, a behavioral PLS analysis showed that individual differences in cognitive flexibility were associated with differences in activity in a number of regions from frontoparietal, salience and default-mode networks during both future imagination conditions, further highlighting that the cognitive flexibility underlying future imagination is grounded in the complex interaction of regions in these networks. PMID:27908591

Systematic Conservation Planning in the Face of Climate Change: Bet-Hedging on the Columbia Plateau

PubMed Central

Schloss, Carrie A.; Lawler, Joshua J.; Larson, Eric R.; Papendick, Hilary L.; Case, Michael J.; Evans, Daniel M.; DeLap, Jack H.; Langdon, Jesse G. R.; Hall, Sonia A.; McRae, Brad H.

2011-01-01

Systematic conservation planning efforts typically focus on protecting current patterns of biodiversity. Climate change is poised to shift species distributions, reshuffle communities, and alter ecosystem functioning. In such a dynamic environment, lands selected to protect today's biodiversity may fail to do so in the future. One proposed approach to designing reserve networks that are robust to climate change involves protecting the diversity of abiotic conditions that in part determine species distributions and ecological processes. A set of abiotically diverse areas will likely support a diversity of ecological systems both today and into the future, although those two sets of systems might be dramatically different. Here, we demonstrate a conservation planning approach based on representing unique combinations of abiotic factors. We prioritize sites that represent the diversity of soils, topographies, and current climates of the Columbia Plateau. We then compare these sites to sites prioritized to protect current biodiversity. This comparison highlights places that are important for protecting both today's biodiversity and the diversity of abiotic factors that will likely determine biodiversity patterns in the future. It also highlights places where a reserve network designed solely to protect today's biodiversity would fail to capture the diversity of abiotic conditions and where such a network could be augmented to be more robust to climate-change impacts. PMID:22174897

Nonequilibrium dynamics of probe filaments in actin-myosin networks

NASA Astrophysics Data System (ADS)

Gladrow, J.; Broedersz, C. P.; Schmidt, C. F.

2017-08-01

Active dynamic processes of cells are largely driven by the cytoskeleton, a complex and adaptable semiflexible polymer network, motorized by mechanoenzymes. Small dimensions, confined geometries, and hierarchical structures make it challenging to probe dynamics and mechanical response of such networks. Embedded semiflexible probe polymers can serve as nonperturbing multiscale probes to detect force distributions in active polymer networks. We show here that motor-induced forces transmitted to the probe polymers are reflected in nonequilibrium bending dynamics, which we analyze in terms of spatial eigenmodes of an elastic beam under steady-state conditions. We demonstrate how these active forces induce correlations among the mode amplitudes, which furthermore break time-reversal symmetry. This leads to a breaking of detailed balance in this mode space. We derive analytical predictions for the magnitude of resulting probability currents in mode space in the white-noise limit of motor activity. We relate the structure of these currents to the spatial profile of motor-induced forces along the probe polymers and provide a general relation for observable currents on two-dimensional hyperplanes.

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT.

PubMed

Lopez-Martin, Manuel; Carro, Belen; Sanchez-Esguevillas, Antonio; Lloret, Jaime

2017-08-26

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

PubMed Central

Carro, Belen; Sanchez-Esguevillas, Antonio

2017-01-01

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery. PMID:28846608

Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage

DOE Data Explorer

Maddalena, Damian; Hoffman, Forrest; Kumar, Jitendra; Hargrove, William

2014-08-01

Sampling networks rarely conform to spatial and temporal ideals, often comprised of network sampling points which are unevenly distributed and located in less than ideal locations due to access constraints, budget limitations, or political conflict. Quantifying the global, regional, and temporal representativeness of these networks by quantifying the coverage of network infrastructure highlights the capabilities and limitations of the data collected, facilitates upscaling and downscaling for modeling purposes, and improves the planning efforts for future infrastructure investment under current conditions and future modeled scenarios. The work presented here utilizes multivariate spatiotemporal clustering analysis and representativeness analysis for quantitative landscape characterization and assessment of the Fluxnet, RAINFOR, and ForestGEO networks. Results include ecoregions that highlight patterns of bioclimatic, topographic, and edaphic variables and quantitative representativeness maps of individual and combined networks.

Optimization of return electrodes in neurostimulating arrays

NASA Astrophysics Data System (ADS)

Flores, Thomas; Goetz, Georges; Lei, Xin; Palanker, Daniel

2016-06-01

Objective. High resolution visual prostheses require dense stimulating arrays with localized inputs of individual electrodes. We study the electric field produced by multielectrode arrays in electrolyte to determine an optimal configuration of return electrodes and activation sequence. Approach. To determine the boundary conditions for computation of the electric field in electrolyte, we assessed current dynamics using an equivalent circuit of a multielectrode array with interleaved return electrodes. The electric field modeled with two different boundary conditions derived from the equivalent circuit was then compared to measurements of electric potential in electrolyte. To assess the effect of return electrode configuration on retinal stimulation, we transformed the computed electric fields into retinal response using a model of neural network-mediated stimulation. Main results. Electric currents at the capacitive electrode-electrolyte interface redistribute over time, so that boundary conditions transition from equipotential surfaces at the beginning of the pulse to uniform current density in steady state. Experimental measurements confirmed that, in steady state, the boundary condition corresponds to a uniform current density on electrode surfaces. Arrays with local return electrodes exhibit improved field confinement and can elicit stronger network-mediated retinal response compared to those with a common remote return. Connecting local return electrodes enhances the field penetration depth and allows reducing the return electrode area. Sequential activation of the pixels in large monopolar arrays reduces electrical cross-talk and improves the contrast in pattern stimulation. Significance. Accurate modeling of multielectrode arrays helps optimize the electrode configuration to maximize the spatial resolution, contrast and dynamic range of retinal prostheses.

Fault-tolerant battery system employing intra-battery network architecture

DOEpatents

Hagen, Ronald A.; Chen, Kenneth W.; Comte, Christophe; Knudson, Orlin B.; Rouillard, Jean

2000-01-01

A distributed energy storing system employing a communications network is disclosed. A distributed battery system includes a number of energy storing modules, each of which includes a processor and communications interface. In a network mode of operation, a battery computer communicates with each of the module processors over an intra-battery network and cooperates with individual module processors to coordinate module monitoring and control operations. The battery computer monitors a number of battery and module conditions, including the potential and current state of the battery and individual modules, and the conditions of the battery's thermal management system. An over-discharge protection system, equalization adjustment system, and communications system are also controlled by the battery computer. The battery computer logs and reports various status data on battery level conditions which may be reported to a separate system platform computer. A module transitions to a stand-alone mode of operation if the module detects an absence of communication connectivity with the battery computer. A module which operates in a stand-alone mode performs various monitoring and control functions locally within the module to ensure safe and continued operation.

Interface Control Document for the EMPACT Module that Estimates Electric Power Transmission System Response to EMP-Caused Damage

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

Werley, Kenneth Alan; Mccown, Andrew William

The EPREP code is designed to evaluate the effects of an Electro-Magnetic Pulse (EMP) on the electric power transmission system. The EPREP code embodies an umbrella framework that allows a user to set up analysis conditions and to examine analysis results. The code links to three major physics/engineering modules. The first module describes the EM wave in space and time. The second module evaluates the damage caused by the wave on specific electric power (EP) transmission system components. The third module evaluates the consequence of the damaged network on its (reduced) ability to provide electric power to meet demand. Thismore » third module is the focus of the present paper. The EMPACT code serves as the third module. The EMPACT name denotes EMP effects on Alternating Current Transmission systems. The EMPACT algorithms compute electric power transmission network flow solutions under severely damaged network conditions. Initial solutions are often characterized by unacceptible network conditions including line overloads and bad voltages. The EMPACT code contains algorithms to adjust optimally network parameters to eliminate network problems while minimizing outages. System adjustments include automatically adjusting control equipment (generator V control, variable transformers, and variable shunts), as well as non-automatic control of generator power settings and minimal load shedding. The goal is to evaluate the minimal loss of customer load under equilibrium (steady-state) conditions during peak demand.« less

DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS

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

McDermott, Jason E.; Costa, Michelle N.; Stevens, S.L.

A difficult problem that is currently growing rapidly due to the sharp increase in the amount of high-throughput data available for many systems is that of determining useful and informative causative influence networks. These networks can be used to predict behavior given observation of a small number of components, predict behavior at a future time point, or identify components that are critical to the functioning of the system under particular conditions. In these endeavors incorporating observations of systems from a wide variety of viewpoints can be particularly beneficial, but has often been undertaken with the objective of inferring networks thatmore » are generally applicable. The focus of the current work is to integrate both general observations and measurements taken for a particular pathology, that of ischemic stroke, to provide improved ability to produce useful predictions of systems behavior. A number of hybrid approaches have recently been proposed for network generation in which the Gene Ontology is used to filter or enrich network links inferred from gene expression data through reverse engineering methods. These approaches have been shown to improve the biological plausibility of the inferred relationships determined, but still treat knowledge-based and machine-learning inferences as incommensurable inputs. In this paper, we explore how further improvements may be achieved through a full integration of network inference insights achieved through application of the Gene Ontology and reverse engineering methods with specific reference to the construction of dynamic models of transcriptional regulatory networks. We show that integrating two approaches to network construction, one based on reverse-engineering from conditional transcriptional data, one based on reverse-engineering from in situ hybridization data, and another based on functional associations derived from Gene Ontology, using probabilities can improve results of clustering as evaluated by a predictive model of transcriptional expression levels.« less

How to Trigger Emergence and Self-Organisation in Learning Networks

NASA Astrophysics Data System (ADS)

Brouns, Francis; Fetter, Sibren; van Rosmalen, Peter

The previous chapters of this section discussed why the social structure of Learning Networks is important and present guidelines on how to maintain and allow the emergence of communities in Learning Networks. Chapter 2 explains how Learning Networks rely on social interaction and active participations of the participants. Chapter 3 then continues by presenting guidelines and policies that should be incorporated into Learning Network Services in order to maintain existing communities by creating conditions that promote social interaction and knowledge sharing. Chapter 4 discusses the necessary conditions required for knowledge sharing to occur and to trigger communities to self-organise and emerge. As pointed out in Chap. 4, ad-hoc transient communities facilitate the emergence of social interaction in Learning Networks, self-organising them into communities, taking into account personal characteristics, community characteristics and general guidelines. As explained in Chap. 4 community members would benefit from a service that brings suitable people together for a specific purpose, because it will allow the participant to focus on the knowledge sharing process by reducing the effort or costs. In the current chapter, we describe an example of a peer support Learning Network Service based on the mechanism of peer tutoring in ad-hoc transient communities.

The 2014-2015 warming anomaly in the Southern California Current System observed by underwater gliders

NASA Astrophysics Data System (ADS)

Zaba, Katherine D.; Rudnick, Daniel L.

2016-02-01

Large-scale patterns of positive temperature anomalies persisted throughout the surface waters of the North Pacific Ocean during 2014-2015. In the Southern California Current System, measurements by our sustained network of underwater gliders reveal the coastal effects of the recent warming. Regional upper ocean temperature anomalies were greatest since the initiation of the glider network in 2006. Additional observed physical anomalies included a depressed thermocline, high stratification, and freshening; induced biological consequences included changes in the vertical distribution of chlorophyll fluorescence. Contemporaneous surface heat flux and wind strength perturbations suggest that local anomalous atmospheric forcing caused the unusual oceanic conditions.

Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks.

PubMed

Ruppin, Eytan; Papin, Jason A; de Figueiredo, Luis F; Schuster, Stefan

2010-08-01

With the advent of modern omics technologies, it has become feasible to reconstruct (quasi-) whole-cell metabolic networks and characterize them in more and more detail. Computer simulations of the dynamic behavior of such networks are difficult due to a lack of kinetic data and to computational limitations. In contrast, network analysis based on appropriate constraints such as the steady-state condition (constraint-based analysis) is feasible and allows one to derive conclusions about the system's metabolic capabilities. Here, we review methods for the reconstruction of metabolic networks, modeling techniques such as flux balance analysis and elementary flux modes and current progress in their development and applications. Game-theoretical methods for studying metabolic networks are discussed as well. Copyright © 2010 Elsevier Ltd. All rights reserved.

The Treatment of Assets and Income From Assets in Income-Conditioned Government Benefit Programs; Technical Papers.

ERIC Educational Resources Information Center

Wisconsin Univ., Madison. Inst. for Research on Poverty.

These technical papers were presented at the Conference on Treatment of Assets and Income from Assets in Income Conditioned Programs. Six papers dealing with current and alternative treatments of assets and income from assets in programs that offer both cash and noncash benefits were presented. The first three authors reviewed the network of rules…

Laboratory Evaluation of Dynamic Traffic Assignment Systems: Requirements, Framework, and System Design

DOT National Transportation Integrated Search

1997-01-01

The success of Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) depends on the availability and dissemination of timely and accurate estimates of current and emerging traffic network conditions. Real-time Dy...

Molecular communication and networking: opportunities and challenges.

PubMed

Nakano, Tadashi; Moore, Michael J; Wei, Fang; Vasilakos, Athanasios V; Shuai, Jianwei

2012-06-01

The ability of engineered biological nanomachines to communicate with biological systems at the molecular level is anticipated to enable future applications such as monitoring the condition of a human body, regenerating biological tissues and organs, and interfacing artificial devices with neural systems. From the viewpoint of communication theory and engineering, molecular communication is proposed as a new paradigm for engineered biological nanomachines to communicate with the natural biological nanomachines which form a biological system. Distinct from the current telecommunication paradigm, molecular communication uses molecules as the carriers of information; sender biological nanomachines encode information on molecules and release the molecules in the environment, the molecules then propagate in the environment to receiver biological nanomachines, and the receiver biological nanomachines biochemically react with the molecules to decode information. Current molecular communication research is limited to small-scale networks of several biological nanomachines. Key challenges to bridge the gap between current research and practical applications include developing robust and scalable techniques to create a functional network from a large number of biological nanomachines. Developing networking mechanisms and communication protocols is anticipated to introduce new avenues into integrating engineered and natural biological nanomachines into a single networked system. In this paper, we present the state-of-the-art in the area of molecular communication by discussing its architecture, features, applications, design, engineering, and physical modeling. We then discuss challenges and opportunities in developing networking mechanisms and communication protocols to create a network from a large number of bio-nanomachines for future applications.

Real-time network security situation visualization and threat assessment based on semi-Markov process

NASA Astrophysics Data System (ADS)

Chen, Junhua

2013-03-01

To cope with a large amount of data in current sensed environments, decision aid tools should provide their understanding of situations in a time-efficient manner, so there is an increasing need for real-time network security situation awareness and threat assessment. In this study, the state transition model of vulnerability in the network based on semi-Markov process is proposed at first. Once events are triggered by an attacker's action or system response, the current states of the vulnerabilities are known. Then we calculate the transition probabilities of the vulnerability from the current state to security failure state. Furthermore in order to improve accuracy of our algorithms, we adjust the probabilities that they exploit the vulnerability according to the attacker's skill level. In the light of the preconditions and post-conditions of vulnerabilities in the network, attack graph is built to visualize security situation in real time. Subsequently, we predict attack path, recognize attack intention and estimate the impact through analysis of attack graph. These help administrators to insight into intrusion steps, determine security state and assess threat. Finally testing in a network shows that this method is reasonable and feasible, and can undertake tremendous analysis task to facilitate administrators' work.

Network discovery with DCM

PubMed Central

Friston, Karl J.; Li, Baojuan; Daunizeau, Jean; Stephan, Klaas E.

2011-01-01

This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity. This network discovery uses Bayesian model selection to identify the sparsity structure (absence of edges or connections) in a graph that best explains observed time-series. The implicit adjacency matrix specifies the form of the network (e.g., cyclic or acyclic) and its graph-theoretical attributes (e.g., degree distribution). The scheme is illustrated using functional magnetic resonance imaging (fMRI) time series to discover functional brain networks. Crucially, it can be applied to experimentally evoked responses (activation studies) or endogenous activity in task-free (resting state) fMRI studies. Unlike conventional approaches to network discovery, DCM permits the analysis of directed and cyclic graphs. Furthermore, it eschews (implausible) Markovian assumptions about the serial independence of random fluctuations. The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks. The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions. We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies. PMID:21182971

Condition monitoring of 3G cellular networks through competitive neural models.

PubMed

Barreto, Guilherme A; Mota, João C M; Souza, Luis G M; Frota, Rewbenio A; Aguayo, Leonardo

2005-09-01

We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms [winner-take-all (WTA), frequency-sensitive competitive learning (FSCL), self-organizing map (SOM), and neural-gas algorithm (NGA)] and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods.

Suppressing epidemic spreading by risk-averse migration in dynamical networks

NASA Astrophysics Data System (ADS)

Yang, Han-Xin; Tang, Ming; Wang, Zhen

2018-01-01

In this paper, we study the interplay between individual behaviors and epidemic spreading in a dynamical network. We distribute agents on a square-shaped region with periodic boundary conditions. Every agent is regarded as a node of the network and a wireless link is established between two agents if their geographical distance is less than a certain radius. At each time, every agent assesses the epidemic situation and make decisions on whether it should stay in or leave its current place. An agent will leave its current place with a speed if the number of infected neighbors reaches or exceeds a critical value E. Owing to the movement of agents, the network's structure is dynamical. Interestingly, we find that there exists an optimal value of E leading to the maximum epidemic threshold. This means that epidemic spreading can be effectively controlled by risk-averse migration. Besides, we find that the epidemic threshold increases as the recovering rate increases, decreases as the contact radius increases, and is maximized by an optimal moving speed. Our findings offer a deeper understanding of epidemic spreading in dynamical networks.

Bilateral Transcranial Direct Current Stimulation Reshapes Resting-State Brain Networks: A Magnetoencephalography Assessment

PubMed Central

Turco, Cristina; Di Pino, Giovanni; Arcara, Giorgio

2018-01-01

Transcranial direct current stimulation (tDCS) can noninvasively induce brain plasticity, and it is potentially useful to treat patients affected by neurological conditions. However, little is known about tDCS effects on resting-state brain networks, which are largely involved in brain physiological functions and in diseases. In this randomized, sham-controlled, double-blind study on healthy subjects, we have assessed the effect of bilateral tDCS applied over the sensorimotor cortices on brain and network activity using a whole-head magnetoencephalography system. Bilateral tDCS, with the cathode (−) centered over C4 and the anode (+) centered over C3, reshapes brain networks in a nonfocal fashion. Compared to sham stimulation, tDCS reduces left frontal alpha, beta, and gamma power and increases global connectivity, especially in delta, alpha, beta, and gamma frequencies. The increase of connectivity is consistent across bands and widespread. These results shed new light on the effects of tDCS and may be of help in personalizing treatments in neurological disorders. PMID:29593782

Analysis of Online DBA Algorithm with Adaptive Sleep Cycle in WDM EPON

NASA Astrophysics Data System (ADS)

Pajčin, Bojan; Matavulj, Petar; Radivojević, Mirjana

2018-05-01

In order to manage Quality of Service (QoS) and energy efficiency in the optical access network, an online Dynamic Bandwidth Allocation (DBA) algorithm with adaptive sleep cycle is presented. This DBA algorithm has the ability to allocate an additional bandwidth to the end user within a single sleep cycle whose duration changes depending on the current buffers occupancy. The purpose of this DBA algorithm is to tune the duration of the sleep cycle depending on the network load in order to provide service to the end user without violating strict QoS requests in all network operating conditions.

A network thermodynamic method for numerical solution of the Nernst-Planck and Poisson equation system with application to ionic transport through membranes.

PubMed

Horno, J; González-Caballero, F; González-Fernández, C F

1990-01-01

Simple techniques of network thermodynamics are used to obtain the numerical solution of the Nernst-Planck and Poisson equation system. A network model for a particular physical situation, namely ionic transport through a thin membrane with simultaneous diffusion, convection and electric current, is proposed. Concentration and electric field profiles across the membrane, as well as diffusion potential, have been simulated using the electric circuit simulation program, SPICE. The method is quite general and extremely efficient, permitting treatments of multi-ion systems whatever the boundary and experimental conditions may be.

Resiliently evolving supply-demand networks

NASA Astrophysics Data System (ADS)

Rubido, Nicolás; Grebogi, Celso; Baptista, Murilo S.

2014-01-01

The ability to design a transport network such that commodities are brought from suppliers to consumers in a steady, optimal, and stable way is of great importance for distribution systems nowadays. In this work, by using the circuit laws of Kirchhoff and Ohm, we provide the exact capacities of the edges that an optimal supply-demand network should have to operate stably under perturbations, i.e., without overloading. The perturbations we consider are the evolution of the connecting topology, the decentralization of hub sources or sinks, and the intermittence of supplier and consumer characteristics. We analyze these conditions and the impact of our results, both on the current United Kingdom power-grid structure and on numerically generated evolving archetypal network topologies.

Project ECHO: A Telementoring Network Model for Continuing Professional Development.

PubMed

Arora, Sanjeev; Kalishman, Summers G; Thornton, Karla A; Komaromy, Miriam S; Katzman, Joanna G; Struminger, Bruce B; Rayburn, William F

2017-01-01

A major challenge with current systems of CME is the inability to translate the explosive growth in health care knowledge into daily practice. Project ECHO (Extension for Community Healthcare Outcomes) is a telementoring network designed for continuing professional development (CPD) and improving patient outcomes. The purpose of this article was to describe how the model has complied with recommendations from several authoritative reports about redesigning and enhancing CPD. This model links primary care clinicians through a knowledge network with an interprofessional team of specialists from an academic medical center who provide telementoring and ongoing education enabling community clinicians to treat patients with a variety of complex conditions. Knowledge and skills are shared during weekly condition-specific videoconferences. The model exemplifies learning as described in the seven levels of CPD by Moore (participation, satisfaction, learning, competence, performance, patient, and community health). The model is also aligned with recommendations from four national reports intended to redesign knowledge transfer in improving health care. Efforts in learning sessions focus on information that is relevant to practice, focus on evidence, education methodology, tailoring of recommendations to individual needs and community resources, and interprofessionalism. Project ECHO serves as a telementoring network model of CPD that aligns with current best practice recommendations for CME. This transformative initiative has the potential to serve as a leading model for larger scale CPD, nationally and globally, to enhance access to care, improve quality, and reduce cost.

Topics on data transmission problem in software definition network

NASA Astrophysics Data System (ADS)

Gao, Wei; Liang, Li; Xu, Tianwei; Gan, Jianhou

2017-08-01

In normal computer networks, the data transmission between two sites go through the shortest path between two corresponding vertices. However, in the setting of software definition network (SDN), it should monitor the network traffic flow in each site and channel timely, and the data transmission path between two sites in SDN should consider the congestion in current networks. Hence, the difference of available data transmission theory between normal computer network and software definition network is that we should consider the prohibit graph structures in SDN, and these forbidden subgraphs represent the sites and channels in which data can't be passed by the serious congestion. Inspired by theoretical analysis of an available data transmission in SDN, we consider some computational problems from the perspective of the graph theory. Several results determined in the paper imply the sufficient conditions of data transmission in SDN in the various graph settings.

Thinking outside the channel: Modeling nitrogen cycling in networked river ecosystems

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

Helton, Ashley; Poole, Geoffrey C.; Meyer, Judy

2011-01-01

Agricultural and urban development alters nitrogen and other biogeochemical cycles in rivers worldwide. Because such biogeochemical processes cannot be measured empirically across whole river networks, simulation models are critical tools for understanding river-network biogeochemistry. However, limitations inherent in current models restrict our ability to simulate biogeochemical dynamics among diverse river networks. We illustrate these limitations using a river-network model to scale up in situ measures of nitrogen cycling in eight catchments spanning various geophysical and land-use conditions. Our model results provide evidence that catchment characteristics typically excluded from models may control river-network biogeochemistry. Based on our findings, we identify importantmore » components of a revised strategy for simulating biogeochemical dynamics in river networks, including approaches to modeling terrestrial-aquatic linkages, hydrologic exchanges between the channel, floodplain/riparian complex, and subsurface waters, and interactions between coupled biogeochemical cycles.« less

Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses.

PubMed

Zhang, Wei; Huang, Tingwen; He, Xing; Li, Chuandong

2017-11-01

In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction based active ramp metering control strategy with mobility and safety assessment

NASA Astrophysics Data System (ADS)

Fang, Jie; Tu, Lili

2018-04-01

Ramp metering is one of the most direct and efficient motorway traffic flow management measures so as to improve traffic conditions. However, owing to short of traffic conditions prediction, in earlier studies, the impact on traffic flow dynamics of the applied RM control was not quantitatively evaluated. In this study, a RM control algorithm adopting Model Predictive Control (MPC) framework to predict and assess future traffic conditions, which taking both the current traffic conditions and the RM-controlled future traffic states into consideration, was presented. The designed RM control algorithm targets at optimizing the network mobility and safety performance. The designed algorithm is evaluated in a field-data-based simulation. Through comparing the presented algorithm controlled scenario with the uncontrolled scenario, it was proved that the proposed RM control algorithm can effectively relieve the congestion of traffic network with no significant compromises in safety aspect.

A permutation testing framework to compare groups of brain networks.

PubMed

Simpson, Sean L; Lyday, Robert G; Hayasaka, Satoru; Marsh, Anthony P; Laurienti, Paul J

2013-01-01

Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Current comparison approaches generally either rely on a summary metric or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network, yielding little power and failing to make network level comparisons. Gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Here we propose a permutation testing framework that allows comparing groups of networks while incorporating topological features inherent in each individual network. We validate our approach using simulated data with known group differences. We then apply the method to functional brain networks derived from fMRI data.

An Optimal Schedule for Urban Road Network Repair Based on the Greedy Algorithm

PubMed Central

Lu, Guangquan; Xiong, Ying; Wang, Yunpeng

2016-01-01

The schedule of urban road network recovery caused by rainstorms, snow, and other bad weather conditions, traffic incidents, and other daily events is essential. However, limited studies have been conducted to investigate this problem. We fill this research gap by proposing an optimal schedule for urban road network repair with limited repair resources based on the greedy algorithm. Critical links will be given priority in repair according to the basic concept of the greedy algorithm. In this study, the link whose restoration produces the ratio of the system-wide travel time of the current network to the worst network is the minimum. We define such a link as the critical link for the current network. We will re-evaluate the importance of damaged links after each repair process is completed. That is, the critical link ranking will be changed along with the repair process because of the interaction among links. We repair the most critical link for the specific network state based on the greedy algorithm to obtain the optimal schedule. The algorithm can still quickly obtain an optimal schedule even if the scale of the road network is large because the greedy algorithm can reduce computational complexity. We prove that the problem can obtain the optimal solution using the greedy algorithm in theory. The algorithm is also demonstrated in the Sioux Falls network. The problem discussed in this paper is highly significant in dealing with urban road network restoration. PMID:27768732

CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data.

PubMed

Zheng, Guangyong; Xu, Yaochen; Zhang, Xiujun; Liu, Zhi-Ping; Wang, Zhuo; Chen, Luonan; Zhu, Xin-Guang

2016-12-23

A gene regulatory network (GRN) represents interactions of genes inside a cell or tissue, in which vertexes and edges stand for genes and their regulatory interactions respectively. Reconstruction of gene regulatory networks, in particular, genome-scale networks, is essential for comparative exploration of different species and mechanistic investigation of biological processes. Currently, most of network inference methods are computationally intensive, which are usually effective for small-scale tasks (e.g., networks with a few hundred genes), but are difficult to construct GRNs at genome-scale. Here, we present a software package for gene regulatory network reconstruction at a genomic level, in which gene interaction is measured by the conditional mutual information measurement using a parallel computing framework (so the package is named CMIP). The package is a greatly improved implementation of our previous PCA-CMI algorithm. In CMIP, we provide not only an automatic threshold determination method but also an effective parallel computing framework for network inference. Performance tests on benchmark datasets show that the accuracy of CMIP is comparable to most current network inference methods. Moreover, running tests on synthetic datasets demonstrate that CMIP can handle large datasets especially genome-wide datasets within an acceptable time period. In addition, successful application on a real genomic dataset confirms its practical applicability of the package. This new software package provides a powerful tool for genomic network reconstruction to biological community. The software can be accessed at http://www.picb.ac.cn/CMIP/ .

Selection of Multiarmed Spiral Waves in a Regular Network of Neurons

PubMed Central

Hu, Bolin; Ma, Jun; Tang, Jun

2013-01-01

Formation and selection of multiarmed spiral wave due to spontaneous symmetry breaking are investigated in a regular network of Hodgkin-Huxley neuron by changing the excitability and imposing spatial forcing currents on the neurons in the network. The arm number of the multiarmed spiral wave is dependent on the distribution of spatial forcing currents and excitability diversity in the network, and the selection criterion for supporting multiarmed spiral waves is discussed. A broken spiral segment is measured by a short polygonal line connected by three adjacent points (controlled nodes), and a double-spiral wave can be developed from the spiral segment. Multiarmed spiral wave is formed when a group of double-spiral waves rotate in the same direction in the network. In the numerical studies, a group of controlled nodes are selected and spatial forcing currents are imposed on these nodes, and our results show that l-arm stable spiral wave (l = 2, 3, 4,...8) can be induced to occupy the network completely. It is also confirmed that low excitability is critical to induce multiarmed spiral waves while high excitability is important to propagate the multiarmed spiral wave outside so that distinct multiarmed spiral wave can occupy the network completely. Our results confirm that symmetry breaking of target wave in the media accounts for emergence of multiarmed spiral wave, which can be developed from a group of spiral waves with single arm under appropriate condition, thus the potential formation mechanism of multiarmed spiral wave in the media is explained. PMID:23935966

On the continuous differentiability of inter-spike intervals of synaptically connected cortical spiking neurons in a neuronal network.

PubMed

Kumar, Gautam; Kothare, Mayuresh V

2013-12-01

We derive conditions for continuous differentiability of inter-spike intervals (ISIs) of spiking neurons with respect to parameters (decision variables) of an external stimulating input current that drives a recurrent network of synaptically connected neurons. The dynamical behavior of individual neurons is represented by a class of discontinuous single-neuron models. We report here that ISIs of neurons in the network are continuously differentiable with respect to decision variables if (1) a continuously differentiable trajectory of the membrane potential exists between consecutive action potentials with respect to time and decision variables and (2) the partial derivative of the membrane potential of spiking neurons with respect to time is not equal to the partial derivative of their firing threshold with respect to time at the time of action potentials. Our theoretical results are supported by showing fulfillment of these conditions for a class of known bidimensional spiking neuron models.

Evaluating the Connectivity of a Protected Areas' Network under the Prism of Global Change: The Efficiency of the European Natura 2000 Network for Four Birds of Prey

PubMed Central

Mazaris, Antonios D.; Papanikolaou, Alexandra D.; Barbet-Massin, Morgane; Kallimanis, Athanasios S.; Jiguet, Frédéric; Schmeller, Dirk S.; Pantis, John D.

2013-01-01

Climate and land use changes are major threats to biodiversity. To preserve biodiversity, networks of protected areas have been established worldwide, like the Natura 2000 network across the European Union (EU). Currently, this reserve network consists of more than 26000 sites covering more than 17% of EU terrestrial territory. Its efficiency to mitigate the detrimental effects of land use and climate change remains an open research question. Here, we examined the potential current and future geographical ranges of four birds of prey under scenarios of both land use and climate changes. By using graph theory, we examined how the current Natura 2000 network will perform in regard to the conservation of these species. This approach determines the importance of a site in regard to the total network and its connectivity. We found that sites becoming unsuitable due to climate change are not a random sample of the network, but are less connected and contribute less to the overall connectivity than the average site and thus their loss does not disrupt the full network. Hence, the connectivity of the remaining network changed only slightly from present day conditions. Our findings highlight the need to establish species-specific management plans with flexible conservation strategies ensuring protection under potential future range expansions. Aquila pomarina is predicted to disappear from the southern part of its range and to become restricted to northeastern Europe. Gyps fulvus, Aquila chrysaetos, and Neophron percnopterus are predicted to locally lose some suitable sites; hence, some isolated small populations may become extinct. However, their geographical range and metapopulation structure will remain relatively unaffected throughout Europe. These species would benefit more from an improved habitat quality and management of the existing network of protected areas than from increased connectivity or assisted migration. PMID:23527237

Common solutions for power, communication and robustness in operations of large measurement networks within Research Infrastructures

NASA Astrophysics Data System (ADS)

Huber, Robert; Beranzoli, Laura; Fiebig, Markus; Gilbert, Olivier; Laj, Paolo; Mazzola, Mauro; Paris, Jean-Daniel; Pedersen, Helle; Stocker, Markus; Vitale, Vito; Waldmann, Christoph

2017-04-01

European Environmental Research Infrastructures (RI) frequently comprise in situ observatories from large-scale networks of platforms or sites to local networks of various sensors. Network operation is usually a cumbersome aspect of these RIs facing specific technological problems related to operations in remote areas, maintenance of the network, transmission of observation values, etc.. Robust inter-connection within and across these networks is still at infancy level and the burden increases with remoteness of the station, harshness of environmental conditions, and unavailability of classic communication systems, which is a common feature here. Despite existing RIs having developed ad-hoc solutions to overcome specific problems and innovative technologies becoming available, no common approach yet exists. Within the European project ENVRIplus, a dedicated work package aims to stimulate common network operation technologies and approaches in terms of power supply and storage, robustness, and data transmission. Major objectives of this task are to review existing technologies and RI requirements, propose innovative solutions and evaluate the standardization potential prior to wider deployment across networks. Focus areas within these efforts are: improving energy production and storage units, testing robustness of RI equipment towards extreme conditions as well as methodologies for robust data transmission. We will introduce current project activities which are coordinated at various levels including the engineering as well as the data management perspective, and explain how environmental RIs can benefit from the developments.

Advanced road scene image segmentation and pavement evaluation using neural networks.

DOT National Transportation Integrated Search

2010-01-01

The current project, funded by MIOH-UTC for the period 9/1/2009-8/31/2010, continues our : efforts in designing an image processing based pavement inspection system for the : assessment of highway surface conditions. One of the most important tasks i...

Assessment of Scanning Tunneling Spectroscopy Modes Inspecting Electron Confinement in Surface-Confined Supramolecular Networks

PubMed Central

Krenner, Wolfgang; Kühne, Dirk; Klappenberger, Florian; Barth, Johannes V.

2013-01-01

Scanning tunneling spectroscopy (STS) enables the local, energy-resolved investigation of a samples surface density of states (DOS) by measuring the differential conductance (dI/dV) being approximately proportional to the DOS. It is popular to examine the electronic structure of elementary samples by acquiring dI/dV maps under constant current conditions. Here we demonstrate the intricacy of STS mapping of samples exhibiting a strong corrugation originating from electronic density and local work function changes. The confinement of the Ag(111) surface state by a porous organic network is studied with maps obtained under constant-current (CC) as well as open-feedback-loop (OFL) conditions. We show how the CC maps deviate markedly from the physically more meaningful OFL maps. By applying a renormalization procedure to the OFL data we can mimic the spurious effects of the CC mode and thereby rationalize the physical effects evoking the artefacts in the CC maps. PMID:23503526

Evaluation of the streamflow-gaging network of Texas and a proposed core network

USGS Publications Warehouse

Slade, Raymond M.; Howard, Teresa; Anaya, Roberto

2001-01-01

The U.S. Geological Survey streamflowgaging network in Texas is operated as part of the National Streamgaging Program and is jointly funded by the Geological Survey and Federal, State, and local agencies. This report documents an evaluation of the existing (as of October 1, 1999) network with regard to four major objectives of streamflow data; and on the basis of that evaluation, proposes a core network of streamflowgaging stations that best meets those objectives. The objectives are (1) regionalization (estimate flows or flow characteristics at ungaged sites in 11 hydrologically similar regions), (2) major flow (obtain flow rates and volumes in large streams), (3) outflow from the State (account for streamflow leaving the State), and (4) streamflow conditions assessment (assess current conditions with regard to long-term data, and define temporal trends in flow). The network analysis resulted in a proposed core network of 263 stations. Of those 263 stations, 43 were discontinued as of October 1, 1999, and 15 were partial-record stations. Fifty-five of the proposed core-network stations meet two of the four major objectives, 16 stations meet three objectives, and 1 station meets all four. One-hundred eighty-five stations with a median record length of 33 years were selected to meet the regionalization objective. Ninety-two stations with a median record length of about 62 years were selected to meet the major-flow objective. Twenty-six stations with a median record length of 59 years were selected to meet the outflow from the State objective. Fifty stations with a median record length of 53 years were selected to meet the streamflow conditions assessment objective.

GENIUS: web server to predict local gene networks and key genes for biological functions.

PubMed

Puelma, Tomas; Araus, Viviana; Canales, Javier; Vidal, Elena A; Cabello, Juan M; Soto, Alvaro; Gutiérrez, Rodrigo A

2017-03-01

GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods. GENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius . genius.psbl@gmail.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Networks within networks: The neuronal control of breathing

PubMed Central

Garcia, Alfredo J.; Zanella, Sebastien; Koch, Henner; Doi, Atsushi; Ramirez, Jan-Marino

2013-01-01

Breathing emerges through complex network interactions involving neurons distributed throughout the nervous system. The respiratory rhythm generating network is composed of micro networks functioning within larger networks to generate distinct rhythms and patterns that characterize breathing. The pre-Bötzinger complex, a rhythm generating network located within the ventrolateral medulla assumes a core function without which respiratory rhythm generation and breathing cease altogether. It contains subnetworks with distinct synaptic and intrinsic membrane properties that give rise to different types of respiratory rhythmic activities including eupneic, sigh, and gasping activities. While critical aspects of these rhythmic activities are preserved when isolated in in vitro preparations, the pre-Bötzinger complex functions in the behaving animal as part of a larger network that receives important inputs from areas such as the pons and parafacial nucleus. The respiratory network is also an integrator of modulatory and sensory inputs that imbue the network with the important ability to adapt to changes in the behavioral, metabolic, and developmental conditions of the organism. This review summarizes our current understanding of these interactions and relates the emerging concepts to insights gained in other rhythm generating networks. PMID:21333801

Controllability and observability of Boolean networks arising from biology

NASA Astrophysics Data System (ADS)

Li, Rui; Yang, Meng; Chu, Tianguang

2015-02-01

Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

Normal streamflows and water levels continue—Summary of hydrologic conditions in Georgia, 2014

USGS Publications Warehouse

Knaak, Andrew E.; Ankcorn, Paul D.; Peck, Michael F.

2016-03-31

The U.S. Geological Survey (USGS) South Atlantic Water Science Center (SAWSC) Georgia office, in cooperation with local, State, and other Federal agencies, maintains a long-term hydrologic monitoring network of more than 350 real-time, continuous-record, streamflow-gaging stations (streamgages). The network includes 14 real-time lake-level monitoring stations, 72 real-time surface-water-quality monitors, and several water-quality sampling programs. Additionally, the SAWSC Georgia office operates more than 204 groundwater monitoring wells, 39 of which are real-time. The wide-ranging coverage of streamflow, reservoir, and groundwater monitoring sites allows for a comprehensive view of hydrologic conditions across the State. One of the many benefits this monitoring network provides is a spatially distributed overview of the hydrologic conditions of creeks, rivers, reservoirs, and aquifers in Georgia.Streamflow and groundwater data are verified throughout the year by USGS hydrographers and made available to water-resource managers, recreationists, and Federal, State, and local agencies. Hydrologic conditions are determined by comparing the statistical analyses of data collected during the current water year to historical data. Changing hydrologic conditions underscore the need for accurate, timely data to allow informed decisions about the management and conservation of Georgia’s water resources for agricultural, recreational, ecological, and water-supply needs and in protecting life and property.

Social networks and alcohol use among older adults: a comparison with middle-aged adults.

PubMed

Kim, Seungyoun; Spilman, Samantha L; Liao, Diana H; Sacco, Paul; Moore, Alison A

2018-04-01

This study compared the association between social networks and alcohol consumption among middle-aged (MA) and older adults (OA) to better understand the nature of the relationship between those two factors among OA and MA. We examined Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Current drinkers aged over 50 were subdivided into two age groups: MA (50-64, n = 5214) and OA (65 and older, n = 3070). Each age group was stratified into drinking levels (low-risk vs. at-risk) based on alcohol consumption. The size and diversity of social networks were measured. Logistic regression models were used to examine age differences in the association between the social networks (size and diversity) and the probability of at-risk drinking among two age groups. A significant association between the social networks diversity and lower odds of at-risk drinking was found among MA and OA. However, the relationship between the diversity of social networks and the likelihood of at-risk drinking was weaker for OA than for MA. The association between social networks size and at-risk drinking was not significant among MA and OA. The current study suggests that the association between social networks diversity and alcohol use among OA differs from the association among MA, and few social networks were associated with alcohol use among OA. In the future, research should consider an in-depth exploration of the nature of social networks and alcohol consumption by using longitudinal designs and advanced methods of exploring drinking networks.

Social networks and alcohol use among older adults: a comparison with middle-aged adults

PubMed Central

Kim, Seungyoun; Spilman, Samantha L.; Liao, Diana H.; Sacco, Paul; Moore, Alison A.

2017-01-01

Objectives This study compared the association between social networks and alcohol consumption among middle-aged (MA) and older adults (OA) to better understand the nature of the relationship between those two factors among OA and MA. Method We examined Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Current drinkers aged over 50 were subdivided into two age groups: MA (50–64, n = 5214) and OA (65 and older, n = 3070). Each age group was stratified into drinking levels (low-risk vs. at-risk) based on alcohol consumption. The size and diversity of social networks were measured. Logistic regression models were used to examine age differences in the association between the social networks (size and diversity) and the probability of at-risk drinking among two age groups. Results A significant association between the social networks diversity and lower odds of at-risk drinking was found among MA and OA. However, the relationship between the diversity of social networks and the likelihood of at-risk drinking was weaker for OA than for MA. The association between social networks size and at-risk drinking was not significant among MA and OA. Conclusion The current study suggests that the association between social networks diversity and alcohol use among OA differs from the association among MA, and few social networks were associated with alcohol use among OA. In the future, research should consider an in-depth exploration of the nature of social networks and alcohol consumption by using longitudinal designs and advanced methods of exploring drinking networks. PMID:28006983

Genomic analysis of regulatory network dynamics reveals large topological changes

NASA Astrophysics Data System (ADS)

Luscombe, Nicholas M.; Madan Babu, M.; Yu, Haiyuan; Snyder, Michael; Teichmann, Sarah A.; Gerstein, Mark

2004-09-01

Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here-particularly the large-scale topological changes and hub transience-will apply to other biological networks, including complex sub-systems in higher eukaryotes.

77 FR 28543 - Nationwide Health Information Network: Conditions for Trusted Exchange

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-15

... expansion, electronic exchange has been governed by a patchwork of contractual relationships, procurement.... Consequently, this ad-hoc governance approach has led to asymmetries in the policies and technical standards... This request for information (RFI) reflects ONC's current thinking regarding the approach ONC should...

Recommendations for a wind profiling network to support Space Shuttle launches

NASA Technical Reports Server (NTRS)

Zamora, R. J.

1992-01-01

The feasibility is examined of a network of clear air radar wind profilers to forecast wind conditions before Space Shuttle launches during winter. Currently, winds are measured only in the vicinity of the shuttle launch site and wind loads on the launch vehicle are estimated using these measurements. Wind conditions upstream of the Cape are not monitored. Since large changes in the wind shear profile can be associated with weather systems moving over the Cape, it may be possible to improve wind forecasts over the launch site if wind measurements are made upstream. A radar wind profiling system is in use at the Space Shuttle launch site. This system can monitor the wind profile continuously. The existing profiler could be combined with a number of radars located upstream of the launch site. Thus, continuous wind measurements would be available upstream and at the Cape. NASA-Marshall representatives have set the requirements for radar wind profiling network. The minimum vertical resolution of the network must be set so that the wind shears over the depths greater than or = 1 km will be detected. The network should allow scientists and engineers to predict the wind profile over the Cape 6 hours before a Space Shuttle launch.

Node fingerprinting: an efficient heuristic for aligning biological networks.

PubMed

Radu, Alex; Charleston, Michael

2014-10-01

With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.

Optimal Performance Monitoring of Hybrid Mid-Infrared Wavelength MIMO Free Space Optical and RF Wireless Networks in Fading Channels

NASA Astrophysics Data System (ADS)

Schmidt, Barnet Michael

An optimal performance monitoring metric for a hybrid free space optical and radio-frequency (RF) wireless network, the Outage Capacity Objective Function, is analytically developed and studied. Current and traditional methods of performance monitoring of both optical and RF wireless networks are centered on measurement of physical layer parameters, the most common being signal-to-noise ratio, error rate, Q factor, and eye diagrams, occasionally combined with link-layer measurements such as data throughput, retransmission rate, and/or lost packet rate. Network management systems frequently attempt to predict or forestall network failures by observing degradations of these parameters and to attempt mitigation (such as offloading traffic, increasing transmitter power, reducing the data rate, or combinations thereof) prior to the failure. These methods are limited by the frequent low sensitivity of the physical layer parameters to the atmospheric optical conditions (measured by optical signal-to-noise ratio) and the radio frequency fading channel conditions (measured by signal-to-interference ratio). As a result of low sensitivity, measurements of this type frequently are unable to predict impending failures sufficiently in advance for the network management system to take corrective action prior to the failure. We derive and apply an optimal measure of hybrid network performance based on the outage capacity of the hybrid optical and RF channel, the outage capacity objective function. The objective function provides high sensitivity and reliable failure prediction, and considers both the effects of atmospheric optical impairments on the performance of the free space optical segment as well as the effect of RF channel impairments on the radio frequency segment. The radio frequency segment analysis considers the three most common RF channel fading statistics: Rayleigh, Ricean, and Nakagami-m. The novel application of information theory to the underlying physics of the gamma-gamma optical channel and radio fading channels in determining the joint hybrid channel outage capacity provides the best performance estimate under any given set of operating conditions. It is shown that, unlike traditional physical layer performance monitoring techniques, the objective function based upon the outage capacity of the hybrid channel at any combination of OSNR and SIR, is able to predict channel degradation and failure well in advance of the actual outage. An outage in the information-theoretic definition occurs when the offered load exceeds the outage capacity under the current conditions of OSNR and SIR. The optical channel is operated at the "long" mid-infrared wavelength of 10000 nm. which provides improved resistance to scattering compared to shorter wavelengths such as 1550 nm.

Brain network disturbance related to posttraumatic stress and traumatic brain injury in veterans.

PubMed

Spielberg, Jeffrey M; McGlinchey, Regina E; Milberg, William P; Salat, David H

2015-08-01

Understanding the neural causes and consequences of posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) is a high research priority, given the high rates of associated disability and suicide. Despite remarkable progress in elucidating the brain mechanisms of PTSD and mTBI, a comprehensive understanding of these conditions at the level of brain networks has yet to be achieved. The present study sought to identify functional brain networks and topological properties (measures of network organization and function) related to current PTSD severity and mTBI. Graph theoretic tools were used to analyze resting-state functional magnetic resonance imaging data from 208 veterans of Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn, all of whom had experienced a traumatic event qualifying for PTSD criterion A. Analyses identified brain networks and topological network properties linked to current PTSD symptom severity, mTBI, and the interaction between PTSD and mTBI. Two brain networks were identified in which weaker connectivity was linked to higher PTSD re-experiencing symptoms, one of which was present only in veterans with comorbid mTBI. Re-experiencing was also linked to worse functional segregation (necessary for specialized processing) and diminished influence of key regions on the network, including the hippocampus. Findings of this study demonstrate that PTSD re-experiencing symptoms are linked to weakened connectivity in a network involved in providing contextual information. A similar relationship was found in a separate network typically engaged in the gating of working memory, but only in veterans with mTBI. Published by Elsevier Inc.

Assessing the sensitivity of coral reef condition indicators to local and global stressors with Bayesian networks

EPA Science Inventory

Coral reefs are highly valued ecosystems that are currently imperiled. Although the value of coral reefs to human societies is only just being investigated and better understood, for many local and global economies coral reefs are important providers of ecosystem services that su...

Growth Cycles of Brain and Mind.

ERIC Educational Resources Information Center

Rose, Samuel P.; Fischer, Kurt W.

1998-01-01

Whereas prior conceptions treated cognitive development as a sequence of stages, current research points to recurring growth cycles between birth and age 30. Each recurrence produces a new capacity for thinking and learning grounded in an expanded, reorganized neural network. Cognitive spurts are evident only under optimal support conditions.…

JRmGRN: Joint reconstruction of multiple gene regulatory networks with common hub genes using data from multiple tissues or conditions.

PubMed

Deng, Wenping; Zhang, Kui; Liu, Sanzhen; Zhao, Patrick; Xu, Shizhong; Wei, Hairong

2018-04-30

Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges. In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision. JRmGRN is available as a R program from: https://github.com/wenpingd. hairong@mtu.edu. Proof of theorem, derivation of algorithm and supplementary data are available at Bioinformatics online.

Enhancing the Reliability of Head Nodes in Underwater Sensor Networks

PubMed Central

Min, Hong; Cho, Yookun; Heo, Junyoung

2012-01-01

Underwater environments are quite different from terrestrial environments in terms of the communication media and operating conditions associated with those environments. In underwater sensor networks, the probability of node failure is high because sensor nodes are deployed in harsher environments than ground-based networks. The sensor nodes are surrounded by salt water and moved around by waves and currents. Many studies have focused on underwater communication environments in an effort to improve the data transmission throughput. In this paper, we present a checkpointing scheme for the head nodes to quickly recover from a head node failure. Experimental results show that the proposed scheme enhances the reliability of the networks and makes them more efficient in terms of energy consumption and the recovery latency compared to the previous scheme without checkpointing. PMID:22438707

Automation of Some Operations of a Wind Tunnel Using Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Buggele, Alvin E.

1996-01-01

Artificial neural networks were used successfully to sequence operations in a small, recently modernized, supersonic wind tunnel at NASA-Lewis Research Center. The neural nets generated correct estimates of shadowgraph patterns, pressure sensor readings and mach numbers for conditions occurring shortly after startup and extending to fully developed flow. Artificial neural networks were trained and tested for estimating: sensor readings from shadowgraph patterns, shadowgraph patterns from shadowgraph patterns and sensor readings from sensor readings. The 3.81 by 10 in. (0.0968 by 0.254 m) tunnel was operated with its mach 2.0 nozzle, and shadowgraph was recorded near the nozzle exit. These results support the thesis that artificial neural networks can be combined with current workstation technology to automate wind tunnel operations.

Connectionist models of conditioning: A tutorial

PubMed Central

Kehoe, E. James

1989-01-01

Models containing networks of neuron-like units have become increasingly prominent in the study of both cognitive psychology and artificial intelligence. This article describes the basic features of connectionist models and provides an illustrative application to compound-stimulus effects in respondent conditioning. Connectionist models designed specifically for operant conditioning are not yet widely available, but some current learning algorithms for machine learning indicate that such models are feasible. Conversely, designers for machine learning appear to have recognized the value of behavioral principles in producing adaptive behavior in their creations. PMID:16812604

Simulation of dynamic expansion, contraction, and connectivity in a mountain stream network

NASA Astrophysics Data System (ADS)

Ward, Adam S.; Schmadel, Noah M.; Wondzell, Steven M.

2018-04-01

Headwater stream networks expand and contract in response to changes in stream discharge. The changes in the extent of the stream network are also controlled by geologic or geomorphic setting - some reaches go dry even under relatively wet conditions, other reaches remain flowing under relatively dry conditions. While such patterns are well recognized, we currently lack tools to predict the extent of the stream network and the times and locations where the network is dry within large river networks. Here, we develop a perceptual model of the river corridor in a headwater mountainous catchment, translate this into a reduced-complexity mechanistic model, and implement the model to examine connectivity and network extent over an entire water year. Our model agreed reasonably well with our observations, showing that the extent and connectivity of the river network was most sensitive to hydrologic forcing under the lowest discharges (Qgauge < 1 L s-1), that at intermediate discharges (1 L s-1 < Qgauge < 10 L s-1) the extent of the network changed dramatically with changes in discharge, and that under wet conditions (Qgauge > 10 L s-1) the extent of the network was relatively insensitive to hydrologic forcing and was instead determined by the network topology. We do not expect that the specific thresholds observed in this study would be transferable to other catchments with different geology, topology, or hydrologic forcing. However, we expect that the general pattern should be robust: the dominant controls will shift from hydrologic forcing to geologic setting as discharge increases. Furthermore, our method is readily transferable as the model can be applied with minimal data requirements (a single stream gauge, a digital terrain model, and estimates of hydrogeologic properties) to estimate flow duration or connectivity along the river corridor in unstudied catchments. As the available information increases, the model could be better calibrated to match site-specific observations of network extent, locations of dry reaches, or solute break through curves as demonstrated in this study. Based on the low initial data requirements and ability to later tune the model to a specific site, we suggest example applications of this parsimonious model that may prove useful to both researchers and managers.

Experimental verification of a radiofrequency power model for Wi-Fi technology.

PubMed

Fang, Minyu; Malone, David

2010-04-01

When assessing the power emitted from a Wi-Fi network, it has been observed that these networks operate at a relatively low duty cycle. In this paper, we extend a recently introduced model of emitted power in Wi-Fi networks to cover conditions where devices do not always have packets to transmit. We present experimental results to validate the original model and its extension by developing approximate, but practical, testbed measurement techniques. The accuracy of the models is confirmed, with small relative errors: less than 5-10%. Moreover, we confirm that the greatest power is emitted when the network is saturated with traffic. Using this, we give a simple technique to quickly estimate power output based on traffic levels and give examples showing how this might be used in practice to predict current or future power output from a Wi-Fi network.

A quantitative approach to measure road network information based on edge diversity

NASA Astrophysics Data System (ADS)

Wu, Xun; Zhang, Hong; Lan, Tian; Cao, Weiwei; He, Jing

2015-12-01

The measure of map information has been one of the key issues in assessing cartographic quality and map generalization algorithms. It is also important for developing efficient approaches to transfer geospatial information. Road network is the most common linear object in real world. Approximately describe road network information will benefit road map generalization, navigation map production and urban planning. Most of current approaches focused on node diversities and supposed that all the edges are the same, which is inconsistent to real-life condition, and thus show limitations in measuring network information. As real-life traffic flow are directed and of different quantities, the original undirected vector road map was first converted to a directed topographic connectivity map. Then in consideration of preferential attachment in complex network study and rich-club phenomenon in social network, the from and to weights of each edge are assigned. The from weight of a given edge is defined as the connectivity of its end node to the sum of the connectivities of all the neighbors of the from nodes of the edge. After getting the from and to weights of each edge, edge information, node information and the whole network structure information entropies could be obtained based on information theory. The approach has been applied to several 1 square mile road network samples. Results show that information entropies based on edge diversities could successfully describe the structural differences of road networks. This approach is a complementarity to current map information measurements, and can be extended to measure other kinds of geographical objects.

PlantPAN 2.0: an update of plant promoter analysis navigator for reconstructing transcriptional regulatory networks in plants.

PubMed

Chow, Chi-Nga; Zheng, Han-Qin; Wu, Nai-Yun; Chien, Chia-Hung; Huang, Hsien-Da; Lee, Tzong-Yi; Chiang-Hsieh, Yi-Fan; Hou, Ping-Fu; Yang, Tien-Yi; Chang, Wen-Chi

2016-01-04

Transcription factors (TFs) are sequence-specific DNA-binding proteins acting as critical regulators of gene expression. The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN2.itps.ncku.edu.tw) provides an informative resource for detecting transcription factor binding sites (TFBSs), corresponding TFs, and other important regulatory elements (CpG islands and tandem repeats) in a promoter or a set of plant promoters. Additionally, TFBSs, CpG islands, and tandem repeats in the conserve regions between similar gene promoters are also identified. The current PlantPAN release (version 2.0) contains 16 960 TFs and 1143 TF binding site matrices among 76 plant species. In addition to updating of the annotation information, adding experimentally verified TF matrices, and making improvements in the visualization of transcriptional regulatory networks, several new features and functions are incorporated. These features include: (i) comprehensive curation of TF information (response conditions, target genes, and sequence logos of binding motifs, etc.), (ii) co-expression profiles of TFs and their target genes under various conditions, (iii) protein-protein interactions among TFs and their co-factors, (iv) TF-target networks, and (v) downstream promoter elements. Furthermore, a dynamic transcriptional regulatory network under various conditions is provided in PlantPAN 2.0. The PlantPAN 2.0 is a systematic platform for plant promoter analysis and reconstructing transcriptional regulatory networks. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Quantification of Microbial Phenotypes

PubMed Central

Martínez, Verónica S.; Krömer, Jens O.

2016-01-01

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis. PMID:27941694

Target Recognition Using Neural Networks for Model Deformation Measurements

NASA Technical Reports Server (NTRS)

Ross, Richard W.; Hibler, David L.

1999-01-01

Optical measurements provide a non-invasive method for measuring deformation of wind tunnel models. Model deformation systems use targets mounted or painted on the surface of the model to identify known positions, and photogrammetric methods are used to calculate 3-D positions of the targets on the model from digital 2-D images. Under ideal conditions, the reflective targets are placed against a dark background and provide high-contrast images, aiding in target recognition. However, glints of light reflecting from the model surface, or reduced contrast caused by light source or model smoothness constraints, can compromise accurate target determination using current algorithmic methods. This paper describes a technique using a neural network and image processing technologies which increases the reliability of target recognition systems. Unlike algorithmic methods, the neural network can be trained to identify the characteristic patterns that distinguish targets from other objects of similar size and appearance and can adapt to changes in lighting and environmental conditions.

Empirical evaluation of H.265/HEVC-based dynamic adaptive video streaming over HTTP (HEVC-DASH)

NASA Astrophysics Data System (ADS)

Irondi, Iheanyi; Wang, Qi; Grecos, Christos

2014-05-01

Real-time HTTP streaming has gained global popularity for delivering video content over Internet. In particular, the recent MPEG-DASH (Dynamic Adaptive Streaming over HTTP) standard enables on-demand, live, and adaptive Internet streaming in response to network bandwidth fluctuations. Meanwhile, emerging is the new-generation video coding standard, H.265/HEVC (High Efficiency Video Coding) promises to reduce the bandwidth requirement by 50% at the same video quality when compared with the current H.264/AVC standard. However, little existing work has addressed the integration of the DASH and HEVC standards, let alone empirical performance evaluation of such systems. This paper presents an experimental HEVC-DASH system, which is a pull-based adaptive streaming solution that delivers HEVC-coded video content through conventional HTTP servers where the client switches to its desired quality, resolution or bitrate based on the available network bandwidth. Previous studies in DASH have focused on H.264/AVC, whereas we present an empirical evaluation of the HEVC-DASH system by implementing a real-world test bed, which consists of an Apache HTTP Server with GPAC, an MP4Client (GPAC) with open HEVC-based DASH client and a NETEM box in the middle emulating different network conditions. We investigate and analyze the performance of HEVC-DASH by exploring the impact of various network conditions such as packet loss, bandwidth and delay on video quality. Furthermore, we compare the Intra and Random Access profiles of HEVC coding with the Intra profile of H.264/AVC when the correspondingly encoded video is streamed with DASH. Finally, we explore the correlation among the quality metrics and network conditions, and empirically establish under which conditions the different codecs can provide satisfactory performance.

Aperiodic linear networked control considering variable channel delays: application to robots coordination.

PubMed

Santos, Carlos; Espinosa, Felipe; Santiso, Enrique; Mazo, Manuel

2015-05-27

One of the main challenges in wireless cyber-physical systems is to reduce the load of the communication channel while preserving the control performance. In this way, communication resources are liberated for other applications sharing the channel bandwidth. The main contribution of this work is the design of a remote control solution based on an aperiodic and adaptive triggering mechanism considering the current network delay of multiple robotics units. Working with the actual network delay instead of the maximum one leads to abandoning this conservative assumption, since the triggering condition is fixed depending on the current state of the network. This way, the controller manages the usage of the wireless channel in order to reduce the channel delay and to improve the availability of the communication resources. The communication standard under study is the widespread IEEE 802.11g, whose channel delay is clearly uncertain. First, the adaptive self-triggered control is validated through the TrueTime simulation tool configured for the mentioned WiFi standard. Implementation results applying the aperiodic linear control laws on four P3-DX robots are also included. Both of them demonstrate the advantage of this solution in terms of network accessing and control performance with respect to periodic and non-adaptive self-triggered alternatives.

Towards Smart Grid Dynamic Ratings

NASA Astrophysics Data System (ADS)

Cheema, Jamal; Clark, Adrian; Kilimnik, Justin; Pavlovski, Chris; Redman, David; Vu, Maria

2011-08-01

The energy distribution industry is giving greater attention to smart grid solutions as a means for increasing the capabilities, efficiency and reliability of the electrical power network. The smart grid makes use of intelligent monitoring and control devices throughout the distribution network to report on electrical properties such as voltage, current and power, as well as raising network alarms and events. A further aspect of the smart grid embodies the dynamic rating of electrical assets of the network. This fundamentally involves a rating of the load current capacity of electrical assets including feeders, transformers and switches. The mainstream approach to rate assets is to apply the vendor plate rating, which often under utilizes assets, or in some cases over utilizes when environmental conditions reduce the effective rated capacity, potentially reducing lifetime. Using active intelligence we have developed a rating system that rates assets in real time based upon several events. This allows for a far more efficient and reliable electrical grid that is able to extend further the life and reliability of the electrical network. In this paper we describe our architecture, the observations made during development and live deployment of the solution into operation. We also illustrate how this solution blends with the smart grid by proposing a dynamic rating system for the smart grid.

The Predictive Capability of Conditioned Simulation of Discrete Fracture Networks using Structural and Hydraulic Data from the ONKALO Underground Research Facility, Finland

NASA Astrophysics Data System (ADS)

Williams, T. R. N.; Baxter, S.; Hartley, L.; Appleyard, P.; Koskinen, L.; Vanhanarkaus, O.; Selroos, J. O.; Munier, R.

2017-12-01

Discrete fracture network (DFN) models provide a natural analysis framework for rock conditions where flow is predominately through a series of connected discrete features. Mechanistic models to predict the structural patterns of networks are generally intractable due to inherent uncertainties (e.g. deformation history) and as such fracture characterisation typically involves empirical descriptions of fracture statistics for location, intensity, orientation, size, aperture etc. from analyses of field data. These DFN models are used to make probabilistic predictions of likely flow or solute transport conditions for a range of applications in underground resource and construction projects. However, there are many instances when the volumes in which predictions are most valuable are close to data sources. For example, in the disposal of hazardous materials such as radioactive waste, accurate predictions of flow-rates and network connectivity around disposal areas are required for long-term safety evaluation. The problem at hand is thus: how can probabilistic predictions be conditioned on local-scale measurements? This presentation demonstrates conditioning of a DFN model based on the current structural and hydraulic characterisation of the Demonstration Area at the ONKALO underground research facility. The conditioned realisations honour (to a required level of similarity) the locations, orientations and trace lengths of fractures mapped on the surfaces of the nearby ONKALO tunnels and pilot drillholes. Other data used as constraints include measurements from hydraulic injection tests performed in pilot drillholes and inflows to the subsequently reamed experimental deposition holes. Numerical simulations using this suite of conditioned DFN models provides a series of prediction-outcome exercises detailing the reliability of the DFN model to make local-scale predictions of measured geometric and hydraulic properties of the fracture system; and provides an understanding of the reduction in uncertainty in model predictions for conditioned DFN models honouring different aspects of this data.

Potential relocation of climatic environments suggests high rates of climate displacement within the North American protection network.

PubMed

Batllori, Enric; Parisien, Marc-André; Parks, Sean A; Moritz, Max A; Miller, Carol

2017-08-01

Ongoing climate change may undermine the effectiveness of protected area networks in preserving the set of biotic components and ecological processes they harbor, thereby jeopardizing their conservation capacity into the future. Metrics of climate change, particularly rates and spatial patterns of climatic alteration, can help assess potential threats. Here, we perform a continent-wide climate change vulnerability assessment whereby we compare the baseline climate of the protected area network in North America (Canada, United States, México-NAM) to the projected end-of-century climate (2071-2100). We estimated the projected pace at which climatic conditions may redistribute across NAM (i.e., climate velocity), and identified future nearest climate analogs to quantify patterns of climate relocation within, among, and outside protected areas. Also, we interpret climatic relocation patterns in terms of associated land-cover types. Our analysis suggests that the conservation capacity of the NAM protection network is likely to be severely compromised by a changing climate. The majority of protected areas (~80%) might be exposed to high rates of climate displacement that could promote important shifts in species abundance or distribution. A small fraction of protected areas (<10%) could be critical for future conservation plans, as they will host climates that represent analogs of conditions currently characterizing almost a fifth of the protected areas across NAM. However, the majority of nearest climatic analogs for protected areas are in nonprotected locations. Therefore, unprotected landscapes could pose additional threats, beyond climate forcing itself, as sensitive biota may have to migrate farther than what is prescribed by the climate velocity to reach a protected area destination. To mitigate future threats to the conservation capacity of the NAM protected area network, conservation plans will need to capitalize on opportunities provided by the existing availability of natural land-cover types outside the current network of NAM protected areas. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Interplay of network dynamics and heterogeneity of ties on spreading dynamics.

PubMed

Ferreri, Luca; Bajardi, Paolo; Giacobini, Mario; Perazzo, Silvia; Venturino, Ezio

2014-07-01

The structure of a network dramatically affects the spreading phenomena unfolding upon it. The contact distribution of the nodes has long been recognized as the key ingredient in influencing the outbreak events. However, limited knowledge is currently available on the role of the weight of the edges on the persistence of a pathogen. At the same time, recent works showed a strong influence of temporal network dynamics on disease spreading. In this work we provide an analytical understanding, corroborated by numerical simulations, about the conditions for infected stable state in weighted networks. In particular, we reveal the role of heterogeneity of edge weights and of the dynamic assignment of weights on the ties in the network in driving the spread of the epidemic. In this context we show that when weights are dynamically assigned to ties in the network, a heterogeneous distribution is able to hamper the diffusion of the disease, contrary to what happens when weights are fixed in time.

Investigation of transient overvoltages in heavily meshed low-voltage underground distribution networks

NASA Astrophysics Data System (ADS)

Salcedo Ulerio, Reynaldo Odalis

The analysis of overvoltages in electrical distribution networks is of considerable significance since they may damage the power system infrastructure and the associated electrical equipment. Overvoltages in distribution networks arise due to switching transients, resonance, lightning strikes and ground faults, among other causes. The operation of network protectors (NWP), low voltage circuit breakers with directional power relay, in a secondary network prevents the continuous flow of reverse power. There are three modes of operation for the network protectors: sensitive, time delayed, and insensitive. In case of a fault, although all of the network protectors sense the fault at the same time, their operation is not simultaneous. Many of them open very quickly with opening times similar to those of the feeder breaker. However, some operate a few cycles later, others take several seconds to open and a few might even fail to operate. Therefore, depending on the settings of the network protectors, faults can last for significantly long time due to backfeeding of current from the low voltage (LV) network into the medium voltage (MV) network. In this work, low voltages are defined as 208V/460V and medium voltage are defined as 25kV/35kV. This thesis presents overvoltages which arise because of the occurrence of a single-line-to-ground (SLG) fault on the MV side (connected in delta) of the system. The thesis reveals that overvoltage stresses are imposed on insulation, micro-processor controlled equipment, and switching devices by overvoltages during current backfeeding. Also, it establishes a relationship between overvoltage magnitude, its duration, and the network loading conditions. Overvoltages above 3 p.u. may be developed as a result of a simultaneous occurrence of three phenomena: neutral displacement, Ferranti effect, and magnetic current chopping. Furthermore, this thesis exposes the possibility of occurrence of the ferro-resonance phenomena in a distribution network having secondary grid, making the study of extreme importance especially in the case of a misoperating network protector. The test systems for both studies were designed following the conventional distribution network with secondary grid, similar to those in the New York City Area. Simulations were performed using the electro-magnetic transient program revised version (EMTP-RV) considering detailed representation of system components as well as the non-linear magnetization and losses of transformers.

Relational event models for longitudinal network data with an application to interhospital patient transfers.

PubMed

Vu, Duy; Lomi, Alessandro; Mascia, Daniele; Pallotti, Francesca

2017-06-30

The main objective of this paper is to introduce and illustrate relational event models, a new class of statistical models for the analysis of time-stamped data with complex temporal and relational dependencies. We outline the main differences between recently proposed relational event models and more conventional network models based on the graph-theoretic formalism typically adopted in empirical studies of social networks. Our main contribution involves the definition and implementation of a marked point process extension of currently available models. According to this approach, the sequence of events of interest is decomposed into two components: (a) event time and (b) event destination. This decomposition transforms the problem of selection of event destination in relational event models into a conditional multinomial logistic regression problem. The main advantages of this formulation are the possibility of controlling for the effect of event-specific data and a significant reduction in the estimation time of currently available relational event models. We demonstrate the empirical value of the model in an analysis of interhospital patient transfers within a regional community of health care organizations. We conclude with a discussion of how the models we presented help to overcome some the limitations of statistical models for networks that are currently available. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

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

PubMed

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

2016-03-15

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

Altered attentional control over the salience network in complex regional pain syndrome.

PubMed

Kim, Jungyoon; Kang, Ilhyang; Chung, Yong-An; Kim, Tae-Suk; Namgung, Eun; Lee, Suji; Oh, Jin Kyoung; Jeong, Hyeonseok S; Cho, Hanbyul; Kim, Myeong Ju; Kim, Tammy D; Choi, Soo Hyun; Lim, Soo Mee; Lyoo, In Kyoon; Yoon, Sujung

2018-05-10

The degree and salience of pain have been known to be constantly monitored and modulated by the brain. In the case of maladaptive neural responses as reported in centralized pain conditions such as complex regional pain syndrome (CRPS), the perception of pain is amplified and remains elevated even without sustained peripheral pain inputs. Given that the attentional state of the brain greatly influences the perception and interpretation of pain, we investigated the role of the attention network and its dynamic interactions with other pain-related networks of the brain in CRPS. We examined alterations in the intra- and inter-network functional connectivities in 21 individuals with CRPS and 49 controls. CRPS-related reduction in intra-network functional connectivity was found in the attention network. Individuals with CRPS had greater inter-network connectivities between the attention and salience networks as compared with healthy controls. Furthermore, individuals within the CRPS group with high levels of pain catastrophizing showed greater inter-network connectivities between the attention and salience networks. Taken together, the current findings suggest that these altered connectivities may be potentially associated with the maladaptive pain coping as found in CRPS patients.

In-network adaptation of SHVC video in software-defined networks

NASA Astrophysics Data System (ADS)

Awobuluyi, Olatunde; Nightingale, James; Wang, Qi; Alcaraz Calero, Jose Maria; Grecos, Christos

2016-04-01

Software Defined Networks (SDN), when combined with Network Function Virtualization (NFV) represents a paradigm shift in how future networks will behave and be managed. SDN's are expected to provide the underpinning technologies for future innovations such as 5G mobile networks and the Internet of Everything. The SDN architecture offers features that facilitate an abstracted and centralized global network view in which packet forwarding or dropping decisions are based on application flows. Software Defined Networks facilitate a wide range of network management tasks, including the adaptation of real-time video streams as they traverse the network. SHVC, the scalable extension to the recent H.265 standard is a new video encoding standard that supports ultra-high definition video streams with spatial resolutions of up to 7680×4320 and frame rates of 60fps or more. The massive increase in bandwidth required to deliver these U-HD video streams dwarfs the bandwidth requirements of current high definition (HD) video. Such large bandwidth increases pose very significant challenges for network operators. In this paper we go substantially beyond the limited number of existing implementations and proposals for video streaming in SDN's all of which have primarily focused on traffic engineering solutions such as load balancing. By implementing and empirically evaluating an SDN enabled Media Adaptation Network Entity (MANE) we provide a valuable empirical insight into the benefits and limitations of SDN enabled video adaptation for real time video applications. The SDN-MANE is the video adaptation component of our Video Quality Assurance Manager (VQAM) SDN control plane application, which also includes an SDN monitoring component to acquire network metrics and a decision making engine using algorithms to determine the optimum adaptation strategy for any real time video application flow given the current network conditions. Our proposed VQAM application has been implemented and evaluated on an SDN allowing us to provide important benchmarks for video streaming over SDN and for SDN control plane latency.

Cross-layer model design in wireless ad hoc networks for the Internet of Things.

PubMed

Yang, Xin; Wang, Ling; Xie, Jian; Zhang, Zhaolin

2018-01-01

Wireless ad hoc networks can experience extreme fluctuations in transmission traffic in the Internet of Things, which is widely used today. Currently, the most crucial issues requiring attention for wireless ad hoc networks are making the best use of low traffic periods, reducing congestion during high traffic periods, and improving transmission performance. To solve these problems, the present paper proposes a novel cross-layer transmission model based on decentralized coded caching in the physical layer and a content division multiplexing scheme in the media access control layer. Simulation results demonstrate that the proposed model effectively addresses these issues by substantially increasing the throughput and successful transmission rate compared to existing protocols without a negative influence on delay, particularly for large scale networks under conditions of highly contrasting high and low traffic periods.

Cross-layer model design in wireless ad hoc networks for the Internet of Things

PubMed Central

Wang, Ling; Xie, Jian; Zhang, Zhaolin

2018-01-01

Wireless ad hoc networks can experience extreme fluctuations in transmission traffic in the Internet of Things, which is widely used today. Currently, the most crucial issues requiring attention for wireless ad hoc networks are making the best use of low traffic periods, reducing congestion during high traffic periods, and improving transmission performance. To solve these problems, the present paper proposes a novel cross-layer transmission model based on decentralized coded caching in the physical layer and a content division multiplexing scheme in the media access control layer. Simulation results demonstrate that the proposed model effectively addresses these issues by substantially increasing the throughput and successful transmission rate compared to existing protocols without a negative influence on delay, particularly for large scale networks under conditions of highly contrasting high and low traffic periods. PMID:29734355

A novel strategy for load balancing of distributed medical applications.

PubMed

Logeswaran, Rajasvaran; Chen, Li-Choo

2012-04-01

Current trends in medicine, specifically in the electronic handling of medical applications, ranging from digital imaging, paperless hospital administration and electronic medical records, telemedicine, to computer-aided diagnosis, creates a burden on the network. Distributed Service Architectures, such as Intelligent Network (IN), Telecommunication Information Networking Architecture (TINA) and Open Service Access (OSA), are able to meet this new challenge. Distribution enables computational tasks to be spread among multiple processors; hence, performance is an important issue. This paper proposes a novel approach in load balancing, the Random Sender Initiated Algorithm, for distribution of tasks among several nodes sharing the same computational object (CO) instances in Distributed Service Architectures. Simulations illustrate that the proposed algorithm produces better network performance than the benchmark load balancing algorithms-the Random Node Selection Algorithm and the Shortest Queue Algorithm, especially under medium and heavily loaded conditions.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

PubMed

Seera, Manjeevan; Lim, Chee Peng; Ishak, Dahaman; Singh, Harapajan

2012-01-01

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Facile fabrication of network film electrodes with ultrathin Au nanowires for nonenzymatic glucose sensing and glucose/O2 fuel cell.

PubMed

Yang, Lu; Zhang, Yijia; Chu, Mi; Deng, Wenfang; Tan, Yueming; Ma, Ming; Su, Xiaoli; Xie, Qingji; Yao, Shuozhuo

2014-02-15

We report here on the facile fabrication of network film electrodes with ultrathin Au nanowires (AuNWs) and their electrochemical applications for high-performance nonenzymatic glucose sensing and glucose/O2 fuel cell under physiological conditions (pH 7.4, containing 0.15M Cl(-)). AuNWs with an average diameter of ~7 or 2 nm were prepared and can self-assemble into robust network films on common electrodes. The network film electrode fabricated with 2-nm AuNWs exhibits high sensitivity (56.0 μA cm(-2)mM(-1)), low detection limit (20 μM), short response time (within 10s), excellent selectivity, and good storage stability for nonenzymatic glucose sensing. Glucose/O2 fuel cells were constructed using network film electrodes as the anode and commercial Pt/C catalyst modified glassy carbon electrode as cathode. The glucose/O2 fuel cell using 2-nm AuNWs as anode catalyst output a maximum power density of is 126 μW cm(-2), an open-circuit cell voltage of 0.425 V, and a short-circuit current density of 1.34 mA cm(-2), respectively. Due to the higher specific electroactive surface area of 2-nm AuNWs, the network film electrode fabricated with 2-nm AuNWs exhibited higher electrocatalytic activity toward glucose oxidation than the network film electrode fabricated with 7-nm AuNWs. The network film electrode exhibits high electrocatalytic activity toward glucose oxidation under physiological conditions, which is helpful for constructing implantable electronic devices. © 2013 Elsevier B.V. All rights reserved.

A smart home application to eldercare: current status and lessons learned.

PubMed

Skubic, Marjorie; Alexander, Gregory; Popescu, Mihail; Rantz, Marilyn; Keller, James

2009-01-01

To address an aging population, we have been investigating sensor networks for monitoring older adults in their homes. In this paper, we report ongoing work in which passive sensor networks have been installed in 17 apartments in an aging in place eldercare facility. The network under development includes simple motion sensors, video sensors, and a bed sensor that captures sleep restlessness and pulse and respiration levels. Data collection has been ongoing for over two years in some apartments. This longevity in sensor data collection is allowing us to study the data and develop algorithms for identifying alert conditions such as falls, as well as extracting typical daily activity patterns for an individual. The goal is to capture patterns representing physical and cognitive health conditions and then recognize when activity patterns begin to deviate from the norm. In doing so, we strive to provide early detection of potential problems which may lead to serious health events if left unattended. We describe the components of the network and show examples of logged sensor data with correlated references to health events. A summary is also included on the challenges encountered and the lessons learned as a result of our experiences in monitoring aging adults in their homes.

Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET

PubMed Central

Androsova, Ganna; del Sol, Antonio

2015-01-01

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states. PMID:26058016

Investigation of mode partition noise in Fabry-Perot laser diode

NASA Astrophysics Data System (ADS)

Guo, Qingyi; Deng, Lanxin; Mu, Jianwei; Li, Xun; Huang, Wei-Ping

2014-09-01

Passive optical network (PON) is considered as the most appealing access network architecture in terms of cost-effectiveness, bandwidth management flexibility, scalability and durability. And to further reduce the cost per subscriber, a Fabry-Perot (FP) laser diode is preferred as the transmitter at the optical network units (ONUs) because of its lower cost compared to distributed feedback (DFB) laser diode. However, the mode partition noise (MPN) associated with the multi-longitudinal-mode FP laser diode becomes the limiting factor in the network. This paper studies the MPN characteristics of the FP laser diode using the time-domain simulation of noise-driven multi-mode laser rate equation. The probability density functions are calculated for each longitudinal mode. The paper focuses on the investigation of the k-factor, which is a simple yet important measure of the noise power, but is usually taken as a fitted or assumed value in the penalty calculations. In this paper, the sources of the k-factor are studied with simulation, including the intrinsic source of the laser Langevin noise, and the extrinsic source of the bit pattern. The photon waveforms are shown under four simulation conditions for regular or random bit pattern, and with or without Langevin noise. The k-factors contributed by those sources are studied with a variety of bias current and modulation current. Simulation results are illustrated in figures, and show that the contribution of Langevin noise to the k-factor is larger than that of the random bit pattern, and is more dominant at lower bias current or higher modulation current.

Replacement predictions for drinking water networks through historical data.

PubMed

Malm, Annika; Ljunggren, Olle; Bergstedt, Olof; Pettersson, Thomas J R; Morrison, Gregory M

2012-05-01

Lifetime distribution functions and current network age data can be combined to provide an assessment of the future replacement needs for drinking water distribution networks. Reliable lifetime predictions are limited by a lack of understanding of deterioration processes for different pipe materials under varied conditions. An alternative approach is the use of real historical data for replacement over an extended time series. In this paper, future replacement needs are predicted through historical data representing more than one hundred years of drinking water pipe replacement in Gothenburg, Sweden. The verified data fits well with commonly used lifetime distribution curves. Predictions for the future are discussed in the context of path dependence theory. Copyright © 2012 Elsevier Ltd. All rights reserved.

Protocol Independent Adaptive Route Update for VANET

PubMed Central

Rasheed, Asim; Qayyum, Amir

2014-01-01

High relative node velocity and high active node density have presented challenges to existing routing approaches within highly scaled ad hoc wireless networks, such as Vehicular Ad hoc Networks (VANET). Efficient routing requires finding optimum route with minimum delay, updating it on availability of a better one, and repairing it on link breakages. Current routing protocols are generally focused on finding and maintaining an efficient route, with very less emphasis on route update. Adaptive route update usually becomes impractical for dense networks due to large routing overheads. This paper presents an adaptive route update approach which can provide solution for any baseline routing protocol. The proposed adaptation eliminates the classification of reactive and proactive by categorizing them as logical conditions to find and update the route. PMID:24723807

Social networking sites and older users - a systematic review.

PubMed

Nef, Tobias; Ganea, Raluca L; Müri, René M; Mosimann, Urs P

2013-07-01

Social networking sites can be beneficial for senior citizens to promote social participation and to enhance intergenerational communication. Particularly for older adults with impaired mobility, social networking sites can help them to connect with family members and other active social networking users. The aim of this systematic review is to give an overview of existing scientific literature on social networking in older users. Computerized databases were searched and 105 articles were identified and screened using exclusion criteria. After exclusion of 87 articles, 18 articles were included, reviewed, classified, and the key findings were extracted. Common findings are identified and critically discussed and possible future research directions are outlined. The main benefit of using social networking sites for older adults is to enter in an intergenerational communication with younger family members (children and grandchildren) that is appreciated by both sides. Identified barriers are privacy concerns, technical difficulties and the fact that current Web design does not take the needs of older users into account. Under the conditions that these problems are carefully addressed, social networking sites have the potential to support today's and tomorrow's communication between older and younger family members.

Rest but busy: Aberrant resting-state functional connectivity of triple network model in insomnia.

PubMed

Dong, Xiaojuan; Qin, Haixia; Wu, Taoyu; Hu, Hua; Liao, Keren; Cheng, Fei; Gao, Dong; Lei, Xu

2018-02-01

One classical hypothesis among many models to explain the etiology and maintenance of insomnia disorder (ID) is hyperarousal. Aberrant functional connectivity among resting-state large-scale brain networks may be the underlying neurological mechanisms of this hypothesis. The aim of current study was to investigate the functional network connectivity (FNC) among large-scale brain networks in patients with insomnia disorder (ID) during resting state. In the present study, the resting-state fMRI was used to evaluate whether patients with ID showed aberrant FNC among dorsal attention network (DAN), frontoparietal control network (FPC), anterior default mode network (aDMN), and posterior default mode network (pDMN) compared with healthy good sleepers (HGSs). The Pearson's correlation analysis was employed to explore whether the abnormal FNC observed in patients with ID was associated with sleep parameters, cognitive and emotional scores, and behavioral performance assessed by questionnaires and tasks. Patients with ID had worse subjective thought control ability measured by Thought Control Ability Questionnaire (TCAQ) and more negative affect than HGSs. Intriguingly, relative to HGSs, patients with ID showed a significant increase in FNC between DAN and FPC, but a significant decrease in FNC between aDMN and pDMN. Exploratory analysis in patients with ID revealed a significantly positive correlation between the DAN-FPC FNC and reaction time (RT) of psychomotor vigilance task (PVT). The current study demonstrated that even during the resting state, the task-activated and task-deactivated large-scale brain networks in insomniacs may still maintain a hyperarousal state, looking quite similar to the pattern in a task condition with external stimuli. Those results support the hyperarousal model of insomnia.

Causal interactions in resting-state networks predict perceived loneliness.

PubMed

Tian, Yin; Yang, Li; Chen, Sifan; Guo, Daqing; Ding, Zechao; Tam, Kin Yip; Yao, Dezhong

2017-01-01

Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual.

Causal interactions in resting-state networks predict perceived loneliness

PubMed Central

Yang, Li; Chen, Sifan; Guo, Daqing; Ding, Zechao; Tam, Kin Yip; Yao, Dezhong

2017-01-01

Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual. PMID:28545125

Using Auditory Steady State Responses to Outline the Functional Connectivity in the Tinnitus Brain

PubMed Central

Schlee, Winfried; Weisz, Nathan; Bertrand, Olivier; Hartmann, Thomas; Elbert, Thomas

2008-01-01

Background Tinnitus is an auditory phantom perception that is most likely generated in the central nervous system. Most of the tinnitus research has concentrated on the auditory system. However, it was suggested recently that also non-auditory structures are involved in a global network that encodes subjective tinnitus. We tested this assumption using auditory steady state responses to entrain the tinnitus network and investigated long-range functional connectivity across various non-auditory brain regions. Methods and Findings Using whole-head magnetoencephalography we investigated cortical connectivity by means of phase synchronization in tinnitus subjects and healthy controls. We found evidence for a deviating pattern of long-range functional connectivity in tinnitus that was strongly correlated with individual ratings of the tinnitus percept. Phase couplings between the anterior cingulum and the right frontal lobe and phase couplings between the anterior cingulum and the right parietal lobe showed significant condition x group interactions and were correlated with the individual tinnitus distress ratings only in the tinnitus condition and not in the control conditions. Conclusions To the best of our knowledge this is the first study that demonstrates existence of a global tinnitus network of long-range cortical connections outside the central auditory system. This result extends the current knowledge of how tinnitus is generated in the brain. We propose that this global extend of the tinnitus network is crucial for the continuos perception of the tinnitus tone and a therapeutical intervention that is able to change this network should result in relief of tinnitus. PMID:19005566

Modelling switching-time effects in high-frequency power conditioning networks

NASA Technical Reports Server (NTRS)

Owen, H. A.; Sloane, T. H.; Rimer, B. H.; Wilson, T. G.

1979-01-01

Power transistor networks which switch large currents in highly inductive environments are beginning to find application in the hundred kilohertz switching frequency range. Recent developments in the fabrication of metal-oxide-semiconductor field-effect transistors in the power device category have enhanced the movement toward higher switching frequencies. Models for switching devices and of the circuits in which they are imbedded are required to properly characterize the mechanisms responsible for turning on and turning off effects. Easily interpreted results in the form of oscilloscope-like plots assist in understanding the effects of parametric studies using topology oriented computer-aided analysis methods.

77 FR 34334 - Western Pacific Pelagic Fisheries; Revised Limits on Sea Turtle Interactions in the Hawaii...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-11

...-set longline fishery. The proposed rule would implement terms and conditions of the current biological..., WordPerfect, or Adobe PDF file formats only. A biological opinion issued under the Endangered Species... Network, et al., v. Department of Commerce, et al., and Hawaii Longline Association, Civil No. 09-00598...

Performance of a Dynamically Controlled Inverter in a Photovoltaic System Interconnected with a Secondary Network Distribution System

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

Coddington, M. H.; Kroposki, B. D.; Basso, T.

In 2008, a 300 kW{sub peak} photovoltaic (PV) system was installed on the rooftop of the Colorado Convention Center (CCC). The installation was unique for the electric utility, Xcel Energy, as it had not previously permitted a PV system to be interconnected on a building served by the local secondary network distribution system (network). The PV system was installed with several provisions; one to prevent reverse power flow, another called a dynamically controlled inverter (DCI), that curtails the output of the PV inverters to maintain an amount of load supplied by Xcel Energy at the CCC. The DCI system utilizesmore » current transformers (CTs) to sense power flow to insure that a minimum threshold is maintained from Xcel Energy through the network transformers. The inverters are set to track the load on each of the three phases and curtail power from the PV system when the generated PV system current reaches 95% of the current on any phase. This is achieved by the DCI, which gathers inputs from current transformers measuring the current from the PV array, Xcel, and the spot network load. Preventing reverse power flow is a critical technical requirement for the spot network which serve this part of the CCC. The PV system was designed with the expectation that the DCI system would not curtail the PV system, as the expected minimum load consumption was historically higher than the designed PV system size. However, the DCI system has operated many days during the course of a year, and the performance has been excellent. The DCI system at the CCC was installed as a secondary measure to insure that a minimum level of power flows to the CCC from the Xcel Energy network. While this DCI system was intended for localized control, the system could also reduce output percent if an external smart grid control signal was employed. This paper specifically focuses on the performance of the innovative design at this installation; however, the DCI system could also be used for new s- art grid-enabled distribution systems where renewables power contributions at certain conditions or times may need to be curtailed.« less

Developments in Test Facility and Data Networking for the Altitude Test Stand at the John C. Stennis Space Center, MS - A General Overview

NASA Technical Reports Server (NTRS)

Hebert, Phillip W., Sr.

2008-01-01

May 2007, NASA's Constellation Program selected John C Stennis Space Center (SSC) near Waveland Mississippi as the site to construct an altitude test facility for the developmental and qualification testing of the Ares1 upper stage (US) engine. Test requirements born out of the Ares1 US propulsion system design necessitate exceptional Data Acquisition System (DAS) design solutions that support facility and propellant systems conditioning, test operations control and test data analysis. This paper reviews the new A3 Altitude Test Facility's DAS design requirements for real-time deterministic digital data, DAS technology enhancements, system trades, technology validation activities, and the current status of this system's new architecture. Also to be discussed will be current network technologies to improve data transfer.

Flight Test Implementation of a Second Generation Intelligent Flight Control System

NASA Technical Reports Server (NTRS)

Williams-Hayes, Peggy S.

2005-01-01

The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team was to develop and flight-test control systems that use neural network technology, to optimize the performance of the aircraft under nominal conditions, and to stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. The Intelligent Flight Control System team is currently in the process of implementing a second generation control scheme, collectively known as Generation 2 or Gen 2, for flight testing on the NASA F-15 aircraft. This report describes the Gen 2 system as implemented by the team for flight test evaluation. Simulation results are shown which describe the experiment to be performed in flight and highlight the ways in which the Gen 2 system meets the defined objectives.

Hydrologic Conditions in Kansas, water year 2015

USGS Publications Warehouse

May, Madison R.

2016-03-31

The U.S. Geological Survey (USGS), in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring sites in Kansas. In 2015, the network included about 200 real-time streamgages (hereafter referred to as “gages”), 12 real-time reservoir-level monitoring stations, and 30 groundwater-level monitoring wells. These data and associated analyses provide a unique overview of hydrologic conditions and help improve the understanding of Kansas’s water resources.Real-time data are verified by the USGS throughout the year with regular measurements of streamflow, lake levels, and groundwater levels. These data are used in protecting life and property; and managing water resources for agricultural, industrial, public supply, ecological, and recreational purposes. Yearly hydrologic conditions are characterized by comparing statistical analyses of current and historical water year (WY) data for the period of record. A WY is the 12-month period from October 1 through September 30 and is designated by the year in which it ends.

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory.

PubMed

Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Xu, Jing; Zheng, Kehong

2015-11-13

In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system.

Bridging groundwater models and decision support with a Bayesian network

USGS Publications Warehouse

Fienen, Michael N.; Masterson, John P.; Plant, Nathaniel G.; Gutierrez, Benjamin T.; Thieler, E. Robert

2013-01-01

Resource managers need to make decisions to plan for future environmental conditions, particularly sea level rise, in the face of substantial uncertainty. Many interacting processes factor in to the decisions they face. Advances in process models and the quantification of uncertainty have made models a valuable tool for this purpose. Long-simulation runtimes and, often, numerical instability make linking process models impractical in many cases. A method for emulating the important connections between model input and forecasts, while propagating uncertainty, has the potential to provide a bridge between complicated numerical process models and the efficiency and stability needed for decision making. We explore this using a Bayesian network (BN) to emulate a groundwater flow model. We expand on previous approaches to validating a BN by calculating forecasting skill using cross validation of a groundwater model of Assateague Island in Virginia and Maryland, USA. This BN emulation was shown to capture the important groundwater-flow characteristics and uncertainty of the groundwater system because of its connection to island morphology and sea level. Forecast power metrics associated with the validation of multiple alternative BN designs guided the selection of an optimal level of BN complexity. Assateague island is an ideal test case for exploring a forecasting tool based on current conditions because the unique hydrogeomorphological variability of the island includes a range of settings indicative of past, current, and future conditions. The resulting BN is a valuable tool for exploring the response of groundwater conditions to sea level rise in decision support.

Two-dimensional plasmons in lateral carbon nanotube network structures and their effect on the terahertz radiation detection

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

Ryzhii, V.; Institute of Ultra High Frequency Semiconductor Electronics of RAS, Moscow 117105; Center for Photonics and Infrared Engineering, Bauman Moscow State Technical University, Moscow 111005

2016-07-28

We consider the carrier transport and plasmonic phenomena in the lateral carbon nanotube (CNT) networks forming the device channel with asymmetric electrodes. One electrode is the Ohmic contact to the CNT network and the other contact is the Schottky contact. These structures can serve as detectors of the terahertz (THz) radiation. We develop the device model for collective response of the lateral CNT networks which comprise a mixture of randomly oriented semiconductor CNTs (s-CNTs) and quasi-metal CNTs (m-CNTs). The proposed model includes the concept of the collective two-dimensional (2D) plasmons in relatively dense networks of randomly oriented CNTs (CNT “felt”)more » and predicts the detector responsivity spectral characteristics exhibiting sharp resonant peaks at the signal frequencies corresponding to the 2D plasmonic resonances. The detection mechanism is the rectification of the ac current due the nonlinearity of the Schottky contact current-voltage characteristics under the conditions of a strong enhancement of the potential drop at this contact associated with the plasmon excitation. The detector responsivity depends on the fractions of the s- and m-CNTs. The burning of the near-contact regions of the m-CNTs or destruction of these CNTs leads to a marked increase in the responsivity in agreement with our experimental data. The resonant THz detectors with sufficiently dense lateral CNT networks can compete and surpass other THz detectors using plasmonic effects at room temperatures.« less

Event-triggered H∞ state estimation for semi-Markov jumping discrete-time neural networks with quantization.

PubMed

Rakkiyappan, R; Maheswari, K; Velmurugan, G; Park, Ju H

2018-05-17

This paper investigates H ∞ state estimation problem for a class of semi-Markovian jumping discrete-time neural networks model with event-triggered scheme and quantization. First, a new event-triggered communication scheme is introduced to determine whether or not the current sampled sensor data should be broad-casted and transmitted to the quantizer, which can save the limited communication resource. Second, a novel communication framework is employed by the logarithmic quantizer that quantifies and reduces the data transmission rate in the network, which apparently improves the communication efficiency of networks. Third, a stabilization criterion is derived based on the sufficient condition which guarantees a prescribed H ∞ performance level in the estimation error system in terms of the linear matrix inequalities. Finally, numerical simulations are given to illustrate the correctness of the proposed scheme. Copyright © 2018 Elsevier Ltd. All rights reserved.

Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

NASA Technical Reports Server (NTRS)

Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

2006-01-01

The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

Growing multiplex networks with arbitrary number of layers

NASA Astrophysics Data System (ADS)

Momeni, Naghmeh; Fotouhi, Babak

2015-12-01

This paper focuses on the problem of growing multiplex networks. Currently, the results on the joint degree distribution of growing multiplex networks present in the literature pertain to the case of two layers and are confined to the special case of homogeneous growth and are limited to the state state (that is, the limit of infinite size). In the present paper, we first obtain closed-form solutions for the joint degree distribution of heterogeneously growing multiplex networks with arbitrary number of layers in the steady state. Heterogeneous growth means that each incoming node establishes different numbers of links in different layers. We consider both uniform and preferential growth. We then extend the analysis of the uniform growth mechanism to arbitrary times. We obtain a closed-form solution for the time-dependent joint degree distribution of a growing multiplex network with arbitrary initial conditions. Throughout, theoretical findings are corroborated with Monte Carlo simulations. The results shed light on the effects of the initial network on the transient dynamics of growing multiplex networks and takes a step towards characterizing the temporal variations of the connectivity of growing multiplex networks, as well as predicting their future structural properties.

Intercontinental Multi-Domain Monitoring for LHC with perfSONAR

NASA Astrophysics Data System (ADS)

Vicinanza, D.

2012-12-01

The Large Hadron Collider (LHC) is currently running at CERN in Geneva, Switzerland. Physicists are using LHC to recreate the conditions just after the Big Bang, by colliding two beams of particles and heavy ions head-on at very high energy. The project is generating more than 15 TB of raw data per year, plus 10 TB of “event summary data”. This data is sent out from CERN to eleven Tier 1 research centres in Europe, Asia, and North America using a multi-gigabits Optical Private Network (OPN), the LHCOPN. Tier 1 sites are then connected to 100+ academic and research institutions in the world (the Tier 2s) through a Multipoint to Multipoint network, the LHC Open Network Environment (LHCONE). Network monitoring on such complex network architecture to ensure robust and reliable operation is of crucial importance. The chosen approach for monitoring the OPN and ONE is based on the perfSONAR framework, which is designed for multi-domain monitoring environments. perfSONAR (www.perfsonar.net) is an infrastructure for performance monitoring data exchange between networks, making it easier to solve performance problems occurring between network measurement points interconnected through several network domains.

NARX neural network Prediction of SYMH and ASYH indices for geomagnetic storms of solar cycle 24 including recent St. Patrick's day, 2015 storm

NASA Astrophysics Data System (ADS)

Bhaskar, A. T.; Vichare, G.

2017-12-01

Here, an attempt is made to develop a prediction model for SYMH and ASYH geomagnetic indices using Artificial Neural Network (ANN). SYMH and ASYH indices represent longitudinal symmetric and asymmetric component of the ring current. The ring current state depends on its past conditions therefore, it is necessary to consider its history for prediction. To account this effect Nonlinear Autoregressive Network with eXogenous inputs (NARX) is implemented. This network considers input history of 30 minutes and output feedback of 120 minutes. Solar wind parameters mainly velocity, density and interplanetary magnetic field are used as inputs. SYMH and ASYH indices during geomagnetic storms of 1998-2013, having minimum SYMH <-85 nT are used as the target for training two independent networks. We present the prediction of SYMH and ASYH indices during 9 geomagnetic storms of solar cycle 24 including the recent largest storm occurred on St. Patrick's day, 2015. The present prediction model reproduces the entire time profile of SYMH and ASYH indices along with small variations of 10-30 minutes to good extent within noise level, indicating significant contribution of interplanetary sources and past state of the magnetosphere. However, during the main phase of major storms, residuals (observed-modeled) are found to be large, suggesting influence of internal factors such as magnetospheric processes.

Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain.

PubMed

Cheng, J C; Rogachov, A; Hemington, K S; Kucyi, A; Bosma, R L; Lindquist, M A; Inman, R D; Davis, K D

2018-04-26

Communication within the brain is dynamic. Chronic pain can also be dynamic, with varying intensities experienced over time. Little is known of how brain dynamics are disrupted in chronic pain, or relates to patients' pain assessed at various time-scales (e.g., short-term state versus long-term trait). Patients experience pain "traits" indicative of their general condition, but also pain "states" that vary day to day. Here, we used network-based multivariate machine learning to determine how patterns in dynamic and static brain communication are related to different characteristics and timescales of chronic pain. Our models were based on resting state dynamic and static functional connectivity (dFC, sFC) in patients with chronic neuropathic pain (NP) or non-NP. The most prominent networks in the models were the default mode, salience, and executive control networks. We also found that cross-network measures of dFC rather than sFC were better associated with patients' pain, but only in those with NP features. These associations were also more highly and widely associated with measures of trait rather than state pain. Furthermore, greater dynamic connectivity with executive control networks was associated with milder neuropathic pain, but greater dynamic connectivity with limbic networks was associated greater neuropathic pain. Compared with healthy individuals, the dFC features most highly related to trait neuropathic pain were also more abnormal in patients with greater pain. Our findings indicate that dFC reflects patients' overall pain condition (i.e., trait pain), not just their current state, and is impacted by complexities in pain features beyond intensity.

Analytical Computation of the Epidemic Threshold on Temporal Networks

NASA Astrophysics Data System (ADS)

Valdano, Eugenio; Ferreri, Luca; Poletto, Chiara; Colizza, Vittoria

2015-04-01

The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science, and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the time-scale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the susceptible-infectious-susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is of both fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.

Greenland Ice Sheet Monitoring Network (GLISN): Contributions to Science and Society

NASA Astrophysics Data System (ADS)

Anderson, K. R.; Bonaime, S.; Clinton, J. F.; Dahl-Jensen, T.; Debski, W. M.; Giardini, D.; Govoni, A.; Kanao, M.; Larsen, T. B.; Lasocki, S.; Lee, W. S.; McCormack, D. A.; Mykkeltveit, S.; Nettles, M.; Stutzmann, E.; Strollo, A.; Sweet, J. R.; Tsuboi, S.; Vallee, M.

2017-12-01

The Greenland Ice Sheet Monitoring Network (GLISN) is a broadband, multi-use seismological network, enhanced by selected geodetic observations, designed with the capability to allow researchers to understand the changes currently occurring in the Arctic, and with the operational characteristics necessary to enable response to those changes as understanding improves. GLISN was established through an international collaboration, with 10 nations coordinating their efforts to develop the current 34-station observing network during the last eight years. All of the data collected are freely and openly available in near-real time. The network was designed to transform the community capability for recording, analysis, and interpretation of seismic signals generated by discrete events in Greenland and the Arctic, as well as those traversing the region. Data from the network support a wide range of uses, including estimation of the properties of the solid Earth that control isostatic adjustment rates and set key boundary conditions for ice-sheet evolution; analysis of tectonic earthquakes throughout Greenland and the Arctic; study of the seismic signals associated with large calving events and changing glacier dynamics; and variations in ice and snow properties within the Greenland Ice Sheet. Recordings from the network have also provided invaluable data for rapid evaluation and understanding of the devastating landslide and tsunami that occurred near Nuugaatsiaq, Greenland, in June, 2017. The GLISN strategy of maximizing data quality from a network of approximately evenly distributed stations, delivering data in near-real time, and archiving a continuous data stream easily accessible to researchers, allows continuous discovery of new uses while also facilitating the generation of data products, such as catalogs of tectonic and glacial earthquakes and GPS-based estimates of snow height, that allow for assessment of change over time.

Emotion regulation, attention to emotion, and the ventral attentional network

PubMed Central

Viviani, Roberto

2013-01-01

Accounts of the effect of emotional information on behavioral response and current models of emotion regulation are based on two opposed but interacting processes: automatic bottom-up processes (triggered by emotionally arousing stimuli) and top-down control processes (mapped to prefrontal cortical areas). Data on the existence of a third attentional network operating without recourse to limited-capacity processes but influencing response raise the issue of how it is integrated in emotion regulation. We summarize here data from attention to emotion, voluntary emotion regulation, and on the origin of biases against negative content suggesting that the ventral network is modulated by exposure to emotional stimuli when the task does not constrain the handling of emotional content. In the parietal lobes, preferential activation of ventral areas associated with “bottom-up” attention by ventral network theorists is strongest in studies of cognitive reappraisal. In conditions when no explicit instruction is given to change one's response to emotional stimuli, control of emotionally arousing stimuli is observed without concomitant activation of the dorsal attentional network, replaced by a shift of activation toward ventral areas. In contrast, in studies where emotional stimuli are placed in the role of distracter, the observed deactivation of these ventral semantic association areas is consistent with the existence of proactive control on the role emotional representations are allowed to take in generating response. It is here argued that attentional orienting mechanisms located in the ventral network constitute an intermediate kind of process, with features only partially in common with effortful and automatic processes, which plays an important role in handling emotion by conveying the influence of semantic networks, with which the ventral network is co-localized. Current neuroimaging work in emotion regulation has neglected this system by focusing on a bottom-up/top-down dichotomy of attentional control. PMID:24223546

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

PubMed

Pena, Rodrigo F O; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C; Lindner, Benjamin

2018-01-01

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.

Dynamic full-scalability conversion in scalable video coding

NASA Astrophysics Data System (ADS)

Lee, Dong Su; Bae, Tae Meon; Thang, Truong Cong; Ro, Yong Man

2007-02-01

For outstanding coding efficiency with scalability functions, SVC (Scalable Video Coding) is being standardized. SVC can support spatial, temporal and SNR scalability and these scalabilities are useful to provide a smooth video streaming service even in a time varying network such as a mobile environment. But current SVC is insufficient to support dynamic video conversion with scalability, thereby the adaptation of bitrate to meet a fluctuating network condition is limited. In this paper, we propose dynamic full-scalability conversion methods for QoS adaptive video streaming in SVC. To accomplish full scalability dynamic conversion, we develop corresponding bitstream extraction, encoding and decoding schemes. At the encoder, we insert the IDR NAL periodically to solve the problems of spatial scalability conversion. At the extractor, we analyze the SVC bitstream to get the information which enable dynamic extraction. Real time extraction is achieved by using this information. Finally, we develop the decoder so that it can manage the changing scalability. Experimental results showed that dynamic full-scalability conversion was verified and it was necessary for time varying network condition.

Flash-induced nanowelding of silver nanowire networks for transparent stretchable electrochromic devices.

PubMed

Lee, Chihak; Oh, Youngsu; Yoon, In Seon; Kim, Sun Hong; Ju, Byeong-Kwon; Hong, Jae-Min

2018-02-09

Electrochromic devices (ECDs) are emerging as a novel technology for various applications like commercialized smart window glasses, and auto-dimming rear-view mirrors. Recently, the development of low-power, lightweight, flexible, and stretchable devices has been accelerated to meet the growing demand in the new wearable devices market. Silver nanowires (AgNWs) can become new primary transparent conducting electrode (TCE) materials to replace indium tin oxide (ITO) for ECDs. However, issues such as substrate adhesion, delamination, and higher resistance still exist with AgNWs. Herein, we report a high-performance stretchable flash-induced AgNW-network-based TCE on surface-treated polydimethylsiloxane (PDMS) substrates. A Xe flash light method was used to create nanowelded networks of AgNWs. Surface silane treatments increased the adhesion and durability of the films as well. Finally, ECDs were fabricated under the optimal conditions and examined under strained conditions to demonstrate the resistance and mechanical behaviours of the devices. Results showed a flexible and durable film maintaining a high level of conductivity and reversible resistance behaviour, beyond those currently achievable with standard ITO/PET flexible TCEs.

Optimal design of reverse osmosis module networks

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

Maskan, F.; Wiley, D.E.; Johnston, L.P.M.

2000-05-01

The structure of individual reverse osmosis modules, the configuration of the module network, and the operating conditions were optimized for seawater and brackish water desalination. The system model included simple mathematical equations to predict the performance of the reverse osmosis modules. The optimization problem was formulated as a constrained multivariable nonlinear optimization. The objective function was the annual profit for the system, consisting of the profit obtained from the permeate, capital cost for the process units, and operating costs associated with energy consumption and maintenance. Optimization of several dual-stage reverse osmosis systems were investigated and compared. It was found thatmore » optimal network designs are the ones that produce the most permeate. It may be possible to achieve economic improvements by refining current membrane module designs and their operating pressures.« less

Conditional bistability, a generic cellular mnemonic mechanism for robust and flexible working memory computations.

PubMed

Rodriguez, Guillaume; Sarazin, Matthieu; Clemente, Alexandra; Holden, Stephanie; Paz, Jeanne T; Delord, Bruno

2018-04-30

Persistent neural activity, the substrate of working memory, is thought to emerge from synaptic reverberation within recurrent networks. However, reverberation models do not robustly explain fundamental dynamics of persistent activity, including high-spiking irregularity, large intertrial variability, and state transitions. While cellular bistability may contribute to persistent activity, its rigidity appears incompatible with persistent activity labile characteristics. Here, we unravel in a cellular model a form of spike-mediated conditional bistability that is robust, generic and provides a rich repertoire of mnemonic computations. Under asynchronous synaptic inputs of the awakened state, conditional bistability generates spiking/bursting episodes, accounting for the irregularity, variability and state transitions characterizing persistent activity. This mechanism has likely been overlooked because of the sub-threshold input it requires and we predict how to assess it experimentally. Our results suggest a reexamination of the role of intrinsic properties in the collective network dynamics responsible for flexible working memory. SIGNIFICANCE STATEMENT This study unravels a novel form of intrinsic neuronal property, i.e. conditional bistability. We show that, thanks of its conditional character, conditional bistability favors the emergence of flexible and robust forms of persistent activity in PFC neural networks, in opposition to previously studied classical forms of absolute bistability. Specifically, we demonstrate for the first time that conditional bistability 1) is a generic biophysical spike-dependent mechanism of layer V pyramidal neurons in the PFC and that 2) it accounts for essential neurodynamical features for the organisation and flexibility of PFC persistent activity (the large irregularity and intertrial variability of the discharge and its organization under discrete stable states), which remain unexplained in a robust fashion by current models. Copyright © 2018 the authors.

Social network media in the forest products industry: A look at a new way of marketing

Treesearch

Iris B. Montague

2011-01-01

The current economic conditions have far reaching financial implications for both consumers and businesses. While consumers devise plans to save money and stretch their dollars, companies are devising plans to stay afloat during economic uncertainty. To be competitive, businesses must find new and innovative ways to conduct everyday business functions and efficiently...

Tree-structured sensor fusion architecture for distributed sensor networks

NASA Astrophysics Data System (ADS)

Iyengar, S. Sitharama; Kashyap, Rangasami L.; Madan, Rabinder N.; Thomas, Daryl D.

1990-10-01

An assessment of numerous activities in the field of multisensor target recognition reveals several trends and conditions which are cause for concern. .These concerns are analyzed in terms of their potential impact on the ultimate employment of automatic target recognition in military systems. Suggestions for additional investigation and guidance for current activities are presented with respect to some of the identified concerns.

Current trends in lifestyle-related disease management by general practitioners: a report from the "Heart Care Network" groups.

PubMed

2009-01-01

In Japan, it is believed that guidelines for lifestyle-related disease are used in routine clinical practice, however, there are few reports on the actual rate of healthcare conducted in accordance with these guidelines by general practitioners and on their usefulness in preventing cardiovascular events. Therefore, the Heart Care Network (HCN) groups were organized mainly by general practitioners treating lifestyle diseases in 62 areas of Japan. The HCN has collected data on lifestyle diseases in high-risk patients in routine practices and investigated management conditions, guideline target achievement rates and medication. Additionally, the incidence of cardiovascular events was assessed. We analyzed 14,064 cases. The lipid profile, blood pressure, glycemic control were significantly improved over the 3 years. The incidence of cardiovascular events were significantly reduced by the achievement of target LDL cholesterol, systolic blood pressure and hemoglobin A1c and even after adjustment for age, gender, history of myocardial infarction, the reduction of these lifestyle-related parameters remains significant. These results revealed the current trends in the healthcare activities of general practitioners, the management conditions for lifestyle diseases in CHD high-risk patients and their effects on reducing cardiovascular events.

An overview of the effect of probiotics and exercise on mood and associated health conditions.

PubMed

Grant, Marie Clare; Baker, Julien S

2017-12-12

The present paper provides a review of the current knowledge relating to the health benefits of probiotics, specially focused on the effects they may have together with physical exercise on mood disorders and related chronic medical conditions.With both these conditions being a substantial contributor to the global disease burden, any alternative therapy must be considered. Probiotics influence the gut microbiota through a complex network of events which can influence mechanisms leading to development of mood disorders such as depression and anxiety. Similarly, through a complex interaction between psychological and neurobiological mechanisms, exercise has been found to play a key role in mood enhancement.

Cluster Synchronization of Diffusively Coupled Nonlinear Systems: A Contraction-Based Approach

NASA Astrophysics Data System (ADS)

Aminzare, Zahra; Dey, Biswadip; Davison, Elizabeth N.; Leonard, Naomi Ehrich

2018-04-01

Finding the conditions that foster synchronization in networked nonlinear systems is critical to understanding a wide range of biological and mechanical systems. However, the conditions proved in the literature for synchronization in nonlinear systems with linear coupling, such as has been used to model neuronal networks, are in general not strict enough to accurately determine the system behavior. We leverage contraction theory to derive new sufficient conditions for cluster synchronization in terms of the network structure, for a network where the intrinsic nonlinear dynamics of each node may differ. Our result requires that network connections satisfy a cluster-input-equivalence condition, and we explore the influence of this requirement on network dynamics. For application to networks of nodes with FitzHugh-Nagumo dynamics, we show that our new sufficient condition is tighter than those found in previous analyses that used smooth or nonsmooth Lyapunov functions. Improving the analytical conditions for when cluster synchronization will occur based on network configuration is a significant step toward facilitating understanding and control of complex networked systems.

Network meta-analysis, electrical networks and graph theory.

PubMed

Rücker, Gerta

2012-12-01

Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

Influence of Telecommunication Modality, Internet Transmission Quality, and Accessories on Speech Perception in Cochlear Implant Users

PubMed Central

Koller, Roger; Guignard, Jérémie; Caversaccio, Marco; Kompis, Martin; Senn, Pascal

2017-01-01

Background Telecommunication is limited or even impossible for more than one-thirds of all cochlear implant (CI) users. Objective We sought therefore to study the impact of voice quality on speech perception with voice over Internet protocol (VoIP) under real and adverse network conditions. Methods Telephone speech perception was assessed in 19 CI users (15-69 years, average 42 years), using the German HSM (Hochmair-Schulz-Moser) sentence test comparing Skype and conventional telephone (public switched telephone networks, PSTN) transmission using a personal computer (PC) and a digital enhanced cordless telecommunications (DECT) telephone dual device. Five different Internet transmission quality modes and four accessories (PC speakers, headphones, 3.5 mm jack audio cable, and induction loop) were compared. As a secondary outcome, the subjective perceived voice quality was assessed using the mean opinion score (MOS). Results Speech telephone perception was significantly better (median 91.6%, P<.001) with Skype compared with PSTN (median 42.5%) under optimal conditions. Skype calls under adverse network conditions (data packet loss > 15%) were not superior to conventional telephony. In addition, there were no significant differences between the tested accessories (P>.05) using a PC. Coupling a Skype DECT phone device with an audio cable to the CI, however, resulted in higher speech perception (median 65%) and subjective MOS scores (3.2) than using PSTN (median 7.5%, P<.001). Conclusions Skype calls significantly improve speech perception for CI users compared with conventional telephony under real network conditions. Listening accessories do not further improve listening experience. Current Skype DECT telephone devices do not fully offer technical advantages in voice quality. PMID:28438727

Influence of Telecommunication Modality, Internet Transmission Quality, and Accessories on Speech Perception in Cochlear Implant Users.

PubMed

Mantokoudis, Georgios; Koller, Roger; Guignard, Jérémie; Caversaccio, Marco; Kompis, Martin; Senn, Pascal

2017-04-24

Telecommunication is limited or even impossible for more than one-thirds of all cochlear implant (CI) users. We sought therefore to study the impact of voice quality on speech perception with voice over Internet protocol (VoIP) under real and adverse network conditions. Telephone speech perception was assessed in 19 CI users (15-69 years, average 42 years), using the German HSM (Hochmair-Schulz-Moser) sentence test comparing Skype and conventional telephone (public switched telephone networks, PSTN) transmission using a personal computer (PC) and a digital enhanced cordless telecommunications (DECT) telephone dual device. Five different Internet transmission quality modes and four accessories (PC speakers, headphones, 3.5 mm jack audio cable, and induction loop) were compared. As a secondary outcome, the subjective perceived voice quality was assessed using the mean opinion score (MOS). Speech telephone perception was significantly better (median 91.6%, P<.001) with Skype compared with PSTN (median 42.5%) under optimal conditions. Skype calls under adverse network conditions (data packet loss > 15%) were not superior to conventional telephony. In addition, there were no significant differences between the tested accessories (P>.05) using a PC. Coupling a Skype DECT phone device with an audio cable to the CI, however, resulted in higher speech perception (median 65%) and subjective MOS scores (3.2) than using PSTN (median 7.5%, P<.001). Skype calls significantly improve speech perception for CI users compared with conventional telephony under real network conditions. Listening accessories do not further improve listening experience. Current Skype DECT telephone devices do not fully offer technical advantages in voice quality. ©Georgios Mantokoudis, Roger Koller, Jérémie Guignard, Marco Caversaccio, Martin Kompis, Pascal Senn. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.04.2017.

Optimization of a Coastal Environmental Monitoring Network Based on the Kriging Method: A Case Study of Quanzhou Bay, China

PubMed Central

Chen, Kai; Ni, Minjie; Wang, Jun; Huang, Dongren; Chen, Huorong; Wang, Xiao; Liu, Mengyang

2016-01-01

Environmental monitoring is fundamental in assessing environmental quality and to fulfill protection and management measures with permit conditions. However, coastal environmental monitoring work faces many problems and challenges, including the fact that monitoring information cannot be linked up with evaluation, monitoring data cannot well reflect the current coastal environmental condition, and monitoring activities are limited by cost constraints. For these reasons, protection and management measures cannot be developed and implemented well by policy makers who intend to solve this issue. In this paper, Quanzhou Bay in southeastern China was selected as a case study; and the Kriging method and a geographic information system were employed to evaluate and optimize the existing monitoring network in a semienclosed bay. This study used coastal environmental monitoring data from 15 sites (including COD, DIN, and PO4-P) to adequately analyze the water quality from 2009 to 2012 by applying the Trophic State Index. The monitoring network in Quanzhou Bay was evaluated and optimized, with the number of sites increased from 15 to 24, and the monitoring precision improved by 32.9%. The results demonstrated that the proposed advanced monitoring network optimization was appropriate for environmental monitoring in Quanzhou Bay. It might provide technical support for coastal management and pollutant reduction in similar areas. PMID:27777951

Optimization of a Coastal Environmental Monitoring Network Based on the Kriging Method: A Case Study of Quanzhou Bay, China.

PubMed

Chen, Kai; Ni, Minjie; Cai, Minggang; Wang, Jun; Huang, Dongren; Chen, Huorong; Wang, Xiao; Liu, Mengyang

2016-01-01

Environmental monitoring is fundamental in assessing environmental quality and to fulfill protection and management measures with permit conditions. However, coastal environmental monitoring work faces many problems and challenges, including the fact that monitoring information cannot be linked up with evaluation, monitoring data cannot well reflect the current coastal environmental condition, and monitoring activities are limited by cost constraints. For these reasons, protection and management measures cannot be developed and implemented well by policy makers who intend to solve this issue. In this paper, Quanzhou Bay in southeastern China was selected as a case study; and the Kriging method and a geographic information system were employed to evaluate and optimize the existing monitoring network in a semienclosed bay. This study used coastal environmental monitoring data from 15 sites (including COD, DIN, and PO 4 -P) to adequately analyze the water quality from 2009 to 2012 by applying the Trophic State Index. The monitoring network in Quanzhou Bay was evaluated and optimized, with the number of sites increased from 15 to 24, and the monitoring precision improved by 32.9%. The results demonstrated that the proposed advanced monitoring network optimization was appropriate for environmental monitoring in Quanzhou Bay. It might provide technical support for coastal management and pollutant reduction in similar areas.

An Iterative Approach for the Optimization of Pavement Maintenance Management at the Network Level

PubMed Central

Torres-Machí, Cristina; Chamorro, Alondra; Videla, Carlos; Yepes, Víctor

2014-01-01

Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Two traditional approaches (sequential and holistic) and four classes of optimization methods (selection based on ranking, mathematical optimization, near optimization, and other methods) have been applied to solve this problem. They vary in the number of alternatives considered and how the selection process is performed. Therefore, a previous understanding of the problem is mandatory to identify the most suitable approach and method for a particular network. This study aims to assist highway agencies, researchers, and practitioners on when and how to apply available methods based on a comparative analysis of the current state of the practice. Holistic approach tackles the problem considering the overall network condition, while the sequential approach is easier to implement and understand, but may lead to solutions far from optimal. Scenarios defining the suitability of these approaches are defined. Finally, an iterative approach gathering the advantages of traditional approaches is proposed and applied in a case study. The proposed approach considers the overall network condition in a simpler and more intuitive manner than the holistic approach. PMID:24741352

An iterative approach for the optimization of pavement maintenance management at the network level.

PubMed

Torres-Machí, Cristina; Chamorro, Alondra; Videla, Carlos; Pellicer, Eugenio; Yepes, Víctor

2014-01-01

Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Two traditional approaches (sequential and holistic) and four classes of optimization methods (selection based on ranking, mathematical optimization, near optimization, and other methods) have been applied to solve this problem. They vary in the number of alternatives considered and how the selection process is performed. Therefore, a previous understanding of the problem is mandatory to identify the most suitable approach and method for a particular network. This study aims to assist highway agencies, researchers, and practitioners on when and how to apply available methods based on a comparative analysis of the current state of the practice. Holistic approach tackles the problem considering the overall network condition, while the sequential approach is easier to implement and understand, but may lead to solutions far from optimal. Scenarios defining the suitability of these approaches are defined. Finally, an iterative approach gathering the advantages of traditional approaches is proposed and applied in a case study. The proposed approach considers the overall network condition in a simpler and more intuitive manner than the holistic approach.

A dense camera network for cropland (CropInsight) - developing high spatiotemporal resolution crop Leaf Area Index (LAI) maps through network images and novel satellite data

NASA Astrophysics Data System (ADS)

Kimm, H.; Guan, K.; Luo, Y.; Peng, J.; Mascaro, J.; Peng, B.

2017-12-01

Monitoring crop growth conditions is of primary interest to crop yield forecasting, food production assessment, and risk management of individual farmers and agribusiness. Despite its importance, there are limited access to field level crop growth/condition information in the public domain. This scarcity of ground truth data also hampers the use of satellite remote sensing for crop monitoring due to the lack of validation. Here, we introduce a new camera network (CropInsight) to monitor crop phenology, growth, and conditions that are designed for the US Corn Belt landscape. Specifically, this network currently includes 40 sites (20 corn and 20 soybean fields) across southern half of the Champaign County, IL ( 800 km2). Its wide distribution and automatic operation enable the network to capture spatiotemporal variations of crop growth condition continuously at the regional scale. At each site, low-maintenance, and high-resolution RGB digital cameras are set up having a downward view from 4.5 m height to take continuous images. In this study, we will use these images and novel satellite data to construct daily LAI map of the Champaign County at 30 m spatial resolution. First, we will estimate LAI from the camera images and evaluate it using the LAI data collected from LAI-2200 (LI-COR, Lincoln, NE). Second, we will develop relationships between the camera-based LAI estimation and vegetation indices derived from a newly developed MODIS-Landsat fusion product (daily, 30 m resolution, RGB + NIR + SWIR bands) and the Planet Lab's high-resolution satellite data (daily, 5 meter, RGB). Finally, we will scale up the above relationships to generate high spatiotemporal resolution crop LAI map for the whole Champaign County. The proposed work has potentials to expand to other agro-ecosystems and to the broader US Corn Belt.

Effects of Distributed Generation on Overcurrent Relay Coordination and an Adaptive Protection Scheme

NASA Astrophysics Data System (ADS)

Ilik, Semih C.; Arsoy, Aysen B.

2017-07-01

Integration of distributed generation (DG) such as renewable energy sources to electrical network becomes more prevalent in recent years. Grid connection of DG has effects on load flow directions, voltage profile, short circuit power and especially protection selectivity. Applying traditional overcurrent protection scheme is inconvenient when system reliability and sustainability are considered. If a fault happens in DG connected network, short circuit contribution of DG, creates additional branch element feeding the fault current; compels to consider directional overcurrent (OC) protection scheme. Protection coordination might get lost for changing working conditions when DG sources are connected. Directional overcurrent relay parameters are determined for downstream and upstream relays when different combinations of DG connected singular or plural, on radial test system. With the help of proposed flow chart, relay parameters are updated and coordination between relays kept sustained for different working conditions in DigSILENT PowerFactory program.

Reliability and availability evaluation of Wireless Sensor Networks for industrial applications.

PubMed

Silva, Ivanovitch; Guedes, Luiz Affonso; Portugal, Paulo; Vasques, Francisco

2012-01-01

Wireless Sensor Networks (WSN) currently represent the best candidate to be adopted as the communication solution for the last mile connection in process control and monitoring applications in industrial environments. Most of these applications have stringent dependability (reliability and availability) requirements, as a system failure may result in economic losses, put people in danger or lead to environmental damages. Among the different type of faults that can lead to a system failure, permanent faults on network devices have a major impact. They can hamper communications over long periods of time and consequently disturb, or even disable, control algorithms. The lack of a structured approach enabling the evaluation of permanent faults, prevents system designers to optimize decisions that minimize these occurrences. In this work we propose a methodology based on an automatic generation of a fault tree to evaluate the reliability and availability of Wireless Sensor Networks, when permanent faults occur on network devices. The proposal supports any topology, different levels of redundancy, network reconfigurations, criticality of devices and arbitrary failure conditions. The proposed methodology is particularly suitable for the design and validation of Wireless Sensor Networks when trying to optimize its reliability and availability requirements.

Reliability and Availability Evaluation of Wireless Sensor Networks for Industrial Applications

PubMed Central

Silva, Ivanovitch; Guedes, Luiz Affonso; Portugal, Paulo; Vasques, Francisco

2012-01-01

Wireless Sensor Networks (WSN) currently represent the best candidate to be adopted as the communication solution for the last mile connection in process control and monitoring applications in industrial environments. Most of these applications have stringent dependability (reliability and availability) requirements, as a system failure may result in economic losses, put people in danger or lead to environmental damages. Among the different type of faults that can lead to a system failure, permanent faults on network devices have a major impact. They can hamper communications over long periods of time and consequently disturb, or even disable, control algorithms. The lack of a structured approach enabling the evaluation of permanent faults, prevents system designers to optimize decisions that minimize these occurrences. In this work we propose a methodology based on an automatic generation of a fault tree to evaluate the reliability and availability of Wireless Sensor Networks, when permanent faults occur on network devices. The proposal supports any topology, different levels of redundancy, network reconfigurations, criticality of devices and arbitrary failure conditions. The proposed methodology is particularly suitable for the design and validation of Wireless Sensor Networks when trying to optimize its reliability and availability requirements. PMID:22368497

Flow-oriented dynamic assembly algorithm in TCP over OBS networks

NASA Astrophysics Data System (ADS)

Peng, Shuping; Li, Zhengbin; He, Yongqi; Xu, Anshi

2008-11-01

OBS is envisioned as a promising infrastructure for the next generation optical network, and TCP is likely to be the dominant transport protocol in the next generation network. Therefore, it is necessary to evaluate the performance of TCP over OBS networks. The assembly at the ingress edge nodes will impact the network performance. There have been several Fixed Assembly Period (FAP) algorithms proposed. However, the assembly period in FAP is fixed, and it can not be adjusted according to the network condition. Moreover, in FAP, the packets from different TCP sources are assembled into one burst. In that case, if such a burst is dropped, the TCP windows of the corresponding sources will shrink and the throughput will be reduced. In this paper, we introduced a flow-oriented Dynamic Assembly Period (DAP) algorithm for TCP over OBS networks. Through comparing the previous and current burst lengths, DAP can track the variation of TCP window, and update the assembly period dynamically for the next assembly. The performance of DAP is evaluated over a single TCP connection and multiple connections, respectively. The simulation results show that DAP performs better than FAP at almost the whole range of burst dropping probability.

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities

PubMed Central

Santangelo, Valerio

2018-01-01

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior. PMID:29535614

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities.

PubMed

Santangelo, Valerio

2018-01-01

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior.

Research on scheme of applying ASON to current networks

NASA Astrophysics Data System (ADS)

Mao, Y. F.; Li, J. R.; Deng, L. J.

2008-10-01

Automatically Switched Optical Network (ASON) is currently a new and hot research subject in the world. It can provide high bandwidth, high assembly flexibility, high network security and reliability, but with a low management cost. It is presented to meet the requirements for high-throughput optical access with stringent Quality of Service (QoS). But as a brand new technology, ASON can not be supported by the traditional protocol software and network equipments. And the approach to build a new ASON network on the basis of completely abandoning the traditional optical network facilities is not desirable, because it costs too much and wastes a lot of network resources can also be used. So how to apply ASON to the current networks and realize the smooth transition between the existing network and ASON has been a serious problem to many network operators. In this research, the status in quo of ASON is introduced first and then the key problems should be considered when applying ASON to current networks are discussed. Based on this, the strategies should be complied with to overcome these key problems are listed. At last, the approach to apply ASON to the current optical networks is proposed and analyzed.

CALYPSO: a new HF RADAR network to monitor sea surface currents in the Malta-Sicily channel (Mediterranean sea)

NASA Astrophysics Data System (ADS)

Cosoli, S.; Ciraolo, G.; Drago, A.; Capodici, F.; Maltese, A.; Gauci, A.; Galea, A.; Azzopardi, J.; Buscaino, G.; Raffa, F.; Mazzola, S.; Sinatra, R.

2016-12-01

Located in one of the main shipping lanes in the Mediterranean Sea, and in a strategic region for oil extraction platforms, the Malta-Sicily channel is exposed to significant oil spill risks. Shipping and extraction activities constitute a major threat for marine areas of relevant ecological value in the area, and impacts of oil spills on the local ecosystems and the economic activities, including tourism and fisheries, can be dramatic. Damages would be even more devastating for the Maltese archipelago, where marine resources represent important economic assets. Additionally, North Africa coastal areas are also under threat, due to their proximity to the Malta-Sicily Channel. Prevention and mitigation measures, together with rapid-response and decision-making in case of emergency situations, are fundamental steps that help accomplishing the tasks of minimizing risks and reducing impacts to the various compartments. Thanks to state-of-art technology for the monitoring of sea-surface currents in real-time under all sea-state conditions, the CALYPSO network of High-Frequency Radars represents an essential and invaluable tool for the specific purpose. HF radars technology provide a unique tool to track surface currents in near-real time, and as such the dispersion of pollutants can be monitored and forecasted and their origin backtracked, for instance through data assimilation into ocean circulation models or through short-term data-driven statistical forecasts of ocean currents. The network is constituted of four SeaSonde systems that work in the 13.5MHz frequency band. The network is operative since August 2012 and has been extensively validated using a variety of independent platforms and devices, including current meter data and drifting buoys. The latter provided clear evidences of the reliability of the collected data as for tracking the drifting objects. Additionally, data have provided a new insight into the oceanographic characteristics of the region, documenting the presence of a number of previously unreported features. The observing network represents an unique opportunity that exploit HF radar in coastal seas to meet Europe's needs for operational mapping of the surface ocean. Additionally, it provides expertise and support for a new generation of scientists and technical staff.

Study of complete interconnect reliability for a GaAs MMIC power amplifier

NASA Astrophysics Data System (ADS)

Lin, Qian; Wu, Haifeng; Chen, Shan-ji; Jia, Guoqing; Jiang, Wei; Chen, Chao

2018-05-01

By combining the finite element analysis (FEA) and artificial neural network (ANN) technique, the complete prediction of interconnect reliability for a monolithic microwave integrated circuit (MMIC) power amplifier (PA) at the both of direct current (DC) and alternating current (AC) operation conditions is achieved effectively in this article. As a example, a MMIC PA is modelled to study the electromigration failure of interconnect. This is the first time to study the interconnect reliability for an MMIC PA at the conditions of DC and AC operation simultaneously. By training the data from FEA, a high accuracy ANN model for PA reliability is constructed. Then, basing on the reliability database which is obtained from the ANN model, it can give important guidance for improving the reliability design for IC.

Compensated control loops for a 30-cm ion thruster

NASA Technical Reports Server (NTRS)

Robson, R. R.

1976-01-01

The vaporizer dynamic control characteristics of a 30-cm diameter mercury ion thruster were determined by operating the thruster in an open loop steady state mode and then introducing a small sinusoidal signal on the main, cathode, or neutralizer vaporizer current and observing the response of the beam current, discharge voltage, and neutralizer keeper voltage, respectively. This was done over a range of frequencies and operating conditions. From these data, Bode plots for gain and phase were made and mathematical models were obtained. The Bode plots and mathematical models were analyzed for stability and appropriate compensation networks determined. The compensated control loops were incorporated into a power processor and operated with a thruster. The time responses of the compensated loops to changes in set points and recovery from arc conditions are presented.

The formation mechanism of defects, spiral wave in the network of neurons.

PubMed

Wu, Xinyi; Ma, Jun

2013-01-01

A regular network of neurons is constructed by using the Morris-Lecar (ML) neuron with the ion channels being considered, and the potential mechnism of the formation of a spiral wave is investigated in detail. Several spiral waves are initiated by blocking the target wave with artificial defects and/or partial blocking (poisoning) in ion channels. Furthermore, possible conditions for spiral wave formation and the effect of partial channel blocking are discussed completely. Our results are summarized as follows. 1) The emergence of a target wave depends on the transmembrane currents with diversity, which mapped from the external forcing current and this kind of diversity is associated with spatial heterogeneity in the media. 2) Distinct spiral wave could be induced to occupy the network when the target wave is broken by partially blocking the ion channels of a fraction of neurons (local poisoned area), and these generated spiral waves are similar with the spiral waves induced by artificial defects. It is confirmed that partial channel blocking of some neurons in the network could play a similar role in breaking a target wave as do artificial defects; 3) Channel noise and additive Gaussian white noise are also considered, and it is confirmed that spiral waves are also induced in the network in the presence of noise. According to the results mentioned above, we conclude that appropriate poisoning in ion channels of neurons in the network acts as 'defects' on the evolution of the spatiotemporal pattern, and accounts for the emergence of a spiral wave in the network of neurons. These results could be helpful to understand the potential cause of the formation and development of spiral waves in the cortex of a neuronal system.

The Formation Mechanism of Defects, Spiral Wave in the Network of Neurons

PubMed Central

Wu, Xinyi; Ma, Jun

2013-01-01

A regular network of neurons is constructed by using the Morris-Lecar (ML) neuron with the ion channels being considered, and the potential mechnism of the formation of a spiral wave is investigated in detail. Several spiral waves are initiated by blocking the target wave with artificial defects and/or partial blocking (poisoning) in ion channels. Furthermore, possible conditions for spiral wave formation and the effect of partial channel blocking are discussed completely. Our results are summarized as follows. 1) The emergence of a target wave depends on the transmembrane currents with diversity, which mapped from the external forcing current and this kind of diversity is associated with spatial heterogeneity in the media. 2) Distinct spiral wave could be induced to occupy the network when the target wave is broken by partially blocking the ion channels of a fraction of neurons (local poisoned area), and these generated spiral waves are similar with the spiral waves induced by artificial defects. It is confirmed that partial channel blocking of some neurons in the network could play a similar role in breaking a target wave as do artificial defects; 3) Channel noise and additive Gaussian white noise are also considered, and it is confirmed that spiral waves are also induced in the network in the presence of noise. According to the results mentioned above, we conclude that appropriate poisoning in ion channels of neurons in the network acts as ‘defects’ on the evolution of the spatiotemporal pattern, and accounts for the emergence of a spiral wave in the network of neurons. These results could be helpful to understand the potential cause of the formation and development of spiral waves in the cortex of a neuronal system. PMID:23383179

EEG current source density and the phenomenology of the default network.

PubMed

Cannon, Rex L; Baldwin, Debora R

2012-10-01

In recent years, there has been an increasing line of research dedicated to the investigation of the default mode network (DMN) of the brain and resting state networks. However, the mental activity of the DMN has not been rigorously assessed to date. The specific aims of the current study were 2-fold: First, we sought to determine whether the current source density (CSD) levels in the DMN would correspond to other neuroimaging techniques. Second, we sought to understand the subjective mental activity of the DMN during baseline recordings. This study was conducted with 63 nonclinical participants, 34 female and 29 males with a mean age of 19.2 years (standard deviation = 2.0). The participants were recorded in 8 conditions. First, 4-minute eyes-closed baseline (ECB) and eyes-opened baseline (EOB) were obtained. The participants then completed 3 assessment instruments and 3 image conditions while the electroencephalography (EEG) was continuously recorded. Participants completed subjective reports for baselines and image conditions. These were rated by 3 independent raters and compared for reliability using a random effects model with an absolute agreement definition. The mean CSD between all conditions differed significantly, in many but not all regions of interest in the DMN. Interestingly, as suggested by other studies, the DMN appears preferential to self-relevant, self-specific, or self-perceptive processes. The reliability analyses show α for interrater agreement for ECB at .95 and EOB at .96. The subjective reports obtained from the participants regarding the mental activities employed during baseline recordings correspond to attentional and self-regulatory processes, which may also implicate the resting state or DMN as playing a direct role in the maintenance of a complex behavior (eg, being still, attending, and self-regulating). Thus, attention and self-regulation constitute the phenomenology of the resting state (DMN) in this study. The results also demonstrate that EEG CSD is a useful method to examine the DMN during concept-specific tasks to elucidate the neural activity associated with these concepts. Standardized low-resolution electromagnetic tomography (sLORETA) can localize to 5 mm(3), which is comparable to the findings in functional magnetic resonance imaging (fMRI). However, sLORETA can provide data about the difference in activity between groups, individuals, or populations which in many cases fMRI cannot provide.

Power Management Based Current Control Technique for Photovoltaic-Battery Assisted Wind-Hydro Hybrid System

NASA Astrophysics Data System (ADS)

Ram Prabhakar, J.; Ragavan, K.

2013-07-01

This article proposes new power management based current control strategy for integrated wind-solar-hydro system equipped with battery storage mechanism. In this control technique, an indirect estimation of load current is done, through energy balance model, DC-link voltage control and droop control. This system features simpler energy management strategy and necessitates few power electronic converters, thereby minimizing the cost of the system. The generation-demand (G-D) management diagram is formulated based on the stochastic weather conditions and demand, which would likely moderate the gap between both. The features of management strategy deploying energy balance model include (1) regulating DC-link voltage within specified tolerances, (2) isolated operation without relying on external electric power transmission network, (3) indirect current control of hydro turbine driven induction generator and (4) seamless transition between grid-connected and off-grid operation modes. Furthermore, structuring of the hybrid system with appropriate selection of control variables enables power sharing among each energy conversion systems and battery storage mechanism. By addressing these intricacies, it is viable to regulate the frequency and voltage of the remote network at load end. The performance of the proposed composite scheme is demonstrated through time-domain simulation in MATLAB/Simulink environment.

Using a hybrid neuron in physiologically inspired models of the basal ganglia.

PubMed

Thibeault, Corey M; Srinivasa, Narayan

2013-01-01

Our current understanding of the basal ganglia (BG) has facilitated the creation of computational models that have contributed novel theories, explored new functional anatomy and demonstrated results complementing physiological experiments. However, the utility of these models extends beyond these applications. Particularly in neuromorphic engineering, where the basal ganglia's role in computation is important for applications such as power efficient autonomous agents and model-based control strategies. The neurons used in existing computational models of the BG, however, are not amenable for many low-power hardware implementations. Motivated by a need for more hardware accessible networks, we replicate four published models of the BG, spanning single neuron and small networks, replacing the more computationally expensive neuron models with an Izhikevich hybrid neuron. This begins with a network modeling action-selection, where the basal activity levels and the ability to appropriately select the most salient input is reproduced. A Parkinson's disease model is then explored under normal conditions, Parkinsonian conditions and during subthalamic nucleus deep brain stimulation (DBS). The resulting network is capable of replicating the loss of thalamic relay capabilities in the Parkinsonian state and its return under DBS. This is also demonstrated using a network capable of action-selection. Finally, a study of correlation transfer under different patterns of Parkinsonian activity is presented. These networks successfully captured the significant results of the originals studies. This not only creates a foundation for neuromorphic hardware implementations but may also support the development of large-scale biophysical models. The former potentially providing a way of improving the efficacy of DBS and the latter allowing for the efficient simulation of larger more comprehensive networks.

Person re-identification over camera networks using multi-task distance metric learning.

PubMed

Ma, Lianyang; Yang, Xiaokang; Tao, Dacheng

2014-08-01

Person reidentification in a camera network is a valuable yet challenging problem to solve. Existing methods learn a common Mahalanobis distance metric by using the data collected from different cameras and then exploit the learned metric for identifying people in the images. However, the cameras in a camera network have different settings and the recorded images are seriously affected by variability in illumination conditions, camera viewing angles, and background clutter. Using a common metric to conduct person reidentification tasks on different camera pairs overlooks the differences in camera settings; however, it is very time-consuming to label people manually in images from surveillance videos. For example, in most existing person reidentification data sets, only one image of a person is collected from each of only two cameras; therefore, directly learning a unique Mahalanobis distance metric for each camera pair is susceptible to over-fitting by using insufficiently labeled data. In this paper, we reformulate person reidentification in a camera network as a multitask distance metric learning problem. The proposed method designs multiple Mahalanobis distance metrics to cope with the complicated conditions that exist in typical camera networks. We address the fact that these Mahalanobis distance metrics are different but related, and learned by adding joint regularization to alleviate over-fitting. Furthermore, by extending, we present a novel multitask maximally collapsing metric learning (MtMCML) model for person reidentification in a camera network. Experimental results demonstrate that formulating person reidentification over camera networks as multitask distance metric learning problem can improve performance, and our proposed MtMCML works substantially better than other current state-of-the-art person reidentification methods.

Chunking and Consolidation: A Theoretical Synthesis of Semantic Networks, Configuring in Conditioning, S--R Versus Cognitive Learning, Normal Forgetting, the Amnesic Syndrome, and the Hippocampal Arousal System.

ERIC Educational Resources Information Center

Wickelgren, Wayne A.

1979-01-01

The relationship between current information processing and prior associative theories of human and animal learning, memory, and amnesia are discussed. The paper focuses on the two components of the amnesic syndrome, retrograde amnesia and anterograde amnesia. A neural theory of chunking and consolidation is proposed. (Author/RD)

A randomized controlled trial testing a social network intervention to promote physical activity among adolescents.

PubMed

van Woudenberg, Thabo J; Bevelander, Kirsten E; Burk, William J; Smit, Crystal R; Buijs, Laura; Buijzen, Moniek

2018-04-23

The current study examined the effectiveness of a social network intervention to promote physical activity among adolescents. Social network interventions utilize peer influence to change behavior by identifying the most influential individuals within social networks (i.e., influence agents), and training them to promote the target behavior. A total of 190 adolescents (46.32% boys; M age = 12.17, age range: 11-14 years) were randomly allocated to either the intervention or control condition. In the intervention condition, the most influential adolescents (based on peer nominations of classmates) in each classroom were trained to promote physical activity among their classmates. Participants received a research smartphone to complete questionnaires and an accelerometer to measure physical activity (steps per day) at baseline, and during the intervention one month later. A multilevel model tested the effectiveness of the intervention, controlling for clustering of data within participants and days. No intervention effect was observed, b = .04, SE = .10, p = .66. This was one of the first studies to test whether physical activity in adolescents could be promoted via influence agents, and the first social network intervention to use smartphones to do so. Important lessons and implications are discussed concerning the selection criterion of the influence agents, the use of smartphones in social network intervention, and the rigorous analyses used to control for confounding factors. Dutch Trial Registry (NTR): NTR6173 . Registered 5 October 2016 Study procedures were approved by the Ethics Committee of the Radboud University (ECSW2014-100614-222).

Modifying Yeast Tolerance to Inhibitory Conditions of Ethanol Production Processes

PubMed Central

Caspeta, Luis; Castillo, Tania; Nielsen, Jens

2015-01-01

Saccharomyces cerevisiae strains having a broad range of substrate utilization, rapid substrate consumption, and conversion to ethanol, as well as good tolerance to inhibitory conditions are ideal for cost-competitive ethanol production from lignocellulose. A major drawback to directly design S. cerevisiae tolerance to inhibitory conditions of lignocellulosic ethanol production processes is the lack of knowledge about basic aspects of its cellular signaling network in response to stress. Here, we highlight the inhibitory conditions found in ethanol production processes, the targeted cellular functions, the key contributions of integrated -omics analysis to reveal cellular stress responses according to these inhibitors, and current status on design-based engineering of tolerant and efficient S. cerevisiae strains for ethanol production from lignocellulose. PMID:26618154

Observability of Boolean multiplex control networks

NASA Astrophysics Data System (ADS)

Wu, Yuhu; Xu, Jingxue; Sun, Xi-Ming; Wang, Wei

2017-04-01

Boolean multiplex (multilevel) networks (BMNs) are currently receiving considerable attention as theoretical arguments for modeling of biological systems and system level analysis. Studying control-related problems in BMNs may not only provide new views into the intrinsic control in complex biological systems, but also enable us to develop a method for manipulating biological systems using exogenous inputs. In this article, the observability of the Boolean multiplex control networks (BMCNs) are studied. First, the dynamical model and structure of BMCNs with control inputs and outputs are constructed. By using of Semi-Tensor Product (STP) approach, the logical dynamics of BMCNs is converted into an equivalent algebraic representation. Then, the observability of the BMCNs with two different kinds of control inputs is investigated by giving necessary and sufficient conditions. Finally, examples are given to illustrate the efficiency of the obtained theoretical results.

Emotion-independent face recognition

NASA Astrophysics Data System (ADS)

De Silva, Liyanage C.; Esther, Kho G. P.

2000-12-01

Current face recognition techniques tend to work well when recognizing faces under small variations in lighting, facial expression and pose, but deteriorate under more extreme conditions. In this paper, a face recognition system to recognize faces of known individuals, despite variations in facial expression due to different emotions, is developed. The eigenface approach is used for feature extraction. Classification methods include Euclidean distance, back propagation neural network and generalized regression neural network. These methods yield 100% recognition accuracy when the training database is representative, containing one image representing the peak expression for each emotion of each person apart from the neutral expression. The feature vectors used for comparison in the Euclidean distance method and for training the neural network must be all the feature vectors of the training set. These results are obtained for a face database consisting of only four persons.

Local Network-Level Integration Mediates Effects of Transcranial Alternating Current Stimulation.

PubMed

Fuscà, Marco; Ruhnau, Philipp; Neuling, Toralf; Weisz, Nathan

2018-05-01

Transcranial alternating current stimulation (tACS) has been proposed as a tool to draw causal inferences on the role of oscillatory activity in cognitive functioning and has the potential to induce long-term changes in cerebral networks. However, effectiveness of tACS underlies high variability and dependencies, which, as previous modeling works have suggested, may be mediated by local and network-level brain states. We used magnetoencephalography to record brain activity from 17 healthy participants at rest as they kept their eyes open (EO) or eyes closed (EC) while being stimulated with sham, weak, or strong alpha-tACS using a montage commonly assumed to target occipital areas. We reconstructed the activity of sources in all stimulation conditions by means of beamforming. The analysis of resting-state brain activity revealed an interaction of the external stimulation with the endogenous alpha power increase from EO to EC. This interaction was localized to the posterior cingulate, a region remote from occipital cortex. This suggests state-dependent (EO vs. EC) long-range effects of tACS. In a follow-up analysis of this online-tACS effect, we find evidence that this state-dependency effect is mediated by functional network changes: connection strength from the precuneus was significantly correlated with the state-dependency effect in the posterior cingulate during tACS. No analogous correlation could be found for alpha power modulations in occipital cortex. Altogether, this is the first strong evidence to illustrate how functional network architectures can shape tACS effects.

Learning dynamics explains human behaviour in prisoner's dilemma on networks.

PubMed

Cimini, Giulio; Sánchez, Angel

2014-05-06

Cooperative behaviour lies at the very basis of human societies, yet its evolutionary origin remains a key unsolved puzzle. Whereas reciprocity or conditional cooperation is one of the most prominent mechanisms proposed to explain the emergence of cooperation in social dilemmas, recent experimental findings on networked Prisoner's Dilemma games suggest that conditional cooperation also depends on the previous action of the player-namely on the 'mood' in which the player is currently in. Roughly, a majority of people behave as conditional cooperators if they cooperated in the past, whereas they ignore the context and free ride with high probability if they did not. However, the ultimate origin of this behaviour represents a conundrum itself. Here, we aim specifically to provide an evolutionary explanation of moody conditional cooperation (MCC). To this end, we perform an extensive analysis of different evolutionary dynamics for players' behavioural traits-ranging from standard processes used in game theory based on pay-off comparison to others that include non-economic or social factors. Our results show that only a dynamic built upon reinforcement learning is able to give rise to evolutionarily stable MCC, and at the end to reproduce the human behaviours observed in the experiments.

Analysis of the streamflow-gaging station network in Ohio for effectiveness in providing regional streamflow information

USGS Publications Warehouse

Straub, D.E.

1998-01-01

The streamflow-gaging station network in Ohio was evaluated for its effectiveness in providing regional streamflow information. The analysis involved application of the principles of generalized least squares regression between streamflow and climatic and basin characteristics. Regression equations were developed for three flow characteristics: (1) the instantaneous peak flow with a 100-year recurrence interval (P100), (2) the mean annual flow (Qa), and (3) the 7-day, 10-year low flow (7Q10). All active and discontinued gaging stations with 5 or more years of unregulated-streamflow data with respect to each flow characteristic were used to develop the regression equations. The gaging-station network was evaluated for the current (1996) condition of the network and estimated conditions of various network strategies if an additional 5 and 20 years of streamflow data were collected. Any active or discontinued gaging station with (1) less than 5 years of unregulated-streamflow record, (2) previously defined basin and climatic characteristics, and (3) the potential for collection of more unregulated-streamflow record were included in the network strategies involving the additional 5 and 20 years of data. The network analysis involved use of the regression equations, in combination with location, period of record, and cost of operation, to determine the contribution of the data for each gaging station to regional streamflow information. The contribution of each gaging station was based on a cost-weighted reduction of the mean square error (average sampling-error variance) associated with each regional estimating equation. All gaging stations included in the network analysis were then ranked according to their contribution to the regional information for each flow characteristic. The predictive ability of the regression equations developed from the gaging station network could be improved for all three flow characteristics with the collection of additional streamflow data. The addition of new gaging stations to the network would result in an even greater improvement of the accuracy of the regional regression equations. Typically, continued data collection at stations with unregulated streamflow for all flow conditions that had less than 11 years of record with drainage areas smaller than 200 square miles contributed the largest cost-weighted reduction to the average sampling-error variance of the regional estimating equations. The results of the network analyses can be used to prioritize the continued operation of active gaging stations or the reactivation of discontinued gaging stations if the objective is to maximize the regional information content in the streamflow-gaging station network.

Establishing a Modern Ground Network for Space Geodesy Applications

NASA Technical Reports Server (NTRS)

Pearlman, M.; Pavlis, E.; Altamimi, Z.; Noll, C.

2010-01-01

Ground-based networks of co-located space-geodesy techniques (VLBI, SLR, GLASS, DORIS) are the basis for the development and maintenance of the :International Terrestrial deference Frame (ITRE), which is the basis for our metric measurements of global change. The Global Geodetic Observing System (GGOS) within the International Association of Geodesy has established a task to develop a strategy to design, integrate and maintain the fundamental geodetic network and supporting infrastructure in a sustainable way to satisfy the long-term requirements for the reference frame. The GGOS goal is an origin definition at I mm or better and a temporal stability on the order of 0.1 mm/y, with similar numbers for the scale and orientation components. These goals are based on scientific requirements to address sea level rise with confidence. As a first step, simulations focused on establishing the optimal global SLR and VLBI network, since these two techniques alone are sufficient to define the reference frame. The GLASS constellations will then distribute the reference frame to users anywhere on the Earth. Using simulated data to be collected by the future networks, we investigated various designs and the resulting accuracy in the origin, scale and orientation of the resulting ITRF. We present here the results of extensive simulation studies aimed at designing optimal global geodetic networks to support GGOS science products. Current estimates are the network will require 24 - 32 globally distributed co-location sites. Stations in the near global network will require geologically stable sites witla good weather, established infrastructure, and local support and personnel. EGOS will seek groups that are interested in participation. GGOS intends to issues a Call for Participation of groups that would like to take part in the network implementation and operation_ Some examples of integrated stations currently in operation or under development will be presented. We will examine necessary conditions and challenges in designing a co-location station.

Size of the social network versus quality of social support: which is more protective against PTSD?

PubMed

Platt, Jonathan; Keyes, Katherine M; Koenen, Karestan C

2014-08-01

Supportive social networks are important to the post-traumatic response process. However, the effects of social network structure may be distinct from the perceived function of those networks. The present study examined the relative importance of role diversity and perceived strength of social support in mitigating post-traumatic stress disorder (PTSD). Data were drawn from respondents who report lifetime potentially traumatic events in the National Epidemiologic Survey on Alcohol and Related Conditions (N = 31,650). The Social Network Index (SNI) was used to measure the diversity of social connections. The Interpersonal Support Evaluation List (ISEL-12) was used to measure the perceived availability of social support within the network. Odds of current PTSD were compared among individuals representing four dichotomous types of social support: high diversity/high perceived strength, high diversity/low perceived strength, low diversity/high perceived strength, and low diversity/low perceived strength to examine which type of support is more protective against PTSD. Unadjusted odds of PTSD were 1.59 (95 % CI 1.39-1.82) for those with low versus high perceived support strength, and 1.10 (0.94-1.28) among those with non-diverse versus diverse social networks. Compared to the reference group (high diversity/high perceived strength), the adjusted odds of current PTSD were higher for two groups: low diversity/low perceived strength (OR = 1.62; 1.33-1.99), and low diversity/high perceived strength (OR = 1.57; 1.3-1.91). The high diversity/low perceived strength group had no greater odds of PTSD (OR = 1.02; 0.81-1.28). The diversity of a social network is potentially more protective against PTSD than the perception of strong social support. This suggests that programs, which engage individuals in social groups and activities may effectively attenuate the risk of PTSD. A better understanding of how these networks operate with respect to PTSD prevention and mitigation holds promise for improving psychiatric health.

Current distribution in conducting nanowire networks

NASA Astrophysics Data System (ADS)

Kumar, Ankush; Vidhyadhiraja, N. S.; Kulkarni, Giridhar U.

2017-07-01

Conducting nanowire networks find diverse applications in solar cells, touch-screens, transparent heaters, sensors, and various related transparent conducting electrode (TCE) devices. The performances of these devices depend on effective resistance, transmittance, and local current distribution in these networks. Although, there have been rigorous studies addressing resistance and transmittance in TCE, not much attention is paid on studying the distribution of current. Present work addresses this compelling issue of understanding current distribution in TCE networks using analytical as well as Monte-Carlo approaches. We quantified the current carrying backbone region against isolated and dangling regions as a function of wire density (ranging from percolation threshold to many multiples of threshold) and compared the wired connectivity with those obtained from template-based methods. Further, the current distribution in the obtained backbone is studied using Kirchhoff's law, which reveals that a significant fraction of the backbone (which is believed to be an active current component) may not be active for end-to-end current transport due to the formation of intervening circular loops. The study shows that conducting wire based networks possess hot spots (extremely high current carrying regions) which can be potential sources of failure. The fraction of these hot spots is found to decrease with increase in wire density, while they are completely absent in template based networks. Thus, the present work discusses unexplored issues related to current distribution in conducting networks, which are necessary to choose the optimum network for best TCE applications.

Comparison of 2002 Water Year and Historical Water-Quality Data, Upper Gunnison River Basin, Colorado

USGS Publications Warehouse

Spahr, N.E.

2003-01-01

Introduction: Population growth and changes in land-use practices have the potential to affect water quality and quantity in the upper Gunnison River basin. In 1995, the U.S. Geological Survey (USGS), in cooperation with local sponsors, City of Gunnison, Colorado River Water Conservation District, Crested Butte South Metropolitan District, Gunnison County, Mount Crested Butte Water and Sanitation District, National Park Service, Town of Crested Butte, and Upper Gunnison River Water Conservancy District, established a water-quality monitoring program in the upper Gunnison River basin to characterize current water-quality conditions and to assess the effects of increased urban development and other land-use changes on water quality. The monitoring network has evolved into two groups of stations, stations that are considered as long term and stations that are rotational. The long-term stations are monitored to assist in defining temporal changes in water quality (how conditions have changed over time). The rotational stations are monitored to assist in the spatial definition of water-quality conditions (how conditions differ throughout the basin) and to address local and short term concerns. Another group of stations (rotational group 2) will be chosen and sampled beginning in water year 2004. Annual summaries of the water-quality data from the monitoring network provide a point of reference for discussions regarding water-quality sampling in the upper Gunnison River basin. This summary includes data collected during water year 2002. The introduction provides a map of the sampling locations, definitions of terms, and a one-page summary of selected water-quality conditions at the network stations. The remainder of the summary is organized around the data collected at individual stations. Data collected during water year 2002 are compared to historical data (data collected for this network since 1995), state water-quality standards, and federal water-quality guidelines. Data were collected during water year 2002 following USGS protocols (U.S. Geological Survey, variously dated).

Network Access Control List Situation Awareness

ERIC Educational Resources Information Center

Reifers, Andrew

2010-01-01

Network security is a large and complex problem being addressed by multiple communities. Nevertheless, current theories in networking security appear to overestimate network administrators' ability to understand network access control lists (NACLs), providing few context specific user analyses. Consequently, the current research generally seems to…

Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology.

PubMed

Bianconi, Fortunato; Baldelli, Elisa; Ludovini, Vienna; Luovini, Vienna; Petricoin, Emanuel F; Crinò, Lucio; Valigi, Paolo

2015-10-19

The study of cancer therapy is a key issue in the field of oncology research and the development of target therapies is one of the main problems currently under investigation. This is particularly relevant in different types of tumor where traditional chemotherapy approaches often fail, such as lung cancer. We started from the general definition of robustness introduced by Kitano and applied it to the analysis of dynamical biochemical networks, proposing a new algorithm based on moment independent analysis of input/output uncertainty. The framework utilizes novel computational methods which enable evaluating the model fragility with respect to quantitative performance measures and parameters such as reaction rate constants and initial conditions. The algorithm generates a small subset of parameters that can be used to act on complex networks and to obtain the desired behaviors. We have applied the proposed framework to the EGFR-IGF1R signal transduction network, a crucial pathway in lung cancer, as an example of Cancer Systems Biology application in drug discovery. Furthermore, we have tested our framework on a pulse generator network as an example of Synthetic Biology application, thus proving the suitability of our methodology to the characterization of the input/output synthetic circuits. The achieved results are of immediate practical application in computational biology, and while we demonstrate their use in two specific examples, they can in fact be used to study a wider class of biological systems.

Evaluation on surface current observing network of high frequency ground wave radars in the Gulf of Thailand

NASA Astrophysics Data System (ADS)

Yin, Xunqiang; Shi, Junqiang; Qiao, Fangli

2018-05-01

Due to the high cost of ocean observation system, the scientific design of observation network becomes much important. The current network of the high frequency radar system in the Gulf of Thailand has been studied using a three-dimensional coastal ocean model. At first, the observations from current radars have been assimilated into this coastal model and the forecast results have improved due to the data assimilation. But the results also show that further optimization of the observing network is necessary. And then, a series of experiments were carried out to assess the performance of the existing high frequency ground wave radar surface current observation system. The simulated surface current data in three regions were assimilated sequentially using an efficient ensemble Kalman filter data assimilation scheme. The experimental results showed that the coastal surface current observation system plays a positive role in improving the numerical simulation of the currents. Compared with the control experiment without assimilation, the simulation precision of surface and subsurface current had been improved after assimilated the surface currents observed at current networks. However, the improvement for three observing regions was quite different and current observing network in the Gulf of Thailand is not effective and a further optimization is required. Based on these evaluations, a manual scheme has been designed by discarding the redundant and inefficient locations and adding new stations where the performance after data assimilation is still low. For comparison, an objective scheme based on the idea of data assimilation has been obtained. Results show that all the two schemes of observing network perform better than the original network and optimal scheme-based data assimilation is much superior to the manual scheme that based on the evaluation of original observing network in the Gulf of Thailand. The distributions of the optimal network of radars could be a useful guidance for future design of observing system in this region.

NetFlow Dynamics

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

Corbet Jr., Thomas F; Beyeler, Walter E; Vanwestrienen, Dirk

NetFlow Dynamics is a web-accessible analysis environment for simulating dynamic flows of materials on model networks. Performing a simulation requires both the NetFlow Dynamics application and a network model which is a description of the structure of the nodes and edges of a network including the flow capacity of each edge and the storage capacity of each node, and the sources and sinks of the material flowing on the network. NetFlow Dynamics consists of databases for storing network models, algorithms to calculate flows on networks, and a GIS-based graphical interface for performing simulations and viewing simulation results. Simulated flows aremore » dynamic in the sense that flows on each edge of the network and inventories at each node change with time and can be out of equilibrium with boundary conditions. Any number of network models could be simulated using Net Flow Dynamics. To date, the models simulated have been models of petroleum infrastructure. The main model has been the National Transportation Fuels Model (NTFM), a network of U.S. oil fields, transmission pipelines, rail lines, refineries, tank farms, and distribution terminals. NetFlow Dynamics supports two different flow algorithms, the Gradient Flow algorithm and the Inventory Control algorithm, that were developed specifically for the NetFlow Dynamics application. The intent is to add additional algorithms in the future as needed. The ability to select from multiple algorithms is desirable because a single algorithm never covers all analysis needs. The current algorithms use a demand-driven capacity-constrained formulation which means that the algorithms strive to use all available capacity and stored inventory to meet desired flows to sinks, subject to the capacity constraints of each network component. The current flow algorithms are best suited for problems in which a material flows on a capacity-constrained network representing a supply chain in which the material supplied can be stored at each node of the network. In the petroleum models, the flowing materials are crude oil and refined products that can be stored at tank farms, refineries, or terminals (i.e. the nodes of the network). Examples of other network models that could be simulated are currency flowing in a financial network, agricultural products moving to market, or natural gas flowing on a pipeline network.« less

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

PubMed Central

Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Xu, Jing; Zheng, Kehong

2015-01-01

In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system. PMID:26580620

Electrophysiologic studies of neronal activities under ischemia condition.

PubMed

Huang, Shun-Ho; Wang, Ping-Hsien; Chen, Jia-Jin Jason

2008-01-01

Substrate with integrated microelectrode arrays (MEAs) provides an alternative electrophysiological method. With MEAS, one can measure the impedance and elicit electrical stimulation from multiple sites of MEAs to determine the electrophysiological conditions of cells. The aims of this research were to construct an impedance and action potential measurement system for neurons cultured on MEAs for observing the electrophysiological signal transmission in neuronal network during glucose and oxygen deprivation (OGD). An extracellular stimulator producing the biphasic micro-current pulse for neuron stimulation was built in this study. From the time-course recording of impedance, OGD condition effectively induced damage in neurons in vitro. It is known that the results of cell stimulation are affected by electrode impedance, so does the result of neuron cells covered on the electrode can measure the sealing resistance. For extracellular stimulation study, cortical neuronal activity was recorded and the suitable stimulation window was determined. However, the stimulation results were affected by electrode impedance as well as sealing impedance resulting from neuron cells covering the electrode. Further development of surface modification for cultured neuron network should provide a better way for in vitro impedance and electrophysiological measurements.

Self-Organizing Maps-based ocean currents forecasting system.

PubMed

Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

2016-03-16

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.

Self-Organizing Maps-based ocean currents forecasting system

PubMed Central

Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

2016-01-01

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training. PMID:26979129

Network Medicine for Alzheimer's Disease and Traditional Chinese Medicine.

PubMed

Jarrell, Juliet T; Gao, Li; Cohen, David S; Huang, Xudong

2018-05-11

Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.

Cross-Domain Analogies as Relating Derived Relations among Two Separate Relational Networks

PubMed Central

Ruiz, Francisco J; Luciano, Carmen

2011-01-01

Contemporary behavior analytic research is making headway in analyzing analogy as the establishment of a relation of coordination among common types of trained or derived relations. Previous studies have been focused on within-domain analogy. The current study expands previous research by analyzing cross-domain analogy as relating relations among separate relational networks and by correlating participants' performance with a standard measure of analogical reasoning. In two experiments, adult participants first completed general intelligence and analogical reasoning tests. Subsequently, they were exposed to a computerized conditional discrimination training procedure designed to create two relational networks, each consisting of two 3-member equivalence classes. The critical test was a two-part analogical test in which participants had to relate combinatorial relations of coordination and distinction between the two relational networks. In Experiment 1, combinatorial relations for each network were individually tested prior to analogical testing, but in Experiment 2 they were not. Across both experiments, 65% of participants passed the analogical test on the first attempt. Moreover, results from the training procedure were strongly correlated with the standard measure of analogical reasoning. PMID:21547072

A Network Approach to Environmental Impact in Psychotic Disorder: Brief Theoretical Framework.

PubMed

Isvoranu, Adela-Maria; Borsboom, Denny; van Os, Jim; Guloksuz, Sinan

2016-07-01

The spectrum of psychotic disorder represents a multifactorial and heterogeneous condition and is thought to result from a complex interplay between genetic and environmental factors. In the current paper, we analyze this interplay using network analysis, which has been recently proposed as a novel psychometric framework for the study of mental disorders. Using general population data, we construct network models for the relation between 3 environmental risk factors (cannabis use, developmental trauma, and urban environment), dimensional measures of psychopathology (anxiety, depression, interpersonal sensitivity, obsessive-compulsive disorder, phobic anxiety, somatizations, and hostility), and a composite measure of psychosis expression. Results indicate the existence of specific paths between environmental factors and symptoms. These paths most often involve cannabis use. In addition, the analyses suggest that symptom networks are more strongly connected for people exposed to environmental risk factors, implying that environmental exposure may lead to less resilient symptom networks. © The Author 2016. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

TCP throughput adaptation in WiMax networks using replicator dynamics.

PubMed

Anastasopoulos, Markos P; Petraki, Dionysia K; Kannan, Rajgopal; Vasilakos, Athanasios V

2010-06-01

The high-frequency segment (10-66 GHz) of the IEEE 802.16 standard seems promising for the implementation of wireless backhaul networks carrying large volumes of Internet traffic. In contrast to wireline backbone networks, where channel errors seldom occur, the TCP protocol in IEEE 802.16 Worldwide Interoperability for Microwave Access networks is conditioned exclusively by wireless channel impairments rather than by congestion. This renders a cross-layer design approach between the transport and physical layers more appropriate during fading periods. In this paper, an adaptive coding and modulation (ACM) scheme for TCP throughput maximization is presented. In the current approach, Internet traffic is modulated and coded employing an adaptive scheme that is mathematically equivalent to the replicator dynamics model. The stability of the proposed ACM scheme is proven, and the dependence of the speed of convergence on various physical-layer parameters is investigated. It is also shown that convergence to the strategy that maximizes TCP throughput may be further accelerated by increasing the amount of information from the physical layer.

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.

PubMed

Movahedi, Faezeh; Coyle, James L; Sejdic, Ervin

2018-05-01

Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this paper, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state-of-the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.

Power supply conditioning circuit

NASA Technical Reports Server (NTRS)

Primas, L. E.; Loveland, R.

1987-01-01

A power supply conditioning circuit that can reduce Periodic and Random Deviations (PARD) on the output voltages of dc power supplies to -150 dBV from dc to several KHz with no measurable periodic deviations is described. The PARD for a typical commercial low noise power supply is -74 dBV for frequencies above 20 Hz and is often much worse at frequencies below 20 Hz. The power supply conditioning circuit described here relies on the large differences in the dynamic impedances of a constant current diode and a zener diode to establish a dc voltage with low PARD. Power supplies with low PARD are especially important in circuitry involving ultrastable frequencies for the Deep Space Network.

Impact of different environmental conditions on lithium-ion batteries performance through the thermal monitoring with fiber sensors

NASA Astrophysics Data System (ADS)

Nascimento, Micael; Ferreira, Marta S.; Pinto, João. L.

2017-08-01

In this work, an optical fiber sensing network has been developed to assess the impact of different environmental conditions on lithium batteries performance through the real time thermal monitoring. The battery is submitted to constant current charge and different discharge C-rates, under normal and abusive operating conditions. The results show that for the discharge C-rate of 5.77C, the LiB under cold and dry climates had 32.5% and 27.2% lower temperature variations, when compared with temperate climates, respectively. The higher temperature shift detected in the temperate climate was related to the battery better performance regarding discharge capacity and power capabilities.

Summary of hydrologic conditions in Kansas, water year 2014

USGS Publications Warehouse

Robison, Andrew L.

2015-01-01

The U.S. Geological Survey Kansas Water Science Center, in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring gages in the State of Kansas. These include 206 real-time streamgages, 12 real-time reservoir-level monitoring stations, and 32 groundwater monitoring wells. These data and associated analyses, accumulated over time, provide a unique overview of hydrologic conditions and help improve our understanding of Kansas’s water resources. Yearly hydrologic conditions are determined by comparing statistical analyses of current and historical water year data for the period of record. These data are used in protecting life and property, and managing water resources for agricultural, industrial, public supply, ecological, and recreational purposes.

The results of systems tests of the 500 kV busbar controllable shunting reactor in the Tavricheskaya substation

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

Gusev, S. I.; Karpov, V. N.; Kiselev, A. N.

2009-09-15

The results of systems tests of the 500 kV busbar magnetization-controllable shunting reactor (CSR), set up in the Tavricheskaya substation, including measurements of the quality of the electric power, the harmonic composition of the network currents of the reactor for different values of the reactive power consumed, the determination of the regulating characteristics of the reactor, the speed of response of the shunting reactor in the current and voltage stabilization modes, and also the operation of the reactor under dynamic conditions for different perturbations, are presented. The results obtained are analyzed.

Brief encounters: what do primary care professionals contribute to peoples' self-care support network for long-term conditions? A mixed methods study.

PubMed

Rogers, Anne; Vassilev, Ivaylo; Brooks, Helen; Kennedy, Anne; Blickem, Christian

2016-02-17

Primary care professionals are presumed to play a central role in delivering long-term condition management. However the value of their contribution relative to other sources of support in the life worlds of patients has been less acknowledged. Here we explore the value of primary care professionals in people's personal communities of support for long-term condition management. A mixed methods survey with nested qualitative study designed to identify relationships and social network member's (SNM) contributions to the support work of managing a long-term condition conducted in 2010 in the North West of England. Through engagement with a concentric circles diagram three hundred participants identified 2544 network members who contributed to illness management. The results demonstrated how primary care professionals are involved relative to others in ongoing self-care management. Primary care professionals constituted 15.5 % of overall network members involved in chronic illness work. Their contribution was identified as being related to illness specific work providing less in terms of emotional work than close family members or pets and little to everyday work. The qualitative accounts suggested that primary care professionals are valued mainly for access to medication and nurses for informational and monitoring activities. Overall primary care is perceived as providing less input in terms of extended self-management support than the current literature on policy and practice suggests. Thus primary care professionals can be described as providing 'minimally provided support'. This sense of a 'minimally' provided input reinforces limited expectations and value about what primary care professionals can provide in terms of support for long-term condition management. Primary care was perceived as having an essential but limited role in making a contribution to support work for long-term conditions. This coalesces with evidence of a restricted capacity of primary care to take on the work load of self-management support work. There is a need to prioritise exploring the means by which extended self-care support could be enhanced out-with primary care. Central to this is building a system capable of engaging network capacity to mobilise resources for self-management support from open settings and the broader community.

Three-Dimensional Bi₂Te₃ Networks of Interconnected Nanowires: Synthesis and Optimization.

PubMed

Ruiz-Clavijo, Alejandra; Caballero-Calero, Olga; Martín-González, Marisol

2018-05-18

Self-standing Bi₂Te₃ networks of interconnected nanowires were fabricated in three-dimensional porous anodic alumina templates (3D⁻AAO) with a porous structure spreading in all three spatial dimensions. Pulsed electrodeposition parameters were optimized to grow highly oriented Bi₂Te₃ interconnected nanowires with stoichiometric composition inside those 3D⁻AAO templates. The nanowire networks were analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX), and Raman spectroscopy. The results are compared to those obtained in films and 1D nanowires grown under similar conditions. The crystalline structure and composition of the 3D Bi⁻Te nanowire network are finely tuned by controlling the applied voltage and the relaxation time off at zero current density during the deposition. With this fabrication method, and controlling the electrodeposition parameters, stoichiometric Bi₂Te₃ networks of interconnected nanowires have been obtained, with a preferential orientation along [1 1 0], which makes them optimal candidates for out-of-plane thermoelectric applications. Moreover, the templates in which they are grown can be dissolved and the network of interconnected nanowires is self-standing without affecting its composition and orientation properties.

Spontaneous Coronary Artery Dissection: A Disease-Specific, Social Networking Community–Initiated Study

PubMed Central

Tweet, Marysia S.; Gulati, Rajiv; Aase, Lee A.; Hayes, Sharonne N.

2011-01-01

OBJECTIVE: To develop and assess the feasibility of a novel method for identification, recruitment, and retrospective and prospective evaluation of patients with rare conditions. PATIENTS AND METHODS: This pilot study is a novel example of “patient-initiated research.” After being approached by several members of an international disease-specific support group on a social networking site, we used it to identify patients who had been diagnosed as having at least 1 episode of spontaneous coronary artery dissection and recruited them to participate in a clinical investigation of their condition. Medical records were collected and reviewed, the original diagnosis was independently confirmed by review of imaging studies, and health status (both interval and current) was assessed via specially designed questionnaires and validated assessment tools. RESULTS: Recruitment of all 12 participants was complete within 1 week of institutional review board approval (March 18, 2010). Data collection was completed November 18, 2010. All participants completed the study questionnaires and provided the required medical records and coronary angiograms and ancillary imaging data. CONCLUSION: This study involving patients with spontaneous coronary artery dissection demonstrates the feasibility of and is a successful model for developing a “virtual” multicenter disease registry through disease-specific social media networks to better characterize an uncommon condition. This study is a prime example of patient-initiated research that could be used by other health care professionals and institutions. PMID:21878595

Spontaneous coronary artery dissection: a disease-specific, social networking community-initiated study.

PubMed

Tweet, Marysia S; Gulati, Rajiv; Aase, Lee A; Hayes, Sharonne N

2011-09-01

To develop and assess the feasibility of a novel method for identification, recruitment, and retrospective and prospective evaluation of patients with rare conditions. This pilot study is a novel example of "patient-initiated research." After being approached by several members of an international disease-specific support group on a social networking site, we used it to identify patients who had been diagnosed as having at least 1 episode of spontaneous coronary artery dissection and recruited them to participate in a clinical investigation of their condition. Medical records were collected and reviewed, the original diagnosis was independently confirmed by review of imaging studies, and health status (both interval and current) was assessed via specially designed questionnaires and validated assessment tools. Recruitment of all 12 participants was complete within 1 week of institutional review board approval (March 18, 2010). Data collection was completed November 18, 2010. All participants completed the study questionnaires and provided the required medical records and coronary angiograms and ancillary imaging data. This study involving patients with spontaneous coronary artery dissection demonstrates the feasibility of and is a successful model for developing a "virtual" multicenter disease registry through disease-specific social media networks to better characterize an uncommon condition. This study is a prime example of patient-initiated research that could be used by other health care professionals and institutions.

Amorphous silicon balanced photodiode for microfluidic applications

NASA Astrophysics Data System (ADS)

Caputo, Domenico; de Cesare, Giampiero; Nascetti, Augusto; Scipinotti, Riccardo

2013-05-01

In this paper, we present the first integration of an amorphous silicon balanced photosensor with a microfluidic network to perform on-chip detection for biomedical applications, where rejection of large background light intensity is needed. This solution allows to achieve high resolution readout without the need of high dynamic range electronics. The balanced photodiode is constituted by two series-connected a-Si:H/a-SiC:H n-i-p stacked junctions, deposited on a glass substrate. The structure is a three terminal device where two electrodes bias the two diodes in reverse conditions while the third electrode (i.e. the connection point of the two diodes) provides the output signal given by the differential current. The microfluidic network is composed of two channels made in PolyDimetilSiloxane (PDMS) positioned over the glass substrate on the photodiode-side aligning each channel with a diode. This configuration guarantees an optimal optical coupling between luminescence events occurring in the channels and the photosensors. The experiments have been carried out measuring the differential current in identical and different conditions for the two channels. We have found that: the measurement dynamic range can be increased by at least an order of magnitude with respect to conventional photodiodes; the balanced photodiode is able to detect the presence or absence of water in the channel; the presence of fluorescent molecules in the channel can be successful detected by our device without any need of optical filter for the excitation light. These preliminary results demonstrate the successful integration of a microfluidic network with a-Si:H photosensor for on-chip detection in biomedical applications.

Wallops Ship Surveillance System

NASA Technical Reports Server (NTRS)

Smith, Donna C.

2011-01-01

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

When global rule reversal meets local task switching: The neural mechanisms of coordinated behavioral adaptation to instructed multi-level demand changes.

PubMed

Shi, Yiquan; Wolfensteller, Uta; Schubert, Torsten; Ruge, Hannes

2018-02-01

Cognitive flexibility is essential to cope with changing task demands and often it is necessary to adapt to combined changes in a coordinated manner. The present fMRI study examined how the brain implements such multi-level adaptation processes. Specifically, on a "local," hierarchically lower level, switching between two tasks was required across trials while the rules of each task remained unchanged for blocks of trials. On a "global" level regarding blocks of twelve trials, the task rules could reverse or remain the same. The current task was cued at the start of each trial while the current task rules were instructed before the start of a new block. We found that partly overlapping and partly segregated neural networks play different roles when coping with the combination of global rule reversal and local task switching. The fronto-parietal control network (FPN) supported the encoding of reversed rules at the time of explicit rule instruction. The same regions subsequently supported local task switching processes during actual implementation trials, irrespective of rule reversal condition. By contrast, a cortico-striatal network (CSN) including supplementary motor area and putamen was increasingly engaged across implementation trials and more so for rule reversal than for nonreversal blocks, irrespective of task switching condition. Together, these findings suggest that the brain accomplishes the coordinated adaptation to multi-level demand changes by distributing processing resources either across time (FPN for reversed rule encoding and later for task switching) or across regions (CSN for reversed rule implementation and FPN for concurrent task switching). © 2017 Wiley Periodicals, Inc.

Interneuron-mediated inhibition synchronizes neuronal activity during slow oscillation.

PubMed

Chen, Jen-Yung; Chauvette, Sylvain; Skorheim, Steven; Timofeev, Igor; Bazhenov, Maxim

2012-08-15

The signature of slow-wave sleep in the electroencephalogram (EEG) is large-amplitude fluctuation of the field potential, which reflects synchronous alternation of activity and silence across cortical neurons. While initiation of the active cortical states during sleep slow oscillation has been intensively studied, the biological mechanisms which drive the network transition from an active state to silence remain poorly understood. In the current study, using a combination of in vivo electrophysiology and thalamocortical network simulation, we explored the impact of intrinsic and synaptic inhibition on state transition during sleep slow oscillation. We found that in normal physiological conditions, synaptic inhibition controls the duration and the synchrony of active state termination. The decline of interneuron-mediated inhibition led to asynchronous downward transition across the cortical network and broke the regular slow oscillation pattern. Furthermore, in both in vivo experiment and computational modelling, we revealed that when the level of synaptic inhibition was reduced significantly, it led to a recovery of synchronized oscillations in the form of seizure-like bursting activity. In this condition, the fast active state termination was mediated by intrinsic hyperpolarizing conductances. Our study highlights the significance of both intrinsic and synaptic inhibition in manipulating sleep slow rhythms.

Interneuron-mediated inhibition synchronizes neuronal activity during slow oscillation

PubMed Central

Chen, Jen-Yung; Chauvette, Sylvain; Skorheim, Steven; Timofeev, Igor; Bazhenov, Maxim

2012-01-01

The signature of slow-wave sleep in the electroencephalogram (EEG) is large-amplitude fluctuation of the field potential, which reflects synchronous alternation of activity and silence across cortical neurons. While initiation of the active cortical states during sleep slow oscillation has been intensively studied, the biological mechanisms which drive the network transition from an active state to silence remain poorly understood. In the current study, using a combination of in vivo electrophysiology and thalamocortical network simulation, we explored the impact of intrinsic and synaptic inhibition on state transition during sleep slow oscillation. We found that in normal physiological conditions, synaptic inhibition controls the duration and the synchrony of active state termination. The decline of interneuron-mediated inhibition led to asynchronous downward transition across the cortical network and broke the regular slow oscillation pattern. Furthermore, in both in vivo experiment and computational modelling, we revealed that when the level of synaptic inhibition was reduced significantly, it led to a recovery of synchronized oscillations in the form of seizure-like bursting activity. In this condition, the fast active state termination was mediated by intrinsic hyperpolarizing conductances. Our study highlights the significance of both intrinsic and synaptic inhibition in manipulating sleep slow rhythms. PMID:22641778

An Empirically Calibrated Model of Cell Fate Decision Following Viral Infection

NASA Astrophysics Data System (ADS)

Coleman, Seth; Igoshin, Oleg; Golding, Ido

The life cycle of the virus (phage) lambda is an established paradigm for the way genetic networks drive cell fate decisions. But despite decades of interrogation, we are still unable to theoretically predict whether the infection of a given cell will result in cell death or viral dormancy. The poor predictive power of current models reflects the absence of quantitative experimental data describing the regulatory interactions between different lambda genes. To address this gap, we are constructing a theoretical model that captures the known interactions in the lambda network. Model assumptions and parameters are calibrated using new single-cell data from our lab, describing the activity of lambda genes at single-molecule resolution. We began with a mean-field model, aimed at exploring the population averaged gene-expression trajectories under different initial conditions. Next, we will develop a stochastic formulation, to capture the differences between individual cells within the population. The eventual goal is to identify how the post-infection decision is driven by the interplay between network topology, initial conditions, and stochastic effects. The insights gained here will inform our understanding of cell fate choices in more complex cellular systems.

Auditory hallucinations.

PubMed

Blom, Jan Dirk

2015-01-01

Auditory hallucinations constitute a phenomenologically rich group of endogenously mediated percepts which are associated with psychiatric, neurologic, otologic, and other medical conditions, but which are also experienced by 10-15% of all healthy individuals in the general population. The group of phenomena is probably best known for its verbal auditory subtype, but it also includes musical hallucinations, echo of reading, exploding-head syndrome, and many other types. The subgroup of verbal auditory hallucinations has been studied extensively with the aid of neuroimaging techniques, and from those studies emerges an outline of a functional as well as a structural network of widely distributed brain areas involved in their mediation. The present chapter provides an overview of the various types of auditory hallucination described in the literature, summarizes our current knowledge of the auditory networks involved in their mediation, and draws on ideas from the philosophy of science and network science to reconceptualize the auditory hallucinatory experience, and point out directions for future research into its neurobiologic substrates. In addition, it provides an overview of known associations with various clinical conditions and of the existing evidence for pharmacologic and non-pharmacologic treatments. © 2015 Elsevier B.V. All rights reserved.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

PubMed Central

Pena, Rodrigo F. O.; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C.; Lindner, Benjamin

2018-01-01

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks. PMID:29551968

Flood monitoring network in southeastern Louisiana

USGS Publications Warehouse

McCallum, Brian E.

1994-01-01

A flood monitoring network has been established to alert emergency operations personnel and the public about hydrologic conditions in the Amite River Basin. The U.S. Geological Survey (USGS), in cooperation with the Louisiana Office of Emergency Preparedness (LOEP), has installed a real-time data acquisition system to monitor rainfall and river stages in the basin. These data will be transmitted for use by emergency operations personnel to develop flood control and evacuation strategies. The current river stages at selected gaging stations in the basin also will be broadcast by local television and radio stations during a flood. Residents can record the changing river stages on a basin monitoring map, similar to a hurricane tracking map.

Thermal non-equilibrium in porous medium adjacent to vertical plate: ANN approach

NASA Astrophysics Data System (ADS)

Ahmed, N. J. Salman; Ahamed, K. S. Nazim; Al-Rashed, Abdullah A. A. A.; Kamangar, Sarfaraz; Athani, Abdulgaphur

2018-05-01

Thermal non-equilibrium in porous medium is a condition that refers to temperature discrepancy in solid matrix and fluid of porous medium. This type of flow is complex flow requiring complex set of partial differential equations that govern the flow behavior. The current work is undertaken to predict the thermal non-equilibrium behavior of porous medium adjacent to vertical plate using artificial neural network. A set of neurons in 3 layers are trained to predict the heat transfer characteristics. It is found that the thermal non-equilibrium heat transfer behavior in terms of Nusselt number of fluid as well as solid phase can be predicted accurately by using well-trained neural network.

Priority-based methods for reducing the impact of packet loss on HEVC encoded video streams

NASA Astrophysics Data System (ADS)

Nightingale, James; Wang, Qi; Grecos, Christos

2013-02-01

The rapid growth in the use of video streaming over IP networks has outstripped the rate at which new network infrastructure has been deployed. These bandwidth-hungry applications now comprise a significant part of all Internet traffic and present major challenges for network service providers. The situation is more acute in mobile networks where the available bandwidth is often limited. Work towards the standardisation of High Efficiency Video Coding (HEVC), the next generation video coding scheme, is currently on track for completion in 2013. HEVC offers the prospect of a 50% improvement in compression over the current H.264 Advanced Video Coding standard (H.264/AVC) for the same quality. However, there has been very little published research on HEVC streaming or the challenges of delivering HEVC streams in resource-constrained network environments. In this paper we consider the problem of adapting an HEVC encoded video stream to meet the bandwidth limitation in a mobile networks environment. Video sequences were encoded using the Test Model under Consideration (TMuC HM6) for HEVC. Network abstraction layers (NAL) units were packetized, on a one NAL unit per RTP packet basis, and transmitted over a realistic hybrid wired/wireless testbed configured with dynamically changing network path conditions and multiple independent network paths from the streamer to the client. Two different schemes for the prioritisation of RTP packets, based on the NAL units they contain, have been implemented and empirically compared using a range of video sequences, encoder configurations, bandwidths and network topologies. In the first prioritisation method the importance of an RTP packet was determined by the type of picture and the temporal switching point information carried in the NAL unit header. Packets containing parameter set NAL units and video coding layer (VCL) NAL units of the instantaneous decoder refresh (IDR) and the clean random access (CRA) pictures were given the highest priority followed by NAL units containing pictures used as reference pictures from which others can be predicted. The second method assigned a priority to each NAL unit based on the rate-distortion cost of the VCL coding units contained in the NAL unit. The sum of the rate-distortion costs of each coding unit contained in a NAL unit was used as the priority weighting. The preliminary results of extensive experiments have shown that all three schemes offered an improvement in PSNR, when comparing original and decoded received streams, over uncontrolled packet loss. Using the first method consistently delivered a significant average improvement of 0.97dB over the uncontrolled scenario while the second method provided a measurable, but less consistent, improvement across the range of testing conditions and encoder configurations.

Depression and Chronic Health Conditions Among Latinos: The Role of Social Networks.

PubMed

Soto, Sandra; Arredondo, Elva M; Villodas, Miguel T; Elder, John P; Quintanar, Elena; Madanat, Hala

2016-12-01

The purpose of this study was to examine the "buffering hypothesis" of social network characteristics in the association between chronic conditions and depression among Latinos. Cross-sectional self-report data from the San Diego Prevention Research Center's community survey of Latinos were used (n = 393). Separate multiple logistic regression models tested the role of chronic conditions and social network characteristics in the likelihood of moderate-to-severe depressive symptoms. Having a greater proportion of the network comprised of friends increased the likelihood of depression among those with high cholesterol. Having a greater proportion of women in the social network was directly related to the increased likelihood of depression, regardless of the presence of chronic health conditions. Findings suggest that network characteristics may play a role in the link between chronic conditions and depression among Latinos. Future research should explore strategies targeting the social networks of Latinos to improve health outcomes.

Decision support systems and methods for complex networks

DOEpatents

Huang, Zhenyu [Richland, WA; Wong, Pak Chung [Richland, WA; Ma, Jian [Richland, WA; Mackey, Patrick S [Richland, WA; Chen, Yousu [Richland, WA; Schneider, Kevin P [Seattle, WA

2012-02-28

Methods and systems for automated decision support in analyzing operation data from a complex network. Embodiments of the present invention utilize these algorithms and techniques not only to characterize the past and present condition of a complex network, but also to predict future conditions to help operators anticipate deteriorating and/or problem situations. In particular, embodiments of the present invention characterize network conditions from operation data using a state estimator. Contingency scenarios can then be generated based on those network conditions. For at least a portion of all of the contingency scenarios, risk indices are determined that describe the potential impact of each of those scenarios. Contingency scenarios with risk indices are presented visually as graphical representations in the context of a visual representation of the complex network. Analysis of the historical risk indices based on the graphical representations can then provide trends that allow for prediction of future network conditions.

Southwestern USA Drought over Multiple Millennia

NASA Astrophysics Data System (ADS)

Salzer, M. W.; Kipfmueller, K. F.

2014-12-01

Severe to extreme drought conditions currently exist across much of the American West. There is increasing concern that climate change may be worsening droughts in the West and particularly the Southwest. Thus, it is important to understand the role of natural variability and to place current conditions in a long-term context. We present a tree-ring derived reconstruction of regional-scale precipitation for the Southwestern USA over several millennia. A network of 48 tree-ring chronologies from California, Nevada, Utah, Arizona, New Mexico, and Colorado was used. All of the chronologies are at least 1,000 years long. The network was subjected to data reduction through PCA and a "nested" multiple linear regression reconstruction approach. The regression model was able to capture 72% of the variance in September-August precipitation over the last 1,000 years and 53% of the variance over the first millennium of the Common Era. Variance captured and spatial coverage further declined back in time as the shorter chronologies dropped out of the model, eventually reaching 24% of variance captured at 3250 BC. Results show regional droughts on decadal- to multi-decadal scales have been prominent and persistent phenomena in the region over the last several millennia. Anthropogenic warming is likely to exacerbate the effects of future droughts on human and other biotic populations.

The impact of Polycomb group (PcG) and Trithorax group (TrxG) epigenetic factors in plant plasticity.

PubMed

de la Paz Sanchez, Maria; Aceves-García, Pamela; Petrone, Emilio; Steckenborn, Stefan; Vega-León, Rosario; Álvarez-Buylla, Elena R; Garay-Arroyo, Adriana; García-Ponce, Berenice

2015-11-01

Current advances indicate that epigenetic mechanisms play important roles in the regulatory networks involved in plant developmental responses to environmental conditions. Hence, understanding the role of such components becomes crucial to understanding the mechanisms underlying the plasticity and variability of plant traits, and thus the ecology and evolution of plant development. We now know that important components of phenotypic variation may result from heritable and reversible epigenetic mechanisms without genetic alterations. The epigenetic factors Polycomb group (PcG) and Trithorax group (TrxG) are involved in developmental processes that respond to environmental signals, playing important roles in plant plasticity. In this review, we discuss current knowledge of TrxG and PcG functions in different developmental processes in response to internal and environmental cues and we also integrate the emerging evidence concerning their function in plant plasticity. Many such plastic responses rely on meristematic cell behavior, including stem cell niche maintenance, cellular reprogramming, flowering and dormancy as well as stress memory. This information will help to determine how to integrate the role of epigenetic regulation into models of gene regulatory networks, which have mostly included transcriptional interactions underlying various aspects of plant development and its plastic response to environmental conditions. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Implementing a social network intervention designed to enhance and diversify support for people with long-term conditions. A qualitative study.

PubMed

Kennedy, Anne; Vassilev, Ivaylo; James, Elizabeth; Rogers, Anne

2016-02-29

For people with long-term conditions, social networks provide a potentially central means of mobilising, mediating and accessing support for health and well-being. Few interventions address the implementation of improving engagement with and through social networks. This paper describes the development and implementation of a web-based tool which comprises: network mapping, user-centred preference elicitation and need assessment and facilitated engagement with resources. The study aimed to determine whether the intervention was acceptable, implementable and acted to enhance support and to add to theory concerning social networks and engagement with resources and activities. A longitudinal design with 15 case studies used ethnographic methods comprising video, non-participant observation of intervention delivery and qualitative interviews (baseline, 6 and 12 months). Participants were people with type 2 diabetes living in a marginalised island community. Facilitators were local health trainers and care navigators. Analysis applied concepts concerning implementation of technology for self-management support to explain how new practices of work were operationalised and how the technology impacted on relationships fit with everyday life and allowed for visual feedback. Most participants reported identifying and taking up new activities as a result of using the tool. Thematic analysis suggested that workability of the tool was predicated on disruption and reconstruction of networks, challenging/supportive facilitation and change and reflection over time concerning network support. Visualisation of the network enabled people to mobilise support and engage in new activities. The tool aligned synergistically with the facilitators' role of linking people to local resources. The social network tool works through a process of initiating positive disruption of established self-management practice through mapping and reflection on personal network membership and support. This opens up possibilities for reconstructing self-management differently from current practice. Key facets of successful implementation were: the visual maps of networks and support options; facilitation characterised by a perceived lack of status difference which assisted engagement and constructive discussion of support and preferences for activities; and background work (a reliable database, tailored preferences, option reduction) for facilitator and user ease of use.

Rodent models of insomnia: a review of experimental procedures that induce sleep disturbances.

PubMed

Revel, Florent G; Gottowik, Juergen; Gatti, Sylvia; Wettstein, Joseph G; Moreau, Jean-Luc

2009-06-01

Insomnia, the most common sleep disorder, is characterized by persistent difficulty in falling or staying asleep despite adequate opportunity to sleep, leading to daytime fatigue and mental dysfunction. As sleep is a sophisticated physiological process generated by a network of neuronal systems that cannot be reproduced in-vitro, pre-clinical development of hypnotic drugs requires in-vivo investigations. Accordingly, this review critically evaluates current and putative rodent models of insomnia which could be used to screen novel hypnotics. Only few valid insomnia models are currently available, although many experimental conditions lead to disturbance of physiological sleep. We categorized these conditions as a function of the procedure used to induce perturbation of sleep, and we discuss their respective advantages and pitfalls with respect to validity, feasibility and translational value to human research.

Conditions for extinction events in chemical reaction networks with discrete state spaces.

PubMed

Johnston, Matthew D; Anderson, David F; Craciun, Gheorghe; Brijder, Robert

2018-05-01

We study chemical reaction networks with discrete state spaces and present sufficient conditions on the structure of the network that guarantee the system exhibits an extinction event. The conditions we derive involve creating a modified chemical reaction network called a domination-expanded reaction network and then checking properties of this network. Unlike previous results, our analysis allows algorithmic implementation via systems of equalities and inequalities and suggests sequences of reactions which may lead to extinction events. We apply the results to several networks including an EnvZ-OmpR signaling pathway in Escherichia coli.

Presynaptic miniature GABAergic currents in developing interneurons.

PubMed

Trigo, Federico F; Bouhours, Brice; Rostaing, Philippe; Papageorgiou, George; Corrie, John E T; Triller, Antoine; Ogden, David; Marty, Alain

2010-04-29

Miniature synaptic currents have long been known to represent random transmitter release under resting conditions, but much remains to be learned about their nature and function in central synapses. In this work, we describe a new class of miniature currents ("preminis") that arise by the autocrine activation of axonal receptors following random vesicular release. Preminis are prominent in gabaergic synapses made by cerebellar interneurons during the development of the molecular layer. Unlike ordinary miniature postsynaptic currents in the same cells, premini frequencies are strongly enhanced by subthreshold depolarization, suggesting that the membrane depolarization they produce belongs to a feedback loop regulating neurotransmitter release. Thus, preminis could guide the formation of the interneuron network by enhancing neurotransmitter release at recently formed synaptic contacts. Copyright 2010 Elsevier Inc. All rights reserved.

A gene network simulator to assess reverse engineering algorithms.

PubMed

Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

2009-03-01

In the context of reverse engineering of biological networks, simulators are helpful to test and compare the accuracy of different reverse-engineering approaches in a variety of experimental conditions. A novel gene-network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics. The simulator generates network topology according to the current knowledge of biological network organization, including scale-free distribution of the connectivity and clustering coefficient independent of the number of nodes in the network. It uses fuzzy logic to represent interactions among the regulators of each gene, integrated with differential equations to generate continuous data, comparable to real data for variety and dynamic complexity. Finally, the simulator accounts for saturation in the response to regulation and transcription activation thresholds and shows robustness to perturbations. It therefore provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data. Since the simulator describes regulatory interactions and expression dynamics as two distinct, although interconnected aspects of regulation, it can also be used to test reverse engineering approaches that use both microarray and protein-protein interaction data in the process of learning. A first software release is available at http://www.dei.unipd.it/~dicamill/software/netsim as an R programming language package.

Design and evaluation of a wireless sensor network based aircraft strength testing system.

PubMed

Wu, Jian; Yuan, Shenfang; Zhou, Genyuan; Ji, Sai; Wang, Zilong; Wang, Yang

2009-01-01

The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system.

Design and Evaluation of a Wireless Sensor Network Based Aircraft Strength Testing System

PubMed Central

Wu, Jian; Yuan, Shenfang; Zhou, Genyuan; Ji, Sai; Wang, Zilong; Wang, Yang

2009-01-01

The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system. PMID:22408521

Time-dependent deformation of polymer network in polymer-stabilized cholesteric liquid crystals (Conference Presentation)

NASA Astrophysics Data System (ADS)

Lee, Kyung Min; Tondiglia, Vincent P.; Bunning, Timothy J.; White, Timothy J.

2017-02-01

Recently, we reported direct current (DC) field controllable electro-optic (EO) responses of negative dielectric anisotropy polymer stabilized cholesteric liquid crystals (PSCLCs). A potential mechanism is: Ions in the liquid crystal mixtures are trapped in/on the polymer network during the fast photopolymerization process, and the movement of ions by the application of the DC field distorts polymer network toward the negative electrode, inducing pitch variation through the cell thickness, i.e., pitch compression on the negative electrode side and pitch expansion on positive electrode side. As the DC voltage is directly applied to a target voltage, charged polymer network is deformed and the reflection band is tuned. Interestingly, the polymer network deforms further (red shift of reflection band) with time when constantly applied DC voltage, illustrating DC field induced time dependent deformation of polymer network (creep-like behavior). This time dependent reflection band changes in PSCLCs are investigated by varying the several factors, such as type and concentration of photoinitiators, liquid crystal monomer content, and curing condition (UV intensity and curing time). In addition, simple linear viscoelastic spring-dashpot models, such as 2-parameter Kelvin and 3-parameter linear models, are used to investigate the time-dependent viscoelastic behaviors of polymer networks in PSCLC.

Analysis Impact of Distributed Generation Injection to Profile of Voltage and Short-Circuit Fault in 20 kV Distribution Network System

NASA Astrophysics Data System (ADS)

Mulyadi, Y.; Sucita, T.; Rahmawan, M. D.

2018-01-01

This study was a case study in PT. PLN (Ltd.) APJ Bandung area with the subject taken was the installation of distributed generation (DG) on 20-kV distribution channels. The purpose of this study is to find out the effect of DG to the changes in voltage profile and three-phase short circuit fault in the 20-kV distribution system with load conditions considered to be balanced. The reason for this research is to know how far DG can improve the voltage profile of the channel and to what degree DG can increase the three-phase short circuit fault on each bus. The method used in this study was comparing the simulation results of power flow and short-circuit fault using ETAP Power System software with manual calculations. The result obtained from the power current simulation before the installation of DG voltage was the drop at the end of the channel at 2.515%. Meanwhile, the three-phase short-circuit current fault before the DG installation at the beginning of the channel was 13.43 kA. After the installation of DG with injection of 50%, DG power obtained voltage drop at the end of the channel was 1.715% and the current fault at the beginning network was 14.05 kA. In addition, with injection of 90%, DG power obtained voltage drop at the end of the channel was 1.06% and the current fault at the beginning network was 14.13%.

Molecular and Genetic Inflammation Networks in Major Human Diseases

PubMed Central

Zhao, Yongzhong; Forst, Christian V.; Sayegh, Camil E.; Wang, I-Ming; Yang, Xia; Zhang, Bin

2016-01-01

It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured most critical inflammation involved molecules, genetic susceptibilities, epigenetic factors, and environmental exposures, our schemata on role of inflammation in complex disease, remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the inflammation molecular and genetic networks underlying major human diseases. In this Review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer Disease, Parkinson disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold a great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervening. PMID:27303926

Using Cell-Phone Tower Signals for Detecting the Precursors of Fog

NASA Astrophysics Data System (ADS)

David, N.; Gao, H. O.

2018-01-01

In the last decade, published research has indicated the potential of commercial microwave links that comprise the data transmission infrastructure of cellular communication networks as an environmental monitoring technology. Different weather phenomena cause interference in the wireless communication links that can therefore essentially act as a low-cost sensor network, already deployed worldwide, for atmospheric monitoring. In this study we focus on the attenuation effect caused in commercial microwave networks due to gradients in the atmospheric refractive index with altitude as a result of the combination of temperature inversions and falls in the atmospheric humidity trapped beneath them. These conditions, when combined with high relative humidity near ground level, are precursors to the creation of fog. The current work utilizes this novel approach to demonstrate the potential for detecting these preconditions of fog, a phenomenon associated with severe visibility limitations that can lead to dangerous accidents, injuries, and loss of lives.

Low power sensor network for wireless condition monitoring

NASA Astrophysics Data System (ADS)

Richter, Ch.; Frankenstein, B.; Schubert, L.; Weihnacht, B.; Friedmann, H.; Ebert, C.

2009-03-01

For comprehensive fatigue tests and surveillance of large scale structures, a vibration monitoring system working in the Hz and sub Hz frequency range was realized and tested. The system is based on a wireless sensor network and focuses especially on the realization of a low power measurement, signal processing and communication. Regarding the development, we met the challenge of synchronizing the wireless connected sensor nodes with sufficient accuracy. The sensor nodes ware realized by compact, sensor near signal processing structures containing components for analog preprocessing of acoustic signals, their digitization, algorithms for data reduction and network communication. The core component is a digital micro controller which performs the basic algorithms necessary for the data acquisition synchronization and the filtering. As a first application, the system was installed in a rotor blade of a wind power turbine in order to monitor the Eigen modes over a longer period of time. Currently the sensor nodes are battery powered.

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis.

PubMed

Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang; Hu, Jianjun

2017-07-28

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

Developing Mesoscale Model of Fibrin-Platelet Network Representing Blood Clotting =

NASA Astrophysics Data System (ADS)

Sun, Yueyi; Nikolov, Svetoslav; Bowie, Sam; Alexeev, Alexander; Lam, Wilbur; Myers, David

Blood clotting disorders which prevent the body's natural ability to achieve hemostasis can lead to a variety of life threatening conditions such as, excessive bleeding, stroke, or heart attack. Treatment of these disorders is highly dependent on understanding the underlying physics behind the clotting process. Since clotting is a highly complex multi scale mechanism developing a fully atomistic model is currently not possible. We develop a mesoscale model based on dissipative particle dynamics (DPD) to gain fundamental understanding of the underlying principles controlling the clotting process. In our study, we examine experimental data on clot contraction using stacks of confocal microscopy images to estimate the crosslink density in the fibrin networks and platelet location. Using this data we reconstruct the platelet rich fibrin network and study how platelet-fibrin interactions affect clotting. Furthermore, we probe how different system parameters affect clot contraction. ANSF CAREER Award DMR-1255288.

Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware

NASA Technical Reports Server (NTRS)

Zee, Frank

1995-01-01

The fault tolerant characteristics of analog-VLSI artificial neural network (with 32 neurons and 532 synapses) chips are studied by exposing them to high energy electrons, high energy protons, and gamma ionizing radiations under biased and unbiased conditions. The biased chips became nonfunctional after receiving a cumulative dose of less than 20 krads, while the unbiased chips only started to show degradation with a cumulative dose of over 100 krads. As the total radiation dose increased, all the components demonstrated graceful degradation. The analog sigmoidal function of the neuron became steeper (increase in gain), current leakage from the synapses progressively shifted the sigmoidal curve, and the digital memory of the synapses and the memory addressing circuits began to gradually fail. From these radiation experiments, we can learn how to modify certain designs of the neural network electronic hardware without using radiation-hardening techniques to increase its reliability and fault tolerance.

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

PubMed Central

Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang

2017-01-01

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions. PMID:28788099

47 CFR 51.313 - Just, reasonable and nondiscriminatory terms and conditions for the provision of unbundled...

Code of Federal Regulations, 2011 CFR

2011-10-01

... and conditions for the provision of unbundled network elements. 51.313 Section 51.313... terms and conditions for the provision of unbundled network elements. (a) The terms and conditions pursuant to which an incumbent LEC provides access to unbundled network elements shall be offered equally...

47 CFR 51.313 - Just, reasonable and nondiscriminatory terms and conditions for the provision of unbundled...

Code of Federal Regulations, 2010 CFR

2010-10-01

... and conditions for the provision of unbundled network elements. 51.313 Section 51.313... terms and conditions for the provision of unbundled network elements. (a) The terms and conditions pursuant to which an incumbent LEC provides access to unbundled network elements shall be offered equally...

The CzeCOS Network

NASA Astrophysics Data System (ADS)

Havránková, Kateřina; Taufarová, Klára; Šigut, Ladislav; McGloin, Ryan; Acosta, Manuel; Dušek, Jiří; Krupková, Lenka; Macálková-Mžourková, Lenka; Pavelka, Marian; Dařenová, Eva; Yadav, Shilpi; Nguyen, Vinh; Guerra, Carlos; Janous, Dalibor; Marek, Michal V.

2017-04-01

The Global Change Research Institute of the Czech Academy of Sciences (CzechGlobe) have established a well-equipped network of ecosystem stations, with modern instrumentation for eco-physiological, plant physiological and micrometeorological studies, and estimation of GHG emissions. The network of stations (CzeCOS) covers the main terrestrial ecosystems of the Czech Republic (young and old coniferous forest, deciduous forest, mixed floodplain forest, grassland, wetland and cropland). The ecosystem stations are equipped with eddy covariance systems, soil and stem chamber systems for CO2 efflux and instruments for making micrometeorological measurements. The network enables detailed research to be conducted on topics such as: the carbon balance of different ecosystems, energy balance closure, the impact of current climate conditions on production and ecosystem disturbances during extreme weather conditions (drought, floods, winter storms, etc.) at regional, national and international scales. As a part of global networks (Fluxnet, ANAEe, ICOS), CzeCOS participates in evaluating and predicting environmental change and helps in the proposal of mitigation measures. Another important issue studied at some of the CzeCOS sites is the use of the eddy covariance method in sloping terrain in order to improve eddy covariance data processing for sites in this kind of terrain. Here we show specific results from the sites and outline the importance of the regional/national network for improving our knowledge about the exchange of matter and energy fluxes at different ecosystems. This study was supported by the Ministry of Education, Youth and Sports of CR within the National Sustainability Program I (NPU I), grant number LO1415 and LD 15040. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme "Projects of Large Research, Development, and Innovations Infrastructures".

Missing Rings in Pinus halepensis – The Missing Link to Relate the Tree-Ring Record to Extreme Climatic Events

PubMed Central

Novak, Klemen; de Luis, Martin; Saz, Miguel A.; Longares, Luis A.; Serrano-Notivoli, Roberto; Raventós, Josep; Čufar, Katarina; Gričar, Jožica; Di Filippo, Alfredo; Piovesan, Gianluca; Rathgeber, Cyrille B. K.; Papadopoulos, Andreas; Smith, Kevin T.

2016-01-01

Climate predictions for the Mediterranean Basin include increased temperatures, decreased precipitation, and increased frequency of extreme climatic events (ECE). These conditions are associated with decreased tree growth and increased vulnerability to pests and diseases. The anatomy of tree rings responds to these environmental conditions. Quantitatively, the width of a tree ring is largely determined by the rate and duration of cell division by the vascular cambium. In the Mediterranean climate, this division may occur throughout almost the entire year. Alternatively, cell division may cease during relatively cool and dry winters, only to resume in the same calendar year with milder temperatures and increased availability of water. Under particularly adverse conditions, no xylem may be produced in parts of the stem, resulting in a missing ring (MR). A dendrochronological network of Pinus halepensis was used to determine the relationship of MR to ECE. The network consisted of 113 sites, 1,509 trees, 2,593 cores, and 225,428 tree rings throughout the distribution range of the species. A total of 4,150 MR were identified. Binomial logistic regression analysis determined that MR frequency increased with increased cambial age. Spatial analysis indicated that the geographic areas of south-eastern Spain and northern Algeria contained the greatest frequency of MR. Dendroclimatic regression analysis indicated a non-linear relationship of MR to total monthly precipitation and mean temperature. MR are strongly associated with the combination of monthly mean temperature from previous October till current February and total precipitation from previous September till current May. They are likely to occur with total precipitation lower than 50 mm and temperatures higher than 5°C. This conclusion is global and can be applied to every site across the distribution area. Rather than simply being a complication for dendrochronology, MR formation is a fundamental response of trees to adverse environmental conditions. The demonstrated relationship of MR formation to ECE across this dendrochronological network in the Mediterranean basin shows the potential of MR analysis to reconstruct the history of past climatic extremes and to predict future forest dynamics in a changing climate. PMID:27303421

Missing Rings in Pinus halepensis - The Missing Link to Relate the Tree-Ring Record to Extreme Climatic Events.

PubMed

Novak, Klemen; de Luis, Martin; Saz, Miguel A; Longares, Luis A; Serrano-Notivoli, Roberto; Raventós, Josep; Čufar, Katarina; Gričar, Jožica; Di Filippo, Alfredo; Piovesan, Gianluca; Rathgeber, Cyrille B K; Papadopoulos, Andreas; Smith, Kevin T

2016-01-01

Climate predictions for the Mediterranean Basin include increased temperatures, decreased precipitation, and increased frequency of extreme climatic events (ECE). These conditions are associated with decreased tree growth and increased vulnerability to pests and diseases. The anatomy of tree rings responds to these environmental conditions. Quantitatively, the width of a tree ring is largely determined by the rate and duration of cell division by the vascular cambium. In the Mediterranean climate, this division may occur throughout almost the entire year. Alternatively, cell division may cease during relatively cool and dry winters, only to resume in the same calendar year with milder temperatures and increased availability of water. Under particularly adverse conditions, no xylem may be produced in parts of the stem, resulting in a missing ring (MR). A dendrochronological network of Pinus halepensis was used to determine the relationship of MR to ECE. The network consisted of 113 sites, 1,509 trees, 2,593 cores, and 225,428 tree rings throughout the distribution range of the species. A total of 4,150 MR were identified. Binomial logistic regression analysis determined that MR frequency increased with increased cambial age. Spatial analysis indicated that the geographic areas of south-eastern Spain and northern Algeria contained the greatest frequency of MR. Dendroclimatic regression analysis indicated a non-linear relationship of MR to total monthly precipitation and mean temperature. MR are strongly associated with the combination of monthly mean temperature from previous October till current February and total precipitation from previous September till current May. They are likely to occur with total precipitation lower than 50 mm and temperatures higher than 5°C. This conclusion is global and can be applied to every site across the distribution area. Rather than simply being a complication for dendrochronology, MR formation is a fundamental response of trees to adverse environmental conditions. The demonstrated relationship of MR formation to ECE across this dendrochronological network in the Mediterranean basin shows the potential of MR analysis to reconstruct the history of past climatic extremes and to predict future forest dynamics in a changing climate.

A real time study on condition monitoring of distribution transformer using thermal imager

NASA Astrophysics Data System (ADS)

Mariprasath, T.; Kirubakaran, V.

2018-05-01

The transformer is one of the critical apparatus in the power system. At any cost, a few minutes of outages harshly influence the power system. Hence, prevention-based maintenance technique is very essential. The continuous conditioning and monitoring technology significantly increases the life span of the transformer, as well as reduces the maintenance cost. Hence, conditioning and monitoring of transformer's temperature are very essential. In this paper, a critical review has been made on various conditioning and monitoring techniques. Furthermore, a new method, hot spot indication technique, is discussed. Also, transformer's operating condition is monitored by using thermal imager. From the thermal analysis, it is inferred that major hotspot locations are appearing at connection lead out; also, the bushing of the transformer is the very hottest spot in transformer, so monitoring the level of oil is essential. Alongside, real time power quality analysis has been carried out using the power analyzer. It shows that industrial drives are injecting current harmonics to the distribution network, which causes the power quality problem on the grid. Moreover, the current harmonic limit has exceeded the IEEE standard limit. Hence, the adequate harmonics suppression technique is need an hour.

Impact of RNA Degradation on Viral Diagnosis: An Understated but Essential Step for the Successful Establishment of a Diagnosis Network

PubMed Central

Relova, Damarys; Acevedo, Ana M.; Coronado, Liani; Perera, Carmen L.

2018-01-01

The current global conditions, which include intensive globalization, climate changes, and viral evolution among other factors, have led to an increased emergence of viruses and new viral diseases; RNA viruses are key drivers of this evolution. Laboratory networks that are linked to central reference laboratories are required to conduct both active and passive environmental surveillance of this complicated global viral environment. These tasks require a continuous exchange of strains or field samples between different diagnostic laboratories. The shipment of these samples on dry ice represents both a biological hazard and a general health risk. Moreover, the requirement to ship on dry ice could be hampered by high costs, particularly in underdeveloped countries or regions located far from each other. To solve these issues, the shipment of RNA isolated from viral suspensions or directly from field samples could be a useful way to share viral genetic material. However, extracted RNA stored in aqueous solutions, even at −70 °C, is highly prone to degradation. The current study evaluated different RNA storage conditions for safety and feasibility for future use in molecular diagnostics. The in vitro RNA-transcripts obtained from an inactivated highly pathogenic avian influenza (HPAI) H5N1 virus was used as a model. The role of secondary structures in the protection of the RNA was also explored. Of the conditions evaluated, the dry pellet matrix was best able to protect viral RNA under extreme storage conditions. This method is safe, cost-effective and assures the integrity of RNA samples for reliable molecular diagnosis. This study aligns with the globally significant “Global One Health” paradigm, especially with respect to the diagnosis of emerging diseases that require confirmation by reference laboratories. PMID:29415432

Influence network linkages across implementation strategy conditions in a randomized controlled trial of two strategies for scaling up evidence-based practices in public youth-serving systems.

PubMed

Palinkas, Lawrence A; Holloway, Ian W; Rice, Eric; Brown, C Hendricks; Valente, Thomas W; Chamberlain, Patricia

2013-11-14

Given the importance of influence networks in the implementation of evidence-based practices and interventions, it is unclear whether such networks continue to operate as sources of information and advice when they are segmented and disrupted by randomization to different implementation strategy conditions. The present study examines the linkages across implementation strategy conditions of social influence networks of leaders of youth-serving systems in 12 California counties participating in a randomized controlled trial of community development teams (CDTs) to scale up use of an evidence-based practice. Semi-structured interviews were conducted with 38 directors, assistant directors, and program managers of county probation, mental health, and child welfare departments. A web-based survey collected additional quantitative data on information and advice networks of study participants. A mixed-methods approach to data analysis was used to create a sociometric data set (n = 176) to examine linkages between treatment and standard conditions. Of those network members who were affiliated with a county (n = 137), only 6 (4.4%) were directly connected to a member of the opposite implementation strategy condition; 19 (13.9%) were connected by two steps or fewer to a member of the opposite implementation strategy condition; 64 (46.7%) were connected by three or fewer steps to a member of the opposite implementation strategy condition. Most of the indirect steps between individuals who were in different implementation strategy conditions were connections involving a third non-county organizational entity that had an important role in the trial in keeping the implementation strategy conditions separate. When these entities were excluded, the CDT network exhibited fewer components and significantly higher betweenness centralization than did the standard condition network. Although the integrity of the RCT in this instance was not compromised by study participant influence networks, RCT designs should consider how influence networks may extend beyond boundaries established by the randomization process in implementation studies. NCT00880126.

Assessment of Water-Quality Monitoring and a Proposed Water-Quality Monitoring Network for the Mosquito Lagoon Basin, East-Central Florida

USGS Publications Warehouse

Kroening, Sharon E.

2008-01-01

Surface- and ground-water quality data from the Mosquito Lagoon Basin were compiled and analyzed to: (1) describe historical and current monitoring in the basin, (2) summarize surface- and ground-water quality conditions with an emphasis on identifying areas that require additional monitoring, and (3) develop a water-quality monitoring network to meet the goals of Canaveral National Seashore (a National Park) and to fill gaps in current monitoring. Water-quality data were compiled from the U.S. Environmental Protection Agency's STORET system, the U.S. Geological Survey's National Water Information System, or from the agency which collected the data. Most water-quality monitoring focused on assessing conditions in Mosquito Lagoon. Significant spatial and/or seasonal variations in water-quality constituents in the lagoon were quantified for pH values, fecal coliform bacteria counts, and concentrations of dissolved oxygen, total nitrogen, total phosphorus, chlorophyll-a, and total suspended solids. Trace element, pesticide, and ground-water-quality data were more limited. Organochlorine insecticides were the major class of pesticides analyzed. A surface- and ground-water-quality monitoring network was designed for the Mosquito Lagoon Basin which emphasizes: (1) analysis of compounds indicative of human activities, including pesticides and other trace organic compounds present in domestic and industrial waste; (2) greater data collection in the southern part of Mosquito Lagoon where spatial variations in water-quality constituents were quantified; and (3) additional ground-water-quality data collection in the surficial aquifer system and Upper Floridan aquifer. Surface-water-quality data collected as part of this network would include a fixed-station monitoring network of eight sites in the southern part of the basin, including a canal draining Oak Hill. Ground-water quality monitoring should be done routinely at about 20 wells in the surficial aquifer system and Upper Floridan aquifer, distributed between developed and undeveloped parts of the basin. Water samples collected should be analyzed for a wide range of constituents, including physical properties, nutrients, suspended sediment, and constituents associated with increased urban development such as pesticides, other trace organic compounds associated with domestic and industrial waste, and trace elements.

Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah

PubMed Central

Tian, Le; Latré, Steven

2017-01-01

IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks. PMID:28677617

Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah.

PubMed

Tian, Le; Khorov, Evgeny; Latré, Steven; Famaey, Jeroen

2017-07-04

IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks.

A top-down manner-based DCNN architecture for semantic image segmentation.

PubMed

Qiao, Kai; Chen, Jian; Wang, Linyuan; Zeng, Lei; Yan, Bin

2017-01-01

Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN) and FCN with conditional random field (DeepLab-CRF) as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU) accuracy improvement on the PASCAL VOC 2011 and 2012 test sets.

NSI operations center

NASA Technical Reports Server (NTRS)

Zanley, Nancy L.

1991-01-01

The NASA Science Internet (NSI) Network Operations Staff is responsible for providing reliable communication connectivity for the NASA science community. As the NSI user community expands, so does the demand for greater interoperability with users and resources on other networks (e.g., NSFnet, ESnet), both nationally and internationally. Coupled with the science community's demand for greater access to other resources is the demand for more reliable communication connectivity. Recognizing this, the NASA Science Internet Project Office (NSIPO) expands its Operations activities. By January 1990, Network Operations was equipped with a telephone hotline, and its staff was expanded to six Network Operations Analysts. These six analysts provide 24-hour-a-day, 7-day-a-week coverage to assist site managers with problem determination and resolution. The NSI Operations staff monitors network circuits and their associated routers. In most instances, NSI Operations diagnoses and reports problems before users realize a problem exists. Monitoring of the NSI TCP/IP Network is currently being done with Proteon's Overview monitoring system. The Overview monitoring system displays a map of the NSI network utilizing various colors to indicate the conditions of the components being monitored. Each node or site is polled via the Simple Network Monitoring Protocol (SNMP). If a circuit goes down, Overview alerts the Network Operations staff with an audible alarm and changes the color of the component. When an alert is received, Network Operations personnel immediately verify and diagnose the problem, coordinate repair with other networking service groups, track problems, and document problem and resolution into a trouble ticket data base. NSI Operations offers the NSI science community reliable connectivity by exercising prompt assessment and resolution of network problems.

Leader-Follower Formation Control of UUVs with Model Uncertainties, Current Disturbances, and Unstable Communication

PubMed Central

Yan, Zheping; Xu, Da; Chen, Tao; Zhang, Wei; Liu, Yibo

2018-01-01

Unmanned underwater vehicles (UUVs) have rapidly developed as mobile sensor networks recently in the investigation, survey, and exploration of the underwater environment. The goal of this paper is to develop a practical and efficient formation control method to improve work efficiency of multi-UUV sensor networks. Distributed leader-follower formation controllers are designed based on a state feedback and consensus algorithm. Considering that each vehicle is subject to model uncertainties and current disturbances, a second-order integral UUV model with a nonlinear function is established using the state feedback linearized method under current disturbances. For unstable communication among UUVs, communication failure and acoustic link noise interference are considered. Two-layer random switching communication topologies are proposed to solve the problem of communication failure. For acoustic link noise interference, accurate representation of valid communication information and noise stripping when designing controllers is necessary. Effective communication topology weights are designed to represent the validity of communication information interfered by noise. Utilizing state feedback and noise stripping, sufficient conditions for design formation controllers are proposed to ensure UUV formation achieves consensus under model uncertainties, current disturbances, and unstable communication. The stability of formation controllers is proven by the Lyapunov-Razumikhin theorem, and the validity is verified by simulation results. PMID:29473919

Current-flow efficiency of networks

NASA Astrophysics Data System (ADS)

Liu, Kai; Yan, Xiaoyong

2018-02-01

Many real-world networks, from infrastructure networks to social and communication networks, can be formulated as flow networks. How to realistically measure the transport efficiency of these networks is of fundamental importance. The shortest-path-based efficiency measurement has limitations, as it assumes that flow travels only along those shortest paths. Here, we propose a new metric named current-flow efficiency, in which we calculate the average reciprocal effective resistance between all pairs of nodes in the network. This metric takes the multipath effect into consideration and is more suitable for measuring the efficiency of many real-world flow equilibrium networks. Moreover, this metric can handle a disconnected graph and can thus be used to identify critical nodes and edges from the efficiency-loss perspective. We further analyze how the topological structure affects the current-flow efficiency of networks based on some model and real-world networks. Our results enable a better understanding of flow networks and shed light on the design and improvement of such networks with higher transport efficiency.

Does context matter in evaluations of stigmatized individuals? An fMRI study.

PubMed

Krendl, Anne C; Moran, Joseph M; Ambady, Nalini

2013-06-01

The manner in which disparate affective responses shape attitudes toward other individuals has received a great deal of attention in neuroscience research. However, the malleability of these affective responses remains largely unexplored. The perceived controllability of a stigma (whether or not the bearer of the stigma is perceived as being responsible for his or her condition) has been found to polarize behavioral affective responses to that stigma. The current study uses functional magnetic resonance imaging to identify the neural correlates underlying the evaluation of stigmatized individuals (people who are homeless) when perceptions of the controllability of their condition are altered. Results demonstrated that perceivers engaged neural networks implicated in inferring intentionality (e.g. the medial prefrontal cortex) when they evaluated a homeless individual who was described as being responsible for becoming homeless. Conversely, neural networks associated with resolving strong affective responses (e.g. insula) were engaged when evaluating a homeless individual who was described as not being responsible for becoming homeless.

Does Posting Facebook Status Updates Increase or Decrease Loneliness? An Online Social Networking Experiment

PubMed Central

Deters, Fenne große; Mehl, Matthias R.

2013-01-01

Online social networking is a pervasive but empirically understudied phenomenon. Strong public opinions on its consequences exist but are backed up by little empirical evidence and almost no causally-conclusive, experimental research. The current study tested the psychological effects of posting status updates on Facebook using an experimental design. For one week, participants in the experimental condition were asked to post more than they usually do, whereas participants in the control condition received no instructions. Participants added a lab “Research Profile” as a Facebook friend allowing for the objective documentation of protocol compliance, participants’ status updates, and friends’ responses. Results revealed (1) that the experimentally-induced increase in status updating activity reduced loneliness, (2) that the decrease in loneliness was due to participants feeling more connected to their friends on a daily basis and (3) that the effect of posting on loneliness was independent of direct social feedback (i.e. responses) by friends. PMID:24224070

A closer look at the relationship between the default network, mind wandering, negative mood, and depression.

PubMed

Konjedi, Shaghayegh; Maleeh, Reza

2017-08-01

By a systematic analysis of the current literature on the neural correlates of mind wandering, that is, the default network (DN), and by shedding light on some determinative factors and conditions which affect the relationship between mind wandering and negative mood, we show that (1) mind wandering per se does not necessarily have a positive correlation with negative mood and, on the higher levels, depression. We propose that negative mood as a consequence of mind wandering generally depends on two determinative conditions, that is, whether mind wandering is with or without meta-awareness and whether mind wandering occurs during high or low vigilance states; (2) increased activity of the DN is not necessarily followed by an increase in unhappiness and depression. We argue that while in some kinds of meditation practices we witness an increase in the structure and in the activity of the DN, no increase in unhappiness and depression is observed.

Development and program implementation of elements for identification of the electromagnet condition for movable element position control

NASA Astrophysics Data System (ADS)

Leukhin, R. I.; Shaykhutdinov, D. V.; Shirokov, K. M.; Narakidze, N. D.; Vlasov, A. S.

2017-02-01

Developing the experimental design of new electromagnetic constructions types in engineering industry enterprises requires solutions of two major problems: regulator’s parameters setup and comprehensive testing of electromagnets. A weber-ampere characteristic as a data source for electromagnet condition identification was selected. Present article focuses on development and implementation of the software for electromagnetic drive control system based on the weber-ampere characteristic measuring. The software for weber-ampere characteristic data processing based on artificial neural network is developed. Results of the design have been integrated into the program code in LabVIEW environment. The license package of LabVIEW graphic programming was used. The hardware is chosen and possibility of its use for control system implementation was proved. The trained artificial neural network defines electromagnetic drive effector position with minimal error. Developed system allows to control the electromagnetic drive powered by the voltage source, the current source and hybrid sources.

An investigation on wireless sensors for asset management and health monitoring of civil structures

NASA Astrophysics Data System (ADS)

Furkan, Mustafa; Mao, Qiang; Mazzotti, Matteo; DeVitis, John; Sumitro, S. Paul; Faridazar, Fred; Aktan, A. Emin; Moon, Franklin; Bartoli, Ivan

2016-04-01

Application of wireless sensors and sensor networks for Structural Health Monitoring has been investigated for a long time. Key limitations for practical use are energy requirements, connectivity, and integration with existing systems. Current sensors and sensor networks mainly rely on wired connectivity for communication and external power source for energy. This paper presents a suite of wireless sensors that are low-cost, maintenance free, rugged, and have long service life. The majority of the sensors considered were designed by transforming existing, proven, and robust wired sensors into wireless units. In this study, the wireless sensors were tested in laboratory conditions for calibration and evaluation along with wired sensors. The experimental results were also compared to theoretical results. The tests mostly show satisfactory performance of the wireless units. This work is part of a broader Federal Highway Administration sponsored project intended to ultimately validate a wireless sensing system on a real, operating structure to account for all the uncertainties, environmental conditions and operational variability that are encountered in the field.

Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: an empirical analysis converting SF-12 into EQ-5D utility index in a national US sample.

PubMed

Le, Quang A; Doctor, Jason N

2011-05-01

As quality-adjusted life years have become the standard metric in health economic evaluations, mapping health-profile or disease-specific measures onto preference-based measures to obtain quality-adjusted life years has become a solution when health utilities are not directly available. However, current mapping methods are limited due to their predictive validity, reliability, and/or other methodological issues. We employ probability theory together with a graphical model, called a Bayesian network, to convert health-profile measures into preference-based measures and to compare the results to those estimated with current mapping methods. A sample of 19,678 adults who completed both the 12-item Short Form Health Survey (SF-12v2) and EuroQoL 5D (EQ-5D) questionnaires from the 2003 Medical Expenditure Panel Survey was split into training and validation sets. Bayesian networks were constructed to explore the probabilistic relationships between each EQ-5D domain and 12 items of the SF-12v2. The EQ-5D utility scores were estimated on the basis of the predicted probability of each response level of the 5 EQ-5D domains obtained from the Bayesian inference process using the following methods: Monte Carlo simulation, expected utility, and most-likely probability. Results were then compared with current mapping methods including multinomial logistic regression, ordinary least squares, and censored least absolute deviations. The Bayesian networks consistently outperformed other mapping models in the overall sample (mean absolute error=0.077, mean square error=0.013, and R overall=0.802), in different age groups, number of chronic conditions, and ranges of the EQ-5D index. Bayesian networks provide a new robust and natural approach to map health status responses into health utility measures for health economic evaluations.

Environmental change makes robust ecological networks fragile

USGS Publications Warehouse

Strona, Giovanni; Lafferty, Kevin D.

2016-01-01

Complex ecological networks appear robust to primary extinctions, possibly due to consumers’ tendency to specialize on dependable (available and persistent) resources. However, modifications to the conditions under which the network has evolved might alter resource dependability. Here, we ask whether adaptation to historical conditions can increase community robustness, and whether such robustness can protect communities from collapse when conditions change. Using artificial life simulations, we first evolved digital consumer-resource networks that we subsequently subjected to rapid environmental change. We then investigated how empirical host–parasite networks would respond to historical, random and expected extinction sequences. In both the cases, networks were far more robust to historical conditions than new ones, suggesting that new environmental challenges, as expected under global change, might collapse otherwise robust natural ecosystems.

Decreased Efficiency of Task-Positive and Task-Negative Networks During Working Memory in Schizophrenia

PubMed Central

Metzak, Paul D.; Riley, Jennifer D.; Wang, Liang; Whitman, Jennifer C.; Ngan, Elton T. C.; Woodward, Todd S.

2012-01-01

Working memory (WM) is one of the most impaired cognitive processes in schizophrenia. Functional magnetic resonance imaging (fMRI) studies in this area have typically found a reduction in information processing efficiency but have focused on the dorsolateral prefrontal cortex. In the current study using the Sternberg Item Recognition Test, we consider networks of regions supporting WM and measure the activation of functionally connected neural networks over different WM load conditions. We used constrained principal component analysis with a finite impulse response basis set to compare the estimated hemodynamic response associated with different WM load condition for 15 healthy control subjects and 15 schizophrenia patients. Three components emerged, reflecting activated (task-positive) and deactivated (task-negative or default-mode) neural networks. Two of the components (with both task-positive and task-negative aspects) were load dependent, were involved in encoding and delay phases (one exclusively encoding and the other both encoding and delay), and both showed evidence for decreased efficiency in patients. The results suggest that WM capacity is reached sooner for schizophrenia patients as the overt levels of WM load increase, to the point that further increases in overt memory load do not increase fMRI activation, and lead to performance impairments. These results are consistent with an account holding that patients show reduced efficiency in task-positive and task-negative networks during WM and also partially support the shifted inverted-U-shaped curve theory of the relationship between WM load and fMRI activation in schizophrenia. PMID:21224491

Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

PubMed Central

Martín-Jiménez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; González, Janneth

2017-01-01

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states. PMID:28243200

Optimal social-networking strategy is a function of socioeconomic conditions.

PubMed

Oishi, Shigehiro; Kesebir, Selin

2012-12-01

In the two studies reported here, we examined the relation among residential mobility, economic conditions, and optimal social-networking strategy. In study 1, a computer simulation showed that regardless of economic conditions, having a broad social network with weak friendship ties is advantageous when friends are likely to move away. By contrast, having a small social network with deep friendship ties is advantageous when the economy is unstable but friends are not likely to move away. In study 2, we examined the validity of the computer simulation using a sample of American adults. Results were consistent with the simulation: American adults living in a zip code where people are residentially stable but economically challenged were happier if they had a narrow but deep social network, whereas in other socioeconomic conditions, people were generally happier if they had a broad but shallow networking strategy. Together, our studies demonstrate that the optimal social-networking strategy varies as a function of socioeconomic conditions.

Network inference from functional experimental data (Conference Presentation)

NASA Astrophysics Data System (ADS)

Desrosiers, Patrick; Labrecque, Simon; Tremblay, Maxime; Bélanger, Mathieu; De Dorlodot, Bertrand; Côté, Daniel C.

2016-03-01

Functional connectivity maps of neuronal networks are critical tools to understand how neurons form circuits, how information is encoded and processed by neurons, how memory is shaped, and how these basic processes are altered under pathological conditions. Current light microscopy allows to observe calcium or electrical activity of thousands of neurons simultaneously, yet assessing comprehensive connectivity maps directly from such data remains a non-trivial analytical task. There exist simple statistical methods, such as cross-correlation and Granger causality, but they only detect linear interactions between neurons. Other more involved inference methods inspired by information theory, such as mutual information and transfer entropy, identify more accurately connections between neurons but also require more computational resources. We carried out a comparative study of common connectivity inference methods. The relative accuracy and computational cost of each method was determined via simulated fluorescence traces generated with realistic computational models of interacting neurons in networks of different topologies (clustered or non-clustered) and sizes (10-1000 neurons). To bridge the computational and experimental works, we observed the intracellular calcium activity of live hippocampal neuronal cultures infected with the fluorescent calcium marker GCaMP6f. The spontaneous activity of the networks, consisting of 50-100 neurons per field of view, was recorded from 20 to 50 Hz on a microscope controlled by a homemade software. We implemented all connectivity inference methods in the software, which rapidly loads calcium fluorescence movies, segments the images, extracts the fluorescence traces, and assesses the functional connections (with strengths and directions) between each pair of neurons. We used this software to assess, in real time, the functional connectivity from real calcium imaging data in basal conditions, under plasticity protocols, and epileptic conditions.

Designing optimized multi-species monitoring networks to detect range shifts driven by climate change: a case study with bats in the North of Portugal.

PubMed

Amorim, Francisco; Carvalho, Sílvia B; Honrado, João; Rebelo, Hugo

2014-01-01

Here we develop a framework to design multi-species monitoring networks using species distribution models and conservation planning tools to optimize the location of monitoring stations to detect potential range shifts driven by climate change. For this study, we focused on seven bat species in Northern Portugal (Western Europe). Maximum entropy modelling was used to predict the likely occurrence of those species under present and future climatic conditions. By comparing present and future predicted distributions, we identified areas where each species is likely to gain, lose or maintain suitable climatic space. We then used a decision support tool (the Marxan software) to design three optimized monitoring networks considering: a) changes in species likely occurrence, b) species conservation status, and c) level of volunteer commitment. For present climatic conditions, species distribution models revealed that areas suitable for most species occur in the north-eastern part of the region. However, areas predicted to become climatically suitable in the future shifted towards west. The three simulated monitoring networks, adaptable for an unpredictable volunteer commitment, included 28, 54 and 110 sampling locations respectively, distributed across the study area and covering the potential full range of conditions where species range shifts may occur. Our results show that our framework outperforms the traditional approach that only considers current species ranges, in allocating monitoring stations distributed across different categories of predicted shifts in species distributions. This study presents a straightforward framework to design monitoring schemes aimed specifically at testing hypotheses about where and when species ranges may shift with climatic changes, while also ensuring surveillance of general population trends.

Connections between intraparietal sulcus and a sensorimotor network underpin sustained tactile attention.

PubMed

Goltz, Dominique; Gundlach, Christopher; Nierhaus, Till; Villringer, Arno; Müller, Matthias; Pleger, Burkhard

2015-05-20

Previous studies on sustained tactile attention draw conclusions about underlying cortical networks by averaging over experimental conditions without considering attentional variance in single trials. This may have formed an imprecise picture of brain processes underpinning sustained tactile attention. In the present study, we simultaneously recorded EEG-fMRI and used modulations of steady-state somatosensory evoked potentials (SSSEPs) as a measure of attentional trial-by-trial variability. Therefore, frequency-tagged streams of vibrotactile stimulations were simultaneously presented to both index fingers. Human participants were cued to sustain attention to either the left or right finger stimulation and to press a button whenever they perceived a target pulse embedded in the to-be-attended stream. In-line with previous studies, a classical general linear model (GLM) analysis based on cued attention conditions revealed increased activity mainly in somatosensory and cerebellar regions. Yet, parametric modeling of the BOLD response using simultaneously recorded SSSEPs as a marker of attentional trial-by-trial variability quarried the intraparietal sulcus (IPS). The IPS in turn showed enhanced functional connectivity to a modality-unspecific attention network. However, this was only revealed on the basis of cued attention conditions in the classical GLM. By considering attentional variability as captured by SSSEPs, the IPS showed increased connectivity to a sensorimotor network, underpinning attentional selection processes between competing tactile stimuli and action choices (press a button or not). Thus, the current findings highlight the potential value by considering attentional variations in single trials and extend previous knowledge on the role of the IPS in tactile attention. Copyright © 2015 the authors 0270-6474/15/357938-12$15.00/0.

Consensus Algorithms for Networks of Systems with Second- and Higher-Order Dynamics

NASA Astrophysics Data System (ADS)

Fruhnert, Michael

This thesis considers homogeneous networks of linear systems. We consider linear feedback controllers and require that the directed graph associated with the network contains a spanning tree and systems are stabilizable. We show that, in continuous-time, consensus with a guaranteed rate of convergence can always be achieved using linear state feedback. For networks of continuous-time second-order systems, we provide a new and simple derivation of the conditions for a second-order polynomials with complex coefficients to be Hurwitz. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. Based on the conditions found, methods to compute feedback gains are proposed. We show that gains can be chosen such that consensus is achieved robustly over a variety of communication structures and system dynamics. We also consider the use of static output feedback. For networks of discrete-time second-order systems, we provide a new and simple derivation of the conditions for a second-order polynomials with complex coefficients to be Schur. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. We show that consensus can always be achieved for marginally stable systems and discretized systems. Simple conditions for consensus achieving controllers are obtained when the Laplacian eigenvalues are all real. For networks of continuous-time time-variant higher-order systems, we show that uniform consensus can always be achieved if systems are quadratically stabilizable. In this case, we provide a simple condition to obtain a linear feedback control. For networks of discrete-time higher-order systems, we show that constant gains can be chosen such that consensus is achieved for a variety of network topologies. First, we develop simple results for networks of time-invariant systems and networks of time-variant systems that are given in controllable canonical form. Second, we formulate the problem in terms of Linear Matrix Inequalities (LMIs). The condition found simplifies the design process and avoids the parallel solution of multiple LMIs. The result yields a modified Algebraic Riccati Equation (ARE) for which we present an equivalent LMI condition.

Science advancements key to increasing management value of life stage monitoring networks for endangered Sacramento River winter-run Chinook salmon in California

USGS Publications Warehouse

Johnson, Rachel C.; Windell, Sean; Brandes, Patricia L.; Conrad, J. Louise; Ferguson, John; Goertler, Pascale A. L.; Harvey, Brett N.; Heublein, Joseph; Isreal, Joshua A.; Kratville, Daniel W.; Kirsch, Joseph E.; Perry, Russell W.; Pisciotto, Joseph; Poytress, William R.; Reece, Kevin; Swart, Brycen G.

2017-01-01

A robust monitoring network that provides quantitative information about the status of imperiled species at key life stages and geographic locations over time is fundamental for sustainable management of fisheries resources. For anadromous species, management actions in one geographic domain can substantially affect abundance of subsequent life stages that span broad geographic regions. Quantitative metrics (e.g., abundance, movement, survival, life history diversity, and condition) at multiple life stages are needed to inform how management actions (e.g., hatcheries, harvest, hydrology, and habitat restoration) influence salmon population dynamics. The existing monitoring network for endangered Sacramento River winterrun Chinook Salmon (SRWRC, Oncorhynchus tshawytscha) in California’s Central Valley was compared to conceptual models developed for each life stage and geographic region of the life cycle to identify relevant SRWRC metrics. We concluded that the current monitoring network was insufficient to diagnose when (life stage) and where (geographic domain) chronic or episodic reductions in SRWRC cohorts occur, precluding within- and among-year comparisons. The strongest quantitative data exist in the Upper Sacramento River, where abundance estimates are generated for adult spawners and emigrating juveniles. However, once SRWRC leave the upper river, our knowledge of their identity, abundance, and condition diminishes, despite the juvenile monitoring enterprise. We identified six system-wide recommended actions to strengthen the value of data generated from the existing monitoring network to assess resource management actions: (1) incorporate genetic run identification; (2) develop juvenile abundance estimates; (3) collect data for life history diversity metrics at multiple life stages; (4) expand and enhance real-time fish survival and movement monitoring; (5) collect fish condition data; and (6) provide timely public access to monitoring data in open data formats. To illustrate how updated technologies can enhance the existing monitoring to provide quantitative data on SRWRC, we provide examples of how each recommendation can address specific management issues.

Application of Phasor Measurement Units for Protection of Distribution Networks with High Penetration of Photovoltaic Sources

NASA Astrophysics Data System (ADS)

Meskin, Matin

The rate of the integration of distributed generation (DG) units to the distribution level to meet the growth in demand increases as a reasonable replacement for costly network expansion. This integration brings many advantages to the consumers and power grids, as well as giving rise to more challenges in relation to protection and control. Recent research has brought to light the negative effects of DG units on short circuit currents and overcurrent (OC) protection systems in distribution networks. Change in the direction of fault current flow, increment or decrement of fault current magnitude, blindness of protection, feeder sympathy trip, nuisance trip of interrupting devices, and the disruption of coordination between protective devices are some potential impacts of DG unit integration. Among other types of DG units, the integration of renewable energy resources into the electric grid has seen a vast improvement in recent years. In particular, the interconnection of photovoltaic (PV) sources to the medium voltage (MV) distribution networks has experienced a rapid increase in the last decade. In this work, the effect of PV source on conventional OC relays in MV distribution networks is shown. It is indicated that the PV output fluctuation, due to changes in solar radiation, causes the magnitude and direction of the current to change haphazardly. These variations may result in the poor operation of OC relays as the main protective devices in the MV distribution networks. In other words, due to the bi-directional power flow characteristic and the fluctuation of current magnitude occurring in the presence of PV sources, a specific setting of OC relays is difficult to realize. Therefore, OC relays may operate in normal conditions. To improve the OC relay operation, a voltage-dependent-overcurrent protection is proposed. Although, this new method prevents the OC relay from maloperation, its ability to detect earth faults and high impedance faults is poor. Thus, a comprehensive protective system is suggested at the end of the dissertation. The proposed method is based on the application of the phasor measurement unit (PMU) and the differential protection method. All of the current magnitudes and angles are collected by PMU and are sent to the phasor data concentrator (PDC), where a differential protection algorithm is applied to these data. If any fault is detected, the trip will be sent back to the corresponding circuit breakers across the network. Higher selectivity, sensitivity, and faster operation in the differential protection are superior to those of other protection schemes. Differential protection operates as unit protection, which means that it operates only when there is a fault in the protection zone. It does not function for faults occurring out of zone. Therefore, no coordination is required between differential protections across the power system. Moreover, the misoperation of this protective scheme is less likely as compared to other protection methods.

Electrode Position and Current Amplitude Modulate Impulsivity after Subthalamic Stimulation in Parkinsons Disease—A Computational Study

PubMed Central

Mandali, Alekhya; Chakravarthy, V. Srinivasa; Rajan, Roopa; Sarma, Sankara; Kishore, Asha

2016-01-01

Background: Subthalamic Nucleus Deep Brain Stimulation (STN-DBS) is highly effective in alleviating motor symptoms of Parkinson's disease (PD) which are not optimally controlled by dopamine replacement therapy. Clinical studies and reports suggest that STN-DBS may result in increased impulsivity and de novo impulse control disorders (ICD). Objective/Hypothesis: We aimed to compare performance on a decision making task, the Iowa Gambling Task (IGT), in healthy conditions (HC), untreated and medically-treated PD conditions with and without STN stimulation. We hypothesized that the position of electrode and stimulation current modulate impulsivity after STN-DBS. Methods: We built a computational spiking network model of basal ganglia (BG) and compared the model's STN output with STN activity in PD. Reinforcement learning methodology was applied to simulate IGT performance under various conditions of dopaminergic and STN stimulation where IGT total and bin scores were compared among various conditions. Results: The computational model reproduced neural activity observed in normal and PD conditions. Untreated and medically-treated PD conditions had lower total IGT scores (higher impulsivity) compared to HC (P < 0.0001). The electrode position that happens to selectively stimulate the part of the STN corresponding to an advantageous panel on IGT resulted in de-selection of that panel and worsening of performance (P < 0.0001). Supratherapeutic stimulation amplitudes also worsened IGT performance (P < 0.001). Conclusion(s): In our computational model, STN stimulation led to impulsive decision making in IGT in PD condition. Electrode position and stimulation current influenced impulsivity which may explain the variable effects of STN-DBS reported in patients. PMID:27965590

42 CFR 486.320 - Condition: Participation in Organ Procurement and Transplantation Network.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 5 2010-10-01 2010-10-01 false Condition: Participation in Organ Procurement and Transplantation Network. 486.320 Section 486.320 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES....320 Condition: Participation in Organ Procurement and Transplantation Network. After being designated...

42 CFR 486.320 - Condition: Participation in Organ Procurement and Transplantation Network.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 5 2011-10-01 2011-10-01 false Condition: Participation in Organ Procurement and Transplantation Network. 486.320 Section 486.320 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES....320 Condition: Participation in Organ Procurement and Transplantation Network. After being designated...

Power Delivery from an Actual Thermoelectric Generation System

NASA Astrophysics Data System (ADS)

Kaibe, Hiromasa; Kajihara, Takeshi; Nagano, Kouji; Makino, Kazuya; Hachiuma, Hirokuni; Natsuume, Daisuke

2014-06-01

Similar to photovoltaic (PV) and fuel cells, thermoelectric generators (TEGs) supply direct-current (DC) power, essentially requiring DC/alternating current (AC) conversion for delivery as electricity into the grid network. Use of PVs is already well established through power conditioning systems (PCSs) that enable DC/AC conversion with maximum-power-point tracking, which enables commercial use by customers. From the economic, legal, and regulatory perspectives, a commercial PCS for PVs should also be available for TEGs, preferably as is or with just simple adjustment. Herein, we report use of a PV PCS with an actual TEG. The results are analyzed, and proper application for TEGs is proposed.

Emergence of Adaptive Computation by Single Neurons in the Developing Cortex

PubMed Central

Famulare, Michael; Gjorgjieva, Julijana; Moody, William J.

2013-01-01

Adaptation is a fundamental computational motif in neural processing. To maintain stable perception in the face of rapidly shifting input, neural systems must extract relevant information from background fluctuations under many different contexts. Many neural systems are able to adjust their input–output properties such that an input's ability to trigger a response depends on the size of that input relative to its local statistical context. This “gain-scaling” strategy has been shown to be an efficient coding strategy. We report here that this property emerges during early development as an intrinsic property of single neurons in mouse sensorimotor cortex, coinciding with the disappearance of spontaneous waves of network activity, and can be modulated by changing the balance of spike-generating currents. Simultaneously, developing neurons move toward a common intrinsic operating point and a stable ratio of spike-generating currents. This developmental trajectory occurs in the absence of sensory input or spontaneous network activity. Through a combination of electrophysiology and modeling, we demonstrate that developing cortical neurons develop the ability to perform nearly perfect gain scaling by virtue of the maturing spike-generating currents alone. We use reduced single neuron models to identify the conditions for this property to hold. PMID:23884925

Detecting phenotype-driven transitions in regulatory network structure.

PubMed

Padi, Megha; Quackenbush, John

2018-01-01

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular and contain dense "communities" of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks and analyzing their topologies separately, there is a lack of robust methods for quantifying differences in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. As an application, we use ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identifies modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors are enriched for genes associated with blood vessel development, and modules found in female breast tissue are enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules suggests that not only can ALPACA identify structural changes in complex networks, but also that these changes may be relevant for characterizing biological phenotypes.

Creation and perturbation of planar networks of chemical oscillators

PubMed Central

Tompkins, Nathan; Cambria, Matthew Carl; Wang, Adam L.; Heymann, Michael; Fraden, Seth

2015-01-01

Methods for creating custom planar networks of diffusively coupled chemical oscillators and perturbing individual oscillators within the network are presented. The oscillators consist of the Belousov-Zhabotinsky (BZ) reaction contained in an emulsion. Networks of drops of the BZ reaction are created with either Dirichlet (constant-concentration) or Neumann (no-flux) boundary conditions in a custom planar configuration using programmable illumination for the perturbations. The differences between the observed network dynamics for each boundary condition are described. Using light, we demonstrate the ability to control the initial conditions of the network and to cause individual oscillators within the network to undergo sustained period elongation or a one-time phase delay. PMID:26117136

Two-point resistance of an m × n resistor network with an arbitrary boundary and its application in RLC network

NASA Astrophysics Data System (ADS)

Zhi-Zhong, Tan

2016-05-01

A rectangular m × n resistor network with an arbitrary boundary is investigated, and a general resistance formula between two nodes on an arbitrary axis is derived by the Recursion-Transform (RT) method, a problem that has never been resolved before, for the Green’s function technique and the Laplacian matrix approach are inapplicable to it. To have the exact solution of resistance is important but it is difficult to obtain under the condition of arbitrary boundary. Our result is directly expressed in a single summation and mainly composed of characteristic roots, which contain both finite and infinite cases. Further, the current distribution is given explicitly as a byproduct of the method. Our framework can be effectively applied to RLC networks. As an application to the LC network, we find that our formulation leads to the occurrence of resonances at h 1 = 1 - cos ϕ i - sin ϕ i cot n ϕ i . This somewhat curious result suggests the possibility of practical applications of our formulae to resonant circuits. Project supported by the Prophase Preparatory Project of Natural Science Foundation of Nantong University, China (Grant No. 15ZY16).

Immigrant maternal depression and social networks. A multilevel Bayesian spatial logistic regression in South Western Sydney, Australia.

PubMed

Eastwood, John G; Jalaludin, Bin B; Kemp, Lynn A; Phung, Hai N; Barnett, Bryanne E W

2013-09-01

The purpose is to explore the multilevel spatial distribution of depressive symptoms among migrant mothers in South Western Sydney and to identify any group level associations that could inform subsequent theory building and local public health interventions. Migrant mothers (n=7256) delivering in 2002 and 2003 were assessed at 2-3 weeks after delivery for risk factors for depressive symptoms. The binary outcome variables were Edinburgh Postnatal Depression Scale scores (EPDS) of >9 and >12. Individual level variables included were: financial income, self-reported maternal health, social support network, emotional support, practical support, baby trouble sleeping, baby demanding and baby not content. The group level variable reported here is aggregated social support networks. We used Bayesian hierarchical multilevel spatial modelling with conditional autoregression. Migrant mothers were at higher risk of having depressive symptoms if they lived in a community with predominantly Australian-born mothers and strong social capital as measured by aggregated social networks. These findings suggest that migrant mothers are socially isolated and current home visiting services should be strengthened for migrant mothers living in communities where they may have poor social networks. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Simulation Modeling of Resilience Assessment in Indonesian Fertiliser Industry Supply Networks

NASA Astrophysics Data System (ADS)

Utami, I. D.; Holt, R. J.; McKay, A.

2018-01-01

Supply network resilience is a significant aspect in the performance of the Indonesian fertiliser industry. Decision makers use risk assessment and port management reports to evaluate the availability of infrastructure. An opportunity was identified to incorporate both types of data into an approach for the measurement of resilience. A framework, based on a synthesis of literature and interviews with industry practitioners, covering both social and technical factors is introduced. A simulation model was then built to allow managers to explore implications for resilience and predict levels of risk in different scenarios. Result of interview with respondens from Indonesian fertiliser industry indicated that the simulation model could be valuable in the assessment. This paper provides details of the simulation model for decision makers to explore levels of risk in supply networks. For practitioners, the model could be used by government to assess the current condition of supply networks in Indonesian industries. On the other hand, for academia, the approach provides a new application of agent-based models in research on supply network resilience and presents a real example of how agent-based modeling could be used as to support the assessment approach.

Defect detection and classification of galvanized stamping parts based on fully convolution neural network

NASA Astrophysics Data System (ADS)

Xiao, Zhitao; Leng, Yanyi; Geng, Lei; Xi, Jiangtao

2018-04-01

In this paper, a new convolution neural network method is proposed for the inspection and classification of galvanized stamping parts. Firstly, all workpieces are divided into normal and defective by image processing, and then the defective workpieces extracted from the region of interest (ROI) area are input to the trained fully convolutional networks (FCN). The network utilizes an end-to-end and pixel-to-pixel training convolution network that is currently the most advanced technology in semantic segmentation, predicts result of each pixel. Secondly, we mark the different pixel values of the workpiece, defect and background for the training image, and use the pixel value and the number of pixels to realize the recognition of the defects of the output picture. Finally, the defect area's threshold depended on the needs of the project is set to achieve the specific classification of the workpiece. The experiment results show that the proposed method can successfully achieve defect detection and classification of galvanized stamping parts under ordinary camera and illumination conditions, and its accuracy can reach 99.6%. Moreover, it overcomes the problem of complex image preprocessing and difficult feature extraction and performs better adaptability.

On Estimating End-to-End Network Path Properties

NASA Technical Reports Server (NTRS)

Allman, Mark; Paxson, Vern

1999-01-01

The more information about current network conditions available to a transport protocol, the more efficiently it can use the network to transfer its data. In networks such as the Internet, the transport protocol must often form its own estimates of network properties based on measurements per-formed by the connection endpoints. We consider two basic transport estimation problems: determining the setting of the retransmission timer (RTO) for are reliable protocol, and estimating the bandwidth available to a connection as it begins. We look at both of these problems in the context of TCP, using a large TCP measurement set [Pax97b] for trace-driven simulations. For RTO estimation, we evaluate a number of different algorithms, finding that the performance of the estimators is dominated by their minimum values, and to a lesser extent, the timer granularity, while being virtually unaffected by how often round-trip time measurements are made or the settings of the parameters in the exponentially-weighted moving average estimators commonly used. For bandwidth estimation, we explore techniques previously sketched in the literature [Hoe96, AD98] and find that in practice they perform less well than anticipated. We then develop a receiver-side algorithm that performs significantly better.

Elucidation of Diels-Alder Reaction Network of 2,5-Dimethylfuran and Ethylene on HY Zeolite Catalyst

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

Do, Phuong T. M.; McAtee, Jesse R.; Watson, Donald A.

2012-12-12

The reaction of 2,5-dimethylfuran and ethylene to produce p-xylene represents a potentially important route for the conversion of biomass to high-value organic chemicals. Current preparation methods suffer from low selectivity and produce a number of byproducts. Using modern separation and analytical techniques, the structures of many of the byproducts produced in this reaction when HY zeolite is employed as a catalyst have been identified. From these data, a detailed reaction network is proposed, demonstrating that hydrolysis and electrophilic alkylation reactions compete with the desired Diels–Alder/dehydration sequence. This information will allow the rational identification of more selective catalysts and more selectivemore » reaction conditions.« less

OCT-based label-free in vivo lymphangiography within human skin and areola

NASA Astrophysics Data System (ADS)

Baran, Utku; Qin, Wan; Qi, Xiaoli; Kalkan, Goknur; Wang, Ruikang K.

2016-02-01

Due to the limitations of current imaging techniques, visualization of lymphatic capillaries within tissue in vivo has been challenging. Here, we present a label-free high resolution optical coherence tomography (OCT) based lymphangiography (OLAG) within human skin in vivo. OLAG enables rapid (~seconds) mapping of lymphatic networks, along with blood vessel networks, over 8 mm x 8 mm of human skin and 5 mm x 5 mm of human areola. Moreover, lymphatic system’s response to inflammation within human skin is monitored throughout an acne lesion development over 7 days. The demonstrated results promise OLAG as a revolutionary tool in the clinical research and treatment of patients with pathologic conditions such as cancer, diabetes, and autoimmune diseases.

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

PubMed

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.

Environmental change makes robust ecological networks fragile

PubMed Central

Strona, Giovanni; Lafferty, Kevin D.

2016-01-01

Complex ecological networks appear robust to primary extinctions, possibly due to consumers' tendency to specialize on dependable (available and persistent) resources. However, modifications to the conditions under which the network has evolved might alter resource dependability. Here, we ask whether adaptation to historical conditions can increase community robustness, and whether such robustness can protect communities from collapse when conditions change. Using artificial life simulations, we first evolved digital consumer-resource networks that we subsequently subjected to rapid environmental change. We then investigated how empirical host–parasite networks would respond to historical, random and expected extinction sequences. In both the cases, networks were far more robust to historical conditions than new ones, suggesting that new environmental challenges, as expected under global change, might collapse otherwise robust natural ecosystems. PMID:27511722

A network security monitor

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

Heberlein, L.T.; Dias, G.V.; Levitt, K.N.

1989-11-01

The study of security in computer networks is a rapidly growing area of interest because of the proliferation of networks and the paucity of security measures in most current networks. Since most networks consist of a collection of inter-connected local area networks (LANs), this paper concentrates on the security-related issues in a single broadcast LAN such as Ethernet. Specifically, we formalize various possible network attacks and outline methods of detecting them. Our basic strategy is to develop profiles of usage of network resources and then compare current usage patterns with the historical profile to determine possible security violations. Thus, ourmore » work is similar to the host-based intrusion-detection systems such as SRI's IDES. Different from such systems, however, is our use of a hierarchical model to refine the focus of the intrusion-detection mechanism. We also report on the development of our experimental LAN monitor currently under implementation. Several network attacks have been simulated and results on how the monitor has been able to detect these attacks are also analyzed. Initial results demonstrate that many network attacks are detectable with our monitor, although it can surely be defeated. Current work is focusing on the integration of network monitoring with host-based techniques. 20 refs., 2 figs.« less

Road safety alerting system with radar and GPS cooperation in a VANET environment

NASA Astrophysics Data System (ADS)

Santamaria, Amilcare Francesco; Sottile, Cesare; De Rango, Floriano; Voznak, Miroslav

2014-05-01

New applications in wireless environments are increasing and keeping even more interests from the developer companies and researchers. In particular, in these last few years the government and institutional organization for road safety spent a lot of resources and money to promote Vehicular Ad-Hoc Network (VANET) technology, also car manufactures are giving a lot of contributions on this field as well. In our paper, we propose an innovative system to increase road safety, matching the requests of the market allowing a cooperation between on-board devices. The vehicles are equipped with On Board Unit (OBU) and On Board Radar Unit (OBRU), which can spread alerting messages around the network regarding warning and dangerous situations exploiting IEEE802.llp standard. Vehicles move along roads observing the environment, traffic and road conditions, and vehicles parameters as well. These information can be elaborated and shared between neighbors, Road Side Unit (RSU)s and, of course, with Internet, allowing inter-system communications exploiting an Road Traffic Manager (RTM). Radar systems task it the detection of the environment in order to increase the knowledge of current conditions of the roads, for example it is important to identify obstacles, road accidents, dangerous situations and so on. Once detected exploiting onboard devices, such as Global Position System (GPS) receiver it is possible to know the exact location of the caught event and after a data elaboration the information is spread along the network. Once the drivers are advised, they can make some precautionary actions such as reduction of traveling speed or modification of current road path. In this work the routing algorithms, which have the main goal to rapidly disseminate information, are also been investigated.

Recovery trajectories of kelp forest animals are rapid yet spatially variable across a network of temperate marine protected areas

NASA Astrophysics Data System (ADS)

Caselle, Jennifer E.; Rassweiler, Andrew; Hamilton, Scott L.; Warner, Robert R.

2015-09-01

Oceans currently face a variety of threats, requiring ecosystem-based approaches to management such as networks of marine protected areas (MPAs). We evaluated changes in fish biomass on temperate rocky reefs over the decade following implementation of a network of MPAs in the northern Channel Islands, California. We found that the biomass of targeted (i.e. fished) species has increased consistently inside all MPAs in the network, with an effect of geography on the strength of the response. More interesting, biomass of targeted fish species also increased outside MPAs, although only 27% as rapidly as in the protected areas, indicating that redistribution of fishing effort has not severely affected unprotected populations. Whether the increase outside of MPAs is due to changes in fishing pressure, fisheries management actions, adult spillover, favorable environmental conditions, or a combination of all four remains unknown. We evaluated methods of controlling for biogeographic or environmental variation across networks of protected areas and found similar performance of models incorporating empirical sea surface temperature versus a simple geographic blocking term based on assemblage structure. The patterns observed are promising indicators of the success of this network, but more work is needed to understand how ecological and physical contexts affect MPA performance.

A radio-aware routing algorithm for reliable directed diffusion in lossy wireless sensor networks.

PubMed

Kim, Yong-Pyo; Jung, Euihyun; Park, Yong-Jin

2009-01-01

In Wireless Sensor Networks (WSNs), transmission errors occur frequently due to node failure, battery discharge, contention or interference by objects. Although Directed Diffusion has been considered as a prominent data-centric routing algorithm, it has some weaknesses due to unexpected network errors. In order to address these problems, we proposed a radio-aware routing algorithm to improve the reliability of Directed Diffusion in lossy WSNs. The proposed algorithm is aware of the network status based on the radio information from MAC and PHY layers using a cross-layer design. The cross-layer design can be used to get detailed information about current status of wireless network such as a link quality or transmission errors of communication links. The radio information indicating variant network conditions and link quality was used to determine an alternative route that provides reliable data transmission under lossy WSNs. According to the simulation result, the radio-aware reliable routing algorithm showed better performance in both grid and random topologies with various error rates. The proposed solution suggested the possibility of providing a reliable transmission method for QoS requests in lossy WSNs based on the radio-awareness. The energy and mobility issues will be addressed in the future work.

Limitations of demand- and pressure-driven modeling for large deficient networks

NASA Astrophysics Data System (ADS)

Braun, Mathias; Piller, Olivier; Deuerlein, Jochen; Mortazavi, Iraj

2017-10-01

The calculation of hydraulic state variables for a network is an important task in managing the distribution of potable water. Over the years the mathematical modeling process has been improved by numerous researchers for utilization in new computer applications and the more realistic modeling of water distribution networks. But, in spite of these continuous advances, there are still a number of physical phenomena that may not be tackled correctly by current models. This paper will take a closer look at the two modeling paradigms given by demand- and pressure-driven modeling. The basic equations are introduced and parallels are drawn with the optimization formulations from electrical engineering. These formulations guarantee the existence and uniqueness of the solution. One of the central questions of the French and German research project ResiWater is the investigation of the network resilience in the case of extreme events or disasters. Under such extraordinary conditions where models are pushed beyond their limits, we talk about deficient network models. Examples of deficient networks are given by highly regulated flow, leakage or pipe bursts and cases where pressure falls below the vapor pressure of water. These examples will be presented and analyzed on the solvability and physical correctness of the solution with respect to demand- and pressure-driven models.

Recovery trajectories of kelp forest animals are rapid yet spatially variable across a network of temperate marine protected areas

PubMed Central

Caselle, Jennifer E.; Rassweiler, Andrew; Hamilton, Scott L.; Warner, Robert R.

2015-01-01

Oceans currently face a variety of threats, requiring ecosystem-based approaches to management such as networks of marine protected areas (MPAs). We evaluated changes in fish biomass on temperate rocky reefs over the decade following implementation of a network of MPAs in the northern Channel Islands, California. We found that the biomass of targeted (i.e. fished) species has increased consistently inside all MPAs in the network, with an effect of geography on the strength of the response. More interesting, biomass of targeted fish species also increased outside MPAs, although only 27% as rapidly as in the protected areas, indicating that redistribution of fishing effort has not severely affected unprotected populations. Whether the increase outside of MPAs is due to changes in fishing pressure, fisheries management actions, adult spillover, favorable environmental conditions, or a combination of all four remains unknown. We evaluated methods of controlling for biogeographic or environmental variation across networks of protected areas and found similar performance of models incorporating empirical sea surface temperature versus a simple geographic blocking term based on assemblage structure. The patterns observed are promising indicators of the success of this network, but more work is needed to understand how ecological and physical contexts affect MPA performance. PMID:26373803

Sensitivity of marine protected area network connectivity to atmospheric variability

NASA Astrophysics Data System (ADS)

Fox, Alan D.; Henry, Lea-Anne; Corne, David W.; Roberts, J. Murray

2016-11-01

International efforts are underway to establish well-connected systems of marine protected areas (MPAs) covering at least 10% of the ocean by 2020. But the nature and dynamics of ocean ecosystem connectivity are poorly understood, with unresolved effects of climate variability. We used 40-year runs of a particle tracking model to examine the sensitivity of an MPA network for habitat-forming cold-water corals in the northeast Atlantic to changes in larval dispersal driven by atmospheric cycles and larval behaviour. Trajectories of Lophelia pertusa larvae were strongly correlated to the North Atlantic Oscillation (NAO), the dominant pattern of interannual atmospheric circulation variability over the northeast Atlantic. Variability in trajectories significantly altered network connectivity and source-sink dynamics, with positive phase NAO conditions producing a well-connected but asymmetrical network connected from west to east. Negative phase NAO produced reduced connectivity, but notably some larvae tracked westward-flowing currents towards coral populations on the mid-Atlantic ridge. Graph theoretical metrics demonstrate critical roles played by seamounts and offshore banks in larval supply and maintaining connectivity across the network. Larval longevity and behaviour mediated dispersal and connectivity, with shorter lived and passive larvae associated with reduced connectivity. We conclude that the existing MPA network is vulnerable to atmospheric-driven changes in ocean circulation.

Network planning under uncertainties

NASA Astrophysics Data System (ADS)

Ho, Kwok Shing; Cheung, Kwok Wai

2008-11-01

One of the main focuses for network planning is on the optimization of network resources required to build a network under certain traffic demand projection. Traditionally, the inputs to this type of network planning problems are treated as deterministic. In reality, the varying traffic requirements and fluctuations in network resources can cause uncertainties in the decision models. The failure to include the uncertainties in the network design process can severely affect the feasibility and economics of the network. Therefore, it is essential to find a solution that can be insensitive to the uncertain conditions during the network planning process. As early as in the 1960's, a network planning problem with varying traffic requirements over time had been studied. Up to now, this kind of network planning problems is still being active researched, especially for the VPN network design. Another kind of network planning problems under uncertainties that has been studied actively in the past decade addresses the fluctuations in network resources. One such hotly pursued research topic is survivable network planning. It considers the design of a network under uncertainties brought by the fluctuations in topology to meet the requirement that the network remains intact up to a certain number of faults occurring anywhere in the network. Recently, the authors proposed a new planning methodology called Generalized Survivable Network that tackles the network design problem under both varying traffic requirements and fluctuations of topology. Although all the above network planning problems handle various kinds of uncertainties, it is hard to find a generic framework under more general uncertainty conditions that allows a more systematic way to solve the problems. With a unified framework, the seemingly diverse models and algorithms can be intimately related and possibly more insights and improvements can be brought out for solving the problem. This motivates us to seek a generic framework for solving the network planning problem under uncertainties. In addition to reviewing the various network planning problems involving uncertainties, we also propose that a unified framework based on robust optimization can be used to solve a rather large segment of network planning problem under uncertainties. Robust optimization is first introduced in the operations research literature and is a framework that incorporates information about the uncertainty sets for the parameters in the optimization model. Even though robust optimization is originated from tackling the uncertainty in the optimization process, it can serve as a comprehensive and suitable framework for tackling generic network planning problems under uncertainties. In this paper, we begin by explaining the main ideas behind the robust optimization approach. Then we demonstrate the capabilities of the proposed framework by giving out some examples of how the robust optimization framework can be applied to the current common network planning problems under uncertain environments. Next, we list some practical considerations for solving the network planning problem under uncertainties with the proposed framework. Finally, we conclude this article with some thoughts on the future directions for applying this framework to solve other network planning problems.

Possibility of using adsorption refrigeration unit in district heating network

NASA Astrophysics Data System (ADS)

Grzebielec, Andrzej; Rusowicz, Artur; Jaworski, Maciej; Laskowski, Rafał

2015-09-01

Adsorption refrigeration systems are able to work with heat sources of temperature starting with 50 °C. The aim of the article is to determine whether in terms of technical and economic issues adsorption refrigeration equipment can work as elements that produce cold using hot water from the district heating network. For this purpose, examined was the work of the adsorption air conditioning equipment cooperating with drycooler, and the opportunities offered by the district heating network in Warsaw during the summer. It turns out that the efficiency of the adsorption device from the economic perspective is not sufficient for production of cold even during the transitional period. The main problem is not the low temperature of the water supply, but the large difference between the coefficients of performance, COPs, of adsorption device and a traditional compressor air conditioning unit. When outside air temperature is 25 °C, the COP of the compressor type reaches a value of 4.49, whereas that of the adsorption device in the same conditions is 0.14. The ratio of the COPs is 32. At the same time ratio between the price of 1 kWh of electric power and 1 kWh of heat is only 2.85. Adsorption refrigeration equipment to be able to compete with compressor devices, should feature COPads efficiency to be greater than 1.52. At such a low driving temperature and even changing the drycooler into the evaporative cooler it is not currently possible to achieve.

Connectivity, excitability and activity patterns in neuronal networks

NASA Astrophysics Data System (ADS)

le Feber, Joost; Stoyanova, Irina I.; Chiappalone, Michela

2014-06-01

Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods currently exist for estimating network connectivity, most of which are related to cross-correlation. An example is the conditional firing probability (CFP) analysis which calculates the pairwise probability (CFPi,j) that electrode j records an action potential at time t = τ, given that electrode i recorded a spike at t = 0. However, electrode i often records multiple spikes within the analysis interval, and CFP values are biased by the on-going dynamic state of the network. Here we show that in a linear approximation this bias may be removed by deconvoluting CFPi,j with the autocorrelation of i (i.e. CFPi,i), to obtain the single pulse response (SPRi,j)—the average response at electrode j to a single spike at electrode i. Thus, in a linear system SPRs would be independent of the dynamic network state. Nonlinear components of synaptic transmission, such as facilitation and short term depression, will however still affect SPRs. Therefore SPRs provide a clean measure of network excitability. We used carbachol and ghrelin to moderately activate cultured cortical networks to affect their dynamic state. Both neuromodulators transformed the bursting firing patterns of the isolated networks into more dispersed firing. We show that the influence of the dynamic state on SPRs is much smaller than the effect on CFPs, but not zero. The remaining difference reflects the alteration in network excitability. We conclude that SPRs are less contaminated by the dynamic network state and that mild excitation may decrease network excitability, possibly through short term synaptic depression.

Electric Current Flow Through Two-Dimensional Networks

NASA Astrophysics Data System (ADS)

Gaspard, Mallory

In modern nanotechnology, two-dimensional atomic network structures boast promising applications as nanoscale circuit boards to serve as the building blocks of more sustainable and efficient, electronic devices. However, properties associated with the network connectivity can be beneficial or deleterious to the current flow. Taking a computational approach, we will study large uniform networks, as well as large random networks using Kirchhoff's Equations in conjunction with graph theoretical measures of network connectedness and flows, to understand how network connectivity affects overall ability for successful current flow throughout a network. By understanding how connectedness affects flow, we may develop new ways to design more efficient two-dimensional materials for the next generation of nanoscale electronic devices, and we will gain a deeper insight into the intricate balance between order and chaos in the universe. Rensselaer Polytechnic Institute, SURP Institutional Grant.

Changing Places and Partners: Associations of Neighborhood Conditions With Sexual Network Turnover Among African American Adults Relocated From Public Housing.

PubMed

Linton, Sabriya L; Cooper, Hannah L F; Luo, Ruiyan; Karnes, Conny; Renneker, Kristen; Haley, Danielle F; Dauria, Emily F; Hunter-Jones, Josalin; Ross, Zev; Wingood, Gina M; Adimora, Adaora A; Bonney, Loida; Rothenberg, Richard

2017-05-01

Neighborhood conditions and sexual network turnover have been associated with the acquisition of HIV and other sexually transmitted infections (STIs). However, few studies investigate the influence of neighborhood conditions on sexual network turnover. This longitudinal study used data collected across 7 visits from a predominantly substance-misusing cohort of 172 African American adults relocated from public housing in Atlanta, Georgia, to determine whether post-relocation changes in exposure to neighborhood conditions influence sexual network stability, the number of new partners joining sexual networks, and the number of partners leaving sexual networks over time. At each visit, participant and sexual network characteristics were captured via survey, and administrative data were analyzed to describe the census tracts where participants lived. Multilevel models were used to longitudinally assess the relationships of tract-level characteristics to sexual network dynamics over time. On average, participants relocated to neighborhoods that were less economically deprived and violent, and had lower alcohol outlet densities. Post-relocation reductions in exposure to alcohol outlet density were associated with fewer new partners joining sexual networks. Reduced perceived community violence was associated with more sexual partners leaving sexual networks. These associations were marginally significant. No post-relocation changes in place characteristics were significantly associated with overall sexual network stability. Neighborhood social context may influence sexual network turnover. To increase understanding of the social-ecological determinants of HIV/STIs, a new line of research should investigate the combined influence of neighborhood conditions and sexual network dynamics on HIV/STI transmission over time.

Ocean-Science Mission Needs: Real-Time AUV Data for Command, Control, and Model Inputs

NASA Technical Reports Server (NTRS)

Carder, Kendall L.; Costello, D. K.; Warrior, H.; Langebrake, L. C.; Hou, W.; Patten, J. T.; Kaltenbacher, E.

2001-01-01

Predictive models for tides, hydrodynamics, and bio-optical properties affecting the visibility and buoyancy of coastal waters are needed to evaluate the safety of personnel and equipment engaged in maritime operations under potentially hazardous conditions. Predicted currents can be markedly different for two-layer systems affected by terrestrial runoff than for well-mixed conditions because the layering decouples the surface and bottom Ekman layers and rectifies the current response to oscillatory upwelling-and downwelling-favorable winds. Standard ocean models (e.g. Princeton Ocean Model) require initial-and boundary data on the physical and optical properties of the multilayered water column to provide accurate simulations of heat budgets and circulation. Two observational systems are designed to measure vertically structured conditions on the West Florida Shelf (WFS): a tethered buoy network and an autonomous underwater vehicle (AUV) observational system. The AUV system is described with a focus on the observational systems that challenge or limit the communications command and control network for various types of measurement programs. These include vertical oscillatory missions on shelf transects to observe the optical and hydrographic properties of the water column, and bottom-following missions for measuring the bottom albedo. Models of light propagation, absorption, and conversion to heat as well as determination of the buoyancy terms for physical models require these measurements. High data rates associated with video bottom imagery are the most challenging for the real-time, command and control communications system, but they are met through a combination of loss-less and lossy data-compression methods, depending upon the data-rate of the radio links.

The effects of tDCS upon sustained visual attention are dependent on cognitive load.

PubMed

Roe, James M; Nesheim, Mathias; Mathiesen, Nina C; Moberget, Torgeir; Alnæs, Dag; Sneve, Markus H

2016-01-08

Transcranial Direct Current Stimulation (tDCS) modulates the excitability of neuronal responses and consequently can affect performance on a variety of cognitive tasks. However, the interaction between cognitive load and the effects of tDCS is currently not well-understood. We recorded the performance accuracy of participants on a bilateral multiple object tracking task while undergoing bilateral stimulation assumed to enhance (anodal) and decrease (cathodal) neuronal excitability. Stimulation was applied to the posterior parietal cortex (PPC), a region inferred to be at the centre of an attentional tracking network that shows load-dependent activation. 34 participants underwent three separate stimulation conditions across three days. Each subject received (1) left cathodal / right anodal PPC tDCS, (2) left anodal / right cathodal PPC tDCS, and (3) sham tDCS. The number of targets-to-be-tracked was also manipulated, giving a low (one target per visual field), medium (two targets per visual field) or high (three targets per visual field) tracking load condition. It was found that tracking performance at high attentional loads was significantly reduced in both stimulation conditions relative to sham, and this was apparent in both visual fields, regardless of the direction of polarity upon the brain's hemispheres. We interpret this as an interaction between cognitive load and tDCS, and suggest that tDCS may degrade attentional performance when cognitive networks become overtaxed and unable to compensate as a result. Systematically varying cognitive load may therefore be a fruitful direction to elucidate the effects of tDCS upon cognitive functions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Mental health network governance: comparative analysis across Canadian regions.

PubMed

Wiktorowicz, Mary E; Fleury, Marie-Josée; Adair, Carol E; Lesage, Alain; Goldner, Elliot; Peters, Suzanne

2010-10-26

Modes of governance were compared in ten local mental health networks in diverse contexts (rural/urban and regionalized/non-regionalized) to clarify the governance processes that foster inter-organizational collaboration and the conditions that support them. Case studies of ten local mental health networks were developed using qualitative methods of document review, semi-structured interviews and focus groups that incorporated provincial policy, network and organizational levels of analysis. Mental health networks adopted either a corporate structure, mutual adjustment or an alliance governance model. A corporate structure supported by regionalization offered the most direct means for local governance to attain inter-organizational collaboration. The likelihood that networks with an alliance model developed coordination processes depended on the presence of the following conditions: a moderate number of organizations, goal consensus and trust among the organizations, and network-level competencies. In the small and mid-sized urban networks where these conditions were met their alliance realized the inter-organizational collaboration sought. In the large urban and rural networks where these conditions were not met, externally brokered forms of network governance were required to support alliance based models. In metropolitan and rural networks with such shared forms of network governance as an alliance or voluntary mutual adjustment, external mediation by a regional or provincial authority was an important lever to foster inter-organizational collaboration.

Analysis of critical operating conditions for LV distribution networks with microgrids

NASA Astrophysics Data System (ADS)

Zehir, M. A.; Batman, A.; Sonmez, M. A.; Font, A.; Tsiamitros, D.; Stimoniaris, D.; Kollatou, T.; Bagriyanik, M.; Ozdemir, A.; Dialynas, E.

2016-11-01

Increase in the penetration of Distributed Generation (DG) in distribution networks, raises the risk of voltage limit violations while contributing to line losses. Especially in low voltage (LV) distribution networks (secondary distribution networks), impacts of active power flows on the bus voltages and on the network losses are more dominant. As network operators must meet regulatory limitations, they have to take into account the most critical operating conditions in their systems. In this study, it is aimed to present the impact of the worst operation cases of LV distribution networks comprising microgrids. Simulation studies are performed on a field data-based virtual test-bed. The simulations are repeated for several cases consisting different microgrid points of connection with different network loading and microgrid supply/demand conditions.

Conformal bi-layered perovskite/spinel coating on a metallic wire network for solid oxide fuel cells via an electrodeposition-based route

NASA Astrophysics Data System (ADS)

Park, Beom-Kyeong; Song, Rak-Hyun; Lee, Seung-Bok; Lim, Tak-Hyoung; Park, Seok-Joo; Jung, WooChul; Lee, Jong-Won

2017-04-01

Solid oxide fuel cells (SOFCs) require low-cost metallic components for current collection from electrodes as well as electrical connection between unit cells; however, the degradation of their electrical properties and surface stability associated with high-temperature oxidation is of great concern. It is thus important to develop protective conducting oxide coatings capable of mitigating the degradation of metallic components under SOFC operating conditions. Here, we report a conformal bi-layered coating composed of perovskite and spinel oxides on a metallic wire network fabricated by a facile electrodeposition-based route. A highly dense, crack-free, and adhesive bi-layered LaMnO3/Co3O4 coating of ∼1.2 μm thickness is conformally formed on the surfaces of wires with ∼100 μm diameter. We demonstrate that the bi-layered LaMnO3/Co3O4 coating plays a key role in improving the power density and durability of a tubular SOFC by stabilizing the surface of the metallic wire network used as a cathode current collector. The electrodeposition-based technique presented in this study offers a low-cost and scalable process to fabricate conformal multi-layered coatings on various metallic structures.

Predictive and Neural Predictive Control of Uncertain Systems

NASA Technical Reports Server (NTRS)

Kelkar, Atul G.

2000-01-01

Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

Prevalence and test characteristics of national health safety network ventilator-associated events.

PubMed

Lilly, Craig M; Landry, Karen E; Sood, Rahul N; Dunnington, Cheryl H; Ellison, Richard T; Bagley, Peter H; Baker, Stephen P; Cody, Shawn; Irwin, Richard S

2014-09-01

The primary aim of the study was to measure the test characteristics of the National Health Safety Network ventilator-associated event/ventilator-associated condition constructs for detecting ventilator-associated pneumonia. Its secondary aims were to report the clinical features of patients with National Health Safety Network ventilator-associated event/ventilator-associated condition, measure costs of surveillance, and its susceptibility to manipulation. Prospective cohort study. Two inpatient campuses of an academic medical center. Eight thousand four hundred eight mechanically ventilated adults discharged from an ICU. None. The National Health Safety Network ventilator-associated event/ventilator-associated condition constructs detected less than a third of ventilator-associated pneumonia cases with a sensitivity of 0.325 and a positive predictive value of 0.07. Most National Health Safety Network ventilator-associated event/ventilator-associated condition cases (93%) did not have ventilator-associated pneumonia or other hospital-acquired complications; 71% met the definition for acute respiratory distress syndrome. Similarly, most patients with National Health Safety Network probable ventilator-associated pneumonia did not have ventilator-associated pneumonia because radiographic criteria were not met. National Health Safety Network ventilator-associated event/ventilator-associated condition rates were reduced 93% by an unsophisticated manipulation of ventilator management protocols. The National Health Safety Network ventilator-associated event/ventilator-associated condition constructs failed to detect many patients who had ventilator-associated pneumonia, detected many cases that did not have a hospital complication, and were susceptible to manipulation. National Health Safety Network ventilator-associated event/ventilator-associated condition surveillance did not perform as well as ventilator-associated pneumonia surveillance and had several undesirable characteristics.

Human Fear Conditioning and Extinction in Neuroimaging: A Systematic Review

PubMed Central

Sehlmeyer, Christina; Schöning, Sonja; Zwitserlood, Pienie; Pfleiderer, Bettina; Kircher, Tilo; Arolt, Volker; Konrad, Carsten

2009-01-01

Fear conditioning and extinction are basic forms of associative learning that have gained considerable clinical relevance in enhancing our understanding of anxiety disorders and facilitating their treatment. Modern neuroimaging techniques have significantly aided the identification of anatomical structures and networks involved in fear conditioning. On closer inspection, there is considerable variation in methodology and results between studies. This systematic review provides an overview of the current neuroimaging literature on fear conditioning and extinction on healthy subjects, taking into account methodological issues such as the conditioning paradigm. A Pubmed search, as of December 2008, was performed and supplemented by manual searches of bibliographies of key articles. Two independent reviewers made the final study selection and data extraction. A total of 46 studies on cued fear conditioning and/or extinction on healthy volunteers using positron emission tomography or functional magnetic resonance imaging were reviewed. The influence of specific experimental factors, such as contingency and timing parameters, assessment of conditioned responses, and characteristics of conditioned and unconditioned stimuli, on cerebral activation patterns was examined. Results were summarized descriptively. A network consisting of fear-related brain areas, such as amygdala, insula, and anterior cingulate cortex, is activated independently of design parameters. However, some neuroimaging studies do not report these findings in the presence of methodological heterogeneities. Furthermore, other brain areas are differentially activated, depending on specific design parameters. These include stronger hippocampal activation in trace conditioning and tactile stimulation. Furthermore, tactile unconditioned stimuli enhance activation of pain related, motor, and somatosensory areas. Differences concerning experimental factors may partly explain the variance between neuroimaging investigations on human fear conditioning and extinction and should, therefore, be taken into serious consideration in the planning and the interpretation of research projects. PMID:19517024

Routing to preserve energy in wireless networks

NASA Astrophysics Data System (ADS)

Block, Frederick J., IV

Many applications for wireless radio networks require that some or all radios in the network rely on batteries as energy sources. In many cases, battery replacement is infeasible, expensive, or impossible. Communication protocols for such networks should be designed to preserve limited energy supplies. Because the choice of a route to a traffic sink influences how often radios must transmit and receive, poor route selection can quickly deplete the batteries of certain nodes. Previous work has shown that a network's lifetime can be extended by assigning higher routing costs to nodes with little remaining energy and nodes that must use high transmitter power to reach neighbor radios. Although using remaining energy levels in routing metrics can increase network lifetime, in practice, there may be significant error in a node's estimate of its battery level. The effect of battery level uncertainty on routing is examined. Routing metrics are presented that are designed to explicitly account for uncertainty in remaining energy. Simulation results using several statistical models for this uncertainty show that the proposed metrics perform well. In addition to knowledge of current battery levels, estimates of how quickly radios are consuming energy may be helpful in extending network lifetime. We present a family of routing metrics that incorporate a radio's rate of energy consumption. Simulation results show that the proposed family of metrics performs well under a variety of traffic models and network topologies. Route selection can also be complicated by time-varying link conditions. Radios may be subject to interference from other nearby communication systems, hostile jammers, and other, non-communication sources of noise. A route that first appears to have only a small cost may later require much greater energy expenditure when transmitting packets. Frequent route selection can help radios avoid using links with interference, but additional routing control messages increase energy consumption. We investigate the effects of time-varying interference on the lifetime of ad hoc networks. It is shown that there is a tradeoff between packet delay and node lifetime. We show that it is possible to design the system to perform well under a wide variety of channel conditions.

Influence network linkages across implementation strategy conditions in a randomized controlled trial of two strategies for scaling up evidence-based practices in public youth-serving systems

PubMed Central

2013-01-01

Background Given the importance of influence networks in the implementation of evidence-based practices and interventions, it is unclear whether such networks continue to operate as sources of information and advice when they are segmented and disrupted by randomization to different implementation strategy conditions. The present study examines the linkages across implementation strategy conditions of social influence networks of leaders of youth-serving systems in 12 California counties participating in a randomized controlled trial of community development teams (CDTs) to scale up use of an evidence-based practice. Methods Semi-structured interviews were conducted with 38 directors, assistant directors, and program managers of county probation, mental health, and child welfare departments. A web-based survey collected additional quantitative data on information and advice networks of study participants. A mixed-methods approach to data analysis was used to create a sociometric data set (n = 176) to examine linkages between treatment and standard conditions. Results Of those network members who were affiliated with a county (n = 137), only 6 (4.4%) were directly connected to a member of the opposite implementation strategy condition; 19 (13.9%) were connected by two steps or fewer to a member of the opposite implementation strategy condition; 64 (46.7%) were connected by three or fewer steps to a member of the opposite implementation strategy condition. Most of the indirect steps between individuals who were in different implementation strategy conditions were connections involving a third non-county organizational entity that had an important role in the trial in keeping the implementation strategy conditions separate. When these entities were excluded, the CDT network exhibited fewer components and significantly higher betweenness centralization than did the standard condition network. Conclusion Although the integrity of the RCT in this instance was not compromised by study participant influence networks, RCT designs should consider how influence networks may extend beyond boundaries established by the randomization process in implementation studies. Trial registration NCT00880126 PMID:24229373

Current Challenges in Geothermal Reservoir Simulation

NASA Astrophysics Data System (ADS)

Driesner, T.

2016-12-01

Geothermal reservoir simulation has long been introduced as a valuable tool for geothermal reservoir management and research. Yet, the current generation of simulation tools faces a number of severe challenges, in particular in the application for novel types of geothermal resources such as supercritical reservoirs or hydraulic stimulation. This contribution reviews a number of key problems: Representing the magmatic heat source of high enthalpy resources in simulations. Current practice is representing the deeper parts of a high enthalpy reservoir by a heat flux or temperature boundary condition. While this is sufficient for many reservoir management purposes it precludes exploring the chances of very high enthalpy resources in the deepest parts of such systems as well as the development of reliable conceptual models. Recent 2D simulations with the CSMP++ simulation platform demonstrate the potential of explicitly including the heat source, namely for understanding supercritical resources. Geometrically realistic incorporation of discrete fracture networks in simulation. A growing number of simulation tools can, in principle, handle flow and heat transport in discrete fracture networks. However, solving the governing equations and representing the physical properties are often biased by introducing strongly simplifying assumptions. Including proper fracture mechanics in complex fracture network simulations remains an open challenge. Improvements of the simulating chemical fluid-rock interaction in geothermal reservoirs. Major improvements have been made towards more stable and faster numerical solvers for multicomponent chemical fluid rock interaction. However, the underlying thermodynamic models and databases are unable to correctly address a number of important regions in temperature-pressure-composition parameter space. Namely, there is currently no thermodynamic formalism to describe relevant chemical reactions in supercritical reservoirs. Overcoming this unsatisfactory situation requires fundamental research in high temperature physical chemistry rather than further numerical development.

Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness

PubMed Central

Kim, Minkyung; Mashour, George A.; Moraes, Stefanie-Blain; Vanini, Giancarlo; Tarnal, Vijay; Janke, Ellen; Hudetz, Anthony G.; Lee, Uncheol

2016-01-01

Sleep, anesthesia, and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four variables that are involved in facilitating ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states. PMID:26834616

Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness.

PubMed

Kim, Minkyung; Mashour, George A; Moraes, Stefanie-Blain; Vanini, Giancarlo; Tarnal, Vijay; Janke, Ellen; Hudetz, Anthony G; Lee, Uncheol

2016-01-01

Sleep, anesthesia, and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four variables that are involved in facilitating ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states.

The National Wind Erosion Research Network: Building a standardized long-term data resource for aeolian research, modeling and land management

USGS Publications Warehouse

Webb, Nicholas P.; Herrick, Jeffrey E.; Van Zee, Justin W; Courtright, Ericha M; Hugenholtz, Ted M; Zobeck, Ted M; Okin, Gregory S.; Barchyn, Thomas E; Billings, Benjamin J; Boyd, Robert A.; Clingan, Scott D; Cooper, Brad F; Duniway, Michael C.; Derner, Justin D.; Fox, Fred A; Havstad, Kris M.; Heilman, Philip; LaPlante, Valerie; Ludwig, Noel A; Metz, Loretta J; Nearing, Mark A; Norfleet, M Lee; Pierson, Frederick B; Sanderson, Matt A; Sharrat, Brenton S; Steiner, Jean L; Tatarko, John; Tedela, Negussie H; Todelo, David; Unnasch, Robert S; Van Pelt, R Scott; Wagner, Larry

2016-01-01

The National Wind Erosion Research Network was established in 2014 as a collaborative effort led by the United States Department of Agriculture’s Agricultural Research Service and Natural Resources Conservation Service, and the United States Department of the Interior’s Bureau of Land Management, to address the need for a long-term research program to meet critical challenges in wind erosion research and management in the United States. The Network has three aims: (1) provide data to support understanding of basic aeolian processes across land use types, land cover types, and management practices, (2) support development and application of models to assess wind erosion and dust emission and their impacts on human and environmental systems, and (3) encourage collaboration among the aeolian research community and resource managers for the transfer of wind erosion technologies. The Network currently consists of thirteen intensively instrumented sites providing measurements of aeolian sediment transport rates, meteorological conditions, and soil and vegetation properties that influence wind erosion. Network sites are located across rangelands, croplands, and deserts of the western US. In support of Network activities, http://winderosionnetwork.org was developed as a portal for information about the Network, providing site descriptions, measurement protocols, and data visualization tools to facilitate collaboration with scientists and managers interested in the Network and accessing Network products. The Network provides a mechanism for engaging national and international partners in a wind erosion research program that addresses the need for improved understanding and prediction of aeolian processes across complex and diverse land use types and management practices.

Social Network Type and Long-Term Condition Management Support: A Cross-Sectional Study in Six European Countries.

PubMed

Vassilev, Ivaylo; Rogers, Anne; Kennedy, Anne; Wensing, Michel; Koetsenruijter, Jan; Orlando, Rosanna; Portillo, Maria Carmen; Culliford, David

2016-01-01

Network types and characteristics have been linked to the capacity of inter-personal environments to mobilise and share resources. The aim of this paper is to examine personal network types in relation to long-term condition management in order to identify the properties of network types most likely to provide support for those with a long-term condition. A cross-sectional observational survey of people with type 2 diabetes using interviews and questionnaires was conducted between April and October 2013 in six European countries: Greece, Spain, Bulgaria, Norway, United Kingdom, and Netherlands. 1862 people with predominantly lower socio-economic status were recruited from each country. We used k-means clustering analysis to derive the network types, and one-way analysis of variance and multivariate logistic regression analysis to explore the relationship between network type socio-economic characteristics, self-management monitoring and skills, well-being, and network member work. Five network types of people with long-term conditions were identified: restricted, minimal family, family, weak ties, and diverse. Restricted network types represented those with the poorest self-management skills and were associated with limited support from social network members. Restricted networks were associated with poor indicators across self-management capacity, network support, and well-being. Diverse networks were associated with more enhanced self-management skills amongst those with a long-term condition and high level of emotional support. It was the three network types which had a large number of network members (diverse, weak ties, and family) where healthcare utilisation was most likely to correspond to existing health needs. Our findings suggest that type of increased social involvement is linked to greater self-management capacity and potentially lower formal health care costs indicating that diverse networks constitute the optimal network type as a policy in terms of the design of LTCM interventions and building support for people with LTCs.

An Internet service for manipulating 3D models of human organs reconstructed from computer tomography and magnetic resonance imaging.

PubMed

Clapworthy, G; Krokos, M; Vasilonikolidakis, N

1997-11-01

Our paper describes an integrated methodology addressing the development of an Internet service for medical professionals, medical students and generally, people interested in medicine. The service (currently developed in the framework of IAEVA, a Telematics Application Programme project of the European Union), incorporates a mechanism for retrieving from a relational database (reference library) 3D volumetric models of human organs reconstructed from computer tomography (CT) and/or magnetic resonance imaging (MRI). Retrieval is implemented in a way transparent to the actual physical location of the database. Prospective users are provided with a Solid Object Viewer that offers them manipulation (rotation, zooming, dissection etc.) of 3D volumetric models. The service constitutes an excellent foundation of understanding for medical professionals/students and a mechanism for broad and rapid dissemination of information related to particular pathological conditions; although pathological conditions of the knee and skin are supported currently, our methodology allows easy service extension into other human organs ultimately covering the entire human body. The service accepts most Internet browsers and supports MS-Windows 32 platforms; no graphics accelerators or any specialised hardware are necessary, thereby allowing service availability to the widest possible audience. Nevertheless, the service operates in near real-time not only over high speed expensive network lines but also over low/medium network connections.

Integrated cellular network of transcription regulations and protein-protein interactions

PubMed Central

2010-01-01

Background With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway. Results In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated. Conclusions We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology. PMID:20211003

Integrated cellular network of transcription regulations and protein-protein interactions.

PubMed

Wang, Yu-Chao; Chen, Bor-Sen

2010-03-08

With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway. In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated. We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

Flow networks for Ocean currents

NASA Astrophysics Data System (ADS)

Tupikina, Liubov; Molkenthin, Nora; Marwan, Norbert; Kurths, Jürgen

2014-05-01

Complex networks have been successfully applied to various systems such as society, technology, and recently climate. Links in a climate network are defined between two geographical locations if the correlation between the time series of some climate variable is higher than a threshold. Therefore, network links are considered to imply heat exchange. However, the relationship between the oceanic and atmospheric flows and the climate network's structure is still unclear. Recently, a theoretical approach verifying the correlation between ocean currents and surface air temperature networks has been introduced, where the Pearson correlation networks were constructed from advection-diffusion dynamics on an underlying flow. Since the continuous approach has its limitations, i.e., by its high computational complexity, we here introduce a new, discrete construction of flow-networks, which is then applied to static and dynamic velocity fields. Analyzing the flow-networks of prototypical flows we find that our approach can highlight the zones of high velocity by degree and transition zones by betweenness, while the combination of these network measures can uncover how the flow propagates within time. We also apply the method to time series data of the Equatorial Pacific Ocean Current and the Gulf Stream ocean current for the changing velocity fields, which could not been done before, and analyse the properties of the dynamical system. Flow-networks can be powerful tools to theoretically understand the step from system's dynamics to network's topology that can be analyzed using network measures and is used for shading light on different climatic phenomena.

Wellness partners: design and evaluation of a web-based physical activity diary with social gaming features for adults.

PubMed

Gotsis, Marientina; Wang, Hua; Spruijt-Metz, Donna; Jordan-Marsh, Maryalice; Valente, Thomas William

2013-02-01

The United States is currently in an age of obesity and inactivity despite increasing public awareness and scientific knowledge of detrimental long-term health effects of this lifestyle. Behavior-tracking diaries offer an effective strategy for physical activity adherence and weight management. Furthermore, Web-based physical activity diaries can engage meaningful partners in people's social networks through fun online gaming interactions and generate motivational mechanisms for effective behavioral change and positive health outcomes. Wellness Partners (WP) is a Web-based intervention in the form of a physical activity diary with social networking and game features. Two versions were designed and developed for the purpose of this study-"Diary" only and "Diary+Game". The objectives of this study included pilot testing the research process of this intervention design, implementation, evaluation, and exploring the effectiveness of social gaming features on adult participants' physical activity and anthropometric measures. We conducted a field experiment with randomized crossover design. Assessments occurred at baseline, first follow-up (FU, 5-8 weeks after using one version of WP), and second FU (5-8 weeks of using the other version of WP). In the control condition, participants started with the "Diary" version of WP while in the experimental condition, participants started with the "Diary+Game" version of WP. A total of 54 adults (egos) ages 44-88, and their family and friends (alters) ages 17-69 participated in the study in ego-network groups. Both egos and their alters completed online surveys about their exercise habits. In addition, egos completed anthropometric measurements of BMI, fat percentage, and fat mass by bioimpedance. From October 2009 to May 2010, flyers, emails, and Web advertisements yielded 335 volunteers who were screened. Rolling recruitment resulted in enrollment of 142 qualified participants in 54 ego-network groups, which were randomly assigned to a study condition. The final analytic sample included 87 individuals from 41 groups. Data were collected from December 2009 to August 2010, and data analysis was completed in 2011. Overall, the participants were given access to the intervention for 10-13 weeks. Statistical analysis suggested an increase in self-reported exercise frequency (mean days per week) from baseline (2.57, SD 1.92) to first FU (3.21, SD 1.74) in both conditions. Stronger effects were seen in the condition where Diary+Game was played first, especially in network groups with larger age variation between the alters and egos. Overall, the decrease in egos' BMI was statistically significant from baseline to first FU, with greater decrease for those in the Diary+Game first condition (-0.26 vs -0.16 in the Diary first condition). The Wellness Partners program increased physical activity among participants and resulted in health benefits among the egos. Web-based diary interventions designed with social gaming features hold potential to promote active lifestyles for middle-age adults and people in their social networks.

Artificial intelligence for analyzing orthopedic trauma radiographs.

PubMed

Olczak, Jakub; Fahlberg, Niklas; Maki, Atsuto; Razavian, Ali Sharif; Jilert, Anthony; Stark, André; Sköldenberg, Olof; Gordon, Max

2017-12-01

Background and purpose - Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods - We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd's Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network's performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results - All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen's kappa under these conditions was 0.76. Interpretation - This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics.

Using Passive Two-Port Networks to Study the Forced Vibrations of Piezoceramic Transducers

NASA Astrophysics Data System (ADS)

Karlash, V. L.

2017-09-01

A generalization and subsequent development of experimental techniques, including methods of studying the phase-frequency relations between the measured components of admittance and instantaneous power are considered. The conditions of electric loading where electric currents, voltages, or instantaneous powers of constant amplitude in the piezoresonators are specified are numerically modeled. It is particularly established that the advanced Mason circuit with additional switch allows acquiring much more data on the forced vibrations of piezoceramic transducers than the classical circuit. The measured (at an arbitrary frequency) voltage drop across the piezoelement, its pull-up resistor, and at the input of the measuring circuit allow determining, with high accuracy, the current, conductivity, impedance, instantaneous power, and phase shifts when the amplitudes of electric current and voltage are given.

Integrated fast assembly of free-standing lithium titanate/carbon nanotube/cellulose nanofiber hybrid network film as flexible paper-electrode for lithium-ion batteries.

PubMed

Cao, Shaomei; Feng, Xin; Song, Yuanyuan; Xue, Xin; Liu, Hongjiang; Miao, Miao; Fang, Jianhui; Shi, Liyi

2015-05-27

A free-standing lithium titanate (Li4Ti5O12)/carbon nanotube/cellulose nanofiber hybrid network film is successfully assembled by using a pressure-controlled aqueous extrusion process, which is highly efficient and easily to scale up from the perspective of disposable and recyclable device production. This hybrid network film used as a lithium-ion battery (LIB) electrode has a dual-layer structure consisting of Li4Ti5O12/carbon nanotube/cellulose nanofiber composites (hereinafter referred to as LTO/CNT/CNF), and carbon nanotube/cellulose nanofiber composites (hereinafter referred to as CNT/CNF). In the heterogeneous fibrous network of the hybrid film, CNF serves simultaneously as building skeleton and a biosourced binder, which substitutes traditional toxic solvents and synthetic polymer binders. Of importance here is that the CNT/CNF layer is used as a lightweight current collector to replace traditional heavy metal foils, which therefore reduces the total mass of the electrode while keeping the same areal loading of active materials. The free-standing network film with high flexibility is easy to handle, and has extremely good conductivity, up to 15.0 S cm(-1). The flexible paper-electrode for LIBs shows very good high rate cycling performance, and the specific charge/discharge capacity values are up to 142 mAh g(-1) even at a current rate of 10 C. On the basis of the mild condition and fast assembly process, a CNF template fulfills multiple functions in the fabrication of paper-electrode for LIBs, which would offer an ever increasing potential for high energy density, low cost, and environmentally friendly flexible electronics.

Mental health network governance: comparative analysis across Canadian regions

PubMed Central

Wiktorowicz, Mary E; Fleury, Marie-Josée; Adair, Carol E; Lesage, Alain; Goldner, Elliot; Peters, Suzanne

2010-01-01

Objective Modes of governance were compared in ten local mental health networks in diverse contexts (rural/urban and regionalized/non-regionalized) to clarify the governance processes that foster inter-organizational collaboration and the conditions that support them. Methods Case studies of ten local mental health networks were developed using qualitative methods of document review, semi-structured interviews and focus groups that incorporated provincial policy, network and organizational levels of analysis. Results Mental health networks adopted either a corporate structure, mutual adjustment or an alliance governance model. A corporate structure supported by regionalization offered the most direct means for local governance to attain inter-organizational collaboration. The likelihood that networks with an alliance model developed coordination processes depended on the presence of the following conditions: a moderate number of organizations, goal consensus and trust among the organizations, and network-level competencies. In the small and mid-sized urban networks where these conditions were met their alliance realized the inter-organizational collaboration sought. In the large urban and rural networks where these conditions were not met, externally brokered forms of network governance were required to support alliance based models. Discussion In metropolitan and rural networks with such shared forms of network governance as an alliance or voluntary mutual adjustment, external mediation by a regional or provincial authority was an important lever to foster inter-organizational collaboration. PMID:21289999

Novel method for water vapour monitoring using wireless communication networks measurements

NASA Astrophysics Data System (ADS)

David, N.; Alpert, P.; Messer, H.

2009-04-01

We propose a new technique for monitoring near-surface water vapour, by estimating humidity from data collected through existing wireless communication networks. Water vapour plays a crucial part in a variety of atmospheric processes. As the most influential of greenhouse gases, it absorbs long-wave terrestrial radiation. The water vapour cycle of evaporation and recondensation is a major energy redistributing mechanism transferring heat energy from the Earth's surface to the atmosphere. Additionally, humidity has an important role in weather forecasting as a key variable required for initialization of atmospheric models and hazard warning techniques. However, current methods of monitoring humidity suffer from low spatial resolution, high cost or a lack of precision when measuring near ground levels. Weather conditions and atmospheric phenomena affect the electromagnetic channel, causing attenuations to the radio signals. Thus, wireless communication networks are in effect built-in environmental monitoring facilities. The wireless microwave links, used in these networks, are widely deployed by cellular providers for backhaul communication between base stations, a few tens of meters above ground level. As a result, the proposed method can provide moisture observations at high temporal and spatial resolution. Further, the implementation cost is minimal, since the data used is already collected and saved by the cellular operators. In addition - many of these links are installed in areas where access is difficult such as orographic terrain and complex topography. As such, our method enables measurements in places that have been hard to measure in the past, or have never been measured before. The technique is restricted to weather conditions which include absence of rain, fog or clouds along the propagation path. We present results from real-data measurements taken from microwave links used in a backhaul cellular network that show very good agreement with surface station humidity measurements.

Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected Inhibitory Neural Networks

PubMed Central

Rich, Scott; Booth, Victoria; Zochowski, Michal

2016-01-01

The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics compared to those in networks of Type I or Type II neurons. To understand these results, we compute neuronal PRCs calculated with a perturbation matching the profile of the synaptic current in our networks. Differences in profiles of these PRCs across the different neuron types reveal mechanisms underlying the divergent network dynamics. PMID:27812323

LANES 1 Users' Guide

NASA Technical Reports Server (NTRS)

Jordan, J.

1985-01-01

This document is intended for users of the Local Area Network Extensible Simulator, version I. This simulator models the performance of a Fiber Optic network under a variety of loading conditions and network characteristics. The options available to the user for defining the network conditions are described in this document. Computer hardware and software requirements are also defined.

Summary of hydrologic conditions in Kansas, water year 2016

USGS Publications Warehouse

Louen, Justin M.

2017-04-06

The U.S. Geological Survey (USGS), in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring sites in Kansas. Real-time data are collected at 216 streamgage sites and are verified throughout the year with regular measurements of streamflow made by USGS personnel. Annual assessments of hydrologic conditions are made by comparing statistical analyses of current and historical water year (WY) data for the period of record. A WY is the 12-month period from October 1 through September 30 and is designated by the calendar year in which the period ends. Long-term monitoring of hydrologic conditions in Kansas provides critical information for water-supply management, flood forecasting, reservoir operations, irrigation scheduling, bridge and culvert design, ecological monitoring, and many other uses.

Optimal line drop compensation parameters under multi-operating conditions

NASA Astrophysics Data System (ADS)

Wan, Yuan; Li, Hang; Wang, Kai; He, Zhe

2017-01-01

Line Drop Compensation (LDC) is a main function of Reactive Current Compensation (RCC) which is developed to improve voltage stability. While LDC has benefit to voltage, it may deteriorate the small-disturbance rotor angle stability of power system. In present paper, an intelligent algorithm which is combined by Genetic Algorithm (GA) and Backpropagation Neural Network (BPNN) is proposed to optimize parameters of LDC. The objective function proposed in present paper takes consideration of voltage deviation and power system oscillation minimal damping ratio under multi-operating conditions. A simulation based on middle area of Jiangxi province power system is used to demonstrate the intelligent algorithm. The optimization result shows that coordinate optimized parameters can meet the multioperating conditions requirement and improve voltage stability as much as possible while guaranteeing enough damping ratio.

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

Method and apparatus for reducing the harmonic currents in alternating-current distribution networks

DOEpatents

Beverly, Leon H.; Hance, Richard D.; Kristalinski, Alexandr L.; Visser, Age T.

1996-01-01

An improved apparatus and method reduce the harmonic content of AC line and neutral line currents in polyphase AC source distribution networks. The apparatus and method employ a polyphase Zig-Zag transformer connected between the AC source distribution network and a load. The apparatus and method also employs a mechanism for increasing the source neutral impedance of the AC source distribution network. This mechanism can consist of a choke installed in the neutral line between the AC source and the Zig-Zag transformer.

Method and apparatus for reducing the harmonic currents in alternating-current distribution networks

DOEpatents

Beverly, L.H.; Hance, R.D.; Kristalinski, A.L.; Visser, A.T.

1996-11-19

An improved apparatus and method reduce the harmonic content of AC line and neutral line currents in polyphase AC source distribution networks. The apparatus and method employ a polyphase Zig-Zag transformer connected between the AC source distribution network and a load. The apparatus and method also employs a mechanism for increasing the source neutral impedance of the AC source distribution network. This mechanism can consist of a choke installed in the neutral line between the AC source and the Zig-Zag transformer. 23 figs.

The effects of alcohol on the nonhuman primate brain: a network science approach to neuroimaging.

PubMed

Telesford, Qawi K; Laurienti, Paul J; Friedman, David P; Kraft, Robert A; Daunais, James B

2013-11-01

Animal studies have long been an important tool for basic research as they offer a degree of control often lacking in clinical studies. Of particular value is the use of nonhuman primates (NHPs) for neuroimaging studies. Currently, studies have been published using functional magnetic resonance imaging (fMRI) to understand the default-mode network in the NHP brain. Network science provides an alternative approach to neuroimaging allowing for evaluation of whole-brain connectivity. In this study, we used network science to build NHP brain networks from fMRI data to understand the basic functional organization of the NHP brain. We also explored how the brain network is affected following an acute ethanol (EtOH) pharmacological challenge. Baseline resting-state fMRI was acquired in an adult male rhesus macaque (n = 1) and a cohort of vervet monkeys (n = 10). A follow-up scan was conducted in the rhesus macaque to assess network variability and to assess the effects of an acute EtOH challenge on the brain network. The most connected regions in the resting-state networks were similar across species and matched regions identified as the default-mode network in previous NHP fMRI studies. Under an acute EtOH challenge, the functional organization of the brain was significantly impacted. Network science offers a great opportunity to understand the brain as a complex system and how pharmacological conditions can affect the system globally. These models are sensitive to changes in the brain and may prove to be a valuable tool in long-term studies on alcohol exposure. Copyright © 2013 by the Research Society on Alcoholism.

Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis.

PubMed

Sonuga-Barke, Edmund J S; Castellanos, F Xavier

2007-01-01

In traditional accounts, fluctuations in sustained and focused attention and associated attentional lapses during task performance are regarded as the result of failures of top-down and effortful higher order processes. The current paper reviews an alternative hypothesis: that spontaneous patterns of very low frequency (<0.1 Hz) coherence within a specific brain network ('default-mode network') thought to support a pattern of generalized task-non-specific cognition during rest, can persist or intrude into periods of active task-specific processing, producing periodic fluctuations in attention that compete with goal-directed activity. We review recent studies supporting the existence of the resting state default network, examine the mechanism underpinning it, describe the consequent temporally distinctive effects on cognition and behaviour of default-mode interference into active processing periods, and suggest some factors that might predispose to it. Finally, we explore the putative role of default-mode interference as a cause of performance variability in attention deficit/hyperactivity disorder.

A study of anthropogenic and climatic disturbance of the New River Estuary using a Bayesian belief network.

PubMed

Nojavan A, Farnaz; Qian, Song S; Paerl, Hans W; Reckhow, Kenneth H; Albright, Elizabeth A

2014-06-15

The present paper utilizes a Bayesian Belief Network (BBN) approach to intuitively present and quantify our current understanding of the complex physical, chemical, and biological processes that lead to eutrophication in an estuarine ecosystem (New River Estuary, North Carolina, USA). The model is further used to explore the effects of plausible future climatic and nutrient pollution management scenarios on water quality indicators. The BBN, through visualizing the structure of the network, facilitates knowledge communication with managers/stakeholders who might not be experts in the underlying scientific disciplines. Moreover, the developed structure of the BBN is transferable to other comparable estuaries. The BBN nodes are discretized exploring a new approach called moment matching method. The conditional probability tables of the variables are driven by a large dataset (four years). Our results show interaction among various predictors and their impact on water quality indicators. The synergistic effects caution future management actions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Long-range acoustic observations of the Eyjafjallajökull eruption, Iceland, April-May 2010

NASA Astrophysics Data System (ADS)

Matoza, Robin S.; Vergoz, Julien; Le Pichon, Alexis; Ceranna, Lars; Green, David N.; Evers, Läslo G.; Ripepe, Maurizio; Campus, Paola; Liszka, Ludwik; Kvaerna, Tormod; Kjartansson, Einar; Höskuldsson, Ármann

2011-03-01

The April-May 2010 summit eruption of Eyjafjallajökull, Iceland, was recorded by 14 atmospheric infrasound sensor arrays at ranges between 1,700 and 3,700 km, indicating that infrasound from modest-size eruptions can propagate for thousands of kilometers in atmospheric waveguides. Although variations in both atmospheric propagation conditions and background noise levels at the sensors generate fluctuations in signal-to-noise ratios and signal detectability, array processing techniques successfully discriminate between volcanic infrasound and ambient coherent and incoherent noise. The current global infrasound network is significantly more dense and sensitive than any previously operated network and signals from large volcanic explosions are routinely recorded. Because volcanic infrasound is generated during the explosive release of fluid into the atmosphere, it is a strong indicator that an eruption has occurred. Therefore, long-range infrasonic monitoring may aid volcanic explosion detection by complementing other monitoring technologies, especially in remote regions with sparse ground-based instrument networks.

Bootstrapping Least Squares Estimates in Biochemical Reaction Networks

PubMed Central

Linder, Daniel F.

2015-01-01

The paper proposes new computational methods of computing confidence bounds for the least squares estimates (LSEs) of rate constants in mass-action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large volume limit of a reaction network, to network’s partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods. PMID:25898769

Statistical analysis of stream water-quality data and sampling network design near Oklahoma City, central Oklahoma, 1977-1999

USGS Publications Warehouse

Brigham, Mark E.; Payne, Gregory A.; Andrews, William J.; Abbott, Marvin M.

2002-01-01

The sampling network was evaluated with respect to areal coverage, sampling frequency, and analytical schedules. Areal coverage could be expanded to include one additional watershed that is not part of the current network. A new sampling site on the North Canadian River might be useful because of expanding urbanization west of the city, but sampling at some other sites could be discontinued or reduced based on comparisons of data between the sites. Additional real-time or periodic monitoring for dissolved oxygen may be useful to prevent anoxic conditions in pools behind new low-water dams. The sampling schedules, both monthly and quarterly, are adequate to evaluate trends, but additional sampling during flow extremes may be needed to quantify loads and evaluate water-quality during flow extremes. Emerging water-quality issues may require sampling for volatile organic compounds, sulfide, total phosphorus, chlorophyll-a, Esherichia coli, and enterococci, as well as use of more sensitive laboratory analytical methods for determination of cadmium, mercury, lead, and silver.

Detection of broken rotor bar faults in induction motor at low load using neural network.

PubMed

Bessam, B; Menacer, A; Boumehraz, M; Cherif, H

2016-09-01

The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

An examination of the default mode network in individuals with autonomous sensory meridian response (ASMR).

PubMed

Smith, Stephen D; Katherine Fredborg, Beverley; Kornelsen, Jennifer

2017-08-01

Autonomous Sensory Meridian Response (ASMR) is a perceptual condition in which specific visual and auditory stimuli consistently trigger tingling sensations on the scalp and neck, sometimes spreading to the back and limbs. These triggering stimuli are often social, almost intimate, in nature (e.g., hearing whispering, or watching someone brush her hair), and often elicit a calm and positive emotional state. Surprisingly, despite its prevalence in the general population, no published study has examined the neural underpinnings of ASMR. In the current study, the default mode network (DMN) of 11 individuals with ASMR was contrasted to that of 11 matched controls. The results indicated that the DMN of individuals with ASMR showed significantly less functional connectivity than that of controls. The DMN of individuals with ASMR also demonstrated increased connectivity between regions in the occipital, frontal, and temporal cortices, suggesting that ASMR was associated with a blending of multiple resting-state networks. This atypical functional connectivity likely influences the unique sensory-emotional experiences associated with ASMR.

Microfluidic circuit analysis II: implications of ion conservation for microchannels connected in series.

PubMed

Biscombe, Christian J C; Davidson, Malcolm R; Harvie, Dalton J E

2012-01-01

A mathematical framework for analysing electrokinetic flow in microchannel networks is outlined. The model is based on conservation of volume and total charge at network junctions, but in contrast to earlier theories also incorporates conservation of ion charge there. The model is applied to mixed pressure-driven/electro-osmotic flows of binary electrolytes through homogeneous microchannels as well as a 4:1:4 contraction-expansion series network. Under conditions of specified volumetric flow rate and ion currents, non-linear steady-state phenomena may arise: when the direction of the net co-ion flux is opposite to the direction of the net volumetric flow, two different fully developed, steady-state flow solutions may be obtained. Model predictions are compared with two-dimensional computational fluid dynamics (CFD) simulations. For systems where two steady states are realisable, the ultimate steady behaviour is shown to depend in part upon the initial state of the system. Copyright © 2011 Elsevier Inc. All rights reserved.

Neuromodulation to the Rescue: Compensation of Temperature-Induced Breakdown of Rhythmic Motor Patterns via Extrinsic Neuromodulatory Input

PubMed Central

Städele, Carola; Heigele, Stefanie; Stein, Wolfgang

2015-01-01

Stable rhythmic neural activity depends on the well-coordinated interplay of synaptic and cell-intrinsic conductances. Since all biophysical processes are temperature dependent, this interplay is challenged during temperature fluctuations. How the nervous system remains functional during temperature perturbations remains mostly unknown. We present a hitherto unknown mechanism of how temperature-induced changes in neural networks are compensated by changing their neuromodulatory state: activation of neuromodulatory pathways establishes a dynamic coregulation of synaptic and intrinsic conductances with opposing effects on neuronal activity when temperature changes, hence rescuing neuronal activity. Using the well-studied gastric mill pattern generator of the crab, we show that modest temperature increase can abolish rhythmic activity in isolated neural circuits due to increased leak currents in rhythm-generating neurons. Dynamic clamp-mediated addition of leak currents was sufficient to stop neuronal oscillations at low temperatures, and subtraction of additional leak currents at elevated temperatures was sufficient to rescue the rhythm. Despite the apparent sensitivity of the isolated nervous system to temperature fluctuations, the rhythm could be stabilized by activating extrinsic neuromodulatory inputs from descending projection neurons, a strategy that we indeed found to be implemented in intact animals. In the isolated nervous system, temperature compensation was achieved by stronger extrinsic neuromodulatory input from projection neurons or by augmenting projection neuron influence via bath application of the peptide cotransmitter Cancer borealis tachykinin-related peptide Ia (CabTRP Ia). CabTRP Ia activates the modulator-induced current IMI (a nonlinear voltage-gated inward current) that effectively acted as a negative leak current and counterbalanced the temperature-induced leak to rescue neuronal oscillations. Computational modelling revealed the ability of IMI to reduce detrimental leak-current influences on neuronal networks over a broad conductance range and indicated that leak and IMI are closely coregulated in the biological system to enable stable motor patterns. In conclusion, these results show that temperature compensation does not need to be implemented within the network itself but can be conditionally provided by extrinsic neuromodulatory input that counterbalances temperature-induced modifications of circuit-intrinsic properties. PMID:26417944

Automated visual inspection system based on HAVNET architecture

NASA Astrophysics Data System (ADS)

Burkett, K.; Ozbayoglu, Murat A.; Dagli, Cihan H.

1994-10-01

In this study, the HAusdorff-Voronoi NETwork (HAVNET) developed at the UMR Smart Engineering Systems Lab is tested in the recognition of mounted circuit components commonly used in printed circuit board assembly systems. The automated visual inspection system used consists of a CCD camera, a neural network based image processing software and a data acquisition card connected to a PC. The experiments are run in the Smart Engineering Systems Lab in the Engineering Management Dept. of the University of Missouri-Rolla. The performance analysis shows that the vision system is capable of recognizing different components under uncontrolled lighting conditions without being effected by rotation or scale differences. The results obtained are promising and the system can be used in real manufacturing environments. Currently the system is being customized for a specific manufacturing application.

Stability and stabilisation of a class of networked dynamic systems

NASA Astrophysics Data System (ADS)

Liu, H. B.; Wang, D. Q.

2018-04-01

We investigate the stability and stabilisation of a linear time invariant networked heterogeneous system with arbitrarily connected subsystems. A new linear matrix inequality based sufficient and necessary condition for the stability is derived, based on which the stabilisation is provided. The obtained conditions efficiently utilise the block-diagonal characteristic of system parameter matrices and the sparseness of subsystem connection matrix. Moreover, a sufficient condition only dependent on each individual subsystem is also presented for the stabilisation of the networked systems with a large scale. Numerical simulations show that these conditions are computationally valid in the analysis and synthesis of a large-scale networked system.

Translational Genomics Research Institute: Identification of Pathways Enriched with Condition-Specific Statistical Dependencies Across Four Subtypes of Glioblastoma Multiforme | Office of Cancer Genomics

Cancer.gov

Evaluation of Differential DependencY (EDDY) is a statistical test for the differential dependency relationship of a set of genes between two given conditions. For each condition, possible dependency network structures are enumerated and their likelihoods are computed to represent a probability distribution of dependency networks. The difference between the probability distributions of dependency networks is computed between conditions, and its statistical significance is evaluated with random permutations of condition labels on the samples.  

Translational Genomics Research Institute (TGen): Identification of Pathways Enriched with Condition-Specific Statistical Dependencies Across Four Subtypes of Glioblastoma Multiforme | Office of Cancer Genomics

Cancer.gov

Evaluation of Differential DependencY (EDDY) is a statistical test for the differential dependency relationship of a set of genes between two given conditions. For each condition, possible dependency network structures are enumerated and their likelihoods are computed to represent a probability distribution of dependency networks. The difference between the probability distributions of dependency networks is computed between conditions, and its statistical significance is evaluated with random permutations of condition labels on the samples.  

Neural network evaluation of tokamak current profiles for real time control

NASA Astrophysics Data System (ADS)

Wróblewski, Dariusz

1997-02-01

Active feedback control of the current profile, requiring real-time determination of the current profile parameters, is envisioned for tokamaks operating in enhanced confinement regimes. The distribution of toroidal current in a tokamak is now routinely evaluated based on external (magnetic probes, flux loops) and internal (motional Stark effect) measurements of the poloidal magnetic field. However, the analysis involves reconstruction of magnetohydrodynamic equilibrium and is too intensive computationally to be performed in real time. In the present study, a neural network is used to provide a mapping from the magnetic measurements (internal and external) to selected parameters of the safety factor profile. The single-pass, feedforward calculation of output of a trained neural network is very fast, making this approach particularly suitable for real-time applications. The network was trained on a large set of simulated equilibrium data for the DIII-D tokamak. The database encompasses a large variety of current profiles including the hollow current profiles important for reversed central shear operation. The parameters of safety factor profile (a quantity related to the current profile through the magnetic field tilt angle) estimated by the neural network include central safety factor, q0, minimum value of q, qmin, and the location of qmin. Very good performance of the trained neural network both for simulated test data and for experimental datais demonstrated.

Neural network evaluation of tokamak current profiles for real time control (abstract)

NASA Astrophysics Data System (ADS)

Wróblewski, Dariusz

1997-01-01

Active feedback control of the current profile, requiring real-time determination of the current profile parameters, is envisioned for tokamaks operating in enhanced confinement regimes. The distribution of toroidal current in a tokamak is now routinely evaluated based on external (magnetic probes, flux loops) and internal (motional Stark effect) measurements of the poloidal magnetic field. However, the analysis involves reconstruction of magnetohydrodynamic equilibrium and is too intensive computationally to be performed in real time. In the present study, a neural network is used to provide a mapping from the magnetic measurements (internal and external) to selected parameters of the safety factor profile. The single-pass, feedforward calculation of output of a trained neural network is very fast, making this approach particularly suitable for real-time applications. The network was trained on a large set of simulated equilibrium data for the DIII-D tokamak. The database encompasses a large variety of current profiles including the hollow current profiles important for reversed central shear operation. The parameters of safety factor profile (a quantity related to the current profile through the magnetic field tilt angle) estimated by the neural network include central safety factor, q0, minimum value of q, qmin, and the location of qmin. Very good performance of the trained neural network both for simulated test data and for experimental data is demonstrated.

Current progress in multiple-image blind demixing algorithms

NASA Astrophysics Data System (ADS)

Szu, Harold H.

2000-06-01

Imagery edges occur naturally in human visual systems as a consequence of redundancy reduction towards `sparse and orthogonality feature maps,' which have been recently derived from the maximum entropy information-theoretical first principle of artificial neural networks. After a brief match review of such an Independent Component Analysis or Blind Source Separation of edge maps, we explore the de- mixing condition for more than two imagery objects recognizable by an intelligent pair of cameras with memory in a time-multiplex fashion.

Public Health Analysis Transport Optimization Model v. 1.0

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

Beyeler, Walt; Finley, Patrick; Walser, Alex

PHANTOM models logistic functions of national public health systems. The system enables public health officials to visualize and coordinate options for public health surveillance, diagnosis, response and administration in an integrated analytical environment. Users may simulate and analyze system performance applying scenarios that represent current conditions or future contingencies what-if analyses of potential systemic improvements. Public health networks are visualized as interactive maps, with graphical displays of relevant system performance metrics as calculated by the simulation modeling components.

Functional roles of fibroblast growth factor receptors (FGFRs) signaling in human cancers.

PubMed

Tiong, Kai Hung; Mah, Li Yen; Leong, Chee-Onn

2013-12-01

The fibroblast growth factor receptors (FGFRs) regulate important biological processes including cell proliferation and differentiation during development and tissue repair. Over the past decades, numerous pathological conditions and developmental syndromes have emerged as a consequence of deregulation in the FGFRs signaling network. This review aims to provide an overview of FGFR family, their complex signaling pathways in tumorigenesis, and the current development and application of therapeutics targeting the FGFRs signaling for treatment of refractory human cancers.

Long-term geomagnetically induced current observations in New Zealand: Earth return corrections and geomagnetic field driver

NASA Astrophysics Data System (ADS)

Mac Manus, Daniel H.; Rodger, Craig J.; Dalzell, Michael; Thomson, Alan W. P.; Clilverd, Mark A.; Petersen, Tanja; Wolf, Moritz M.; Thomson, Neil R.; Divett, Tim

2017-08-01

Transpower New Zealand Limited has measured DC currents in transformer neutrals in the New Zealand electrical network at multiple South Island locations. Near-continuous archived DC current data exist since 2001, starting with 12 different substations and expanding from 2009 to include 17 substations. From 2001 to 2015 up to 58 individual transformers were simultaneously monitored. Primarily, the measurements were intended to monitor the impact of the high-voltage DC system linking the North and South Islands when it is operating in "Earth return" mode. However, after correcting for Earth return operation, as described here, the New Zealand measurements provide an unusually long and spatially detailed set of geomagnetically induced current (GIC) measurements. We examine the peak GIC magnitudes observed from these observations during two large geomagnetic storms on 6 November 2001 and 2 October 2013. Currents of 30-50 A are observed, depending on the measurement location. There are large spatial variations in the GIC observations over comparatively small distances, which likely depend upon network layout and ground conductivity. We then go on to examine the GIC in transformers throughout the South Island during more than 151 h of geomagnetic storm conditions. We compare the GIC to the various magnitude and rate of change components of the magnetic field. Our results show that there is a strong correlation between the magnitude of the GIC and the rate of change of the horizontal magnetic field (H'). This correlation is particularly clear for transformers that show large GIC current during magnetic storms.

A Web-Based Decision Support System for Assessing Regional Water-Quality Conditions and Management Actions

NASA Astrophysics Data System (ADS)

Booth, N. L.; Everman, E.; Kuo, I.; Sprague, L.; Murphy, L.

2011-12-01

A new web-based decision support system has been developed as part of the U.S. Geological Survey (USGS) National Water Quality Assessment Program's (NAWQA) effort to provide ready access to Spatially Referenced Regressions On Watershed attributes (SPARROW) results of stream water-quality conditions and to offer sophisticated scenario testing capabilities for research and water-quality planning via an intuitive graphical user interface with a map-based display. The SPARROW Decision Support System (DSS) is delivered through a web browser over an Internet connection, making it widely accessible to the public in a format that allows users to easily display water-quality conditions, distribution of nutrient sources, nutrient delivery to downstream waterbodies, and simulations of altered nutrient inputs including atmospheric and agricultural sources. The DSS offers other features for analysis including various background map layers, model output exports, and the ability to save and share prediction scenarios. SPARROW models currently supported by the DSS are based on the modified digital versions of the 1:500,000-scale River Reach File (RF1) and 1:100,000-scale National Hydrography Dataset (medium-resolution, NHDPlus) stream networks. The underlying modeling framework and server infrastructure illustrate innovations in the information technology and geosciences fields for delivering SPARROW model predictions over the web by performing intensive model computations and map visualizations of the predicted conditions within the stream network.

Development and implementation of a GEOGLAM Crop Monitor web interface

NASA Astrophysics Data System (ADS)

Oliva, P.; Sanchez, A.; Humber, M. L.; Becker-Reshef, I.; Justice, C. J.; McGaughey, K.; Barker, B.

2016-12-01

Beginning in September 2013, the GEOGLAM Crop Monitor activity has provided earth observation (EO) data to a network of partners and collected crop assessments on a subnational basis through a web interface known as the Crop Assessment Tool. Based on the collection of monthly crop assessments, a monthly crop condition bulletin is published in the Agricultural Market Information System (AMIS) Market Monitor report. This workflow has been successfully applied to food security applications through the Early Warning Crop Monitor activity. However, a lack of timely and accurate information on crop conditions and prospects at the national scale is a critical issue in the majority of southern and eastern African countries and some South American countries. Such information is necessary for informed and prompt decision making in the face of emergencies, food insecurity and planning requirements for agricultural markets. This project addresses these needs through the development of relevant, user-friendly remote sensing monitor systems, collaborative internet technology, and collaboration with national and regional agricultural monitoring networks. By building on current projects and relationships established through the various GEOGLAM Crop Monitor activities, this project aims to ultimately provide EO-informed crop condition maps and charts designed for economics and policy oriented audiences, thereby providing quick and easy to understand products on crop conditions as the season progresses. Integrating these data and assessments vertically throughout the system provides a basis for regional sharing and collaboration in food security applications.

Inverse analysis of turbidites by machine learning

NASA Astrophysics Data System (ADS)

Naruse, H.; Nakao, K.

2017-12-01

This study aims to propose a method to estimate paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine-learning technique. In this method, numerical simulation was repeated under various initial conditions, which produces a data set of characteristic features of turbidites. Then, this data set of turbidites is used for supervised training of a deep-learning neural network (NN). Quantities of characteristic features of turbidites in the training data set are given to input nodes of NN, and output nodes are expected to provide the estimates of initial condition of the turbidity current. The optimization of weight coefficients of NN is then conducted to reduce root-mean-square of the difference between the true conditions and the output values of NN. The empirical relationship with numerical results and the initial conditions is explored in this method, and the discovered relationship is used for inversion of turbidity currents. This machine learning can potentially produce NN that estimates paleo-hydraulic conditions from data of ancient turbidites. We produced a preliminary implementation of this methodology. A forward model based on 1D shallow-water equations with a correction of density-stratification effect was employed. This model calculates a behavior of a surge-like turbidity current transporting mixed-size sediment, and outputs spatial distribution of volume per unit area of each grain-size class on the uniform slope. Grain-size distribution was discretized 3 classes. Numerical simulation was repeated 1000 times, and thus 1000 beds of turbidites were used as the training data for NN that has 21000 input nodes and 5 output nodes with two hidden-layers. After the machine learning finished, independent simulations were conducted 200 times in order to evaluate the performance of NN. As a result of this test, the initial conditions of validation data were successfully reconstructed by NN. The estimated values show very small deviation from the true parameters. Comparing to previous inverse modeling of turbidity currents, our methodology is superior especially in the efficiency of computation. Also, our methodology has advantage in extensibility and applicability to various sediment transport processes such as pyroclastic flows or debris flows.

An Evaluation Network for Educational Change.

ERIC Educational Resources Information Center

Marchesi, Alvaro; Martin, Elena; Martinez Arias, Rosario; Tiana, Alejandro; Moreno, Amparo

2003-01-01

Describes the experience of an evaluation network being put into practice in Spain to encourage educational change. Outlines the most relevant conditions needed for educational change and develops the description of a school evaluation network that includes all the previously described conditions. (SLD)

Neural network to diagnose lining condition

NASA Astrophysics Data System (ADS)

Yemelyanov, V. A.; Yemelyanova, N. Y.; Nedelkin, A. A.; Zarudnaya, M. V.

2018-03-01

The paper presents data on the problem of diagnosing the lining condition at the iron and steel works. The authors describe the neural network structure and software that are designed and developed to determine the lining burnout zones. The simulation results of the proposed neural networks are presented. The authors note the low learning and classification errors of the proposed neural networks. To realize the proposed neural network, the specialized software has been developed.

LTER network data access policy revision: report and recommendations.

Treesearch

James Brunt; Peter McCartney; Stuart Gage; Don Henshaw

2004-01-01

This document is a report on work carried out to update the LTER Network Data Access Policy. The current LTER Network Data Access Policy, approved by the coordinating committee in 1997, has been in use since 1990. An analysis of the current policies related to the release, access, and use of LTER data has been undertaken by a sub-committee of the LTER Network...

Comparison of Water Years 2004-05 and Historical Water-Quality Data, Upper Gunnison River Basin, Colorado

USGS Publications Warehouse

Spahr, Norman E.; Hartle, David M.; Diaz, Paul

2008-01-01

Population growth and changes in land use have the potential to affect water quality and quantity in the upper Gunnison River Basin. In 1995, the U.S. Geological Survey (USGS), in cooperation with the Bureau of Land Management, City of Gunnison, Colorado River Water Conservation District, Crested Butte South Metropolitan District, Gunnison County, Hinsdale County, Mount Crested Butte Water and Sanitation District, National Park Service, Town of Crested Butte, Upper Gunnison River Water Conservancy District, and Western State College, established a water-quality monitoring program in the upper Gunnison River Basin to characterize current water-quality conditions and to assess the effects of increased urban development and other land-use changes on water quality. The monitoring network has evolved into two groups of stations - stations that are considered long term and stations that are considered rotational. The long-term stations are monitored to assist in defining temporal changes in water quality (how conditions may change over time). The rotational stations are monitored to assist in the spatial definition of water-quality conditions (how conditions differ throughout the basin) and to address local and short-term concerns. Some stations in the rotational group were changed beginning in water year 2007. Annual summaries of the water-quality data from the monitoring network provide a point of reference for discussions regarding water-quality monitoring in the upper Gunnison River Basin. This summary includes data collected during water years 2004 and 2005. The introduction provides a map of the sampling sites, definitions of terms, and a one-page summary of selected water-quality conditions at the network stations. The remainder of the summary is organized around the data collected at individual stations. Data collected during water years 2004 and 2005 are compared to historical data, State water-quality standards, and Federal water-quality guidelines. Data were collected following USGS protocols.

Comparison of 2006-2007 Water Years and Historical Water-Quality Data, Upper Gunnison River Basin, Colorado

USGS Publications Warehouse

Solberg, P.A.; Moore, Bryan; Smits, Dennis

2009-01-01

Population growth and changes in land use have the potential to affect water quality and quantity in the upper Gunnison River basin. In 1995, the U.S. Geological Survey (USGS), in cooperation with the Bureau of Land Management, City of Gunnison, Colorado River Water Conservation District, Crested Butte South Metropolitan District, Gunnison County, Hinsdale County, Mount Crested Butte Water and Sanitation District, National Park Service, Town of Crested Butte, Upper Gunnison River Water Conservancy District, and Western State College established a water-quality monitoring program in the upper Gunnison River basin to characterize current water-quality conditions and to assess the effects of increased urban development and other land-use changes on water quality. The monitoring network has evolved into two groups of stations - stations that are considered long term and stations that are considered rotational. The long-term stations are monitored to assist in defining temporal changes in water quality (how conditions may change over time). The rotational stations are monitored to assist in the spatial definition of water-quality conditions (how conditions differ throughout the basin) and to address local and short-term concerns. Some stations in the rotational group were changed beginning in water year 2007. Annual summaries of the water-quality data from the monitoring network provide a point of reference for discussions regarding water-quality monitoring in the upper Gunnison River basin. This summary includes data collected during water years 2006 and 2007. The introduction provides a map of the sampling sites, definitions of terms, and a one-page summary of selected water-quality conditions at the network stations. The remainder of the summary is organized around the data collected at individual stations. Data collected during water years 2006 and 2007 are compared to historical data, State water-quality standards, and Federal water-quality guidelines. Data were collected following USGS protocols (U.S. Geological Survey, variously dated).

Revealing the microstructure of the giant component in random graph ensembles

NASA Astrophysics Data System (ADS)

Tishby, Ido; Biham, Ofer; Katzav, Eytan; Kühn, Reimer

2018-04-01

The microstructure of the giant component of the Erdős-Rényi network and other configuration model networks is analyzed using generating function methods. While configuration model networks are uncorrelated, the giant component exhibits a degree distribution which is different from the overall degree distribution of the network and includes degree-degree correlations of all orders. We present exact analytical results for the degree distributions as well as higher-order degree-degree correlations on the giant components of configuration model networks. We show that the degree-degree correlations are essential for the integrity of the giant component, in the sense that the degree distribution alone cannot guarantee that it will consist of a single connected component. To demonstrate the importance and broad applicability of these results, we apply them to the study of the distribution of shortest path lengths on the giant component, percolation on the giant component, and spectra of sparse matrices defined on the giant component. We show that by using the degree distribution on the giant component one obtains high quality results for these properties, which can be further improved by taking the degree-degree correlations into account. This suggests that many existing methods, currently used for the analysis of the whole network, can be adapted in a straightforward fashion to yield results conditioned on the giant component.

A new evaluation of the USGS streamgaging network

USGS Publications Warehouse

,

1998-01-01

Since 1889, the U.S. Geological Survey (USGS) has operated a streamgaging network to collect information about the Nation's water resources. It is a multipurpose network funded by the USGS and many other Federal, State and local agencies. Individual streamgaging stations are supported for specific purposes such as water allocation, reservoir operations, or regulating permit requirements, but the data are used by others for many purposes. Collectively, the USGS streamgaging network produces valuable data that are used for current forecasting and operational decisions as well as long-term resource planning, infrastructure design, and flood hazard mitigation. The guiding principles of the network are: Streamgaging stations are funded by the USGS and many agencies to achieve the Federal mission goals of the USGS and the individual goals of the funding agencies. Data are freely available to the public and all partners. USGS operates the network on behalf of all partners, which achieves economies because it eliminates the need for multiple infrastructures for testing equipment, providing training to staff, developing and maintaining the communications and database systems, and conducting quality assurance. USGS brings the capability of its national staff to bear on challenging problems such as responding to catastrophic floods or finding solutions to unique streamgaging conditions. This report has been prepared in response to a request from the U.S. House of Representatives Subcommittee on Interior Appropriations in its report to accompany H.R. 4193.

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

NASA Astrophysics Data System (ADS)

Sun, Wei; Chang, K. C.

2005-05-01

Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.

A social network-based intervention stimulating peer influence on children's self-reported water consumption: A randomized control trial.

PubMed

Smit, Crystal R; de Leeuw, Rebecca N H; Bevelander, Kirsten E; Burk, William J; Buijzen, Moniek

2016-08-01

The current pilot study examined the effectiveness of a social network-based intervention using peer influence on self-reported water consumption. A total of 210 children (52% girls; M age = 10.75 ± SD = 0.80) were randomly assigned to either the intervention (n = 106; 52% girls) or control condition (n = 104; 52% girls). In the intervention condition, the most influential children in each classroom were trained to promote water consumption among their peers for eight weeks. The schools in the control condition did not receive any intervention. Water consumption, sugar-sweetened beverage (SSB) consumption, and intentions to drink more water in the near future were assessed by self-report measures before and immediately after the intervention. A repeated measure MANCOVA showed a significant multivariate interaction effect between condition and time (V = 0.07, F(3, 204) = 5.18, p = 0.002, pη(2) = 0.07) on the dependent variables. Further examination revealed significant univariate interaction effects between condition and time on water (p = 0.021) and SSB consumption (p = 0.015) as well as water drinking intentions (p = 0.049). Posthoc analyses showed that children in the intervention condition reported a significant increase in their water consumption (p = 0.018) and a decrease in their SSB consumption (p < 0.001) over time, compared to the control condition (p-values > 0.05). The children who were exposed to the intervention did not report a change in their water drinking intentions over time (p = 0.576) whereas the nonexposed children decreased their intentions (p = 0.026). These findings show promise for a social network-based intervention using peer influence to positively alter consumption behaviors. This RCT was registered in the Australian New Zealand Clinical Trials Registry (ACTRN12614001179628). Study procedures were approved by the Ethics Committee of the Faculty of Social Sciences at Radboud University (ECSW2014-1003-203). Copyright © 2016 Elsevier Ltd. All rights reserved.

Informing Environmental Water Management Decisions: Using Conditional Probability Networks to Address the Information Needs of Planning and Implementation Cycles.

PubMed

Horne, Avril C; Szemis, Joanna M; Webb, J Angus; Kaur, Simranjit; Stewardson, Michael J; Bond, Nick; Nathan, Rory

2018-03-01

One important aspect of adaptive management is the clear and transparent documentation of hypotheses, together with the use of predictive models (complete with any assumptions) to test those hypotheses. Documentation of such models can improve the ability to learn from management decisions and supports dialog between stakeholders. A key challenge is how best to represent the existing scientific knowledge to support decision-making. Such challenges are currently emerging in the field of environmental water management in Australia, where managers are required to prioritize the delivery of environmental water on an annual basis, using a transparent and evidence-based decision framework. We argue that the development of models of ecological responses to environmental water use needs to support both the planning and implementation cycles of adaptive management. Here we demonstrate an approach based on the use of Conditional Probability Networks to translate existing ecological knowledge into quantitative models that include temporal dynamics to support adaptive environmental flow management. It equally extends to other applications where knowledge is incomplete, but decisions must still be made.

A Novel Component Carrier Configuration and Switching Scheme for Real-Time Traffic in a Cognitive-Radio-Based Spectrum Aggregation System

PubMed Central

Fu, Yunhai; Ma, Lin; Xu, Yubin

2015-01-01

In spectrum aggregation (SA), two or more component carriers (CCs) of different bandwidths in different bands can be aggregated to support a wider transmission bandwidth. The scheduling delay is the most important design constraint for the broadband wireless trunking (BWT) system, especially in the cognitive radio (CR) condition. The current resource scheduling schemes for spectrum aggregation become questionable and are not suitable for meeting the challenge of the delay requirement. Consequently, the authors propose a novel component carrier configuration and switching scheme for real-time traffic (RT-CCCS) to satisfy the delay requirement in the CR-based SA system. In this work, the authors consider a sensor-network-assisted CR network. The authors first introduce a resource scheduling structure for SA in the CR condition. Then the proposed scheme is analyzed in detail. Finally, simulations are carried out to verify the analysis on the proposed scheme. Simulation results prove that our proposed scheme can satisfy the delay requirement in the CR-based SA system. PMID:26393594

Informing Environmental Water Management Decisions: Using Conditional Probability Networks to Address the Information Needs of Planning and Implementation Cycles

NASA Astrophysics Data System (ADS)

Horne, Avril C.; Szemis, Joanna M.; Webb, J. Angus; Kaur, Simranjit; Stewardson, Michael J.; Bond, Nick; Nathan, Rory

2018-03-01

One important aspect of adaptive management is the clear and transparent documentation of hypotheses, together with the use of predictive models (complete with any assumptions) to test those hypotheses. Documentation of such models can improve the ability to learn from management decisions and supports dialog between stakeholders. A key challenge is how best to represent the existing scientific knowledge to support decision-making. Such challenges are currently emerging in the field of environmental water management in Australia, where managers are required to prioritize the delivery of environmental water on an annual basis, using a transparent and evidence-based decision framework. We argue that the development of models of ecological responses to environmental water use needs to support both the planning and implementation cycles of adaptive management. Here we demonstrate an approach based on the use of Conditional Probability Networks to translate existing ecological knowledge into quantitative models that include temporal dynamics to support adaptive environmental flow management. It equally extends to other applications where knowledge is incomplete, but decisions must still be made.

A science-based, watershed strategy to support effective remediation of abandoned mine lands

USGS Publications Warehouse

Buxton, Herbert T.; Nimick, David A.; Von Guerard, Paul; Church, Stan E.; Frazier, Ann G.; Gray, John R.; Lipin, Bruce R.; Marsh, Sherman P.; Woodward, Daniel F.; Kimball, Briant A.; Finger, Susan E.; Ischinger, Lee S.; Fordham, John C.; Power, Martha S.; Bunch, Christine M.; Jones, John W.

1997-01-01

A U.S. Geological Survey Abandoned Mine Lands Initiative will develop a strategy for gathering and communicating the scientific information needed to formulate effective and cost-efficient remediation of abandoned mine lands. A watershed approach will identify, characterize, and remediate contaminated sites that have the most profound effect on water and ecosystem quality within a watershed. The Initiative will be conducted during 1997 through 2001 in two pilot watersheds, the Upper Animas River watershed in Colorado and the Boulder River watershed in Montana. Initiative efforts are being coordinated with the U.S. Forest Service, Bureau of Land Management, National Park Service, and other stakeholders which are using the resulting scientific information to design and implement remediation activities. The Initiative has the following eight objective-oriented components: estimate background (pre-mining) conditions; define baseline (current) conditions; identify target sites (major contaminant sources); characterize target sites and processes affecting contaminant dispersal; characterize ecosystem health and controlling processes at target sites; develop remediation goals and monitoring network; provide an integrated, quality-assured and accessible data network; and document lessons learned for future applications of the watershed approach.

Comparison of response surface methodology and artificial neural network to enhance the release of reducing sugars from non-edible seed cake by autoclave assisted HCl hydrolysis.

PubMed

Shet, Vinayaka B; Palan, Anusha M; Rao, Shama U; Varun, C; Aishwarya, Uday; Raja, Selvaraj; Goveas, Louella Concepta; Vaman Rao, C; Ujwal, P

2018-02-01

In the current investigation, statistical approaches were adopted to hydrolyse non-edible seed cake (NESC) of Pongamia and optimize the hydrolysis process by response surface methodology (RSM). Through the RSM approach, the optimized conditions were found to be 1.17%v/v of HCl concentration at 54.12 min for hydrolysis. Under optimized conditions, the release of reducing sugars was found to be 53.03 g/L. The RSM data were used to train the artificial neural network (ANN) and the predictive ability of both models was compared by calculating various statistical parameters. A three-layered ANN model consisting of 2:12:1 topology was developed; the response of the ANN model indicates that it is precise when compared with the RSM model. The fit of the models was expressed with the regression coefficient R 2 , which was found to be 0.975 and 0.888, respectively, for the ANN and RSM models. This further demonstrated that the performance of ANN was better than that of RSM.

Aberrant cerebellar connectivity in bipolar disorder with psychosis.

PubMed

Shinn, Ann K; Roh, Youkyung S; Ravichandran, Caitlin T; Baker, Justin T; Öngür, Dost; Cohen, Bruce M

2017-07-01

The cerebellum, which modulates affect and cognition in addition to motor functions, may contribute substantially to the pathophysiology of mood and psychotic disorders, such as bipolar disorder. A growing literature points to cerebellar abnormalities in bipolar disorder. However, no studies have investigated the topographic representations of resting state cerebellar networks in bipolar disorder, specifically their functional connectivity to cerebral cortical networks. Using a well-defined cerebral cortical parcellation scheme as functional connectivity seeds, we compared ten cerebellar resting state networks in 49 patients with bipolar disorder and a lifetime history of psychotic features and 55 healthy control participants matched for age, sex, and image signal-to-noise ratio. Patients with psychotic bipolar disorder showed reduced cerebro-cerebellar functional connectivity in somatomotor A, ventral attention, salience, and frontoparietal control A and B networks relative to healthy control participants. These findings were not significantly correlated with current symptoms. Patients with psychotic bipolar disorder showed evidence of cerebro-cerebellar dysconnectivity in selective networks. These disease-related changes were substantial and not explained by medication exposure or substance use. Therefore, they may be mechanistically relevant to the underlying susceptibility to mood dysregulation and psychosis. Cerebellar mechanisms deserve further exploration in psychiatric conditions, and this study's findings may have value in guiding future studies on pathophysiology and treatment of mood and psychotic disorders, in particular.

Dynamic neural networking as a basis for plasticity in the control of heart rate.

PubMed

Kember, G; Armour, J A; Zamir, M

2013-01-21

A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. Copyright © 2012 Elsevier Ltd. All rights reserved.

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

An fMRI examination of the effects of acoustic-phonetic and lexical competition on access to the lexical-semantic network.

PubMed

Minicucci, Domenic; Guediche, Sara; Blumstein, Sheila E

2013-08-01

The current study explored how factors of acoustic-phonetic and lexical competition affect access to the lexical-semantic network during spoken word recognition. An auditory semantic priming lexical decision task was presented to subjects while in the MR scanner. Prime-target pairs consisted of prime words with the initial voiceless stop consonants /p/, /t/, and /k/ followed by word and nonword targets. To examine the neural consequences of lexical and sound structure competition, primes either had voiced minimal pair competitors or they did not, and they were either acoustically modified to be poorer exemplars of the voiceless phonetic category or not. Neural activation associated with semantic priming (Unrelated-Related conditions) revealed a bilateral fronto-temporo-parietal network. Within this network, clusters in the left insula/inferior frontal gyrus (IFG), left superior temporal gyrus (STG), and left posterior middle temporal gyrus (pMTG) showed sensitivity to lexical competition. The pMTG also demonstrated sensitivity to acoustic modification, and the insula/IFG showed an interaction between lexical competition and acoustic modification. These findings suggest the posterior lexical-semantic network is modulated by both acoustic-phonetic and lexical structure, and that the resolution of these two sources of competition recruits frontal structures. Copyright © 2013 Elsevier Ltd. All rights reserved.

A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation

PubMed Central

2011-01-01

Background Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods are now also being applied to organisms such as Populus, a woody perennial tree, in order to understand the specific characteristics of these species. Results We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis. Conclusions We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis. PMID:21232107

Northern long-eared bat day-roosting and prescribed fire in the central Appalachians

USGS Publications Warehouse

Ford, W. Mark; Silvis, Alexander; Johnson, Joshua B.; Edwards, John W.; Karp, Milu

2016-01-01

The northern long-eared bat (Myotis septentrionalis Trovessart) is a cavity-roosting species that forages in cluttered upland and riparian forests throughout the oak-dominated Appalachian and Central Hardwoods regions. Common prior to white-nose syndrome, the population of this bat species has declined to functional extirpation in some regions in the Northeast and Mid-Atlantic, including portions of the central Appalachians. Our long-term research in the central Appalachians has shown that maternity colonies of this species form non-random assorting networks in patches of suitable trees that result from long- and short-term forest disturbance processes, and that roost loss can occur with these disturbances. Following two consecutive prescribed burns on the Fernow Experimental Forest in the central Appalachians, West Virginia, USA, in 2007 to 2008, post-fire counts of suitable black locust (Robinia pseudoacacia L.; the most selected species for roosting) slightly decreased by 2012. Conversely, post-fire numbers of suitable maple (Acer spp. L.), primarily red maple (Acer rubrum L.), increased by a factor of three, thereby ameliorating black locust reduction. Maternity colony network metrics such as roost degree (use) and network density for two networks in the burned compartment were similar to the single network observed in unburned forest. However, roost clustering and degree of roost centralization was greater for the networks in the burned forest area. Accordingly, the short-term effects of prescribed fire are slightly or moderately positive in impact to day-roost habitat for the northern long-eared bat in the central Appalachians from a social dynamic perspective. Listing of northern long-eared bats as federally threatened will bring increased scrutiny of immediate fire impacts from direct take as well as indirect impacts from long-term changes to roosting and foraging habitat in stands being returned to historic fire-return conditions. Unfortunately, definitive impacts will remain speculative owing to the species’ current rarity and the paucity of forest stand data that considers tree condition or that adequately tracks snags spatially and temporally.

People and places: Relocating to neighborhoods with better economic and social conditions is associated with less risky drug/alcohol network characteristics among African American adults in Atlanta, GA.

PubMed

Linton, Sabriya L; Cooper, Hannah L F; Luo, Ruiyan; Karnes, Conny; Renneker, Kristen; Haley, Danielle F; Hunter-Jones, Josalin; Ross, Zev; Bonney, Loida; Rothenberg, Richard

2016-03-01

Few studies assess whether place characteristics are associated with social network characteristics that create vulnerability to substance use. This longitudinal study analyzed 7 waves of data (2009-2014) from a predominantly substance-using cohort of 172 African American adults relocated from public housing complexes in Atlanta, GA, to determine whether post-relocation changes in exposure to neighborhood conditions were associated with four network characteristics related to substance use: number of social network members who used illicit drugs or alcohol in excess in the past six months ("drug/alcohol network"), drug/alcohol network stability, and turnover into and out of drug/alcohol networks. Individual- and network-level characteristics were captured via survey and administrative data were used to describe census tracts where participants lived. Multilevel models were used to assess relationships of census tract-level characteristics to network outcomes over time. On average, participants relocated to census tracts that had less economic disadvantage, social disorder, and renter-occupied housing. Post-relocation reductions in exposure to economic disadvantage were associated with fewer drug/alcohol network members and less turnover into drug/alcohol networks. Post-relocation improvements in exposure to multiple census tract-level social conditions and reductions in perceived community violence were associated with fewer drug/alcohol network members, less turnover into drug/alcohol networks, less drug/alcohol network stability, and more turnover out of drug/alcohol networks. Relocating to neighborhoods with less economic disadvantage and better social conditions may weaken relationships with substance-using individuals. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Underwater acoustic wireless sensor networks: advances and future trends in physical, MAC and routing layers.

PubMed

Climent, Salvador; Sanchez, Antonio; Capella, Juan Vicente; Meratnia, Nirvana; Serrano, Juan Jose

2014-01-06

This survey aims to provide a comprehensive overview of the current research on underwater wireless sensor networks, focusing on the lower layers of the communication stack, and envisions future trends and challenges. It analyzes the current state-of-the-art on the physical, medium access control and routing layers. It summarizes their security threads and surveys the currently proposed studies. Current envisioned niches for further advances in underwater networks research range from efficient, low-power algorithms and modulations to intelligent, energy-aware routing and medium access control protocols.

Artificial intelligence for analyzing orthopedic trauma radiographs

PubMed Central

Olczak, Jakub; Fahlberg, Niklas; Maki, Atsuto; Razavian, Ali Sharif; Jilert, Anthony; Stark, André; Sköldenberg, Olof

2017-01-01

Background and purpose — Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods — We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd’s Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network’s performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results — All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen’s kappa under these conditions was 0.76. Interpretation — This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics. PMID:28681679

Large-scale P2P network based distributed virtual geographic environment (DVGE)

NASA Astrophysics Data System (ADS)

Tan, Xicheng; Yu, Liang; Bian, Fuling

2007-06-01

Virtual Geographic Environment has raised full concern as a kind of software information system that helps us understand and analyze the real geographic environment, and it has also expanded to application service system in distributed environment--distributed virtual geographic environment system (DVGE), and gets some achievements. However, limited by the factor of the mass data of VGE, the band width of network, as well as numerous requests and economic, etc. DVGE still faces some challenges and problems which directly cause the current DVGE could not provide the public with high-quality service under current network mode. The Rapid development of peer-to-peer network technology has offered new ideas of solutions to the current challenges and problems of DVGE. Peer-to-peer network technology is able to effectively release and search network resources so as to realize efficient share of information. Accordingly, this paper brings forth a research subject on Large-scale peer-to-peer network extension of DVGE as well as a deep study on network framework, routing mechanism, and DVGE data management on P2P network.

The Geomorphological Effects of Old Routes

NASA Astrophysics Data System (ADS)

Martinek, Jan; Bíl, Michal

2017-04-01

The communication network in rural areas in the historical Czech Lands predominantly consisted of unpaved routes prior to the eighteenth century. Certain parts of the network were transformed gradually into the current roads and are now being used by motor traffic. The majority of the old routes form, however, an abandoned network the remnants of which (abandoned during the Middle Ages or even earlier) are currently being discovered. Certain segments of used unpaved routes were, over the course of time, transformed into holloways (sunken lanes) and consequently also abandoned. The degree of incision of the holloway into the soil was determined by local geological conditions. Routes, which were abandoned due to more difficult transport in holloways, have distinct linear forms and can often be found as a grouping of parallel holloways. This indicates that these routes were frequently used or localized on low-resistant ground. Analyses of the precise digital elevation models, derived from LIDAR data, can reveal the distinct pattern of an old route network quite often interacting with other geomorphological phenomena (e.g., landslides, streams) or old human constructions (e.g., fortified settlements). We will present several cases where old paths interacted with landslides. These facts can consequently be used for dating the purposes of both the landslides and the old path sections. General erosion impacts, the degree of incision of the old transportation lines, can also be quantified through analyses of digital elevation models taking into consideration the former and new, incised, surface. We will demonstrate the methodology used for these analyses and the preliminary results.

Interaction of language, auditory and memory brain networks in auditory verbal hallucinations.

PubMed

Ćurčić-Blake, Branislava; Ford, Judith M; Hubl, Daniela; Orlov, Natasza D; Sommer, Iris E; Waters, Flavie; Allen, Paul; Jardri, Renaud; Woodruff, Peter W; David, Olivier; Mulert, Christoph; Woodward, Todd S; Aleman, André

2017-01-01

Auditory verbal hallucinations (AVH) occur in psychotic disorders, but also as a symptom of other conditions and even in healthy people. Several current theories on the origin of AVH converge, with neuroimaging studies suggesting that the language, auditory and memory/limbic networks are of particular relevance. However, reconciliation of these theories with experimental evidence is missing. We review 50 studies investigating functional (EEG and fMRI) and anatomic (diffusion tensor imaging) connectivity in these networks, and explore the evidence supporting abnormal connectivity in these networks associated with AVH. We distinguish between functional connectivity during an actual hallucination experience (symptom capture) and functional connectivity during either the resting state or a task comparing individuals who hallucinate with those who do not (symptom association studies). Symptom capture studies clearly reveal a pattern of increased coupling among the auditory, language and striatal regions. Anatomical and symptom association functional studies suggest that the interhemispheric connectivity between posterior auditory regions may depend on the phase of illness, with increases in non-psychotic individuals and first episode patients and decreases in chronic patients. Leading hypotheses involving concepts as unstable memories, source monitoring, top-down attention, and hybrid models of hallucinations are supported in part by the published connectivity data, although several caveats and inconsistencies remain. Specifically, possible changes in fronto-temporal connectivity are still under debate. Precise hypotheses concerning the directionality of connections deduced from current theoretical approaches should be tested using experimental approaches that allow for discrimination of competing hypotheses. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Next Generation Access Network Deployment in Croatia: Optical Access Networks and Current IoT/5G Status

NASA Astrophysics Data System (ADS)

Breskovic, Damir; Sikirica, Mladen; Begusic, Dinko

2018-05-01

This paper gives an overview and background of optical access network deployment in Croatia. Optical access network development in Croatia has been put into a global as well as in the European Union context. All the challenges and the driving factors for optical access networks deployment are considered. Optical access network architectures that have been deployed by most of the investors in Croatian telecommunication market are presented, as well as the architectures that are in early phase of deployment. Finally, an overview on current status of mobile networks of the fifth generation and Internet of Things is given.

Evaluation of urban drainage network based geographycal information system (GIS) in Sumenep City

NASA Astrophysics Data System (ADS)

Agrianto, F.; Hadiani, R.; Purwana, Y. M.

2017-02-01

Sumenep City frequently hit by floods. Drainage network conditions greatly affect the performance of her maid, especially those aspects that affect the capacity of the drainage channel. Aspects that affect the capacity of the drainage channel in the form of sedimentation rate and complementary buildings on drainage channels, for example, the presence of street inlet and trash rack. The method used is a drainage channel capacity level approach that level assessment of each segment drainage network conditions by calculating the ratio of the channel cross-sectional area that is filled with sediment to the total cross-sectional area wet and the existence of complementary buildings. Having obtained the condition index value of each segment, the subsequent analysis is spatial analysis using ArcGIS applications to obtain a map of the drainage network information. The analysis showed that the level condition of drainage network in the city of Sumenep in 2016 that of the total 428 drainage network there are 43 sections belonging to the state level “Good”, 198 drainage network belong to the state level “Enough”, 115 drainage network belong to the state “Mild Damaged”, 50 sections belonging to the state “Heavy Damage” and 22 drainage network belong to the state of “Dysfunction”.

Human embryonic stem cell-derived neurons adopt and regulate the activity of an established neural network

PubMed Central

Weick, Jason P.; Liu, Yan; Zhang, Su-Chun

2011-01-01

Whether hESC-derived neurons can fully integrate with and functionally regulate an existing neural network remains unknown. Here, we demonstrate that hESC-derived neurons receive unitary postsynaptic currents both in vitro and in vivo and adopt the rhythmic firing behavior of mouse cortical networks via synaptic integration. Optical stimulation of hESC-derived neurons expressing Channelrhodopsin-2 elicited both inhibitory and excitatory postsynaptic currents and triggered network bursting in mouse neurons. Furthermore, light stimulation of hESC-derived neurons transplanted to the hippocampus of adult mice triggered postsynaptic currents in host pyramidal neurons in acute slice preparations. Thus, hESC-derived neurons can participate in and modulate neural network activity through functional synaptic integration, suggesting they are capable of contributing to neural network information processing both in vitro and in vivo. PMID:22106298

The role of reservoir storage in large-scale surface water availability analysis for Europe

NASA Astrophysics Data System (ADS)

Garrote, L. M.; Granados, A.; Martin-Carrasco, F.; Iglesias, A.

2017-12-01

A regional assessment of current and future water availability in Europe is presented in this study. The assessment was made using the Water Availability and Adaptation Policy Analysis (WAAPA) model. The model was built on the river network derived from the Hydro1K digital elevation maps, including all major river basins of Europe. Reservoir storage volume was taken from the World Register of Dams of ICOLD, including all dams with storage capacity over 5 hm3. Potential Water Availability is defined as the maximum amount of water that could be supplied at a certain point of the river network to satisfy a regular demand under pre-specified reliability requirements. Water availability is the combined result of hydrological processes, which determine streamflow in natural conditions, and human intervention, which determines the available hydraulic infrastructure to manage water and establishes water supply conditions through operating rules. The WAAPA algorithm estimates the maximum demand that can be supplied at every node of the river network accounting for the regulation capacity of reservoirs under different management scenarios. The model was run for a set of hydrologic scenarios taken from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), where the PCRGLOBWB hydrological model was forced with results from five global climate models. Model results allow the estimation of potential water stress by comparing water availability to projections of water abstractions along the river network under different management alternatives. The set of sensitivity analyses performed showed the effect of policy alternatives on water availability and highlighted the large uncertainties linked to hydrological and anthropological processes.

Brain network dysfunction in youth with obsessive-compulsive disorder induced by simple uni-manual behavior: The role of the dorsal anterior cingulate cortex

PubMed Central

Friedman, Amy L.; Burgess, Ashley; Ramaseshan, Karthik; Easter, Phil; Khatib, Dalal; Chowdury, Asadur; Arnold, Paul D.; Hanna, Gregory L.; Rosenberg, David R.; Diwadkar, Vaibhav A.

2017-01-01

In an effort to elucidate differences in functioning brain networks between youth with obsessive-compulsive disorder and controls, we used fMRI signals to analyze brain network interactions of the dorsal anterior cingulate cortex (dACC) during visually coordinated motor responses. Subjects made a uni-manual response to briefly presented probes, at periodic (allowing participants to maintain a “motor set”) or random intervals (demanding reactive responses). Network interactions were assessed using psycho-physiological interaction (PPI), a basic model of functional connectivity evaluating modulatory effects of the dACC in the context of each task condition. Across conditions, OCD were characterized by hyper-modulation by the dACC, with loci alternatively observed as both condition-general and condition-specific. Thus, dynamically driven task demands during simple uni-manual motor control induce compensatory network interactions in cortical-thalamic regions in OCD. These findings support previous research in OCD showing compensatory network interactions during complex memory tasks, but establish that these network effects are observed during basic sensorimotor processing. Thus, these patterns of network dysfunction may in fact be independent of the complexity of tasks used to induce brain network activity. Hypothesis-driven approaches coupled with sophisticated network analyses are a highly valuable approach in using fMRI to uncover mechanisms in disorders like OCD. PMID:27992792

Modulation of brain response to emotional conflict as a function of current mood in bipolar disorder: preliminary findings from a follow-up state-based fMRI study.

PubMed

Rey, Gwladys; Desseilles, Martin; Favre, Sophie; Dayer, Alexandre; Piguet, Camille; Aubry, Jean-Michel; Vuilleumier, Patrik

2014-08-30

We used functional magnetic resonance imaging (fMRI) to examine affective control longitudinally in a group of patients with bipolar disorder (BD). Participants comprised 12 BD patients who underwent repeated fMRI scans in euthymic (n=11), depressed (n=9), or hypomanic (n=9) states, and were compared with 12 age-matched healthy controls. During fMRI, participants performed an emotional face-word interference task with either low or high attentional demands. Relative to healthy controls, patients showed decreased activation of the cognitive control network normally associated with conflict processing, more severely during hypomania than during depression, but regardless of level of task demand in both cases. During euthymia, a decreased response to conflict was observed only during the high load condition. Additionally, unlike healthy participants, patients exhibited deactivation in several key areas in response to emotion-conflict trials - including the rostral anterior cingulate cortex during euthymia, the hippocampus during depression, and the posterior cingulate cortex during hypomania. Our results indicate that the ability of BD patients to recruit control networks when processing affective conflict, and the abnormal suppression of activity in distinct components of the default mode network, may depend on their current clinical state and attentional demand. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Status of the NASA Micro Pulse Lidar Network (MPLNET): overview of the network and future plans, new version 3 data products, and the polarized MPL

NASA Astrophysics Data System (ADS)

Welton, Ellsworth J.; Stewart, Sebastian A.; Lewis, Jasper R.; Belcher, Larry R.; Campbell, James R.; Lolli, Simone

2018-04-01

The NASA Micro Pulse Lidar Network (MPLNET) is a global federated network of Micro-Pulse Lidars (MPL) co-located with the NASA Aerosol Robotic Network (AERONET). MPLNET began in 2000, and there are currently 17 long-term sites, numerous field campaigns, and more planned sites on the way. We have developed a new Version 3 processing system including the deployment of polarized MPLs across the network. Here we provide an overview of Version 3, the polarized MPL, and current and future plans.

Monitoring network-design influence on assessment of ecological condition in wadeable streams

EPA Science Inventory

We investigated outcomes of three monitoring networks for assessing ecological character and condition of wadeable streams in the Waikato region, New Zealand. Sites were selected 1) based on a professional judgment network, 2) within categories of stream and watershed characteris...

Stability analysis of fractional-order Hopfield neural networks with time delays.

PubMed

Wang, Hu; Yu, Yongguang; Wen, Guoguang

2014-07-01

This paper investigates the stability for fractional-order Hopfield neural networks with time delays. Firstly, the fractional-order Hopfield neural networks with hub structure and time delays are studied. Some sufficient conditions for stability of the systems are obtained. Next, two fractional-order Hopfield neural networks with different ring structures and time delays are developed. By studying the developed neural networks, the corresponding sufficient conditions for stability of the systems are also derived. It is shown that the stability conditions are independent of time delays. Finally, numerical simulations are given to illustrate the effectiveness of the theoretical results obtained in this paper. Copyright © 2014 Elsevier Ltd. All rights reserved.

High-Density, High-Resolution, Low-Cost Air Quality Sensor Networks for Urban Air Monitoring

NASA Astrophysics Data System (ADS)

Mead, M. I.; Popoola, O. A.; Stewart, G.; Bright, V.; Kaye, P.; Saffell, J.

2012-12-01

Monitoring air quality in highly granular environments such as urban areas which are spatially heterogeneous with variable emission sources, measurements need to be made at appropriate spatial and temporal scales. Current routine air quality monitoring networks generally are either composed of sparse expensive installations (incorporating e.g. chemiluminescence instruments) or higher density low time resolution systems (e.g. NO2 diffusion tubes). Either approach may not accurately capture important effects such as pollutant "hot spots" or adequately capture spatial (or temporal) variability. As a result, analysis based on data from traditional low spatial resolution networks, such as personal exposure, may be inaccurate. In this paper we present details of a sophisticated, low-cost, multi species (gas phase, speciated PM, meteorology) air quality measurement network methodology incorporating GPS and GPRS which has been developed for high resolution air quality measurements in urban areas. Sensor networks developed in the Centre for Atmospheric Science (University of Cambridge) incorporated electrochemical gas sensors configured for use in urban air quality studies operating at parts-per-billion (ppb) levels. It has been demonstrated that these sensors can be used to measure key air quality gases such as CO, NO and NO2 at the low ppb mixing ratios present in the urban environment (estimated detection limits <4ppb for CO and NO and <1ppb for NO2. Mead et al (submitted Aug., 2012)). Based on this work, a state of the art multi species instrument package for deployment in scalable sensor networks has been developed which has general applicability. This is currently being employed as part of a major 3 year UK program at London Heathrow airport (the Sensor Networks for Air Quality (SNAQ) Heathrow project). The main project outcome is the creation of a calibrated, high spatial and temporal resolution data set for O3, NO, NO2, SO2, CO, CO2, VOCstotal, size-speciated PM, temperature, relative humidity, wind speed and direction. The network incorporates existing GPRS infrastructures for real time sending of data with low overheads in terms of cost, effort and installation. In this paper we present data from the SNAQ Heathrow project as well as previous deployments showing measurement capability at the ppb level for NO, NO2 and CO. We show that variability can be observed and measured quantitatively using these sensor networks over widely differing time scales from individual emission events, diurnal variability associated with traffic and meteorological conditions, through to longer term synoptic weather conditions and seasonal behaviour. This work demonstrates a widely applicable generic capability to urban areas, airports as well as other complex emissions environments making this sensor system methodology valuable for scientific, policy and regulatory issues. We conclude that the low-cost high-density network philosophy has the potential to provide a more complete assessment of the high-granularity air quality structure generally observed in the environment. Further, when appropriately deployed, has the potential to offer a new paradigm in air quality quantification and monitoring.

Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography

PubMed Central

2018-01-01

Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one. PMID:29695066

Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography.

PubMed

Coutinho, Murilo; de Oliveira Albuquerque, Robson; Borges, Fábio; García Villalba, Luis Javier; Kim, Tai-Hoon

2018-04-24

Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one.

Overview of physical dosimetry methods for triage application integrated in the new European network RENEB.

PubMed

Trompier, François; Burbidge, Christopher; Bassinet, Céline; Baumann, Marion; Bortolin, Emanuela; De Angelis, Cinzia; Eakins, Jonathan; Della Monaca, Sara; Fattibene, Paola; Quattrini, Maria Cristina; Tanner, Rick; Wieser, Albrecht; Woda, Clemens

2017-01-01

In the EC-funded project RENEB (Realizing the European Network in Biodosimetry), physical methods applied to fortuitous dosimetric materials are used to complement biological dosimetry, to increase dose assessment capacity for large-scale radiation/nuclear accidents. This paper describes the work performed to implement Optically Stimulated Luminescence (OSL) and Electron Paramagnetic Resonance (EPR) dosimetry techniques. OSL is applied to electronic components and EPR to touch-screen glass from mobile phones. To implement these new approaches, several blind tests and inter-laboratory comparisons (ILC) were organized for each assay. OSL systems have shown good performances. EPR systems also show good performance in controlled conditions, but ILC have also demonstrated that post-irradiation exposure to sunlight increases the complexity of the EPR signal analysis. Physically-based dosimetry techniques present high capacity, new possibilities for accident dosimetry, especially in the case of large-scale events. Some of the techniques applied can be considered as operational (e.g. OSL on Surface Mounting Devices [SMD]) and provide a large increase of measurement capacity for existing networks. Other techniques and devices currently undergoing validation or development in Europe could lead to considerable increases in the capacity of the RENEB accident dosimetry network.

Synchronization in complex oscillator networks and smart grids.

PubMed

Dörfler, Florian; Chertkov, Michael; Bullo, Francesco

2013-02-05

The emergence of synchronization in a network of coupled oscillators is a fascinating topic in various scientific disciplines. A widely adopted model of a coupled oscillator network is characterized by a population of heterogeneous phase oscillators, a graph describing the interaction among them, and diffusive and sinusoidal coupling. It is known that a strongly coupled and sufficiently homogeneous network synchronizes, but the exact threshold from incoherence to synchrony is unknown. Here, we present a unique, concise, and closed-form condition for synchronization of the fully nonlinear, nonequilibrium, and dynamic network. Our synchronization condition can be stated elegantly in terms of the network topology and parameters or equivalently in terms of an intuitive, linear, and static auxiliary system. Our results significantly improve upon the existing conditions advocated thus far, they are provably exact for various interesting network topologies and parameters; they are statistically correct for almost all networks; and they can be applied equally to synchronization phenomena arising in physics and biology as well as in engineered oscillator networks, such as electrical power networks. We illustrate the validity, the accuracy, and the practical applicability of our results in complex network scenarios and in smart grid applications.

A Hybrid LCC-VSC HVDC Transmission System Supplying a Passive Load

NASA Astrophysics Data System (ADS)

Kotb, Omar

High Voltage Direct Current (HVDC) transmission systems continue to be an excellent asset in modern power systems, mainly for their ability to overcome the problems of AC transmission, such as the interconnection of asynchronous grids, stability of long transmission lines, and use of long cables for power transmission. In the past 20 years, Voltage Source Converter (VSC)-HVDC transmission systems were developed and installed in many projects, thereby adding more operational benefits to DC transmission option, such as high controllability, ability to supply weak networks, and reduced converter reactive power demand. Nevertheless, VSC-HVDC transmission suffers from the disadvantages of high losses and cost. In this research, a hybrid HVDC employing a Line Commutated Converter (LCC) as rectifier and a VSC as inverter is used to supply a passive network through a DC cable. The hybrid system is best suited for unidirectional power transmission scenarios, such as power transmission to islands and remote load centers, where the construction of new transmission lines is prohibitively expensive. Control modes for the rectifier and inverter are selected and implemented using Proportional Integral (PI) controllers. Special control schemes are developed for abnormal operating conditions such as starting at light load and recovering from AC network faults. The system performance under steady state and transient conditions is investigated by EMTP-RV simulations. The results show the feasibility of the hybrid system.

Functional Connectivity Substrates for tDCS Response in Minimally Conscious State Patients

PubMed Central

Cavaliere, Carlo; Aiello, Marco; Di Perri, Carol; Amico, Enrico; Martial, Charlotte; Thibaut, Aurore; Laureys, Steven; Soddu, Andrea

2016-01-01

Transcranial direct current stimulation (tDCS) is a non-invasive technique recently employed in disorders of consciousness, and determining a transitory recovery of signs of consciousness in almost half of minimally conscious state (MCS) patients. Although the rising evidences about its possible role in the treatment of many neurological and psychiatric conditions exist, no evidences exist about brain functional connectivity substrates underlying tDCS response. We retrospectively evaluated resting state functional Magnetic Resonance Imaging (fMRI) of 16 sub-acute and chronic MCS patients (6 tDCS responders) who successively received a single left dorsolateral prefrontal cortex (DLPFC) tDCS in a double-blind randomized cross-over trial. A seed-based approach for regions of left extrinsic control network (ECN) and default-mode network (DMN) was performed. tDCS responders showed an increased left intra-network connectivity for regions co-activated with left DLPFC, and significantly with left inferior frontal gyrus. Non-responders (NR) MCS patients showed an increased connectivity between left DLPFC and midline cortical structures, including anterior cingulate cortex and precuneus. Our findings suggest that a prior high connectivity with regions belonging to ECN can facilitate transitory recovery of consciousness in a subgroup of MCS patients that underwent tDCS treatment. Therefore, resting state-fMRI could be very valuable in detecting the neuronal conditions necessary for tDCS to improve behavior in MCS. PMID:27857682

Building an ecosystem model using mismatched and fragmented data: A probabilistic network of early marine survival for coho salmon Oncorhynchus kisutch in the Strait of Georgia

NASA Astrophysics Data System (ADS)

Andres Araujo, H.; Holt, Carrie; Curtis, Janelle M. R.; Perry, R. I.; Irvine, James R.; Michielsens, Catherine G. J.

2013-08-01

We evaluated the effects of biophysical conditions and hatchery production on the early marine survival of coho salmon Oncorhynchus kisutch in the Strait of Georgia, British Columbia, Canada. Due to a paucity of balanced multivariate ecosystem data, we developed a probabilistic network that integrated physical and ecological data and information from literature, expert opinion, oceanographic models, and in situ observations. This approach allowed us to evaluate alternate hypotheses about drivers of early marine survival while accounting for uncertainties in relationships among variables. Probabilistic networks allow users to explore multiple environmental settings and evaluate the consequences of management decisions under current and projected future states. We found that the zooplankton biomass anomaly, calanoid copepod biomass, and herring biomass were the best indicators of early marine survival. It also appears that concentrating hatchery supplementation during periods of negative PDO and ENSO (Pacific Decadal and El Niño Southern Oscillation respectively), indicative of generally favorable ocean conditions for salmon, tends to increase survival of hatchery coho salmon while minimizing negative impacts on the survival of wild juveniles. Scientists and managers can benefit from the approach presented here by exploring multiple scenarios, providing a basis for open and repeatable ecosystem-based risk assessments when data are limited.

Functional Connectivity Substrates for tDCS Response in Minimally Conscious State Patients.

PubMed

Cavaliere, Carlo; Aiello, Marco; Di Perri, Carol; Amico, Enrico; Martial, Charlotte; Thibaut, Aurore; Laureys, Steven; Soddu, Andrea

2016-01-01

Transcranial direct current stimulation (tDCS) is a non-invasive technique recently employed in disorders of consciousness, and determining a transitory recovery of signs of consciousness in almost half of minimally conscious state (MCS) patients. Although the rising evidences about its possible role in the treatment of many neurological and psychiatric conditions exist, no evidences exist about brain functional connectivity substrates underlying tDCS response. We retrospectively evaluated resting state functional Magnetic Resonance Imaging (fMRI) of 16 sub-acute and chronic MCS patients (6 tDCS responders) who successively received a single left dorsolateral prefrontal cortex (DLPFC) tDCS in a double-blind randomized cross-over trial. A seed-based approach for regions of left extrinsic control network (ECN) and default-mode network (DMN) was performed. tDCS responders showed an increased left intra-network connectivity for regions co-activated with left DLPFC, and significantly with left inferior frontal gyrus. Non-responders (NR) MCS patients showed an increased connectivity between left DLPFC and midline cortical structures, including anterior cingulate cortex and precuneus. Our findings suggest that a prior high connectivity with regions belonging to ECN can facilitate transitory recovery of consciousness in a subgroup of MCS patients that underwent tDCS treatment. Therefore, resting state-fMRI could be very valuable in detecting the neuronal conditions necessary for tDCS to improve behavior in MCS.

Remote Assessment of Forest Ecosystem Stress (RAFES): Development of a Real Time Decision Support Tool for the Eastern U.S

NASA Astrophysics Data System (ADS)

Clinton, B.; Vose, J.; Novick, K.; Liu, Y.

2011-12-01

Drier and warmer conditions predicted with climate change models are likely to significantly impact forest ecosystems over the next several decades. The U.S. has experienced significant droughts over the past several years that have increased the susceptibility of forests to insect outbreaks, disease, and wildfire. Weather data collected with traditional approaches provide an indirect measure of drought or temperature stress; however, the significance of short-term or prolonged climate-related stress varies considerably across the landscape as topography, elevations, edaphic condition and antecedent conditions vary. This limits the capacity of land managers to anticipate and initiate management activities that could offset the impacts of climate-related forest stress. Decision support tools are needed that allow fine scale monitoring of stress conditions in forest ecosystems in real time to help land managers evaluate response strategies. To assist land managers in managing the impacts of climate change, we are developing a stress monitoring and decision support system across multiple sites in the eastern U.S. that (1) provides remote data capture of environmental parameters that quantify climate-related forest stress, (2) links remotely captured data with physiologically-based indices of tree water stress, and (3) provides a PC-based analytical tool for land managers to monitor and assess the severity of climate-related stress. Currently the network represents southern coastal plain pine plantation, Atlantic coastal flatwoods mixed pine-hardwood, southern piedmont upland mixed pine-hardwood, southern Appalachian dry ridge and mesic riparian, southern Arkansas managed mature pine, and northern Minnesota mature aspen. The strategy for selecting additional sites for the network will be a focus on at-risk ecosystems deemed particularly vulnerable to the affects of predicted climate change such as those in ecotonal transition regions, or those at the fringes of their ranges. The sensor arrays at each site detect water and temperature stress variables and transmit those data to a field office. Sensors include air and soil temperature, relative humidity, fuel moisture and temperature, xylem sap flux density, soil moisture and matric potential, precipitation, and solar radiation. Data are transmitted in real-time to the NOAA Geostationary Operational Environmental Satellite (GOES). A PC-based software program that downloads monitoring data from the GOES satellite, analyzes the data, and provides the land manager with an assessment of climate-related stress conditions and potential forest health threat levels in real time is under development. Data collection began in early 2010 on most sites, and we have at least one year of data from all nine sites within the network. We are currently comparing estimates of stress levels on our sites with estimates of stress from common drought indices. For this presentation, we are comparing and contrasting four sites representing an environmental gradient within the network.

Source Localization in Wireless Sensor Networks with Randomly Distributed Elements under Multipath Propagation Conditions

DTIC Science & Technology

2009-03-01

IN WIRELESS SENSOR NETWORKS WITH RANDOMLY DISTRIBUTED ELEMENTS UNDER MULTIPATH PROPAGATION CONDITIONS by Georgios Tsivgoulis March 2009...COVERED Engineer’s Thesis 4. TITLE Source Localization in Wireless Sensor Networks with Randomly Distributed Elements under Multipath Propagation...the non-line-of-sight information. 15. NUMBER OF PAGES 111 14. SUBJECT TERMS Wireless Sensor Network , Direction of Arrival, DOA, Random

Wellness Partners: Design and Evaluation of a Web-Based Physical Activity Diary with Social Gaming Features for Adults

PubMed Central

Wang, Hua; Spruijt-Metz, Donna; Jordan-Marsh, Maryalice; Valente, Thomas William

2013-01-01

Background The United States is currently in an age of obesity and inactivity despite increasing public awareness and scientific knowledge of detrimental long-term health effects of this lifestyle. Behavior-tracking diaries offer an effective strategy for physical activity adherence and weight management. Furthermore, Web-based physical activity diaries can engage meaningful partners in people’s social networks through fun online gaming interactions and generate motivational mechanisms for effective behavioral change and positive health outcomes. Objective Wellness Partners (WP) is a Web-based intervention in the form of a physical activity diary with social networking and game features. Two versions were designed and developed for the purpose of this study—“Diary” only and “Diary+Game”. The objectives of this study included pilot testing the research process of this intervention design, implementation, evaluation, and exploring the effectiveness of social gaming features on adult participants’ physical activity and anthropometric measures. Methods We conducted a field experiment with randomized crossover design. Assessments occurred at baseline, first follow-up (FU, 5-8 weeks after using one version of WP), and second FU (5-8 weeks of using the other version of WP). In the control condition, participants started with the “Diary” version of WP while in the experimental condition, participants started with the “Diary+Game” version of WP. A total of 54 adults (egos) ages 44-88, and their family and friends (alters) ages 17-69 participated in the study in ego-network groups. Both egos and their alters completed online surveys about their exercise habits. In addition, egos completed anthropometric measurements of BMI, fat percentage, and fat mass by bioimpedance. Results From October 2009 to May 2010, flyers, emails, and Web advertisements yielded 335 volunteers who were screened. Rolling recruitment resulted in enrollment of 142 qualified participants in 54 ego-network groups, which were randomly assigned to a study condition. The final analytic sample included 87 individuals from 41 groups. Data were collected from December 2009 to August 2010, and data analysis was completed in 2011. Overall, the participants were given access to the intervention for 10-13 weeks. Statistical analysis suggested an increase in self-reported exercise frequency (mean days per week) from baseline (2.57, SD 1.92) to first FU (3.21, SD 1.74) in both conditions. Stronger effects were seen in the condition where Diary+Game was played first, especially in network groups with larger age variation between the alters and egos. Overall, the decrease in egos’ BMI was statistically significant from baseline to first FU, with greater decrease for those in the Diary+Game first condition (-0.26 vs -0.16 in the Diary first condition). Conclusions The Wellness Partners program increased physical activity among participants and resulted in health benefits among the egos. Web-based diary interventions designed with social gaming features hold potential to promote active lifestyles for middle-age adults and people in their social networks. PMID:23611986

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

2017-02-01

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

The patient perspective in health care networks.

PubMed

Raus, Kasper; Mortier, Eric; Eeckloo, Kristof

2018-06-05

Health care organization is entering a new age. Focus is increasingly shifting from individual health care institutions to interorganizational collaboration and health care networks. Much hope is set on such networks which have been argued to improve economic efficiency and quality of care. However, this does not automatically mean they are always ethically justified. A relevant question that remains is what ethical obligations or duties one can ascribe to these networks especially because networks involve many risks. Due to their often amorphous and complex structure, collective responsibility and accountability may increase while individual responsibility goes down. We argue that a business ethics approach to ethical obligations for health care networks, is problematic and we propose to opt for a patient perspective. Using the classic four principles of biomedical ethics (justice, nonmaleficence, beneficence and autonomy) it is possible to identify specific ethical duties. Based on the principle of justice, health care networks have an ethical duty to provide just and fair access for all patients and to be transparent to patients about how access is regulated. The principle of nonmaleficence implies an obligation to guarantee patient safety, whereas the principle of beneficence implies an obligation for health care networks to guarantee continuity of care in all its dimensions. Finally, the principle of autonomy is translated into a specific obligation to promote and respect patient choice. Networks that fail to meet any of these conditions are suspect and cannot be justified ethically. Faced with daunting challenges, the health care system is changing rapidly. Currently many hopes ride on integrated care and broad health care networks. Such networks are the topic of empirical debate, but more attention should be given to the ethical aspects. Health care networks raise new and pressing ethical issues and we are in need of a framework for assessing how and when such networks are justified.

Social Networks, the ‘Work’ and Work Force of Chronic Illness Self-Management: A Survey Analysis of Personal Communities

PubMed Central

Vassilev, Ivaylo; Rogers, Anne; Blickem, Christian; Brooks, Helen; Kapadia, Dharmi; Kennedy, Anne; Sanders, Caroline; Kirk, Sue; Reeves, David

2013-01-01

Self-management support forms a central aspect of chronic Illness management nationally and globally. Evidence for the success of self-management support has mainly focussed on individually-centred outcomes of behavioural change. While it is recognised that social network members play an important role there is currently a gap in knowledge regarding who provides what type of support and under what circumstances. This is relevant for understanding the division of labour and the meeting of needs for those living with a long-term condition. We therefore took a network approach to explore self-management support conceptualising it as types of illness ‘work’ undertaken within peoples’ social networks. 300 people from deprived areas and with chronic illnesses took part in a survey conducted in 2010 in the North West of England. A concentric circles diagram was used as a research tool with which participants identified 2,544 network members who contributed to illness management. The results provide an articulation of how social network members are substantially involved in illness management. Whilst partners and close family make the highest contributions there is evidence of inputs from a wide range of relationships. Network member characteristics (type of relationship, proximity, frequency of contact) impact on the amount of illness work undertaken in peoples’ networks. In networks with ‘no partner’ other people tend to contribute more in the way of illness related work than in networks with a partner. This indicates a degree of substitutability between differently constituted networks, and that the level and type of input by different members of a network might change according to circumstances. A network perspective offers an opportunity to redress the balance of an exclusively individual focus on self-management because it addresses the broader set of contributions and resources available to people in need of chronic illness management and support. PMID:23565162

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

PubMed

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-11

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

NASA Astrophysics Data System (ADS)

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-01

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Detecting subnetwork-level dynamic correlations.

PubMed

Yan, Yan; Qiu, Shangzhao; Jin, Zhuxuan; Gong, Sihong; Bai, Yun; Lu, Jianwei; Yu, Tianwei

2017-01-15

The biological regulatory system is highly dynamic. The correlations between many functionally related genes change over different biological conditions. Finding dynamic relations on the existing biological network may reveal important regulatory mechanisms. Currently no method is available to detect subnetwork-level dynamic correlations systematically on the genome-scale network. Two major issues hampered the development. The first is gene expression profiling data usually do not contain time course measurements to facilitate the analysis of dynamic relations, which can be partially addressed by using certain genes as indicators of biological conditions. Secondly, it is unclear how to effectively delineate subnetworks, and define dynamic relations between them. Here we propose a new method named LANDD (Liquid Association for Network Dynamics Detection) to find subnetworks that show substantial dynamic correlations, as defined by subnetwork A is concentrated with Liquid Association scouting genes for subnetwork B. The method produces easily interpretable results because of its focus on subnetworks that tend to comprise functionally related genes. Also, the collective behaviour of genes in a subnetwork is a much more reliable indicator of underlying biological conditions compared to using single genes as indicators. We conducted extensive simulations to validate the method's ability to detect subnetwork-level dynamic correlations. Using a real gene expression dataset and the human protein-protein interaction network, we demonstrate the method links subnetworks of distinct biological processes, with both confirmed relations and plausible new functional implications. We also found signal transduction pathways tend to show extensive dynamic relations with other functional groups. The R package is available at https://cran.r-project.org/web/packages/LANDD CONTACTS: yunba@pcom.edu, jwlu33@hotmail.com or tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A novel paradigm to evaluate conditioned pain modulation in fibromyalgia.

PubMed

Schoen, Cynthia J; Ablin, Jacob N; Ichesco, Eric; Bhavsar, Rupal J; Kochlefl, Laura; Harris, Richard E; Clauw, Daniel J; Gracely, Richard H; Harte, Steven E

2016-01-01

Application of noxious stimulation to one body area reduces pain sensitivity in a remote body area through activation of an endogenous pain-inhibitory network, a behavioral phenomenon referred to as conditioned pain modulation (CPM). The efficiency of CPM is predictive of a variety of health outcomes, while impaired CPM has been associated with various chronic pain conditions. Current methods used to assess CPM vary widely, and interest in CPM method development remains strong. Here, we evaluated a novel method for assessing CPM in healthy controls and fibromyalgia (FM) patients using thumb pressure as both a test and conditioning stimulus. Sixteen female FM patients and 14 matched healthy controls underwent CPM testing with thumbnail pressure as the test stimulus, and either cold water or noxious pressure as the conditioning stimulus. CPM magnitude was evaluated as the difference in pain rating of the test stimulus applied before and during the conditioning stimulus. In healthy controls, application of either pressure or cold water conditioning stimulation induced CPM as evidenced by a significant reduction in test stimulus pain rating during conditioning ( P =0.007 and P =0.021, respectively). In contrast, in FM patients, neither conditioning stimulus induced a significant CPM effect ( P >0.274). There was a significant difference in CPM magnitude for FM patients compared to healthy controls with noxious pressure conditioning stimulation ( P =0.023); however, no significant difference in CPM was found between groups using cold water as a conditioning stimulus ( P =0.269). The current study demonstrates that thumbnail pressure can be used as both a test and conditioning stimulus in the assessment of CPM. This study further confirms previous findings of attenuated CPM in FM patients compared with healthy controls.

A novel paradigm to evaluate conditioned pain modulation in fibromyalgia

PubMed Central

Schoen, Cynthia J; Ablin, Jacob N; Ichesco, Eric; Bhavsar, Rupal J; Kochlefl, Laura; Harris, Richard E; Clauw, Daniel J; Gracely, Richard H; Harte, Steven E

2016-01-01

Introduction Application of noxious stimulation to one body area reduces pain sensitivity in a remote body area through activation of an endogenous pain-inhibitory network, a behavioral phenomenon referred to as conditioned pain modulation (CPM). The efficiency of CPM is predictive of a variety of health outcomes, while impaired CPM has been associated with various chronic pain conditions. Current methods used to assess CPM vary widely, and interest in CPM method development remains strong. Here, we evaluated a novel method for assessing CPM in healthy controls and fibromyalgia (FM) patients using thumb pressure as both a test and conditioning stimulus. Methods Sixteen female FM patients and 14 matched healthy controls underwent CPM testing with thumbnail pressure as the test stimulus, and either cold water or noxious pressure as the conditioning stimulus. CPM magnitude was evaluated as the difference in pain rating of the test stimulus applied before and during the conditioning stimulus. Results In healthy controls, application of either pressure or cold water conditioning stimulation induced CPM as evidenced by a significant reduction in test stimulus pain rating during conditioning (P=0.007 and P=0.021, respectively). In contrast, in FM patients, neither conditioning stimulus induced a significant CPM effect (P>0.274). There was a significant difference in CPM magnitude for FM patients compared to healthy controls with noxious pressure conditioning stimulation (P=0.023); however, no significant difference in CPM was found between groups using cold water as a conditioning stimulus (P=0.269). Conclusion The current study demonstrates that thumbnail pressure can be used as both a test and conditioning stimulus in the assessment of CPM. This study further confirms previous findings of attenuated CPM in FM patients compared with healthy controls. PMID:27713648

78 FR 17946 - Consolidated Tape Association; Notice of Filing and Immediate Effectiveness of the Sixteenth...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-25

... Network A and Network B data feeds. Consistent with current practice, within each of a firm's billable... fee schedules by compressing the current 14-tier Network A device rate schedule into four tiers, by... products, unprecedented levels of trading, internationalization and developments in portfolio analysis and...

Sieve-based relation extraction of gene regulatory networks from biological literature

PubMed Central

2015-01-01

Background Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. Results We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming data into skip-mention sequences is appropriate for detecting relations between distant mentions. Conclusions Linear-chain conditional random fields, along with appropriate data transformations, can be efficiently used to extract relations. The sieve-based architecture simplifies the system as new sieves can be easily added or removed and each sieve can utilize the results of previous ones. Furthermore, sieves with conditional random fields can be trained on arbitrary text data and hence are applicable to broad range of relation extraction tasks and data domains. PMID:26551454

Sieve-based relation extraction of gene regulatory networks from biological literature.

PubMed

Žitnik, Slavko; Žitnik, Marinka; Zupan, Blaž; Bajec, Marko

2015-01-01

Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming data into skip-mention sequences is appropriate for detecting relations between distant mentions. Linear-chain conditional random fields, along with appropriate data transformations, can be efficiently used to extract relations. The sieve-based architecture simplifies the system as new sieves can be easily added or removed and each sieve can utilize the results of previous ones. Furthermore, sieves with conditional random fields can be trained on arbitrary text data and hence are applicable to broad range of relation extraction tasks and data domains.

Reconstruction of the experimentally supported human protein interactome: what can we learn?

PubMed

Klapa, Maria I; Tsafou, Kalliopi; Theodoridis, Evangelos; Tsakalidis, Athanasios; Moschonas, Nicholas K

2013-10-02

Understanding the topology and dynamics of the human protein-protein interaction (PPI) network will significantly contribute to biomedical research, therefore its systematic reconstruction is required. Several meta-databases integrate source PPI datasets, but the protein node sets of their networks vary depending on the PPI data combined. Due to this inherent heterogeneity, the way in which the human PPI network expands via multiple dataset integration has not been comprehensively analyzed. We aim at assembling the human interactome in a global structured way and exploring it to gain insights of biological relevance. First, we defined the UniProtKB manually reviewed human "complete" proteome as the reference protein-node set and then we mined five major source PPI datasets for direct PPIs exclusively between the reference proteins. We updated the protein and publication identifiers and normalized all PPIs to the UniProt identifier level. The reconstructed interactome covers approximately 60% of the human proteome and has a scale-free structure. No apparent differentiating gene functional classification characteristics were identified for the unrepresented proteins. The source dataset integration augments the network mainly in PPIs. Polyubiquitin emerged as the highest-degree node, but the inclusion of most of its identified PPIs may be reconsidered. The high number (>300) of connections of the subsequent fifteen proteins correlates well with their essential biological role. According to the power-law network structure, the unrepresented proteins should mainly have up to four connections with equally poorly-connected interactors. Reconstructing the human interactome based on the a priori definition of the protein nodes enabled us to identify the currently included part of the human "complete" proteome, and discuss the role of the proteins within the network topology with respect to their function. As the network expansion has to comply with the scale-free theory, we suggest that the core of the human interactome has essentially emerged. Thus, it could be employed in systems biology and biomedical research, despite the considerable number of currently unrepresented proteins. The latter are probably involved in specialized physiological conditions, justifying the scarcity of related PPI information, and their identification can assist in designing relevant functional experiments and targeted text mining algorithms.

A network property necessary for concentration robustness

NASA Astrophysics Data System (ADS)

Eloundou-Mbebi, Jeanne M. O.; Küken, Anika; Omranian, Nooshin; Kleessen, Sabrina; Neigenfind, Jost; Basler, Georg; Nikoloski, Zoran

2016-10-01

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications.

A network property necessary for concentration robustness.

PubMed

Eloundou-Mbebi, Jeanne M O; Küken, Anika; Omranian, Nooshin; Kleessen, Sabrina; Neigenfind, Jost; Basler, Georg; Nikoloski, Zoran

2016-10-19

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications.

A network property necessary for concentration robustness

PubMed Central

Eloundou-Mbebi, Jeanne M. O.; Küken, Anika; Omranian, Nooshin; Kleessen, Sabrina; Neigenfind, Jost; Basler, Georg; Nikoloski, Zoran

2016-01-01

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications. PMID:27759015

Interplay of node connectivity and epidemic rates in the dynamics of epidemic networks

DOE PAGES

Kostova, Tanya

2010-07-09

We present and analyze a discrete-time susceptible-infected epidemic network model which represents each host as a separate entity and allows heterogeneous hosts and contacts. We establish a necessary and sufficient condition for global stability of the disease-free equilibrium of the system (defined as epidemic controllability) which defines the epidemic reproduction number of the network. When this condition is not fulfilled, we show that the system has a unique, locally stable equilibrium. As a result, we further derive sufficient conditions for epidemic controllability in terms of the epidemic rates and the network topology.

Scaling Dissolved Nutrient Removal in River Networks: A Comparative Modeling Investigation

NASA Astrophysics Data System (ADS)

Ye, Sheng; Reisinger, Alexander J.; Tank, Jennifer L.; Baker, Michelle A.; Hall, Robert O.; Rosi, Emma J.; Sivapalan, Murugesu

2017-11-01

Along the river network, water, sediment, and nutrients are transported, cycled, and altered by coupled hydrological and biogeochemical processes. Our current understanding of the rates and processes controlling the cycling and removal of dissolved inorganic nutrients in river networks is limited due to a lack of empirical measurements in large, (nonwadeable), rivers. The goal of this paper was to develop a coupled hydrological and biogeochemical process model to simulate nutrient uptake at the network scale during summer base flow conditions. The model was parameterized with literature values from headwater streams, and empirical measurements made in 15 rivers with varying hydrological, biological, and topographic characteristics, to simulate nutrient uptake at the network scale. We applied the coupled model to 15 catchments describing patterns in uptake for three different solutes to determine the role of rivers in network-scale nutrient cycling. Model simulation results, constrained by empirical data, suggested that rivers contributed proportionally more to nutrient removal than headwater streams given the fraction of their length represented in a network. In addition, variability of nutrient removal patterns among catchments was varied among solutes, and as expected, was influenced by nutrient concentration and discharge. Net ammonium uptake was not significantly correlated with any environmental descriptor. In contrast, net daily nitrate removal was linked to suspended chlorophyll a (an indicator of primary producers) and land use characteristics. Finally, suspended sediment characteristics and agricultural land use were correlated with net daily removal of soluble reactive phosphorus, likely reflecting abiotic sorption dynamics. Rivers are understudied relative to streams, and our model suggests that rivers can contribute more to network-scale nutrient removal than would be expected based upon their representative fraction of network channel length.

Automated analysis of information processing, kinetic independence and modular architecture in biochemical networks using MIDIA.

PubMed

Bowsher, Clive G

2011-02-15

Understanding the encoding and propagation of information by biochemical reaction networks and the relationship of such information processing properties to modular network structure is of fundamental importance in the study of cell signalling and regulation. However, a rigorous, automated approach for general biochemical networks has not been available, and high-throughput analysis has therefore been out of reach. Modularization Identification by Dynamic Independence Algorithms (MIDIA) is a user-friendly, extensible R package that performs automated analysis of how information is processed by biochemical networks. An important component is the algorithm's ability to identify exact network decompositions based on both the mass action kinetics and informational properties of the network. These modularizations are visualized using a tree structure from which important dynamic conditional independence properties can be directly read. Only partial stoichiometric information needs to be used as input to MIDIA, and neither simulations nor knowledge of rate parameters are required. When applied to a signalling network, for example, the method identifies the routes and species involved in the sequential propagation of information between its multiple inputs and outputs. These routes correspond to the relevant paths in the tree structure and may be further visualized using the Input-Output Path Matrix tool. MIDIA remains computationally feasible for the largest network reconstructions currently available and is straightforward to use with models written in Systems Biology Markup Language (SBML). The package is distributed under the GNU General Public License and is available, together with a link to browsable Supplementary Material, at http://code.google.com/p/midia. Further information is at www.maths.bris.ac.uk/~macgb/Software.html.

Will choice-based reform work for Medicare? Evidence from the Federal Employees Health Benefits Program.

PubMed

Florence, Curtis S; Atherly, Adam; Thorpe, Kenneth E

2006-10-01

. To examine the effect of premiums and benefits on the health plan choices of older enrollees who choose Federal Employees Health Benefits Program (FEHBP) health plans as their primary payer. Administrative enrollment data from the Office of Personnel Management (OPM) and plan premiums and benefits data taken from the Checkbook Guide to health plans. We estimate individual plan choice models where the choice of health plan is a function of out-of-pocket premium, actuarial value, plan attributes, and individual characteristics. Plan attributes include plan structure (fee-for-service/preferred provider organization, point-of-service, or health maintenance organization), drug benefit structure, and whether or not the plan covers other types of spending such as dental services and diabetic supplies. The models are estimated by conditional logit. Our study focuses on three populations that currently choose FEHBP as their primary health care coverage and are similar to the Medicare population: current employees and retirees who are approaching the age of Medicare eligibility (ages 60-64) and current federal employees age 65+. Current employees age 65+ are eligible for Medicare, but their FEHBP plan is their primary payer. Retirees and employees 60-64 are not yet eligible for Medicare but are similar in many respects to recently age-eligible Medicare beneficiaries. We also estimate our model for current employees age 55 and younger as a comparison group. We select a random sample of retirees and employees age 60-64, as well as all current employees age 65+, from the OPM administrative database for the calendar year 2001. The plan choices available to each person are determined by the plans participating in their metropolitan statistical area. We match plan premium and attribute information from the Checkbook Guide to each plan in the enrollee's list of choices. We find that current workers 65+, 60-64, and non-Medicare eligible retirees are sensitive to variation in plan premiums. The premium elasticities for these groups are similar in magnitude to those of the age 55 and under employee group. Older workers and retirees not yet eligible for Medicare are willing to pay a substantial amount for plans with open provider networks. The willingness to pay for open networks is significantly greater for these groups than for younger employees. Willingness to pay for open network plans varies significantly by income, but varies little by age within group. Our finding that older workers and non-Medicare eligible retirees are sensitive to plan premiums suggests that choice-based reform of Medicare would lead to cost-conscious choices by Medicare beneficiaries. However, our finding that these groups are willing to pay more for open network plans than younger employees suggest that higher risk individuals may migrate toward higher benefit, higher cost plans. Our findings on the relationship between income and willingness to pay for open network plans suggest that means testing is a viable reform for lowering Medicare program costs.

Underwater Acoustic Wireless Sensor Networks: Advances and Future Trends in Physical, MAC and Routing Layers

PubMed Central

Climent, Salvador; Sanchez, Antonio; Capella, Juan Vicente; Meratnia, Nirvana; Serrano, Juan Jose

2014-01-01

This survey aims to provide a comprehensive overview of the current research on underwater wireless sensor networks, focusing on the lower layers of the communication stack, and envisions future trends and challenges. It analyzes the current state-of-the-art on the physical, medium access control and routing layers. It summarizes their security threads and surveys the currently proposed studies. Current envisioned niches for further advances in underwater networks research range from efficient, low-power algorithms and modulations to intelligent, energy-aware routing and medium access control protocols. PMID:24399155

Commercial satellite broadcasting for Europe

NASA Astrophysics Data System (ADS)

Forrest, J. R.

1988-12-01

A review is presented of the current television broadcasting situation in European countries, which involves a varied mix of terrestrial VHF or UHF systems and cable networks. A small market has emerged in Europe for receivers using the low-power telecommunications satellite transmission between the program providers and cable network companies. This is expected to change with the launch of medium-power pan-European telecommunication satellites (e.g. ASTRA, EUTELSAT II), which are now directly addressing the market of home reception. DBS (direct broadcast satellite) in the UK, using the D-MAC transmission standard, will offer three additional television channels, data broadcasting services, and a planned evolution to compatible forms of wide-screen, high-definition television. Comments are given on receiver and conditional access system standardization. Some views are expressed on satellite broadcasting as part of an overall broadcasting framework for the future.

Neural Network based Control of SG based Standalone Generating System with Energy Storage for Power Quality Enhancement

NASA Astrophysics Data System (ADS)

Nayar, Priya; Singh, Bhim; Mishra, Sukumar

2017-08-01

An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.

Line width measurement below 60 nm using an optical interferometer and artificial neural network

NASA Astrophysics Data System (ADS)

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

2007-03-01

We have recently described a technique for optical line-width measurements. The system currently is capable of measuring line-width down to 60 nm with a precision of 2 nm, and potentially should be able to measure down to 10nm. The system consists of an ultra-stable interferometer and artificial neural networks (ANNs). The former is used to generate optical profiles which are input to the ANNs. The outputs of the ANNs are the desired sample parameters. Different types of samples have been tested with equally impressive results. In this paper we will discuss the factors that are essential to extend the application of the technique. Two of the factors are signal conditioning and sample classification. Methods, including principal component analysis, that are capable of performing these tasks will be considered.

General analytical solutions for DC/AC circuit-network analysis

NASA Astrophysics Data System (ADS)

Rubido, Nicolás; Grebogi, Celso; Baptista, Murilo S.

2017-06-01

In this work, we present novel general analytical solutions for the currents that are developed in the edges of network-like circuits when some nodes of the network act as sources/sinks of DC or AC current. We assume that Ohm's law is valid at every edge and that charge at every node is conserved (with the exception of the source/sink nodes). The resistive, capacitive, and/or inductive properties of the lines in the circuit define a complex network structure with given impedances for each edge. Our solution for the currents at each edge is derived in terms of the eigenvalues and eigenvectors of the Laplacian matrix of the network defined from the impedances. This derivation also allows us to compute the equivalent impedance between any two nodes of the circuit and relate it to currents in a closed circuit which has a single voltage generator instead of many input/output source/sink nodes. This simplifies the treatment that could be done via Thévenin's theorem. Contrary to solving Kirchhoff's equations, our derivation allows to easily calculate the redistribution of currents that occurs when the location of sources and sinks changes within the network. Finally, we show that our solutions are identical to the ones found from Circuit Theory nodal analysis.

Identifying messaging completion in a parallel computer by checking for change in message received and transmitted count at each node

DOEpatents

Archer, Charles J [Rochester, MN; Hardwick, Camesha R [Fayetteville, NC; McCarthy, Patrick J [Rochester, MN; Wallenfelt, Brian P [Eden Prairie, MN

2009-06-23

Methods, parallel computers, and products are provided for identifying messaging completion on a parallel computer. The parallel computer includes a plurality of compute nodes, the compute nodes coupled for data communications by at least two independent data communications networks including a binary tree data communications network optimal for collective operations that organizes the nodes as a tree and a torus data communications network optimal for point to point operations that organizes the nodes as a torus. Embodiments include reading all counters at each node of the torus data communications network; calculating at each node a current node value in dependence upon the values read from the counters at each node; and determining for all nodes whether the current node value for each node is the same as a previously calculated node value for each node. If the current node is the same as the previously calculated node value for all nodes of the torus data communications network, embodiments include determining that messaging is complete and if the current node is not the same as the previously calculated node value for all nodes of the torus data communications network, embodiments include determining that messaging is currently incomplete.

DiffNet: automatic differential functional summarization of dE-MAP networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes

2014-10-01

The study of genetic interaction networks that respond to changing conditions is an emerging research problem. Recently, Bandyopadhyay et al. (2010) proposed a technique to construct a differential network (dE-MAPnetwork) from two static gene interaction networks in order to map the interaction differences between them under environment or condition change (e.g., DNA-damaging agent). This differential network is then manually analyzed to conclude that DNA repair is differentially effected by the condition change. Unfortunately, manual construction of differential functional summary from a dE-MAP network that summarizes all pertinent functional responses is time-consuming, laborious and error-prone, impeding large-scale analysis on it. To this end, we propose DiffNet, a novel data-driven algorithm that leverages Gene Ontology (go) annotations to automatically summarize a dE-MAP network to obtain a high-level map of functional responses due to condition change. We tested DiffNet on the dynamic interaction networks following MMS treatment and demonstrated the superiority of our approach in generating differential functional summaries compared to state-of-the-art graph clustering methods. We studied the effects of parameters in DiffNet in controlling the quality of the summary. We also performed a case study that illustrates its utility. Copyright © 2014 Elsevier Inc. All rights reserved.

Geohydrology of the Antelope Valley Area, California and design for a ground-water-quality monitoring network

USGS Publications Warehouse

Duell, L.F.

1987-01-01

A basinwide ideal network and an actual network were designed to identify ambient groundwater quality, trends in groundwater quality, and degree of threat from potential pollution sources in Antelope Valley, California. In general, throughout the valley groundwater quality has remained unchanged, and no specific trends are apparent. The main source of groundwater for the valley is generally suitable for domestic, irrigation, and most industrial uses. Water quality data for selected constituents of some network wells and surface-water sites are presented. The ideal network of 77 sites was selected on the basis of site-specific criteria, geohydrology, and current land use (agricultural, residential, and industrial). These sites were used as a guide in the design of the actual network consisting of 44 existing wells. Wells are currently being monitored and were selected whenever possible because of budgetary constraints. Of the remaining ideal sites, 20 have existing wells not part of a current water quality network, and 13 are locations where no wells exist. The methodology used for the selection of sites, constituents monitored, and frequency of analysis will enable network users to make appropriate future changes to the monitoring network. (USGS)

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

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

Potok, Thomas E; Schuman, Catherine D; Young, Steven R

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determinemore » network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.« less

Load flows and faults considering dc current injections

NASA Technical Reports Server (NTRS)

Kusic, G. L.; Beach, R. F.

1991-01-01

The authors present novel methods for incorporating current injection sources into dc power flow computations and determining network fault currents when electronic devices limit fault currents. Combinations of current and voltage sources into a single network are considered in a general formulation. An example of relay coordination is presented. The present study is pertinent to the development of the Space Station Freedom electrical generation, transmission, and distribution system.

Assessing the Liquidity of Firms: Robust Neural Network Regression as an Alternative to the Current Ratio

NASA Astrophysics Data System (ADS)

de Andrés, Javier; Landajo, Manuel; Lorca, Pedro; Labra, Jose; Ordóñez, Patricia

Artificial neural networks have proven to be useful tools for solving financial analysis problems such as financial distress prediction and audit risk assessment. In this paper we focus on the performance of robust (least absolute deviation-based) neural networks on measuring liquidity of firms. The problem of learning the bivariate relationship between the components (namely, current liabilities and current assets) of the so-called current ratio is analyzed, and the predictive performance of several modelling paradigms (namely, linear and log-linear regressions, classical ratios and neural networks) is compared. An empirical analysis is conducted on a representative data base from the Spanish economy. Results indicate that classical ratio models are largely inadequate as a realistic description of the studied relationship, especially when used for predictive purposes. In a number of cases, especially when the analyzed firms are microenterprises, the linear specification is improved by considering the flexible non-linear structures provided by neural networks.

GERIREX - growing a second generation medical expert system

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

Kocur, J. Jr.; Suh, S.C.

This article describes GERIREX, a medical expert system as the core module of an integrated system for total management of a medical practice. GERIREX is currently a first-generation consultant in the domain of prescribing for the geriatric patient with multiple ailments. Employing rule and objective probabilistic knowledge representations, the system performs at the near-expert level, correctly ranking single and multiple drug therapy for hypertension and/or congestive heart failure in the presence of between two and seven of 18 common accompanying or underlying conditions. GERIREX creates permanent consultation records and can access patient information from existing databases. System requirements are metmore » by very modest PCs, yet power, speed, flexibility, and ease of use rival or exceed those of many other systems. GERIREX interfaces with a variety of configurations and applications, including text, spreadsheets, databases, and executables, to fit in with current plans to upgrade to a second generation system, providing a degree of self-maintenance through intelligent parsing of a drug data source such as the Physicians` Desk Reference (PDR - CDROM version). Another option under consideration is developing neural networks to both replace the current knowledge base, and to embody the rationale employed by the medical expert in evaluating drug data for treatment selection. In this version, the current drug database would be used as warning data for the network tasked with adding new drugs to the drug database, imitating the process whereby a physician determines their personal arsenal from among the wide range of available options.« less

Probability Density Functions of the Solar Wind Driver of the Magnetopshere-Ionosphere System

NASA Astrophysics Data System (ADS)

Horton, W.; Mays, M. L.

2007-12-01

The solar-wind driven magnetosphere-ionosphere system is a complex dynamical system in that it exhibits (1) sensitivity to initial conditions; (2) multiple space-time scales; (3) bifurcation sequences with hysteresis in transitions between attractors; and (4) noncompositionality. This system is modeled by WINDMI--a network of eight coupled ordinary differential equations which describe the transfer of power from the solar wind through the geomagnetic tail, the ionosphere, and ring current in the system. The model captures both storm activity from the plasma ring current energy, which yields a model Dst index result, and substorm activity from the region 1 field aligned current, yielding model AL and AU results. The input to the model is the solar wind driving voltage calculated from ACE solar wind parameter data, which has a regular coherent component and broad-band turbulent component. Cross correlation functions of the input-output data time series are computed and the conditional probability density function for the occurrence of substorms given earlier IMF conditions are derived. The model shows a high probability of substorms for solar activity that contains a coherent, rotating IMF with magnetic cloud features. For a theoretical model of the imprint of solar convection on the solar wind we have used the Lorenz attractor (Horton et al., PoP, 1999, doi:10.10631.873683) as a solar wind driver. The work is supported by NSF grant ATM-0638480.

Current challenges in quantifying preferential flow through the vadose zone

NASA Astrophysics Data System (ADS)

Koestel, John; Larsbo, Mats; Jarvis, Nick

2017-04-01

In this presentation, we give an overview of current challenges in quantifying preferential flow through the vadose zone. A review of the literature suggests that current generation models do not fully reflect the present state of process understanding and empirical knowledge of preferential flow. We believe that the development of improved models will be stimulated by the increasingly widespread application of novel imaging technologies as well as future advances in computational power and numerical techniques. One of the main challenges in this respect is to bridge the large gap between the scales at which preferential flow occurs (pore to Darcy scales) and the scale of interest for management (fields, catchments, regions). Studies at the pore scale are being supported by the development of 3-D non-invasive imaging and numerical simulation techniques. These studies are leading to a better understanding of how macropore network topology and initial/boundary conditions control key state variables like matric potential and thus the strength of preferential flow. Extrapolation of this knowledge to larger scales would require support from theoretical frameworks such as key concepts from percolation and network theory, since we lack measurement technologies to quantify macropore networks at these large scales. Linked hydro-geophysical measurement techniques that produce highly spatially and temporally resolved data enable investigation of the larger-scale heterogeneities that can generate preferential flow patterns at pedon, hillslope and field scales. At larger regional and global scales, improved methods of data-mining and analyses of large datasets (machine learning) may help in parameterizing models as well as lead to new insights into the relationships between soil susceptibility to preferential flow and site attributes (climate, land uses, soil types).

New insights into the placebo and nocebo responses.

PubMed

Enck, Paul; Benedetti, Fabrizio; Schedlowski, Manfred

2008-07-31

In modern medicine, the placebo response or placebo effect has often been regarded as a nuisance in basic research and particularly in clinical research. The latest scientific evidence has demonstrated, however, that the placebo effect and the nocebo effect, the negative effects of placebo, stem from highly active processes in the brain that are mediated by psychological mechanisms such as expectation and conditioning. These processes have been described in some detail for many diseases and treatments, and we now know that they can represent both strength and vulnerability in the course of a disease as well as in the response to a therapy. However, recent research and current knowledge raise several issues that we shall address in this review. We will discuss current neurobiological models like expectation-induced activation of the brain reward circuitry, Pavlovian conditioning, and anxiety mechanisms of the nocebo response. We will further explore the nature of the placebo responses in clinical trials and address major questions for future research such as the relationship between expectations and conditioning in placebo effects, the existence of a consistent brain network for all placebo effects, the role of gender in placebo effects, and the impact of getting drug-like effects without drugs.

Preparation of Professional Training Teachers for Network Cooperation between Educational Establishments during Labor Preparation

ERIC Educational Resources Information Center

Tarasjuk, Olga V.; Fedulova, Ksenia A.; Fedulova, Marina A.; Kryukova, Polina S.; Yadretsov, Vyacheslav ?.

2016-01-01

Relevance of the problem being investigated is conditioned by the necessity to arrange network cooperation between educational institutions during labor force preparation in the conditions of informatization of educational and technological processes. The aim of the article is to prove the necessity of including basics of network cooperation…

Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

NASA Astrophysics Data System (ADS)

Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.

2017-02-01

An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.

Using ordinal partition transition networks to analyze ECG data

NASA Astrophysics Data System (ADS)

Kulp, Christopher W.; Chobot, Jeremy M.; Freitas, Helena R.; Sprechini, Gene D.

2016-07-01

Electrocardiogram (ECG) data from patients with a variety of heart conditions are studied using ordinal pattern partition networks. The ordinal pattern partition networks are formed from the ECG time series by symbolizing the data into ordinal patterns. The ordinal patterns form the nodes of the network and edges are defined through the time ordering of the ordinal patterns in the symbolized time series. A network measure, called the mean degree, is computed from each time series-generated network. In addition, the entropy and number of non-occurring ordinal patterns (NFP) is computed for each series. The distribution of mean degrees, entropies, and NFPs for each heart condition studied is compared. A statistically significant difference between healthy patients and several groups of unhealthy patients with varying heart conditions is found for the distributions of the mean degrees, unlike for any of the distributions of the entropies or NFPs.

Community networks in chronic disease management.

PubMed

Pyne, Diane

2009-01-01

Community networks are being established as part of the Chronic Disease Management program in Edmonton, Alberta. These networks are programs and services from profit and not-for-profit organizations that support people with chronic conditions to address lifestyle choices and issues. Evidence-informed standards and criteria have been developed that have to be met to belong to such a network. The community network approach is developing a "community" of resources that are available and committed to assist healthcare professionals and the public with health promotion for people with chronic conditions.

Simulation of Electromigration Based on Resistor Networks

NASA Astrophysics Data System (ADS)

Patrinos, Anthony John

A two dimensional computer simulation of electromigration based on resistor networks was designed and implemented. The model utilizes a realistic grain structure generated by the Monte Carlo method and takes specific account of the local effects through which electromigration damage progresses. The dynamic evolution of the simulated thin film is governed by the local current and temperature distributions. The current distribution is calculated by superimposing a two dimensional electrical network on the lattice whose nodes correspond to the particles in the lattice and the branches to interparticle bonds. Current is assumed to flow from site to site via nearest neighbor bonds. The current distribution problem is solved by applying Kirchhoff's rules on the resulting electrical network. The calculation of the temperature distribution in the lattice proceeds by discretizing the partial differential equation for heat conduction, with appropriate material parameters chosen for the lattice and its defects. SEReNe (for Simulation of Electromigration using Resistor Networks) was tested by applying it to common situations arising in experiments with real films with satisfactory results. Specifically, the model successfully reproduces the expected grain size, line width and bamboo effects, the lognormal failure time distribution and the relationship between current density exponent and current density. It has also been modified to simulate temperature ramp experiments but with mixed, in this case, results.

Synchronization of Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions and Infinite Delays.

PubMed

Sheng, Yin; Zhang, Hao; Zeng, Zhigang

2017-10-01

This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.

Biblio-MetReS: A bibliometric network reconstruction application and server

PubMed Central

2011-01-01

Background Reconstruction of genes and/or protein networks from automated analysis of the literature is one of the current targets of text mining in biomedical research. Some user-friendly tools already perform this analysis on precompiled databases of abstracts of scientific papers. Other tools allow expert users to elaborate and analyze the full content of a corpus of scientific documents. However, to our knowledge, no user friendly tool that simultaneously analyzes the latest set of scientific documents available on line and reconstructs the set of genes referenced in those documents is available. Results This article presents such a tool, Biblio-MetReS, and compares its functioning and results to those of other user-friendly applications (iHOP, STRING) that are widely used. Under similar conditions, Biblio-MetReS creates networks that are comparable to those of other user friendly tools. Furthermore, analysis of full text documents provides more complete reconstructions than those that result from using only the abstract of the document. Conclusions Literature-based automated network reconstruction is still far from providing complete reconstructions of molecular networks. However, its value as an auxiliary tool is high and it will increase as standards for reporting biological entities and relationships become more widely accepted and enforced. Biblio-MetReS is an application that can be downloaded from http://metres.udl.cat/. It provides an easy to use environment for researchers to reconstruct their networks of interest from an always up to date set of scientific documents. PMID:21975133

The Global Seismographic Network (GSN): Deployment of Next Generation VBB Borehole Sensors and Improving Overall Network Noise Performance

NASA Astrophysics Data System (ADS)

Hafner, K.; Davis, P.; Wilson, D.; Sumy, D.

2017-12-01

The Global Seismographic Network (GSN) recently received delivery of the next generation Very Broadband (VBB) borehole sensors purchased through funding from the DOE. Deployment of these sensors will be underway during the end of summer and fall of 2017 and they will eventually replace the aging KS54000 sensors at approximately one-third of the GSN network stations. We will present the latest methods of deploying these sensors in the existing deep boreholes. To achieve lower noise performance at some sites, emplacement in shallow boreholes might result in lower noise performance for the existing site conditions. In some cases shallow borehole installations may be adapted to vault stations (which make up two thirds of the network), as a means of reducing tilt-induced signals on the horizontal components. The GSN is creating a prioritized list of equipment upgrades at selected stations with the ultimate goal of optimizing overall network data availability and noise performance. For an overview of the performance of the current GSN relative to selected set of metrics, we are utilizing data quality metrics and Probability Density Functions (PDFs)) generated by the IRIS Data Management Centers' (DMC) MUSTANG (Modular Utility for Statistical Knowledge Gathering) and LASSO (Latest Assessment of Seismic Station Observations) tools. We will present our metric analysis of GSN performance in 2016, and show the improvements at GSN sites resulting from recent instrumentation and infrastructure upgrades.

A novel neural network for variational inequalities with linear and nonlinear constraints.

PubMed

Gao, Xing-Bao; Liao, Li-Zhi; Qi, Liqun

2005-11-01

Variational inequality is a uniform approach for many important optimization and equilibrium problems. Based on the sufficient and necessary conditions of the solution, this paper presents a novel neural network model for solving variational inequalities with linear and nonlinear constraints. Three sufficient conditions are provided to ensure that the proposed network with an asymmetric mapping is stable in the sense of Lyapunov and converges to an exact solution of the original problem. Meanwhile, the proposed network with a gradient mapping is also proved to be stable in the sense of Lyapunov and to have a finite-time convergence under some mild condition by using a new energy function. Compared with the existing neural networks, the new model can be applied to solve some nonmonotone problems, has no adjustable parameter, and has lower complexity. Thus, the structure of the proposed network is very simple. Since the proposed network can be used to solve a broad class of optimization problems, it has great application potential. The validity and transient behavior of the proposed neural network are demonstrated by several numerical examples.

Why trace and delay conditioning are sometimes (but not always) hippocampal dependent: A computational model

PubMed Central

Moustafa, Ahmed A.; Wufong, Ella; Servatius, Richard J.; Pang, Kevin C. H.; Gluck, Mark A.; Myers, Catherine E.

2013-01-01

A recurrent-network model provides a unified account of the hippocampal region in mediating the representation of temporal information in classical eyeblink conditioning. Much empirical research is consistent with a general conclusion that delay conditioning (in which the conditioned stimulus CS and unconditioned stimulus US overlap and co-terminate) is independent of the hippocampal system, while trace conditioning (in which the CS terminates before US onset) depends on the hippocampus. However, recent studies show that, under some circumstances, delay conditioning can be hippocampal-dependent and trace conditioning can be spared following hippocampal lesion. Here, we present an extension of our prior trial-level models of hippocampal function and stimulus representation that can explain these findings within a unified framework. Specifically, the current model includes adaptive recurrent collateral connections that aid in the representation of intra-trial temporal information. With this model, as in our prior models, we argue that the hippocampus is not specialized for conditioned response timing, but rather is a general-purpose system that learns to predict the next state of all stimuli given the current state of variables encoded by activity in recurrent collaterals. As such, the model correctly predicts that hippocampal involvement in classical conditioning should be critical not only when there is an intervening trace interval, but also when there is a long delay between CS onset and US onset. Our model simulates empirical data from many variants of classical conditioning, including delay and trace paradigms in which the length of the CS, the inter-stimulus interval, or the trace interval is varied. Finally, we discuss model limitations, future directions, and several novel empirical predictions of this temporal processing model of hippocampal function and learning. PMID:23178699

Correlation mapping microscopy

NASA Astrophysics Data System (ADS)

McGrath, James; Alexandrov, Sergey; Owens, Peter; Subhash, Hrebesh M.; Leahy, Martin J.

2015-03-01

Changes in the microcirculation are associated with conditions such as Raynauds disease. Current modalities used to assess the microcirculation such as nailfold capillaroscopy are limited due to their depth ambiguity. A correlation mapping technique was recently developed to extend the capabilities of Optical Coherence Tomography to generate depth resolved images of the microcirculation. Here we present the extension of this technique to microscopy modalities, including confocal microscopy. It is shown that this correlation mapping microscopy technique can extend the capabilities of conventional microscopy to enable mapping of vascular networks in vivo with high spatial resolution.

Differential Effects of Left and Right Prefrontal High-Frequency Repetitive Transcranial Magnetic Stimulation on Resting-State Functional Magnetic Resonance Imaging in Healthy Individuals.

PubMed

Schluter, Renée S; Jansen, Jochem M; van Holst, Ruth J; van den Brink, Wim; Goudriaan, Anna E

2018-03-01

High-frequency repetitive transcranial magnetic stimulation (HF-rTMS) has gained great interest in multiple clinical and research fields and is believed to accomplish its effect by influencing neuronal networks. The dorsolateral prefrontal cortex (dlPFC) is frequently chosen as the cortical target for HF-rTMS. However, very little is known about the differential effect of HF-rTMS over the left and right dlPFC on intrinsic functional connectivity networks in patients or in healthy individuals. The current study assessed the differential effects of left or right HF-rTMS (corrected for sham) on intrinsic independent component analysis (ICA)-defined functional connectivity networks in a sample of 45 healthy individuals. All subjects had a first scanning session in which baseline functional connectivity was assessed. During the second session, individuals received one session of left, right, or sham dlPFC HF-rTMS (60 5-sec trains of 10 Hz at 110% motor threshold). The sham condition was used to correct for time and placebo effects. ICAs were performed to assess baseline differences and stimulation effects on within- and between-network functional connectivity. Stimulation of the left dlPFC resulted in decreased functional connectivity in the salience network, whereas right dlPFC stimulation resulted in increased functional connectivity within this network. No differences between left or right dlPFC stimulation were found in between-network connectivity. These results suggest that left and right HF-rTMS may have differential effects, and more research is needed on the clinical consequences.

The effect of music-induced mood on attentional networks.

PubMed

Jiang, Jun; Scolaro, Ashley J; Bailey, Kira; Chen, Antao

2011-06-01

Attention network theory suggests that there are three separate neural networks that execute the discrete functions of alerting, orienting, and executive attention. Previous research on the influence of mood on attention has shown subtle and inconsistent results. The attention network theory may aid in clarifying the influence of mood on attention. The present study investigated the influence of mood on attentional networks in a normal population. Participants performed the Attention Network Test (ANT), which provides functional measures of alerting, orienting, and executive attention. Positive or negative mood was induced by listening to music with a positive or negative valence, respectively; neutral mood was induced by reading a collection of basic facts about China. The results revealed that negative mood led to a significantly higher alerting efficiency relative to other moods, while there were no significant mood effects on orienting or executive attention efficiency. According to the algorithm underlying the ANT, the higher alerting efficiency in the negative mood condition can be attributed to relatively greater benefits of cueing effects. The findings are discussed in the context of the noradrenergic system and of evolutionary significance. Specifically, the increase in the alerting function during negative mood states may be due to the modulation effect of negative mood on the noradrenergic system, and/or to the survival benefit resulting from an increase in automatic vigilance towards negative information. The current results suggest that as the influence of negative mood on attention appears to specifically consist in an enhanced alerting function, it may not be found in studies where the three attentional networks are not dissociated.

Intelligent automotive battery systems

NASA Astrophysics Data System (ADS)

Witehira, P.

A single power-supply battery is incompatible with modern vehicles. A one-cmbination 12 cell/12 V battery, developed by Power Beat International Limited (PBIL), is described. The battery is designed to be a 'drop in' replacement for existing batteries. The cell structures, however, are designed according to load function, i.e., high-current shallow-discharge cycles and low-current deep-discharge cycles. The preferred energy discharge management logic and integration into the power distribution network of the vehicle to provide safe user-friendly usage is described. The system is designed to operate transparent to the vehicle user. The integrity of the volatile high-current cells is maintained by temperature-sensitive voltage control and discharge management. The deep-cycle cells can be fully utilized without affecting startability under extreme conditions. Electric energy management synchronization with engine starting will provide at least 6% overall reduction in hydrocarbon emissions using an intelligent on-board power-supply technology developed by PBIL.

Improvement of the conductive network of positive electrodes and the performance of Ni-MH battery

NASA Astrophysics Data System (ADS)

Morimoto, Katsuya; Nakayama, Kousuke; Maki, Hideshi; Inoue, Hiroshi; Mizuhata, Minoru

2017-06-01

The pretreatment to modify the valence of cobalt by discharging at 0.2 C rate for 7.5 h before the first initial activation charge process is effective in improving the surface electronic conductivity among fine particles of positive electrode active materials. The discharge curves indicate the same locus within 1800 cycles, and the capacity of the pretreated battery is stable for over 4000 cycles. However, in-situ cell pretreatment with constant current has negative influence on other components. During the constant current pretreatment, the cell voltage rapidly falls to -0.5 V in the first 10 s of in-situ pretreatment. Therefore, we investigate the pretreatment by supplying a constant voltage to the battery instead of a constant current, and find the effective condition to improve the electrochemical performance and not to have any influence on other components of the battery.

Neural correlates of fixation duration in natural reading: Evidence from fixation-related fMRI.

PubMed

Henderson, John M; Choi, Wonil; Luke, Steven G; Desai, Rutvik H

2015-10-01

A key assumption of current theories of natural reading is that fixation duration reflects underlying attentional, language, and cognitive processes associated with text comprehension. The neurocognitive correlates of this relationship are currently unknown. To investigate this relationship, we compared neural activation associated with fixation duration in passage reading and a pseudo-reading control condition. The results showed that fixation duration was associated with activation in oculomotor and language areas during text reading. Fixation duration during pseudo-reading, on the other hand, showed greater involvement of frontal control regions, suggesting flexibility and task dependency of the eye movement network. Consistent with current models, these results provide support for the hypothesis that fixation duration in reading reflects attentional engagement and language processing. The results also demonstrate that fixation-related fMRI provides a method for investigating the neurocognitive bases of natural reading. Copyright © 2015 Elsevier Inc. All rights reserved.

Characterization of Adaptation by Morphology in a Planar Biological Network of Plasmodial Slime Mold

NASA Astrophysics Data System (ADS)

Ito, Masateru; Okamoto, Riki; Takamatsu, Atsuko

2011-07-01

Growth processes of a planar biological network of plasmodium of a true slime mold, Physarum polycephalum, were analyzed quantitatively. The plasmodium forms a transportation network through which protoplasm conveys nutrients, oxygen, and cellular organelles similarly to blood in a mammalian vascular network. To analyze the network structure, vertices were defined at tube bifurcation points. Then edges were defined for the tubes connecting both end vertices. Morphological analysis was attempted along with conventional topological analysis, revealing that the growth process of the plasmodial network structure depends on environmental conditions. In an attractive condition, the network is a polygonal lattice with more than six edges per vertex at the early stage and the hexagonal lattice at a later stage. Through all growing stages, the tube structure was not highly developed but an unstructured protoplasmic thin sheet was dominantly formed. The network size is small. In contrast, in the repulsive condition, the network is a mixture of polygonal lattice and tree-graph. More specifically, the polygonal lattice has more than six edges per vertex in the early stage, then a tree-graph structure is added to the lattice network at a later stage. The thick tube structure was highly developed. The network size, in the meaning of Euclidean distance but not topological one, grows considerably. Finally, the biological meaning of the environment-dependent network structure in the plasmodium is discussed.

The Current State of Human Performance Technology: A Citation Network Analysis of "Performance Improvement Quarterly," 1988-2010

ERIC Educational Resources Information Center

Cho, Yonjoo; Jo, Sung Jun; Park, Sunyoung; Kang, Ingu; Chen, Zengguan

2011-01-01

This study conducted a citation network analysis (CNA) of human performance technology (HPT) to examine its current state of the field. Previous reviews of the field have used traditional research methods, such as content analysis, survey, Delphi, and citation analysis. The distinctive features of CNA come from using a social network analysis…

An Ad-hoc Satellite Network to Measure Filamentary Current Structures in the Auroral Zone

NASA Astrophysics Data System (ADS)

Nabong, C.; Fritz, T. A.; Semeter, J. L.

2014-12-01

An ad-hoc cubesat-based satellite network project known as ANDESITE is under development at Boston University. It aims to develop a dense constellation of easy-to-use, rapidly-deployable low-cost wireless sensor nodes in space. The objectives of the project are threefold: 1) Demonstrate viability of satellite based sensor networks by deploying an 8-node miniature sensor network to study the filamentation of the field aligned currents in the auroral zones of the Earth's magnetosphere. 2) Test the scalability of proposed protocols, including localization techniques, tracking, data aggregation, and routing, for a 3 dimensional wireless sensor network using a "flock" of nodes. 3) Construct a 6U Cube-sat running the Android OS as an integrated constellation manager, data mule and sensor node deplorer. This small network of sensor nodes will resolve current densities at different spatial resolutions in the near-Earth magnetosphere using measurements from magnetometers with 1-nT sensitivities and 0.2 nT/√Hz self-noise. Mapping of these currents will provide new constraints for models of auroral particle acceleration, wave-particle interactions, ionospheric destabilization, and other kinetic processes operating in the low-beta plasma of the near Earth magnetosphere.

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt.

PubMed

Jayaraman, Prem Prakash; Yavari, Ali; Georgakopoulos, Dimitrios; Morshed, Ahsan; Zaslavsky, Arkady

2016-11-09

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis

PubMed Central

Fan, Yannan; Siklenka, Keith; Arora, Simran K.; Ribeiro, Paula; Kimmins, Sarah; Xia, Jianguo

2016-01-01

MicroRNAs (miRNAs) can regulate nearly all biological processes and their dysregulation is implicated in various complex diseases and pathological conditions. Recent years have seen a growing number of functional studies of miRNAs using high-throughput experimental technologies, which have produced a large amount of high-quality data regarding miRNA target genes and their interactions with small molecules, long non-coding RNAs, epigenetic modifiers, disease associations, etc. These rich sets of information have enabled the creation of comprehensive networks linking miRNAs with various biologically important entities to shed light on their collective functions and regulatory mechanisms. Here, we introduce miRNet, an easy-to-use web-based tool that offers statistical, visual and network-based approaches to help researchers understand miRNAs functions and regulatory mechanisms. The key features of miRNet include: (i) a comprehensive knowledge base integrating high-quality miRNA-target interaction data from 11 databases; (ii) support for differential expression analysis of data from microarray, RNA-seq and quantitative PCR; (iii) implementation of a flexible interface for data filtering, refinement and customization during network creation; (iv) a powerful fully featured network visualization system coupled with enrichment analysis. miRNet offers a comprehensive tool suite to enable statistical analysis and functional interpretation of various data generated from current miRNA studies. miRNet is freely available at http://www.mirnet.ca. PMID:27105848

Evolution of network architecture in a granular material under compression

NASA Astrophysics Data System (ADS)

Papadopoulos, Lia; Puckett, James G.; Daniels, Karen E.; Bassett, Danielle S.

2016-09-01

As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. Force chains are a prime example of such structures, and are thought to constrain bulk properties such as mechanical stability and acoustic transmission. However, capturing and characterizing the evolving nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. A growing body of work has shown that graph theoretic approaches may provide a useful foundation for tackling these problems. Here, we extend the current approaches by utilizing multilayer networks as a framework for directly quantifying the progression of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and interparticle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the changes in this structure throughout the compression process. We separately consider the network of normal and tangential forces, and find that they display a different progression throughout compression. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be achieved by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than a purely local measure of interparticle forces alone. The results discussed throughout this study suggest that these network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup.

Default mode network as a potential biomarker of chemotherapy-related brain injury

PubMed Central

Kesler, Shelli R.

2014-01-01

Chronic medical conditions and/or their treatments may interact with aging to alter or even accelerate brain senescence. Adult onset cancer, for example, is a disease associated with advanced aging and emerging evidence suggests a profile of subtle but diffuse brain injury following cancer chemotherapy. Breast cancer is currently the primary model for studying these “chemobrain” effects. Given the widespread changes to brain structure and function as well as the common impairment of integrated cognitive skills observed following breast cancer chemotherapy, it is likely that large-scale brain networks are involved. Default mode network (DMN) is a strong candidate considering its preferential vulnerability to aging and sensitivity to toxicity and disease states. Additionally, chemotherapy is associated with several physiologic effects including increased inflammation and oxidative stress that are believed to elevate toxicity in the DMN. Biomarkers of DMN connectivity could aid in the development of treatments for chemotherapy-related cognitive decline. For example, certain nutritional interventions could potentially reduce the metabolic changes (e.g. amyloid beta toxicity) associated with DMN disruption. PMID:24913897

Accountable care organizations may have difficulty avoiding the failures of integrated delivery networks of the 1990s.

PubMed

Burns, Lawton R; Pauly, Mark V

2012-11-01

Accountable care organizations are intended to improve the quality and lower the cost of health care through several mechanisms, such as disease management programs, care coordination, and aligning financial incentives for hospitals and physicians. Providers employed several of these mechanisms in forming the integrated delivery networks of the 1990s. The networks failed, however, because of heavy financial losses stemming from hospitals' purchase of physician practices and their inability to align incentives, garner capitated contracts, and develop the infrastructure to manage risk. Although the current mechanisms underlying accountable care organizations continue to evolve, whether and how they will have an impact on quality and costs remains open to question. Care coordination and information technology are proving more complicated and expensive to implement than anticipated, providers may lack the ability to implement these mechanisms, and primary care providers are in short supply. As in the 1990s, success depends on targeting specific populations, such as people with multiple chronic conditions who need and may benefit from coordinated care.

Physiological and neural correlates of worry and rumination: Support for the contrast avoidance model of worry.

PubMed

Steinfurth, Elisa C K; Alius, Manuela G; Wendt, Julia; Hamm, Alfons O

2017-02-01

The current experiments tested neural and physiological correlates of worry and rumination in comparison to thinking about neutral events. According to the avoidance model-stating that worry is a strategy to reduce intense emotions-physiological and neurobiological activity during worried thinking should not differ from activation during neutral thinking. According to the contrast avoidance model-stating that worry is a strategy to reduce abrupt shifts of emotions-activity should be increased. To test these competing models, we induced worry and neutral thinking in healthy participants using personal topics. A rumination condition was added to investigate the specificity of changes induced by the mental process. Two experiments were conducted assessing the effects on different response levels: (1) neural activation using fMRI, and (2) physiological response mobilization using startle and autonomic measures. During worry, participants showed a potentiated startle response and BOLD activity indicative of emotional network activation. These data partly support the contrast avoidance model of worry. Both mental processes showed elevated activity in a common network referred to as default network indicating self-referential activity. © 2016 Society for Psychophysiological Research.

Security Analysis of DTN Architecture and Bundle Protocol Specification for Space-Based Networks

NASA Technical Reports Server (NTRS)

Ivancic, William D.

2009-01-01

A Delay-Tolerant Network (DTN) Architecture (Request for Comment, RFC-4838) and Bundle Protocol Specification, RFC-5050, have been proposed for space and terrestrial networks. Additional security specifications have been provided via the Bundle Security Specification (currently a work in progress as an Internet Research Task Force internet-draft) and, for link-layer protocols applicable to Space networks, the Licklider Transport Protocol Security Extensions. This document provides a security analysis of the current DTN RFCs and proposed security related internet drafts with a focus on space-based communication networks, which is a rather restricted subset of DTN networks. Note, the original focus and motivation of DTN work was for the Interplanetary Internet . This document does not address general store-and-forward network overlays, just the current work being done by the Internet Research Task Force (IRTF) and the Consultative Committee for Space Data Systems (CCSDS) Space Internetworking Services Area (SIS) - DTN working group under the DTN and Bundle umbrellas. However, much of the analysis is relevant to general store-and-forward overlays.

Optimization of Emissions Sensor Networks Incorporating Tradeoffs Between Different Sensor Technologies

NASA Astrophysics Data System (ADS)

Nicholson, B.; Klise, K. A.; Laird, C. D.; Ravikumar, A. P.; Brandt, A. R.

2017-12-01

In order to comply with current and future methane emissions regulations, natural gas producers must develop emissions monitoring strategies for their facilities. In addition, regulators must develop air monitoring strategies over wide areas incorporating multiple facilities. However, in both of these cases, only a limited number of sensors can be deployed. With a wide variety of sensors to choose from in terms of cost, precision, accuracy, spatial coverage, location, orientation, and sampling frequency, it is difficult to design robust monitoring strategies for different scenarios while systematically considering the tradeoffs between different sensor technologies. In addition, the geography, weather, and other site specific conditions can have a large impact on the performance of a sensor network. In this work, we demonstrate methods for calculating optimal sensor networks. Our approach can incorporate tradeoffs between vastly different sensor technologies, optimize over typical wind conditions for a particular area, and consider different objectives such as time to detection or geographic coverage. We do this by pre-computing site specific scenarios and using them as input to a mixed-integer, stochastic programming problem that solves for a sensor network that maximizes the effectiveness of the detection program. Our methods and approach have been incorporated within an open source Python package called Chama with the goal of providing facility operators and regulators with tools for designing more effective and efficient monitoring systems. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energys National Nuclear Security Administration under contract DE-NA0003525.

Conjunctive management of multi-reservoir network system and groundwater system

NASA Astrophysics Data System (ADS)

Mani, A.; Tsai, F. T. C.

2015-12-01

This study develops a successive mixed-integer linear fractional programming (successive MILFP) method to conjunctively manage water resources provided by a multi-reservoir network system and a groundwater system. The conjunctive management objectives are to maximize groundwater withdrawals and maximize reservoir storages while satisfying water demands and raising groundwater level to a target level. The decision variables in the management problem are reservoir releases and spills, network flows and groundwater pumping rates. Using the fractional programming approach, the objective function is defined as a ratio of total groundwater withdraws to total reservoir storage deficits from the maximum storages. Maximizing this ratio function tends to maximizing groundwater use and minimizing surface water use. This study introduces a conditional constraint on groundwater head in order to sustain aquifers from overpumping: if current groundwater level is less than a target level, groundwater head at the next time period has to be raised; otherwise, it is allowed to decrease up to a certain extent. This conditional constraint is formulated into a set of mixed binary nonlinear constraints and results in a mixed-integer nonlinear fractional programming (MINLFP) problem. To solve the MINLFP problem, we first use the response matrix approach to linearize groundwater head with respect to pumping rate and reduce the problem to an MILFP problem. Using the Charnes-Cooper transformation, the MILFP is transformed to an equivalent mixed-integer linear programming (MILP). The solution of the MILP is successively updated by updating the response matrix in every iteration. The study uses IBM CPLEX to solve the MILP problem. The methodology is applied to water resources management in northern Louisiana. This conjunctive management approach aims to recover the declining groundwater level of the stressed Sparta aquifer by using surface water from a network of four reservoirs as an alternative source of supply.

Geospatial Data Integration for Assessing Landslide Hazard on Engineered Slopes

NASA Astrophysics Data System (ADS)

Miller, P. E.; Mills, J. P.; Barr, S. L.; Birkinshaw, S. J.

2012-07-01

Road and rail networks are essential components of national infrastructures, underpinning the economy, and facilitating the mobility of goods and the human workforce. Earthwork slopes such as cuttings and embankments are primary components, and their reliability is of fundamental importance. However, instability and failure can occur, through processes such as landslides. Monitoring the condition of earthworks is a costly and continuous process for network operators, and currently, geospatial data is largely underutilised. The research presented here addresses this by combining airborne laser scanning and multispectral aerial imagery to develop a methodology for assessing landslide hazard. This is based on the extraction of key slope stability variables from the remotely sensed data. The methodology is implemented through numerical modelling, which is parameterised with the slope stability information, simulated climate conditions, and geotechnical properties. This allows determination of slope stability (expressed through the factor of safety) for a range of simulated scenarios. Regression analysis is then performed in order to develop a functional model relating slope stability to the input variables. The remotely sensed raster datasets are robustly re-sampled to two-dimensional cross-sections to facilitate meaningful interpretation of slope behaviour and mapping of landslide hazard. Results are stored in a geodatabase for spatial analysis within a GIS environment. For a test site located in England, UK, results have shown the utility of the approach in deriving practical hazard assessment information. Outcomes were compared to the network operator's hazard grading data, and show general agreement. The utility of the slope information was also assessed with respect to auto-population of slope geometry, and found to deliver significant improvements over the network operator's existing field-based approaches.

Is U.S. climatic diversity well represented within the existing federal protection network?

PubMed

Batllori, Enric; Miller, Carol; Parisien, Marc-Andre; Parks, Sean A; Moritz, Max A

Establishing protection networks to ensure that biodiversity and associated ecosystem services persist under changing environments is a major challenge for conservation planning. The potential consequences of altered climates for the structure and function of ecosystems necessitates new and complementary approaches be incorporated into traditional conservation plans. The conterminous United States of America (CONUS) has an extensive system of protected areas managed by federal agencies, but a comprehensive assessment of how this network represents CONUS climate is lacking. We present a quantitative classification of the climate space that is independent from the geographic locations to evaluate the climatic representation of the existing protected area network. We use this classification to evaluate the coverage of each agency's jurisdiction and to identify current conservation deficits. Our findings reveal that the existing network poorly represents CONUS climatic diversity. Although rare climates are generally well represented by the network, the most common climates are particularly underrepresented. Overall, 83% of the area of the CONUS corresponds to climates underrepresented by the network. The addition of some currently unprotected federal lands to the network would enhance the coverage of CONUS climates. However, to fully palliate current conservation deficits, large-scale private-land conservation initiatives will be critical.

Flight control with adaptive critic neural network

NASA Astrophysics Data System (ADS)

Han, Dongchen

2001-10-01

In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.

Stress-induced electric current fluctuations in rocks: a superstatistical model

NASA Astrophysics Data System (ADS)

Cartwright-Taylor, Alexis; Vallianatos, Filippos; Sammonds, Peter

2017-04-01

We recorded spontaneous electric current flow in non-piezoelectric Carrara marble samples during triaxial deformation. Mechanical data, ultrasonic velocities and acoustic emissions were acquired simultaneously with electric current to constrain the relationship between electric current flow, differential stress and damage. Under strain-controlled loading, spontaneous electric current signals (nA) were generated and sustained under all conditions tested. In dry samples, a detectable electric current arises only during dilatancy and the overall signal is correlated with the damage induced by microcracking. Our results show that fracture plays a key role in the generation of electric currents in deforming rocks (Cartwright-Taylor et al., in prep). We also analysed the high-frequency fluctuations of these electric current signals and found that they are not normally distributed - they exhibit power-law tails (Cartwright-Taylor et al., 2014). We modelled these distributions with q-Gaussian statistics, derived by maximising the Tsallis entropy. This definition of entropy is particularly applicable to systems which are strongly correlated and far from equilibrium. Good agreement, at all experimental conditions, between the distributions of electric current fluctuations and the q-Gaussian function with q-values far from one, illustrates the highly correlated, fractal nature of the electric source network within the samples and provides further evidence that the source of the electric signals is the developing fractal network of cracks. It has been shown (Beck, 2001) that q-Gaussian distributions can arise from the superposition of local relaxations in the presence of a slowly varying driving force, thus providing a dynamic reason for the appearance of Tsallis statistics in systems with a fluctuating energy dissipation rate. So, the probability distribution for a dynamic variable, u under some external slow forcing, β, can be obtained as a superposition of temporary local equilibrium processes whose variance fluctuates over time. The appearance of q-Gaussian statistics are caused by the fluctuating β parameter, which effectively models the fluctuating energy dissipation rate in the system. This concept is known as superstatistics and is physically relevant for modelling driven non-equilibrium systems where the environmental conditions fluctuate on a large scale. The idea is that the environmental variable, such as temperature or pressure, changes so slowly that a rapidly fluctuating variable within that environment has time to relax back to equilibrium between each change in the environment. The application of superstatistical techniques to our experimental electric current fluctuations show that they can indeed be described, to good approximation, by the superposition of local Gaussian processes with fluctuating variance. We conclude, then, that the measured electric current fluctuates in response to intermittent energy dissipation and is driven to varying temporary local equilibria during deformation by the variations in stress intensity. The advantage of this technique is that, once the model has been established to be a good description of the system in question, the average β parameter (a measure of the average energy dissipation rate) for the system can be obtained simply from the macroscopic q-Gaussian distribution parameters.

Fibril growth kinetics link buffer conditions and topology of 3D collagen I networks.

PubMed

Kalbitzer, Liv; Pompe, Tilo

2018-02-01

Three-dimensional fibrillar networks reconstituted from collagen I are widely used as biomimetic scaffolds for in vitro and in vivo cell studies. Various physicochemical parameters of buffer conditions for in vitro fibril formation are well known, including pH-value, ion concentrations and temperature. However, there is a lack of a detailed understanding of reconstituting well-defined 3D network topologies, which is required to mimic specific properties of the native extracellular matrix. We screened a wide range of relevant physicochemical buffer conditions and characterized the topology of the reconstituted 3D networks in terms of mean pore size and fibril diameter. A congruent analysis of fibril formation kinetics by turbidimetry revealed the adjustment of the lateral growth phase of fibrils by buffer conditions to be key in the determination of pore size and fibril diameter of the networks. Although the kinetics of nucleation and linear growth phase were affected by buffer conditions as well, network topology was independent of those two growth phases. Overall, the results of our study provide necessary insights into how to engineer 3D collagen matrices with an independent control over topology parameters, in order to mimic in vivo tissues in in vitro experiments and tissue engineering applications. The study reports a comprehensive analysis of physicochemical conditions of buffer solutions to reconstitute defined 3D collagen I matrices. By a combined analysis of network topology, i.e., pore size and fibril diameter, and the kinetics of fibril formation we can reveal the dependence of 3D network topology on buffer conditions, such as pH-value, phosphate concentration and sodium chloride content. With those results we are now able to provide engineering strategies to independently tune the topology parameters of widely used 3D collagen scaffolds based on the buffer conditions. By that, we enable the straightforward mimicking of extracellular matrices of in vivo tissues for in vitro cell culture experiments and tissue engineering applications. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Accounting for heterogeneity of nutrient dynamics in riverscapes through spatially distributed models

NASA Astrophysics Data System (ADS)

Wollheim, W. M.; Stewart, R. J.

2011-12-01

Numerous types of heterogeneity exist within river systems, leading to hotspots of nutrient sources, sinks, and impacts embedded within an underlying gradient defined by river size. This heterogeneity influences the downstream propagation of anthropogenic impacts across flow conditions. We applied a river network model to explore how nitrogen saturation at river network scales is influenced by the abundance and distribution of potential nutrient processing hotspots (lakes, beaver ponds, tributary junctions, hyporheic zones) under different flow conditions. We determined that under low flow conditions, whole network nutrient removal is relatively insensitive to the number of hotspots because the underlying river network structure has sufficient nutrient processing capacity. However, hotspots become more important at higher flows and greatly influence the spatial distribution of removal within the network at all flows, suggesting that identification of heterogeneity is critical to develop predictive understanding of nutrient removal processes under changing loading and climate conditions. New temporally intensive data from in situ sensors can potentially help to better understand and constrain these dynamics.

Assessing cortical synchronization during transcranial direct current stimulation: A graph-theoretical analysis.

PubMed

Mancini, Matteo; Brignani, Debora; Conforto, Silvia; Mauri, Piercarlo; Miniussi, Carlo; Pellicciari, Maria Concetta

2016-10-15

Transcranial direct current stimulation (tDCS) is a neuromodulation technique that can alter cortical excitability and modulate behaviour in a polarity-dependent way. Despite the widespread use of this method in the neuroscience field, its effects on ongoing local or global (network level) neuronal activity are still not foreseeable. A way to shed light on the neuronal mechanisms underlying the cortical connectivity changes induced by tDCS is provided by the combination of tDCS with electroencephalography (EEG). In this study, twelve healthy subjects underwent online tDCS-EEG recording (i.e., simultaneous), during resting-state, using 19 EEG channels. The protocol involved anodal, cathodal and sham stimulation conditions, with the active and the reference electrodes in the left frontocentral area (FC3) and on the forehead over the right eyebrow, respectively. The data were processed using a network model, based on graph theory and the synchronization likelihood. The resulting graphs were analysed for four frequency bands (theta, alpha, beta and gamma) to evaluate the presence of tDCS-induced differences in synchronization patterns and graph theory measures. The resting state network connectivity resulted altered during tDCS, in a polarity-specific manner for theta and alpha bands. Anodal tDCS weakened synchronization with respect to the baseline over the fronto-central areas in the left hemisphere, for theta band (p<0.05). In contrast, during cathodal tDCS a significant increase in inter-hemispheric synchronization connectivity was observed over the centro-parietal, centro-occipital and parieto-occipital areas for the alpha band (p<0.05). Local graph measures showed a tDCS-induced polarity-specific differences that regarded modifications of network activities rather than specific region properties. Our results show that applying tDCS during the resting state modulates local synchronization as well as network properties in slow frequency bands, in a polarity-specific manner. Copyright © 2016 Elsevier Inc. All rights reserved.

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study.

PubMed

Feltus, F Alex; Ficklin, Stephen P; Gibson, Scott M; Smith, Melissa C

2013-06-05

In genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues. When large sets of samples are obtained from public repositories it is often unmanageable to associate samples into condition-specific groups, and combining samples from various conditions has a negative effect on network size. A fixed significance threshold is often applied also limiting the size of the final network. Therefore, we propose pre-clustering of input expression samples to approximate condition-specific grouping of samples and individual network construction of each group as a means for dynamic significance thresholding. The net effect is increase sensitivity thus maximizing the total co-expression relationships in the final co-expression network compendium. A total of 86 Arabidopsis thaliana co-expression networks were constructed after k-means partitioning of 7,105 publicly available ATH1 Affymetrix microarray samples. We term each pre-sorted network a Gene Interaction Layer (GIL). Random Matrix Theory (RMT), an un-supervised thresholding method, was used to threshold each of the 86 networks independently, effectively providing a dynamic (non-global) threshold for the network. The overall gene count across all GILs reached 19,588 genes (94.7% measured gene coverage) and 558,022 unique co-expression relationships. In comparison, network construction without pre-sorting of input samples yielded only 3,297 genes (15.9%) and 129,134 relationships. in the global network. Here we show that pre-clustering of microarray samples helps approximate condition-specific networks and allows for dynamic thresholding using un-supervised methods. Because RMT ensures only highly significant interactions are kept, the GIL compendium consists of 558,022 unique high quality A. thaliana co-expression relationships across almost all of the measurable genes on the ATH1 array. For A. thaliana, these networks represent the largest compendium to date of significant gene co-expression relationships, and are a means to explore complex pathway, polygenic, and pleiotropic relationships for this focal model plant. The networks can be explored at sysbio.genome.clemson.edu. Finally, this method is applicable to any large expression profile collection for any organism and is best suited where a knowledge-independent network construction method is desired.

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study

PubMed Central

2013-01-01

Background In genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues. When large sets of samples are obtained from public repositories it is often unmanageable to associate samples into condition-specific groups, and combining samples from various conditions has a negative effect on network size. A fixed significance threshold is often applied also limiting the size of the final network. Therefore, we propose pre-clustering of input expression samples to approximate condition-specific grouping of samples and individual network construction of each group as a means for dynamic significance thresholding. The net effect is increase sensitivity thus maximizing the total co-expression relationships in the final co-expression network compendium. Results A total of 86 Arabidopsis thaliana co-expression networks were constructed after k-means partitioning of 7,105 publicly available ATH1 Affymetrix microarray samples. We term each pre-sorted network a Gene Interaction Layer (GIL). Random Matrix Theory (RMT), an un-supervised thresholding method, was used to threshold each of the 86 networks independently, effectively providing a dynamic (non-global) threshold for the network. The overall gene count across all GILs reached 19,588 genes (94.7% measured gene coverage) and 558,022 unique co-expression relationships. In comparison, network construction without pre-sorting of input samples yielded only 3,297 genes (15.9%) and 129,134 relationships. in the global network. Conclusions Here we show that pre-clustering of microarray samples helps approximate condition-specific networks and allows for dynamic thresholding using un-supervised methods. Because RMT ensures only highly significant interactions are kept, the GIL compendium consists of 558,022 unique high quality A. thaliana co-expression relationships across almost all of the measurable genes on the ATH1 array. For A. thaliana, these networks represent the largest compendium to date of significant gene co-expression relationships, and are a means to explore complex pathway, polygenic, and pleiotropic relationships for this focal model plant. The networks can be explored at sysbio.genome.clemson.edu. Finally, this method is applicable to any large expression profile collection for any organism and is best suited where a knowledge-independent network construction method is desired. PMID:23738693

Interaction of cellular and network mechanisms for efficient pheromone coding in moths.

PubMed

Belmabrouk, Hana; Nowotny, Thomas; Rospars, Jean-Pierre; Martinez, Dominique

2011-12-06

Sensory systems, both in the living and in machines, have to be optimized with respect to their environmental conditions. The pheromone subsystem of the olfactory system of moths is a particularly well-defined example in which rapid variations of odor content in turbulent plumes require fast, concentration-invariant neural representations. It is not clear how cellular and network mechanisms in the moth antennal lobe contribute to coding efficiency. Using computational modeling, we show that intrinsic potassium currents (I(A) and I(SK)) in projection neurons may combine with extrinsic inhibition from local interneurons to implement a dual latency code for both pheromone identity and intensity. The mean latency reflects stimulus intensity, whereas latency differences carry concentration-invariant information about stimulus identity. In accordance with physiological results, the projection neurons exhibit a multiphasic response of inhibition-excitation-inhibition. Together with synaptic inhibition, intrinsic currents I(A) and I(SK) account for the first and second inhibitory phases and contribute to a rapid encoding of pheromone information. The first inhibition plays the role of a reset to limit variability in the time to first spike. The second inhibition prevents responses of excessive duration to allow tracking of intermittent stimuli.

A smart indoor air quality sensor network

NASA Astrophysics Data System (ADS)

Wen, Jin

2006-03-01

The indoor air quality (IAQ) has an important impact on public health. Currently, the indoor air pollution, caused by gas, particle, and bio-aerosol pollutants, is considered as the top five environmental risks to public health and has an estimated cost of $2 billion/year due to medical cost and lost productivity. Furthermore, current buildings are especially vulnerable for chemical and biological warfare (CBW) agent contamination because the central air conditioning and ventilation system serve as a nature carrier to spread the released agent from one location to the whole indoor environment within a short time period. To assure the IAQ and safety for either new or existing buildings, real time comprehensive IAQ and CBW measurements are needed. With the development of new sensing technologies, economic and reliable comprehensive IAQ and CBW sensors become promising. However, few studies exist that examine the design and evaluation issues related to IAQ and CBW sensor network. In this paper, relevant research areas including IAQ and CBW sensor development, demand control ventilation, indoor CBW sensor system design, and sensor system design for other areas such as water system protection, fault detection and diagnosis, are reviewed and summarized. Potential research opportunities for IAQ and CBW sensor system design and evaluation are discussed.

The medial frontal cortex contributes to but does not organize rat exploratory behavior.

PubMed

Blankenship, Philip A; Stuebing, Sarah L; Winter, Shawn S; Cheatwood, Joseph L; Benson, James D; Whishaw, Ian Q; Wallace, Douglas G

2016-11-12

Animals use multiple strategies to maintain spatial orientation. Dead reckoning is a form of spatial navigation that depends on self-movement cue processing. During dead reckoning, the generation of self-movement cues from a starting position to an animal's current position allow for the estimation of direction and distance to the position movement originated. A network of brain structures has been implicated in dead reckoning. Recent work has provided evidence that the medial frontal cortex may contribute to dead reckoning in this network of brain structures. The current study investigated the organization of rat exploratory behavior subsequent to medial frontal cortex aspiration lesions under light and dark conditions. Disruptions in exploratory behavior associated with medial frontal lesions were consistent with impaired motor coordination, response inhibition, or egocentric reference frame. These processes are necessary for spatial orientation; however, they are not sufficient for self-movement cue processing. Therefore it is possible that the medial frontal cortex provides processing resources that support dead reckoning in other brain structures but does not of itself compute the kinematic details of dead reckoning. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Genome-wide profiling of 24 hr diel rhythmicity in the water flea, Daphnia pulex: network analysis reveals rhythmic gene expression and enhances functional gene annotation.

PubMed

Rund, Samuel S C; Yoo, Boyoung; Alam, Camille; Green, Taryn; Stephens, Melissa T; Zeng, Erliang; George, Gary F; Sheppard, Aaron D; Duffield, Giles E; Milenković, Tijana; Pfrender, Michael E

2016-08-18

Marine and freshwater zooplankton exhibit daily rhythmic patterns of behavior and physiology which may be regulated directly by the light:dark (LD) cycle and/or a molecular circadian clock. One of the best-studied zooplankton taxa, the freshwater crustacean Daphnia, has a 24 h diel vertical migration (DVM) behavior whereby the organism travels up and down through the water column daily. DVM plays a critical role in resource tracking and the behavioral avoidance of predators and damaging ultraviolet radiation. However, there is little information at the transcriptional level linking the expression patterns of genes to the rhythmic physiology/behavior of Daphnia. Here we analyzed genome-wide temporal transcriptional patterns from Daphnia pulex collected over a 44 h time period under a 12:12 LD cycle (diel) conditions using a cosine-fitting algorithm. We used a comprehensive network modeling and analysis approach to identify novel co-regulated rhythmic genes that have similar network topological properties and functional annotations as rhythmic genes identified by the cosine-fitting analyses. Furthermore, we used the network approach to predict with high accuracy novel gene-function associations, thus enhancing current functional annotations available for genes in this ecologically relevant model species. Our results reveal that genes in many functional groupings exhibit 24 h rhythms in their expression patterns under diel conditions. We highlight the rhythmic expression of immunity, oxidative detoxification, and sensory process genes. We discuss differences in the chronobiology of D. pulex from other well-characterized terrestrial arthropods. This research adds to a growing body of literature suggesting the genetic mechanisms governing rhythmicity in crustaceans may be divergent from other arthropod lineages including insects. Lastly, these results highlight the power of using a network analysis approach to identify differential gene expression and provide novel functional annotation.

A Probabilistic Approach to Network Event Formation from Pre-Processed Waveform Data

NASA Astrophysics Data System (ADS)

Kohl, B. C.; Given, J.

2017-12-01

The current state of the art for seismic event detection still largely depends on signal detection at individual sensor stations, including picking accurate arrivals times and correctly identifying phases, and relying on fusion algorithms to associate individual signal detections to form event hypotheses. But increasing computational capability has enabled progress toward the objective of fully utilizing body-wave recordings in an integrated manner to detect events without the necessity of previously recorded ground truth events. In 2011-2012 Leidos (then SAIC) operated a seismic network to monitor activity associated with geothermal field operations in western Nevada. We developed a new association approach for detecting and quantifying events by probabilistically combining pre-processed waveform data to deal with noisy data and clutter at local distance ranges. The ProbDet algorithm maps continuous waveform data into continuous conditional probability traces using a source model (e.g. Brune earthquake or Mueller-Murphy explosion) to map frequency content and an attenuation model to map amplitudes. Event detection and classification is accomplished by combining the conditional probabilities from the entire network using a Bayesian formulation. This approach was successful in producing a high-Pd, low-Pfa automated bulletin for a local network and preliminary tests with regional and teleseismic data show that it has promise for global seismic and nuclear monitoring applications. The approach highlights several features that we believe are essential to achieving low-threshold automated event detection: Minimizes the utilization of individual seismic phase detections - in traditional techniques, errors in signal detection, timing, feature measurement and initial phase ID compound and propagate into errors in event formation, Has a formalized framework that utilizes information from non-detecting stations, Has a formalized framework that utilizes source information, in particular the spectral characteristics of events of interest, Is entirely model-based, i.e. does not rely on a priori's - particularly important for nuclear monitoring, Does not rely on individualized signal detection thresholds - it's the network solution that matters.

Transcranial Direct Current Stimulation Modulates Neuronal Networks in Attention Deficit Hyperactivity Disorder.

PubMed

Sotnikova, Anna; Soff, Cornelia; Tagliazucchi, Enzo; Becker, Katja; Siniatchkin, Michael

2017-09-01

Anodal transcranial direct current stimulation (tDCS) of the prefrontal cortex has been repeatedly shown to improve working memory (WM). Since patients with attention deficit hyperactivity disorder (ADHD) are characterized by both underactivation of the prefrontal cortex and deficits in WM, the modulation of prefrontal activity with tDCS in ADHD patients may increase their WM performance as well as improve the activation and connectivity of the WM network. In the present study, this hypothesis was tested using a double-blind sham-controlled experimental design. After randomization, sixteen adolescents with ADHD underwent either anodal tDCS over the left dorsolateral prefrontal cortex (DLPFC, 1 mA, 20 min) or sham stimulation with simultaneous fMRI during n-back WM task. Both in one-back and two-back conditions, tDCS led to a greater activation (compared with sham stimulation) of the left DLPFC (under the electrode), left premotor cortex, left supplementary motor cortex, and precuneus. The effects of tDCS were long-lasting and influenced resting state functional connectivity even 20 min after the stimulation, with patterns of strengthened DLPFC connectivity after tDCS outlining the WM network. In summary, anodal tDCS caused increased neuronal activation and connectivity, not only in the brain area under the stimulating electrode (i.e. left DLPFC) but also in other, more remote brain regions. Because of moderate behavioral effects of tDCS, the significance of this technique for ADHD treatment has to be investigated in further studies.

Neurocircuit function in eating disorders.

PubMed

Friederich, Hans-Christoph; Wu, Mudan; Simon, Joe J; Herzog, Wolfgang

2013-07-01

Eating disorders are serious psychosomatic disorders with high morbidity and lifetime mortality. Inadequate response to current therapeutic interventions constitutes a challenging clinical problem. A better understanding of the underlying neurobiological mechanisms could improve psychotherapeutic and drug treatment strategies. A review highlighting the current state of brain imaging in eating disorders related to the anxiety and pathological fear learning model of anorexia nervosa (AN) and the impulsivity model of binge eating in bulimia nervosa (BN). Available neuroimaging studies in patients with acute AN primarily suggest a hyper-responsive emotional and fear network to food, but not necessarily to eating disorder-unrelated, salient stimuli. Furthermore, patients with AN show decreased activation in the ventral fronto-striatal circuits during the performance of a cognitive flexibility task. Results in patients with BN primarily suggest a hypo-responsive reward system to food stimuli, especially to taste reward. Additionally, patients with BN exhibit impaired brain activation in the inhibitory control network during the performance of general response-inhibition tasks. Anxiety and pathological fear learning may lead to conditioned neural stimulus-response patterns to food stimuli and increased cognitive rigidity, which could account for the phobic avoidance of food intake in patients with acute AN. However, further neurobiological studies are required to investigate pathological fear learning in patients with AN. Patients with BN may binge eat to compensate for a hypo-responsive reward system. The impaired brain activation in the inhibitory control network may facilitate the loss of control over food intake in patients with BN. Copyright © 2013 Wiley Periodicals, Inc.

Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill

NASA Astrophysics Data System (ADS)

Barrios, José Angel; Torres-Alvarado, Miguel; Cavazos, Alberto; Leduc, Luis

2011-10-01

In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker entry temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.

New Insights into Mechanism of Surface Reactions of ZnO Nanorods During Electrons Beam Irradiation.

PubMed

Cho, Youngseung; Ji, Hyunjin; Kim, Hyoungsub; Yoon, Jinsuop; Choi, Byoungdeog

2018-09-01

This study provides new insight into mechanisms of ionic reactions on the surface of ZnO nanorod networks, which could result in enhanced performance in optical or molecular sensors. The current- voltage characteristics of ZnO nanorod network devices exhibit typical nonlinear behavior in air, which implies the formation of a Schottky barrier when metals are used as contacts. The conductance of the device increased significantly in vacuum, which can be explained by the desorption of hydroxyl groups at very low pressure. While physisorbed water or oxygen-related ions can detach from the ZnO surface during evacuation, exposure to high energy in the electron beam is believed to detach the chemisorbed anions of O- and O-2 from the surface of ZnO nanorods, which releases more electrons into the channel. The increase in available electrons enhances the conductance of the ZnO nanorods. Slow initialization of the conductance under ambient conditions indicates that the ionic re-adsorption is inactive under these conditions. Thus, the electron irradiation process can be used to reset the surface ionic molecules on metal oxide nano-structures by tuning the surface potential prior to the passivation process.

HTTP-based remote operational options for the Vacuum Tower Telescope, Tenerife

NASA Astrophysics Data System (ADS)

Staiger, J.

2012-09-01

We are currently developing network based tools for the Vacuum Tower Telescope (VTT), Tenerife which will allow to operate the telescope together with the newly developed 2D-spectrometer HELLRIDE under remote control conditions. The computational configuration can be viewed as a distributed system linking hardware components of various functionality from different locations. We have developed a communication protocol which is basically an extension of the HTTP standard. It will serve as a carrier for command- and data-transfers. The server-client software is based on Berkley-Unix sockets in a C++ programming environment. A customized CMS will allow to create browser accessible information on-the-fly. Java-based applet pages have been tested as optional user access GUI's. An access tool has been implemented to download near-realtime, web-based target information from NASA/SDO. Latency tests have been carried out at the VTT and the Swedish STT at La Palma for concept verification. Short response times indicate that under favorable network conditions remote interactive telescope handling may be possible. The scientific focus of possible future remote operations will be set on the helioseismology of the solar atmosphere, the monitoring of flares and the footpoint analysis of coronal loops and chromospheric events.

Identification of Load Categories in Rotor System Based on Vibration Analysis

PubMed Central

Yang, Zhaojian

2017-01-01

Rotating machinery is often subjected to variable loads during operation. Thus, monitoring and identifying different load types is important. Here, five typical load types have been qualitatively studied for a rotor system. A novel load category identification method for rotor system based on vibration signals is proposed. This method is a combination of ensemble empirical mode decomposition (EEMD), energy feature extraction, and back propagation (BP) neural network. A dedicated load identification test bench for rotor system was developed. According to loads characteristics and test conditions, an experimental plan was formulated, and loading tests for five loads were conducted. Corresponding vibration signals of the rotor system were collected for each load condition via eddy current displacement sensor. Signals were reconstructed using EEMD, and then features were extracted followed by energy calculations. Finally, characteristics were input to the BP neural network, to identify different load types. Comparison and analysis of identifying data and test data revealed a general identification rate of 94.54%, achieving high identification accuracy and good robustness. This shows that the proposed method is feasible. Due to reliable and experimentally validated theoretical results, this method can be applied to load identification and fault diagnosis for rotor equipment used in engineering applications. PMID:28726754

A Wireless Sensor System for Real-Time Monitoring and Fault Detection of Motor Arrays

PubMed Central

Medina-García, Jonathan; Sánchez-Rodríguez, Trinidad; Galán, Juan Antonio Gómez; Delgado, Aránzazu; Gómez-Bravo, Fernando; Jiménez, Raúl

2017-01-01

This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system. PMID:28245623

Graph theory in brain-to-brain connectivity: A simulation study and an application to an EEG hyperscanning experiment.

PubMed

Toppi, J; Ciaramidaro, A; Vogel, P; Mattia, D; Babiloni, F; Siniatchkin, M; Astolfi, L

2015-08-01

Hyperscanning consists in the simultaneous recording of hemodynamic or neuroelectrical signals from two or more subjects acting in a social context. Well-established methodologies for connectivity estimation have already been adapted to hyperscanning purposes. The extension of graph theory approach to multi-subjects case is still a challenging issue. In the present work we aim to test the ability of the currently used graph theory global indices in describing the properties of a network given by two interacting subjects. The testing was conducted first on surrogate brain-to-brain networks reproducing typical social scenarios and then on real EEG hyperscanning data recorded during a Joint Action task. The results of the simulation study highlighted the ability of all the investigated indexes in modulating their values according to the level of interaction between subjects. However, only global efficiency and path length indexes demonstrated to be sensitive to an asymmetry in the communication between the two subjects. Such results were, then, confirmed by the application on real EEG data. Global efficiency modulated, in fact, their values according to the inter-brain density, assuming higher values in the social condition with respect to the non-social condition.

A Wireless Sensor System for Real-Time Monitoring and Fault Detection of Motor Arrays.

PubMed

Medina-García, Jonathan; Sánchez-Rodríguez, Trinidad; Galán, Juan Antonio Gómez; Delgado, Aránzazu; Gómez-Bravo, Fernando; Jiménez, Raúl

2017-02-25

This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system.

[Attentional impairment in children with attention deficit and hyperactivity disorder].

PubMed

Abbes, Zeineb; Bouden, Asma; Amado, Isabelle; Chantal Bourdel, Marie; Tabbane, Karim; Béchir Halayem, Mohamed

2009-10-01

Attention-deficit hyperactivity disorder (ADHD) is a heterogeneous disorder currently defined by clinical history and behavioral report of impairment. The Attention Network test (ANT) gives measures of different aspects of the complex process of attention. We ask if children with Attention Deficit Hyperactivity Disorder (ADHD) will show a characteristic pattern of deficits on this test. The sample included 40 children (M=9 years) who performed the "Attention network test". Children with an ADHD diagnosis (N=20) were compared to a control group (N=20). The group of children with ADHD showed slower reaction times in all conditions (mean RT=866 ms; SD=234,063). Children with ADHD showed a significant impairment in their executive control system compared to healthy subjects, with slower reaction times in incongruent conditions and lower accuracy scores (RT=1064 ms; F(1.38) p=0.02). Our results showed that spatial orienting and alerting in ADHD was no different than controls (p=0,68). ADHD group showed a greater variable response (p=0,0001). The present study showed that impairment in executive control system and variability measures are the characteristic pattern of deficits in children with ADHD.

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

PubMed Central

Hu, Xintao; Zhu, Dajiang; Lv, Peili; Li, Kaiming; Han, Junwei; Wang, Lihong; Shen, Dinggang; Guo, Lei; Liu, Tianming

2014-01-01

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures. PMID:23319242

Closeness Possible through Computer Networking.

ERIC Educational Resources Information Center

Dodd, Julie E.

1989-01-01

Points out the benefits of computer networking for scholastic journalism. Discusses three systems currently offering networking possibilities for publications: the Student Press Information Network; the Youth Communication Service; and the Dow Jones Newspaper Fund's electronic mail system. (MS)

State feedback controller design for the synchronization of Boolean networks with time delays

NASA Astrophysics Data System (ADS)

Li, Fangfei; Li, Jianning; Shen, Lijuan

2018-01-01

State feedback control design to make the response Boolean network synchronize with the drive Boolean network is far from being solved in the literature. Motivated by this, this paper studies the feedback control design for the complete synchronization of two coupled Boolean networks with time delays. A necessary condition for the existence of a state feedback controller is derived first. Then the feedback control design procedure for the complete synchronization of two coupled Boolean networks is provided based on the necessary condition. Finally, an example is given to illustrate the proposed design procedure.