Characterization of the Decision Network for Wing Expansion in Drosophila Using Targeted Expression of the TRPM8 Channel

PubMed Central

Peabody, Nathan C.; Pohl, Jascha B.; Diao, Fengqiu; Vreede, Andrew P.; Sandstrom, David J.; Wang, Howard; Zelensky, Paul K.; White, Benjamin H.

2009-01-01

After emergence, adult flies and other insects select a suitable perch and expand their wings. Wing expansion is governed by the hormone bursicon and can be delayed under adverse environmental conditions. How environmental factors delay bursicon release and alter perch selection and expansion behaviors has not been investigated in detail. Here we provide evidence that in Drosophila the motor programs underlying perch selection and wing expansion have different environmental dependencies. Using physical manipulations, we demonstrate that the decision to perch is based primarily on environmental valuations and is incrementally delayed under conditions of increasing perturbation and confinement. In contrast, the all-or-none motor patterns underlying wing expansion are relatively invariant in length regardless of environmental conditions. Using a novel technique for targeted activation of neurons, we show that the highly stereotyped wing expansion motor patterns can be initiated by stimulation of NCCAP, a small network of central neurons that regulates the release of bursicon. Activation of this network using the cold-sensitive rat TRPM8 channel is sufficient to trigger all essential behavioral and somatic processes required for wing expansion. The delay of wing expansion under adverse circumstances thus couples an environmentally-sensitive decision network to a command-like network that initiates a fixed action pattern. Because NCCAP mediates environmentally-insensitive ecdysis-related behaviors in Drosophila development prior to adult emergence, the study of wing expansion promises insights not only into how networks mediate behavioral choices, but also into how decision networks develop. PMID:19295141

Geospatial Data Fusion and Multigroup Decision Support for Surface Water Quality Management

NASA Astrophysics Data System (ADS)

Sun, A. Y.; Osidele, O.; Green, R. T.; Xie, H.

2010-12-01

Social networking and social media have gained significant popularity and brought fundamental changes to many facets of our everyday life. With the ever-increasing adoption of GPS-enabled gadgets and technology, location-based content is likely to play a central role in social networking sites. While location-based content is not new to the geoscience community, where geographic information systems (GIS) are extensively used, the delivery of useful geospatial data to targeted user groups for decision support is new. Decision makers and modelers ought to make more effective use of the new web-based tools to expand the scope of environmental awareness education, public outreach, and stakeholder interaction. Environmental decision processes are often rife with uncertainty and controversy, requiring integration of multiple sources of information and compromises between diverse interests. Fusing of multisource, multiscale environmental data for multigroup decision support is a challenging task. Toward this goal, a multigroup decision support platform should strive to achieve transparency, impartiality, and timely synthesis of information. The latter criterion often constitutes a major technical bottleneck to traditional GIS-based media, featuring large file or image sizes and requiring special processing before web deployment. Many tools and design patterns have appeared in recent years to ease the situation somewhat. In this project, we explore the use of Web 2.0 technologies for “pushing” location-based content to multigroups involved in surface water quality management and decision making. In particular, our granular bottom-up approach facilitates effective delivery of information to most relevant user groups. Our location-based content includes in-situ and remotely sensed data disseminated by NASA and other national and local agencies. Our project is demonstrated for managing the total maximum daily load (TMDL) program in the Arroyo Colorado coastal river basin in Texas. The overall design focuses on assigning spatial information to decision support elements and on efficiently using Web 2.0 technologies to relay scientific information to the nonscientific community. We conclude that (i) social networking, if appropriately used, has great potential for mitigating difficulty associated with multigroup decision making; (ii) all potential stakeholder groups should be involved in creating a useful decision support system; and (iii) environmental decision support systems should be considered a must-have, instead of an optional component of TMDL decision support projects. Acknowledgment: This project was supported by NASA grant NNX09AR63G.

Evolving neural networks for strategic decision-making problems.

PubMed

Kohl, Nate; Miikkulainen, Risto

2009-04-01

Evolution of neural networks, or neuroevolution, has been a successful approach to many low-level control problems such as pole balancing, vehicle control, and collision warning. However, certain types of problems-such as those involving strategic decision-making-have remained difficult for neuroevolution to solve. This paper evaluates the hypothesis that such problems are difficult because they are fractured: The correct action varies discontinuously as the agent moves from state to state. A method for measuring fracture using the concept of function variation is proposed and, based on this concept, two methods for dealing with fracture are examined: neurons with local receptive fields, and refinement based on a cascaded network architecture. Experiments in several benchmark domains are performed to evaluate how different levels of fracture affect the performance of neuroevolution methods, demonstrating that these two modifications improve performance significantly. These results form a promising starting point for expanding neuroevolution to strategic tasks.

Jimena: efficient computing and system state identification for genetic regulatory networks.

PubMed

Karl, Stefan; Dandekar, Thomas

2013-10-11

Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones). Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.

Lambda network having 2.sup.m-1 nodes in each of m stages with each node coupled to four other nodes for bidirectional routing of data packets between nodes

DOEpatents

Napolitano, Jr., Leonard M.

1995-01-01

The Lambda network is a single stage, packet-switched interprocessor communication network for a distributed memory, parallel processor computer. Its design arises from the desired network characteristics of minimizing mean and maximum packet transfer time, local routing, expandability, deadlock avoidance, and fault tolerance. The network is based on fixed degree nodes and has mean and maximum packet transfer distances where n is the number of processors. The routing method is detailed, as are methods for expandability, deadlock avoidance, and fault tolerance.

The challenge of sustaining effectiveness over time: the case of the global network to stop tuberculosis

PubMed Central

Quissell, Kathryn; Walt, Gill

2016-01-01

Where once global health decisions were largely the domain of national governments and the World Health Organization, today networks of international organizations, governments, private philanthropies and other entities are actively shaping public policy. However, there is still limited understanding of how global networks form, how they create institutions, how they promote and sustain collective action, and how they adapt to changes in the policy environment. Understanding these processes is crucial to understanding their effectiveness: whether and how global networks influence policy and public health outcomes. This study seeks to address these gaps through the examination of the global network to stop tuberculosis (TB) and the factors influencing its effectiveness over time. Drawing from ∼200 document sources and 16 interviews with key informants, we trace the development of the Global Partnership to Stop TB and its work over the past decade. We find that having a centralized core group and a strategic brand helped the network to coalesce around a primary intervention strategy, directly observed treatment short course. This strategy was created before the network was formalized, and helped bring in donors, ministries of health and other organizations committed to fighting TB—growing the network. Adaptations to this strategy, the creation of a consensus-based Global Plan, and the creation of a variety of participatory venues for discussion, helped to expand and sustain the network. Presently, however, tensions have become more apparent within the network as it struggles with changing internal political dynamics and the evolution of the disease. While centralization and stability helped to launch and grow the network, the institutionalization of governance and strategy may have constrained adaptation. Institutionalization and centralization may, therefore, facilitate short-term success for networks, but may end up complicating longer-term effectiveness. PMID:26282859

78 FR 2571 - Special Access for Price Cap Local Exchange Carriers; AT&T Corporation Petition for Rulemaking To...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-11

...., observed sales and purchases). This allows for an analysis that controls for factors that may vary widely... those factors that affect providers' decisions to expand existing networks, e.g., the non-price factors... Street SW., Room CY-A257, Washington, DC 20554. The complete text may be purchased from Best Copy and...

Activating clinical trials: a process improvement approach.

PubMed

Martinez, Diego A; Tsalatsanis, Athanasios; Yalcin, Ali; Zayas-Castro, José L; Djulbegovic, Benjamin

2016-02-24

The administrative process associated with clinical trial activation has been criticized as costly, complex, and time-consuming. Prior research has concentrated on identifying administrative barriers and proposing various solutions to reduce activation time, and consequently associated costs. Here, we expand on previous research by incorporating social network analysis and discrete-event simulation to support process improvement decision-making. We searched for all operational data associated with the administrative process of activating industry-sponsored clinical trials at the Office of Clinical Research of the University of South Florida in Tampa, Florida. We limited the search to those trials initiated and activated between July 2011 and June 2012. We described the process using value stream mapping, studied the interactions of the various process participants using social network analysis, and modeled potential process modifications using discrete-event simulation. The administrative process comprised 5 sub-processes, 30 activities, 11 decision points, 5 loops, and 8 participants. The mean activation time was 76.6 days. Rate-limiting sub-processes were those of contract and budget development. Key participants during contract and budget development were the Office of Clinical Research, sponsors, and the principal investigator. Simulation results indicate that slight increments on the number of trials, arriving to the Office of Clinical Research, would increase activation time by 11 %. Also, incrementing the efficiency of contract and budget development would reduce the activation time by 28 %. Finally, better synchronization between contract and budget development would reduce time spent on batching documentation; however, no improvements would be attained in total activation time. The presented process improvement analytic framework not only identifies administrative barriers, but also helps to devise and evaluate potential improvement scenarios. The strength of our framework lies in its system analysis approach that recognizes the stochastic duration of the activation process and the interdependence between process activities and entities.

The influence of management and environment on local health department organizational structure and adaptation: a longitudinal network analysis.

PubMed

Keeling, Jonathan W; Pryde, Julie A; Merrill, Jacqueline A

2013-01-01

The nation's 2862 local health departments (LHDs) are the primary means for assuring public health services for all populations. The objective of this study is to assess the effect of organizational network analysis on management decisions in LHDs and to demonstrate the technique's ability to detect organizational adaptation over time. We conducted a longitudinal network analysis in a full-service LHD with 113 employees serving about 187,000 persons. Network survey data were collected from employees at 3 times: months 0, 8, and 34. At time 1 the initial analysis was presented to LHD managers as an intervention with information on evidence-based management strategies to address the findings. At times 2 and 3 interviews documented managers' decision making and events in the task environment. Response rates for the 3 network analyses were 90%, 97%, and 83%. Postintervention (time 2) results showed beneficial changes in network measures of communication and integration. Screening and case identification increased for chlamydia and for gonorrhea. Outbreak mitigation was accelerated by cross-divisional teaming. Network measurements at time 3 showed LHD adaptation to H1N1 and budget constraints with increased centralization. Task redundancy increased dramatically after National Incident Management System training. Organizational network analysis supports LHD management with empirical evidence that can be translated into strategic decisions about communication, allocation of resources, and addressing knowledge gaps. Specific population health outcomes were traced directly to management decisions based on network evidence. The technique can help managers improve how LHDs function as organizations and contribute to our understanding of public health systems.

Establishing a regional network of academic centers to support decision making for new vaccine introduction in Latin America and the Caribbean: the ProVac experience.

PubMed

Toscano, C M; Jauregui, B; Janusz, C B; Sinha, A; Clark, A D; Sanderson, C; Resch, S; Ruiz Matus, C; Andrus, J K

2013-07-02

The Pan American Health Organization's ProVac Initiative, designed to strengthen national decision making regarding the introduction of new vaccines, was initiated in 2004. Central to realizing ProVac's vision of regional capacity building, the ProVac Network of Centers of Excellence (CoEs) was established in 2010 to provide research support to the ProVac Initiative, leveraging existing capacity at Latin American and Caribbean (LAC) universities. We describe the process of establishing the ProVac Network of CoEs and its initial outcomes and challenges. A survey was sent to academic, not-for-profit institutions in LAC that had recently published work in the areas of clinical decision sciences and health economic analysis. Centers invited to join the Network were selected by an international committee on the basis of the survey results. Selection criteria included academic productivity in immunization-related work, team size and expertise, successful collaboration with governmental agencies and international organizations, and experience in training and education. The Network currently includes five academic institutions across LAC. Through open dialog and negotiation, specific projects were assigned to centers according to their areas of expertise. Collaboration among centers was highly encouraged. Faculty from ProVac's technical partners were assigned as focal points for each project. The resulting work led to the development and piloting of tools, methodological guides, and training materials that support countries in assessing existing evidence and generating new evidence on vaccine introduction. The evidence generated is shared with country-level decision makers and the scientific community. As the ProVac Initiative expands to other regions of the world with support from immunization and public health partners, the establishment of other regional and global networks of CoEs will be critical. The experience of LAC in creating the current network could benefit the formation of similar structures that support evidence-based decisions regarding new public health interventions. Copyright © 2013 Elsevier Ltd. All rights reserved.

[Audiovisual telecommunication by multimedia technology in HNO medicine. ISDN--internet--ATM].

PubMed

Plinkert, P K; Plinkert, B; Kurek, R; Zenner, H P

2000-11-01

Telemedicine includes all medical activities in diagnosis, therapeutics, or social medicine undertaken by means of an electronic transfer medium, enabling the transmission of visual and acoustic information over long distances to doctors not personally present at the place of the requested consultation. Most experience with telemedicine applications has been gained in the field of diagnosis (teleconsultation, teleradiology, telepathology) and is expanding to quality control and quality assurance. Decisive for each form of application is its availability, practicability, cost, safety, and especially quality of audiovisual transmission. For telesurgical applications, particularly the use of minimally invasive techniques in otorhinolaryngology, head, and neck surgery, the high quality transmission of audiovisual data in real time is necessary. Rapid expansion and further developments in transmission technologies and networks in the last decade have created several technologies with increased quality and costs. In this paper, we tested different transmission media for audiovisual telecommunication--integrated services digital network (ISDN), Internet, and asynchronous transfer mode (ATM)--using real time video transmission of typical operations in otorhinolaryngology. Their applications, costs, and future perspectives are discussed.

From Thoughts To Action - Linking Practice, Science, Policy And Decision Making: Dissemination Activities Of The Global Risk Forum, GRF Davos

NASA Astrophysics Data System (ADS)

Stal, Marc; Sutter, Corina; Ammann, Walter

2010-05-01

The world's growing population in combination with expanding urbanisation, globalisation and climate change has greatly aggravated the risk potential to all communities and nations. These increasing risks imply the intensification of worldwide disasters, hence collaborations and worldwide knowledge exchange to mitigate these negative impacts is mandatory. How can these exchange and collaboration activities take place? The Global Risk Forum, GRF Davos addresses the variety of risks that face communities with a special focus on climate change, natural hazards, environmental degradation as well as technical, biological risks, pandemics and terrorism - all across different political institutions, national and international organisations, countries and business sectors. One of GRF's main goals is to bridge the gap between science and practice and to promote and accelerate the worldwide exchange of know-how and experience. GRF Davos aims at targeting solutions and promoting good practice in integral risk management and climate change adaptation.. The Forum also provides and manages a network for decision-makers, practitioners and experts from politics, government, IGOs, business, science, NGOs, media and the public and works on maintaining and expanding these networks constantly to enable the dissemination of disaster and risk reduction techniques. In order to link practice, science, policy and decision making, GRF Davos has three pillars, the Risk Academy, the International Disaster and Risk Conferences and Workshops (IDRC) as well as the online Platform for Networks. With its pillars, the GRFs aims at reducing vulnerability for all types of risks and disasters to protect life, property, environment, critical infrastructure and all means of business for the worldwide community on a sustainable basis.

Lambda network having 2{sup m{minus}1} nodes in each of m stages with each node coupled to four other nodes for bidirectional routing of data packets between nodes

DOEpatents

Napolitano, L.M. Jr.

1995-11-28

The Lambda network is a single stage, packet-switched interprocessor communication network for a distributed memory, parallel processor computer. Its design arises from the desired network characteristics of minimizing mean and maximum packet transfer time, local routing, expandability, deadlock avoidance, and fault tolerance. The network is based on fixed degree nodes and has mean and maximum packet transfer distances where n is the number of processors. The routing method is detailed, as are methods for expandability, deadlock avoidance, and fault tolerance. 14 figs.

Dynamic modeling and optimization for space logistics using time-expanded networks

NASA Astrophysics Data System (ADS)

Ho, Koki; de Weck, Olivier L.; Hoffman, Jeffrey A.; Shishko, Robert

2014-12-01

This research develops a dynamic logistics network formulation for lifecycle optimization of mission sequences as a system-level integrated method to find an optimal combination of technologies to be used at each stage of the campaign. This formulation can find the optimal transportation architecture considering its technology trades over time. The proposed methodologies are inspired by the ground logistics analysis techniques based on linear programming network optimization. Particularly, the time-expanded network and its extension are developed for dynamic space logistics network optimization trading the quality of the solution with the computational load. In this paper, the methodologies are applied to a human Mars exploration architecture design problem. The results reveal multiple dynamic system-level trades over time and give recommendation of the optimal strategy for the human Mars exploration architecture. The considered trades include those between In-Situ Resource Utilization (ISRU) and propulsion technologies as well as the orbit and depot location selections over time. This research serves as a precursor for eventual permanent settlement and colonization of other planets by humans and us becoming a multi-planet species.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

PubMed

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Decision time, slow inhibition, and theta rhythm.

PubMed

Smerieri, Anteo; Rolls, Edmund T; Feng, Jianfeng

2010-10-20

In this paper, we examine decision making in a spiking neuronal network and show that longer time constants for the inhibitory neurons can decrease the reaction times and produce theta rhythm. We analyze the mechanism and find that the spontaneous firing rate before the decision cues are applied can drift, and thereby influence the speed of the reaction time when the decision cues are applied. The drift of the firing rate in the population that will win the competition is larger if the time constant of the inhibitory interneurons is increased from 10 to 33 ms, and even larger if there are two populations of inhibitory neurons with time constants of 10 and 100 ms. Of considerable interest is that the decision that will be made can be influenced by the noise-influenced drift of the spontaneous firing rate over many seconds before the decision cues are applied. The theta rhythm associated with the longer time constant networks mirrors the greater integration in the firing rate drift produced by the recurrent connections over long time periods in the networks with slow inhibition. The mechanism for the effect of slow waves in the theta and delta range on decision times is suggested to be increased neuronal spiking produced by depolarization of the membrane potential on the positive part of the slow waves when the neuron's membrane potential is close to the firing threshold.

Minimizing species extinctions through strategic planning for conservation fencing.

PubMed

Ringma, Jeremy L; Wintle, Brendan; Fuller, Richard A; Fisher, Diana; Bode, Michael

2017-10-01

Conservation fences are an increasingly common management action, particularly for species threatened by invasive predators. However, unlike many conservation actions, fence networks are expanding in an unsystematic manner, generally as a reaction to local funding opportunities or threats. We conducted a gap analysis of Australia's large predator-exclusion fence network by examining translocation of Australian mammals relative to their extinction risk. To address gaps identified in species representation, we devised a systematic prioritization method for expanding the conservation fence network that explicitly incorporated population viability analysis and minimized expected species' extinctions. The approach was applied to New South Wales, Australia, where the state government intends to expand the existing conservation fence network. Existing protection of species in fenced areas was highly uneven; 67% of predator-sensitive species were unrepresented in the fence network. Our systematic prioritization yielded substantial efficiencies in that it reduced expected number of species extinctions up to 17 times more effectively than ad hoc approaches. The outcome illustrates the importance of governance in coordinating management action when multiple projects have similar objectives and rely on systematic methods rather than expanding networks opportunistically. © 2017 Society for Conservation Biology.

Business Associations, Conservative Networks, and the Ongoing Republican War over Medicaid Expansion.

PubMed

Hertel-Fernandez, Alexander; Skocpol, Theda; Lynch, Daniel

2016-04-01

A major component of the Affordable Care Act involves the expansion of state Medicaid programs to cover the uninsured poor. In the wake of the 2012 Supreme Court decision upholding and modifying reform legislation, states can decide whether to expand Medicaid-and twenty states are still not proceeding as of August 2015. What explains state choices about participation in expansion, including governors' decisions to endorse expansion or not as well as final state decisions? We tackle this puzzle, focusing closely on outcomes and battles in predominantly Republican-led states. Like earlier scholars, we find that partisan differences between Democrats and Republicans are central, but we go beyond earlier analyses to measure added effects from two dueling factions within the Republican coalition: statewide business associations and cross-state networks of ideologically conservative organizations. Using both statistical modeling and case studies, we show that GOP-leaning or GOP-dominated states have been most likely to embrace the expansion when organized business support outweighs pressures from conservative networks. Our findings help make sense of ongoing state-level debates over a core part of health reform and shed new light on mounting policy tensions within the Republican Party. Copyright © 2016 by Duke University Press.

Neurodynamics of an election.

PubMed

da Rocha, Armando Freitas; Rocha, Fábio Theoto; Burattini, Marcelo Nascimento; Massad, Eduardo

2010-09-10

Variables influencing decision-making in real settings, as in the case of voting decisions, are uncontrollable and in many times even unknown to the experimenter. In this case, the experimenter has to study the intention to decide (vote) as close as possible in time to the moment of the real decision (election day). Here, we investigated the brain activity associated with the voting intention declared 1 week before the election day of the Brazilian Firearms Control Referendum about prohibiting the commerce of firearms. Two alliances arose in the Congress to run the campaigns for YES (for the prohibition of firearm commerce) and NO (against the prohibition of firearm commerce) voting. Time constraints imposed by the necessity of studying a reasonable number (here, 32) of voters during a very short time (5 days) made the EEG the tool of choice for recording the brain activity associated with voting decision. Recent fMRI and EEG studies have shown decision-making as a process due to the enrollment of defined neuronal networks. In this work, a special EEG technique is applied to study the topology of the voting decision-making networks and is compared to the results of standard ERP procedures. The results show that voting decision-making enrolled networks in charge of calculating the benefits and risks of the decision of prohibiting or allowing firearm commerce and that the topology of such networks was vote- (i.e., YES/NO-) sensitive. 2010 Elsevier B.V. All rights reserved.

The Forbin Paper.

DTIC Science & Technology

1987-07-01

Network for Constructing a Widget and Gizmo 46 14 The Task Network After One Round of Expansion ............. 48 15 The Further Expansion of the MAKE...Widget Task .............. 49 16 The Further Expansion of the MAKE Gizmo Task ............ ... 50 17 Choosing the INSTALL-I METHOD...component of the planner’s knowledge. The task expander implements the 101 Network A MAKE Widget I time=O MAKE Gizmo Network B MAKE Widget time=35 MAKE

Exploring the future with anticipatory networks

NASA Astrophysics Data System (ADS)

Skulimowski, A. M. J.

2013-01-01

This paper presents a theory of anticipatory networks that originates from anticipatory models of consequences in multicriteria decision problems. When making a decision, the decision maker takes into account the anticipated outcomes of each future decision problem linked by the causal relations with the present one. In a network of linked decision problems, the causal relations are defined between time-ordered nodes. The scenarios of future consequences of each decision are modeled by multiple vertices starting from an appropriate node. The network is supplemented by one or more relations of anticipation, or future feedback, which describe a situation where decision makers take into account the anticipated results of some future optimization problems while making their choice. So arises a multigraph of decision problems linked causally and by one or more anticipation relation, termed here the anticipatory network. We will present the properties of anticipatory networks and propose a method of reducing, transforming and using them to solve current decision problems. Furthermore, it will be shown that most anticipatory networks can be regarded as superanticipatory systems, i.e. systems that are anticipatory in the Rosen sense and contain a future model of at least one other anticipatory system. The anticipatory networks can also be applied to filter the set of future scenarios in a foresight exercise.

A recurrent network mechanism of time integration in perceptual decisions.

PubMed

Wong, Kong-Fatt; Wang, Xiao-Jing

2006-01-25

Recent physiological studies using behaving monkeys revealed that, in a two-alternative forced-choice visual motion discrimination task, reaction time was correlated with ramping of spike activity of lateral intraparietal cortical neurons. The ramping activity appears to reflect temporal accumulation, on a timescale of hundreds of milliseconds, of sensory evidence before a decision is reached. To elucidate the cellular and circuit basis of such integration times, we developed and investigated a simplified two-variable version of a biophysically realistic cortical network model of decision making. In this model, slow time integration can be achieved robustly if excitatory reverberation is primarily mediated by NMDA receptors; our model with only fast AMPA receptors at recurrent synapses produces decision times that are not comparable with experimental observations. Moreover, we found two distinct modes of network behavior, in which decision computation by winner-take-all competition is instantiated with or without attractor states for working memory. Decision process is closely linked to the local dynamics, in the "decision space" of the system, in the vicinity of an unstable saddle steady state that separates the basins of attraction for the two alternative choices. This picture provides a rigorous and quantitative explanation for the dependence of performance and response time on the degree of task difficulty, and the reason for which reaction times are longer in error trials than in correct trials as observed in the monkey experiment. Our reduced two-variable neural model offers a simple yet biophysically plausible framework for studying perceptual decision making in general.

Using Social Network Analysis to Investigate Positive EOL Communication.

PubMed

Xu, Jiayun; Yang, Rumei; Wilson, Andrew; Reblin, Maija; Clayton, Margaret F; Ellington, Lee

2018-04-30

End of life (EOL) communication is a complex process involving the whole family and multiple care providers. Applications of analysis techniques that account for communication beyond the patient and patient/provider, will improve clinical understanding of EOL communication. To introduce the use of social network analysis to EOL communication data, and to provide an example of applying social network analysis to home hospice interactions. We provide a description of social network analysis using social network analysis to model communication patterns during home hospice nursing visits. We describe three social network attributes (i.e. magnitude, directionality, and reciprocity) in the expression of positive emotion among hospice nurses, family caregivers, and hospice cancer patients. Differences in communication structure by primary family caregiver gender and across time were also examined. Magnitude (frequency) in the expression of positive emotion occurred most often between nurses and caregivers or nurses and patients. Female caregivers directed more positive emotion to nurses, and nurses directed more positive emotion to other family caregivers when the primary family caregiver was male. Reciprocity (mutuality) in positive emotion declined towards day of death, but increased on day of actual patient death. There was variation in reciprocity by the type of positive emotion expressed. Our example demonstrates that social network analysis can be used to better understand the process of EOL communication. Social network analysis can be expanded to other areas of EOL research, such as EOL decision-making and health care teamwork. Copyright © 2018. Published by Elsevier Inc.

New methodologies for multi-scale time-variant reliability analysis of complex lifeline networks

NASA Astrophysics Data System (ADS)

Kurtz, Nolan Scot

The cost of maintaining existing civil infrastructure is enormous. Since the livelihood of the public depends on such infrastructure, its state must be managed appropriately using quantitative approaches. Practitioners must consider not only which components are most fragile to hazard, e.g. seismicity, storm surge, hurricane winds, etc., but also how they participate on a network level using network analysis. Focusing on particularly damaged components does not necessarily increase network functionality, which is most important to the people that depend on such infrastructure. Several network analyses, e.g. S-RDA, LP-bounds, and crude-MCS, and performance metrics, e.g. disconnection bounds and component importance, are available for such purposes. Since these networks are existing, the time state is also important. If networks are close to chloride sources, deterioration may be a major issue. Information from field inspections may also have large impacts on quantitative models. To address such issues, hazard risk analysis methodologies for deteriorating networks subjected to seismicity, i.e. earthquakes, have been created from analytics. A bridge component model has been constructed for these methodologies. The bridge fragilities, which were constructed from data, required a deeper level of analysis as these were relevant for specific structures. Furthermore, chloride-induced deterioration network effects were investigated. Depending on how mathematical models incorporate new information, many approaches are available, such as Bayesian model updating. To make such procedures more flexible, an adaptive importance sampling scheme was created for structural reliability problems. Additionally, such a method handles many kinds of system and component problems with singular or multiple important regions of the limit state function. These and previously developed analysis methodologies were found to be strongly sensitive to the network size. Special network topologies may be more or less computationally difficult, while the resolution of the network also has large affects. To take advantage of some types of topologies, network hierarchical structures with super-link representation have been used in the literature to increase the computational efficiency by analyzing smaller, densely connected networks; however, such structures were based on user input and subjective at times. To address this, algorithms must be automated and reliable. These hierarchical structures may indicate the structure of the network itself. This risk analysis methodology has been expanded to larger networks using such automated hierarchical structures. Component importance is the most important objective from such network analysis; however, this may only provide the information of which bridges to inspect/repair earliest and little else. High correlations influence such component importance measures in a negative manner. Additionally, a regional approach is not appropriately modelled. To investigate a more regional view, group importance measures based on hierarchical structures have been created. Such structures may also be used to create regional inspection/repair approaches. Using these analytical, quantitative risk approaches, the next generation of decision makers may make both component and regional-based optimal decisions using information from both network function and further effects of infrastructure deterioration.

The challenge of sustaining effectiveness over time: the case of the global network to stop tuberculosis.

PubMed

Quissell, Kathryn; Walt, Gill

2016-04-01

Where once global health decisions were largely the domain of national governments and the World Health Organization, today networks of international organizations, governments, private philanthropies and other entities are actively shaping public policy. However, there is still limited understanding of how global networks form, how they create institutions, how they promote and sustain collective action, and how they adapt to changes in the policy environment. Understanding these processes is crucial to understanding their effectiveness: whether and how global networks influence policy and public health outcomes. This study seeks to address these gaps through the examination of the global network to stop tuberculosis (TB) and the factors influencing its effectiveness over time. Drawing from ∼ 200 document sources and 16 interviews with key informants, we trace the development of the Global Partnership to Stop TB and its work over the past decade. We find that having a centralized core group and a strategic brand helped the network to coalesce around a primary intervention strategy, directly observed treatment short course. This strategy was created before the network was formalized, and helped bring in donors, ministries of health and other organizations committed to fighting TB-growing the network. Adaptations to this strategy, the creation of a consensus-based Global Plan, and the creation of a variety of participatory venues for discussion, helped to expand and sustain the network. Presently, however, tensions have become more apparent within the network as it struggles with changing internal political dynamics and the evolution of the disease. While centralization and stability helped to launch and grow the network, the institutionalization of governance and strategy may have constrained adaptation. Institutionalization and centralization may, therefore, facilitate short-term success for networks, but may end up complicating longer-term effectiveness. © Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2015; all rights reserved.

Rationality Validation of a Layered Decision Model for Network Defense

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

Wei, Huaqiang; Alves-Foss, James; Zhang, Du

2007-08-31

We propose a cost-effective network defense strategy built on three key: three decision layers: security policies, defense strategies, and real-time defense tactics for countering immediate threats. A layered decision model (LDM) can be used to capture this decision process. The LDM helps decision-makers gain insight into the hierarchical relationships among inter-connected entities and decision types, and supports the selection of cost-effective defense mechanisms to safeguard computer networks. To be effective as a business tool, it is first necessary to validate the rationality of model before applying it to real-world business cases. This paper describes our efforts in validating the LDMmore » rationality through simulation.« less

Network-centric decision architecture for financial or 1/f data models

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Massey, Stoney; Case, Carl T.; Songy, Claude G.

2002-12-01

This paper presents a decision architecture algorithm for training neural equation based networks to make autonomous multi-goal oriented, multi-class decisions. These architectures make decisions based on their individual goals and draw from the same network centric feature set. Traditionally, these architectures are comprised of neural networks that offer marginal performance due to lack of convergence of the training set. We present an approach for autonomously extracting sample points as I/O exemplars for generation of multi-branch, multi-node decision architectures populated by adaptively derived neural equations. To test the robustness of this architecture, open source data sets in the form of financial time series were used, requiring a three-class decision space analogous to the lethal, non-lethal, and clutter discrimination problem. This algorithm and the results of its application are presented here.

The Evolution of ICT Markets: An Agent-Based Model on Complex Networks

NASA Astrophysics Data System (ADS)

Zhao, Liangjie; Wu, Bangtao; Chen, Zhong; Li, Li

Information and communication technology (ICT) products exhibit positive network effects.The dynamic process of ICT markets evolution has two intrinsic characteristics: (1) customers are influenced by each others’ purchasing decision; (2) customers are intelligent agents with bounded rationality.Guided by complex systems theory, we construct an agent-based model and simulate on complex networks to examine how the evolution can arise from the interaction of customers, which occur when they make expectations about the future installed base of a product by the fraction of neighbors who are using the same product in his personal network.We demonstrate that network effects play an important role in the evolution of markets share, which make even an inferior product can dominate the whole market.We also find that the intensity of customers’ communication can influence whether the best initial strategy for firms is to improve product quality or expand their installed base.

The 2nd Generation Real Time Mission Monitor (RTMM) Development

NASA Technical Reports Server (NTRS)

Blakeslee, Richard; Goodman, Michael; Meyer, Paul; Hardin, Danny; Hall, John; He, Yubin; Regner, Kathryn; Conover, Helen; Smith, Tammy; Lu, Jessica;

Over-the-counter access to emergency contraception without age restriction: an opinion of the Women's Health Practice and Research Network of the American College of Clinical Pharmacy.

PubMed

Rafie, Sally; McIntosh, Jennifer; Gardner, Debra K; Gawronski, Kristen M; Karaoui, Lamis R; Koepf, Erin R; Lehman, Katherine Joy; McBane, Sarah; Patel-Shori, Nima M

2013-05-01

Family planning remains a high priority area for the United States, with goals to increase the proportion of pregnancies that are intended, reduce pregnancy rates among adolescents, and increase contraceptive use prioritized in the Healthy People 2020 objectives. Contraception intended for use after unprotected intercourse, known as emergency contraception, remains underutilized. Levonorgestrel is one method of oral emergency contraception, which prevents fertilization and does not disrupt an already established pregnancy; thus, timing of administration is critical. Despite data demonstrating safety and efficacy, evidence-based decision making has been overshadowed by politically charged actions involving levonorgestrel emergency contraception for over a decade. The Women's Health Practice and Research Network of the American College of Clinical Pharmacy supports expanded access to levonorgestrel emergency contraception and removal of barriers such as age restrictions on the nonprescription drug product. Pharmacists remain a key provider of emergency contraceptive services and can help ensure timely access. In states where direct pharmacy access to emergency contraception is available, pharmacists are encouraged to participate. Education, research, and advocacy are other important responsibilities for pharmacists in this arena. © 2013 Pharmacotherapy Publications, Inc.

A business planning model to identify new safety net clinic locations.

PubMed

Langabeer, James; Helton, Jeffrey; DelliFraine, Jami; Dotson, Ebbin; Watts, Carolyn; Love, Karen

2014-01-01

Community health clinics serving the poor and underserved are geographically expanding due to changes in U.S. health care policy. This paper describes the experience of a collaborative alliance of health care providers in a large metropolitan area who develop a conceptual and mathematical decision model to guide decisions on expanding its network of community health clinics. Community stakeholders participated in a collaborative process that defined constructs they deemed important in guiding decisions on the location of community health clinics. This collaboration also defined key variables within each construct. Scores for variables within each construct were then totaled and weighted into a community-specific optimal space planning equation. This analysis relied entirely on secondary data available from published sources. The model built from this collaboration revolved around the constructs of demand, sustainability, and competition. It used publicly available data defining variables within each construct to arrive at an optimal location that maximized demand and sustainability and minimized competition. This is a model that safety net clinic planners and community stakeholders can use to analyze demographic and utilization data to optimize capacity expansion to serve uninsured and Medicaid populations. Communities can use this innovative model to develop a locally relevant clinic location-planning framework.

U.S. Department of the Interior Climate Science Centers and U.S. Geological Survey National Climate Change and Wildlife Science Center—Annual report for 2015

USGS Publications Warehouse

Varela Minder, Elda; Padgett, Holly A.

2016-04-07

2015 was another great year for the Department of the Interior (DOI) Climate Science Centers (CSCs) and U.S. Geological Survey (USGS) National Climate Change and Wildlife Science Center (NCCWSC) network. The DOI CSCs and USGS NCCWSC continued their mission of providing the science, data, and tools that are needed for on-the-ground decision making by natural and cultural resource managers to address the effects of climate change on fish, wildlife, ecosystems, and communities. Our many accomplishments in 2015 included initiating a national effort to understand the influence of drought on wildlife and ecosystems; providing numerous opportunities for students and early career researchers to expand their networks and learn more about climate change effects; and working with tribes and indigenous communities to expand their knowledge of and preparation for the impacts of climate change on important resources and traditional ways of living. Here we illustrate some of these 2015 activities from across the CSCs and NCCWSC.

Alternative Fuels Data Center: Electric Vehicle Charging Network Expands at

Science.gov Websites

National Parks Electric Vehicle Charging Network Expands at National Parks to someone by E-mail Share Alternative Fuels Data Center: Electric Vehicle Charging Network Expands at National Parks on Facebook Tweet about Alternative Fuels Data Center: Electric Vehicle Charging Network Expands at National

Building University Capacity to Visualize Solutions to Complex Problems in the Arctic

NASA Astrophysics Data System (ADS)

Broderson, D.; Veazey, P.; Raymond, V. L.; Kowalski, K.; Prakash, A.; Signor, B.

2016-12-01

Rapidly changing environments are creating complex problems across the globe, which are particular magnified in the Arctic. These worldwide challenges can best be addressed through diverse and interdisciplinary research teams. It is incumbent on such teams to promote co-production of knowledge and data-driven decision-making by identifying effective methods to communicate their findings and to engage with the public. Decision Theater North (DTN) is a new semi-immersive visualization system that provides a space for teams to collaborate and develop solutions to complex problems, relying on diverse sets of skills and knowledge. It provides a venue to synthesize the talents of scientists, who gather information (data); modelers, who create models of complex systems; artists, who develop visualizations; communicators, who connect and bridge populations; and policymakers, who can use the visualizations to develop sustainable solutions to pressing problems. The mission of Decision Theater North is to provide a cutting-edge visual environment to facilitate dialogue and decision-making by stakeholders including government, industry, communities and academia. We achieve this mission by adopting a multi-faceted approach reflected in the theater's design, technology, networking capabilities, user support, community relationship building, and strategic partnerships. DTN is a joint project of Alaska's National Science Foundation Experimental Program to Stimulate Competitive Research (NSF EPSCoR) and the University of Alaska Fairbanks (UAF), who have brought the facility up to full operational status and are now expanding its development space to support larger team science efforts. Based in Fairbanks, Alaska, DTN is uniquely poised to address changes taking place in the Arctic and subarctic, and is connected with a larger network of decision theaters that include the Arizona State University Decision Theater Network and the McCain Institute in Washington, DC.

A multi-criteria decision aid methodology to design electric vehicles public charging networks

NASA Astrophysics Data System (ADS)

Raposo, João; Rodrigues, Ana; Silva, Carlos; Dentinho, Tomaz

2015-05-01

This article presents a new multi-criteria decision aid methodology, dynamic-PROMETHEE, here used to design electric vehicle charging networks. In applying this methodology to a Portuguese city, results suggest that it is effective in designing electric vehicle charging networks, generating time and policy based scenarios, considering offer and demand and the city's urban structure. Dynamic-PROMETHE adds to the already known PROMETHEE's characteristics other useful features, such as decision memory over time, versatility and adaptability. The case study, used here to present the dynamic-PROMETHEE, served as inspiration and base to create this new methodology. It can be used to model different problems and scenarios that may present similar requirement characteristics.

Changes of mind in an attractor network of decision-making.

PubMed

Albantakis, Larissa; Deco, Gustavo

2011-06-01

Attractor networks successfully account for psychophysical and neurophysiological data in various decision-making tasks. Especially their ability to model persistent activity, a property of many neurons involved in decision-making, distinguishes them from other approaches. Stable decision attractors are, however, counterintuitive to changes of mind. Here we demonstrate that a biophysically-realistic attractor network with spiking neurons, in its itinerant transients towards the choice attractors, can replicate changes of mind observed recently during a two-alternative random-dot motion (RDM) task. Based on the assumption that the brain continues to evaluate available evidence after the initiation of a decision, the network predicts neural activity during changes of mind and accurately simulates reaction times, performance and percentage of changes dependent on difficulty. Moreover, the model suggests a low decision threshold and high incoming activity that drives the brain region involved in the decision-making process into a dynamical regime close to a bifurcation, which up to now lacked evidence for physiological relevance. Thereby, we further affirmed the general conformance of attractor networks with higher level neural processes and offer experimental predictions to distinguish nonlinear attractor from linear diffusion models.

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.

Designing and Implementation of River Classification Assistant Management System

NASA Astrophysics Data System (ADS)

Zhao, Yinjun; Jiang, Wenyuan; Yang, Rujun; Yang, Nan; Liu, Haiyan

2018-03-01

In an earlier publication, we proposed a new Decision Classifier (DCF) for Chinese river classification based on their structures. To expand, enhance and promote the application of the DCF, we build a computer system to support river classification named River Classification Assistant Management System. Based on ArcEngine and ArcServer platform, this system implements many functions such as data management, extraction of river network, river classification, and results publication under combining Client / Server with Browser / Server framework.

Application of bayesian networks to real-time flood risk estimation

NASA Astrophysics Data System (ADS)

Garrote, L.; Molina, M.; Blasco, G.

2003-04-01

This paper presents the application of a computational paradigm taken from the field of artificial intelligence - the bayesian network - to model the behaviour of hydrologic basins during floods. The final goal of this research is to develop representation techniques for hydrologic simulation models in order to define, develop and validate a mechanism, supported by a software environment, oriented to build decision models for the prediction and management of river floods in real time. The emphasis is placed on providing decision makers with tools to incorporate their knowledge of basin behaviour, usually formulated in terms of rainfall-runoff models, in the process of real-time decision making during floods. A rainfall-runoff model is only a step in the process of decision making. If a reliable rainfall forecast is available and the rainfall-runoff model is well calibrated, decisions can be based mainly on model results. However, in most practical situations, uncertainties in rainfall forecasts or model performance have to be incorporated in the decision process. The computation paradigm adopted for the simulation of hydrologic processes is the bayesian network. A bayesian network is a directed acyclic graph that represents causal influences between linked variables. Under this representation, uncertain qualitative variables are related through causal relations quantified with conditional probabilities. The solution algorithm allows the computation of the expected probability distribution of unknown variables conditioned to the observations. An approach to represent hydrologic processes by bayesian networks with temporal and spatial extensions is presented in this paper, together with a methodology for the development of bayesian models using results produced by deterministic hydrologic simulation models

A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

NASA Astrophysics Data System (ADS)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

Progress and lessons learned from water-quality monitoring networks

USGS Publications Warehouse

Myers, Donna N.; Ludtke, Amy S.

2017-01-01

Stream-quality monitoring networks in the United States were initiated and expanded after passage of successive federal water-pollution control laws from 1948 to 1972. The first networks addressed information gaps on the extent and severity of stream pollution and served as early warning systems for spills. From 1965 to 1972, monitoring networks expanded to evaluate compliance with stream standards, track emerging issues, and assess water-quality status and trends. After 1972, concerns arose regarding the ability of monitoring networks to determine if water quality was getting better or worse and why. As a result, monitoring networks adopted a hydrologic systems approach targeted to key water-quality issues, accounted for human and natural factors affecting water quality, innovated new statistical methods, and introduced geographic information systems and models that predict water quality at unmeasured locations. Despite improvements, national-scale monitoring networks have declined over time. Only about 1%, or 217, of more than 36,000 US Geological Survey monitoring sites sampled from 1975 to 2014 have been operated throughout the four decades since passage of the 1972 Clean Water Act. Efforts to sustain monitoring networks are important because these networks have collected information crucial to the description of water-quality trends over time and are providing information against which to evaluate future trends.

Heuristic urban transportation network design method, a multilayer coevolution approach

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Effective monitoring of agriculture: a response.

PubMed

Sachs, Jeffrey D; Remans, Roseline; Smukler, Sean M; Winowiecki, Leigh; Andelman, Sandy J; Cassman, Kenneth G; Castle, David; DeFries, Ruth; Denning, Glenn; Fanzo, Jessica; Jackson, Louise E; Leemans, Rik; Lehmann, Johannes; Milder, Jeffrey C; Naeem, Shahid; Nziguheba, Generose; Palm, Cheryl A; Pingali, Prabhu L; Reganold, John P; Richter, Daniel D; Scherr, Sara J; Sircely, Jason; Sullivan, Clare; Tomich, Thomas P; Sanchez, Pedro A

2012-03-01

The development of effective agricultural monitoring networks is essential to track, anticipate and manage changes in the social, economic and environmental aspects of agriculture. We welcome the perspective of Lindenmayer and Likens (J. Environ. Monit., 2011, 13, 1559) as published in the Journal of Environmental Monitoring on our earlier paper, "Monitoring the World's Agriculture" (Sachs et al., Nature, 2010, 466, 558-560). In this response, we address their three main critiques labeled as 'the passive approach', 'the problem with uniform metrics' and 'the problem with composite metrics'. We expand on specific research questions at the core of the network design, on the distinction between key universal and site-specific metrics to detect change over time and across scales, and on the need for composite metrics in decision-making. We believe that simultaneously measuring indicators of the three pillars of sustainability (environmentally sound, social responsible and economically viable) in an effectively integrated monitoring system will ultimately allow scientists and land managers alike to find solutions to the most pressing problems facing global food security. This journal is © The Royal Society of Chemistry 2012

Promoting the dissemination of decision aids: an odyssey in a dysfunctional health care financing system.

PubMed

Billings, John

2004-01-01

The usefulness of patient decision aids (PtDAs) is well documented, yet they are not in widespread use. Barriers include assuring balance and fairness (auspices matter), the cost of producing and maintaining them, and getting them into the hands of patients at the right time. The Foundation for Informed Medical Decision Making and its for-profit partner, Health Dialog, have developed a creative business model that helps overcome these barriers and has greatly expanded the reach of decision aids.

Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.

PubMed

Bennett, Casey C; Hauser, Kris

2013-01-01

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor". This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record. The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine. Copyright © 2012 Elsevier B.V. All rights reserved.

Overview 2010 of ARL Program on Network Science for Human Decision Making

DTIC Science & Technology

2011-01-01

pressure and subliminal seduction; but always through a sequence of decisions, either conscious or not. Network characteristics cannot be deduced from the...Time, Communication , and the Nervous System”, in Norbert Wiener: Collected Works, Volume IV, pp.220-252, The MIT Press, Cambridge, MA (1985). 16. P

Identifying Hierarchical and Overlapping Protein Complexes Based on Essential Protein-Protein Interactions and “Seed-Expanding” Method

PubMed Central

Ren, Jun; Zhou, Wei; Wang, Jianxin

2014-01-01

Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on λ-module and “seed-expanding.” First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a λ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter λ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of λ_th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes. PMID:25143945

Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule.

PubMed

Beyeler, Michael; Dutt, Nikil D; Krichmar, Jeffrey L

2013-12-01

Understanding how the human brain is able to efficiently perceive and understand a visual scene is still a field of ongoing research. Although many studies have focused on the design and optimization of neural networks to solve visual recognition tasks, most of them either lack neurobiologically plausible learning rules or decision-making processes. Here we present a large-scale model of a hierarchical spiking neural network (SNN) that integrates a low-level memory encoding mechanism with a higher-level decision process to perform a visual classification task in real-time. The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a spike-timing-dependent plasticity (STDP) synaptic learning rule with additional synaptic dynamics for memory encoding, and an accumulator model for memory retrieval and categorization. The full network, which comprised 71,026 neurons and approximately 133 million synapses, ran in real-time on a single off-the-shelf graphics processing unit (GPU). The network was constructed on a publicly available SNN simulator that supports general-purpose neuromorphic computer chips. The network achieved 92% correct classifications on MNIST in 100 rounds of random sub-sampling, which is comparable to other SNN approaches and provides a conservative and reliable performance metric. Additionally, the model correctly predicted reaction times from psychophysical experiments. Because of the scalability of the approach and its neurobiological fidelity, the current model can be extended to an efficient neuromorphic implementation that supports more generalized object recognition and decision-making architectures found in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Optimal routing of hazardous substances in time-varying, stochastic transportation networks

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

Woods, A.L.; Miller-Hooks, E.; Mahmassani, H.S.

This report is concerned with the selection of routes in a network along which to transport hazardous substances, taking into consideration several key factors pertaining to the cost of transport and the risk of population exposure in the event of an accident. Furthermore, the fact that travel time and the risk measures are not constant over time is explicitly recognized in the routing decisions. Existing approaches typically assume static conditions, possibly resulting in inefficient route selection and unnecessary risk exposure. The report described the application of recent advances in network analysis methodologies to the problem of routing hazardous substances. Severalmore » specific problem formulations are presented, reflecting different degrees of risk aversion on the part of the decision-maker, as well as different possible operational scenarios. All procedures explicitly consider travel times and travel costs (including risk measures) to be stochastic time-varying quantities. The procedures include both exact algorithms, which may require extensive computational effort in some situations, as well as more efficient heuristics that may not guarantee a Pareto-optimal solution. All procedures are systematically illustrated for an example application using the Texas highway network, for both normal and incident condition scenarios. The application illustrates the trade-offs between the information obtained in the solution and computational efficiency, and highlights the benefits of incorporating these procedures in a decision-support system for hazardous substance shipment routing decisions.« less

IEEE 802.21 Assisted Seamless and Energy Efficient Handovers in Mixed Networks

NASA Astrophysics Data System (ADS)

Liu, Huaiyu; Maciocco, Christian; Kesavan, Vijay; Low, Andy L. Y.

Network selection is the decision process for a mobile terminal to handoff between homogeneous or heterogeneous networks. With multiple available networks, the selection process must evaluate factors like network services/conditions, monetary cost, system conditions, user preferences etc. In this paper, we investigate network selection using a cost function and information provided by IEEE 802.21. The cost function provides flexibility to balance different factors in decision making and our research is focused on improving both seamlessness and energy efficiency of handovers. Our solution is evaluated using real WiFi, WiMax, and 3G signal strength traces. The results show that appropriate networks were selected based on selection policies, handovers were triggered at optimal times to increase overall network connectivity as compared to traditional triggering schemes, while at the same time the energy consumption of multi-radio devices for both on-going operations as well as during handovers is optimized.

Formation of nanoscale networks: selectively swelling amphiphilic block copolymers with CO2-expanded liquids

NASA Astrophysics Data System (ADS)

Gong, Jianliang; Zhang, Aijuan; Bai, Hua; Zhang, Qingkun; Du, Can; Li, Lei; Hong, Yanzhen; Li, Jun

2013-01-01

Polymeric films with nanoscale networks were prepared by selectively swelling an amphiphilic diblock copolymer, polystyrene-block-poly(4-vinylpyridine) (PS-b-P4VP), with the CO2-expanded liquid (CXL), CO2-methanol. The phase behavior of the CO2-methanol system was investigated by both theoretical calculation and experiments, revealing that methanol can be expanded by CO2, forming homogeneous CXL under the experimental conditions. When treated with the CO2-methanol system, the spin cast compact PS-b-P4VP film was transformed into a network with interconnected pores, in a pressure range of 12-20 MPa and a temperature range of 45-60 °C. The formation mechanism of the network, involving plasticization of PS and selective swelling of P4VP, was proposed. Because the diblock copolymer diffusion process is controlled by the activated hopping of individual block copolymer chains with the thermodynamic barrier for moving PVP segments from one to another, the formation of the network structures is achieved in a short time scale and shows ``thermodynamically restricted'' character. Furthermore, the resulting polymer networks were employed as templates, for the preparation of polypyrrole networks, by an electrochemical polymerization process. The prepared porous polypyrrole film was used to fabricate a chemoresistor-type gas sensor which showed high sensitivity towards ammonia.Polymeric films with nanoscale networks were prepared by selectively swelling an amphiphilic diblock copolymer, polystyrene-block-poly(4-vinylpyridine) (PS-b-P4VP), with the CO2-expanded liquid (CXL), CO2-methanol. The phase behavior of the CO2-methanol system was investigated by both theoretical calculation and experiments, revealing that methanol can be expanded by CO2, forming homogeneous CXL under the experimental conditions. When treated with the CO2-methanol system, the spin cast compact PS-b-P4VP film was transformed into a network with interconnected pores, in a pressure range of 12-20 MPa and a temperature range of 45-60 °C. The formation mechanism of the network, involving plasticization of PS and selective swelling of P4VP, was proposed. Because the diblock copolymer diffusion process is controlled by the activated hopping of individual block copolymer chains with the thermodynamic barrier for moving PVP segments from one to another, the formation of the network structures is achieved in a short time scale and shows ``thermodynamically restricted'' character. Furthermore, the resulting polymer networks were employed as templates, for the preparation of polypyrrole networks, by an electrochemical polymerization process. The prepared porous polypyrrole film was used to fabricate a chemoresistor-type gas sensor which showed high sensitivity towards ammonia. Electronic supplementary information (ESI) available. See DOI: 10.1039/c2nr33188h

Noise in Attractor Networks in the Brain Produced by Graded Firing Rate Representations

PubMed Central

Webb, Tristan J.; Rolls, Edmund T.; Deco, Gustavo; Feng, Jianfeng

2011-01-01

Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given mean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that is usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is indeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions. The greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are applied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the spontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent collateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can increase the speed of operation of cortical circuitry. PMID:21931607

Fuzzy-based decision strategy in real-time strategic games

NASA Astrophysics Data System (ADS)

Volna, Eva

2017-11-01

The aim of this article is to describe our own gaming artificial intelligence for OpenTTD, which is a real-time building strategy game. A multi-agent system with fuzzy decision-making was used for the proposal itself. The multiagent system was chosen because real-time strategy games achieve great complexity and require decomposition of the problem into individual problems, which are then solved by individual cooperating agents. The system becomes then more stable and easily expandable. The fuzzy approach makes the decision-making process of strategies easier thanks to the use of uncertainty. In the conclusion, own experimental results were compared with other approaches.

The Path to NGATS

NASA Technical Reports Server (NTRS)

Scardina, John

2006-01-01

NGATS operational Improvements and benefits include: 1) Broad area and precision navigation to access and capacity; 2) Airspace access and management to capacity; 3) 4D trajectory based ATM to capacity and efficiency; 4) Reduced separation between aircraft to capacity; 5) Flight deck situational awareness and delegation to capacity and safety; 6) ATM decision support to capacity; 7) Improved weather data and dissemination to capacity and safety; 8) Reduced cost to deliver ATM services to cost; 9) Greatly expanded airport network and improved terminals to capacity.

Information processing by networks of quantum decision makers

NASA Astrophysics Data System (ADS)

Yukalov, V. I.; Yukalova, E. P.; Sornette, D.

2018-02-01

We suggest a model of a multi-agent society of decision makers taking decisions being based on two criteria, one is the utility of the prospects and the other is the attractiveness of the considered prospects. The model is the generalization of quantum decision theory, developed earlier for single decision makers realizing one-step decisions, in two principal aspects. First, several decision makers are considered simultaneously, who interact with each other through information exchange. Second, a multistep procedure is treated, when the agents exchange information many times. Several decision makers exchanging information and forming their judgment, using quantum rules, form a kind of a quantum information network, where collective decisions develop in time as a result of information exchange. In addition to characterizing collective decisions that arise in human societies, such networks can describe dynamical processes occurring in artificial quantum intelligence composed of several parts or in a cluster of quantum computers. The practical usage of the theory is illustrated on the dynamic disjunction effect for which three quantitative predictions are made: (i) the probabilistic behavior of decision makers at the initial stage of the process is described; (ii) the decrease of the difference between the initial prospect probabilities and the related utility factors is proved; (iii) the existence of a common consensus after multiple exchange of information is predicted. The predicted numerical values are in very good agreement with empirical data.

An Artificial Neural Network-Based Decision-Support System for Integrated Network Security

DTIC Science & Technology

2014-09-01

group that they need to know in order to make team-based decisions in real-time environments, (c) Employ secure cloud computing services to host mobile...THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force...out-of-the-loop syndrome and create complexity creep. As a result, full automation efforts can lead to inappropriate decision-making despite a

Managing outbreaks of invasive species - a new method to prioritize preemptive quarantine efforts across large geographic regions

Treesearch

J.R. Withrow; E.L. Smith; F.H. Koch; D. Yemshanov

2015-01-01

In pest risk assessment it is frequently necessary to make time-critical decisions regarding management of expanding pest populations. When an invasive pest outbreak is expanding rapidly, preemptive quarantine of areas that are under imminent threat of infestation is one of only a few available management tools that can be implemented quickly to help control the...

CISN ShakeAlert Earthquake Early Warning System Monitoring Tools

NASA Astrophysics Data System (ADS)

Henson, I. H.; Allen, R. M.; Neuhauser, D. S.

2015-12-01

CISN ShakeAlert is a prototype earthquake early warning system being developed and tested by the California Integrated Seismic Network. The system has recently been expanded to support redundant data processing and communications. It now runs on six machines at three locations with ten Apache ActiveMQ message brokers linking together 18 waveform processors, 12 event association processes and 4 Decision Module alert processes. The system ingests waveform data from about 500 stations and generates many thousands of triggers per day, from which a small portion produce earthquake alerts. We have developed interactive web browser system-monitoring tools that display near real time state-of-health and performance information. This includes station availability, trigger statistics, communication and alert latencies. Connections to regional earthquake catalogs provide a rapid assessment of the Decision Module hypocenter accuracy. Historical performance can be evaluated, including statistics for hypocenter and origin time accuracy and alert time latencies for different time periods, magnitude ranges and geographic regions. For the ElarmS event associator, individual earthquake processing histories can be examined, including details of the transmission and processing latencies associated with individual P-wave triggers. Individual station trigger and latency statistics are available. Detailed information about the ElarmS trigger association process for both alerted events and rejected events is also available. The Google Web Toolkit and Map API have been used to develop interactive web pages that link tabular and geographic information. Statistical analysis is provided by the R-Statistics System linked to a PostgreSQL database.

The Alaska Volcano Observatory - Expanded Monitoring of Volcanoes Yields Results

USGS Publications Warehouse

Brantley, Steven R.; McGimsey, Robert G.; Neal, Christina A.

2004-01-01

Recent explosive eruptions at some of Alaska's 52 historically active volcanoes have significantly affected air traffic over the North Pacific, as well as Alaska's oil, power, and fishing industries and local communities. Since its founding in the late 1980s, the Alaska Volcano Observatory (AVO) has installed new monitoring networks and used satellite data to track activity at Alaska's volcanoes, providing timely warnings and monitoring of frequent eruptions to the aviation industry and the general public. To minimize impacts from future eruptions, scientists at AVO continue to assess volcano hazards and to expand monitoring networks.

Methods and decision making on a Mars rover for identification of fossils

NASA Technical Reports Server (NTRS)

Eberlein, Susan; Yates, Gigi

1989-01-01

A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mars

Tax-exempt/proprietary partnerships: how the deal gets done.

PubMed

Anthony, M F

1997-01-01

Joint venture partnerships between tax-exempt healthcare providers and proprietary companies represent a type of provider-sponsored network. Tax-exempt /proprietary partnerships can help tax-exempt providers attain their strategic objectives and, at the same time, retain some governance involvement and healthcare decision-making authority. Proprietary companies that enter into such partnerships are able to expand their market presence and revenue potential without spending capital on an acquisition. Proprietary companies also gain the tax-exempt partners' goodwill, which could take them years to develop on their own. Before negotiating a partnership agreement, potential partners must assess their respective financial, cultural, organizational, and strategic strengths and weaknesses as well as their overall compatibility. Then they must develop contract terms to bring into the partnership negotiations. These terms include purpose, legal structure, assets/liabilities, governance, management, valuation, profit/loss sharing, capitalization/working capital, human resources, withdrawal from the partnership, noncompete covernants, and tax exemption issues.

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.

Building clinical networks: a developmental evaluation framework.

PubMed

Carswell, Peter; Manning, Benjamin; Long, Janet; Braithwaite, Jeffrey

2014-05-01

Clinical networks have been designed as a cross-organisational mechanism to plan and deliver health services. With recent concerns about the effectiveness of these structures, it is timely to consider an evidence-informed approach for how they can be developed and evaluated. To document an evaluation framework for clinical networks by drawing on the network evaluation literature and a 5-year study of clinical networks. We searched literature in three domains: network evaluation, factors that aid or inhibit network development, and on robust methods to measure network characteristics. This material was used to build a framework required for effective developmental evaluation. The framework's architecture identifies three stages of clinical network development; partner selection, network design and network management. Within each stage is evidence about factors that act as facilitators and barriers to network growth. These factors can be used to measure progress via appropriate methods and tools. The framework can provide for network growth and support informed decisions about progress. For the first time in one place a framework incorporating rigorous methods and tools can identify factors known to affect the development of clinical networks. The target user group is internal stakeholders who need to conduct developmental evaluation to inform key decisions along their network's developmental pathway.

Design and implementation of wireless dose logger network for radiological emergency decision support system.

PubMed

Gopalakrishnan, V; Baskaran, R; Venkatraman, B

2016-08-01

A decision support system (DSS) is implemented in Radiological Safety Division, Indira Gandhi Centre for Atomic Research for providing guidance for emergency decision making in case of an inadvertent nuclear accident. Real time gamma dose rate measurement around the stack is used for estimating the radioactive release rate (source term) by using inverse calculation. Wireless gamma dose logging network is designed, implemented, and installed around the Madras Atomic Power Station reactor stack to continuously acquire the environmental gamma dose rate and the details are presented in the paper. The network uses XBee-Pro wireless modules and PSoC controller for wireless interfacing, and the data are logged at the base station. A LabView based program is developed to receive the data, display it on the Google Map, plot the data over the time scale, and register the data in a file to share with DSS software. The DSS at the base station evaluates the real time source term to assess radiation impact.

Design and implementation of wireless dose logger network for radiological emergency decision support system

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

Gopalakrishnan, V.; Baskaran, R.; Venkatraman, B.

A decision support system (DSS) is implemented in Radiological Safety Division, Indira Gandhi Centre for Atomic Research for providing guidance for emergency decision making in case of an inadvertent nuclear accident. Real time gamma dose rate measurement around the stack is used for estimating the radioactive release rate (source term) by using inverse calculation. Wireless gamma dose logging network is designed, implemented, and installed around the Madras Atomic Power Station reactor stack to continuously acquire the environmental gamma dose rate and the details are presented in the paper. The network uses XBee–Pro wireless modules and PSoC controller for wireless interfacing,more » and the data are logged at the base station. A LabView based program is developed to receive the data, display it on the Google Map, plot the data over the time scale, and register the data in a file to share with DSS software. The DSS at the base station evaluates the real time source term to assess radiation impact.« less

Research and collaboration overview of Institut Pasteur International Network: a bibliometric approach toward research funding decisions.

PubMed

Mostafavi, Ehsan; Bazrafshan, Azam

2014-01-01

Institut Pasteur International Network (IPIN), which includes 32 research institutes around the world, is a network of research and expertise to fight against infectious diseases. A scientometric approach was applied to describe research and collaboration activities of IPIN. Publications were identified using a manual search of IPIN member addresses in Science Citation Index Expanded (SCIE) between 2006 and 2011. Total publications were then subcategorized by geographic regions. Several scientometric indicators and the H-index were employed to estimate the scientific production of each IPIN member. Subject and geographical overlay maps were also applied to visualize the network activities of the IPIN members. A total number of 12667 publications originated from IPIN members. Each author produced an average number of 2.18 papers and each publication received an average of 13.40 citations. European Pasteur Institutes had the largest amount of publications, authored papers, and H-index values. Biochemistry and molecular biology, microbiology, immunology and infectious diseases were the most important research topics, respectively. Geographic mapping of IPIN publications showed wide international collaboration among IPIN members around the world. IPIN has strong ties with national and international authorities and organizations to investigate the current and future health issues. It is recommended to use scientometric and collaboration indicators as measures of research performance in IPIN future policies and investment decisions.

Theory of nonstationary Hawkes processes

NASA Astrophysics Data System (ADS)

Tannenbaum, Neta Ravid; Burak, Yoram

2017-12-01

We expand the theory of Hawkes processes to the nonstationary case, in which the mutually exciting point processes receive time-dependent inputs. We derive an analytical expression for the time-dependent correlations, which can be applied to networks with arbitrary connectivity, and inputs with arbitrary statistics. The expression shows how the network correlations are determined by the interplay between the network topology, the transfer functions relating units within the network, and the pattern and statistics of the external inputs. We illustrate the correlation structure using several examples in which neural network dynamics are modeled as a Hawkes process. In particular, we focus on the interplay between internally and externally generated oscillations and their signatures in the spike and rate correlation functions.

78 FR 44136 - Agency Information Collection Activities: Office of Biometric Identity Management (OBIM...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-23

..., state, and local law enforcement agencies; and the Intelligence Community to assist in the decisions..., US-VISIT expanded the collection of fingerprints from two prints to ten. The new collection time of...

A Novel Hybrid MADM Based Competence Set Expansions of a SOC Design Service Firm

NASA Astrophysics Data System (ADS)

Huang, Chi-Yo; Tzeng, Gwo-Hshiung; Lue, Yeou-Feng; Chuang, Hsiu-Tyan

As the IC (integrated circuit) industry migrates to the System-on-Chip (SOC) era, a novel business model, the SOC design service (DS), is emerging. However, how to expand a firm’s innovation competences while satisfying multiple objectives including highest quality, lowest cost, and fastest time to market as well as most revenues for economics of scale are always problems for a design service firm. Therefore, attempts to expand the innovation competences, and thus the competitiveness, of latecomers in the SOC DS industry have already become the most critical issue facing the top managers of SOC design service firms. In this paper, a novel multiple attribute decision making (MADM) analytic framework based on the concept of competence set expansion, as well as MADM methods consisting with DEMATEL, ANP and multiple objective decision making (MODM) will be proposed in order to define a path for expanding a late-coming SOC DS firm’s innovation capabilities. An empirical study on expanding innovation competence sets, of a late-coming Taiwanese DS firm then will be presented.

Social Network and Content Analysis of the North American Carbon Program as a Scientific Community of Practice

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Ihli, Monica; Hendrick, Oscar; Delgado-Arias, Sabrina; Escobar, Vanessa M.; Griffith, Peter

2015-01-01

The North American Carbon Program (NACP) was formed to further the scientific understanding of sources, sinks, and stocks of carbon in Earth's environment. Carbon cycle science integrates multidisciplinary research, providing decision-support information for managing climate and carbon-related change across multiple sectors of society. This investigation uses the conceptual framework of com-munities of practice (CoP) to explore the role that the NACP has played in connecting researchers into a carbon cycle knowledge network, and in enabling them to conduct physical science that includes ideas from social science. A CoP describes the communities formed when people consistently engage in shared communication and activities toward a common passion or learning goal. We apply the CoP model by using keyword analysis of abstracts from scientific publications to analyze the research outputs of the NACP in terms of its knowledge domain. We also construct a co-authorship network from the publications of core NACP members, describe the structure and social pathways within the community. Results of the content analysis indicate that the NACP community of practice has substantially expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. Results of the co-authorship social network analysis demonstrate that the NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and that it has also expanded its network of institutions involved in carbon cycle research over the past seven years.

Brain network response underlying decisions about abstract reinforcers.

PubMed

Mills-Finnerty, Colleen; Hanson, Catherine; Hanson, Stephen Jose

2014-12-01

Decision making studies typically use tasks that involve concrete action-outcome contingencies, in which subjects do something and get something. No studies have addressed decision making involving abstract reinforcers, where there are no action-outcome contingencies and choices are entirely hypothetical. The present study examines these kinds of choices, as well as whether the same biases that exist for concrete reinforcer decisions, specifically framing effects, also apply during abstract reinforcer decisions. We use both General Linear Model as well as Bayes network connectivity analysis using the Independent Multi-sample Greedy Equivalence Search (IMaGES) algorithm to examine network response underlying choices for abstract reinforcers under positive and negative framing. We find for the first time that abstract reinforcer decisions activate the same network of brain regions as concrete reinforcer decisions, including the striatum, insula, anterior cingulate, and VMPFC, results that are further supported via comparison to a meta-analysis of decision making studies. Positive and negative framing activated different parts of this network, with stronger activation in VMPFC during negative framing and in DLPFC during positive, suggesting different decision making pathways depending on frame. These results were further clarified using connectivity analysis, which revealed stronger connections between anterior cingulate, insula, and accumbens during negative framing compared to positive. Taken together, these results suggest that not only do abstract reinforcer decisions rely on the same brain substrates as concrete reinforcers, but that the response underlying framing effects on abstract reinforcers also resemble those for concrete reinforcers, specifically increased limbic system connectivity during negative frames. Copyright © 2014 Elsevier Inc. All rights reserved.

A Multimetric Approach for Handoff Decision in Heterogeneous Wireless Networks

NASA Astrophysics Data System (ADS)

Kustiawan, I.; Purnama, W.

2018-02-01

Seamless mobility and service continuity anywhere at any time are an important issue in the wireless Internet. This research proposes a scheme to make handoff decisions effectively in heterogeneous wireless networks using a fuzzy system. Our design lies in an inference engine which takes RSS (received signal strength), data rate, network latency, and user preference as strategic determinants. The logic of our engine is realized on a UE (user equipment) side in faster reaction to network dynamics while roaming across different radio access technologies. The fuzzy system handles four metrics jointly to deduce a moderate decision about when to initiate handoff. The performance of our design is evaluated by simulating move-out mobility scenarios. Simulation results show that our scheme outperforms other approaches in terms of reducing unnecessary handoff.

Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race.

PubMed

Warnke, Tom; Reinhardt, Oliver; Klabunde, Anna; Willekens, Frans; Uhrmacher, Adelinde M

2017-10-01

Individuals' decision processes play a central role in understanding modern migration phenomena and other demographic processes. Their integration into agent-based computational demography depends largely on suitable support by a modelling language. We are developing the Modelling Language for Linked Lives (ML3) to describe the diverse decision processes of linked lives succinctly in continuous time. The context of individuals is modelled by networks the individual is part of, such as family ties and other social networks. Central concepts, such as behaviour conditional on agent attributes, age-dependent behaviour, and stochastic waiting times, are tightly integrated in the language. Thereby, alternative decisions are modelled by concurrent processes that compete by stochastic race. Using a migration model, we demonstrate how this allows for compact description of complex decisions, here based on the Theory of Planned Behaviour. We describe the challenges for the simulation algorithm posed by stochastic race between multiple concurrent complex decisions.

Research of Ad Hoc Networks Access Algorithm

NASA Astrophysics Data System (ADS)

Xiang, Ma

With the continuous development of mobile communication technology, Ad Hoc access network has become a hot research, Ad Hoc access network nodes can be used to expand capacity of multi-hop communication range of mobile communication system, even business adjacent to the community, improve edge data rates. When the ad hoc network is the access network of the internet, the gateway discovery protocol is very important to choose the most appropriate gateway to guarantee the connectivity between ad hoc network and IP based fixed networks. The paper proposes a QoS gateway discovery protocol which uses the time delay and stable route to the gateway selection conditions. And according to the gateway discovery protocol, it also proposes a fast handover scheme which can decrease the handover time and improve the handover efficiency.

Value of Information for Optimal Adaptive Routing in Stochastic Time-Dependent Traffic Networks: Algorithms and Computational Tools

DOT National Transportation Integrated Search

2010-10-25

Real-time information is important for travelers' routing decisions in uncertain networks by enabling online adaptation to revealed traffic conditions. Usually there are spatial and/or temporal limitations in traveler information. In this research, a...

A peer review process as part of the implementation of clinical pathways in radiation oncology: Does it improve compliance?

PubMed

Gebhardt, Brian J; Heron, Dwight E; Beriwal, Sushil

Clinical pathways are patient management plans that standardize evidence-based practices to ensure high-quality and cost-effective medical care. Implementation of a pathway is a collaborative process in our network, requiring the active involvement of physicians. This approach promotes acceptance of pathway recommendations, although a peer review process is necessary to ensure compliance and to capture and approve off-pathway selections. We investigated the peer review process and factors associated with time to completion of peer review. Our cancer center implemented radiation oncology pathways for every disease site throughout a large, integrated network. Recommendations are written based upon national guidelines, published literature, and institutional experience with evidence evaluated hierarchically in order of efficacy, toxicity, and then cost. Physicians enter decisions into an online, menu-driven decision support tool that integrates with medical records. Data were collected from the support tool and included the rate of on- and off-pathway selections, peer review decisions performed by disease site directors, and time to complete peer review. A total of 6965 treatment decisions were entered in 2015, and 605 (8.7%) were made off-pathway and were subject to peer review. The median time to peer review decision was 2 days (interquartile range, 0.2-6.8). Factors associated with time to peer review decision >48 hours on univariate analysis include disease site (P < .0001) with a trend toward significance (P = .066) for radiation therapy modality. There was no difference between recurrent and non-recurrent disease (P = .267). Multivariable analysis revealed disease site was associated with time to peer review (P < .001), with lymphoma and skin/sarcoma most strongly influencing decision time >48 hours. Clinical pathways are an integral tool for standardizing evidence-based care throughout our large, integrated network, with 91.3% of all treatment decisions being made as per pathway. The peer review process was feasible, with <1% selections ultimately rejected, suggesting that awareness of peer review of treatment decisions encourages compliance with clinical pathway recommendations. Copyright © 2017 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Engineering a Functional Small RNA Negative Autoregulation Network with Model-Guided Design.

PubMed

Hu, Chelsea Y; Takahashi, Melissa K; Zhang, Yan; Lucks, Julius B

2018-05-22

RNA regulators are powerful components of the synthetic biology toolbox. Here, we expand the repertoire of synthetic gene networks built from these regulators by constructing a transcriptional negative autoregulation (NAR) network out of small RNAs (sRNAs). NAR network motifs are core motifs of natural genetic networks, and are known for reducing network response time and steady state signal. Here we use cell-free transcription-translation (TX-TL) reactions and a computational model to design and prototype sRNA NAR constructs. Using parameter sensitivity analysis, we design a simple set of experiments that allow us to accurately predict NAR function in TX-TL. We transfer successful network designs into Escherichia coli and show that our sRNA transcriptional network reduces both network response time and steady-state gene expression. This work broadens our ability to construct increasingly sophisticated RNA genetic networks with predictable function.

Temporal coding of reward-guided choice in the posterior parietal cortex

PubMed Central

Hawellek, David J.; Wong, Yan T.; Pesaran, Bijan

2016-01-01

Making a decision involves computations across distributed cortical and subcortical networks. How such distributed processing is performed remains unclear. We test how the encoding of choice in a key decision-making node, the posterior parietal cortex (PPC), depends on the temporal structure of the surrounding population activity. We recorded spiking and local field potential (LFP) activity in the PPC while two rhesus macaques performed a decision-making task. We quantified the mutual information that neurons carried about an upcoming choice and its dependence on LFP activity. The spiking of PPC neurons was correlated with LFP phases at three distinct time scales in the theta, beta, and gamma frequency bands. Importantly, activity at these time scales encoded upcoming decisions differently. Choice information contained in neural firing varied with the phase of beta and gamma activity. For gamma activity, maximum choice information occurred at the same phase as the maximum spike count. However, for beta activity, choice information and spike count were greatest at different phases. In contrast, theta activity did not modulate the encoding properties of PPC units directly but was correlated with beta and gamma activity through cross-frequency coupling. We propose that the relative timing of local spiking and choice information reveals temporal reference frames for computations in either local or large-scale decision networks. Differences between the timing of task information and activity patterns may be a general signature of distributed processing across large-scale networks. PMID:27821752

Quantum stochastic walks on networks for decision-making.

PubMed

Martínez-Martínez, Ismael; Sánchez-Burillo, Eduardo

2016-03-31

Recent experiments report violations of the classical law of total probability and incompatibility of certain mental representations when humans process and react to information. Evidence shows promise of a more general quantum theory providing a better explanation of the dynamics and structure of real decision-making processes than classical probability theory. Inspired by this, we show how the behavioral choice-probabilities can arise as the unique stationary distribution of quantum stochastic walkers on the classical network defined from Luce's response probabilities. This work is relevant because (i) we provide a very general framework integrating the positive characteristics of both quantum and classical approaches previously in confrontation, and (ii) we define a cognitive network which can be used to bring other connectivist approaches to decision-making into the quantum stochastic realm. We model the decision-maker as an open system in contact with her surrounding environment, and the time-length of the decision-making process reveals to be also a measure of the process' degree of interplay between the unitary and irreversible dynamics. Implementing quantum coherence on classical networks may be a door to better integrate human-like reasoning biases in stochastic models for decision-making.

Quantum stochastic walks on networks for decision-making

NASA Astrophysics Data System (ADS)

Martínez-Martínez, Ismael; Sánchez-Burillo, Eduardo

2016-03-01

Recent experiments report violations of the classical law of total probability and incompatibility of certain mental representations when humans process and react to information. Evidence shows promise of a more general quantum theory providing a better explanation of the dynamics and structure of real decision-making processes than classical probability theory. Inspired by this, we show how the behavioral choice-probabilities can arise as the unique stationary distribution of quantum stochastic walkers on the classical network defined from Luce’s response probabilities. This work is relevant because (i) we provide a very general framework integrating the positive characteristics of both quantum and classical approaches previously in confrontation, and (ii) we define a cognitive network which can be used to bring other connectivist approaches to decision-making into the quantum stochastic realm. We model the decision-maker as an open system in contact with her surrounding environment, and the time-length of the decision-making process reveals to be also a measure of the process’ degree of interplay between the unitary and irreversible dynamics. Implementing quantum coherence on classical networks may be a door to better integrate human-like reasoning biases in stochastic models for decision-making.

Quantum stochastic walks on networks for decision-making

PubMed Central

Martínez-Martínez, Ismael; Sánchez-Burillo, Eduardo

2016-01-01

Recent experiments report violations of the classical law of total probability and incompatibility of certain mental representations when humans process and react to information. Evidence shows promise of a more general quantum theory providing a better explanation of the dynamics and structure of real decision-making processes than classical probability theory. Inspired by this, we show how the behavioral choice-probabilities can arise as the unique stationary distribution of quantum stochastic walkers on the classical network defined from Luce’s response probabilities. This work is relevant because (i) we provide a very general framework integrating the positive characteristics of both quantum and classical approaches previously in confrontation, and (ii) we define a cognitive network which can be used to bring other connectivist approaches to decision-making into the quantum stochastic realm. We model the decision-maker as an open system in contact with her surrounding environment, and the time-length of the decision-making process reveals to be also a measure of the process’ degree of interplay between the unitary and irreversible dynamics. Implementing quantum coherence on classical networks may be a door to better integrate human-like reasoning biases in stochastic models for decision-making. PMID:27030372

Improving the energy efficiency of telecommunication networks

NASA Astrophysics Data System (ADS)

Lange, Christoph; Gladisch, Andreas

2011-05-01

The energy consumption of telecommunication networks has gained increasing interest throughout the recent past: Besides its environmental implications it has been identified to be a major contributor to operational expenditures of network operators. Targeting at sustainable telecommunication networks, thus, it is important to find appropriate strategies for improving their energy efficiency before the background of rapidly increasing traffic volumes. Besides the obvious benefits of increasing energy efficiency of network elements by leveraging technology progress, load-adaptive network operation is a very promising option, i.e. using network resources only to an extent and for the time they are actually needed. In contrast, current network operation takes almost no advantage of the strongly time-variant behaviour of the network traffic load. Mechanisms for energy-aware load-adaptive network operation can be subdivided in techniques based on local autonomous or per-link decisions and in techniques relying on coordinated decisions incorporating information from several links. For the transformation from current network structures and operation paradigms towards energy-efficient and sustainable networks it will be essential to use energy-optimized network elements as well as including the overall energy consumption in network design and planning phases together with the energy-aware load-adaptive operation. In load-adaptive operation it will be important to establish the optimum balance between local and overarching power management concepts in telecommunication networks.

Using Co-authorship Networks to Map and Analyse Global Neglected Tropical Disease Research with an Affiliation to Germany

PubMed Central

Bender, Max Ernst; Edwards, Suzanne; von Philipsborn, Peter; Steinbeis, Fridolin; Keil, Thomas; Tinnemann, Peter

2015-01-01

Background Research on Neglected Tropical Diseases (NTDs) has increased in recent decades, and significant need-gaps in diagnostic and treatment tools remain. Analysing bibliometric data from published research is a powerful method for revealing research efforts, partnerships and expertise. We aim to identify and map NTD research networks in Germany and their partners abroad to enable an informed and transparent evaluation of German contributions to NTD research. Methodology/Principal Findings A SCOPUS database search for articles with German author affiliations that were published between 2002 and 2012 was conducted for kinetoplastid and helminth diseases. Open-access tools were used for data cleaning and scientometrics (OpenRefine), geocoding (OpenStreetMaps) and to create (Table2Net), visualise and analyse co-authorship networks (Gephi). From 26,833 publications from around the world that addressed 11 diseases, we identified 1,187 (4.4%) with at least one German author affiliation, and we processed 972 publications for the five most published-about diseases. Of those, we extracted 4,007 individual authors and 863 research institutions to construct co-author networks. The majority of co-authors outside Germany were from high-income countries and Brazil. Collaborations with partners on the African continent remain scattered. NTD research within Germany was distributed among 220 research institutions. We identified strong performers on an individual level by using classic parameters (number of publications, h-index) and social network analysis parameters (betweenness centrality). The research network characteristics varied strongly between diseases. Conclusions/Significance The share of NTD publications with German affiliations is approximately half of its share in other fields of medical research. This finding underlines the need to identify barriers and expand Germany’s otherwise strong research activities towards NTDs. A geospatial analysis of research collaborations with partners abroad can support decisions to strengthen research capacity, particularly in low- and middle-income countries, which were less involved in collaborations than high-income countries. Identifying knowledge hubs within individual researcher networks complements traditional scientometric indicators that are used to identify opportunities for collaboration. Using free tools to analyse research processes and output could facilitate data-driven health policies. Our findings contribute to the prioritisation of efforts in German NTD research at a time of impending local and global policy decisions. PMID:26719978

Metamodeling and the Critic-based approach to multi-level optimization.

PubMed

Werbos, Ludmilla; Kozma, Robert; Silva-Lugo, Rodrigo; Pazienza, Giovanni E; Werbos, Paul J

2012-08-01

Large-scale networks with hundreds of thousands of variables and constraints are becoming more and more common in logistics, communications, and distribution domains. Traditionally, the utility functions defined on such networks are optimized using some variation of Linear Programming, such as Mixed Integer Programming (MIP). Despite enormous progress both in hardware (multiprocessor systems and specialized processors) and software (Gurobi) we are reaching the limits of what these tools can handle in real time. Modern logistic problems, for example, call for expanding the problem both vertically (from one day up to several days) and horizontally (combining separate solution stages into an integrated model). The complexity of such integrated models calls for alternative methods of solution, such as Approximate Dynamic Programming (ADP), which provide a further increase in the performance necessary for the daily operation. In this paper, we present the theoretical basis and related experiments for solving the multistage decision problems based on the results obtained for shorter periods, as building blocks for the models and the solution, via Critic-Model-Action cycles, where various types of neural networks are combined with traditional MIP models in a unified optimization system. In this system architecture, fast and simple feed-forward networks are trained to reasonably initialize more complicated recurrent networks, which serve as approximators of the value function (Critic). The combination of interrelated neural networks and optimization modules allows for multiple queries for the same system, providing flexibility and optimizing performance for large-scale real-life problems. A MATLAB implementation of our solution procedure for a realistic set of data and constraints shows promising results, compared to the iterative MIP approach. Copyright © 2012 Elsevier Ltd. All rights reserved.

Planning in subsumption architectures

NASA Technical Reports Server (NTRS)

Chalfant, Eugene C.

1994-01-01

A subsumption planner using a parallel distributed computational paradigm based on the subsumption architecture for control of real-world capable robots is described. Virtual sensor state space is used as a planning tool to visualize the robot's anticipated effect on its environment. Decision sequences are generated based on the environmental situation expected at the time the robot must commit to a decision. Between decision points, the robot performs in a preprogrammed manner. A rudimentary, domain-specific partial world model contains enough information to extrapolate the end results of the rote behavior between decision points. A collective network of predictors operates in parallel with the reactive network forming a recurrrent network which generates plans as a hierarchy. Details of a plan segment are generated only when its execution is imminent. The use of the subsumption planner is demonstrated by a simple maze navigation problem.

Using Neural Networks in Decision Making for a Reconfigurable Electro Mechanical Actuator (EMA)

NASA Technical Reports Server (NTRS)

Latino, Carl D.

2001-01-01

The objectives of this project were to demonstrate applicability and advantages of a neural network approach for evaluating the performance of an electro-mechanical actuator (EMA). The EMA in question was intended for the X-37 Advanced Technology Vehicle. It will have redundant components for safety and reliability. The neural networks for this application are to monitor the operation of the redundant electronics that control the actuator in real time and decide on the operating configuration. The system we proposed consists of the actuator, sensors, control circuitry and dedicated (embedded) processors. The main purpose of the study was to develop suitable hardware and neural network capable of allowing real time reconfiguration decisions to be made. This approach was to be compared to other methods such as fuzzy logic and knowledge based systems considered for the same application. Over the course of the project a more general objective was the identification of the other neural network applications and the education of interested NASA personnel on the topic of Neural Networks.

A Real-Time Decision Support System for Voltage Collapse Avoidance in Power Supply Networks

NASA Astrophysics Data System (ADS)

Chang, Chen-Sung

This paper presents a real-time decision support system (RDSS) based on artificial intelligence (AI) for voltage collapse avoidance (VCA) in power supply networks. The RDSS scheme employs a fuzzy hyperrectangular composite neural network (FHRCNN) to carry out voltage risk identification (VRI). In the event that a threat to the security of the power supply network is detected, an evolutionary programming (EP)-based algorithm is triggered to determine the operational settings required to restore the power supply network to a secure condition. The effectiveness of the RDSS methodology is demonstrated through its application to the American Electric Power Provider System (AEP, 30-bus system) under various heavy load conditions and contingency scenarios. In general, the numerical results confirm the ability of the RDSS scheme to minimize the risk of voltage collapse in power supply networks. In other words, RDSS provides Power Provider Enterprises (PPEs) with a viable tool for performing on-line voltage risk assessment and power system security enhancement functions.

Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior

PubMed Central

Kochemazov, Stepan; Semenov, Alexander

2014-01-01

In this paper, we propose an approach for modeling and analysis of a number of phenomena of collective behavior. By collectives we mean multi-agent systems that transition from one state to another at discrete moments of time. The behavior of a member of a collective (agent) is called conforming if the opinion of this agent at current time moment conforms to the opinion of some other agents at the previous time moment. We presume that at each moment of time every agent makes a decision by choosing from the set (where 1-decision corresponds to action and 0-decision corresponds to inaction). In our approach we model collective behavior with synchronous Boolean networks. We presume that in a network there can be agents that act at every moment of time. Such agents are called instigators. Also there can be agents that never act. Such agents are called loyalists. Agents that are neither instigators nor loyalists are called simple agents. We study two combinatorial problems. The first problem is to find a disposition of instigators that in several time moments transforms a network from a state where the majority of simple agents are inactive to a state with the majority of active agents. The second problem is to find a disposition of loyalists that returns the network to a state with the majority of inactive agents. Similar problems are studied for networks in which simple agents demonstrate the contrary to conforming behavior that we call anticonforming. We obtained several theoretical results regarding the behavior of collectives of agents with conforming or anticonforming behavior. In computational experiments we solved the described problems for randomly generated networks with several hundred vertices. We reduced corresponding combinatorial problems to the Boolean satisfiability problem (SAT) and used modern SAT solvers to solve the instances obtained. PMID:25526612

Eradicating the grey squirrel Sciurus carolinensis from urban areas: an innovative decision-making approach based on lessons learnt in Italy.

PubMed

La Morgia, Valentina; Paoloni, Daniele; Genovesi, Piero

2017-02-01

Eradication of invasive alien species supports the recovery of native biodiversity. A new European Union Regulation introduces obligations to eradicate the most harmful invasive species. However, eradications of charismatic mammals may encounter strong opposition. Considering the case study of the eastern grey squirrel (Sciurus carolinensis Gmelin, 1788) in central Italy, we developed a structured decision-making technique based on a Bayesian decision network model and explicitly considering the plurality of environmental values of invasive species management to reduce potential social conflicts. The model identified priority areas for management activities. These areas corresponded to the core of the grey squirrel range, but they also included peripheral zones, where rapid eradication is fundamental to prevent the spread of squirrels. However, when the model was expanded to integrate the attitude of citizens towards the project, the intervention strategy slightly changed. In some areas, the citizens' support was limited, and this resulted in a reduced overall utility of intervention. The suggested approach extends the scientific basis for management decisions, evaluated in terms of technical efficiency, feasibility and social impact. Here, the Bayesian decision network model analysed the potential technical and social consequences of management actions, and it responded to the need for transparency in the decision-making process, but it can easily be extended to consider further issues that are common in many mammal eradication programmes. Owing to its flexibility and comprehensiveness, it provides an innovative example of how to plan rapid eradication or control activities, as required by the new EU Regulation. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing.

PubMed

Tulabandhula, Theja; Rudin, Cynthia

2014-06-01

Our goal is to design a prediction and decision system for real-time use during a professional car race. In designing a knowledge discovery process for racing, we faced several challenges that were overcome only when domain knowledge of racing was carefully infused within statistical modeling techniques. In this article, we describe how we leveraged expert knowledge of the domain to produce a real-time decision system for tire changes within a race. Our forecasts have the potential to impact how racing teams can optimize strategy by making tire-change decisions to benefit their rank position. Our work significantly expands previous research on sports analytics, as it is the only work on analytical methods for within-race prediction and decision making for professional car racing.

Goal-Directed Decision Making with Spiking Neurons.

PubMed

Friedrich, Johannes; Lengyel, Máté

2016-02-03

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. Copyright © 2016 the authors 0270-6474/16/361529-18$15.00/0.

Goal-Directed Decision Making with Spiking Neurons

PubMed Central

Lengyel, Máté

2016-01-01

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. PMID:26843636

The Evolution and Expansion of Regional Disease Surveillance Networks and Their Role in Mitigating the Threat of Infectious Disease Outbreaks

PubMed Central

Bond, Katherine C.; Macfarlane, Sarah B.; Burke, Charlanne; Ungchusak, Kumnuan; Wibulpolprasert, Suwit

2013-01-01

We examine the emergence, development, and value of regional infectious disease surveillance networks that neighboring countries worldwide are organizing to control cross-border outbreaks at their source. The regional perspective represented in the paper is intended to serve as an instructive framework for others who decide to launch such networks as new technologies and emerging threats bring countries even closer together. Distinct from more formal networks in geographic regions designated by the World Health Organization (WHO), these networks usually involve groupings of fewer countries chosen by national governments to optimize surveillance efforts. Sometimes referred to as sub-regional, these “self-organizing” networks complement national and local government recognition with informal relationships across borders among epidemiologists, scientists, ministry officials, health workers, border officers, and community members. Their development over time reflects both incremental learning and growing connections among network actors; and changing disease patterns, with infectious disease threats shifting over time from local to regional to global levels. Not only has this regional disease surveillance network model expanded across the globe, it has also expanded from a mostly practitioner-based network model to one that covers training, capacity-building, and multidisciplinary research. Today, several of these networks are linked through Connecting Organizations for Regional Disease Surveillance (CORDS). We explore how regional disease surveillance networks add value to global disease detection and response by complementing other systems and efforts, by harnessing their power to achieve other goals such as health and human security, and by helping countries adapt to complex challenges via multi-sectoral solutions. We note that governmental commitment and trust among participating individuals are critical to the success of regional infectious disease surveillance networks. PMID:23362414

Neural and neurochemical basis of reinforcement-guided decision making.

PubMed

Khani, Abbas; Rainer, Gregor

2016-08-01

Decision making is an adaptive behavior that takes into account several internal and external input variables and leads to the choice of a course of action over other available and often competing alternatives. While it has been studied in diverse fields ranging from mathematics, economics, ecology, and ethology to psychology and neuroscience, recent cross talk among perspectives from different fields has yielded novel descriptions of decision processes. Reinforcement-guided decision making models are based on economic and reinforcement learning theories, and their focus is on the maximization of acquired benefit over a defined period of time. Studies based on reinforcement-guided decision making have implicated a large network of neural circuits across the brain. This network includes a wide range of cortical (e.g., orbitofrontal cortex and anterior cingulate cortex) and subcortical (e.g., nucleus accumbens and subthalamic nucleus) brain areas and uses several neurotransmitter systems (e.g., dopaminergic and serotonergic systems) to communicate and process decision-related information. This review discusses distinct as well as overlapping contributions of these networks and neurotransmitter systems to the processing of decision making. We end the review by touching on neural circuitry and neuromodulatory regulation of exploratory decision making. Copyright © 2016 the American Physiological Society.

Managing risk and expected financial return from selective expansion of operating room capacity: mean-variance analysis of a hospital's portfolio of surgeons.

PubMed

Dexter, Franklin; Ledolter, Johannes

2003-07-01

Surgeons using the same amount of operating room (OR) time differ in their achieved hospital contribution margins (revenue minus variable costs) by >1000%. Thus, to improve the financial return from perioperative facilities, OR strategic decisions should selectively focus additional OR capacity and capital purchasing on a few surgeons or subspecialties. These decisions use estimates of each surgeon's and/or subspecialty's contribution margin per OR hour. The estimates are subject to uncertainty (e.g., from outliers). We account for the uncertainties by using mean-variance portfolio analysis (i.e., quadratic programming). This method characterizes the problem of selectively expanding OR capacity based on the expected financial return and risk of different portfolios of surgeons. The assessment reveals whether the choices, of which surgeons have their OR capacity expanded, are sensitive to the uncertainties in the surgeons' contribution margins per OR hour. Thus, mean-variance analysis reduces the chance of making strategic decisions based on spurious information. We also assess the financial benefit of using mean-variance portfolio analysis when the planned expansion of OR capacity is well diversified over at least several surgeons or subspecialties. Our results show that, in such circumstances, there may be little benefit from further changing the portfolio to reduce its financial risk. Surgeon and subspecialty specific hospital financial data are uncertain, a fact that should be taken into account when making decisions about expanding operating room capacity. We show that mean-variance portfolio analysis can incorporate this uncertainty, thereby guiding operating room management decision-making and reducing the chance of a strategic decision being made based on spurious information.

Speaker verification using committee neural networks.

PubMed

Reddy, Narender P; Buch, Ojas A

2003-10-01

Security is a major problem in web based access or remote access to data bases. In the present study, the technique of committee neural networks was developed for speech based speaker verification. Speech data from the designated speaker and several imposters were obtained. Several parameters were extracted in the time and frequency domains, and fed to neural networks. Several neural networks were trained and the five best performing networks were recruited into the committee. The committee decision was based on majority voting of the member networks. The committee opinion was evaluated with further testing data. The committee correctly identified the designated speaker in (50 out of 50) 100% of the cases and rejected imposters in (150 out of 150) 100% of the cases. The committee decision was not unanimous in majority of the cases tested.

Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

PubMed Central

Zhang, Wenyu; Zhang, Zhenjiang

2015-01-01

Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier’s training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster’s combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule. PMID:26295399

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

Tuxen, L.

The Integrated Risk Information System (IRIS) is an electronic information system developed by the US Environmental Protection Agency (EPA) containing information related to health risk assessment. IRIS is the Agency`s primary vehicle for communication of chronic health hazard information that represents Agency consensus following comprehensive review by intra-Agency work groups. The original purpose for developing IRIS was to provide guidance to EPA personnel in making risk management decisions. This original purpose for developing IRIS was to guidance to EPA personnel in making risk management decisions. This role has expanded and evolved with wider access and use of the system. IRISmore » contains chemical-specific information in summary format for approximately 500 chemicals. IRIS is available to the general public on the National Library of Medicine`s Toxicology Data Network (TOXNET) and on diskettes through the National Technical Information Service (NTIS).« less

Network approach for decision making under risk-How do we choose among probabilistic options with the same expected value?

PubMed

Pan, Wei; Chen, Yi-Shin

2018-01-01

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing 'goal' and 'time' factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight.

Individual differences in intrinsic brain connectivity predict decision strategy.

PubMed

Barnes, Kelly Anne; Anderson, Kevin M; Plitt, Mark; Martin, Alex

2014-10-15

When humans are provided with ample time to make a decision, individual differences in strategy emerge. Using an adaptation of a well-studied decision making paradigm, motion direction discrimination, we probed the neural basis of individual differences in strategy. We tested whether strategies emerged from moment-to-moment reconfiguration of functional brain networks involved in decision making with task-evoked functional MRI (fMRI) and whether intrinsic properties of functional brain networks, measured at rest with functional connectivity MRI (fcMRI), were associated with strategy use. We found that human participants reliably selected one of two strategies across 2 days of task performance, either continuously accumulating evidence or waiting for task difficulty to decrease. Individual differences in decision strategy were predicted both by the degree of task-evoked activation of decision-related brain regions and by the strength of pretask correlated spontaneous brain activity. These results suggest that spontaneous brain activity constrains strategy selection on perceptual decisions.

Brain pathways for cognitive-emotional decision making in the human animal.

PubMed

Levine, Daniel S

2009-04-01

As roles for different brain regions become clearer, a picture emerges of how primate prefrontal cortex executive circuitry influences subcortical decision making pathways inherited from other mammals. The human's basic needs or drives can be interpreted as residing in an on-center off-surround network in motivational regions of the hypothalamus and brain stem. Such a network has multiple attractors that, in this case, represent the amount of satisfaction of these needs, and we consider and interpret neurally a continuous-time simulated annealing algorithm for moving between attractors under the influence of noise that represents "discontent" combined with "initiative." For decision making on specific tasks, we employ a variety of rules whose neural circuitry appears to involve the amygdala and the orbital, cingulate, and dorsolateral regions of prefrontal cortex. These areas can be interpreted as connected in a three-layer adaptive resonance network. The vigilance of the network, which is influenced by the state of the hypothalamic needs network, determines the level of sophistication of the rule being utilized.

Overlapping Networks Engaged during Spoken Language Production and Its Cognitive Control

PubMed Central

Wise, Richard J.S.; Mehta, Amrish; Leech, Robert

2014-01-01

Spoken language production is a complex brain function that relies on large-scale networks. These include domain-specific networks that mediate language-specific processes, as well as domain-general networks mediating top-down and bottom-up attentional control. Language control is thought to involve a left-lateralized fronto-temporal-parietal (FTP) system. However, these regions do not always activate for language tasks and similar regions have been implicated in nonlinguistic cognitive processes. These inconsistent findings suggest that either the left FTP is involved in multidomain cognitive control or that there are multiple spatially overlapping FTP systems. We present evidence from an fMRI study using multivariate analysis to identify spatiotemporal networks involved in spoken language production in humans. We compared spoken language production (Speech) with multiple baselines, counting (Count), nonverbal decision (Decision), and “rest,” to pull apart the multiple partially overlapping networks that are involved in speech production. A left-lateralized FTP network was activated during Speech and deactivated during Count and nonverbal Decision trials, implicating it in cognitive control specific to sentential spoken language production. A mirror right-lateralized FTP network was activated in the Count and Decision trials, but not Speech. Importantly, a second overlapping left FTP network showed relative deactivation in Speech. These three networks, with distinct time courses, overlapped in the left parietal lobe. Contrary to the standard model of the left FTP as being dominant for speech, we revealed a more complex pattern within the left FTP, including at least two left FTP networks with competing functional roles, only one of which was activated in speech production. PMID:24966373

Overlapping networks engaged during spoken language production and its cognitive control.

PubMed

Geranmayeh, Fatemeh; Wise, Richard J S; Mehta, Amrish; Leech, Robert

2014-06-25

Spoken language production is a complex brain function that relies on large-scale networks. These include domain-specific networks that mediate language-specific processes, as well as domain-general networks mediating top-down and bottom-up attentional control. Language control is thought to involve a left-lateralized fronto-temporal-parietal (FTP) system. However, these regions do not always activate for language tasks and similar regions have been implicated in nonlinguistic cognitive processes. These inconsistent findings suggest that either the left FTP is involved in multidomain cognitive control or that there are multiple spatially overlapping FTP systems. We present evidence from an fMRI study using multivariate analysis to identify spatiotemporal networks involved in spoken language production in humans. We compared spoken language production (Speech) with multiple baselines, counting (Count), nonverbal decision (Decision), and "rest," to pull apart the multiple partially overlapping networks that are involved in speech production. A left-lateralized FTP network was activated during Speech and deactivated during Count and nonverbal Decision trials, implicating it in cognitive control specific to sentential spoken language production. A mirror right-lateralized FTP network was activated in the Count and Decision trials, but not Speech. Importantly, a second overlapping left FTP network showed relative deactivation in Speech. These three networks, with distinct time courses, overlapped in the left parietal lobe. Contrary to the standard model of the left FTP as being dominant for speech, we revealed a more complex pattern within the left FTP, including at least two left FTP networks with competing functional roles, only one of which was activated in speech production. Copyright © 2014 Geranmayeh et al.

Non-equilibrium physics of neural networks for leaning, memory and decision making: landscape and flux perspectives

NASA Astrophysics Data System (ADS)

Wang, Jin

Cognitive behaviors are determined by underlying neural networks. Many brain functions, such as learning and memory, can be described by attractor dynamics. We developed a theoretical framework for global dynamics by quantifying the landscape associated with the steady state probability distributions and steady state curl flux, measuring the degree of non-equilibrium through detailed balance breaking. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. Both landscape and flux determine the kinetic paths and speed of decision making. The kinetics and global stability of decision making are explored by quantifying the landscape topography through the barrier heights and the mean first passage time. The theoretical predictions are in agreement with experimental observations: more errors occur under time pressure. We quantitatively explored two mechanisms of the speed-accuracy tradeoff with speed emphasis and further uncovered the tradeoffs among speed, accuracy, and energy cost. Our results show an optimal balance among speed, accuracy, and the energy cost in decision making. We uncovered possible mechanisms of changes of mind and how mind changes improve performance in decision processes. Our landscape approach can help facilitate an understanding of the underlying physical mechanisms of cognitive processes and identify the key elements in neural networks.

Fast decision algorithms in low-power embedded processors for quality-of-service based connectivity of mobile sensors in heterogeneous wireless sensor networks.

PubMed

Jaraíz-Simón, María D; Gómez-Pulido, Juan A; Vega-Rodríguez, Miguel A; Sánchez-Pérez, Juan M

2012-01-01

When a mobile wireless sensor is moving along heterogeneous wireless sensor networks, it can be under the coverage of more than one network many times. In these situations, the Vertical Handoff process can happen, where the mobile sensor decides to change its connection from a network to the best network among the available ones according to their quality of service characteristics. A fitness function is used for the handoff decision, being desirable to minimize it. This is an optimization problem which consists of the adjustment of a set of weights for the quality of service. Solving this problem efficiently is relevant to heterogeneous wireless sensor networks in many advanced applications. Numerous works can be found in the literature dealing with the vertical handoff decision, although they all suffer from the same shortfall: a non-comparable efficiency. Therefore, the aim of this work is twofold: first, to develop a fast decision algorithm that explores the entire space of possible combinations of weights, searching that one that minimizes the fitness function; and second, to design and implement a system on chip architecture based on reconfigurable hardware and embedded processors to achieve several goals necessary for competitive mobile terminals: good performance, low power consumption, low economic cost, and small area integration.

Using Network Science Measures to Predict the Lexical Decision Performance of Adults Who Stutter.

PubMed

Castro, Nichol; Pelczarski, Kristin M; Vitevitch, Michael S

2017-07-12

Methods from network science have examined various aspects of language processing. Clinical populations may also benefit from these novel analyses. Phonological and lexical factors have been examined in adults who stutter (AWS) as potential contributing factors to stuttering, although differences reported are often subtle. We reexamined the performance of AWS and adults who do not stutter (AWNS) from a previously conducted lexical decision task in an attempt to determine if network science measures would provide additional insight into the phonological network of AWS beyond traditional psycholinguistic measures. Multiple regression was used to examine the influence of several traditional psycholinguistic measures as well as several new measures from network science on response times. AWS responded to low-frequency words more slowly than AWNS; responses for both groups were equivalent for high-frequency words. AWS responded to shorter words more slowly than AWNS, producing a reverse word-length effect. For the network measures, degree/neighborhood density and closeness centrality, but not whether a word was inside or outside the giant component, influenced response times similarly between groups. Network analyses suggest that multiple levels of the phonological network might influence phonological processing, not just the micro-level traditionally considered by mainstream psycholinguistics.

Master Clock and Time-Signal-Distribution System

NASA Technical Reports Server (NTRS)

Tjoelker, Robert; Calhoun, Malcolm; Kuhnle, Paul; Sydnor, Richard; Lauf, John

2007-01-01

A timing system comprising an electronic master clock and a subsystem for distributing time signals from the master clock to end users is undergoing development to satisfy anticipated timing requirements of NASA s Deep Space Network (DSN) for the next 20 to 30 years. This system has a modular, flexible, expandable architecture that is easier to operate and maintain than the present frequency and timing subsystem (FTS).

A study of the temporal robustness of the growing global container-shipping network

PubMed Central

Wang, Nuo; Wu, Nuan; Dong, Ling-ling; Yan, Hua-kun; Wu, Di

2016-01-01

Whether they thrive as they grow must be determined for all constantly expanding networks. However, few studies have focused on this important network feature or the development of quantitative analytical methods. Given the formation and growth of the global container-shipping network, we proposed the concept of network temporal robustness and quantitative method. As an example, we collected container liner companies’ data at two time points (2004 and 2014) and built a shipping network with ports as nodes and routes as links. We thus obtained a quantitative value of the temporal robustness. The temporal robustness is a significant network property because, for the first time, we can clearly recognize that the shipping network has become more vulnerable to damage over the last decade: When the node failure scale reached 50% of the entire network, the temporal robustness was approximately −0.51% for random errors and −12.63% for intentional attacks. The proposed concept and analytical method described in this paper are significant for other network studies. PMID:27713549

DREAMS and IMAGE: A Model and Computer Implementation for Concurrent, Life-Cycle Design of Complex Systems

NASA Technical Reports Server (NTRS)

Hale, Mark A.; Craig, James I.; Mistree, Farrokh; Schrage, Daniel P.

1995-01-01

Computing architectures are being assembled that extend concurrent engineering practices by providing more efficient execution and collaboration on distributed, heterogeneous computing networks. Built on the successes of initial architectures, requirements for a next-generation design computing infrastructure can be developed. These requirements concentrate on those needed by a designer in decision-making processes from product conception to recycling and can be categorized in two areas: design process and design information management. A designer both designs and executes design processes throughout design time to achieve better product and process capabilities while expanding fewer resources. In order to accomplish this, information, or more appropriately design knowledge, needs to be adequately managed during product and process decomposition as well as recomposition. A foundation has been laid that captures these requirements in a design architecture called DREAMS (Developing Robust Engineering Analysis Models and Specifications). In addition, a computing infrastructure, called IMAGE (Intelligent Multidisciplinary Aircraft Generation Environment), is being developed that satisfies design requirements defined in DREAMS and incorporates enabling computational technologies.

Socioemotional selectivity theory, aging, and health: the increasingly delicate balance between regulating emotions and making tough choices.

PubMed

Löckenhoff, Corinna E; Carstensen, Laura L

2004-12-01

After providing an introductory overview of socioemotional selectivity theory, we review empirical evidence for its basic postulates and consider the implications of the predicted cognitive and behavioral changes for physical health. The main assertion of socioemotional selectivity theory is that when boundaries on time are perceived, present-oriented goals related to emotional meaning are prioritized over future-oriented goals aimed at acquiring information and expanding horizons. Such motivational changes, which are strongly correlated with chronological age, systematically influence social preferences, social network composition, emotion regulation, and cognitive processing. On the one hand, there is considerable reason to believe that such changes are good for well-being and social adjustment. On the other hand, the very same motivational changes may limit health-related information-seeking and influence attention, memory, and decision-making such that positive material is favored over negative information. Grounding our arguments in socioemotional selectivity theory, we consider possible ways to tailor contexts such that disadvantages are avoided.

Power flow analysis and optimal locations of resistive type superconducting fault current limiters.

PubMed

Zhang, Xiuchang; Ruiz, Harold S; Geng, Jianzhao; Shen, Boyang; Fu, Lin; Zhang, Heng; Coombs, Tim A

2016-01-01

Based on conventional approaches for the integration of resistive-type superconducting fault current limiters (SFCLs) on electric distribution networks, SFCL models largely rely on the insertion of a step or exponential resistance that is determined by a predefined quenching time. In this paper, we expand the scope of the aforementioned models by considering the actual behaviour of an SFCL in terms of the temperature dynamic power-law dependence between the electrical field and the current density, characteristic of high temperature superconductors. Our results are compared to the step-resistance models for the sake of discussion and clarity of the conclusions. Both SFCL models were integrated into a power system model built based on the UK power standard, to study the impact of these protection strategies on the performance of the overall electricity network. As a representative renewable energy source, a 90 MVA wind farm was considered for the simulations. Three fault conditions were simulated, and the figures for the fault current reduction predicted by both fault current limiting models have been compared in terms of multiple current measuring points and allocation strategies. Consequently, we have shown that the incorporation of the E - J characteristics and thermal properties of the superconductor at the simulation level of electric power systems, is crucial for estimations of reliability and determining the optimal locations of resistive type SFCLs in distributed power networks. Our results may help decision making by distribution network operators regarding investment and promotion of SFCL technologies, as it is possible to determine the maximum number of SFCLs necessary to protect against different fault conditions at multiple locations.

The Pioneering Role of the Vaccine Safety Datalink Project (VSD) to Advance Collaborative Research and Distributed Data Networks

PubMed Central

Fahey, Kevin R.

2015-01-01

Introduction: Large-scale distributed data networks consisting of diverse stakeholders including providers, patients, and payers are changing health research in terms of methods, speed and efficiency. The Vaccine Safety Datalink (VSD) set the stage for expanded involvement of health plans in collaborative research. Expanding Surveillance Capacity and Progress Toward a Learning Health System: From an initial collaboration of four integrated health systems with fewer than 10 million covered lives to 16 diverse health plans with nearly 100 million lives now in the FDA Sentinel, the expanded engagement of health plan researchers has been essential to increase the value and impact of these efforts. The collaborative structure of the VSD established a pathway toward research efforts that successfully engage all stakeholders in a cohesive rather than competitive manner. The scientific expertise and methodology developed through the VSD such as rapid cycle analysis (RCA) to conduct near real-time safety surveillance allowed for the development of the expanded surveillance systems that now exist. Building on Success and Lessons Learned: These networks have learned from and built on the knowledge base and infrastructure created by the VSD investigators. This shared technical knowledge and experience expedited the development of systems like the FDA’s Mini-Sentinel and the Patient Centered Outcomes Research Institute (PCORI)’s PCORnet Conclusion: This narrative reviews the evolution of the VSD, its contribution to other collaborative research networks, longer-term sustainability of this type of distributed research, and how knowledge gained from the earlier efforts can contribute to a continually learning health system. PMID:26793736

Gigabit Ethernet: A Technical Assessment.

ERIC Educational Resources Information Center

Axner, David

1997-01-01

Describes gigabit ethernet for LAN (local area network) technology that will expand ethernet bandwidth. Technical details are discussed, including protocol stacks, optical fiber, deployment strategy for performance improvement, ATM (Asynchronous Transfer Mode), real-time protocol, reserve reservation protocol, and standards. (LRW)

13 CFR 120.837 - SBA decision on application for a new CDC or for an existing CDC to expand Area of Operations.

Code of Federal Regulations, 2013 CFR

2013-01-01

... new CDC or for an existing CDC to expand Area of Operations. 120.837 Section 120.837 Business Credit...) Extending A Cdc's Area of Operations § 120.837 SBA decision on application for a new CDC or for an existing CDC to expand Area of Operations. The processing District Office must solicit the comments of any...

13 CFR 120.837 - SBA decision on application for a new CDC or for an existing CDC to expand Area of Operations.

Code of Federal Regulations, 2014 CFR

2014-01-01

... new CDC or for an existing CDC to expand Area of Operations. 120.837 Section 120.837 Business Credit...) Extending A Cdc's Area of Operations § 120.837 SBA decision on application for a new CDC or for an existing CDC to expand Area of Operations. The processing District Office must solicit the comments of any...

13 CFR 120.837 - SBA decision on application for a new CDC or for an existing CDC to expand Area of Operations.

Code of Federal Regulations, 2011 CFR

2011-01-01

... new CDC or for an existing CDC to expand Area of Operations. 120.837 Section 120.837 Business Credit...) Extending A Cdc's Area of Operations § 120.837 SBA decision on application for a new CDC or for an existing CDC to expand Area of Operations. The processing District Office must solicit the comments of any...

13 CFR 120.837 - SBA decision on application for a new CDC or for an existing CDC to expand Area of Operations.

Code of Federal Regulations, 2012 CFR

2012-01-01

... new CDC or for an existing CDC to expand Area of Operations. 120.837 Section 120.837 Business Credit...) Extending A Cdc's Area of Operations § 120.837 SBA decision on application for a new CDC or for an existing CDC to expand Area of Operations. The processing District Office must solicit the comments of any...

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

PubMed Central

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

PubMed

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Decision-Making in National Security Affairs: Toward a Typology.

DTIC Science & Technology

1985-06-07

decisional model, and thus provide the necessary linkage between observation and application of theory in explaining and/or predicting policy decisions . r...examines theories and models of decision -making processes from an interdisciplinary perspective, with a view toward deriving means by which the behavior of...processes, game theory , linear programming, network and graph theory , time series analysis, and the like. The discipline of decision analysis is a relatively

Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

PubMed

Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

2014-09-01

Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

Our Selections and Decisions: Inherent Features of the Nervous System?

NASA Astrophysics Data System (ADS)

Rösler, Frank

The chapter summarizes findings on the neuronal bases of decisionmaking. Taking the phenomenon of selection it will be explained that systems built only from excitatory and inhibitory neuron (populations) have the emergent property of selecting between different alternatives. These considerations suggest that there exists a hierarchical architecture with central selection switches. However, in such a system, functions of selection and decision-making are not localized, but rather emerge from an interaction of several participating networks. These are, on the one hand, networks that process specific input and output representations and, on the other hand, networks that regulate the relative activation/inhibition of the specific input and output networks. These ideas are supported by recent empirical evidence. Moreover, other studies show that rather complex psychological variables, like subjective probability estimates, expected gains and losses, prediction errors, etc., do have biological correlates, i.e., they can be localized in time and space as activation states of neural networks and single cells. These findings suggest that selections and decisions are consequences of an architecture which, seen from a biological perspective, is fully deterministic. However, a transposition of such nomothetic functional principles into the idiographic domain, i.e., using them as elements for comprehensive 'mechanistic' explanations of individual decisions, seems not to be possible because of principle limitations. Therefore, individual decisions will remain predictable by means of probabilistic models alone.

A framework for visualization of battlefield network behavior

NASA Astrophysics Data System (ADS)

Perzov, Yury; Yurcik, William

2006-05-01

An extensible network simulation application was developed to study wireless battlefield communications. The application monitors node mobility and depicts broadcast and unicast traffic as expanding rings and directed links. The network simulation was specially designed to support fault injection to show the impact of air strikes on disabling nodes. The application takes standard ns-2 trace files as an input and provides for performance data output in different graphical forms (histograms and x/y plots). Network visualization via animation of simulation output can be saved in AVI format that may serve as a basis for a real-time battlefield awareness system.

Road Network State Estimation Using Random Forest Ensemble Learning

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

Hou, Yi; Edara, Praveen; Chang, Yohan

Network-scale travel time prediction not only enables traffic management centers (TMC) to proactively implement traffic management strategies, but also allows travelers make informed decisions about route choices between various origins and destinations. In this paper, a random forest estimator was proposed to predict travel time in a network. The estimator was trained using two years of historical travel time data for a case study network in St. Louis, Missouri. Both temporal and spatial effects were considered in the modeling process. The random forest models predicted travel times accurately during both congested and uncongested traffic conditions. The computational times for themore » models were low, thus useful for real-time traffic management and traveler information applications.« less

Demonstrating an Effective Marine Biodiversity Observation Network in the Santa Barbara Channel

NASA Astrophysics Data System (ADS)

Miller, R. J.

2016-02-01

The Santa Barbara Channel (SBC) is a transition zone characterized by high species and habitat diversity and strong environmental gradients within a relatively small area where cold- and warm-water species found from Baja to the Bering Sea coexist. These characteristics make SBC an ideal setting for our demonstration Marine Biodiversity Observation Network (BON) project that integrates biological levels from genes to habitats and links biodiversity observations to environmental forcing and biogeography. SBC BON is building a comprehensive demonstration system that includes representation of all levels of biotic diversity, key new tools to expand the scales of present observation, and a data management network to integrate new and existing data sources. Our system will be scalable to expand into a full regional Marine BON, and the methods and decision support tools we develop will be transferable to other regions. Incorporating a broad set of habitats including nearshore coast, continental shelf, and pelagic, and taxonomic breadth from microbes to whales will facilitate this transferability. The Santa Barbara Channel marine BON has three broad objectives: 1. Integrate biodiversity data to enable inferences about regional biodiversity 2. Develop advanced methods in optical and acoustic imaging and genomics for monitoring biodiversity in partnership with ongoing monitoring and research programs to begin filling the gaping gaps in our knowledge. 3. Implement a tradeoff framework that optimizes allocation of sampling effort. Here we discuss our progress towards these goals and challenges in developing an effective MBON.

Campaign-level dynamic network modelling for spaceflight logistics for the flexible path concept

NASA Astrophysics Data System (ADS)

Ho, Koki; de Weck, Olivier L.; Hoffman, Jeffrey A.; Shishko, Robert

2016-06-01

This paper develops a network optimization formulation for dynamic campaign-level space mission planning. Although many past space missions have been designed mainly from a mission-level perspective, a campaign-level perspective will be important for future space exploration. In order to find the optimal campaign-level space transportation architecture, a mixed-integer linear programming (MILP) formulation with a generalized multi-commodity flow and a time-expanded network is developed. Particularly, a new heuristics-based method, a partially static time-expanded network, is developed to provide a solution quickly. The developed method is applied to a case study containing human exploration of a near-Earth object (NEO) and Mars, related to the concept of the Flexible Path. The numerical results show that using the specific combinations of propulsion technologies, in-situ resource utilization (ISRU), and other space infrastructure elements can reduce the initial mass in low-Earth orbit (IMLEO) significantly. In addition, the case study results also show that we can achieve large IMLEO reduction by designing NEO and Mars missions together as a campaign compared with designing them separately owing to their common space infrastructure pre-deployment. This research will be an important step toward efficient and flexible campaign-level space mission planning.

Custom Ontologies for Expanded Network Analysis

DTIC Science & Technology

2006-12-01

for Expanded Network Analysis. In Visualising Network Information (pp. 6-1 – 6-10). Meeting Proceedings RTO-MP-IST-063, Paper 6. Neuilly-sur-Seine...Even to this day, current research groups are working to develop an approach that involves taking all available text, video, imagery and audio and

Request-Driven Schedule Automation for the Deep Space Network

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Tran, Daniel; Arroyo, Belinda; Call, Jared; Mercado, Marisol

2010-01-01

The DSN Scheduling Engine (DSE) has been developed to increase the level of automated scheduling support available to users of NASA s Deep Space Network (DSN). We have adopted a request-driven approach to DSN scheduling, in contrast to the activity-oriented approach used up to now. Scheduling requests allow users to declaratively specify patterns and conditions on their DSN service allocations, including timing, resource requirements, gaps, overlaps, time linkages among services, repetition, priorities, and a wide range of additional factors and preferences. The DSE incorporates a model of the key constraints and preferences of the DSN scheduling domain, along with algorithms to expand scheduling requests into valid resource allocations, to resolve schedule conflicts, and to repair unsatisfied requests. We use time-bounded systematic search with constraint relaxation to return nearby solutions if exact ones cannot be found, where the relaxation options and order are under user control. To explore the usability aspects of our approach we have developed a graphical user interface incorporating some crucial features to make it easier to work with complex scheduling requests. Among these are: progressive revelation of relevant detail, immediate propagation and visual feedback from a user s decisions, and a meeting calendar metaphor for repeated patterns of requests. Even as a prototype, the DSE has been deployed and adopted as the initial step in building the operational DSN schedule, thus representing an important initial validation of our overall approach. The DSE is a core element of the DSN Service Scheduling Software (S(sup 3)), a web-based collaborative scheduling system now under development for deployment to all DSN users.

Producing regionally-relevant multiobjective tradeoffs to engage with Colorado water managers

NASA Astrophysics Data System (ADS)

Smith, R.; Kasprzyk, J. R.; Basdekas, L.; Dilling, L.

2016-12-01

Disseminating results from water resources systems analysis research can be challenging when there are political or regulatory barriers associated with real-world models, or when a research model does not incorporate management context to which practitioners can relate. As part of a larger transdisciplinary study, we developed a broadly-applicable case study in collaboration with our partners at six diverse water utilities in the Front Range of Colorado, USA. Our model, called the "Eldorado Utility Planning Model", incorporates realistic water management decisions and objectives and achieves a pragmatic balance between system complexity and simplicity. Using the sophisticated modeling platform RiverWare, we modeled a spatially distributed regional network in which, under varying climate scenarios, the Eldorado Utility can meet growing demand from its variety of sources and by interacting with other users in the network. In accordance with complicated Front Range water laws, ownership, priority of use, and restricted uses of water are tracked through RiverWare's accounting functionality. To achieve good system performance, Eldorado can make decisions such as expand/build a reservoir, purchase rights from one or more actors, and enact conservation. This presentation introduces the model, and motivates how it can be used to aid researchers in developing multi-objective evolutionary algorithm (MOEA)-based optimization for similar multi-reservoir systems in Colorado and the Western US. Within the optimization, system performance is quantified by 5 objectives: minimizing time in restrictions; new storage capacity; newly developed supply; and uncaptured water; and maximizing year-end storage. Our results demonstrate critical tradeoffs between the objectives and show how these tradeoffs are affected by several realistic climate change scenarios. These results were used within an interactive workshop that helped demonstrate the application of MOEA-based optimization for water management in the western US.

Abductive networks applied to electronic combat

NASA Astrophysics Data System (ADS)

Montgomery, Gerard J.; Hess, Paul; Hwang, Jong S.

1990-08-01

A practical approach to dealing with combinatorial decision problems and uncertainties associated with electronic combat through the use of networks of high-level functional elements called abductive networks is presented. It describes the application of the Abductory Induction Mechanism (AIMTM) a supervised inductive learning tool for synthesizing polynomial abductive networks to the electronic combat problem domain. From databases of historical expert-generated or simulated combat engagements AIM can often induce compact and robust network models for making effective real-time electronic combat decisions despite significant uncertainties or a combinatorial explosion of possible situations. The feasibility of applying abductive networks to realize advanced combat decision aiding capabilities was demonstrated by applying AIM to a set of electronic combat simulations. The networks synthesized by AIM generated accurate assessments of the intent lethality and overall risk associated with a variety of simulated threats and produced reasonable estimates of the expected effectiveness of a group of electronic countermeasures for a large number of simulated combat scenarios. This paper presents the application of abductive networks to electronic combat summarizes the results of experiments performed using AIM discusses the benefits and limitations of applying abductive networks to electronic combat and indicates why abductive networks can often result in capabilities not attainable using alternative approaches. 1. ELECTRONIC COMBAT. UNCERTAINTY. AND MACHINE LEARNING Electronic combat has become an essential part of the ability to make war and has become increasingly complex since

A Markov chain model for image ranking system in social networks

NASA Astrophysics Data System (ADS)

Zin, Thi Thi; Tin, Pyke; Toriu, Takashi; Hama, Hiromitsu

2014-03-01

In today world, different kinds of networks such as social, technological, business and etc. exist. All of the networks are similar in terms of distributions, continuously growing and expanding in large scale. Among them, many social networks such as Facebook, Twitter, Flickr and many others provides a powerful abstraction of the structure and dynamics of diverse kinds of inter personal connection and interaction. Generally, the social network contents are created and consumed by the influences of all different social navigation paths that lead to the contents. Therefore, identifying important and user relevant refined structures such as visual information or communities become major factors in modern decision making world. Moreover, the traditional method of information ranking systems cannot be successful due to their lack of taking into account the properties of navigation paths driven by social connections. In this paper, we propose a novel image ranking system in social networks by using the social data relational graphs from social media platform jointly with visual data to improve the relevance between returned images and user intentions (i.e., social relevance). Specifically, we propose a Markov chain based Social-Visual Ranking algorithm by taking social relevance into account. By using some extensive experiments, we demonstrated the significant and effectiveness of the proposed social-visual ranking method.

Update on Plans to Establish a National Phenology Network in the U.S.A.

NASA Astrophysics Data System (ADS)

Betancourt, J.; Schwartz, M.; Breshears, D.; Cayan, D.; Dettinger, M.; Inouye, D.; Post, E.; Reed, B.; Gray, S.

2005-12-01

The passing of the seasons is the most pervasive source of climatic and biological variability on Earth, yet phenological monitoring has been spotty worldwide. Formal phenological networks were recently established in Europe and Canada, and we are now following their lead in organizing a National Phenology Network (NPN) for the U.S.A. With support from federal agencies (NSF, USGS, NPS, USDA-FS, EPA, NOAA, NASA), on Aug. 22-26 we organized a workshop in Tucson, Arizona to begin planning a national-scale, multi-tiered phenological network. A prototype for a web-based NPN and preliminary workshop results are available at http://www.npn.uwm.edu. The main goals of NPN will be to: (1) facilitate thorough understanding of phenological phenomena, including causes and effects; (2) provide ground truthing to make the most of heavy public investment in remote sensing data; (3) allow detection and prediction of environmental change for a wide of variety of applications; (4) harness the power of mass participation and engage tens of thousands of "citizen scientists" in meeting national needs in Education, Health, Commerce, Natural Resources and Agriculture; (5) develop a model system for substantive collaboration across different levels of government, academia and the private sector. Just as the national networks of weather stations and stream gauges are critical for providing weather, climate and water-related information, NPN will help safeguard and procure goods and services that ecosystems provide. We expect that NPN will consist of a four-tiered, expandable structure: 1) a backbone network linked to existing weather stations, run by recruited public observers; 2) A smaller, second tier of intensive observations, run by scientists at established research sites; 3) a much larger network of observations made by citizen scientists; and 4) remote sensing observations that can be validated with surface observations, thereby providing wall-to-wall coverage for the U.S.A. Key to the success of NPN will be formal linkages with other ecological networks (e.g., LTER, AmeriFlux, NEON, USDA-FS Inventory and Analysis, NPS Inventory and Monitoring) and strategic co-location of phenological measurements with weather stations (e.g., NOAA's Real-Time Observation Network and state mesonets). Establishment and operation of NPN will require partnerships among multiple federal and state agencies, universities, and NGO's. Interagency agreements will facilitate data sharing, staff commitments, and the transfer of funds, while demonstrating policy-level support for NPN and smoothing the path for use of phenological data in decision-making. A formal implementation report will be completed and circulated for review by Dec. 1, 2005. As soon as the network can start assimilating observations from the public at large (tier 3), NPN will start recruiting observers through NGO's, as well as regional and national media. Every effort will be made to start making observations and expanding the monitoring network by Spring 2006.

Optimization and resilience in natural resources management

USGS Publications Warehouse

Williams, Byron K.; Johnson, Fred A.

2015-01-01

We consider the putative tradeoff between optimization and resilience in the management of natural resources, using a framework that incorporates different sources of uncertainty that are common in natural resources management. We address one-time decisions, and then expand the decision context to the more complex problem of iterative decision making. For both cases we focus on two key sources of uncertainty: partial observability of system state and uncertainty as to system dynamics. Optimal management strategies will vary considerably depending on the timeframe being considered and the amount and quality of information that is available to characterize system features and project the consequences of potential decisions. But in all cases an optimal decision making framework, if properly identified and focused, can be useful in recognizing sound decisions. We argue that under the conditions of deep uncertainty that characterize many resource systems, an optimal decision process that focuses on robustness does not automatically induce a loss of resilience.

Smart-Grid Backbone Network Real-Time Delay Reduction via Integer Programming.

PubMed

Pagadrai, Sasikanth; Yilmaz, Muhittin; Valluri, Pratyush

2016-08-01

This research investigates an optimal delay-based virtual topology design using integer linear programming (ILP), which is applied to the current backbone networks such as smart-grid real-time communication systems. A network traffic matrix is applied and the corresponding virtual topology problem is solved using the ILP formulations that include a network delay-dependent objective function and lightpath routing, wavelength assignment, wavelength continuity, flow routing, and traffic loss constraints. The proposed optimization approach provides an efficient deterministic integration of intelligent sensing and decision making, and network learning features for superior smart grid operations by adaptively responding the time-varying network traffic data as well as operational constraints to maintain optimal virtual topologies. A representative optical backbone network has been utilized to demonstrate the proposed optimization framework whose simulation results indicate that superior smart-grid network performance can be achieved using commercial networks and integer programming.

Optical Meteor Systems Used by the NASA Meteoroid Environment Office

NASA Technical Reports Server (NTRS)

Kingery, A. M.; Blaauw, R. C.; Cooke, W. J.; Moser, D. E.

2015-01-01

The NASA Meteoroid Environment Office (MEO) uses two main meteor camera networks to characterize the meteoroid environment: an all sky system and a wide field system to study cm and mm size meteors respectively. The NASA All Sky Fireball Network consists of fifteen meteor video cameras in the United States, with plans to expand to eighteen cameras by the end of 2015. The camera design and All-Sky Guided and Real-time Detection (ASGARD) meteor detection software [1, 2] were adopted from the University of Western Ontario's Southern Ontario Meteor Network (SOMN). After seven years of operation, the network has detected over 12,000 multi-station meteors, including meteors from at least 53 different meteor showers. The network is used for speed distribution determination, characterization of meteor showers and sporadic sources, and for informing the public on bright meteor events. The NASA Wide Field Meteor Network was established in December of 2012 with two cameras and expanded to eight cameras in December of 2014. The two camera configuration saw 5470 meteors over two years of operation with two cameras, and has detected 3423 meteors in the first five months of operation (Dec 12, 2014 - May 12, 2015) with eight cameras. We expect to see over 10,000 meteors per year with the expanded system. The cameras have a 20 degree field of view and an approximate limiting meteor magnitude of +5. The network's primary goal is determining the nightly shower and sporadic meteor fluxes. Both camera networks function almost fully autonomously with little human interaction required for upkeep and analysis. The cameras send their data to a central server for storage and automatic analysis. Every morning the servers automatically generates an e-mail and web page containing an analysis of the previous night's events. The current status of the networks will be described, alongside with preliminary results. In addition, future projects, CCD photometry and broadband meteor color camera system, will be discussed.

Computing by robust transience: How the fronto-parietal network performs sequential category-based decisions

PubMed Central

Chaisangmongkon, Warasinee; Swaminathan, Sruthi K.; Freedman, David J.; Wang, Xiao-Jing

2017-01-01

Summary Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a ‘neural landscape’, consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally-relevant circuit motifs and generalize the framework to solve other categorization tasks. PMID:28334612

Medicaid Expansion: A Tale of Two Governors.

PubMed

Flagg, Robin

2016-10-01

This is a study of why two seemingly similar governors made divergent decisions on expanding Medicaid under the Patient Protection and Affordable Care Act (ACA). Performing a case study of Governors John Kasich (OH) and Scott Walker (WI), I explore the roles played by electoral pressures, political party, governor's ideology, the state's policy heritage, stakeholder advocacy, and the economy in each governor's decision about whether to expand Medicaid. Electoral pressure was the most significant factor for both governors. I demonstrate that even Walker succumbed to state electoral pressures and expanded Medicaid, albeit in a manner unique to Wisconsin. He did this despite his emphatic national rhetoric rejecting Obamacare and expansion. Additionally, existing state political institutions drove each governor to decide in a manner unique to his state: previous Medicaid decisions in Wisconsin and direct democracy in Ohio provided additional pressures and divergent starting points. The remaining factors served less as a driving force behind the decision and more as a frame to justify the decision ex post facto. Case studies allow for a more complex view of how political pressures fit together; differences can be explained and expanded, and an enhanced understanding of political processes can be gleaned. Copyright © 2016 by Duke University Press.

IMPROVEMENT OF BUSINESS EFFICIENCY USING A MULTI-AGENT SIMULATION FOR HIGHWAY PATROL ON URBAN EXPRESSWAY

NASA Astrophysics Data System (ADS)

Okamoto, Taro; Taniguchi, Eiichi; Yamada, Tadashi

In Japan, the network of urban expressway has been expanding with the development of urban areas. However, the patrol systems in the urban expressway has not been operated on the basis of scientific evidence, but of conformity and experience. It is therefore crucial to efficiently operate such systems, not only to facilitate the rapid recovery of decreased expressway functionality, but also to acquire the income that supports the operation of privatized expressway companies. Therefore, we develop a multiagent simulation model consisting of the decision-making of four agents, including expressway company, highway patol company, road network users and road authority. These agents determines their schemes depending on their profit obtained. Results of the simulation identyfies the schemes that could offer the profits to the expressway companies in terms of the convenience of the users and the improvement of their operation.

A controllable sensor management algorithm capable of learning

NASA Astrophysics Data System (ADS)

Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

2005-03-01

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

Tending the Flock

ERIC Educational Resources Information Center

Walker, Nathalie

2011-01-01

Many universities, colleges, and independent schools face the reality of geographically expanding alumni bases, likely combined with constant or decreasing budgets for alumni activities. Well-intentioned colleagues understand the potential power of a geographically diverse alumni network but do not always grasp the time and complexity associated…

PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space.

PubMed

Ma, Chihua; Luciani, Timothy; Terebus, Anna; Liang, Jie; Marai, G Elisabeta

2017-02-15

Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists' understanding of phenotypic behavior associated with specific genes. We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases.

Efficient methods and readily customizable libraries for managing complexity of large networks.

PubMed

Dogrusoz, Ugur; Karacelik, Alper; Safarli, Ilkin; Balci, Hasan; Dervishi, Leonard; Siper, Metin Can

2018-01-01

One common problem in visualizing real-life networks, including biological pathways, is the large size of these networks. Often times, users find themselves facing slow, non-scaling operations due to network size, if not a "hairball" network, hindering effective analysis. One extremely useful method for reducing complexity of large networks is the use of hierarchical clustering and nesting, and applying expand-collapse operations on demand during analysis. Another such method is hiding currently unnecessary details, to later gradually reveal on demand. Major challenges when applying complexity reduction operations on large networks include efficiency and maintaining the user's mental map of the drawing. We developed specialized incremental layout methods for preserving a user's mental map while managing complexity of large networks through expand-collapse and hide-show operations. We also developed open-source JavaScript libraries as plug-ins to the web based graph visualization library named Cytsocape.js to implement these methods as complexity management operations. Through efficient specialized algorithms provided by these extensions, one can collapse or hide desired parts of a network, yielding potentially much smaller networks, making them more suitable for interactive visual analysis. This work fills an important gap by making efficient implementations of some already known complexity management techniques freely available to tool developers through a couple of open source, customizable software libraries, and by introducing some heuristics which can be applied upon such complexity management techniques to ensure preserving mental map of users.

Application of Network and Decision Theory to Routing Problems.

DTIC Science & Technology

1982-03-01

special thanks to Major Hal Carter, faculty member, for his help in getting the authors to understand one of the underlying algorithms in the methodology...61 26. General Methodology Flowchart .......... .. 64 27. Least Cost/Time Path Algorithm Flowchart . . 65 28. Possible Redundant Arc of Time...minimum time to travel. This was neces- sary because: 1. The DTN designers did not have a procedure to do so. 2. The various network algorithms to

Optimization and resilience of complex supply-demand networks

NASA Astrophysics Data System (ADS)

Zhang, Si-Ping; Huang, Zi-Gang; Dong, Jia-Qi; Eisenberg, Daniel; Seager, Thomas P.; Lai, Ying-Cheng

2015-06-01

Supply-demand processes take place on a large variety of real-world networked systems ranging from power grids and the internet to social networking and urban systems. In a modern infrastructure, supply-demand systems are constantly expanding, leading to constant increase in load requirement for resources and consequently, to problems such as low efficiency, resource scarcity, and partial system failures. Under certain conditions global catastrophe on the scale of the whole system can occur through the dynamical process of cascading failures. We investigate optimization and resilience of time-varying supply-demand systems by constructing network models of such systems, where resources are transported from the supplier sites to users through various links. Here by optimization we mean minimization of the maximum load on links, and system resilience can be characterized using the cascading failure size of users who fail to connect with suppliers. We consider two representative classes of supply schemes: load driven supply and fix fraction supply. Our findings are: (1) optimized systems are more robust since relatively smaller cascading failures occur when triggered by external perturbation to the links; (2) a large fraction of links can be free of load if resources are directed to transport through the shortest paths; (3) redundant links in the performance of the system can help to reroute the traffic but may undesirably transmit and enlarge the failure size of the system; (4) the patterns of cascading failures depend strongly upon the capacity of links; (5) the specific location of the trigger determines the specific route of cascading failure, but has little effect on the final cascading size; (6) system expansion typically reduces the efficiency; and (7) when the locations of the suppliers are optimized over a long expanding period, fewer suppliers are required. These results hold for heterogeneous networks in general, providing insights into designing optimal and resilient complex supply-demand systems that expand constantly in time.

Behavioral and multimodal neuroimaging evidence for a deficit in brain timing networks in stuttering: a hypothesis and theory

PubMed Central

Etchell, Andrew C.; Johnson, Blake W.; Sowman, Paul F.

2014-01-01

The fluent production of speech requires accurately timed movements. In this article, we propose that a deficit in brain timing networks is one of the core neurophysiological deficits in stuttering. We first discuss the experimental evidence supporting the involvement of the basal ganglia and supplementary motor area (SMA) in stuttering and the involvement of the cerebellum as a possible mechanism for compensating for the neural deficits that underlie stuttering. Next, we outline the involvement of the right inferior frontal gyrus (IFG) as another putative compensatory locus in stuttering and suggest a role for this structure in an expanded core timing-network. Subsequently, we review behavioral studies of timing in people who stutter and examine their behavioral performance as compared to people who do not stutter. Finally, we highlight challenges to existing research and provide avenues for future research with specific hypotheses. PMID:25009487

The Ising Decision Maker: a binary stochastic network for choice response time.

PubMed

Verdonck, Stijn; Tuerlinckx, Francis

2014-07-01

The Ising Decision Maker (IDM) is a new formal model for speeded two-choice decision making derived from the stochastic Hopfield network or dynamic Ising model. On a microscopic level, it consists of 2 pools of binary stochastic neurons with pairwise interactions. Inside each pool, neurons excite each other, whereas between pools, neurons inhibit each other. The perceptual input is represented by an external excitatory field. Using methods from statistical mechanics, the high-dimensional network of neurons (microscopic level) is reduced to a two-dimensional stochastic process, describing the evolution of the mean neural activity per pool (macroscopic level). The IDM can be seen as an abstract, analytically tractable multiple attractor network model of information accumulation. In this article, the properties of the IDM are studied, the relations to existing models are discussed, and it is shown that the most important basic aspects of two-choice response time data can be reproduced. In addition, the IDM is shown to predict a variety of observed psychophysical relations such as Piéron's law, the van der Molen-Keuss effect, and Weber's law. Using Bayesian methods, the model is fitted to both simulated and real data, and its performance is compared to the Ratcliff diffusion model. (c) 2014 APA, all rights reserved.

Adapting forest health assessments to changing perspectives on threats--a case example from Sweden.

PubMed

Wulff, Sören; Lindelöw, Åke; Lundin, Lars; Hansson, Per; Axelsson, Anna-Lena; Barklund, Pia; Wijk, Sture; Ståhl, Göran

2012-04-01

A revised Swedish forest health assessment system is presented. The assessment system is composed of several interacting components which target information needs for strategic and operational decision making and accommodate a continuously expanding knowledge base. The main motivation for separating information for strategic and operational decision making is that major damage outbreaks are often scattered throughout the landscape. Generally, large-scale inventories (such as national forest inventories) cannot provide adequate information for mitigation measures. In addition to broad monitoring programs that provide time-series information on known damaging agents and their effects, there is also a need for local and regional inventories adapted to specific damage events. While information for decision making is the major focus of the health assessment system, the system also contributes to expanding the knowledge base of forest conditions. For example, the integrated monitoring programs provide a better understanding of ecological processes linked to forest health. The new health assessment system should be able to respond to the need for quick and reliable information and thus will be an important part of the future monitoring of Swedish forests.

Satellite image processing for precision agriculture and agroindustry using convolutional neural network and genetic algorithm

NASA Astrophysics Data System (ADS)

Firdaus; Arkeman, Y.; Buono, A.; Hermadi, I.

2017-01-01

Translating satellite imagery to a useful data for decision making during this time are usually done manually by human. In this research, we are going to translate satellite imagery by using artificial intelligence method specifically using convolutional neural network and genetic algorithm to become a useful data for decision making, especially for precision agriculture and agroindustry. In this research, we are focused on how to made a sustainable land use planning with 3 objectives. The first is maximizing economic factor. Second is minimizing CO2 emission and the last is minimizing land degradation. Results show that by using artificial intelligence method, can produced a good pareto optimum solutions in a short time.

Achieving full connectivity of sites in the multiperiod reserve network design problem

USGS Publications Warehouse

Jafari, Nahid; Nuse, Bryan L.; Moore, Clinton; Dilkina, Bistra; Hepinstall-Cymerman, Jeffrey

2017-01-01

The conservation reserve design problem is a challenge to solve because of the spatial and temporal nature of the problem, uncertainties in the decision process, and the possibility of alternative conservation actions for any given land parcel. Conservation agencies tasked with reserve design may benefit from a dynamic decision system that provides tactical guidance for short-term decision opportunities while maintaining focus on a long-term objective of assembling the best set of protected areas possible. To plan cost-effective conservation over time under time-varying action costs and budget, we propose a multi-period mixed integer programming model for the budget-constrained selection of fully connected sites. The objective is to maximize a summed conservation value over all network parcels at the end of the planning horizon. The originality of this work is in achieving full spatial connectivity of the selected sites during the schedule of conservation actions.

A study of the security technology and a new security model for WiFi network

NASA Astrophysics Data System (ADS)

Huang, Jing

2013-07-01

The WiFi network is one of the most rapidly developing wireless communication networks, which makes wireless office and wireless life possible and greatly expands the application form and scope of the internet. At the same time, the WiFi network security has received wide attention, and this is also the key factor of WiFi network development. This paper makes a systematic introduction to the WiFi network and WiFi network security problems, and the WiFi network security technology are reviewed and compared. In order to solve the security problems in WiFi network, this paper presents a new WiFi network security model and the key exchange algorithm. Experiments are performed to test the performance of the model, the results show that the new security model can withstand external network attack and ensure stable and safe operation of WiFi network.

Disorder generated by interacting neural networks: application to econophysics and cryptography

NASA Astrophysics Data System (ADS)

Kinzel, Wolfgang; Kanter, Ido

2003-10-01

When neural networks are trained on their own output signals they generate disordered time series. In particular, when two neural networks are trained on their mutual output they can synchronize; they relax to a time-dependent state with identical synaptic weights. Two applications of this phenomenon are discussed for (a) econophysics and (b) cryptography. (a) When agents competing in a closed market (minority game) are using neural networks to make their decisions, the total system relaxes to a state of good performance. (b) Two partners communicating over a public channel can find a common secret key.

Complex network analysis of conventional and Islamic stock market in Indonesia

NASA Astrophysics Data System (ADS)

Rahmadhani, Andri; Purqon, Acep; Kim, Sehyun; Kim, Soo Yong

2015-09-01

The rising popularity of Islamic financial products in Indonesia has become a new interesting topic to be analyzed recently. We introduce a complex network analysis to compare conventional and Islamic stock market in Indonesia. Additionally, Random Matrix Theory (RMT) has been added as a part of reference to expand the analysis of the result. Both of them are based on the cross correlation matrix of logarithmic price returns. Closing price data, which is taken from June 2011 to July 2012, is used to construct logarithmic price returns. We also introduce the threshold value using winner-take-all approach to obtain scale-free property of the network. This means that the nodes of the network that has a cross correlation coefficient below the threshold value should not be connected with an edge. As a result, we obtain 0.5 as the threshold value for all of the stock market. From the RMT analysis, we found that there is only market wide effect on both stock market and no clustering effect has been found yet. From the network analysis, both of stock market networks are dominated by the mining sector. The length of time series of closing price data must be expanded to get more valuable results, even different behaviors of the system.

Utilization of Live Localized Weather Information for Sustainable Agriculture

NASA Astrophysics Data System (ADS)

Anderson, J.; Usher, J.

2010-09-01

Authors: Jim Anderson VP, Global Network and Business Development WeatherBug® Professional Jeremy Usher Managing Director, Europe WeatherBug® Professional Localized, real-time weather information is vital for day-to-day agronomic management of all crops. The challenge for agriculture is twofold in that local and timely weather data is not often available for producers and farmers, and it is not integrated into decision-support tools they require. Many of the traditional sources of weather information are not sufficient for agricultural applications because of the long distances between weather stations, meaning the data is not always applicable for on-farm decision making processes. The second constraint with traditional weather information is the timeliness of the data. Most delivery systems are designed on a one-hour time step, whereas many decisions in agriculture are based on minute-by-minute weather conditions. This is especially true for decisions surrounding chemical and fertilizer application and frost events. This presentation will outline how the creation of an agricultural mesonet (weather network) can enable producers and farmers with live, local weather information from weather stations installed in farm/field locations. The live weather information collected from each weather station is integrated into a web-enabled decision support tool, supporting numerous on-farm agronomic activities such as pest management, or dealing with heavy rainfall and frost events. Agronomic models can be used to assess the potential of disease pressure, enhance the farmer's abilities to time pesticide applications, or assess conditions contributing to yield and quality fluctuations. Farmers and industry stakeholders may also view quality-assured historical weather variables at any location. This serves as a record-management tool for viewing previously uncharted agronomic weather events in graph or table form. This set of weather tools is unique and provides a significant enhancement to the agronomic decision-support process. Direct benefits to growers can take the form of increased yield and grade potential, as well as savings in money and time. Pest management strategies become more efficient due to timely and localized disease and pest modelling, and increased efficacy of pest and weed control. Examples from the Canadian Wheat Board (CWB) WeatherFarm weather network will be utilized to illustrate the processes, decision tools and benefits to producers and farmers.

Better Decisions through Consultation and Collaboration

EPA Pesticide Factsheets

This manual discusses the benefits of public involvement to agency decision makers, including expanding shared baseline knowledge, generating support for the decision, and developing ongoing relationships that will help in implementing decisions.

Existence and global exponential stability of periodic solution of memristor-based BAM neural networks with time-varying delays.

PubMed

Li, Hongfei; Jiang, Haijun; Hu, Cheng

2016-03-01

In this paper, we investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, boundedness and ultimate boundedness of solutions of memristor-based BAM neural networks are guaranteed by Chain rule and inequalities technique. Moreover, a new method involving Yoshizawa-like theorem is favorably employed to acquire the existence of periodic solution. By applying the theory of set-valued maps and functional differential inclusions, an available Lyapunov functional and some new testable algebraic criteria are derived for ensuring the uniqueness and global exponential stability of periodic solution of memristor-based BAM neural networks. The obtained results expand and complement some previous work on memristor-based BAM neural networks. Finally, a numerical example is provided to show the applicability and effectiveness of our theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improving the Quality of and Access to Federally Funded, Digital Out of School Time Tutoring

ERIC Educational Resources Information Center

Burch, Patricia; Heinrich, Carolyn; Good, Annalee

2013-01-01

Because digital tutoring is rapidly expanding, more rigorous, independent evaluations of their effectiveness is critical to inform federal, state, and local policy decisions that influence their role and application of technology in educating underserved students. The in-depth observations and vignettes in this paper illustrate the challenges in…

78 FR 5776 - University of Colorado Boulder, et al.; Notice of Consolidated Decision on Applications for Duty...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-28

... capabilities of this instrument are expanded spectral reach, x-ray beams with controllable polarization, and... optical antennas, plasmonics in metals and semiconductors (including graphene), photonic crystals, and... the Linac Coherent Light Source II project's ray probe pulses with controllable inter-pulse time delay...

Legal Reforms Affecting Child and Youth Services: An Introduction.

ERIC Educational Resources Information Center

Melton, Gary B.

1982-01-01

Several Supreme Court decisions in the last 15 years have demonstrated expanding recognition of the rights of minors and other dependent groups. At the same time, these recent trends have engendered conflicts concerning minors' competence to make judgments, relationships between child and family autonomy, and differing interests in child advocacy.…

Dynamic decision making for dam-break emergency management - Part 1: Theoretical framework

NASA Astrophysics Data System (ADS)

Peng, M.; Zhang, L. M.

2013-02-01

An evacuation decision for dam breaks is a very serious issue. A late decision may lead to loss of lives and properties, but a very early evacuation will incur unnecessary expenses. This paper presents a risk-based framework of dynamic decision making for dam-break emergency management (DYDEM). The dam-break emergency management in both time scale and space scale is introduced first to define the dynamic decision problem. The probability of dam failure is taken as a stochastic process and estimated using a time-series analysis method. The flood consequences are taken as functions of warning time and evaluated with a human risk analysis model (HURAM) based on Bayesian networks. A decision criterion is suggested to decide whether to evacuate the population at risk (PAR) or to delay the decision. The optimum time for evacuating the PAR is obtained by minimizing the expected total loss, which integrates the time-related probabilities and flood consequences. When a delayed decision is chosen, the decision making can be updated with available new information. A specific dam-break case study is presented in a companion paper to illustrate the application of this framework to complex dam-breaching problems.

Improving management decision processes through centralized communication linkages

NASA Technical Reports Server (NTRS)

Simanton, D. F.; Garman, J. R.

1985-01-01

Information flow is a critical element to intelligent and timely decision-making. At NASA's Johnson Space Center the flow of information is being automated through the use of a centralized backbone network. The theoretical basis of this network, its implications to the horizontal and vertical flow of information, and the technical challenges involved in its implementation are the focus of this paper. The importance of the use of common tools among programs and some future concerns related to file transfer, graphics transfer, and merging of voice and data are also discussed.

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.

The Real Time Mission Monitor: A Situational Awareness Tool For Managing Experiment Assets

NASA Technical Reports Server (NTRS)

Blakeslee, Richard; Hall, John; Goodman, Michael; Parker, Philip; Freudinger, Larry; He, Matt

2007-01-01

The NASA Real Time Mission Monitor (RTMM) is a situational awareness tool that integrates satellite, airborne and surface data sets; weather information; model and forecast outputs; and vehicle state data (e.g., aircraft navigation, satellite tracks and instrument field-of-views) for field experiment management RTMM optimizes science and logistic decision-making during field experiments by presenting timely data and graphics to the users to improve real time situational awareness of the experiment's assets. The RTMM is proven in the field as it supported program managers, scientists, and aircraft personnel during the NASA African Monsoon Multidisciplinary Analyses experiment during summer 2006 in Cape Verde, Africa. The integration and delivery of this information is made possible through data acquisition systems, network communication links and network server resources built and managed by collaborators at NASA Dryden Flight Research Center (DFRC) and Marshall Space Flight Center (MSFC). RTMM is evolving towards a more flexible and dynamic combination of sensor ingest, network computing, and decision-making activities through the use of a service oriented architecture based on community standards and protocols.

Generalized activity equations for spiking neural network dynamics.

PubMed

Buice, Michael A; Chow, Carson C

2013-01-01

Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales-the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances.

Robust stability bounds for multi-delay networked control systems

NASA Astrophysics Data System (ADS)

Seitz, Timothy; Yedavalli, Rama K.; Behbahani, Alireza

2018-04-01

In this paper, the robust stability of a perturbed linear continuous-time system is examined when controlled using a sampled-data networked control system (NCS) framework. Three new robust stability bounds on the time-invariant perturbations to the original continuous-time plant matrix are presented guaranteeing stability for the corresponding discrete closed-loop augmented delay-free system (ADFS) with multiple time-varying sensor and actuator delays. The bounds are differentiated from previous work by accounting for the sampled-data nature of the NCS and for separate communication delays for each sensor and actuator, not a single delay. Therefore, this paper expands the knowledge base in multiple inputs multiple outputs (MIMO) sampled-data time delay systems. Bounds are presented for unstructured, semi-structured, and structured perturbations.

Networked Information Resources. SPEC Kit 253.

ERIC Educational Resources Information Center

Bleiler, Richard, Comp.; Plum, Terry, Comp.

1999-01-01

This SPEC Kit, published six times per year, examines how Association of Research Libraries (ARL) libraries have structured themselves to identify networked information resources in the market, to evaluate them for purchase, to make purchasing decisions, to publicize them, and to assess their continued utility. In the summer of 1999, the survey…

A New Vertebral Body Replacement Strategy Using Expandable Polymeric Cages

PubMed Central

Liu, Xifeng; Paulsen, Alex; Giambini, Hugo; Guo, Ji; Miller, A. Lee; Lin, Po-Chun; Yaszemski, Michael J.

2017-01-01

We have developed a novel polymeric expandable cage that can be delivered via a posterior-only surgical approach for the treatment of noncontained vertebral defects. This approach is less invasive than an anterior-only or combined approach and much more cost-effective than currently used expandable metal cages. The polymeric expandable cage is composed of oligo poly(ethylene glycol) fumarate (OPF), a hydrogel that has been previously shown to have excellent nerve and bone tissue biocompatibility. OPF hydrogel cages can expand to twice their original diameter and length within a surgical time frame following hydration. Modulation of parameters such as polymeric network crosslink density or the introduction of charge to the network allowed for precise expansion kinetics. To meet specific requirements due to size variations in patient vertebral bodies, we fabricated a series of molds with varied diameters and explored the expansion kinetics of the OPF cages. Results showed a stable expansion ratio of approximately twofold to the original size within 20 min, regardless of the absolute value of the cage size. Following implantation of a dried OPF cage into a noncontained vertebral defect and its in situ expansion with normal saline, other augmentation biomaterials, such as poly(propylene fumarate) (PPF), can be injected to the lumen of the OPF cage and allowed to crosslink in situ. The OPF/PPF composite scaffold can provide the necessary rigidity and stability to the augmented spine. PMID:27835935

Role of Structural Asymmetry in Controlling Drop Spacing in Microfluidic Ladder Networks

NASA Astrophysics Data System (ADS)

Wang, William; Maddala, Jeevan; Vanapalli, Siva; Rengasamy, Raghunathan

2012-02-01

Manipulation of drop spacing is crucial to many processes in microfluidic devices including drop coalescence, detection and storage. Microfluidic ladder networks ---where two droplet-carrying parallel channels are connected by narrow bypass channels through which the motion of drops is forbidden---have been proposed as a means to control relative separation between pairs of drops. Prior studies in microfluidic ladder networks with vertical bypasses, which possess fore-aft structural symmetry, have revealed that pairs of drops can only undergo reduction in drop spacing at the ladder exit. We investigate the dynamics of drops in microfluidic ladder networks with both vertical and slanted bypasses. Our analytical results indicate that unlike symmetric ladder networks, structural asymmetry introduced by a single slanted bypass can be used to modulate the relative spacing between drops, enabling them to contract, synchronize, expand or even flip at the ladder exit. Our experiments confirm all the behaviors predicted by theory. Numerical analysis further shows that ladders containing several identical bypasses can only linearly transform the input drop spacing. Finally, we find that ladders with specific combinations of vertical and slanted bypasses can generate non-linear transformation of input drop spacing, despite the absence of drop decision-making events at the bypass junctions.

Bayesian networks in overlay recipe optimization

NASA Astrophysics Data System (ADS)

Binns, Lewis A.; Reynolds, Greg; Rigden, Timothy C.; Watkins, Stephen; Soroka, Andrew

2005-05-01

Currently, overlay measurements are characterized by "recipe", which defines both physical parameters such as focus, illumination et cetera, and also the software parameters such as algorithm to be used and regions of interest. Setting up these recipes requires both engineering time and wafer availability on an overlay tool, so reducing these requirements will result in higher tool productivity. One of the significant challenges to automating this process is that the parameters are highly and complexly correlated. At the same time, a high level of traceability and transparency is required in the recipe creation process, so a technique that maintains its decisions in terms of well defined physical parameters is desirable. Running time should be short, given the system (automatic recipe creation) is being implemented to reduce overheads. Finally, a failure of the system to determine acceptable parameters should be obvious, so a certainty metric is also desirable. The complex, nonlinear interactions make solution by an expert system difficult at best, especially in the verification of the resulting decision network. The transparency requirements tend to preclude classical neural networks and similar techniques. Genetic algorithms and other "global minimization" techniques require too much computational power (given system footprint and cost requirements). A Bayesian network, however, provides a solution to these requirements. Such a network, with appropriate priors, can be used during recipe creation / optimization not just to select a good set of parameters, but also to guide the direction of search, by evaluating the network state while only incomplete information is available. As a Bayesian network maintains an estimate of the probability distribution of nodal values, a maximum-entropy approach can be utilized to obtain a working recipe in a minimum or near-minimum number of steps. In this paper we discuss the potential use of a Bayesian network in such a capacity, reducing the amount of engineering intervention. We discuss the benefits of this approach, especially improved repeatability and traceability of the learning process, and quantification of uncertainty in decisions made. We also consider the problems associated with this approach, especially in detailed construction of network topology, validation of the Bayesian network and the recipes it generates, and issues arising from the integration of a Bayesian network with a complex multithreaded application; these primarily relate to maintaining Bayesian network and system architecture integrity.

Mobile and static sensors in a citizen-based observatory of water

NASA Astrophysics Data System (ADS)

Brauchli, Tristan; Weijs, Steven V.; Lehning, Michael; Huwald, Hendrik

2014-05-01

Understanding and forecasting water resources and components of the water cycle require spatially and temporally resolved observations of numerous water-related variables. Such observations are often obtained from wireless networks of automated weather stations. The "WeSenseIt" project develops a citizen- and community-based observatory of water to improve the water and risk management at the catchment scale and to support decision-making of stakeholders. It is implemented in three case studies addressing various questions related to flood, drought, water resource management, water quality and pollution. Citizens become potential observers and may transmit water-related measurements and information. Combining the use of recent technologies (wireless communication, internet, smartphone) with the development of innovative low cost sensors enables the implementation of heterogeneous observatories, which (a) empower citizens and (b) expand and complement traditional operational sensing networks. With the goal of increasing spatial coverage of observations and decreasing cost for sensors, this study presents the examples of measuring (a) flow velocity in streams using smartphones and (b) sensible heat flux using simple sensors at the nodes of wireless sensor networks.

Web-Based Learning in the Computer-Aided Design Curriculum.

ERIC Educational Resources Information Center

Sung, Wen-Tsai; Ou, S. C.

2002-01-01

Applies principles of constructivism and virtual reality (VR) to computer-aided design (CAD) curriculum, particularly engineering, by integrating network, VR and CAD technologies into a Web-based learning environment that expands traditional two-dimensional computer graphics into a three-dimensional real-time simulation that enhances user…

Limited time perspective increases the value of calm.

PubMed

Jiang, Da; Fung, Helene H; Sims, Tamara; Tsai, Jeanne L; Zhang, Fan

2016-02-01

Previous findings indirectly suggest that the more people perceive their time in life as limited, the more they value calm. No study, however, has directly tested this hypothesis. To this end, using a combination of survey, experience sampling, and experimental methods, we examined the relationship between future time perspective and the affective states that people ideally want to feel (i.e., their "ideal affect"). In Study 1, the more people reported a limited time perspective, the more they wanted to feel calm and experience other low-arousal positive states. In Study 2, participants were randomly assigned to a limited time or an expanded time condition. Participants in the limited time condition reported valuing calm and other low arousal positive states more than those in the expanded time condition. We discuss the implications of these findings for broadening our understanding of the factors that shape how people ideally want to feel, and their consequences for decision making. (c) 2016 APA, all rights reserved).

Generalized priority-queue network dynamics: Impact of team and hierarchy

NASA Astrophysics Data System (ADS)

Cho, Won-Kuk; Min, Byungjoon; Goh, K.-I.; Kim, I.-M.

2010-06-01

We study the effect of team and hierarchy on the waiting-time dynamics of priority-queue networks. To this end, we introduce generalized priority-queue network models incorporating interaction rules based on team-execution and hierarchy in decision making, respectively. It is numerically found that the waiting-time distribution exhibits a power law for long waiting times in both cases, yet with different exponents depending on the team size and the position of queue nodes in the hierarchy, respectively. The observed power-law behaviors have in many cases a corresponding single or pairwise-interacting queue dynamics, suggesting that the pairwise interaction may constitute a major dynamic consequence in the priority-queue networks. It is also found that the reciprocity of influence is a relevant factor for the priority-queue network dynamics.

Adaptive Network Dynamics - Modeling and Control of Time-Dependent Social Contacts

PubMed Central

Schwartz, Ira B.; Shaw, Leah B.; Shkarayev, Maxim S.

2013-01-01

Real networks consisting of social contacts do not possess static connections. That is, social connections may be time dependent due to a variety of individual behavioral decisions based on current network connections. Examples of adaptive networks occur in epidemics, where information about infectious individuals may change the rewiring of healthy people, or in the recruitment of individuals to a cause or fad, where rewiring may optimize recruitment of susceptible individuals. In this paper, we will review some of the dynamical properties of adaptive networks, and show how they predict novel phenomena as well as yield insight into new controls. The applications will be control of epidemic outbreaks and terrorist recruitment modeling. PMID:25414913

Medical and pharmacy coverage decision making at the population level.

PubMed

Mohr, Penny E; Tunis, Sean R

2014-06-01

Medicare is one of the largest health care payers in the United States. As a result, its decisions about coverage have profound implications for patient access to care. In this commentary, the authors describe how Medicare used evidence on heterogeneity of treatment effects to make population-based decisions on health care coverage for implantable cardiac defibrillators. This case is discussed in the context of the rapidly expanding availability of comparative effectiveness research. While there is a potential tension between population-based and patient-centered decision making, the expanded diversity of populations and settings included in comparative effectiveness research can provide useful information for making more discerning and informed policy and clinical decisions.

Designing and implementing transparency for real time inspection of autonomous robots

NASA Astrophysics Data System (ADS)

Theodorou, Andreas; Wortham, Robert H.; Bryson, Joanna J.

2017-07-01

The EPSRC's Principles of Robotics advises the implementation of transparency in robotic systems, however research related to AI transparency is in its infancy. This paper introduces the reader of the importance of having transparent inspection of intelligent agents and provides guidance for good practice when developing such agents. By considering and expanding upon other prominent definitions found in literature, we provide a robust definition of transparency as a mechanism to expose the decision-making of a robot. The paper continues by addressing potential design decisions developers need to consider when designing and developing transparent systems. Finally, we describe our new interactive intelligence editor, designed to visualise, develop and debug real-time intelligence.

OnEarth: An Open Source Solution for Efficiently Serving High-Resolution Mapped Image Products

NASA Astrophysics Data System (ADS)

Thompson, C. K.; Plesea, L.; Hall, J. R.; Roberts, J. T.; Cechini, M. F.; Schmaltz, J. E.; Alarcon, C.; Huang, T.; McGann, J. M.; Chang, G.; Boller, R. A.; Ilavajhala, S.; Murphy, K. J.; Bingham, A. W.

2013-12-01

This presentation introduces OnEarth, a server side software package originally developed at the Jet Propulsion Laboratory (JPL), that facilitates network-based, minimum-latency geolocated image access independent of image size or spatial resolution. The key component in this package is the Meta Raster Format (MRF), a specialized raster file extension to the Geospatial Data Abstraction Library (GDAL) consisting of an internal indexed pyramid of image tiles. Imagery to be served is converted to the MRF format and made accessible online via an expandable set of server modules handling requests in several common protocols, including the Open Geospatial Consortium (OGC) compliant Web Map Tile Service (WMTS) as well as Tiled WMS and Keyhole Markup Language (KML). OnEarth has recently transitioned to open source status and is maintained and actively developed as part of GIBS (Global Imagery Browse Services), a collaborative project between JPL and Goddard Space Flight Center (GSFC). The primary function of GIBS is to enhance and streamline the data discovery process and to support near real-time (NRT) applications via the expeditious ingestion and serving of full-resolution imagery representing science products from across the NASA Earth Science spectrum. Open source software solutions are leveraged where possible in order to utilize existing available technologies, reduce development time, and enlist wider community participation. We will discuss some of the factors and decision points in transitioning OnEarth to a suitable open source paradigm, including repository and licensing agreement decision points, institutional hurdles, and perceived benefits. We will also provide examples illustrating how OnEarth is integrated within GIBS and other applications.

Expanding the vision of the Experimental Forest and Range network to urban areas

Treesearch

J. Morgan Grove

2014-01-01

After 100 years, the USDA Forest Service has emerging opportunities to expand the Experimental Forest and Range (EFR) network to urban areas. The purpose of this expansion would be to broaden the types of ecosystems studied, interdisciplinary approaches used, and relevance to society of the EFR network through long-term and large-scale social-ecological projects in...

Inference of Gene Regulatory Networks Using Time-Series Data: A Survey

PubMed Central

Sima, Chao; Hua, Jianping; Jung, Sungwon

2009-01-01

The advent of high-throughput technology like microarrays has provided the platform for studying how different cellular components work together, thus created an enormous interest in mathematically modeling biological network, particularly gene regulatory network (GRN). Of particular interest is the modeling and inference on time-series data, which capture a more thorough picture of the system than non-temporal data do. We have given an extensive review of methodologies that have been used on time-series data. In realizing that validation is an impartible part of the inference paradigm, we have also presented a discussion on the principles and challenges in performance evaluation of different methods. This survey gives a panoramic view on these topics, with anticipation that the readers will be inspired to improve and/or expand GRN inference and validation tool repository. PMID:20190956

Supply chain network design problem for a new market opportunity in an agile manufacturing system

NASA Astrophysics Data System (ADS)

Babazadeh, Reza; Razmi, Jafar; Ghodsi, Reza

2012-08-01

The characteristics of today's competitive environment, such as the speed with which products are designed, manufactured, and distributed, and the need for higher responsiveness and lower operational cost, are forcing companies to search for innovative ways to do business. The concept of agile manufacturing has been proposed in response to these challenges for companies. This paper copes with the strategic and tactical level decisions in agile supply chain network design. An efficient mixed-integer linear programming model that is able to consider the key characteristics of agile supply chain such as direct shipments, outsourcing, different transportation modes, discount, alliance (process and information integration) between opened facilities, and maximum waiting time of customers for deliveries is developed. In addition, in the proposed model, the capacity of facilities is determined as decision variables, which are often assumed to be fixed. Computational results illustrate that the proposed model can be applied as a power tool in agile supply chain network design as well as in the integration of strategic decisions with tactical decisions.

Real-Time Multimission Event Notification System for Mars Relay

NASA Technical Reports Server (NTRS)

Wallick, Michael N.; Allard, Daniel A.; Gladden, Roy E.; Wang, Paul; Hy, Franklin H.

2013-01-01

As the Mars Relay Network is in constant flux (missions and teams going through their daily workflow), it is imperative that users are aware of such state changes. For example, a change by an orbiter team can affect operations on a lander team. This software provides an ambient view of the real-time status of the Mars network. The Mars Relay Operations Service (MaROS) comprises a number of tools to coordinate, plan, and visualize various aspects of the Mars Relay Network. As part of MaROS, a feature set was developed that operates on several levels of the software architecture. These levels include a Web-based user interface, a back-end "ReSTlet" built in Java, and databases that store the data as it is received from the network. The result is a real-time event notification and management system, so mission teams can track and act upon events on a moment-by-moment basis. This software retrieves events from MaROS and displays them to the end user. Updates happen in real time, i.e., messages are pushed to the user while logged into the system, and queued when the user is not online for later viewing. The software does not do away with the email notifications, but augments them with in-line notifications. Further, this software expands the events that can generate a notification, and allows user-generated notifications. Existing software sends a smaller subset of mission-generated notifications via email. A common complaint of users was that the system-generated e-mails often "get lost" with other e-mail that comes in. This software allows for an expanded set (including user-generated) of notifications displayed in-line of the program. By separating notifications, this can improve a user's workflow.

Exploring the Persistence of Adult Women at a Midwest Community College

ERIC Educational Resources Information Center

Cox, Elizabeth M.; Ebbers, Larry H.

2010-01-01

The purpose of this study was to describe, interpret, and analyze the educational experiences and factors contributing to the decision to persist for adult, female, part-time students currently enrolled at a community college in the Midwest. This study sought to expand the research on student retention by describing the perspectives of adult…

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.

Developing an index to measure the voluntariness of consent to research.

PubMed

Dugosh, Karen L; Festinger, David S; Marlowe, Douglas B; Clements, Nicolle T

2014-10-01

The goals of the current study were to expand the content domain and further validate the Coercion Assessment Scale (CAS), a measure of perceived coercion for criminally involved substance abusers being recruited into research. Unlike the few existing measures of this construct, the CAS identifies specific external sources of pressure that may influence one's decision to participate. In Phase 1, we conducted focus groups with criminal justice clients and stakeholders to expand the instrument by identifying additional sources of pressure. In Phase 2, we evaluated the expanded measure (i.e., endorsement rates, reliability, validity) in an ongoing research trial. Results identified new sources of pressure and provided evidence supporting the CAS's utility and reliability over time as well as convergent and discriminative validity. © The Author(s) 2014.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

PubMed

Pecevski, Dejan; Maass, Wolfgang

2016-01-01

Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

PubMed Central

Pecevski, Dejan

2016-01-01

Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

Exploring Competencies for Manufacturing Education Partnership Centers

ERIC Educational Resources Information Center

Chapman, Diane D.; Guerdat, Kate G.

2012-01-01

The National Institute of Standards and Technology's Hollings Manufacturing Extension Partnership works with U.S. manufacturers to help them create and retain jobs, increase profits, and save time and money. Members of the Manufacturing Extension Partnership recognized the need to expand capacity and capabilities of their network to address the…

Well-Connected: Exploring Parent Social Networks in a Gentrifying School

ERIC Educational Resources Information Center

Cappelletti, Gina A.

2017-01-01

The enrollment and engagement of middle-class families in historically low-income urban public schools can generate school improvements, including increased resources and expanded extracurricular programming. At the same time, prior research has highlighted the marginalization of low-income parents as one consequence of middle-class parent…

Enhanced Handover Decision Algorithm in Heterogeneous Wireless Network

PubMed Central

Abdullah, Radhwan Mohamed; Zukarnain, Zuriati Ahmad

2017-01-01

Transferring a huge amount of data between different network locations over the network links depends on the network’s traffic capacity and data rate. Traditionally, a mobile device may be moved to achieve the operations of vertical handover, considering only one criterion, that is the Received Signal Strength (RSS). The use of a single criterion may cause service interruption, an unbalanced network load and an inefficient vertical handover. In this paper, we propose an enhanced vertical handover decision algorithm based on multiple criteria in the heterogeneous wireless network. The algorithm consists of three technology interfaces: Long-Term Evolution (LTE), Worldwide interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). It also employs three types of vertical handover decision algorithms: equal priority, mobile priority and network priority. The simulation results illustrate that the three types of decision algorithms outperform the traditional network decision algorithm in terms of handover number probability and the handover failure probability. In addition, it is noticed that the network priority handover decision algorithm produces better results compared to the equal priority and the mobile priority handover decision algorithm. Finally, the simulation results are validated by the analytical model. PMID:28708067

Information Technology: Better Informed Decision Making Needed on Navy’s Next Generation Enterprise Network Acquisition

DTIC Science & Technology

2011-03-01

million. To bridge the time frame between the end of the NMCI contract and the full transition to NGEN, DON awarded a $3.7 billion continuity of...leasehold improvements; and moveable infrastructure associated with local network operations. End-User Hardware December 2011 Provide end-user

Large-Scale Simulation Network Design Study

DTIC Science & Technology

1983-10-01

video displays: three for the tank commander, three for the driver, one for the loader, and one for the gunner. The solid angles subtended by these...Newman Inc Range Sortr This process sorts the expanded display lists into range order for drawing according to the "painter’s algorithm’" The range sorter ...session could then be continued as soon as the network recovered. and the elapsed session time would not be wasted . The SimNet design is much more tolerant

Operability engineering in the Deep Space Network

NASA Technical Reports Server (NTRS)

Wilkinson, Belinda

1993-01-01

Many operability problems exist at the three Deep Space Communications Complexes (DSCC's) of the Deep Space Network (DSN). Four years ago, the position of DSN Operability Engineer was created to provide the opportunity for someone to take a system-level approach to solving these problems. Since that time, a process has been developed for personnel and development engineers and for enforcing user interface standards in software designed for the DSCC's. Plans are for the participation of operations personnel in the product life-cycle to expand in the future.

Natural Gas Pipeline Network: Changing and Growing

EIA Publications

1996-01-01

This chapter focuses upon the capabilities of the national natural gas pipeline network, examining how it has expanded during this decade and how it may expand further over the coming years. It also looks at some of the costs of this expansion, including the environmental costs which may be extensive. Changes in the network as a result of recent regional market shifts are also discussed.

Parental Intentions to Enroll Children in a Voluntary Expanded Newborn Screening Program

PubMed Central

Paquin, Ryan S.; Peay, Holly L.; Gehtland, Lisa M.; Lewis, Megan A.; Bailey, Donald B.

2016-01-01

Background and Objectives Nearly all babies in the United States are tested at birth for rare, serious, and treatable disorders through mandatory state newborn screening (NBS). Recently, there have been calls for an expanded, voluntary model to facilitate early diagnosis and treatment of a wider range of disorders. We applied the reasoned action framework to examine parental intentions to participate in voluntary expanded screening. Methods We recruited a national cohort of recent and expectant parents living in the U.S. who completed a self-administered online survey (N = 1,001). Using a mixed-level fractional factorial experiment, we studied parental participation intentions and preferences for timing of consent, cost, consent format, and testing options. Results We conducted a hierarchical regression analysis assessing parental intentions to participate in voluntary expanded NBS. Attitudes, perceived normative influence, and perceived behavioral control explained substantial variance in intention, with perceived normative influence emerging as the strongest predictor. We found no evidence that the manipulated program features altered mean levels of intention, but timing of parental permission, cost, and permission format moderated the relative importance of reasoned action constructs on intention. Conclusion Program design features may impact the psychological mechanisms underlying parental decision making for voluntary expanded screening. These results have important implications for parent education, outreach, and informed parental permission procedures. PMID:27526258

Epidemic outbreaks in growing scale-free networks with local structure

NASA Astrophysics Data System (ADS)

Ni, Shunjiang; Weng, Wenguo; Shen, Shifei; Fan, Weicheng

2008-09-01

The class of generative models has already attracted considerable interest from researchers in recent years and much expanded the original ideas described in BA model. Most of these models assume that only one node per time step joins the network. In this paper, we grow the network by adding n interconnected nodes as a local structure into the network at each time step with each new node emanating m new edges linking the node to the preexisting network by preferential attachment. This successfully generates key features observed in social networks. These include power-law degree distribution pk∼k, where μ=(n-1)/m is a tuning parameter defined as the modularity strength of the network, nontrivial clustering, assortative mixing, and modular structure. Moreover, all these features are dependent in a similar way on the parameter μ. We then study the susceptible-infected epidemics on this network with identical infectivity, and find that the initial epidemic behavior is governed by both of the infection scheme and the network structure, especially the modularity strength. The modularity of the network makes the spreading velocity much lower than that of the BA model. On the other hand, increasing the modularity strength will accelerate the propagation velocity.

Visualizing protein partnerships in living cells and organisms.

PubMed

Lowder, Melissa A; Appelbaum, Jacob S; Hobert, Elissa M; Schepartz, Alanna

2011-12-01

In recent years, scientists have expanded their focus from cataloging genes to characterizing the multiple states of their translated products. One anticipated result is a dynamic map of the protein association networks and activities that occur within the cellular environment. While in vitro-derived network maps can illustrate which of a multitude of possible protein-protein associations could exist, they supply a falsely static picture lacking the subtleties of subcellular location (where) or cellular state (when). Generating protein association network maps that are informed by both subcellular location and cell state requires novel approaches that accurately characterize the state of protein associations in living cells and provide precise spatiotemporal resolution. In this review, we highlight recent advances in visualizing protein associations and networks under increasingly native conditions. These advances include second generation protein complementation assays (PCAs), chemical and photo-crosslinking techniques, and proximity-induced ligation approaches. The advances described focus on background reduction, signal optimization, rapid and reversible reporter assembly, decreased cytotoxicity, and minimal functional perturbation. Key breakthroughs have addressed many challenges and should expand the repertoire of tools useful for generating maps of protein interactions resolved in both time and space. Copyright © 2011 Elsevier Ltd. All rights reserved.

Network approach for decision making under risk—How do we choose among probabilistic options with the same expected value?

PubMed Central

Chen, Yi-Shin

2018-01-01

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing ‘goal’ and ‘time’ factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight. PMID:29702665

Expanding the Operational Use of Total Lightning Ahead of GOES-R

NASA Technical Reports Server (NTRS)

Stano, Geoffrey T.; Wood, Lance; Garner, Tim; Nunez, Roland; Kann, Deirdre; Reynolds, James; Rydell, Nezette; Cox, Rob; Bobb, William R.

2015-01-01

NASA's Short-term Prediction Research and Transition Center (SPoRT) has been transitioning real-time total lightning observations from ground-based lightning mapping arrays since 2003. This initial effort was with the local Weather Forecast Offices (WFO) that could use the North Alabama Lightning Mapping Array (NALMA). These early collaborations established a strong interest in the use of total lightning for WFO operations. In particular the focus started with warning decision support, but has since expanded to include impact-based decision support and lightning safety. SPoRT has used its experience to establish connections with new lightning mapping arrays as they become available. The GOES-R / JPSS Visiting Scientist Program has enabled SPoRT to conduct visits to new partners and expand the number of operational users with access to total lightning observations. In early 2014, SPoRT conducted the most recent visiting scientist trips to meet with forecast offices that will used the Colorado, Houston, and Langmuir Lab (New Mexico) lightning mapping arrays. In addition, SPoRT met with the corresponding Center Weather Service Units (CWSUs) to expand collaborations with the aviation community. These visits were an opportunity to learn about the forecast needs of each office visited as well as to provide on-site training for the use of total lightning, setting the stage for a real-time assessment during May-July 2014. With five lightning mapping arrays covering multiple geographic locations, the 2014 assessment has demonstrated numerous uses of total lightning in varying situations. Several highlights include a much broader use of total lightning for impact-based decision support ranging from airport weather warnings, supporting fire crews, and protecting large outdoor events. The inclusion of the CWSUs has broadened the operational scope of total lightning, demonstrating how these data can support air traffic management, particularly in the Terminal Radar Approach Control Facilities (TRACON) region around an airport. These collaborations continue to demonstrate, from the operational perspective, the utility of total lightning and the importance of continued training and preparation in advance of the Geostationary Lightning Mapper.

A consensual neural network

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Ersoy, O. K.; Swain, P. H.

1991-01-01

A neural network architecture called a consensual neural network (CNN) is proposed for the classification of data from multiple sources. Its relation to hierarchical and ensemble neural networks is discussed. CNN is based on the statistical consensus theory and uses nonlinearly transformed input data. The input data are transformed several times, and the different transformed data are applied as if they were independent inputs. The independent inputs are classified using stage neural networks and outputs from the stage networks are then weighted and combined to make a decision. Experimental results based on remote-sensing data and geographic data are given.

Preserving the Finger Lakes for the Future: A Prototype Decision Support System for Water Resource Management, Open Space, and Agricultural Protection

NASA Technical Reports Server (NTRS)

Brower, Robert

2003-01-01

As described herein, this project has progressed well, with the initiation or completion of a number of program facets at programmatic, technical, and inter-agency levels. The concept of the Virtual Management Operations Center has taken shape, grown, and has been well received by parties from a wide variety of agencies and organizations in the Finger Lakes region and beyond. As it has evolved in design and functionality, and to better illustrate its current focus for this project, it has been given the expanded name of Watershed Virtual Management Operations Center (W-VMOC). It offers the advanced, compelling functionality of interactive 3D visualization interfaced with 2D mapping, all accessed via Internet or virtually any kind of distributed computer network. This strong foundation will allow the development of a Decision Support System (DSS) with anticipated enhanced functionality to be applied to the myriad issues involved in the wise management of the Finger Lakes region.

Utilization of Non-Dentist Providers and Attitudes Toward New Provider Models: Findings from The National Dental Practice-Based Research Network

PubMed Central

Blue, Christine M.; Funkhouser, D. Ellen; Riggs, Sheila; Rindal, D. Brad; Worley, Donald; Pihlstrom, Daniel J.; Benjamin, Paul; Gilbert, Gregg H.

2014-01-01

Objectives The purpose of this study was to quantify within The National Dental Practice-Based Research Network current utilization of dental hygienists and assistants with expanded functions and quantify network dentists’ attitudes toward a new non-dentist provider model - the dental therapist. Methods Dental practice-based research network practitioner-investigators participated in a single, cross-sectional administration of a questionnaire. Results Current non-dentist providers are not being utilized by network practitioner-investigators to the fullest extent allowed by law. Minnesota practitioners, practitioners in large group practices, and those with prior experience with expanded function non-dentist providers delegate at a higher rate and had more-positive perceptions of the new dental therapist model. Conclusions Expanding scopes of practice for dental hygienists and assistants has not translated to the maximal delegation allowed by law among network practices. This finding may provide insight into dentists’ acceptance of newer non-dentist provider models. PMID:23668892

Big Data Analytics for Disaster Preparedness and Response of Mobile Communication Infrastructure during Natural Hazards

NASA Astrophysics Data System (ADS)

Zhong, L.; Takano, K.; Ji, Y.; Yamada, S.

2015-12-01

The disruption of telecommunications is one of the most critical disasters during natural hazards. As the rapid expanding of mobile communications, the mobile communication infrastructure plays a very fundamental role in the disaster response and recovery activities. For this reason, its disruption will lead to loss of life and property, due to information delays and errors. Therefore, disaster preparedness and response of mobile communication infrastructure itself is quite important. In many cases of experienced disasters, the disruption of mobile communication networks is usually caused by the network congestion and afterward long-term power outage. In order to reduce this disruption, the knowledge of communication demands during disasters is necessary. And big data analytics will provide a very promising way to predict the communication demands by analyzing the big amount of operational data of mobile users in a large-scale mobile network. Under the US-Japan collaborative project on 'Big Data and Disaster Research (BDD)' supported by the Japan Science and Technology Agency (JST) and National Science Foundation (NSF), we are going to investigate the application of big data techniques in the disaster preparedness and response of mobile communication infrastructure. Specifically, in this research, we have considered to exploit the big amount of operational information of mobile users for predicting the communications needs in different time and locations. By incorporating with other data such as shake distribution of an estimated major earthquake and the power outage map, we are able to provide the prediction information of stranded people who are difficult to confirm safety or ask for help due to network disruption. In addition, this result could further facilitate the network operators to assess the vulnerability of their infrastructure and make suitable decision for the disaster preparedness and response. In this presentation, we are going to introduce the results we obtained based on the big data analytics of mobile user statistical information and discuss the implications of these results.

Dynamic Divisive Normalization Predicts Time-Varying Value Coding in Decision-Related Circuits

PubMed Central

LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W.

2014-01-01

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. PMID:25429145

Linear Goal Programming as a Military Decision Aid.

DTIC Science & Technology

1988-04-01

JAMES F. MAJOR9 USAF 13a. TYPE OF REPORT 13b. TIME COVERED 14. DATE OF REPORT (Year, Month, Day) 15. PAGE COUNT IFROM____ TO 1988 APRIL 64 16...air warfare, advanced armour warfare, the potential f or space warfare, and many other advances have expanded the breadth of weapons employed to the...written by A. Charnes and W. W. Cooper, Management Models and Industrial Applications of Linear Programming In 1961.(3:5) Since this time linear

Toward an Expanded Definition of Adaptive Decision Making.

ERIC Educational Resources Information Center

Phillips, Susan D.

1997-01-01

Uses the lifespan, life-space model to examine the definition of adaptive decision making. Reviews the existing definition of adaptive decision making as "rational" decision making and offers alternate perspectives on decision making with an emphasis on the implications of using the model. Makes suggestions for future theory, research,…

Closed loop supply chain network design with fuzzy tactical decisions

NASA Astrophysics Data System (ADS)

Sherafati, Mahtab; Bashiri, Mahdi

2016-09-01

One of the most strategic and the most significant decisions in supply chain management is reconfiguration of the structure and design of the supply chain network. In this paper, a closed loop supply chain network design model is presented to select the best tactical and strategic decision levels simultaneously considering the appropriate transportation mode in activated links. The strategic decisions are made for a long term; thus, it is more satisfactory and more appropriate when the decision variables are considered uncertain and fuzzy, because it is more flexible and near to the real world. This paper is the first research which considers fuzzy decision variables in the supply chain network design model. Moreover, in this study a new fuzzy optimization approach is proposed to solve a supply chain network design problem with fuzzy tactical decision variables. Finally, the proposed approach and model are verified using several numerical examples. The comparison of the results with other existing approaches confirms efficiency of the proposed approach. Moreover the results confirms that by considering the vagueness of tactical decisions some properties of the supply chain network will be improved.

Evolutionary image simplification for lung nodule classification with convolutional neural networks.

PubMed

Lückehe, Daniel; von Voigt, Gabriele

2018-05-29

Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

13 CFR 120.837 - SBA decision on application for a new CDC or for an existing CDC to expand Area of Operations.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 13 Business Credit and Assistance 1 2010-01-01 2010-01-01 false SBA decision on application for a new CDC or for an existing CDC to expand Area of Operations. 120.837 Section 120.837 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS Development Company Loan Program (504...

Real-Time Adaptation of Decision Thresholds in Sensor Networks for Detection of Moving Targets (PREPRINT)

DTIC Science & Technology

2010-01-01

target kinematics for multiple sensor detections is referred to as the track - before - detect strategy, and is commonly adopted in multi-sensor surveillance...of moving targets. Wettergren [4] presented an application of track - before - detect strategies to undersea distributed sensor networks. In de- signing...the deployment of a distributed passive sensor network that employs this track - before - detect procedure, it is impera- tive that the placement of

Ad Hoc Access Gateway Selection Algorithm

NASA Astrophysics Data System (ADS)

Jie, Liu

With the continuous development of mobile communication technology, Ad Hoc access network has become a hot research, Ad Hoc access network nodes can be used to expand capacity of multi-hop communication range of mobile communication system, even business adjacent to the community, improve edge data rates. For mobile nodes in Ad Hoc network to internet, internet communications in the peer nodes must be achieved through the gateway. Therefore, the key Ad Hoc Access Networks will focus on the discovery gateway, as well as gateway selection in the case of multi-gateway and handover problems between different gateways. This paper considers the mobile node and the gateway, based on the average number of hops from an average access time and the stability of routes, improved gateway selection algorithm were proposed. An improved gateway selection algorithm, which mainly considers the algorithm can improve the access time of Ad Hoc nodes and the continuity of communication between the gateways, were proposed. This can improve the quality of communication across the network.

The Influence of Closeness Centrality on Lexical Processing

PubMed Central

Goldstein, Rutherford; Vitevitch, Michael S.

2017-01-01

The present study examined how the network science measure known as closeness centrality (which measures the average distance between a node and all other nodes in the network) influences lexical processing. In the mental lexicon, a word such as CAN has high closeness centrality, because it is close to many other words in the lexicon. Whereas, a word such as CURE has low closeness centrality because it is far from other words in the lexicon. In an auditory lexical decision task (Experiment 1) participants responded more quickly to words with high closeness centrality. In Experiment 2 an auditory lexical decision task was again used, but with a wider range of stimulus characteristics. Although, there was no main effect of closeness centrality in Experiment 2, an interaction between closeness centrality and frequency of occurrence was observed on reaction times. The results are explained in terms of partial activation gradually strengthening over time word-forms that are centrally located in the phonological network. PMID:29018396

Multisensor Network System for Wildfire Detection Using Infrared Image Processing

PubMed Central

Bosch, I.; Serrano, A.; Vergara, L.

2013-01-01

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA). The necessity of including decision fusion is thereby demonstrated. PMID:23843734

Multisensor network system for wildfire detection using infrared image processing.

PubMed

Bosch, I; Serrano, A; Vergara, L

2013-01-01

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA). The necessity of including decision fusion is thereby demonstrated.

Approaches to Forecasting Demands for Library Network Services. Report No. 10.

ERIC Educational Resources Information Center

Kang, Jong Hoa

The problem of forecasting monthly demands for library network services is considered in terms of using forecasts as inputs to policy analysis models, and in terms of using forecasts to aid in the making of budgeting and staffing decisions. Box-Jenkins time-series methodology, adaptive filtering, and regression approaches are examined and compared…

Design of a stateless low-latency router architecture for green software-defined networking

NASA Astrophysics Data System (ADS)

Saldaña Cercós, Silvia; Ramos, Ramon M.; Ewald Eller, Ana C.; Martinello, Magnos; Ribeiro, Moisés. R. N.; Manolova Fagertun, Anna; Tafur Monroy, Idelfonso

2015-01-01

Expanding software defined networking (SDN) to transport networks requires new strategies to deal with the large number of flows that future core networks will have to face. New south-bound protocols within SDN have been proposed to benefit from having control plane detached from the data plane offering a cost- and energy-efficient forwarding engine. This paper presents an overview of a new approach named KeyFlow to simultaneously reduce latency, jitter, and power consumption in core network nodes. Results on an emulation platform indicate that round trip time (RTT) can be reduced above 50% compared to the reference protocol OpenFlow, specially when flow tables are densely populated. Jitter reduction has been demonstrated experimentally on a NetFPGA-based platform, and 57.3% power consumption reduction has been achieved.

Social Network Analysis and Its Applications in Wireless Sensor and Vehicular Networks

NASA Astrophysics Data System (ADS)

Papadimitriou, Alexis; Katsaros, Dimitrios; Manolopoulos, Yannis

Ever since the introduction of wireless sensor networks in the research and development agenda, the corresponding community has been eager to harness the endless possibilities that this new technology has to offer. These micro sensor nodes, whose capabilities have skyrocketed over the last couple of years, have allowed for a wide range of applications to be created; applications that not so long ago would seem impossible, impractical and time-consuming. It would only be logical to expect that researchers from other fields would take an interest in sensor networks, hence expanding the already wide variety of algorithms, theoretical proofs and applications that existed beforehand. Social Network Analysis is one such field, which has instigated a paradigm shift in the way we view sensor nodes.

The urban watershed continuum: evolving spatial and temporal dimensions

Treesearch

Sujay S. Kaushal; Kenneth T. Belt

2012-01-01

Urban ecosystems are constantly evolving, and they are expected to change in both space and time with active management or degradation. An urban watershed continuum framework recognizes a continuum of engineered and natural hydrologic flowpaths that expands hydrologic networks in ways that are seldom considered. It recognizes that the nature of hydrologic connectivity...

The "Magic" of Wireless Access in the Library

ERIC Educational Resources Information Center

Balas, Janet L.

2006-01-01

It seems that the demand for public access computers grows exponentially every time a library network is expanded, making it impossible to ever have enough computers available for patrons. One solution that many libraries are implementing to ease the demand for public computer use is to offer wireless technology that allows patrons to bring in…

Hydrology of small forest streams in western Oregon.

Treesearch

R. Dennis Harr

1976-01-01

The hydrology of small forest streams in western Oregon varies by time and space in terms of both streamflow and channel hydraulics. Overland flow rarely occurs on undisturbed soils. Instead, water is transmitted rapidly through soils to stream channels by displacement of stored soil water. Drainage networks expand and contract according to the interaction between...

Self-organization of bacterial biofilms is facilitated by extracellular DNA

PubMed Central

Gloag, Erin S.; Turnbull, Lynne; Huang, Alan; Vallotton, Pascal; Wang, Huabin; Nolan, Laura M.; Mililli, Lisa; Hunt, Cameron; Lu, Jing; Osvath, Sarah R.; Monahan, Leigh G.; Cavaliere, Rosalia; Charles, Ian G.; Wand, Matt P.; Gee, Michelle L.; Prabhakar, Ranganathan; Whitchurch, Cynthia B.

2013-01-01

Twitching motility-mediated biofilm expansion is a complex, multicellular behavior that enables the active colonization of surfaces by many species of bacteria. In this study we have explored the emergence of intricate network patterns of interconnected trails that form in actively expanding biofilms of Pseudomonas aeruginosa. We have used high-resolution, phase-contrast time-lapse microscopy and developed sophisticated computer vision algorithms to track and analyze individual cell movements during expansion of P. aeruginosa biofilms. We have also used atomic force microscopy to examine the topography of the substrate underneath the expanding biofilm. Our analyses reveal that at the leading edge of the biofilm, highly coherent groups of bacteria migrate across the surface of the semisolid media and in doing so create furrows along which following cells preferentially migrate. This leads to the emergence of a network of trails that guide mass transit toward the leading edges of the biofilm. We have also determined that extracellular DNA (eDNA) facilitates efficient traffic flow throughout the furrow network by maintaining coherent cell alignments, thereby avoiding traffic jams and ensuring an efficient supply of cells to the migrating front. Our analyses reveal that eDNA also coordinates the movements of cells in the leading edge vanguard rafts and is required for the assembly of cells into the “bulldozer” aggregates that forge the interconnecting furrows. Our observations have revealed that large-scale self-organization of cells in actively expanding biofilms of P. aeruginosa occurs through construction of an intricate network of furrows that is facilitated by eDNA. PMID:23798445

Clinical decision support for personalized medicine: an opportunity for pharmacist-physician collaboration.

PubMed

Barlow, Jane F

2012-06-01

Pharmacogenomics has significant potential to improve the efficacy and safety of medication therapy, but it requires new expertise and adds a new layer of complexity for all healthcare professionals. Pharmacists and pharmacy management systems can play a leading role in providing clinical decision support for the use and interpretation of pharmacogenomic tests. To serve this role effectively, pharmacists will need to expand their expertise in the emerging field of clinical pharmacogenomics. Pharmacy-based clinical programs can expedite the use of pharmacogenomic testing, help physicians interpret the test results and identify future medication risks associated with the patient's phenotype. Over time, some of these functions can be embedded in clinical decision support systems as part of the broader automation of the healthcare system.

A Study of Commuters’ Decision-Making When Delaying Departure for Work-Home Trips

NASA Astrophysics Data System (ADS)

Que, Fangjie; Wang, Wei

2017-12-01

Studies on the travel behaviors and patterns of residents are important to the arrangement of urban layouts and urban traffic planning. However, research on the characteristics of the decision-making behavior regarding departure time is not fully expanded yet. In this paper, the research focuses on commuters’ decision-making behavior regarding departure delay. According to the 2013 travel survey data of Suzhou City, a nested logit (NL) model was built to represent the probabilities of individual choices. Parameter calibration was conducted, so that the significant factors influencing the departure delay were obtained. Ultimately, the results of the NL model indicated that it performed better and with higher precision, compared to the traditional multinomial logit (MNL) model.

Recent Improvements to the U.S. Geological Survey Streamgaging Program...from the National Streamflow Information Program

USGS Publications Warehouse

Blanchard, Stephen F.

2007-01-01

INTRODUCTION The U.S. Geological Survey (USGS) established its first streamgage in 1889 on the Rio Grande River at Embudo, N.M. As the need for streamflow information increased, the USGS streamgaging network expanded to its current (2007) size of approximately 7,400 streamgages nationwide. The USGS streamgaging network, for most of its history, required mechanical measuring and recording devices to collect station data. Time-consuming and labor-intensive site visits were required to gather the recorded data for processing in the office. Eventually the data were published in paper reports. The USGS has progressively improved the streamgaging program by incorporating new technologies and techniques that streamline data collection, data delivery, and records processing while increasing the number and quality of product types that can be derived from the data. Improvements in recent decades that have expanded and broadened the streamgaging program are included the fact sheet.

Time-Varying, Multi-Scale Adaptive System Reliability Analysis of Lifeline Infrastructure Networks

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

Gearhart, Jared Lee; Kurtz, Nolan Scot

2014-09-01

The majority of current societal and economic needs world-wide are met by the existing networked, civil infrastructure. Because the cost of managing such infrastructure is high and increases with time, risk-informed decision making is essential for those with management responsibilities for these systems. To address such concerns, a methodology that accounts for new information, deterioration, component models, component importance, group importance, network reliability, hierarchical structure organization, and efficiency concerns has been developed. This methodology analyzes the use of new information through the lens of adaptive Importance Sampling for structural reliability problems. Deterioration, multi-scale bridge models, and time-variant component importance aremore » investigated for a specific network. Furthermore, both bridge and pipeline networks are studied for group and component importance, as well as for hierarchical structures in the context of specific networks. Efficiency is the primary driver throughout this study. With this risk-informed approach, those responsible for management can address deteriorating infrastructure networks in an organized manner.« less

Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

PubMed

Liu, Qingshan; Wang, Jun

2011-04-01

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

Time optimized path-choice in the termite hunting ant Megaponera analis.

PubMed

Frank, Erik T; Hönle, Philipp O; Linsenmair, K Eduard

2018-05-10

Trail network systems among ants have received a lot of scientific attention due to their various applications in problem solving of networks. Recent studies have shown that ants select the fastest available path when facing different velocities on different substrates, rather than the shortest distance. The progress of decision-making by these ants is determined by pheromone-based maintenance of paths, which is a collective decision. However, path optimization through individual decision-making remains mostly unexplored. Here we present the first study of time-optimized path selection via individual decision-making by scout ants. Megaponera analis scouts search for termite foraging sites and lead highly organized raid columns to them. The path of the scout determines the path of the column. Through installation of artificial roads around M. analis nests we were able to influence the pathway choice of the raids. After road installation 59% of all recorded raids took place completely or partly on the road, instead of the direct, i.e. distance-optimized, path through grass from the nest to the termites. The raid velocity on the road was more than double the grass velocity, the detour thus saved 34.77±23.01% of the travel time compared to a hypothetical direct path. The pathway choice of the ants was similar to a mathematical model of least time allowing us to hypothesize the underlying mechanisms regulating the behavior. Our results highlight the importance of individual decision-making in the foraging behavior of ants and show a new procedure of pathway optimization. © 2018. Published by The Company of Biologists Ltd.

An Updated Decision Support Interface: A Tool for Remote Monitoring of Crop Growing Conditions

NASA Astrophysics Data System (ADS)

Husak, G. J.; Budde, M. E.; Rowland, J.; Verdin, J. P.; Funk, C. C.; Landsfeld, M. F.

2014-12-01

Remote sensing of agroclimatological variables to monitor food production conditions is a critical component of the Famine Early Warning Systems Network portfolio of tools for assessing food security in the developing world. The Decision Support Interface (DSI) seeks to integrate a number of remotely sensed and modeled variables to create a single, simplified portal for analysis of crop growing conditions. The DSI has been reformulated to incorporate more variables and give the user more freedom in exploring the available data. This refinement seeks to transition the DSI from a "first glance" agroclimatic indicator to one better suited for the differentiation of drought events. The DSI performs analysis of variables over primary agricultural zones at the first sub-national administrative level. It uses the spatially averaged rainfall, normalized difference vegetation index (NDVI), water requirement satisfaction index (WRSI), and actual evapotranspiration (ETa) to identify potential hazards to food security. Presenting this information in a web-based client gives food security analysts and decision makers a lightweight portal for information on crop growing conditions in the region. The crop zones used for the aggregation contain timing information which is critical to the DSI presentation. Rainfall and ETa are accumulated from different points in the crop phenology to identify season-long deficits in rainfall or transpiration that adversely affect the crop-growing conditions. Furthermore, the NDVI and WRSI serve as their own seasonal accumulated measures of growing conditions by capturing vegetation vigor or actual evapotranspiration deficits. The DSI is currently active for major growing regions of sub-Saharan Africa, with intention of expanding to other areas over the coming years.

In search of tools to aid logical thinking and communicating about medical decision making.

PubMed

Hunink, M G

2001-01-01

To have real-time impact on medical decision making, decision analysts need a wide variety of tools to aid logical thinking and communication. Decision models provide a formal framework to integrate evidence and values, but they are commonly perceived as complex and difficult to understand by those unfamiliar with the methods, especially in the context of clinical decision making. The theory of constraints, introduced by Eliyahu Goldratt in the business world, provides a set of tools for logical thinking and communication that could potentially be useful in medical decision making. The author used the concept of a conflict resolution diagram to analyze the decision to perform carotid endarterectomy prior to coronary artery bypass grafting in a patient with both symptomatic coronary and asymptomatic carotid artery disease. The method enabled clinicians to visualize and analyze the issues, identify and discuss the underlying assumptions, search for the best available evidence, and use the evidence to make a well-founded decision. The method also facilitated communication among those involved in the care of the patient. Techniques from fields other than decision analysis can potentially expand the repertoire of tools available to support medical decision making and to facilitate communication in decision consults.

Prediction of Sybil attack on WSN using Bayesian network and swarm intelligence

NASA Astrophysics Data System (ADS)

Muraleedharan, Rajani; Ye, Xiang; Osadciw, Lisa Ann

2008-04-01

Security in wireless sensor networks is typically sacrificed or kept minimal due to limited resources such as memory and battery power. Hence, the sensor nodes are prone to Denial-of-service attacks and detecting the threats is crucial in any application. In this paper, the Sybil attack is analyzed and a novel prediction method, combining Bayesian algorithm and Swarm Intelligence (SI) is proposed. Bayesian Networks (BN) is used in representing and reasoning problems, by modeling the elements of uncertainty. The decision from the BN is applied to SI forming an Hybrid Intelligence Scheme (HIS) to re-route the information and disconnecting the malicious nodes in future routes. A performance comparison based on the prediction using HIS vs. Ant System (AS) helps in prioritizing applications where decisions are time-critical.

Frontopolar cortex and decision-making efficiency: comparing brain activity of experts with different professional background during an exploration-exploitation task.

PubMed

Laureiro-Martínez, Daniella; Canessa, Nicola; Brusoni, Stefano; Zollo, Maurizio; Hare, Todd; Alemanno, Federica; Cappa, Stefano F

2013-01-01

An optimal balance between efficient exploitation of available resources and creative exploration of alternatives is critical for adaptation and survival. Previous studies associated these behavioral drives with, respectively, the dopaminergic mesocorticolimbic system and frontopolar-intraparietal networks. We study the activation of these systems in two age and gender-matched groups of experienced decision-makers differing in prior professional background, with the aim to understand the neural bases of individual differences in decision-making efficiency (performance divided by response time). We compare brain activity of entrepreneurs (who currently manage the organization they founded based on their venture idea) and managers (who are constantly involved in making strategic decisions but have no venture experience) engaged in a gambling-task assessing exploitative vs. explorative decision-making. Compared with managers, entrepreneurs showed higher decision-making efficiency, and a stronger activation in regions of frontopolar cortex (FPC) previously associated with explorative choice. Moreover, activity across a network of regions previously linked to explore/exploit tradeoffs explained individual differences in choice efficiency. These results suggest new avenues for the study of individual differences in the neural antecedents of efficient decision-making.

Frontopolar cortex and decision-making efficiency: comparing brain activity of experts with different professional background during an exploration-exploitation task

PubMed Central

Laureiro-Martínez, Daniella; Canessa, Nicola; Brusoni, Stefano; Zollo, Maurizio; Hare, Todd; Alemanno, Federica; Cappa, Stefano F.

2014-01-01

An optimal balance between efficient exploitation of available resources and creative exploration of alternatives is critical for adaptation and survival. Previous studies associated these behavioral drives with, respectively, the dopaminergic mesocorticolimbic system and frontopolar-intraparietal networks. We study the activation of these systems in two age and gender-matched groups of experienced decision-makers differing in prior professional background, with the aim to understand the neural bases of individual differences in decision-making efficiency (performance divided by response time). We compare brain activity of entrepreneurs (who currently manage the organization they founded based on their venture idea) and managers (who are constantly involved in making strategic decisions but have no venture experience) engaged in a gambling-task assessing exploitative vs. explorative decision-making. Compared with managers, entrepreneurs showed higher decision-making efficiency, and a stronger activation in regions of frontopolar cortex (FPC) previously associated with explorative choice. Moreover, activity across a network of regions previously linked to explore/exploit tradeoffs explained individual differences in choice efficiency. These results suggest new avenues for the study of individual differences in the neural antecedents of efficient decision-making. PMID:24478664

Task-related modulation of effective connectivity during perceptual decision making: dissociation between dorsal and ventral prefrontal cortex.

PubMed

Akaishi, Rei; Ueda, Naoko; Sakai, Katsuyuki

2013-01-01

The dorsal and ventral parts of the lateral prefrontal cortex have been thought to play distinct roles in decision making. Although its dorsal part such as the frontal eye field (FEF) is shown to play roles in accumulation of sensory information during perceptual decision making, the role of the ventral prefrontal cortex (PFv) is not well-documented. Previous studies have suggested that the PFv is involved in selective attention to the task-relevant information and is associated with accuracy of the behavioral performance. It is unknown, however, whether the accumulation and selection processes are anatomically dissociated between the FEF and PFv. Here we show that, by using concurrent TMS and EEG recording, the short-latency (20-40 ms) TMS-evoked potentials after stimulation of the FEF change as a function of the time to behavioral response, whereas those after stimulation of the PFv change depending on whether the response is correct or not. The potentials after stimulation of either region did not show significant interaction between time to response and performance accuracy, suggesting dissociation between the processes subserved by the FEF and PFv networks. The results are consistent with the idea that the network involving the FEF plays a role in information accumulation, whereas the network involving the PFv plays a role in selecting task relevant information. In addition, stimulation of the FEF and PFv induced activation in common regions in the dorsolateral and medial frontal cortices, suggesting convergence of information processed in the two regions. Taken together, the results suggest dissociation between the FEF and PFv networks for their computational roles in perceptual decision making. The study also highlights the advantage of TMS-EEG technique in investigating the computational processes subserved by the neural network in the human brain with a high temporal resolution.

Weak and strong publics: drawing on Nancy Fraser to explore parental participation in neonatal networks.

PubMed

Gibson, Andrew J; Lewando-Hundt, Gillian; Blaxter, Loraine

2014-02-01

We draw on the work of Nancy Fraser, and in particular her concepts of weak and strong publics, to analyze the process of parental involvement in managed neonatal network boards. Public involvement has moved beyond the individual level to include greater involvement of both patients and the public in governance. However, there is relatively little literature that explores the nature and outcomes of long-term patient involvement initiatives or has attempted to theorize, particularly at the level of corporate decision making, the process of patient and public involvement. A repeated survey of all neonatal network managers in England was carried out in 2006-07 to capture developments and changes in parental representation over this time period. This elicited information about the current status of parent representation on neonatal network boards. Four networks were also selected as case studies. This involved interviews with key members of each network board, interviews with parent representatives, observation of meetings and access to board minutes. Data collected show that a wide range of approaches to involving parents has been adopted. These range from decisions not to involve parents at this level to relatively well-developed systems designed to link parent representatives on network boards to parents in neonatal units. Despite these variations, we suggest that parental participation within neonatal services remains an example of a weak public because the parent representatives had limited participation with little influence on decision making. © 2011 John Wiley & Sons Ltd.

Modulation of Saccade Vigor during Value-Based Decision Making.

PubMed

Reppert, Thomas R; Lempert, Karolina M; Glimcher, Paul W; Shadmehr, Reza

2015-11-18

During value-based decision-making, individuals consider the various options and select the one that provides the maximum subjective value. Although the brain integrates abstract information to compute and compare these values, the only behavioral outcome is often the decision itself. However, if the options are visual stimuli, during deliberation the brain moves the eyes from one stimulus to the other. Previous work suggests that saccade vigor, i.e., peak velocity as a function of amplitude, is greater if reward is associated with the visual stimulus. This raises the possibility that vigor during the free viewing of options may be influenced by the valuation of each option. Here, humans chose between a small, immediate monetary reward and a larger but delayed reward. As the deliberation began, vigor was similar for the saccades made to the two options but diverged 0.5 s before decision time, becoming greater for the preferred option. This difference in vigor increased as a function of the difference in the subjective values that the participant assigned to the delayed and immediate options. After the decision was made, participants continued to gaze at the options, but with reduced vigor, making it possible to infer timing of the decision from the sudden drop in vigor. Therefore, the subjective value that the brain assigned to a stimulus during decision-making affected the motor system via the vigor with which the eyes moved toward that stimulus. We find that, as individuals deliberate between two rewarding options and arrive at a decision, the vigor with which they make saccades to each option reflects a real-time evaluation of that option. With deliberation, saccade vigor diverges between the two options, becoming greater for the option that the individual will eventually choose. The results suggest a shared element between the network that assigns value to a stimulus during the process of decision-making and the network that controls vigor of movements toward that stimulus. Copyright © 2015 the authors 0270-6474/15/3515369-10$15.00/0.

Modulation of Saccade Vigor during Value-Based Decision Making

PubMed Central

Lempert, Karolina M.; Glimcher, Paul W.; Shadmehr, Reza

2015-01-01

During value-based decision-making, individuals consider the various options and select the one that provides the maximum subjective value. Although the brain integrates abstract information to compute and compare these values, the only behavioral outcome is often the decision itself. However, if the options are visual stimuli, during deliberation the brain moves the eyes from one stimulus to the other. Previous work suggests that saccade vigor, i.e., peak velocity as a function of amplitude, is greater if reward is associated with the visual stimulus. This raises the possibility that vigor during the free viewing of options may be influenced by the valuation of each option. Here, humans chose between a small, immediate monetary reward and a larger but delayed reward. As the deliberation began, vigor was similar for the saccades made to the two options but diverged 0.5 s before decision time, becoming greater for the preferred option. This difference in vigor increased as a function of the difference in the subjective values that the participant assigned to the delayed and immediate options. After the decision was made, participants continued to gaze at the options, but with reduced vigor, making it possible to infer timing of the decision from the sudden drop in vigor. Therefore, the subjective value that the brain assigned to a stimulus during decision-making affected the motor system via the vigor with which the eyes moved toward that stimulus. SIGNIFICANCE STATEMENT We find that, as individuals deliberate between two rewarding options and arrive at a decision, the vigor with which they make saccades to each option reflects a real-time evaluation of that option. With deliberation, saccade vigor diverges between the two options, becoming greater for the option that the individual will eventually choose. The results suggest a shared element between the network that assigns value to a stimulus during the process of decision-making and the network that controls vigor of movements toward that stimulus. PMID:26586823

Disclosing Sexual Assault Within Social Networks: A Mixed-Method Investigation.

PubMed

Dworkin, Emily R; Pittenger, Samantha L; Allen, Nicole E

2016-03-01

Most survivors of sexual assault disclose their experiences within their social networks, and these disclosure decisions can have important implications for their entry into formal systems and well-being, but no research has directly examined these networks as a strategy to understand disclosure decisions. Using a mixed-method approach that combined survey data, social network analysis, and interview data, we investigate whom, among potential informal responders in the social networks of college students who have experienced sexual assault, survivors contact regarding their assault, and how survivors narrate the role of networks in their decisions about whom to contact. Quantitative results suggest that characteristics of survivors, their social networks, and members of these networks are associated with disclosure decisions. Using data from social network analysis, we identified that survivors tended to disclose to a smaller proportion of their network when many network members had relationships with each other or when the network had more subgroups. Our qualitative analysis helps to contextualize these findings. © Society for Community Research and Action 2016.

Tuning the Brake While Raising the Stake: Network Dynamics during Sequential Decision-Making.

PubMed

Meder, David; Haagensen, Brian Numelin; Hulme, Oliver; Morville, Tobias; Gelskov, Sofie; Herz, Damian Marc; Diomsina, Beata; Christensen, Mark Schram; Madsen, Kristoffer Hougaard; Siebner, Hartwig Roman

2016-05-11

When gathering valued goods, risk and reward are often coupled and escalate over time, for instance, during foraging, trading, or gambling. This escalating frame requires agents to continuously balance expectations of reward against those of risk. To address how the human brain dynamically computes these tradeoffs, we performed whole-brain fMRI while healthy young individuals engaged in a sequential gambling task. Participants were repeatedly confronted with the option to continue with throwing a die to accumulate monetary reward under escalating risk, or the alternative option to stop to bank the current balance. Within each gambling round, the accumulation of gains gradually increased reaction times for "continue" choices, indicating growing uncertainty in the decision to continue. Neural activity evoked by "continue" choices was associated with growing activity and connectivity of a cortico-subcortical "braking" network that positively scaled with the accumulated gains, including pre-supplementary motor area (pre-SMA), inferior frontal gyrus, caudate, and subthalamic nucleus (STN). The influence of the STN on continue-evoked activity in the pre-SMA was predicted by interindividual differences in risk-aversion attitudes expressed during the gambling task. Furthermore, activity in dorsal anterior cingulate cortex (ACC) reflected individual choice tendencies by showing increased activation when subjects made nondefault "continue" choices despite an increasing tendency to stop, but ACC activity did not change in proportion with subjective choice uncertainty. Together, the results implicate a key role of dorsal ACC, pre-SMA, inferior frontal gyrus, and STN in computing the trade-off between escalating reward and risk in sequential decision-making. Using a paradigm where subjects experienced increasing potential rewards coupled with increasing risk, this study addressed two unresolved questions in the field of decision-making: First, we investigated an "inhibitory" network of regions that has so far been investigated with externally cued action inhibition. In this study, we show that the dynamics in this network under increasingly risky decisions are predictive of subjects' risk attitudes. Second, we contribute to a currently ongoing debate about the anterior cingulate cortex's role in sequential foraging decisions by showing that its activity is related to making nondefault choices rather than to choice uncertainty. Copyright © 2016 Meder, Haagensen, et al.

Expanding poliomyelitis and measles surveillance networks to establish surveillance for acute meningitis and encephalitis syndromes--Bangladesh, China, and India, 2006-2008.

PubMed

2012-12-14

Quality surveillance is critical to the control and elimination of vaccine-preventable diseases (VPDs). A key strategy for enhancing VPD surveillance, outlined in the World Health Organization (WHO) Global Framework for Immunization Monitoring and Surveillance (GFIMS), is to expand and link existing VPD surveillance systems (particularly those developed for polio eradication and measles elimination) to include other priority VPDs. Since the launch of the Global Polio Eradication Initiative in 1988, the incidence of polio has decrease by 99% worldwide. A cornerstone of this success is a sensitive surveillance system based on the rapid and timely reporting of all acute flaccid paralysis (AFP) cases in children aged <15 years, with confirmatory diagnostic testing performed by laboratories that are part of a global network. As countries achieve polio-free status, many have expanded syndromic surveillance to include persons with rash and fever, and have built measles diagnostic capacity in existing polio reference laboratories. Acute meningitis/encephalitis syndrome (AMES) and acute encephalitis syndrome (AES) are candidates for expanded surveillance because they are most often caused by VPDs of public health importance for which confirmatory laboratory tests exist. Vaccine-preventable cases of encephalitis include approximately 68,000 Japanese encephalitis (JE) cases, resulting in 13,000-20,000 deaths each year in Asia. Moreover, although bacterial meningitis incidence in Asia is not as well-documented, pneumococcal and meningococcal meningitis outbreaks have been reported in Bangladesh and China, and the incidence of Haemophilus influenzae type b (Hib) meningitis in children aged <5 years in India has been estimated to be 7.1 per 100,000 population, similar to that in European countries before the introduction of vaccine. This report describes a prototype for expanding existing polio and measles surveillance networks in Bangladesh, China, and India to include surveillance for viral and bacterial vaccine-preventable causes of AMES and AES and presents data from 2006-2008.

ProVac Global Initiative: a vision shaped by ten years of supporting evidence-based policy decisions.

PubMed

Jauregui, Barbara; Janusz, Cara Bess; Clark, Andrew D; Sinha, Anushua; Garcia, Ana Gabriela Felix; Resch, Stephen; Toscano, Cristiana M; Sanderson, Colin; Andrus, Jon Kim

2015-05-07

The Pan American Health Organization (PAHO) created the ProVac Initiative in 2004 with the goal of strengthening national technical capacity to make evidence-based decisions on new vaccine introduction, focusing on economic evaluations. In view of the 10th anniversary of the ProVac Initiative, this article describes its progress and reflects on lessons learned to guide the next phase. We quantified the output of the Initiative's capacity-building efforts and critically assess its progress toward achieving the milestones originally proposed in 2004. Additionally, we reviewed how country studies supported by ProVac have directly informed and strengthened the deliberations around new vaccine introduction. Since 2004, ProVac has conducted four regional workshops and supported 24 health economic analyses in 15 Latin American and Caribbean countries. Five Regional Centers of Excellence were funded, resulting in six operational research projects and nine publications. Twenty four decisions on new vaccine introductions were supported with ProVac studies. Enduring products include the TRIVAC and CERVIVAC cost-effectiveness models, the COSTVAC program costing model, methodological guides, workshop training materials and the OLIVES on-line data repository. Ten NITAGs were strengthened through ProVac activities. The evidence accumulated suggests that initiatives with emphasis on sustainable training and direct support for countries to generate evidence themselves, can help accelerate the introduction of the most valuable new vaccines. International and Regional Networks of Collaborators are necessary to provide technical support and tools to national teams conducting analyses. Timeliness, integration, quality and country ownership of the process are four necessary guiding principles for national economic evaluations to have an impact on policymaking. It would be an asset to have a model that offers different levels of complexity to choose from depending on the vaccine being evaluated, the availability of data, and the time frame of the decision. Decision support for new vaccine introduction in low- and middle-income countries is critical to maximizing the efficiency and impact of vaccination programs. Global technical cooperation will be required. In the future, PAHO and WHO have an opportunity to expand the reach of the ProVac philosophy, models, and methods to additional regions and countries requiring real-time support. The ProVac Global Initiative is proposed as an effective mechanism to do so. Copyright © 2015. Published by Elsevier Ltd.

Federated queries of clinical data repositories: Scaling to a national network.

PubMed

Weber, Griffin M

2015-06-01

Federated networks of clinical research data repositories are rapidly growing in size from a handful of sites to true national networks with more than 100 hospitals. This study creates a conceptual framework for predicting how various properties of these systems will scale as they continue to expand. Starting with actual data from Harvard's four-site Shared Health Research Information Network (SHRINE), the framework is used to imagine a future 4000 site network, representing the majority of hospitals in the United States. From this it becomes clear that several common assumptions of small networks fail to scale to a national level, such as all sites being online at all times or containing data from the same date range. On the other hand, a large network enables researchers to select subsets of sites that are most appropriate for particular research questions. Developers of federated clinical data networks should be aware of how the properties of these networks change at different scales and design their software accordingly. Copyright © 2015 Elsevier Inc. All rights reserved.

Exploring relationship between human mobility and social ties: Physical distance is not dead

NASA Astrophysics Data System (ADS)

Jin, Bo; Liao, Binbing; Yuan, Ning; Wang, Wenjun

2015-06-01

Partly due to the difficulty of the access to a worldwide dataset that simultaneously captures the location history and social networks, our understanding of the relationship between human mobility and the social ties has been limited. However, this topic is essential for a deeper study from human dynamics and social networks aspects. In this paper, we examine the location history data and social networks data of 712 email users and 399 offline events users from a map-editing based social network website. Based on these data, we expand all our experiment both from individual aspect and community aspect. We find that the physical distance is still the most influential factor to social ties among the nine representative human mobility features extracted from our GPS trajectory dataset, although Internet revolution has made long-distance communication dramatically faster, easier and cheaper than ever before, and in turn, partly expand the physical scope of social networks. Furthermore, we find that to a certain extent, the proximity of South-North direction is more influential than East-West direction to social ties. To the our best of our knowledge, this difference between South-North and East-West is the first time to be raised and quantitatively supported by a large dataset. We believe our findings on the interplay of human mobility and social ties offer a new perspective to this field of study.

A review of the Forest Service Remote Automated Weather Station (RAWS) network

Treesearch

John Zachariassen; Karl F. Zeller; Ned Nikolov; Tom McClelland

2003-01-01

The RAWS network and RAWS data-use systems are closely reviewed and summarized in this report. RAWS is an active program created by the many land-management agencies that share a common need for accurate and timely weather data from remote locations for vital operational and program decisions specific to wildland and prescribed fires. A RAWS measures basic observable...

Advancements in Risk-Informed Performance-Based Asset Management for Commercial Nuclear Power Plants

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

Liming, James K.; Ravindra, Mayasandra K.

2006-07-01

Over the past several years, ABSG Consulting Inc. (ABS Consulting) and the South Texas Project Nuclear Operating Company (STPNOC) have developed a decision support process and associated software for risk-informed, performance-based asset management (RIPBAM) of nuclear power plant facilities. RIPBAM applies probabilistic risk assessment (PRA) tools and techniques in the realm of plant physical and financial asset management. The RIPBAM process applies a tiered set of models and supporting performance measures (or metrics) that can ultimately be applied to support decisions affecting the allocation and management of plant resources (e.g., funding, staffing, scheduling, etc.). In general, the ultimate goal ofmore » the RIPBAM process is to continually support decision-making to maximize a facility's net present value (NPV) and long-term profitability for its owners. While the initial applications of RIPBAM have been for nuclear power stations, the methodology can easily be adapted to other types of power station or complex facility decision-making support. RIPBAM can also be designed to focus on performance metrics other than NPV and profitability (e.g., mission reliability, operational availability, probability of mission success per dollar invested, etc.). Recent advancements in the RIPBAM process focus on expanding the scope of previous RIPBAM applications to include not only operations, maintenance, and safety issues, but also broader risk perception components affecting plant owner (stockholder), operator, and regulator biases. Conceptually, RIPBAM is a comprehensive risk-informed cash flow model for decision support. It originated as a tool to help manage plant refueling outage scheduling, and was later expanded to include the full spectrum of operations and maintenance decision support. However, it differs from conventional business modeling tools in that it employs a systems engineering approach with broadly based probabilistic analysis of organizational 'value streams'. The scope of value stream inclusion in the process can be established by the user, but in its broadest applications, RIPBAM can be used to address how risk perceptions of plant owners and regulators are impacted by plant performance. Plant staffs can expand and refine RIPBAM models scope via a phased program of activities over time. This paper shows how the multi-metric uncertainty analysis feature of RIPBAM can apply a wide spectrum of decision-influencing factors to support decisions designed to maximize the probability of achieving, maintaining, and improving upon plant goals and objectives. In this paper, the authors show how this approach can be extremely valuable to plant owners and operators in supporting plant value-impacting decision-making processes. (authors)« less

Dysfunctional default mode network and executive control network in people with Internet gaming disorder: Independent component analysis under a probability discounting task.

PubMed

Wang, L; Wu, L; Lin, X; Zhang, Y; Zhou, H; Du, X; Dong, G

2016-04-01

The present study identified the neural mechanism of risky decision-making in Internet gaming disorder (IGD) under a probability discounting task. Independent component analysis was used on the functional magnetic resonance imaging data from 19 IGD subjects (22.2 ± 3.08 years) and 21 healthy controls (HC, 22.8 ± 3.5 years). For the behavioral results, IGD subjects prefer the risky to the fixed options and showed shorter reaction time compared to HC. For the imaging results, the IGD subjects showed higher task-related activity in default mode network (DMN) and less engagement in the executive control network (ECN) than HC when making the risky decisions. Also, we found the activities of DMN correlate negatively with the reaction time and the ECN correlate positively with the probability discounting rates. The results suggest that people with IGD show altered modulation in DMN and deficit in executive control function, which might be the reason for why the IGD subjects continue to play online games despite the potential negative consequences. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Tarighati, Alla; Gross, James; Jalden, Joakim

2017-09-01

We consider the problem of decentralized hypothesis testing in a network of energy harvesting sensors, where sensors make noisy observations of a phenomenon and send quantized information about the phenomenon towards a fusion center. The fusion center makes a decision about the present hypothesis using the aggregate received data during a time interval. We explicitly consider a scenario under which the messages are sent through parallel access channels towards the fusion center. To avoid limited lifetime issues, we assume each sensor is capable of harvesting all the energy it needs for the communication from the environment. Each sensor has an energy buffer (battery) to save its harvested energy for use in other time intervals. Our key contribution is to formulate the problem of decentralized detection in a sensor network with energy harvesting devices. Our analysis is based on a queuing-theoretic model for the battery and we propose a sensor decision design method by considering long term energy management at the sensors. We show how the performance of the system changes for different battery capacities. We then numerically show how our findings can be used in the design of sensor networks with energy harvesting sensors.

Randomness in the network inhibits cooperation based on the bounded rational collective altruistic decision

NASA Astrophysics Data System (ADS)

Ohdaira, Tetsushi

2014-07-01

Previous studies discussing cooperation employ the best decision that every player knows all information regarding the payoff matrix and selects the strategy of the highest payoff. Therefore, they do not discuss cooperation based on the altruistic decision with limited information (bounded rational altruistic decision). In addition, they do not cover the case where every player can submit his/her strategy several times in a match of the game. This paper is based on Ohdaira's reconsideration of the bounded rational altruistic decision, and also employs the framework of the prisoner's dilemma game (PDG) with sequential strategy. The distinction between this study and the Ohdaira's reconsideration is that the former covers the model of multiple groups, but the latter deals with the model of only two groups. Ohdaira's reconsideration shows that the bounded rational altruistic decision facilitates much more cooperation in the PDG with sequential strategy than Ohdaira and Terano's bounded rational second-best decision does. However, the detail of cooperation of multiple groups based on the bounded rational altruistic decision has not been resolved yet. This study, therefore, shows how randomness in the network composed of multiple groups affects the increase of the average frequency of mutual cooperation (cooperation between groups) based on the bounded rational altruistic decision of multiple groups. We also discuss the results of the model in comparison with related studies which employ the best decision.

Case analysis online: a strategic management case model for the health industry.

PubMed

Walsh, Anne; Bearden, Eithne

2004-01-01

Despite the plethora of methods and tools available to support strategic management, the challenge for health executives in the next century will relate to their ability to access and interpret data from multiple and intricate communication networks. Integrated digital networks and satellite systems will expand the scope and ease of sharing information between business divisions, and networked systems will facilitate the use of virtual case discussions across universities. While the internet is frequently used to support clinical decisions in the healthcare industry, few executives rely upon the internetfor strategic analysis. Although electronic technologies can easily synthesize data from multiple information channels, research as well as technical issues may deter their application in strategic analysis. As digital models transform access to information, online models may become increasingly relevant in designing strategic solutions. While there are various pedagogical models available to support the strategic management process, this framework was designed to enhance strategic analysis through the application of technology and electronic research. A strategic analysis framework, which incorporated internet research and case analysis in a strategic managementcourse, is described alongwith design and application issues that emerged during the case analysis process.

Parametric Study of Diffusion-Enhancement Networks for Spatiotemporal Grouping in Real-Time Artificial Vision

DTIC Science & Technology

1993-04-01

suggesting it occurs in later visual motion processing (long-range or second-order system). STIMULUS PERCEPT L" FLASH DURATION FLASH DURATION (a) TIME ( b ...TIME Figure 2. Gamma motion. (a) A light of fixed spatial extent is illuminated then extim- guished. ( b ) The percept is of a light expanding and then...while smaller, type- B cells provide input to its parvocellular subdivision. From here the magnocellular pathway progresses up through visual cortex area V

A neuromorphic network for generic multivariate data classification

PubMed Central

Schmuker, Michael; Pfeil, Thomas; Nawrot, Martin Paul

2014-01-01

Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using “virtual receptors” (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a naïve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-to-trial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to real-world computing problems. PMID:24469794

The Importance of Transition Metals in the Expanding Network of Microbial Metabolism in the Archean Eon

NASA Astrophysics Data System (ADS)

Moore, E. K.; Jelen, B. I.; Giovannelli, D.; Prabhu, A.; Raanan, H.; Falkowski, P. G.

2017-12-01

Deep time changes in Earth surface redox conditions, particularly due to global oxygenation, has impacted the availability of different metals and substrates that are central in biology. Oxidoreductase proteins are molecular nanomachines responsible for all biological electron transfer processes across the tree of life. These enzymes largely contain transition metals in their active sites. Microbial metabolic pathways form a global network of electron transfer, which expanded throughout the Archean eon. Older metabolisms (sulfur reduction, methanogenesis, anoxygenic photosynthesis) accessed negative redox potentials, while later evolving metabolisms (oxygenic photosynthesis, nitrification/denitrification, aerobic respiration) accessed positive redox potentials. The incorporation of different transition metals facilitated biological innovation and the expansion of the network of microbial metabolism. Network analysis was used to examine the connections between microbial taxa, metabolic pathways, crucial metallocofactors, and substrates in deep time by incorporating biosignatures preserved in the geologic record. Nitrogen fixation and aerobic respiration have the highest level of betweenness among metabolisms in the network, indicating that the oldest metabolisms are not the most central. Fe has by far the highest betweenness among metals. Clustering analysis largely separates High Metal Bacteria (HMB), Low Metal Bacteria (LMB), and Archaea showing that simple un-weighted links between taxa, metabolism, and metals have phylogenetic relevance. On average HMB have the highest betweenness among taxa, followed by Archaea and LMB. There is a correlation between the number of metallocofactors and metabolic pathways in representative bacterial taxa, but Archaea do not follow this trend. In many cases older and more recently evolved metabolisms were clustered together supporting previous findings that proliferation of metabolic pathways is not necessarily chronological.

Data Acquisition for Land Subsidence Control

NASA Astrophysics Data System (ADS)

Zhu, Y.; Balke, K.

2009-12-01

For controlling land subsidence caused by groundwater over-exploitation, loading of engineered structures, mining and other anthropogenic activities in this fast changing world, a large variety of different data of various scales of concerning areas are needed for scientific study and administrative operational purposes. The economical, social and environmental impacts of anthropogenic land subsidence have long been recognized by many scientific institutions and management authorities based on results of monitoring and analysis at an interdisciplinary level. The land subsidence information systems composed of the surface and subsurface monitoring nets (monitoring and development wells, GPS stations and other facilities) and local data processing centers as a system management tool in Shanghai City was started with the use of GPS technology to monitor land subsidence in 1998. After years of experiences with a set of initiatives by adopting adequate countermeasures, the particular attention given to new improved methodologies to monitor and model the process of land subsidence in a simple and timely way, this is going to be promoted in the whole Yangtze River Delta region in China, where land subsidence expands in the entire region of urban cluster. The Delta land subsidence monitoring network construction aims to establish an efficient and coordinated water resource management system. The land subsidence monitoring network records "living history" of land subsidence, produces detailed scheduled reports and environmental impact statements. For the different areas with local factors and site characteristics, parallel packages need to be designed for predicting changes, land sensitivity and uncertainty analysis, especially for the risk analysis in the rapid growth of megacities and urban areas. In such cases, the new models with new types of local data and the new ways of data acquisition provide the best information for the decision makers for their mitigating decisions. The problems with outputs to professional and non-professional users, planning vs exploitation conflicts, 3D modeling and visualization are not yet solved due to the complex issues.

Toward a Multilingual, Experiential Environment for Learning Decision Technology.

ERIC Educational Resources Information Center

Yeo, Gee Kin; Tan, Seng Teen

1999-01-01

Describes work at the National University of Singapore on the Internet in expanding a simulation game used in supporting a course in decision technology. Topics include decision support systems, multilingual support for cross-cultural decision studies, process support in a World Wide Web-enhanced multiuser domain (MUD) learning environment, and…

Expansion of Surveillance for Vaccine-preventable Diseases: Building on the Global Polio Laboratory Network and the Global Measles and Rubella Laboratory Network Platforms.

PubMed

Mulders, Mick N; Serhan, Fatima; Goodson, James L; Icenogle, Joseph; Johnson, Barbara W; Rota, Paul A

2017-07-01

Laboratory networks were established to provide accurate and timely laboratory confirmation of infections, an essential component of disease surveillance systems. The World Health Organization (WHO) coordinates global laboratory surveillance of vaccine-preventable diseases (VPDs), including polio, measles and rubella, yellow fever, Japanese encephalitis, rotavirus, and invasive bacterial diseases. In addition to providing high-quality laboratory surveillance data to help guide disease control, elimination, and eradication programs, these global networks provide capacity-building and an infrastructure for public health laboratories. There are major challenges with sustaining and expanding the global laboratory surveillance capacity: limited resources and the need for expansion to meet programmatic goals. Here, we describe the WHO-coordinated laboratory networks supporting VPD surveillance and present a plan for the further development of these networks. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.

Extending the soil moisture record of the climate reference network with machine learning

USDA-ARS?s Scientific Manuscript database

Soil moisture estimation is crucial for agricultural decision-support and a key component of hydrological and climatic research. Unfortunately, quality-controlled soil moisture time series data are uncommon before the most recent decade. However, time series data for precipitation are accessible at ...

A tool for corporate decision making about cognitive pharmaceutical services.

PubMed

Tipton, D J

2001-01-01

To present and discuss the models, theories, ideas, and frameworks that corporate decision makers would apply to the implementation of cognitive pharmaceutical services. Large chains and integrated delivery networks dominate the pharmacy marketplace. As a result, in many instances implementing cognitive pharmaceutical services, or expanding their delivery, first requires approval of a corporate decision maker, often not a pharmacist, who is schooled in marketing, management, and finance, and who necessarily views proposals for cognitive pharmaceutical services from those frames of reference. This article focuses on the following six marketing and management questions that corporate decision makers likely want answered before approving and funding the implementation of cognitive pharmaceutical services: (1) What is our product? (2) Who will pay, and what is the price? (3) Is there a market, and can it be reached? (4) What procedures must be put in place? (5) Who will deliver the service? (6) Where are the services to be delivered, and how is the facility to be presented? For a pharmacy manager charged with bringing cognitive pharmaceutical services to the marketplace, consideration of the issues detailed here meets a reasonable test of due diligence in committing human, financial, and organizational resources. It is natural for a pharmacist to look at cognitive pharmaceutical services through a professional lens. It is just as natural for a corporate decision marker to look at cognitive pharmaceutical services through a marketing and management lens. Unless both lenses are put together, one gets only half the picture.

An Early Look at the Effects of Success Academy Charter Schools

ERIC Educational Resources Information Center

Unterman, Rebecca

2017-01-01

Success Academy is a rapidly expanding charter school network in New York City, with schools located in the Bronx, Brooklyn, Manhattan, and Queens. In the 2016-2017 school year, Success Academy served roughly 14,000 students across 41 elementary, middle, and high schools, which at the time was about 13 percent of the students attending charter…

Healthcare4VideoStorm: Making Smart Decisions Based on Storm Metrics.

PubMed

Zhang, Weishan; Duan, Pengcheng; Chen, Xiufeng; Lu, Qinghua

2016-04-23

Storm-based stream processing is widely used for real-time large-scale distributed processing. Knowing the run-time status and ensuring performance is critical to providing expected dependability for some applications, e.g., continuous video processing for security surveillance. The existing scheduling strategies' granularity is too coarse to have good performance, and mainly considers network resources without computing resources while scheduling. In this paper, we propose Healthcare4Storm, a framework that finds Storm insights based on Storm metrics to gain knowledge from the health status of an application, finally ending up with smart scheduling decisions. It takes into account both network and computing resources and conducts scheduling at a fine-grained level using tuples instead of topologies. The comprehensive evaluation shows that the proposed framework has good performance and can improve the dependability of the Storm-based applications.

Documenting Uncertainty and Error in Gridded Growing Degree Day and Spring Onset Maps Generated by the USA National Phenology Network

NASA Astrophysics Data System (ADS)

Crimmins, T. M.; Switzer, J.; Rosemartin, A.; Marsh, L.; Gerst, K.; Crimmins, M.; Weltzin, J. F.

2016-12-01

Since 2016 the USA National Phenology Network (USA-NPN; www.usanpn.org) has produced and delivered daily maps and short-term forecasts of accumulated growing degree days and spring onset dates at fine spatial scale for the conterminous United States. Because accumulated temperature is a strong driver of phenological transitions in plants and animals, including leaf-out, flowering, fruit ripening, and migration, these data products have utility for a wide range of natural resource planning and management applications, including scheduling invasive species and pest detection and control activities, determining planting dates, anticipating allergy outbreaks and planning agricultural harvest dates. The USA-NPN is a national-scale program that supports scientific advancement and decision-making by collecting, storing, and sharing phenology data and information. We will be expanding the suite of gridded map products offered by the USA-NPN to include predictive species-specific maps of phenological transitions in plants and animals at fine spatial and temporal resolution in the future. Data products, such as the gridded maps currently produced by the USA-NPN, inherently contain uncertainty and error arising from multiple sources, including error propagated forward from underlying climate data and from the models implemented. As providing high-quality, vetted data in a transparent way is central to the USA-NPN, we aim to identify and report the sources and magnitude of uncertainty and error in gridded maps and forecast products. At present, we compare our real-time gridded products to independent, trustworthy data sources, such as the Climate Reference Network, on a daily basis and report Mean Absolute Error and bias through an interactive online dashboard.

Expanded Access Programs

PubMed Central

Van Campen, Luann E.; Garnett, Timothy

2015-01-01

Expanded access is a regulatory mechanism by which an investigational drug can be made available outside of a clinical trial to treat patients with serious or life-threatening conditions for which there are no satisfactory treatment options. An expanded access program (EAP) is the formal plan under which preapproval access to an investigational drug can be provided to a group of patients. Although an EAP is a regulated program, the decision to authorize an EAP is the responsibility of the biopharmaceutical sponsor. Because of the significant impact an EAP can have on current patients, drug development, and future patients, we propose that a sponsor’s decision must be based not only on regulatory criteria but also on ethical and practical considerations regarding implementation of an EAP. Such an approach will help ensure that decisions and plans uphold ethical precepts such as fairness, promoting good, and minimizing risk of harm. PMID:29473010

Decision support system for outage management and automated crew dispatch

DOEpatents

Kang, Ning; Mousavi, Mirrasoul

2018-01-23

A decision support system is provided for utility operations to assist with crew dispatch and restoration activities following the occurrence of a disturbance in a multiphase power distribution network, by providing a real-time visualization of possible location(s). The system covers faults that occur on fuse-protected laterals. The system uses real-time data from intelligent electronics devices coupled with other data sources such as static feeder maps to provide a complete picture of the disturbance event, guiding the utility crew to the most probable location(s). This information is provided in real-time, reducing restoration time and avoiding more costly and laborious fault location finding practices.

Message survival and decision dynamics in a class of reactive complex systems subject to external fields

NASA Astrophysics Data System (ADS)

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

2014-07-01

In this study, the dynamics of decisions in complex networks subject to external fields are studied within a Markov process framework using nonlinear dynamical systems theory. A mathematical discrete-time model is derived using a set of basic assumptions regarding the convincement mechanisms associated with two competing opinions. The model is analyzed with respect to the multiplicity of critical points and the stability of extinction states. Sufficient conditions for extinction are derived in terms of the convincement probabilities and the maximum eigenvalues of the associated connectivity matrices. The influences of exogenous (e.g., mass media-based) effects on decision behavior are analyzed qualitatively. The current analysis predicts: (i) the presence of fixed-point multiplicity (with a maximum number of four different fixed points), multi-stability, and sensitivity with respect to the process parameters; and (ii) the bounded but significant impact of exogenous perturbations on the decision behavior. These predictions were verified using a set of numerical simulations based on a scale-free network topology.

Methodology for designing and implementing a class for service for the transmission of medical images over a common network

NASA Astrophysics Data System (ADS)

Dimond, David A.; Burgess, Robert; Barrios, Nolan; Johnson, Neil D.

2000-05-01

Traditionally, to guarantee the network performance of medical image data transmission, imaging traffic was isolated on a separate network. Organizations are depending on a new generation of multi-purpose networks to transport both normal information and image traffic as they expand access to images throughout the enterprise. These organi want to leverage their existing infrastructure for imaging traffic, but are not willing to accept degradations in overall network performance. To guarantee 'on demand' network performance for image transmissions anywhere at any time, networks need to be designed with the ability to 'carve out' bandwidth for specific applications and to minimize the chances of network failures. This paper will present the methodology Cincinnati Children's Hospital Medical Center (CHMC) used to enhance the physical and logical network design of the existing hospital network to guarantee a class of service for imaging traffic. PACS network designs should utilize the existing enterprise local area network i.e. (LAN) infrastructure where appropriate. Logical separation or segmentation provides the application independence from other clinical and administrative applications as required, ensuring bandwidth and service availability.

End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging.

PubMed

Cai, Chuangjian; Deng, Kexin; Ma, Cheng; Luo, Jianwen

2018-06-15

An end-to-end deep neural network, ResU-net, is developed for quantitative photoacoustic imaging. A residual learning framework is used to facilitate optimization and to gain better accuracy from considerably increased network depth. The contracting and expanding paths enable ResU-net to extract comprehensive context information from multispectral initial pressure images and, subsequently, to infer a quantitative image of chromophore concentration or oxygen saturation (sO 2 ). According to our numerical experiments, the estimations of sO 2 and indocyanine green concentration are accurate and robust against variations in both optical property and object geometry. An extremely short reconstruction time of 22 ms is achieved.

Dynamic divisive normalization predicts time-varying value coding in decision-related circuits.

PubMed

Louie, Kenway; LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W

2014-11-26

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. Copyright © 2014 the authors 0270-6474/14/3416046-12$15.00/0.

How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks

PubMed Central

Chen, Heng; Chen, Xinying

2018-01-01

Language is a complex adaptive system, but how does it change? For investigating this process, four diachronic Chinese word co-occurrence networks have been built based on texts that were written during the last 2,000 years. By comparing the network indicators that are associated with the hierarchical features in language networks, we learn that the hierarchy of Chinese lexical networks has indeed evolved over time at three different levels. The connections of words at the micro level are continually weakening; the number of words in the meso-level communities has increased significantly; and the network is expanding at the macro level. This means that more and more words tend to be connected to medium-central words and form different communities. Meanwhile, fewer high-central words link these communities into a highly efficient small-world network. Understanding this process may be crucial for understanding the increasing structural complexity of the language system. PMID:29489837

How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks.

PubMed

Chen, Heng; Chen, Xinying; Liu, Haitao

2018-01-01

Language is a complex adaptive system, but how does it change? For investigating this process, four diachronic Chinese word co-occurrence networks have been built based on texts that were written during the last 2,000 years. By comparing the network indicators that are associated with the hierarchical features in language networks, we learn that the hierarchy of Chinese lexical networks has indeed evolved over time at three different levels. The connections of words at the micro level are continually weakening; the number of words in the meso-level communities has increased significantly; and the network is expanding at the macro level. This means that more and more words tend to be connected to medium-central words and form different communities. Meanwhile, fewer high-central words link these communities into a highly efficient small-world network. Understanding this process may be crucial for understanding the increasing structural complexity of the language system.

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

PubMed

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

2013-03-01

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

Overcoming Indecision by Changing the Decision Boundary

PubMed Central

2017-01-01

The dominant theoretical framework for decision making asserts that people make decisions by integrating noisy evidence to a threshold. It has recently been shown that in many ecologically realistic situations, decreasing the decision boundary maximizes the reward available from decisions. However, empirical support for decreasing boundaries in humans is scant. To investigate this problem, we used an ideal observer model to identify the conditions under which participants should change their decision boundaries with time to maximize reward rate. We conducted 6 expanded-judgment experiments that precisely matched the assumptions of this theoretical model. In this paradigm, participants could sample noisy, binary evidence presented sequentially. Blocks of trials were fixed in duration, and each trial was an independent reward opportunity. Participants therefore had to trade off speed (getting as many rewards as possible) against accuracy (sampling more evidence). Having access to the actual evidence samples experienced by participants enabled us to infer the slope of the decision boundary. We found that participants indeed modulated the slope of the decision boundary in the direction predicted by the ideal observer model, although we also observed systematic deviations from optimality. Participants using suboptimal boundaries do so in a robust manner, so that any error in their boundary setting is relatively inexpensive. The use of a normative model provides insight into what variable(s) human decision makers are trying to optimize. Furthermore, this normative model allowed us to choose diagnostic experiments and in doing so we present clear evidence for time-varying boundaries. PMID:28406682

Using Decision Analysis to Improve Malaria Control Policy Making

PubMed Central

Kramer, Randall; Dickinson, Katherine L.; Anderson, Richard M.; Fowler, Vance G.; Miranda, Marie Lynn; Mutero, Clifford M.; Saterson, Kathryn A.; Wiener, Jonathan B.

2013-01-01

Malaria and other vector-borne diseases represent a significant and growing burden in many tropical countries. Successfully addressing these threats will require policies that expand access to and use of existing control methods, such as insecticide-treated bed nets and artemesinin combination therapies for malaria, while weighing the costs and benefits of alternative approaches over time. This paper argues that decision analysis provides a valuable framework for formulating such policies and combating the emergence and re-emergence of malaria and other diseases. We outline five challenges that policy makers and practitioners face in the struggle against malaria, and demonstrate how decision analysis can help to address and overcome these challenges. A prototype decision analysis framework for malaria control in Tanzania is presented, highlighting the key components that a decision support tool should include. Developing and applying such a framework can promote stronger and more effective linkages between research and policy, ultimately helping to reduce the burden of malaria and other vector-borne diseases. PMID:19356821

Patients' attitudes to their embryos and their destiny: social conditioning?

PubMed

de Lacey, Sheryl

2007-02-01

The clinical management of embryo storage and disposal is dynamic and subject to changes in the cultural context such as public debate and the implementation of public policy. Studies of the decisions made by patient couples for their embryos, and trends in decision-making over time and in relation to issues arising in the cultural context are rare. Studies of the attitudes that patient couples have towards their frozen embryos have largely focused on measuring patients' intentions in relation to publicly contentious outcomes. A small but expanding number of interview studies are illuminating the meaning that couples attribute to frozen embryos and how this influences decisions for their destiny. This chapter maps both quantitative and qualitative studies of patients' attitudes and decisions illuminating similarities and contradictions in study findings, and ultimately highlights the range of attitudes in patients, clinics and the community towards what is evidently a difficult and morally challenging decision to end the storage of frozen embryos.

Risk Management for Weapon Systems Acquisition: A Decision Support System

DTIC Science & Technology

1985-02-28

includes the program evaluation and review technique (PERT) for network analysis, the PMRM for quantifying risk , an optimization package for generating...Despite the inclusion of uncertainty in time, PERT can at best be considered as a tool for quantifying risk with regard to the time element only. Moreover

Hypothesis generation using network structures on community health center cancer-screening performance.

PubMed

Carney, Timothy Jay; Morgan, Geoffrey P; Jones, Josette; McDaniel, Anna M; Weaver, Michael T; Weiner, Bryan; Haggstrom, David A

2015-10-01

Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments. Copyright © 2015 Elsevier Inc. All rights reserved.

Portable emergency telemedicine system over wireless broadband and 3G networks.

PubMed

Hong, SungHye; Kim, SangYong; Kim, JungChae; Lim, DongKyu; Jung, SeokMyung; Kim, DongKeun; Yoo, Sun K

2009-01-01

The telemedicine system aims at monitoring patients remotely without limit in time and space. However the existing telemedicine systems exchange medical information simply in a specified location. Due to increasing speed in processing data and expanding bandwidth of wireless networks, it is possible to perform telemedicine services on personal digital assistants (PDA). In this paper, a telemedicine system on PDA was developed using wideband mobile networks such as Wi-Fi, HSDPA, and WiBro for high speed bandwidths. This system enables to utilize and exchange variety and reliable patient information of video, biosignals, chatting messages, and triage data. By measuring bandwidths of individual data of the system over wireless networks, and evaluating the performance of this system using PDA, we demonstrated the feasibility of the designed portable emergency telemedicine system.

GNSS Active Network of West of Sao Paulo State Applied to Ionosphere Monitoring

NASA Astrophysics Data System (ADS)

Aguiar, C. R.; Camargo, P. D.

2008-12-01

In Brazil, a research project of atmospheric studies from reference stations equipped with dual frequency GNSS receivers is in initial phase. These stations have composed the GNSS Active Network of West Sao Paulo State (Network-GNSS-SP) and have been broadcasting GNSS data in real time. Network-GNSS-SP is in tests phase and it's the first Brazilian network to provide GNSS measurements in real time. In Spatial Geodesy Study Brazilian Group (GEGE) has been researched the ionosphere effects on L band signal, as well as the GPS potential on ionosphere dynamic monitoring and, consequently, the application of this one to spatial geophysics study, besides dynamic ionosphere modeling. An algorithm based on Kalman filter has been developed for ionosphere modeling at low latitude regions and estimation of ionospheric parameters as absolute vertical TEC (VTEC) for the monitoring of ionosphere behavior. The approach used in this study is to apply a model for the ionospheric vertical delay. In the algorithm, the ionospheric vertical delay is modeled and expanded by Fourier series. In this paper has been realized on-line processing of the Network-GNSS-SP data and the initial results reached with the algorithm can already be analyzed. The results show the ionospheric maps created from real time TEC estimates.

Topology of Innovation Spaces in the Knowledge Networks Emerging through Questions-And-Answers

PubMed Central

Andjelković, Miroslav; Tadić, Bosiljka; Mitrović Dankulov, Marija; Rajković, Milan; Melnik, Roderick

2016-01-01

The communication processes of knowledge creation represent a particular class of human dynamics where the expertise of individuals plays a substantial role, thus offering a unique possibility to study the structure of knowledge networks from online data. Here, we use the empirical evidence from questions-and-answers in mathematics to analyse the emergence of the network of knowledge contents (or tags) as the individual experts use them in the process. After removing extra edges from the network-associated graph, we apply the methods of algebraic topology of graphs to examine the structure of higher-order combinatorial spaces in networks for four consecutive time intervals. We find that the ranking distributions of the suitably scaled topological dimensions of nodes fall into a unique curve for all time intervals and filtering levels, suggesting a robust architecture of knowledge networks. Moreover, these networks preserve the logical structure of knowledge within emergent communities of nodes, labeled according to a standard mathematical classification scheme. Further, we investigate the appearance of new contents over time and their innovative combinations, which expand the knowledge network. In each network, we identify an innovation channel as a subgraph of triangles and larger simplices to which new tags attach. Our results show that the increasing topological complexity of the innovation channels contributes to network’s architecture over different time periods, and is consistent with temporal correlations of the occurrence of new tags. The methodology applies to a wide class of data with the suitable temporal resolution and clearly identified knowledge-content units. PMID:27171149

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

NASA Astrophysics Data System (ADS)

Qiu, Bin

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

Neural network for processing both spatial and temporal data with time based back-propagation

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)

1993-01-01

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

Digital watermarking for secure and adaptive teleconferencing

NASA Astrophysics Data System (ADS)

Vorbrueggen, Jan C.; Thorwirth, Niels

2002-04-01

The EC-sponsored project ANDROID aims to develop a management system for secure active networks. Active network means allowing the network's customers to execute code (Java-based so-called proxylets) on parts of the network infrastructure. Secure means that the network operator nonetheless retains full control over the network and its resources, and that proxylets use ANDROID-developed facilities to provide secure applications. Management is based on policies and allows autonomous, distributed decisions and actions to be taken. Proxylets interface with the system via policies; among actions they can take is controlling execution of other proxylets or redirection of network traffic. Secure teleconferencing is used as the application to demonstrate the approach's advantages. A way to control a teleconference's data streams is to use digital watermarking of the video, audio and/or shared-whiteboard streams, providing an imperceptible and inseparable side channel that delivers information from originating or intermediate stations to downstream stations. Depending on the information carried by the watermark, these stations can take many different actions. Examples are forwarding decisions based on security classifications (possibly time-varying) at security boundaries, set-up and tear-down of virtual private networks, intelligent and adaptive transcoding, recorder or playback control (e.g., speaking off the record), copyright protection, and sender authentication.

Preparing for a decision support system.

PubMed

Callan, K

2000-08-01

The increasing pressure to reduce costs and improve outcomes is driving the health care industry to view information as a competitive advantage. Timely information is required to help reduce inefficiencies and improve patient care. Numerous disparate operational or transactional information systems with inconsistent and often conflicting data are no longer adequate to meet the information needs of integrated care delivery systems and networks in competitive managed care environments. This article reviews decision support system characteristics and describes a process to assess the preparedness of an organization to implement and use decision support systems to achieve a more effective, information-based decision process. Decision support tools included in this article range from reports to data mining.

Impact of New Water Sources on the Overall Water Network: An Optimisation Approach

PubMed Central

Jones, Brian C.; Hove-Musekwa, Senelani D.

2014-01-01

A mathematical programming problem is formulated for a water network with new water sources included. Salinity and water hardness are considered in the model, which is later solved using the Max-Min Ant System (MMAS) to assess the impact of new water sources on the total cost of the existing network. It is efficient to include new water sources if the distances to them are short or if there is a high penalty associated with failure to meet demand. Desalination unit costs also significantly affect the decision whether to install new water sources into the existing network while softening costs are generally negligible in making such decisions. Experimental results show that, in the example considered, it is efficient to reduce number of desalination plants to remain with one central plant. The Max-Min Ant System algorithm seems to be an effective method as shown by least computational time as compared to the commercial solver Cplex. PMID:27382617

Probabilistic resource allocation system with self-adaptive capability

NASA Technical Reports Server (NTRS)

Yufik, Yan M. (Inventor)

1996-01-01

A probabilistic resource allocation system is disclosed containing a low capacity computational module (Short Term Memory or STM) and a self-organizing associative network (Long Term Memory or LTM) where nodes represent elementary resources, terminal end nodes represent goals, and directed links represent the order of resource association in different allocation episodes. Goals and their priorities are indicated by the user, and allocation decisions are made in the STM, while candidate associations of resources are supplied by the LTM based on the association strength (reliability). Reliability values are automatically assigned to the network links based on the frequency and relative success of exercising those links in the previous allocation decisions. Accumulation of allocation history in the form of an associative network in the LTM reduces computational demands on subsequent allocations. For this purpose, the network automatically partitions itself into strongly associated high reliability packets, allowing fast approximate computation and display of allocation solutions satisfying the overall reliability and other user-imposed constraints. System performance improves in time due to modification of network parameters and partitioning criteria based on the performance feedback.

Probabilistic resource allocation system with self-adaptive capability

NASA Technical Reports Server (NTRS)

Yufik, Yan M. (Inventor)

1998-01-01

A probabilistic resource allocation system is disclosed containing a low capacity computational module (Short Term Memory or STM) and a self-organizing associative network (Long Term Memory or LTM) where nodes represent elementary resources, terminal end nodes represent goals, and weighted links represent the order of resource association in different allocation episodes. Goals and their priorities are indicated by the user, and allocation decisions are made in the STM, while candidate associations of resources are supplied by the LTM based on the association strength (reliability). Weights are automatically assigned to the network links based on the frequency and relative success of exercising those links in the previous allocation decisions. Accumulation of allocation history in the form of an associative network in the LTM reduces computational demands on subsequent allocations. For this purpose, the network automatically partitions itself into strongly associated high reliability packets, allowing fast approximate computation and display of allocation solutions satisfying the overall reliability and other user-imposed constraints. System performance improves in time due to modification of network parameters and partitioning criteria based on the performance feedback.

AST: Activity-Security-Trust driven modeling of time varying networks.

PubMed

Wang, Jian; Xu, Jiake; Liu, Yanheng; Deng, Weiwen

2016-02-18

Network modeling is a flexible mathematical structure that enables to identify statistical regularities and structural principles hidden in complex systems. The majority of recent driving forces in modeling complex networks are originated from activity, in which an activity potential of a time invariant function is introduced to identify agents' interactions and to construct an activity-driven model. However, the new-emerging network evolutions are already deeply coupled with not only the explicit factors (e.g. activity) but also the implicit considerations (e.g. security and trust), so more intrinsic driving forces behind should be integrated into the modeling of time varying networks. The agents undoubtedly seek to build a time-dependent trade-off among activity, security, and trust in generating a new connection to another. Thus, we reasonably propose the Activity-Security-Trust (AST) driven model through synthetically considering the explicit and implicit driving forces (e.g. activity, security, and trust) underlying the decision process. AST-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process, allowing a profound understanding of the effects of security and trust in driving network evolution, and improving the biases induced by only involving activity representations in analyzing the dynamical processes.

A Heuristic Decision Making Model to Mitigate Adverse Consequences in a Network Centric Warfare/Sense and Respond System

DTIC Science & Technology

2005-05-01

made. 4. Do military decision makers identify / analyze adverse consequences presently? Few do based on this research and most don’t do it effectively ...A HEURISTIC DECISION MAKING MODEL TO MITIGATE ADVERSE CONSEQUENCES IN A NETWORK CENTRIC WARFARE / SENSE AND RESPOND SYSTEM...ENS/05-01 A HEURISTIC DECISION MAKING MODEL TO MITIGATE ADVERSE CONSEQUENCES IN A NETWORK CENTRIC WARFARE / SENSE AND RESPOND SYSTEM

Mobile Support For Logistics

DTIC Science & Technology

2016-03-01

Infrastructure to Support Mobile Devices (Takai, 2012, p. 2). The objectives needed in order to meet this goal are to: evolve spectrum management, expand... infrastructure to support wireless capabilities, and establish a mobile device security architecture (Takai, 2012, p. 2). By expanding infrastructure to...often used on Mobile Ad-Hoc Networks (MANETs). MANETS are infrastructure -less networks that include, but are not limited to, mobile devices. These

Mapping longitudinal scientific progress, collaboration and impact of the Alzheimer's disease neuroimaging initiative.

PubMed

Yao, Xiaohui; Yan, Jingwen; Ginda, Michael; Börner, Katy; Saykin, Andrew J; Shen, Li

2017-01-01

Alzheimer's disease neuroimaging initiative (ADNI) is a landmark imaging and omics study in AD. ADNI research literature has increased substantially over the past decade, which poses challenges for effectively communicating information about the results and impact of ADNI-related studies. In this work, we employed advanced information visualization techniques to perform a comprehensive and systematic mapping of the ADNI scientific growth and impact over a period of 12 years. Citation information of ADNI-related publications from 01/01/2003 to 05/12/2015 were downloaded from the Scopus database. Five fields, including authors, years, affiliations, sources (journals), and keywords, were extracted and preprocessed. Statistical analyses were performed on basic publication data as well as journal and citations information. Science mapping workflows were conducted using the Science of Science (Sci2) Tool to generate geospatial, topical, and collaboration visualizations at the micro (individual) to macro (global) levels such as geospatial layouts of institutional collaboration networks, keyword co-occurrence networks, and author collaboration networks evolving over time. During the studied period, 996 ADNI manuscripts were published across 233 journals and conference proceedings. The number of publications grew linearly from 2008 to 2015, so did the number of involved institutions. ADNI publications received much more citations than typical papers from the same set of journals. Collaborations were visualized at multiple levels, including authors, institutions, and research areas. The evolution of key ADNI research topics was also plotted over the studied period. Both statistical and visualization results demonstrate the increasing attention of ADNI research, strong citation impact of ADNI publications, the expanding collaboration networks among researchers, institutions and ADNI core areas, and the dynamic evolution of ADNI research topics. The visualizations presented here can help improve daily decision making based on a deep understanding of existing patterns and trends using proven and replicable data analysis and visualization methods. They have great potential to provide new insights and actionable knowledge for helping translational research in AD.

Resolving uncertainties in the urban air quality, climate, and vegetation nexus through citizen science, satellite imagery, and atmospheric modeling

NASA Astrophysics Data System (ADS)

Jenerette, D.; Wang, J.; Chandler, M.; Ripplinger, J.; Koutzoukis, S.; Ge, C.; Castro Garcia, L.; Kucera, D.; Liu, X.

2017-12-01

Large uncertainties remain in identifying the distribution of urban air quality and temperature risks across neighborhood to regional scales. Nevertheless, many cities are actively expanding vegetation with an expectation to moderate both climate and air quality risks. We address these uncertainties through an integrated analysis of satellite data, atmospheric modeling, and in-situ environmental sensor networks maintained by citizen scientists. During the summer of 2017 we deployed neighborhood-scale networks of air temperature and ozone sensors through three campaigns across urbanized southern California. During each five-week campaign we deployed six sensor nodes that included an EPA federal equivalent method ozone sensor and a suite of meteorological sensors. Each node was further embedded in a network of 100 air temperature sensors that combined a randomized design developed by the research team and a design co-created by citizen scientists. Between 20 and 60 citizen scientists were recruited for each campaign, with local partners supporting outreach and training to ensure consistent deployment and data gathering. We observed substantial variation in both temperature and ozone concentrations at scales less than 4km, whole city, and the broader southern California region. At the whole city scale the average spatial variation with our ozone sensor network just for city of Long Beach was 26% of the mean, while corresponding variation in air temperature was only 7% of the mean. These findings contrast with atmospheric model estimates of variation at the regional scale of 11% and 1%. Our results show the magnitude of fine-scale variation underestimated by current models and may also suggest scaling functions that can connect neighborhood and regional variation in both ozone and temperature risks in southern California. By engaging citizen science with high quality sensors, satellite data, and real-time forecasting, our results help identify magnitudes of climate and air quality risk variation across scales and can guide individual decisions and urban policies surrounding vegetation to moderate these risks.

Mapping longitudinal scientific progress, collaboration and impact of the Alzheimer’s disease neuroimaging initiative

PubMed Central

Yao, Xiaohui; Yan, Jingwen; Ginda, Michael; Börner, Katy; Saykin, Andrew J.

2017-01-01

Background Alzheimer’s disease neuroimaging initiative (ADNI) is a landmark imaging and omics study in AD. ADNI research literature has increased substantially over the past decade, which poses challenges for effectively communicating information about the results and impact of ADNI-related studies. In this work, we employed advanced information visualization techniques to perform a comprehensive and systematic mapping of the ADNI scientific growth and impact over a period of 12 years. Methods Citation information of ADNI-related publications from 01/01/2003 to 05/12/2015 were downloaded from the Scopus database. Five fields, including authors, years, affiliations, sources (journals), and keywords, were extracted and preprocessed. Statistical analyses were performed on basic publication data as well as journal and citations information. Science mapping workflows were conducted using the Science of Science (Sci2) Tool to generate geospatial, topical, and collaboration visualizations at the micro (individual) to macro (global) levels such as geospatial layouts of institutional collaboration networks, keyword co-occurrence networks, and author collaboration networks evolving over time. Results During the studied period, 996 ADNI manuscripts were published across 233 journals and conference proceedings. The number of publications grew linearly from 2008 to 2015, so did the number of involved institutions. ADNI publications received much more citations than typical papers from the same set of journals. Collaborations were visualized at multiple levels, including authors, institutions, and research areas. The evolution of key ADNI research topics was also plotted over the studied period. Conclusions Both statistical and visualization results demonstrate the increasing attention of ADNI research, strong citation impact of ADNI publications, the expanding collaboration networks among researchers, institutions and ADNI core areas, and the dynamic evolution of ADNI research topics. The visualizations presented here can help improve daily decision making based on a deep understanding of existing patterns and trends using proven and replicable data analysis and visualization methods. They have great potential to provide new insights and actionable knowledge for helping translational research in AD. PMID:29095836

Analysis Resilient Algorithm on Artificial Neural Network Backpropagation

NASA Astrophysics Data System (ADS)

Saputra, Widodo; Tulus; Zarlis, Muhammad; Widia Sembiring, Rahmat; Hartama, Dedy

2017-12-01

Prediction required by decision makers to anticipate future planning. Artificial Neural Network (ANN) Backpropagation is one of method. This method however still has weakness, for long training time. This is a reason to improve a method to accelerate the training. One of Artificial Neural Network (ANN) Backpropagation method is a resilient method. Resilient method of changing weights and bias network with direct adaptation process of weighting based on local gradient information from every learning iteration. Predicting data result of Istanbul Stock Exchange training getting better. Mean Square Error (MSE) value is getting smaller and increasing accuracy.

Decision Modeling Framework to Minimize Arrival Delays from Ground Delay Programs

NASA Astrophysics Data System (ADS)

Mohleji, Nandita

Convective weather and other constraints create uncertainty in air transportation, leading to costly delays. A Ground Delay Program (GDP) is a strategy to mitigate these effects. Systematic decision support can increase GDP efficacy, reduce delays, and minimize direct operating costs. In this study, a decision analysis (DA) model is constructed by combining a decision tree and Bayesian belief network. Through a study of three New York region airports, the DA model demonstrates that larger GDP scopes that include more flights in the program, along with longer lead times that provide stakeholders greater notice of a pending program, trigger the fewest average arrival delays. These findings are demonstrated to result in a savings of up to $1,850 per flight. Furthermore, when convective weather is predicted, forecast weather confidences remain the same level or greater at least 70% of the time, supporting more strategic decision making. The DA model thus enables quantification of uncertainties and insights on causal relationships, providing support for future GDP decisions.

Reduction of streamflow monitoring networks by a reference point approach

NASA Astrophysics Data System (ADS)

Cetinkaya, Cem P.; Harmancioglu, Nilgun B.

2014-05-01

Adoption of an integrated approach to water management strongly forces policy and decision-makers to focus on hydrometric monitoring systems as well. Existing hydrometric networks need to be assessed and revised against the requirements on water quantity data to support integrated management. One of the questions that a network assessment study should resolve is whether a current monitoring system can be consolidated in view of the increased expenditures in time, money and effort imposed on the monitoring activity. Within the last decade, governmental monitoring agencies in Turkey have foreseen an audit on all their basin networks in view of prevailing economic pressures. In particular, they question how they can decide whether monitoring should be continued or terminated at a particular site in a network. The presented study is initiated to address this question by examining the applicability of a method called “reference point approach” (RPA) for network assessment and reduction purposes. The main objective of the study is to develop an easily applicable and flexible network reduction methodology, focusing mainly on the assessment of the “performance” of existing streamflow monitoring networks in view of variable operational purposes. The methodology is applied to 13 hydrometric stations in the Gediz Basin, along the Aegean coast of Turkey. The results have shown that the simplicity of the method, in contrast to more complicated computational techniques, is an asset that facilitates the involvement of decision makers in application of the methodology for a more interactive assessment procedure between the monitoring agency and the network designer. The method permits ranking of hydrometric stations with regard to multiple objectives of monitoring and the desired attributes of the basin network. Another distinctive feature of the approach is that it also assists decision making in cases with limited data and metadata. These features of the RPA approach highlight its advantages over the existing network assessment and reduction methods.

Atmospheric deposition of inorganic nitrogen in Spanish forests of Quercus ilex measured with ion-exchange resins and conventional collectors

Treesearch

Héctor García-Gomez; Sheila Izquieta-Rojano; Laura Aguillaume; Ignacio González-Fernández; Fernando Valiño; David Elustondo; Jesús M. Santamaría; Anna Àvila; Mark E. Fenn; Rocío Alonso

2016-01-01

Atmospheric nitrogen deposition is one of the main threats for biodiversity and ecosystem functioning. Measurement techniques like ion-exchange resin collectors (IECs), which are less expensive and time-consuming than conventional methods, are gaining relevance in the study of atmospheric deposition and are recommended to expand monitoring networks. In the present work...

Effects of Ben 10 on Kids in the Age-Group 5 to 8 Years

ERIC Educational Resources Information Center

Sandhu, Devendar

2014-01-01

"The problem with our society is that our values aren't in the right place. There's an awful lot of bleeding and naked bodies on prime-time networks, but not nearly enough cable television on public programming." --Bauvard, Evergreens Are Prudish Technology has expanded the availability of information through various routes, such as,…

The mixed reality of things: emerging challenges for human-information interaction

NASA Astrophysics Data System (ADS)

Spicer, Ryan P.; Russell, Stephen M.; Rosenberg, Evan Suma

2017-05-01

Virtual and mixed reality technology has advanced tremendously over the past several years. This nascent medium has the potential to transform how people communicate over distance, train for unfamiliar tasks, operate in challenging environments, and how they visualize, interact, and make decisions based on complex data. At the same time, the marketplace has experienced a proliferation of network-connected devices and generalized sensors that are becoming increasingly accessible and ubiquitous. As the "Internet of Things" expands to encompass a predicted 50 billion connected devices by 2020, the volume and complexity of information generated in pervasive and virtualized environments will continue to grow exponentially. The convergence of these trends demands a theoretically grounded research agenda that can address emerging challenges for human-information interaction (HII). Virtual and mixed reality environments can provide controlled settings where HII phenomena can be observed and measured, new theories developed, and novel algorithms and interaction techniques evaluated. In this paper, we describe the intersection of pervasive computing with virtual and mixed reality, identify current research gaps and opportunities to advance the fundamental understanding of HII, and discuss implications for the design and development of cyber-human systems for both military and civilian use.

A Conceptual Model to be Used for Community-based Drinking-water Improvements

PubMed Central

Ahmed, Mushfique

2006-01-01

A conceptual model that can be applied to improve community-based drinking-water in crisis-type situations has been developed from the original general science and technology/development bridging concept and from a case study in Northwest Bangladesh. The main feature of this model is the strengthened role of communities in identifying and implementing appropriate drinking-water improvements with facilitation by multi-disciplinary collaborative regional agency networks. These combined representative community/regional agency networks make decisions and take actions that involve environmental and health data, related capacity factors, and appropriateness of drinking-water improvements. They also progressively link regional decisions and actions together, expanding them nationally and preferably within a sustainable national policy-umbrella. This use of the model reflects stronger community control and input with more appropriate solutions to such drinking-water crisis situations and minimization of risk from potentially-inappropriate ‘externally-imposed’ processes. The application here is not intended as a generic or complete poverty-alleviation strategy by itself but as a crisis-solving intervention, complementary to existing and developing sustainable national policies and to introduce how key principles and concepts can relate in the wider context. In terms of the Bangladesh arsenic crisis, this translates into community/regional networks in geographic regions making assessments on the appropriateness of their drinking-water configuration. Preferred improvement options are decided and acted upon in a technological framework. Options include: pond-sand filters, rainwater harvesting, dugwell, deep-protected tubewell, and shallow tubewell with treatment devices. Bedding in the regional drinking-water improvement configuration protocols then occurs. This involves establishing ongoing representative monitoring and screening, clear delineation of arsenic-contaminated wells with inter-regional linking, and national expansion within national drinking-water policy frameworks. PMID:17366766

A conceptual model to be used for community-based drinking-water improvements.

PubMed

Anstiss, Richard G; Ahmed, Mushfique

2006-09-01

A conceptual model that can be applied to improve community-based drinking-water in crisis-type situations has been developed from the original general science and technology/development bridging concept and from a case study in Northwest Bangladesh. The main feature of this model is the strengthened role of communities in identifying and implementing appropriate drinking-water improvements with facilitation by multi-disciplinary collaborative regional agency networks. These combined representative community/regional agency networks make decisions and take actions that involve environmental and health data, related capacity factors, and appropriateness of drinking-water improvements. They also progressively link regional decisions and actions together, expanding them nationally and preferably within a sustainable national policy-umbrella. This use of the model reflects stronger community control and input with more appropriate solutions to such drinking-water crisis situations and minimization of risk from potentially-inappropriate 'externally-imposed' processes. The application here is not intended as a generic or complete poverty-alleviation strategy by itself but as a crisis-solving intervention, complementary to existing and developing sustainable national policies and to introduce how key principles and concepts can relate in the wider context. In terms of the Bangladesh arsenic crisis, this translates into community/regional networks in geographic regions making assessments on the appropriateness of their drinking-water configuration. Preferred improvement options are decided and acted upon in a technological framework. Options include: pond-sand filters, rainwater harvesting, dugwell, deep-protected tubewell, and shallow tubewell with treatment devices. Bedding in the regional drinking-water improvement configuration protocols then occurs. This involves establishing ongoing representative monitoring and screening, clear delineation of arsenic-contaminated wells with inter-regional linking, and national expansion within national drinking-water policy frameworks.

Exploring biological and social networks to better understand and treat diabetes mellitus. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

Belgardt, Bengt-Frederik; Jarasch, Alexander; Lammert, Eckhard

2018-03-01

Improvements and breakthroughs in computational sciences in the last 20 years have paralleled the rapid gain of influence of social networks on our daily life. As timely reviewed by Perc and colleagues [1], understanding and treating complex human diseases, such as type 2 diabetes (T2D), from which already more than 5% of the global population suffer, will necessitate analyzing and understanding the multi-layered and interconnected networks that usually keep physiological functions intact, but are disturbed in disease states. These networks range from intra- and intercellular networks influencing cell behavior (e.g., secretion of insulin in response to food intake and anabolic response to insulin) to social networks influencing human behavior (e.g., food intake and physical activity). This commentary first expands on the background of pancreatic beta cell networks in human health and T2D, briefly introduces exosomes as novel signals exchanged between distant cellular networks, and finally discusses potential pitfalls and chances in network analyses with regards to experimental data acquisition and processing.

SDN-Enabled Dynamic Feedback Control and Sensing in Agile Optical Networks

NASA Astrophysics Data System (ADS)

Lin, Likun

Fiber optic networks are no longer just pipelines for transporting data in the long haul backbone. Exponential growth in traffic in metro-regional areas has pushed higher capacity fiber toward the edge of the network, and highly dynamic patterns of heterogeneous traffic have emerged that are often bursty, severely stressing the historical "fat and dumb pipe" static optical network, which would need to be massively over-provisioned to deal with these loads. What is required is a more intelligent network with a span of control over the optical as well as electrical transport mechanisms which enables handling of service requests in a fast and efficient way that guarantees quality of service (QoS) while optimizing capacity efficiency. An "agile" optical network is a reconfigurable optical network comprised of high speed intelligent control system fed by real-time in situ network sensing. It provides fast response in the control and switching of optical signals in response to changing traffic demands and network conditions. This agile control of optical signals is enabled by pushing switching decisions downward in the network stack to the physical layer. Implementing such agility is challenging due to the response dynamics and interactions of signals in the physical layer. Control schemes must deal with issues such as dynamic power equalization, EDFA transients and cascaded noise effects, impairments due to self-phase modulation and dispersion, and channel-to-channel cross talk. If these issues are not properly predicted and mitigated, attempts at dynamic control can drive the optical network into an unstable state. In order to enable high speed actuation of signal modulators and switches, the network controller must be able to make decisions based on predictive models. In this thesis, we consider how to take advantage of Software Defined Networking (SDN) capabilities for network reconfiguration, combined with embedded models that access updates from deployed network monitoring sensors. In order to maintain signal quality while optimizing network resources, we find that it is essential to model and update estimates of the physical link impairments in real-time. In this thesis, we consider the key elements required to enable an agile optical network, with contributions as follows: • Control Framework: extended the SDN concept to include the optical transport network through extensions to the OpenFlow (OF) protocol. A unified SDN control plane is built to facilitate control and management capability across the electrical/packet-switched and optical/circuit-switched portions of the network seamlessly. The SDN control plane serves as a platform to abstract the resources of multilayer/multivendor networks. Through this platform, applications can dynamically request the network resources to meet their service requirements. • Use of In-situ Monitors: enabled real-time physical impairment sensing in the control plane using in-situ Optical Performance Monitoring (OPM) and bit error rate (BER) analyzers. OPM and BER values are used as quantitative indicators of the link status and are fed to the control plane through a high-speed data collection interface to form a closed-loop feedback system to enable adaptive resource allocation. • Predictive Network Model: used a network model embedded in the control layer to study the link status. The estimated results of network status is fed into the control decisions to precompute the network resources. The performance of the network model can be enhanced by the sensing results. • Real-Time Control Algorithms: investigated various dynamic resource allocation mechanisms supporting an agile optical network. Intelligent routing and wavelength switching for recovering from traffic impairments is achieved experimentally in the agile optical network within one second. A distance-adaptive spectrum allocation scheme to address transmission impairments caused by cascaded Wavelength Selective Switches (WSS) is proposed and evaluated for improving network spectral efficiency.

Value encoding in single neurons in the human amygdala during decision making.

PubMed

Jenison, Rick L; Rangel, Antonio; Oya, Hiroyuki; Kawasaki, Hiroto; Howard, Matthew A

2011-01-05

A growing consensus suggests that the brain makes simple choices by assigning values to the stimuli under consideration and then comparing these values to make a decision. However, the network involved in computing the values has not yet been fully characterized. Here, we investigated whether the human amygdala plays a role in the computation of stimulus values at the time of decision making. We recorded single neuron activity from the amygdala of awake patients while they made simple purchase decisions over food items. We found 16 amygdala neurons, located primarily in the basolateral nucleus that responded linearly to the values assigned to individual items.

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.

The Application of Wireless Sensor Networks in Management of Orchard

NASA Astrophysics Data System (ADS)

Zhu, Guizhi

A monitoring system based on wireless sensor network is established, aiming at the difficulty of information acquisition in the orchard on the hill at present. The temperature and humidity sensors are deployed around fruit trees to gather the real-time environmental parameters, and the wireless communication modules with self-organized form, which transmit the data to a remote central server, can realize the function of monitoring. By setting the parameters of data intelligent analysis judgment, the information on remote diagnosis and decision support can be timely and effectively feed back to users.

Quantifying Economic and Environmental Impacts of Transportation Network Disruptions with Dynamic Traffic Simulation

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

Shekar, Venkateswaran; Fiondella, Lance; Chatterjee, Samrat

Several transportation network vulnerability models have been proposed. However, most only consider disruptions as a static snapshot in time and the impact on total travel time. These approaches cannot consider the time-varying nature of travel demand nor other undesirable outcomes that follow from transportation network disruptions. This paper proposes an algorithmic approach to assess the vulnerability of a transportation network that considers the time-varying demand with an open source dynamic transportation simulation tool. The open source nature of the tool allows us to systematically consider many disruption scenarios and quantitatively compare their relative criticality. This is far more efficient thanmore » traditional approaches which would require days or weeks of a transportation engineers time to manually set up, run, and assess these simulations. In addition to travel time, we also collect statistics on additional fuel consumed and the corresponding carbon dioxide emissions. Our approach, thus provides a more systematic approach that is both time-varying and can consider additional negative consequences of disruptions for decision makers to evaluate.« less

Decision support system for the optimal location of electrical and electronic waste treatment plants: a case study in greece.

PubMed

Achillas, Ch; Vlachokostas, Ch; Moussiopoulos, Nu; Banias, G

2010-05-01

Environmentally sound end-of-life management of Electrical and Electronic Equipment has been realised as a top priority issue internationally, both due to the waste stream's continuously increasing quantities, as well as its content in valuable and also hazardous materials. In an effort to manage Waste Electrical and Electronic Equipment (WEEE), adequate infrastructure in treatment and recycling facilities is considered a prerequisite. A critical number of such plants are mandatory to be installed in order: (i) to accommodate legislative needs, (ii) decrease transportation cost, and (iii) expand reverse logistics network and cover more areas. However, WEEE recycling infrastructures require high expenditures and therefore the decision maker need to be most precautious. In this context, special care should be given on the viability of infrastructure which is heavily dependent on facilities' location. To this end, a methodology aiming towards optimal location of Units of Treatment and Recycling is developed, taking into consideration economical together with social criteria, in an effort to interlace local acceptance and financial viability. For the decision support system's needs, ELECTRE III is adopted as a multicriteria analysis technique. The methodology's applicability is demonstrated with a real-world case study in Greece. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Pharmaceutical expenditure forecast model to support health policy decision making.

PubMed

Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

2014-01-01

With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project 'European Union (EU) Pharmaceutical expenditure forecast' - http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). A model was built to assess policy scenarios' impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate, and distribution. Reducing, even slightly, the prices of generics had a major impact on savings. However, very aggressive pricing of generic and biosimilar products might make this market unattractive and can be counterproductive. Worth noting, delaying time to access innovative products was also identified as an effective leverage to increase savings but might not be a desirable policy for breakthrough products. Increasing patient financial contributions, either directly or indirectly via their private insurances, is a more likely scenario rather than expanding the national pharmaceutical expenditure coverage.

Pharmaceutical expenditure forecast model to support health policy decision making

PubMed Central

Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

2014-01-01

Background and objective With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project ‘European Union (EU) Pharmaceutical expenditure forecast’ – http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Methods A model was built to assess policy scenarios’ impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Results Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Conclusions Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate, and distribution. Reducing, even slightly, the prices of generics had a major impact on savings. However, very aggressive pricing of generic and biosimilar products might make this market unattractive and can be counterproductive. Worth noting, delaying time to access innovative products was also identified as an effective leverage to increase savings but might not be a desirable policy for breakthrough products. Increasing patient financial contributions, either directly or indirectly via their private insurances, is a more likely scenario rather than expanding the national pharmaceutical expenditure coverage. PMID:27226830

User Access Management Based on Network Pricing for Social Network Applications

PubMed Central

Ma, Xingmin; Gu, Qing

2018-01-01

Social applications play a very important role in people’s lives, as users communicate with each other through social networks on a daily basis. This presents a challenge: How does one receive high-quality service from social networks at a low cost? Users can access different kinds of wireless networks from various locations. This paper proposes a user access management strategy based on network pricing such that networks can increase its income and improve service quality. Firstly, network price is treated as an optimizing access parameter, and an unascertained membership algorithm is used to make pricing decisions. Secondly, network price is adjusted dynamically in real time according to network load. Finally, selecting a network is managed and controlled in terms of the market economy. Simulation results show that the proposed scheme can effectively balance network load, reduce network congestion, improve the user's quality of service (QoS) requirements, and increase the network’s income. PMID:29495252

The Army’s Military Decision Making: Adequate or Update and Expand

DTIC Science & Technology

2008-05-22

requires creative efforts by every Soldier and Marine.”63 Expanding the soldier base would allow for greater creativity in order to better deal with...military can overcome these deficiencies? I believe that to achieve the initial stage of success would be to create a segment of soldier telecommuters ...problems. By expanding the thinking base, the Army can expand the breadth and depth into areas currently unreachable. Telecommuting allows for several

An Interaction Library for the FcεRI Signaling Network

DOE PAGES

Chylek, Lily A.; Holowka, David A.; Baird, Barbara A.; ...

2014-04-15

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions.more » This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP 3 at the plasma membrane and the soluble second messenger IP 3. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.« less

An Interaction Library for the FcεRI Signaling Network

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

Chylek, Lily A.; Holowka, David A.; Baird, Barbara A.

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions.more » This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP 3 at the plasma membrane and the soluble second messenger IP 3. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.« less

Predict or classify: The deceptive role of time-locking in brain signal classification

NASA Astrophysics Data System (ADS)

Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

A decision network account of reasoning about other people's choices

PubMed Central

Jern, Alan; Kemp, Charles

2015-01-01

The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference. PMID:26010559

Using Trust to Establish a Secure Routing Model in Cognitive Radio Network.

PubMed

Zhang, Guanghua; Chen, Zhenguo; Tian, Liqin; Zhang, Dongwen

2015-01-01

Specific to the selective forwarding attack on routing in cognitive radio network, this paper proposes a trust-based secure routing model. Through monitoring nodes' forwarding behaviors, trusts of nodes are constructed to identify malicious nodes. In consideration of that routing selection-based model must be closely collaborative with spectrum allocation, a route request piggybacking available spectrum opportunities is sent to non-malicious nodes. In the routing decision phase, nodes' trusts are used to construct available path trusts and delay measurement is combined for making routing decisions. At the same time, according to the trust classification, different responses are made specific to their service requests. By adopting stricter punishment on malicious behaviors from non-trusted nodes, the cooperation of nodes in routing can be stimulated. Simulation results and analysis indicate that this model has good performance in network throughput and end-to-end delay under the selective forwarding attack.

The Mechanosensory Lateral Line System Mediates Activation of Socially-Relevant Brain Regions during Territorial Interactions.

PubMed

Butler, Julie M; Maruska, Karen P

2016-01-01

Animals use multiple senses during social interactions and must integrate this information in the brain to make context-dependent behavioral decisions. For fishes, the largest group of vertebrates, the mechanosensory lateral line system provides crucial hydrodynamic information for survival behaviors, but little is known about its function in social communication. Our previous work using the African cichlid fish, Astatotilapia burtoni, provided the first empirical evidence that fish use their lateral line system to detect water movements from conspecifics for mutual assessment and behavioral choices. It is unknown, however, where this socially-relevant mechanosensory information is processed in the brain to elicit adaptive behavioral responses. To examine for the first time in any fish species which brain regions receive contextual mechanosensory information, we quantified expression of the immediate early gene cfos as a proxy for neural activation in sensory and socially-relevant brain nuclei from lateral line-intact and -ablated fish following territorial interactions. Our in situ hybridization results indicate that in addition to known lateral line processing regions, socially-relevant mechanosensory information is processed in the ATn (ventromedial hypothalamus homolog), Dl (putative hippocampus homolog), and Vs (putative medial extended amygdala homolog). In addition, we identified a functional network within the conserved social decision-making network (SDMN) whose co-activity corresponds with mutual assessment and behavioral choice. Lateral line-intact and -ablated fight winners had different patterns of co-activity of these function networks and group identity could be determined solely by activation patterns, indicating the importance of mechanoreception to co-activity of the SDMN. These data show for the first time that the mechanosensory lateral line system provides relevant information to conserved decision-making centers of the brain during territorial interactions to mediate crucial behavioral choices such as whether or not to engage in a territorial fight. To our knowledge, this is also the first evidence of a subpallial nucleus receiving mechanosensory input, providing important information for elucidating homologies of decision-making circuits across vertebrates. These novel results highlight the importance of considering multimodal sensory input in mediating context-appropriate behaviors that will provide broad insights on the evolution of decision-making networks across all taxa.

Household food waste collection: Building service networks through neighborhood expansion.

PubMed

Armington, William R; Chen, Roger B

2018-04-17

In this paper we develop a residential food waste collection analysis and modeling framework that captures transportation costs faced by service providers in their initial stages of service provision. With this framework and model, we gain insights into network transportation costs and investigate possible service expansion scenarios faced by these organizations. We solve a vehicle routing problem (VRP) formulated for the residential neighborhood context using a heuristic approach developed. The scenarios considered follow a narrative where service providers start with an initial neighborhood or community and expands to incorporate other communities and their households. The results indicate that increasing household participation, decreases the travel time and cost per household, up to a critical threshold, beyond which we see marginal time and cost improvements. Additionally, the results indicate different outcomes in expansion scenarios depending on the household density of incorporated neighborhoods. As household participation and density increases, the travel time per household in the network decreases. However, at approximately 10-20 households per km 2 , the decrease in travel time per household is marginal, suggesting a lowerbound household density threshold. Finally, we show in food waste collection, networks share common scaling effects with respect to travel time and costs, regardless of the number of nodes and links. Copyright © 2018 Elsevier Ltd. All rights reserved.

Improve Governance for Charters

ERIC Educational Resources Information Center

Finn, Chester E., Jr.; Manno, Bruno V.; Wright, Brandon L.

2017-01-01

With 25 years of experience, the charter sector has had enough time to experience a host of unanticipated and unresolved problems related to the complex ways in which charter school governance relates to school leadership. The time has come for the sector to revisit some fundamental decisions about how charter schools and networks are governed,…

Development of a Neural Network-Based Renewable Energy Forecasting Framework for Process Industries

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

Lee, Soobin; Ryu, Jun-Hyung; Hodge, Bri-Mathias

2016-06-25

This paper presents a neural network-based forecasting framework for photovoltaic power (PV) generation as a decision-supporting tool to employ renewable energies in the process industry. The applicability of the proposed framework is illustrated by comparing its performance against other methodologies such as linear and nonlinear time series modelling approaches. A case study of an actual PV power plant in South Korea is presented.

Social Learning Networks: From Data Analytics to Active Sensing

DTIC Science & Technology

2017-10-13

time updating of user models that in turn dictate the learning path of each student . In particular, we have designed , implemented, and evaluated our...decision, unless so designated by other documentation. 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS (ES) U.S. Army Research Office P.O. Box...social network that exists between students , instructors, and modules of learning. Between 2015 and 2017, we completed a variety of data-driven

The Value of Information in Distributed Decision Networks

DTIC Science & Technology

2016-03-04

formulation, and then we describe the various results at- tained. 1 Mathematical description of Distributed Decision Network un- der Information...Constraints We now define a mathematical framework for networks. Let G = (V,E) be an undirected random network (graph) drawn from a known distribution pG, 1

From social network (centralized vs. decentralized) to collective decision-making (unshared vs. shared consensus).

PubMed

Sueur, Cédric; Deneubourg, Jean-Louis; Petit, Odile

2012-01-01

Relationships we have with our friends, family, or colleagues influence our personal decisions, as well as decisions we make together with others. As in human beings, despotism and egalitarian societies seem to also exist in animals. While studies have shown that social networks constrain many phenomena from amoebae to primates, we still do not know how consensus emerges from the properties of social networks in many biological systems. We created artificial social networks that represent the continuum from centralized to decentralized organization and used an agent-based model to make predictions about the patterns of consensus and collective movements we observed according to the social network. These theoretical results showed that different social networks and especially contrasted ones--star network vs. equal network--led to totally different patterns. Our model showed that, by moving from a centralized network to a decentralized one, the central individual seemed to lose its leadership in the collective movement's decisions. We, therefore, showed a link between the type of social network and the resulting consensus. By comparing our theoretical data with data on five groups of primates, we confirmed that this relationship between social network and consensus also appears to exist in animal societies.

Physical mechanism of mind changes and tradeoffs among speed, accuracy, and energy cost in brain decision making: Landscape, flux, and path perspectives

NASA Astrophysics Data System (ADS)

Han, Yan; Kun, Zhang; Jin, Wang

2016-07-01

Cognitive behaviors are determined by underlying neural networks. Many brain functions, such as learning and memory, have been successfully described by attractor dynamics. For decision making in the brain, a quantitative description of global attractor landscapes has not yet been completely given. Here, we developed a theoretical framework to quantify the landscape associated with the steady state probability distributions and associated steady state curl flux, measuring the degree of non-equilibrium through the degree of detailed balance breaking for decision making. We quantified the decision-making processes with optimal paths from the undecided attractor states to the decided attractor states, which are identified as basins of attractions, on the landscape. Both landscape and flux determine the kinetic paths and speed. The kinetics and global stability of decision making are explored by quantifying the landscape topography through the barrier heights and the mean first passage time. Our theoretical predictions are in agreement with experimental observations: more errors occur under time pressure. We quantitatively explored two mechanisms of the speed-accuracy tradeoff with speed emphasis and further uncovered the tradeoffs among speed, accuracy, and energy cost. Our results imply that there is an optimal balance among speed, accuracy, and the energy cost in decision making. We uncovered the possible mechanisms of changes of mind and how mind changes improve performance in decision processes. Our landscape approach can help facilitate an understanding of the underlying physical mechanisms of cognitive processes and identify the key factors in the corresponding neural networks. Project supported by the National Natural Science Foundation of China (Grant Nos. 21190040, 91430217, and 11305176).

Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

NASA Astrophysics Data System (ADS)

Chen, Yan; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

The key in stock trading model is to take the right actions for trading at the right time, primarily based on the accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creating a stock trading model. In this paper, we propose a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are three important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the candlestick charts according to the real time stock prices. Second, we combine RTU-GNP with a Sarsa learning algorithm to create the programs efficiently. Also, sub-nodes are introduced in each judgment and processing node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. Third, a Real Time Updating system has been firstly introduced in our paper considering the change of the trend of stock prices. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without real time updating. We also compared the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

Pulse-coupled neural network implementation in FPGA

NASA Astrophysics Data System (ADS)

Waldemark, Joakim T. A.; Lindblad, Thomas; Lindsey, Clark S.; Waldemark, Karina E.; Oberg, Johnny; Millberg, Mikael

1998-03-01

Pulse Coupled Neural Networks (PCNN) are biologically inspired neural networks, mainly based on studies of the visual cortex of small mammals. The PCNN is very well suited as a pre- processor for image processing, particularly in connection with object isolation, edge detection and segmentation. Several implementations of PCNN on von Neumann computers, as well as on special parallel processing hardware devices (e.g. SIMD), exist. However, these implementations are not as flexible as required for many applications. Here we present an implementation in Field Programmable Gate Arrays (FPGA) together with a performance analysis. The FPGA hardware implementation may be considered a platform for further, extended implementations and easily expanded into various applications. The latter may include advanced on-line image analysis with close to real-time performance.

Computationally-efficient stochastic cluster dynamics method for modeling damage accumulation in irradiated materials

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

Hoang, Tuan L.; Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, CA 94550; Marian, Jaime, E-mail: jmarian@ucla.edu

2015-11-01

An improved version of a recently developed stochastic cluster dynamics (SCD) method (Marian and Bulatov, 2012) [6] is introduced as an alternative to rate theory (RT) methods for solving coupled ordinary differential equation (ODE) systems for irradiation damage simulations. SCD circumvents by design the curse of dimensionality of the variable space that renders traditional ODE-based RT approaches inefficient when handling complex defect population comprised of multiple (more than two) defect species. Several improvements introduced here enable efficient and accurate simulations of irradiated materials up to realistic (high) damage doses characteristic of next-generation nuclear systems. The first improvement is a proceduremore » for efficiently updating the defect reaction-network and event selection in the context of a dynamically expanding reaction-network. Next is a novel implementation of the τ-leaping method that speeds up SCD simulations by advancing the state of the reaction network in large time increments when appropriate. Lastly, a volume rescaling procedure is introduced to control the computational complexity of the expanding reaction-network through occasional reductions of the defect population while maintaining accurate statistics. The enhanced SCD method is then applied to model defect cluster accumulation in iron thin films subjected to triple ion-beam (Fe{sup 3+}, He{sup +} and H{sup +}) irradiations, for which standard RT or spatially-resolved kinetic Monte Carlo simulations are prohibitively expensive.« less

Computationally-efficient stochastic cluster dynamics method for modeling damage accumulation in irradiated materials

NASA Astrophysics Data System (ADS)

Hoang, Tuan L.; Marian, Jaime; Bulatov, Vasily V.; Hosemann, Peter

2015-11-01

An improved version of a recently developed stochastic cluster dynamics (SCD) method (Marian and Bulatov, 2012) [6] is introduced as an alternative to rate theory (RT) methods for solving coupled ordinary differential equation (ODE) systems for irradiation damage simulations. SCD circumvents by design the curse of dimensionality of the variable space that renders traditional ODE-based RT approaches inefficient when handling complex defect population comprised of multiple (more than two) defect species. Several improvements introduced here enable efficient and accurate simulations of irradiated materials up to realistic (high) damage doses characteristic of next-generation nuclear systems. The first improvement is a procedure for efficiently updating the defect reaction-network and event selection in the context of a dynamically expanding reaction-network. Next is a novel implementation of the τ-leaping method that speeds up SCD simulations by advancing the state of the reaction network in large time increments when appropriate. Lastly, a volume rescaling procedure is introduced to control the computational complexity of the expanding reaction-network through occasional reductions of the defect population while maintaining accurate statistics. The enhanced SCD method is then applied to model defect cluster accumulation in iron thin films subjected to triple ion-beam (Fe3+, He+ and H+) irradiations, for which standard RT or spatially-resolved kinetic Monte Carlo simulations are prohibitively expensive.

Short paths in expander graphs

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

Kleinberg, J.; Rubinfeld, R.

Graph expansion has proved to be a powerful general tool for analyzing the behavior of routing algorithms and the interconnection networks on which they run. We develop new routing algorithms and structural results for bounded-degree expander graphs. Our results are unified by the fact that they are all based upon, and extend, a body of work asserting that expanders are rich in short, disjoint paths. In particular, our work has consequences for the disjoint paths problem, multicommodify flow, and graph minor containment. We show: (i) A greedy algorithm for approximating the maximum disjoint paths problem achieves a polylogarithmic approximation ratiomore » in bounded-degree expanders. Although our algorithm is both deterministic and on-line, its performance guarantee is an improvement over previous bounds in expanders. (ii) For a multicommodily flow problem with arbitrary demands on a bounded-degree expander, there is a (1 + {epsilon})-optimal solution using only flow paths of polylogarithmic length. It follows that the multicommodity flow algorithm of Awerbuch and Leighton runs in nearly linear time per commodity in expanders. Our analysis is based on establishing the following: given edge weights on an expander G, one can increase some of the weights very slightly so the resulting shortest-path metric is smooth - the min-weight path between any pair of nodes uses a polylogarithmic number of edges. (iii) Every bounded-degree expander on n nodes contains every graph with O(n/log{sup O(1)} n) nodes and edges as a minor.« less

Developing Evidence to Inform Decisions about Effectiveness (DeCIDE) Network

Cancer.gov

The Developing Evidence to Inform Decisions about Effectiveness Network is a network of research centers that the Agency for Healthcare Research and Quality created to conduct practical studies about health care items and services.

Information Security and Privacy in Network Environments.

ERIC Educational Resources Information Center

Congress of the U.S., Washington, DC. Office of Technology Assessment.

The use of information networks for business and government is expanding enormously. Government use of networks features prominently in plans to make government more efficient, effective, and responsive. But the transformation brought about by the networking also raises new concerns for the security and privacy of networked information. This…

Bayesian Decision Support for Adaptive Lung Treatments

NASA Astrophysics Data System (ADS)

McShan, Daniel; Luo, Yi; Schipper, Matt; TenHaken, Randall

2014-03-01

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827

Statistical inference approach to structural reconstruction of complex networks from binary time series

NASA Astrophysics Data System (ADS)

Ma, Chuang; Chen, Han-Shuang; Lai, Ying-Cheng; Zhang, Hai-Feng

2018-02-01

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems.

Statistical inference approach to structural reconstruction of complex networks from binary time series.

PubMed

Ma, Chuang; Chen, Han-Shuang; Lai, Ying-Cheng; Zhang, Hai-Feng

2018-02-01

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems.

A decision network account of reasoning about other people's choices.

PubMed

Jern, Alan; Kemp, Charles

2015-09-01

The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference. Copyright © 2015 Elsevier B.V. All rights reserved.

Cloud-Hosted Real-time Data Services for the Geosciences (CHORDS)

NASA Astrophysics Data System (ADS)

Daniels, M. D.; Graves, S. J.; Vernon, F.; Kerkez, B.; Chandra, C. V.; Keiser, K.; Martin, C.

2014-12-01

Cloud-Hosted Real-time Data Services for the Geosciences (CHORDS) Access, utilization and management of real-time data continue to be challenging for decision makers, as well as researchers in several scientific fields. This presentation will highlight infrastructure aimed at addressing some of the gaps in handling real-time data, particularly in increasing accessibility of these data to the scientific community through cloud services. The Cloud-Hosted Real-time Data Services for the Geosciences (CHORDS) system addresses the ever-increasing importance of real-time scientific data, particularly in mission critical scenarios, where informed decisions must be made rapidly. Advances in the distribution of real-time data are leading many new transient phenomena in space-time to be observed, however real-time decision-making is infeasible in many cases that require streaming scientific data as these data are locked down and sent only to proprietary in-house tools or displays. This lack of accessibility to the broader scientific community prohibits algorithm development and workflows initiated by these data streams. As part of NSF's EarthCube initiative, CHORDS proposes to make real-time data available to the academic community via cloud services. The CHORDS infrastructure will enhance the role of real-time data within the geosciences, specifically expanding the potential of streaming data sources in enabling adaptive experimentation and real-time hypothesis testing. Adherence to community data and metadata standards will promote the integration of CHORDS real-time data with existing standards-compliant analysis, visualization and modeling tools.

Better Data Help Make Better Decisions: Disseminating Information During Hurricane Harvey

NASA Astrophysics Data System (ADS)

Conner, K.; Lindner, J.; Moore, M.

2017-12-01

During large scale natural disasters, like hurricane Harvey, time-critical decisions are made on a constant basis. From evacuation orders, allocation of emergency resources, or allowing people to return home, decisions are only as good as the information upon which they are based. Better real-time data lead to better decisions which ultimately leads to improved disaster response and recovery. In 2015 Harris County Flood Control District (HCFCD) in Houston, TX began upgrading their automatic flood warning system (FWS) that dates back to the 1980s. The HCFCD network consists of 154 remote stations that report precipitation intensities and stream levels in near real time. Since the upgrades were completed in 2016 the Houston area has experienced multiple 100+ rain events, the most recent being Hurricane Harvey. The FWS generated accurate, reliable, real-time data throughout the entirety of the record breaking, four-day event. This information was disseminated to state, local and federal agencies, news outlets and the public via web sites and social media. Without this quality of data, disaster management decisions could not have been made effectively, ultimately leading to greater destruction of property and loss of life.

Primary care nurses’ experiences of how the mass media influence frontline healthcare in the UK

PubMed Central

2013-01-01

Background Mass media plays an important role in communicating about health research and services to patients, and in shaping public perceptions and decisions about health. Healthcare professionals also play an important role in providing patients with credible, evidence-based and up-to-date information on a wide range of health issues. This study aims to explore primary care nurses’ experiences of how mass media influences frontline healthcare. Methods In-depth telephone interviews were carried out with 18 primary care nurses (nine health visitors and nine practice nurses) working in the United Kingdom (UK). Interviews were recorded and transcribed. The data was analysed using thematic analysis, with a focus on constant comparative analysis. Results Three themes emerged from the data. First, participants reported that their patients were frequently influenced by controversial health stories reported in the media, which affected their perceptions of, and decisions about, care. This, in turn, impinged upon participants’ workloads as they had to spend additional time discussing information and reassuring patients. Second, participants also recalled times in their own careers when media reports had contributed to a decline in their confidence in current healthcare practices and treatments. Third, the participants in this study suggested a real need for additional resources to support and expand their own media literacy skills, which could be shared with patients. Conclusion In an ever expanding media landscape with greater reporting on health, nurses working in the primary care setting face increasing pressure to effectively manage media stories that dispute current health policies and practices. These primary care nurses were keen to expand their media literacy skills to develop critical autonomy in relation to all media, and to facilitate more meaningful conversations with their patients about their health concerns and choices. PMID:24267614

Primary care nurses' experiences of how the mass media influence frontline healthcare in the UK.

PubMed

van Bekkum, Jennifer E; Hilton, Shona

2013-11-24

Mass media plays an important role in communicating about health research and services to patients, and in shaping public perceptions and decisions about health. Healthcare professionals also play an important role in providing patients with credible, evidence-based and up-to-date information on a wide range of health issues. This study aims to explore primary care nurses' experiences of how mass media influences frontline healthcare. In-depth telephone interviews were carried out with 18 primary care nurses (nine health visitors and nine practice nurses) working in the United Kingdom (UK). Interviews were recorded and transcribed. The data was analysed using thematic analysis, with a focus on constant comparative analysis. Three themes emerged from the data. First, participants reported that their patients were frequently influenced by controversial health stories reported in the media, which affected their perceptions of, and decisions about, care. This, in turn, impinged upon participants' workloads as they had to spend additional time discussing information and reassuring patients. Second, participants also recalled times in their own careers when media reports had contributed to a decline in their confidence in current healthcare practices and treatments. Third, the participants in this study suggested a real need for additional resources to support and expand their own media literacy skills, which could be shared with patients. In an ever expanding media landscape with greater reporting on health, nurses working in the primary care setting face increasing pressure to effectively manage media stories that dispute current health policies and practices. These primary care nurses were keen to expand their media literacy skills to develop critical autonomy in relation to all media, and to facilitate more meaningful conversations with their patients about their health concerns and choices.

Improvement Science and NetworkED Improvement Communities: An Interview with Dr. Anthony Bryk. An ExpandED Schools Resource Guide

ERIC Educational Resources Information Center

ExpandED Schools, 2016

2016-01-01

During the summer, we interviewed Tony Bryk, Author of Learning to Improve: How America's Schools Can Get Better at Getting Better and head of the Carnegie Foundation for the Advancement of Teaching. His work was seminal in the creation of the Framework for Great Schools, which has spurred New York City schools to rethink the structures and…

Are mobile health applications useful for supporting shared decision making in diagnostic and treatment decisions?

PubMed Central

Abbasgholizadeh Rahimi, Samira; Menear, Matthew; Robitaille, Hubert; Légaré, France

2017-01-01

ABSTRACT Mobile health (mHealth) applications intended to support shared decision making in diagnostic and treatment decisions are increasingly available. In this paper, we discuss some recent studies on mHealth applications with relevance to shared decision making. We discuss the potential advantages and disadvantages of using mHealth in shared decision making in various contexts, and suggest some directions for future research in this quickly expanding field. PMID:28838306

An fMRI and effective connectivity study investigating miss errors during advice utilization from human and machine agents.

PubMed

Goodyear, Kimberly; Parasuraman, Raja; Chernyak, Sergey; de Visser, Ewart; Madhavan, Poornima; Deshpande, Gopikrishna; Krueger, Frank

2017-10-01

As society becomes more reliant on machines and automation, understanding how people utilize advice is a necessary endeavor. Our objective was to reveal the underlying neural associations during advice utilization from expert human and machine agents with fMRI and multivariate Granger causality analysis. During an X-ray luggage-screening task, participants accepted or rejected good or bad advice from either the human or machine agent framed as experts with manipulated reliability (high miss rate). We showed that the machine-agent group decreased their advice utilization compared to the human-agent group and these differences in behaviors during advice utilization could be accounted for by high expectations of reliable advice and changes in attention allocation due to miss errors. Brain areas involved with the salience and mentalizing networks, as well as sensory processing involved with attention, were recruited during the task and the advice utilization network consisted of attentional modulation of sensory information with the lingual gyrus as the driver during the decision phase and the fusiform gyrus as the driver during the feedback phase. Our findings expand on the existing literature by showing that misses degrade advice utilization, which is represented in a neural network involving salience detection and self-processing with perceptual integration.

AST: Activity-Security-Trust driven modeling of time varying networks

PubMed Central

Wang, Jian; Xu, Jiake; Liu, Yanheng; Deng, Weiwen

2016-01-01

Network modeling is a flexible mathematical structure that enables to identify statistical regularities and structural principles hidden in complex systems. The majority of recent driving forces in modeling complex networks are originated from activity, in which an activity potential of a time invariant function is introduced to identify agents’ interactions and to construct an activity-driven model. However, the new-emerging network evolutions are already deeply coupled with not only the explicit factors (e.g. activity) but also the implicit considerations (e.g. security and trust), so more intrinsic driving forces behind should be integrated into the modeling of time varying networks. The agents undoubtedly seek to build a time-dependent trade-off among activity, security, and trust in generating a new connection to another. Thus, we reasonably propose the Activity-Security-Trust (AST) driven model through synthetically considering the explicit and implicit driving forces (e.g. activity, security, and trust) underlying the decision process. AST-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process, allowing a profound understanding of the effects of security and trust in driving network evolution, and improving the biases induced by only involving activity representations in analyzing the dynamical processes. PMID:26888717

A collaborative framework for contributing DICOM RT PHI (Protected Health Information) to augment data mining in clinical decision support

NASA Astrophysics Data System (ADS)

Deshpande, Ruchi; Thuptimdang, Wanwara; DeMarco, John; Liu, Brent J.

2014-03-01

We have built a decision support system that provides recommendations for customizing radiation therapy treatment plans, based on patient models generated from a database of retrospective planning data. This database consists of relevant metadata and information derived from the following DICOM objects - CT images, RT Structure Set, RT Dose and RT Plan. The usefulness and accuracy of such patient models partly depends on the sample size of the learning data set. Our current goal is to increase this sample size by expanding our decision support system into a collaborative framework to include contributions from multiple collaborators. Potential collaborators are often reluctant to upload even anonymized patient files to repositories outside their local organizational network in order to avoid any conflicts with HIPAA Privacy and Security Rules. We have circumvented this problem by developing a tool that can parse DICOM files on the client's side and extract de-identified numeric and text data from DICOM RT headers for uploading to a centralized system. As a result, the DICOM files containing PHI remain local to the client side. This is a novel workflow that results in adding only relevant yet valuable data from DICOM files to the centralized decision support knowledge base in such a way that the DICOM files never leave the contributor's local workstation in a cloud-based environment. Such a workflow serves to encourage clinicians to contribute data for research endeavors by ensuring protection of electronic patient data.

From Population Databases to Research and Informed Health Decisions and Policy.

PubMed

Machluf, Yossy; Tal, Orna; Navon, Amir; Chaiter, Yoram

2017-01-01

In the era of big data, the medical community is inspired to maximize the utilization and processing of the rapidly expanding medical datasets for clinical-related and policy-driven research. This requires a medical database that can be aggregated, interpreted, and integrated at both the individual and population levels. Policymakers seek data as a lever for wise, evidence-based decision-making and information-driven policy. Yet, bridging the gap between data collection, research, and policymaking, is a major challenge. To bridge this gap, we propose a four-step model: (A) creating a conjoined task force of all relevant parties to declare a national program to promote collaborations; (B) promoting a national digital records project, or at least a network of synchronized and integrated databases, in an accessible transparent manner; (C) creating an interoperative national research environment to enable the analysis of the organized and integrated data and to generate evidence; and (D) utilizing the evidence to improve decision-making, to support a wisely chosen national policy. For the latter purpose, we also developed a novel multidimensional set of criteria to illuminate insights and estimate the risk for future morbidity based on current medical conditions. Used by policymakers, providers of health plans, caregivers, and health organizations, we presume this model will assist transforming evidence generation to support the design of health policy and programs, as well as improved decision-making about health and health care, at all levels: individual, communal, organizational, and national.

Using Predictive Analytics to Predict Power Outages from Severe Weather

NASA Astrophysics Data System (ADS)

Wanik, D. W.; Anagnostou, E. N.; Hartman, B.; Frediani, M. E.; Astitha, M.

2015-12-01

The distribution of reliable power is essential to businesses, public services, and our daily lives. With the growing abundance of data being collected and created by industry (i.e. outage data), government agencies (i.e. land cover), and academia (i.e. weather forecasts), we can begin to tackle problems that previously seemed too complex to solve. In this session, we will present newly developed tools to aid decision-support challenges at electric distribution utilities that must mitigate, prepare for, respond to and recover from severe weather. We will show a performance evaluation of outage predictive models built for Eversource Energy (formerly Connecticut Light & Power) for storms of all types (i.e. blizzards, thunderstorms and hurricanes) and magnitudes (from 20 to >15,000 outages). High resolution weather simulations (simulated with the Weather and Research Forecast Model) were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This research has the potential to be used for other critical infrastructure systems (such as telecommunications, drinking water and gas distribution networks), and can be readily expanded to the entire New England region to facilitate better planning and coordination among decision-makers when severe weather strikes.

Battle-Wise: Seeking Time-Information Superiority in Networked Warfare

DTIC Science & Technology

2006-07-01

idiosyncratic and thus not repeatable—each person’s perceptions , experiences, and thought processes are different. This makes it difficult to rely on... perception management’ is central to the conduct of its war with the West.”26 It does not require dedicated information-network infrastructure or expensive...to leave a house after several failed attempts to fight flare-ups in the first floor kitchen. The chief attributed his decision to extrasensory

Real-Time Analysis of a Sensor's Data for Automated Decision Making in an IoT-Based Smart Home.

PubMed

Khan, Nida Saddaf; Ghani, Sayeed; Haider, Sajjad

2018-05-25

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor's streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

Dissociating neural variability related to stimulus quality and response times in perceptual decision-making.

PubMed

Bode, Stefan; Bennett, Daniel; Sewell, David K; Paton, Bryan; Egan, Gary F; Smith, Philip L; Murawski, Carsten

2018-03-01

According to sequential sampling models, perceptual decision-making is based on accumulation of noisy evidence towards a decision threshold. The speed with which a decision is reached is determined by both the quality of incoming sensory information and random trial-by-trial variability in the encoded stimulus representations. To investigate those decision dynamics at the neural level, participants made perceptual decisions while functional magnetic resonance imaging (fMRI) was conducted. On each trial, participants judged whether an image presented under conditions of high, medium, or low visual noise showed a piano or a chair. Higher stimulus quality (lower visual noise) was associated with increased activation in bilateral medial occipito-temporal cortex and ventral striatum. Lower stimulus quality was related to stronger activation in posterior parietal cortex (PPC) and dorsolateral prefrontal cortex (DLPFC). When stimulus quality was fixed, faster response times were associated with a positive parametric modulation of activation in medial prefrontal and orbitofrontal cortex, while slower response times were again related to more activation in PPC, DLPFC and insula. Our results suggest that distinct neural networks were sensitive to the quality of stimulus information, and to trial-to-trial variability in the encoded stimulus representations, but that reaching a decision was a consequence of their joint activity. Copyright © 2018 Elsevier Ltd. All rights reserved.

50 years of Global Seismic Observations

NASA Astrophysics Data System (ADS)

Anderson, K. R.; Butler, R.; Berger, J.; Davis, P.; Derr, J.; Gee, L.; Hutt, C. R.; Leith, W. S.; Park, J. J.

2007-12-01

Seismological recordings have been made on Earth for hundreds of years in some form or another, however, global monitoring of earthquakes only began in the 1890's when John Milne created 40 seismic observatories to measure the waves from these events. Shortly after the International Geophysical Year (IGY), a concerted effort was made to establish and maintain a more modern standardized seismic network on the global scale. In the early 1960's, the World-Wide Standardized Seismograph Network (WWSSN) was established through funding from the Advanced Research Projects Agency (ARPA) and was installed and maintained by the USGS's Albuquerque Seismological Laboratory (then a part of the US Coast and Geodetic Survey). This network of identical seismic instruments consisted of 120 stations in 60 countries. Although the network was motivated by nuclear test monitoring, the WWSSN facilitated numerous advances in observational seismology. From the IGY to the present, the network has been upgraded (High-Gain Long-Period Seismograph Network, Seismic Research Observatories, Digital WWSSN, Global Telemetered Seismograph Network, etc.) and expanded (International Deployment of Accelerometers, US National Seismic Network, China Digital Seismograph Network, Joint Seismic Project, etc.), bringing the modern day Global Seismographic Network (GSN) to a current state of approximately 150 stations. The GSN consists of state-of-the-art very broadband seismic transducers, continuous power and communications, and ancillary sensors including geodetic, geomagnetic, microbarographic, meteorological and other related instrumentation. Beyond the GSN, the system of global network observatories includes contributions from other international partners (e.g., GEOSCOPE, GEOFON, MEDNET, F-Net, CTBTO), forming an even larger backbone of permanent seismological observatories as a part of the International Federation of Digital Seismograph Networks. 50 years of seismic network operations have provided valuable data for earth science research. Developments in communications and other technological advances have expanded the role of the GSN in rapid earthquake analysis, tsunami warning, and nuclear test monitoring. With such long-term observations, scientists are now getting a glimpse of Earth structure changes on human time scales, such as the rotation of the inner core, as well as views into climate processes. Continued observations for the next 50 years will enhance our image of the Earth and its processes.

The NASA Real Time Mission Monitor - A Situational Awareness Tool for Conducting Tropical Cyclone Field Experiments

NASA Technical Reports Server (NTRS)

Goodman, Michael; Blakeslee, Richard; Hall, John; Parker, Philip; He, Yubin

2008-01-01

The NASA Real Time Mission Monitor (RTMM) is a situational awareness tool that integrates satellite, aircraft state information, airborne and surface instruments, and weather state data in to a single visualization package for real time field experiment management. RTMM optimizes science and logistic decision-making during field experiments by presenting timely data and graphics to the users to improve real time situational awareness of the experiment's assets. The RTMM is proven in the field as it supported program managers, scientists, and aircraft personnel during the NASA African Monsoon Multidisciplinary Analyses (investigated African easterly waves and Tropical Storm Debby and Helene) during August-September 2006 in Cape Verde, the Tropical Composition, Cloud and Climate Coupling experiment during July-August 2007 in Costa Rica, and the Hurricane Aerosonde mission into Hurricane Noel in 2-3 November 2007. The integration and delivery of this information is made possible through data acquisition systems, network communication links, and network server resources built and managed by collaborators at NASA Marshall Space Flight Center (MSFC) and Dryden Flight Research Center (DFRC). RTMM is evolving towards a more flexible and dynamic combination of sensor ingest, network computing, and decision-making activities through the use of a service oriented architecture based on community standards and protocols. Each field experiment presents unique challenges and opportunities for advancing the functionality of RTMM. A description of RTMM, the missions it has supported, and its new features that are under development will be presented.

Biology-inspired Architecture for Situation Management

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

2006-01-01

Situation Management is a rapidly developing science combining new techniques for data collection with advanced methods of data fusion to facilitate the process leading to correct decisions prescribing action. Current research focuses on reducing increasing amounts of diverse data to knowledge used by decision makers and on reducing time between observations, decisions and actions. No new technology is more promising for increasing the diversity and fidelity of observations than sensor networks. However, current research on sensor networks concentrates on a centralized network architecture. We believe this trend will not realize the full potential of situation management. We propose a new architecture modeled after biological ecosystems where motes are autonomous and intelligent, yet cooperate with local neighborhoods. Providing a layered approach, they sense and act independently when possible, and cooperate with neighborhoods when necessary. The combination of their local actions results in global effects. While situation management research is currently dominated by military applications, advances envisioned for industrial and business applications have similar requirements. NASA has requirements for intelligent and autonomous systems in future missions that can benefit from advances in situation management. We describe requirements for the Integrated Vehicle Health Management program where our biology-inspired architecture provides a layered approach and decisions can be made at the proper level to improve safety, reduce costs, and improve efficiency in making diagnostic and prognostic assessments of the structural integrity, aerodynamic characteristics, and operation of aircraft.

Willed action, free will, and the stochastic neurodynamics of decision-making

PubMed Central

Rolls, Edmund T.

2012-01-01

It is shown that the randomness of the firing times of neurons in decision-making attractor neuronal networks that is present before the decision cues are applied can cause statistical fluctuations that influence the decision that will be taken. In this rigorous sense, it is possible to partially predict decisions before they are made. This raises issues about free will and determinism. There are many decision-making networks in the brain. Some decision systems operate to choose between gene-specified rewards such as taste, touch, and beauty (in for example the peacock's tail). Other processes capable of planning ahead with multiple steps held in working memory may require correction by higher order thoughts that may involve explicit, conscious, processing. The explicit system can allow the gene-specified rewards not to be selected or deferred. The decisions between the selfish gene-specified rewards, and the explicitly calculated rewards that are in the interests of the individual, the phenotype, may themselves be influenced by noise in the brain. When the explicit planning system does take the decision, it can report on its decision-making, and can provide a causal account rather than a confabulation about the decision process. We might use the terms “willed action” and “free will” to refer to the operation of the planning system that can think ahead over several steps held in working memory with which it can take explicit decisions. Reduced connectivity in some of the default mode cortical regions including the precuneus that are active during self-initiated action appears to be related to the reduction in the sense of self and agency, of causing willed actions, that can be present in schizophrenia. PMID:22973205

From Social Network (Centralized vs. Decentralized) to Collective Decision-Making (Unshared vs. Shared Consensus)

PubMed Central

Sueur, Cédric; Deneubourg, Jean-Louis; Petit, Odile

2012-01-01

Relationships we have with our friends, family, or colleagues influence our personal decisions, as well as decisions we make together with others. As in human beings, despotism and egalitarian societies seem to also exist in animals. While studies have shown that social networks constrain many phenomena from amoebae to primates, we still do not know how consensus emerges from the properties of social networks in many biological systems. We created artificial social networks that represent the continuum from centralized to decentralized organization and used an agent-based model to make predictions about the patterns of consensus and collective movements we observed according to the social network. These theoretical results showed that different social networks and especially contrasted ones – star network vs. equal network - led to totally different patterns. Our model showed that, by moving from a centralized network to a decentralized one, the central individual seemed to lose its leadership in the collective movement's decisions. We, therefore, showed a link between the type of social network and the resulting consensus. By comparing our theoretical data with data on five groups of primates, we confirmed that this relationship between social network and consensus also appears to exist in animal societies. PMID:22393416

A Genetic Algorithm for the Bi-Level Topological Design of Local Area Networks

PubMed Central

Camacho-Vallejo, José-Fernando; Mar-Ortiz, Julio; López-Ramos, Francisco; Rodríguez, Ricardo Pedraza

2015-01-01

Local access networks (LAN) are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network with minimum connection cost. Therefore, the decision of optimally assigning users to clusters will be made by the leader and the follower will make the decision of connecting all the clusters while forming a spanning tree. In this paper, we propose a genetic algorithm for solving the bi-level topological design of a Local Access Network. Our solution method considers the Stackelberg equilibrium to solve the bi-level problem. The Stackelberg-Genetic algorithm procedure deals with the fact that the follower’s problem cannot be optimally solved in a straightforward manner. The computational results obtained from two different sets of instances show that the performance of the developed algorithm is efficient and that it is more suitable for solving the bi-level problem than a previous Nash-Genetic approach. PMID:26102502

The Dynamics of Coalition Formation on Complex Networks

NASA Astrophysics Data System (ADS)

Auer, S.; Heitzig, J.; Kornek, U.; Schöll, E.; Kurths, J.

2015-08-01

Complex networks describe the structure of many socio-economic systems. However, in studies of decision-making processes the evolution of the underlying social relations are disregarded. In this report, we aim to understand the formation of self-organizing domains of cooperation (“coalitions”) on an acquaintance network. We include both the network’s influence on the formation of coalitions and vice versa how the network adapts to the current coalition structure, thus forming a social feedback loop. We increase complexity from simple opinion adaptation processes studied in earlier research to more complex decision-making determined by costs and benefits, and from bilateral to multilateral cooperation. We show how phase transitions emerge from such coevolutionary dynamics, which can be interpreted as processes of great transformations. If the network adaptation rate is high, the social dynamics prevent the formation of a grand coalition and therefore full cooperation. We find some empirical support for our main results: Our model develops a bimodal coalition size distribution over time similar to those found in social structures. Our detection and distinguishing of phase transitions may be exemplary for other models of socio-economic systems with low agent numbers and therefore strong finite-size effects.

Many-objective Groundwater Monitoring Network Design Using Bias-Aware Ensemble Kalman Filtering and Evolutionary Optimization

NASA Astrophysics Data System (ADS)

Kollat, J. B.; Reed, P. M.

2009-12-01

This study contributes the ASSIST (Adaptive Strategies for Sampling in Space and Time) framework for improving long-term groundwater monitoring decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). The ASSIST framework combines contaminant flow-and-transport modeling, bias-aware ensemble Kalman filtering (EnKF) and many-objective evolutionary optimization. Our goal in this work is to provide decision makers with a fuller understanding of the information tradeoffs they must confront when performing long-term groundwater monitoring network design. Our many-objective analysis considers up to 6 design objectives simultaneously and consequently synthesizes prior monitoring network design methodologies into a single, flexible framework. This study demonstrates the ASSIST framework using a tracer study conducted within a physical aquifer transport experimental tank located at the University of Vermont. The tank tracer experiment was extensively sampled to provide high resolution estimates of tracer plume behavior. The simulation component of the ASSIST framework consists of stochastic ensemble flow-and-transport predictions using ParFlow coupled with the Lagrangian SLIM transport model. The ParFlow and SLIM ensemble predictions are conditioned with tracer observations using a bias-aware EnKF. The EnKF allows decision makers to enhance plume transport predictions in space and time in the presence of uncertain and biased model predictions by conditioning them on uncertain measurement data. In this initial demonstration, the position and frequency of sampling were optimized to: (i) minimize monitoring cost, (ii) maximize information provided to the EnKF, (iii) minimize failure to detect the tracer, (iv) maximize the detection of tracer flux, (v) minimize error in quantifying tracer mass, and (vi) minimize error in quantifying the moment of the tracer plume. The results demonstrate that the many-objective problem formulation provides a tremendous amount of information for decision makers. Specifically our many-objective analysis highlights the limitations and potentially negative design consequences of traditional single and two-objective problem formulations. These consequences become apparent through visual exploration of high-dimensional tradeoffs and the identification of regions with interesting compromise solutions. The prediction characteristics of these compromise designs are explored in detail, as well as their implications for subsequent design decisions in both space and time.

TOXNET: Toxicology Data Network

MedlinePlus

... 4. Supporting Data for Carcinogenicity Expand II.B. Quantitative Estimate of Carcinogenic Risk from Oral Exposure II. ... of Confidence (Carcinogenicity, Oral Exposure) Expand II.C. Quantitative Estimate of Carcinogenic Risk from Inhalation Exposure II. ...

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

PubMed Central

Lam, William H. K.; Li, Qingquan

2017-01-01

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. PMID:29210978

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks.

PubMed

Shi, Chaoyang; Chen, Bi Yu; Lam, William H K; Li, Qingquan

2017-12-06

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

A spiking neural integrator model of the adaptive control of action by the medial prefrontal cortex.

PubMed

Bekolay, Trevor; Laubach, Mark; Eliasmith, Chris

2014-01-29

Subjects performing simple reaction-time tasks can improve reaction times by learning the expected timing of action-imperative stimuli and preparing movements in advance. Success or failure on the previous trial is often an important factor for determining whether a subject will attempt to time the stimulus or wait for it to occur before initiating action. The medial prefrontal cortex (mPFC) has been implicated in enabling the top-down control of action depending on the outcome of the previous trial. Analysis of spike activity from the rat mPFC suggests that neural integration is a key mechanism for adaptive control in precisely timed tasks. We show through simulation that a spiking neural network consisting of coupled neural integrators captures the neural dynamics of the experimentally recorded mPFC. Errors lead to deviations in the normal dynamics of the system, a process that could enable learning from past mistakes. We expand on this coupled integrator network to construct a spiking neural network that performs a reaction-time task by following either a cue-response or timing strategy, and show that it performs the task with similar reaction times as experimental subjects while maintaining the same spiking dynamics as the experimentally recorded mPFC.

EIGENVECTOR-BASED CENTRALITY MEASURES FOR TEMPORAL NETWORKS*

PubMed Central

TAYLOR, DANE; MYERS, SEAN A.; CLAUSET, AARON; PORTER, MASON A.; MUCHA, PETER J.

2017-01-01

Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centrality. We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT × NT whose dominant eigenvector gives the centrality of each node i at each time t. We refer to this eigenvector and its components as a joint centrality, as it reflects the importances of both the node i and the time layer t. We also introduce the concepts of marginal and conditional centralities, which facilitate the study of centrality trajectories over time. We find that the strength of coupling between layers is important for determining multiscale properties of centrality, such as localization phenomena and the time scale of centrality changes. In the strong-coupling regime, we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion. We also study first-order terms to obtain first-order-mover scores, which concisely describe the magnitude of nodes’ centrality changes over time. As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the United States Supreme Court. PMID:29046619

Temporal Dynamics of Sensorimotor Networks in Effort-Based Cost-Benefit Valuation: Early Emergence and Late Net Value Integration.

PubMed

Harris, Alison; Lim, Seung-Lark

2016-07-06

Although physical effort can impose significant costs on decision-making, when and how effort cost information is incorporated into choice remains contested, reflecting a larger debate over the role of sensorimotor networks in specifying behavior. Serial information processing models, in which motor circuits simply implement the output of cognitive systems, hypothesize that effort cost factors into decisions relatively late, via integration with stimulus values into net (combined) value signals in dorsomedial frontal cortex (dmFC). In contrast, ethology-inspired approaches suggest a more active role for the dorsal sensorimotor stream, with effort cost signals emerging rapidly after stimulus onset. Here we investigated the time course of effort cost integration using event-related potentials in hungry human subjects while they made decisions about expending physical effort for appetitive foods. Consistent with the ethological perspective, we found that effort cost was represented from as early as 100-250 ms after stimulus onset, localized to dorsal sensorimotor regions including middle cingulate, somatosensory, and motor/premotor cortices. However, examining the same data time-locked to motor output revealed net value signals combining stimulus value and effort cost approximately -400 ms before response, originating from sensorimotor areas including dmFC, precuneus, and posterior parietal cortex. Granger causal connectivity analysis of the motor effector signal in the time leading to response showed interactions between these sensorimotor regions and ventrolateral prefrontal cortex, a structure associated with adjusting behavior-response mappings. These results suggest that rapid activation of sensorimotor regions interacts with cognitive valuation systems, producing a net value signal reflecting both physical effort and reward contingencies. Although physical effort imposes a cost on choice, when and how effort cost influences neural correlates of decision-making remains contested. This dispute reflects a larger disagreement between cognitive neuroscience and ethology over the role of sensorimotor systems in behavior: are sensorimotor circuits merely implementing the late-stage output of cognitive systems, or engaged rapidly and interactively from early in decision-making? We find that, although early representation of effort cost is associated with sensorimotor regions, these signals are also integrated with cognitive stimulus value representations in the time leading up to motor response. These data suggest that sensorimotor networks interact dynamically with cognitive systems to guide decision-making, providing a first step toward reconciling differing perspectives on sensorimotor roles in valuation and choice. Copyright © 2016 the authors 0270-6474/16/367167-17$15.00/0.

A Search for the tt¯H (H → bb) Large Hadron Collider with the atlas detector using a matrix element method

NASA Astrophysics Data System (ADS)

Basye, Austin T.

A matrix element method analysis of the Standard Model Higgs boson, produced in association with two top quarks decaying to the lepton-plus-jets channel is presented. Based on 20.3 fb--1 of s=8 TeV data, produced at the Large Hadron Collider and collected by the ATLAS detector, this analysis utilizes multiple advanced techniques to search for ttH signatures with a 125 GeV Higgs boson decaying to two b -quarks. After categorizing selected events based on their jet and b-tag multiplicities, signal rich regions are analyzed using the matrix element method. Resulting variables are then propagated to two parallel multivariate analyses utilizing Neural Networks and Boosted Decision Trees respectively. As no significant excess is found, an observed (expected) limit of 3.4 (2.2) times the Standard Model cross-section is determined at 95% confidence, using the CLs method, for the Neural Network analysis. For the Boosted Decision Tree analysis, an observed (expected) limit of 5.2 (2.7) times the Standard Model cross-section is determined at 95% confidence, using the CLs method. Corresponding unconstrained fits of the Higgs boson signal strength to the observed data result in the measured signal cross-section to Standard Model cross-section prediction of mu = 1.2 +/- 1.3(total) +/- 0.7(stat.) for the Neural Network analysis, and mu = 2.9 +/- 1.4(total) +/- 0.8(stat.) for the Boosted Decision Tree analysis.

Beyond Decision Making for Outdoor Leaders: Expanding the Safety Behavior Research Agenda

ERIC Educational Resources Information Center

Jackson, Jeff S.

2016-01-01

The study of safety behaviour of designated outdoor leaders primarily revolves around their decision making and judgement. The last ten years, however, have seen relatively little peer-reviewed research regarding guide or instructor safety cognition and behaviour. The narrow decision making focus of modern work makes for a field of study…

Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: a randomized clinical trial.

PubMed

Dexheimer, Judith W; Abramo, Thomas J; Arnold, Donald H; Johnson, Kevin; Shyr, Yu; Ye, Fei; Fan, Kang-Hsien; Patel, Neal; Aronsky, Dominik

2014-11-01

The use of evidence-based guidelines can improve the care for asthma patients. We implemented a computerized asthma management system in a pediatric emergency department (ED) to integrate national guidelines. Our objective was to determine whether patient eligibility identification by a probabilistic disease detection system (Bayesian network) combined with an asthma management system embedded in the workflow decreases time to disposition decision. We performed a prospective, randomized controlled trial in an urban, tertiary care pediatric ED. All patients 2-18 years of age presenting to the ED between October 2010 and February 2011 were screened for inclusion by the disease detection system. Patients identified to have an asthma exacerbation were randomized to intervention or control. For intervention patients, asthma management was computer-driven and workflow-integrated including computer-based asthma scoring in triage, and time-driven display of asthma-related reminders for re-scoring on the electronic patient status board combined with guideline-compliant order sets. Control patients received standard asthma management. The primary outcome measure was the time from triage to disposition decision. The Bayesian network identified 1339 patients with asthma exacerbations, of which 788 had an asthma diagnosis determined by an ED physician-established reference standard (positive predictive value 69.9%). The median time to disposition decision did not differ among the intervention (228 min; IQR=(141, 326)) and control group (223 min; IQR=(129, 316)); (p=0.362). The hospital admission rate was unchanged between intervention (25%) and control groups (26%); (p=0.867). ED length of stay did not differ among intervention (262 min; IQR=(165, 410)) and control group (247 min; IQR=(163, 379)); (p=0.818). The control and intervention groups were similar in regards to time to disposition; the computerized management system did not add additional wait time. The time to disposition decision did not change; however the management system integrated several different information systems to support clinicians' communication. Copyright © 2014. Published by Elsevier Ireland Ltd.

Network reconstruction and systems analysis of plant cell wall deconstruction by Neurospora crassa.

PubMed

Samal, Areejit; Craig, James P; Coradetti, Samuel T; Benz, J Philipp; Eddy, James A; Price, Nathan D; Glass, N Louise

2017-01-01

Plant biomass degradation by fungal-derived enzymes is rapidly expanding in economic importance as a clean and efficient source for biofuels. The ability to rationally engineer filamentous fungi would facilitate biotechnological applications for degradation of plant cell wall polysaccharides. However, incomplete knowledge of biomolecular networks responsible for plant cell wall deconstruction impedes experimental efforts in this direction. To expand this knowledge base, a detailed network of reactions important for deconstruction of plant cell wall polysaccharides into simple sugars was constructed for the filamentous fungus Neurospora crassa . To reconstruct this network, information was integrated from five heterogeneous data types: functional genomics, transcriptomics, proteomics, genetics, and biochemical characterizations. The combined information was encapsulated into a feature matrix and the evidence weighted to assign annotation confidence scores for each gene within the network. Comparative analyses of RNA-seq and ChIP-seq data shed light on the regulation of the plant cell wall degradation network, leading to a novel hypothesis for degradation of the hemicellulose mannan. The transcription factor CLR-2 was subsequently experimentally shown to play a key role in the mannan degradation pathway of N. crassa . Here we built a network that serves as a scaffold for integration of diverse experimental datasets. This approach led to the elucidation of regulatory design principles for plant cell wall deconstruction by filamentous fungi and a novel function for the transcription factor CLR-2. This expanding network will aid in efforts to rationally engineer industrially relevant hyper-production strains.

A Strategic Approach to Network Defense: Framing the Cloud

DTIC Science & Technology

2011-03-10

accepted network defensive principles, to reduce risks associated with emerging virtualization capabilities and scalability of cloud computing . This expanded...defensive framework can assist enterprise networking and cloud computing architects to better design more secure systems.

Decision Making Analysis: Critical Factors-Based Methodology

DTIC Science & Technology

2010-04-01

the pitfalls associated with current wargaming methods such as assuming a western view of rational values in decision - making regardless of the cultures...Utilization theory slightly expands the rational decision making model as it states that “actors try to maximize their expected utility by weighing the...items to categorize the decision - making behavior of political leaders which tend to demonstrate either a rational or cognitive leaning. Leaders

A feedback control model for network flow with multiple pure time delays

NASA Technical Reports Server (NTRS)

Press, J.

1972-01-01

A control model describing a network flow hindered by multiple pure time (or transport) delays is formulated. Feedbacks connect each desired output with a single control sector situated at the origin. The dynamic formulation invokes the use of differential difference equations. This causes the characteristic equation of the model to consist of transcendental functions instead of a common algebraic polynomial. A general graphical criterion is developed to evaluate the stability of such a problem. A digital computer simulation confirms the validity of such criterion. An optimal decision making process with multiple delays is presented.

Overview of the Smart Network Element Architecture and Recent Innovations

NASA Technical Reports Server (NTRS)

Perotti, Jose M.; Mata, Carlos T.; Oostdyk, Rebecca L.

2008-01-01

In industrial environments, system operators rely on the availability and accuracy of sensors to monitor processes and detect failures of components and/or processes. The sensors must be networked in such a way that their data is reported to a central human interface, where operators are tasked with making real-time decisions based on the state of the sensors and the components that are being monitored. Incorporating health management functions at this central location aids the operator by automating the decision-making process to suggest, and sometimes perform, the action required by current operating conditions. Integrated Systems Health Management (ISHM) aims to incorporate data from many sources, including real-time and historical data and user input, and extract information and knowledge from that data to diagnose failures and predict future failures of the system. By distributing health management processing to lower levels of the architecture, there is less bandwidth required for ISHM, enhanced data fusion, make systems and processes more robust, and improved resolution for the detection and isolation of failures in a system, subsystem, component, or process. The Smart Network Element (SNE) has been developed at NASA Kennedy Space Center to perform intelligent functions at sensors and actuators' level in support of ISHM.

The application of network teaching in applied optics teaching

NASA Astrophysics Data System (ADS)

Zhao, Huifu; Piao, Mingxu; Li, Lin; Liu, Dongmei

2017-08-01

Network technology has become a creative tool of changing human productivity, the rapid development of it has brought profound changes to our learning, working and life. Network technology has many advantages such as rich contents, various forms, convenient retrieval, timely communication and efficient combination of resources. Network information resources have become the new education resources, get more and more application in the education, has now become the teaching and learning tools. Network teaching enriches the teaching contents, changes teaching process from the traditional knowledge explanation into the new teaching process by establishing situation, independence and cooperation in the network technology platform. The teacher's role has shifted from teaching in classroom to how to guide students to learn better. Network environment only provides a good platform for the teaching, we can get a better teaching effect only by constantly improve the teaching content. Changchun university of science and technology introduced a BB teaching platform, on the platform, the whole optical classroom teaching and the classroom teaching can be improved. Teachers make assignments online, students learn independently offline or the group learned cooperatively, this expands the time and space of teaching. Teachers use hypertext form related knowledge of applied optics, rich cases and learning resources, set up the network interactive platform, homework submission system, message board, etc. The teaching platform simulated the learning interest of students and strengthens the interaction in the teaching.

Visualization of metabolic interaction networks in microbial communities using VisANT 5.0

DOE PAGES

Granger, Brian R.; Chang, Yi -Chien; Wang, Yan; ...

2016-04-15

Here, the complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique meta-graph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction networkmore » between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues.« less

Isolated in a technologically connected world?: Changes in the core professional ties of female researchers in Ghana, Kenya, and Kerala, India.

PubMed

Miller, B Paige; Shrum, Wesley

2012-01-01

Using panel data gathered across two waves (2001 and 2005) from researchers in Ghana, Kenya, and Kerala, India, we examine three questions: (1) To what extent do gender differences exist in the core professional networks of scientists in low-income areas? (2) How do gender differences shift over time? (3) Does use of information and communication technologies (ICTs) mediate the relationship between gender and core network composition? Our results indicate that over a period marked by dramatic increases in access to and use of various ICTs, the composition and size of female researchers core professional ties have either not changed significantly or have changed in an unexpected direction. Indeed, the size of women's ties are retracting over time rather than expanding.

A Layered Decision Model for Cost-Effective System Security

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

Wei, Huaqiang; Alves-Foss, James; Soule, Terry

System security involves decisions in at least three areas: identification of well-defined security policies, selection of cost-effective defence strategies, and implementation of real-time defence tactics. Although choices made in each of these areas affect the others, existing decision models typically handle these three decision areas in isolation. There is no comprehensive tool that can integrate them to provide a single efficient model for safeguarding a network. In addition, there is no clear way to determine which particular combinations of defence decisions result in cost-effective solutions. To address these problems, this paper introduces a Layered Decision Model (LDM) for use inmore » deciding how to address defence decisions based on their cost-effectiveness. To validate the LDM and illustrate how it is used, we used simulation to test model rationality and applied the LDM to the design of system security for an e-commercial business case.« less

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0

PubMed Central

Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

2016-01-01

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT’s unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the “symbiotic layout” of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu. PMID:27081850

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

PubMed

Granger, Brian R; Chang, Yi-Chien; Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

2016-04-01

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

Subthalamic stimulation, oscillatory activity and connectivity reveal functional role of STN and network mechanisms during decision making under conflict.

PubMed

Hell, Franz; Taylor, Paul C J; Mehrkens, Jan H; Bötzel, Kai

2018-05-01

Inhibitory control is an important executive function that is necessary to suppress premature actions and to block interference from irrelevant stimuli. Current experimental studies and models highlight proactive and reactive mechanisms and claim several cortical and subcortical structures to be involved in response inhibition. However, the involved structures, network mechanisms and the behavioral relevance of the underlying neural activity remain debated. We report cortical EEG and invasive subthalamic local field potential recordings from a fully implanted sensing neurostimulator in Parkinson's patients during a stimulus- and response conflict task with and without deep brain stimulation (DBS). DBS made reaction times faster overall while leaving the effects of conflict intact: this lack of any effect on conflict may have been inherent to our task encouraging a high level of proactive inhibition. Drift diffusion modelling hints that DBS influences decision thresholds and drift rates are modulated by stimulus conflict. Both cortical EEG and subthalamic (STN) LFP oscillations reflected reaction times (RT). With these results, we provide a different interpretation of previously conflict-related oscillations in the STN and suggest that the STN implements a general task-specific decision threshold. The timecourse and topography of subthalamic-cortical oscillatory connectivity suggest the involvement of motor, frontal midline and posterior regions in a larger network with complementary functionality, oscillatory mechanisms and structures. While beta oscillations are functionally associated with motor cortical-subthalamic connectivity, low frequency oscillations reveal a subthalamic-frontal-posterior network. With our results, we suggest that proactive as well as reactive mechanisms and structures are involved in implementing a task-related dynamic inhibitory signal. We propose that motor and executive control networks with complementary oscillatory mechanisms are tonically active, react to stimuli and release inhibition at the response when uncertainty is resolved and return to their default state afterwards. Copyright © 2018 Elsevier Inc. All rights reserved.

Investigation of the implementation of a probe-vehicle based pavement roughness estimation system.

DOT National Transportation Integrated Search

2011-08-01

As roadway systems age and maintenance budgets shrink, a need emerges for timely and roughness data for pavement maintenance decision-making. The Virginia Department of Transportation (VDOT) maintains the third-largest state network of roadways in Am...

Multiple attribute decision making model and application to food safety risk evaluation.

PubMed

Ma, Lihua; Chen, Hong; Yan, Huizhe; Yang, Lifeng; Wu, Lifeng

2017-01-01

Decision making for supermarket food purchase decisions are characterized by network relationships. This paper analyzed factors that influence supermarket food selection and proposes a supplier evaluation index system based on the whole process of food production. The author established the intuitive interval value fuzzy set evaluation model based on characteristics of the network relationship among decision makers, and validated for a multiple attribute decision making case study. Thus, the proposed model provides a reliable, accurate method for multiple attribute decision making.

Reconstruction of extended Petri nets from time series data and its application to signal transduction and to gene regulatory networks

PubMed Central

2011-01-01

Background Network inference methods reconstruct mathematical models of molecular or genetic networks directly from experimental data sets. We have previously reported a mathematical method which is exclusively data-driven, does not involve any heuristic decisions within the reconstruction process, and deliveres all possible alternative minimal networks in terms of simple place/transition Petri nets that are consistent with a given discrete time series data set. Results We fundamentally extended the previously published algorithm to consider catalysis and inhibition of the reactions that occur in the underlying network. The results of the reconstruction algorithm are encoded in the form of an extended Petri net involving control arcs. This allows the consideration of processes involving mass flow and/or regulatory interactions. As a non-trivial test case, the phosphate regulatory network of enterobacteria was reconstructed using in silico-generated time-series data sets on wild-type and in silico mutants. Conclusions The new exact algorithm reconstructs extended Petri nets from time series data sets by finding all alternative minimal networks that are consistent with the data. It suggested alternative molecular mechanisms for certain reactions in the network. The algorithm is useful to combine data from wild-type and mutant cells and may potentially integrate physiological, biochemical, pharmacological, and genetic data in the form of a single model. PMID:21762503

Weighting Statistical Inputs for Data Used to Support Effective Decision Making During Severe Emergency Weather and Environmental Events

NASA Technical Reports Server (NTRS)

Gardner, Adrian

2010-01-01

National Aeronautical and Space Administration (NASA) weather and atmospheric environmental organizations are insatiable consumers of geophysical, hydrometeorological and solar weather statistics. The expanding array of internet-worked sensors producing targeted physical measurements has generated an almost factorial explosion of near real-time inputs to topical statistical datasets. Normalizing and value-based parsing of such statistical datasets in support of time-constrained weather and environmental alerts and warnings is essential, even with dedicated high-performance computational capabilities. What are the optimal indicators for advanced decision making? How do we recognize the line between sufficient statistical sampling and excessive, mission destructive sampling ? How do we assure that the normalization and parsing process, when interpolated through numerical models, yields accurate and actionable alerts and warnings? This presentation will address the integrated means and methods to achieve desired outputs for NASA and consumers of its data.

Dynamic Graph Analytic Framework (DYGRAF): greater situation awareness through layered multi-modal network analysis

NASA Astrophysics Data System (ADS)

Margitus, Michael R.; Tagliaferri, William A., Jr.; Sudit, Moises; LaMonica, Peter M.

2012-06-01

Understanding the structure and dynamics of networks are of vital importance to winning the global war on terror. To fully comprehend the network environment, analysts must be able to investigate interconnected relationships of many diverse network types simultaneously as they evolve both spatially and temporally. To remove the burden from the analyst of making mental correlations of observations and conclusions from multiple domains, we introduce the Dynamic Graph Analytic Framework (DYGRAF). DYGRAF provides the infrastructure which facilitates a layered multi-modal network analysis (LMMNA) approach that enables analysts to assemble previously disconnected, yet related, networks in a common battle space picture. In doing so, DYGRAF provides the analyst with timely situation awareness, understanding and anticipation of threats, and support for effective decision-making in diverse environments.

The ASP Sensor Network: Infrastructure for the Next Generation of NASA Airborne Science

NASA Astrophysics Data System (ADS)

Myers, J. S.; Sorenson, C. E.; Van Gilst, D. P.; Duley, A.

2012-12-01

A state-of-the-art real-time data communications network is being implemented across the NASA Airborne Science Program core platforms. Utilizing onboard Ethernet networks and satellite communications systems, it is intended to maximize the science return from both single-platform missions and complex multi-aircraft Earth science campaigns. It also provides an open platform for data visualization and synthesis software tools, for use by the science instrument community. This paper will describe the prototype implementations currently deployed on the NASA DC-8 and Global Hawk aircraft, and the ongoing effort to expand the capability to other science platforms. Emphasis will be on the basic network architecture, the enabling hardware, and new standardized instrument interfaces. The new Mission Tools Suite, which provides an web-based user interface, will be also described; together with several example use-cases of this evolving technology.

Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement.

PubMed

Yue, Shigang; Rind, F Claire

2006-05-01

The lobula giant movement detector (LGMD) is an identified neuron in the locust brain that responds most strongly to the images of an approaching object such as a predator. Its computational model can cope with unpredictable environments without using specific object recognition algorithms. In this paper, an LGMD-based neural network is proposed with a new feature enhancement mechanism to enhance the expanded edges of colliding objects via grouped excitation for collision detection with complex backgrounds. The isolated excitation caused by background detail will be filtered out by the new mechanism. Offline tests demonstrated the advantages of the presented LGMD-based neural network in complex backgrounds. Real time robotics experiments using the LGMD-based neural network as the only sensory system showed that the system worked reliably in a wide range of conditions; in particular, the robot was able to navigate in arenas with structured surrounds and complex backgrounds.

Dynamical evolution of domain walls in an expanding universe

NASA Technical Reports Server (NTRS)

Press, William H.; Ryden, Barbara S.; Spergel, David N.

1989-01-01

Whenever the potential of a scalar field has two or more separated, degenerate minima, domain walls form as the universe cools. The evolution of the resulting network of domain walls is calculated for the case of two potential minima in two and three dimensions, including wall annihilation, crossing, and reconnection effects. The nature of the evolution is found to be largely independent of the rate at which the universe expands. Wall annihilation and reconnection occur almost as fast as causality allows, so that the horizon volume is 'swept clean' and contains, at any time, only about one, fairly smooth, wall. Quantitative statistics are given. The total area of wall per volume decreases as the first power of time. The relative slowness of the decrease and the smoothness of the wall on the horizon scale make it impossible for walls to both generate large-scale structure and be consistent with quadrupole microwave background anisotropy limits.

Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming.

PubMed

Fenton, Michael; Lynch, David; Kucera, Stepan; Claussen, Holger; O'Neill, Michael

2017-09-01

Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs. The evolved heuristics yield minimum downlink rates three times higher than a baseline method, and twice that of a state-of-the-art benchmark. Furthermore, a greater number of UEs receive transmissions under the proposed scheme than in either the baseline or benchmark cases.

Comparing MODIS C6 'Deep Blue' and 'Dark Target' Aerosol Data

NASA Technical Reports Server (NTRS)

Hsu, N. C.; Sayer, A. M.; Bettenhausen, C.; Lee, J.; Levy, R. C.; Mattoo, S.; Munchak, L. A.; Kleidman, R.

2014-01-01

The MODIS Collection 6 Atmospheres product suite includes refined versions of both 'Deep Blue' (DB) and 'Dark Target' (DT) aerosol algorithms, with the DB dataset now expanded to include coverage over vegetated land surfaces. This means that, over much of the global land surface, users will have both DB and DT data to choose from. A 'merged' dataset is also provided, primarily for visualization purposes, which takes retrievals from either or both algorithms based on regional and seasonal climatologies of normalized difference vegetation index (NDVI). This poster present some comparisons of these two C6 aerosol algorithms, focusing on AOD at 550 nm derived from MODIS Aqua measurements, with each other and with Aerosol Robotic Network (AERONET) data, with the intent to facilitate user decisions about the suitability of the two datasets for their desired applications.

Elements of an integrated health monitoring framework

NASA Astrophysics Data System (ADS)

Fraser, Michael; Elgamal, Ahmed; Conte, Joel P.; Masri, Sami; Fountain, Tony; Gupta, Amarnath; Trivedi, Mohan; El Zarki, Magda

2003-07-01

Internet technologies are increasingly facilitating real-time monitoring of Bridges and Highways. The advances in wireless communications for instance, are allowing practical deployments for large extended systems. Sensor data, including video signals, can be used for long-term condition assessment, traffic-load regulation, emergency response, and seismic safety applications. Computer-based automated signal-analysis algorithms routinely process the incoming data and determine anomalies based on pre-defined response thresholds and more involved signal analysis techniques. Upon authentication, appropriate action may be authorized for maintenance, early warning, and/or emergency response. In such a strategy, data from thousands of sensors can be analyzed with near real-time and long-term assessment and decision-making implications. Addressing the above, a flexible and scalable (e.g., for an entire Highway system, or portfolio of Networked Civil Infrastructure) software architecture/framework is being developed and implemented. This framework will network and integrate real-time heterogeneous sensor data, database and archiving systems, computer vision, data analysis and interpretation, physics-based numerical simulation of complex structural systems, visualization, reliability & risk analysis, and rational statistical decision-making procedures. Thus, within this framework, data is converted into information, information into knowledge, and knowledge into decision at the end of the pipeline. Such a decision-support system contributes to the vitality of our economy, as rehabilitation, renewal, replacement, and/or maintenance of this infrastructure are estimated to require expenditures in the Trillion-dollar range nationwide, including issues of Homeland security and natural disaster mitigation. A pilot website (http://bridge.ucsd.edu/compositedeck.html) currently depicts some basic elements of the envisioned integrated health monitoring analysis framework.

Leaking privacy and shadow profiles in online social networks.

PubMed

Garcia, David

2017-08-01

Social interaction and data integration in the digital society can affect the control that individuals have on their privacy. Social networking sites can access data from other services, including user contact lists where nonusers are listed too. Although most research on online privacy has focused on inference of personal information of users, this data integration poses the question of whether it is possible to predict personal information of nonusers. This article tests the shadow profile hypothesis, which postulates that the data given by the users of an online service predict personal information of nonusers. Using data from a disappeared social networking site, we perform a historical audit to evaluate whether personal data of nonusers could have been predicted with the personal data and contact lists shared by the users of the site. We analyze personal information of sexual orientation and relationship status, which follow regular mixing patterns in the social network. Going back in time over the growth of the network, we measure predictor performance as a function of network size and tendency of users to disclose their contact lists. This article presents robust evidence supporting the shadow profile hypothesis and reveals a multiplicative effect of network size and disclosure tendencies that accelerates the performance of predictors. These results call for new privacy paradigms that take into account the fact that individual privacy decisions do not happen in isolation and are mediated by the decisions of others.

A century of sprawl in the United States

PubMed Central

Barrington-Leigh, Christopher; Millard-Ball, Adam

2015-01-01

The urban street network is one of the most permanent features of cities. Once laid down, the pattern of streets determines urban form and the level of sprawl for decades to come. We present a high-resolution time series of urban sprawl, as measured through street network connectivity, in the United States from 1920 to 2012. Sprawl started well before private car ownership was dominant and grew steadily until the mid-1990s. Over the last two decades, however, new streets have become significantly more connected and grid-like; the peak in street-network sprawl in the United States occurred in ∼1994. By one measure of connectivity, the mean nodal degree of intersections, sprawl fell by ∼9% between 1994 and 2012. We analyze spatial variation in these changes and demonstrate the persistence of sprawl. Places that were built with a low-connectivity street network tend to stay that way, even as the network expands. We also find suggestive evidence that local government policies impact sprawl, as the largest increases in connectivity have occurred in places with policies to promote gridded streets and similar New Urbanist design principles. We provide for public use a county-level version of our street-network sprawl dataset comprising a time series of nearly 100 y. PMID:26080422

A century of sprawl in the United States.

PubMed

Barrington-Leigh, Christopher; Millard-Ball, Adam

2015-07-07

The urban street network is one of the most permanent features of cities. Once laid down, the pattern of streets determines urban form and the level of sprawl for decades to come. We present a high-resolution time series of urban sprawl, as measured through street network connectivity, in the United States from 1920 to 2012. Sprawl started well before private car ownership was dominant and grew steadily until the mid-1990s. Over the last two decades, however, new streets have become significantly more connected and grid-like; the peak in street-network sprawl in the United States occurred in ∼ 1994. By one measure of connectivity, the mean nodal degree of intersections, sprawl fell by ∼ 9% between 1994 and 2012. We analyze spatial variation in these changes and demonstrate the persistence of sprawl. Places that were built with a low-connectivity street network tend to stay that way, even as the network expands. We also find suggestive evidence that local government policies impact sprawl, as the largest increases in connectivity have occurred in places with policies to promote gridded streets and similar New Urbanist design principles. We provide for public use a county-level version of our street-network sprawl dataset comprising a time series of nearly 100 y.

Dynamic Network Communication in the Human Functional Connectome Predicts Perceptual Variability in Visual Illusion.

PubMed

Wang, Zhiwei; Zeljic, Kristina; Jiang, Qinying; Gu, Yong; Wang, Wei; Wang, Zheng

2018-01-01

Ubiquitous variability between individuals in visual perception is difficult to standardize and has thus essentially been ignored. Here we construct a quantitative psychophysical measure of illusory rotary motion based on the Pinna-Brelstaff figure (PBF) in 73 healthy volunteers and investigate the neural circuit mechanisms underlying perceptual variation using functional magnetic resonance imaging (fMRI). We acquired fMRI data from a subset of 42 subjects during spontaneous and 3 stimulus conditions: expanding PBF, expanding modified-PBF (illusion-free) and expanding modified-PBF with physical rotation. Brain-wide graph analysis of stimulus-evoked functional connectivity patterns yielded a functionally segregated architecture containing 3 discrete hierarchical networks, commonly shared between rest and stimulation conditions. Strikingly, communication efficiency and strength between 2 networks predominantly located in visual areas robustly predicted individual perceptual differences solely in the illusory stimulus condition. These unprecedented findings demonstrate that stimulus-dependent, not spontaneous, dynamic functional integration between distributed brain networks contributes to perceptual variability in humans. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Sustaining a Mature Teacher Inquiry Network

ERIC Educational Resources Information Center

Satter, Sarah Bea

2014-01-01

This research consisted of a case study of an active network for teacher inquiry. Specifically, I investigated how an organization dedicated to teacher inquiry had provided the structure, leadership, and resources to sustain, maintain, and expand the network. The group studied was the Mid-Ohio Writing Project, a teacher inquiry network affiliated…

Dynamic Pricing in Electronic Commerce Using Neural Network

NASA Astrophysics Data System (ADS)

Ghose, Tapu Kumar; Tran, Thomas T.

In this paper, we propose an approach where feed-forward neural network is used for dynamically calculating a competitive price of a product in order to maximize sellers’ revenue. In the approach we considered that along with product price other attributes such as product quality, delivery time, after sales service and seller’s reputation contribute in consumers purchase decision. We showed that once the sellers, by using their limited prior knowledge, set an initial price of a product our model adjusts the price automatically with the help of neural network so that sellers’ revenue is maximized.

Networks of conforming or nonconforming individuals tend to reach satisfactory decisions.

PubMed

Ramazi, Pouria; Riehl, James; Cao, Ming

2016-11-15

Binary decisions of agents coupled in networks can often be classified into two types: "coordination," where an agent takes an action if enough neighbors are using that action, as in the spread of social norms, innovations, and viral epidemics, and "anticoordination," where too many neighbors taking a particular action causes an agent to take the opposite action, as in traffic congestion, crowd dispersion, and division of labor. Both of these cases can be modeled using linear-threshold-based dynamics, and a fundamental question is whether the individuals in such networks are likely to reach decisions with which they are satisfied. We show that, in the coordination case, and perhaps more surprisingly, also in the anticoordination case, the agents will indeed always tend to reach satisfactory decisions, that is, the network will almost surely reach an equilibrium state. This holds for every network topology and every distribution of thresholds, for both asynchronous and partially synchronous decision-making updates. These results reveal that irregular network topology, population heterogeneity, and partial synchrony are not sufficient to cause cycles or nonconvergence in linear-threshold dynamics; rather, other factors such as imitation or the coexistence of coordinating and anticoordinating agents must play a role.

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

PubMed Central

2009-01-01

Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality. PMID:19758426

Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions.

PubMed

Green, Mark B; Campbell, John L; Yanai, Ruth D; Bailey, Scott W; Bailey, Amey S; Grant, Nicholas; Halm, Ian; Kelsey, Eric P; Rustad, Lindsey E

2018-01-01

The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been monitored with a network established in 1955 that has grown to 23 gauges distributed across nine small catchments. This high sampling intensity allowed us to simulate reduced sampling schemes and thereby evaluate the effect of decommissioning gauges on the quality of precipitation estimates. We considered all possible scenarios of sampling intensity for the catchments on the south-facing slope (2047 combinations) and the north-facing slope (4095 combinations), from the current scenario with 11 or 12 gauges to only 1 gauge remaining. Gauge scenarios differed by as much as 6.0% from the best estimate (based on all the gauges), depending on the catchment, but 95% of the scenarios gave estimates within 2% of the long-term average annual precipitation. The insensitivity of precipitation estimates and the catchment fluxes that depend on them under many reduced monitoring scenarios allowed us to base our reduction decision on other factors such as technician safety, the time required for monitoring, and co-location with other hydrometeorological measurements (snow, air temperature). At Hubbard Brook, precipitation gauges could be reduced from 23 to 10 with a change of <2% in the long-term precipitation estimates. The decision-making approach illustrated in this case study is applicable to the redesign of monitoring networks when reduction of effort seems warranted.

Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

PubMed Central

Wimmer, Klaus; Compte, Albert; Roxin, Alex; Peixoto, Diogo; Renart, Alfonso; de la Rocha, Jaime

2015-01-01

Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability. PMID:25649611

Effects of Gain/Loss Framing in Cyber Defense Decision-Making

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

Bos, Nathan; Paul, Celeste; Gersh, John

Cyber defense requires decision making under uncertainty. Yet this critical area has not been a strong focus of research in judgment and decision-making. Future defense systems, which will rely on software-defined networks and may employ ‘moving target’ defenses, will increasingly automate lower level detection and analysis, but will still require humans in the loop for higher level judgment. We studied the decision making process and outcomes of 17 experienced network defense professionals who worked through a set of realistic network defense scenarios. We manipulated gain versus loss framing in a cyber defense scenario, and found significant effects in one ofmore » two focal problems. Defenders that began with a network already in quarantine (gain framing) used a quarantine system more than those that did not (loss framing). We also found some difference in perceived workload and efficacy. Alternate explanations of these findings and implications for network defense are discussed.« less

The application of neural networks to myoelectric signal analysis: a preliminary study.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1990-03-01

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multidegree of freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameters for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least squares (SLS) algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single site MES based on two features, specifically the first time series parameter, and the signal power. Using these features, the perceptron is trained to distinguish between four separate arm functions. The two-dimensional decision boundaries used by the perceptron classifier are delineated. It is also demonstrated that the perceptron is able to rapidly compensate for variations when new data are incorporated into the training set. This adaptive quality suggests that perceptrons may provide a useful tool for future MES analysis.

CyAN satellite-derived Cyanobacteria products in support of Public Health Protection

EPA Science Inventory

The timely distribution of satellite-derived cyanoHAB data is necessary for adaptive water quality management decision-making and for targeted deployment of existing government and non-government water quality monitoring resources. The Cyanobacteria Assessment Network (CyAN) is a...

Networking CD-ROMs: The Decision Maker's Guide to Local Area Network Solutions.

ERIC Educational Resources Information Center

Elshami, Ahmed M.

In an era when patrons want access to CD-ROM resources but few libraries can afford to buy multiple copies, CD-ROM local area networks (LANs) are emerging as a cost-effective way to provide shared access. To help librarians make informed decisions, this manual offers information on: (1) the basics of LANs, a "local area network primer";…

User data dissemination concepts for earth resources: Executive summary

NASA Technical Reports Server (NTRS)

Davies, R.; Scott, M.; Mitchell, C.; Torbett, A.

1976-01-01

The impact of the future capabilities of earth-resources data sensors (both satellite and airborne) and their requirements on the data dissemination network were investigated and optimum ways of configuring this network were determined. The scope of this study was limited to the continental U.S.A. (including Alaska) and to the 1985-1995 time period. Some of the conclusions and recommendations reached were: (1) Data from satellites in sun-synchronous polar orbits (700-920 km) will generate most of the earth-resources data in the specified time period. (2) Data from aircraft and shuttle sorties cannot be readily integrated in a data-dissemination network unless already preprocessed in a digitized form to a standard geometric coordinate system. (3) Data transmission between readout stations and central preprocessing facilities, and between processing facilities and user facilities are most economically performed by domestic communication satellites. (4) The effect of the following factors should be studied: cloud cover, expanded coverage, pricing strategies, multidiscipline missions.

Choosing a College Major: Factors that Might Influence the Way Students Make Decisions

ERIC Educational Resources Information Center

Lee, Wei-Chun Vanessa

2009-01-01

This current study investigated Janis and Mann's (1977) Conflict Model of Decision Making. Specifically, Janis and Mann's model was tested to examine decision-making styles (coping patterns) and students who either have already decided or who have yet to decide on their college major. Furthermore, the current study is aimed to expand Janis and…

Active Stream Length Dynamics in Headwater Catchments Spanning Physiographic Provinces in the Appalachian Highlands

NASA Astrophysics Data System (ADS)

Jensen, C.; McGuire, K. J.

2015-12-01

One of the most basic descriptions of streams is the presence of channelized flow. However, this seemingly simple query goes unanswered for the majority of headwater networks, as stream length expands and contracts with the wetness of catchments seasonally, interannually, and in response to storm events. Although streams are known to grow and shrink, a lack of information on longitudinal dynamics across different geographic regions precludes effective management. Understanding the temporal variation in temporary network length over a broad range of settings is critical for policy decisions that impact aquatic ecosystem health. This project characterizes changes in active stream length for forested headwater catchments spanning four physiographic provinces of the Appalachian Highlands: the New England at Hubbard Brook Experimental Forest, New Hampshire; Valley and Ridge at Poverty Creek and the North Fork of Big Stony Creek in Jefferson National Forest, Virginia; Blue Ridge at Coweeta Hydrologic Laboratory, North Carolina; and Appalachian Plateau at Fernow Experimental Forest, West Virginia. Multivariate statistical analysis confirms these provinces exhibit characteristic topographies reflecting differences in climate, geology, and environmental history and, thus, merit separate consideration. The active streams of three watersheds (<45 ha) in each study area were mapped six times to capture a variety of moderate flow conditions that can be expected most of the time (i.e., exceedance probabilities between 25 to 75%). The geomorphic channel and channel heads were additionally mapped to determine how active stream length variability relates to the development of the geomorphic network. We found that drainage density can vary up to four-fold with discharge. Stream contraction primarily proceeds by increasing disconnection and disintegration into pools, while the number of flow origins remains constant except at high and low extremes of discharge. This work demonstrates that streams can remain active in the form of isolated, disconnected sections along even the most upstream reaches during low flows. This finding suggests that we must consider the maximum stream extent for conservation and management strategies much more frequently than for just periods of high stream flow.

Genetic counseling: Growth of the profession and the professional.

PubMed

Baty, Bonnie J

2018-03-01

Growth of the profession of genetic counseling has gone hand-in-hand with professional development of individual genetic counselors. Genetic counseling has achieved most of the typical early milestones in the development of a profession. The profession is maturing at a time when the number of practitioners is predicted to vastly expand. The last two decades have seen a proliferation of genetic counselor roles and practice areas, and a distinct professional identity. It is likely that the next two decades will see an increase in educational paths, practice areas, and possibilities for professional advancement. How this maturation proceeds will be impacted by overall trends in healthcare, decisions made by international genetic counseling organizations, and thousands of individual decisions about career trajectories. © 2018 Wiley Periodicals, Inc.

Can Limiting Choice Increase Social Welfare? The Elderly and Health Insurance

PubMed Central

Hanoch, Yaniv; Rice, Thomas

2006-01-01

Herbert Simon's work on bounded rationality has had little impact on health policy discourse, despite numerous supportive findings. This is particularly surprising in regard to the elderly, a group marked by a decline in higher cognitive functions. Elders' cognitive capacity to make decisions will be challenged even further with the introduction of the new Medicare prescription drug benefit program, mainly because of the many options available. At the same time, a growing body of evidence points to the perils of having too many choices. By combining research from decision science, economics, and psychology, we highlight the potential problems with the expanding health insurance choices facing the elderly and conclude with some policy suggestions to alleviate the problem. PMID:16529568

SANDS: an architecture for clinical decision support in a National Health Information Network.

PubMed

Wright, Adam; Sittig, Dean F

2007-10-11

A new architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support) is introduced and its performance evaluated. The architecture provides a method for performing clinical decision support across a network, as in a health information exchange. Using the prototype we demonstrated that, first, a number of useful types of decision support can be carried out using our architecture; and, second, that the architecture exhibits desirable reliability and performance characteristics.

Autonomous control of production networks using a pheromone approach

NASA Astrophysics Data System (ADS)

Armbruster, D.; de Beer, C.; Freitag, M.; Jagalski, T.; Ringhofer, C.

2006-04-01

The flow of parts through a production network is usually pre-planned by a central control system. Such central control fails in presence of highly fluctuating demand and/or unforeseen disturbances. To manage such dynamic networks according to low work-in-progress and short throughput times, an autonomous control approach is proposed. Autonomous control means a decentralized routing of the autonomous parts themselves. The parts’ decisions base on backward propagated information about the throughput times of finished parts for different routes. So, routes with shorter throughput times attract parts to use this route again. This process can be compared to ants leaving pheromones on their way to communicate with following ants. The paper focuses on a mathematical description of such autonomously controlled production networks. A fluid model with limited service rates in a general network topology is derived and compared to a discrete-event simulation model. Whereas the discrete-event simulation of production networks is straightforward, the formulation of the addressed scenario in terms of a fluid model is challenging. Here it is shown, how several problems in a fluid model formulation (e.g. discontinuities) can be handled mathematically. Finally, some simulation results for the pheromone-based control with both the discrete-event simulation model and the fluid model are presented for a time-dependent influx.

The Xpress Transfer Protocol (XTP): A tutorial (expanded version)

NASA Technical Reports Server (NTRS)

Sanders, Robert M.; Weaver, Alfred C.

1990-01-01

The Xpress Transfer Protocol (XTP) is a reliable, real-time, light weight transfer layer protocol. Current transport layer protocols such as DoD's Transmission Control Protocol (TCP) and ISO's Transport Protocol (TP) were not designed for the next generation of high speed, interconnected reliable networks such as fiber distributed data interface (FDDI) and the gigabit/second wide area networks. Unlike all previous transport layer protocols, XTP is being designed to be implemented in hardware as a VLSI chip set. By streamlining the protocol, combining the transport and network layers and utilizing the increased speed and parallelization possible with a VLSI implementation, XTP will be able to provide the end-to-end data transmission rates demanded in high speed networks without compromising reliability and functionality. This paper describes the operation of the XTP protocol and in particular, its error, flow and rate control; inter-networking addressing mechanisms; and multicast support features, as defined in the XTP Protocol Definition Revision 3.4.

The effects of pre-entry career maturity and support networks in workplace on newcomers' mental health.

PubMed

Kawai, Kaoru; Yamazaki, Yoshihiko

2006-11-01

The present study examined the effects of pre-entry experiences (i.e. career maturity), as well as support networks (i.e. informational and friendship), on newcomers' mental health (i.e. depression, self-esteem, psychosomatic symptoms, and work motivation). We performed a longitudinal study of 890 men and women who first entered the workplace in 2003. Surveys were distributed at two time points: just prior to entering the workplace, and two months after entering. Results indicated that career maturity related positively to newcomers' mental health, and newcomers with high career maturity were more successful in establishing positive relationships with superiors and co-workers. Although, informational support networks positively related to work motivation, friendship networks did not show any direct effects on mental health. These results underscore the crucial roles of career maturity and informational networks in facilitating the transition to the workplace. The results also provide empirical support for an expanded view of the importance of pre-entry experiences to workplace newcomers' mental health.

Evidence-based Sensor Tasking for Space Domain Awareness

NASA Astrophysics Data System (ADS)

Jaunzemis, A.; Holzinger, M.; Jah, M.

2016-09-01

Space Domain Awareness (SDA) is the actionable knowledge required to predict, avoid, deter, operate through, recover from, and/or attribute cause to the loss and/or degradation of space capabilities and services. A main purpose for SDA is to provide decision-making processes with a quantifiable and timely body of evidence of behavior(s) attributable to specific space threats and/or hazards. To fulfill the promise of SDA, it is necessary for decision makers and analysts to pose specific hypotheses that may be supported or refuted by evidence, some of which may only be collected using sensor networks. While Bayesian inference may support some of these decision making needs, it does not adequately capture ambiguity in supporting evidence; i.e., it struggles to rigorously quantify 'known unknowns' for decision makers. Over the past 40 years, evidential reasoning approaches such as Dempster Shafer theory have been developed to address problems with ambiguous bodies of evidence. This paper applies mathematical theories of evidence using Dempster Shafer expert systems to address the following critical issues: 1) How decision makers can pose critical decision criteria as rigorous, testable hypotheses, 2) How to interrogate these hypotheses to reduce ambiguity, and 3) How to task a network of sensors to gather evidence for multiple competing hypotheses. This theory is tested using a simulated sensor tasking scenario balancing search versus track responsibilities.

Pervasive Monitoring—An Intelligent Sensor Pod Approach for Standardised Measurement Infrastructures

PubMed Central

Resch, Bernd; Mittlboeck, Manfred; Lippautz, Michael

2010-01-01

Geo-sensor networks have traditionally been built up in closed monolithic systems, thus limiting trans-domain usage of real-time measurements. This paper presents the technical infrastructure of a standardised embedded sensing device, which has been developed in the course of the Live Geography approach. The sensor pod implements data provision standards of the Sensor Web Enablement initiative, including an event-based alerting mechanism and location-aware Complex Event Processing functionality for detection of threshold transgression and quality assurance. The goal of this research is that the resultant highly flexible sensing architecture will bring sensor network applications one step further towards the realisation of the vision of a “digital skin for planet earth”. The developed infrastructure can potentially have far-reaching impacts on sensor-based monitoring systems through the deployment of ubiquitous and fine-grained sensor networks. This in turn allows for the straight-forward use of live sensor data in existing spatial decision support systems to enable better-informed decision-making. PMID:22163537

Pervasive monitoring--an intelligent sensor pod approach for standardised measurement infrastructures.

PubMed

Resch, Bernd; Mittlboeck, Manfred; Lippautz, Michael

2010-01-01

Geo-sensor networks have traditionally been built up in closed monolithic systems, thus limiting trans-domain usage of real-time measurements. This paper presents the technical infrastructure of a standardised embedded sensing device, which has been developed in the course of the Live Geography approach. The sensor pod implements data provision standards of the Sensor Web Enablement initiative, including an event-based alerting mechanism and location-aware Complex Event Processing functionality for detection of threshold transgression and quality assurance. The goal of this research is that the resultant highly flexible sensing architecture will bring sensor network applications one step further towards the realisation of the vision of a "digital skin for planet earth". The developed infrastructure can potentially have far-reaching impacts on sensor-based monitoring systems through the deployment of ubiquitous and fine-grained sensor networks. This in turn allows for the straight-forward use of live sensor data in existing spatial decision support systems to enable better-informed decision-making.

Portable data collection terminal in the automated power consumption measurement system

NASA Astrophysics Data System (ADS)

Vologdin, S. V.; Shushkov, I. D.; Bysygin, E. K.

2018-01-01

Aim of efficiency increasing, automation process of electric energy data collection and processing is very important at present time. High cost of classic electric energy billing systems prevent from its mass application. Udmurtenergo Branch of IDGC of Center and Volga Region developed electronic automated system called “Mobile Energy Billing” based on data collection terminals. System joins electronic components based on service-oriented architecture, WCF services. At present time all parts of Udmurtenergo Branch electric network are connected to “Mobile Energy Billing” project. System capabilities are expanded due to flexible architecture.

Design and Implementation of a Wireless Sensor Network-Based Remote Water-Level Monitoring System

PubMed Central

Li, Xiuhong; Cheng, Xiao; Gong, Peng; Yan, Ke

2011-01-01

The proposed remote water-level monitoring system (RWMS) consists of a field sensor module, a base station module, adata center module and aWEB releasing module. It has advantages in real time and synchronized remote control, expandability, and anti-jamming capabilities. The RWMS can realize real-time remote monitoring, providing early warning of events and protection of the safety of monitoring personnel under certain dangerous circumstances. This system has been successfully applied in Poyanghu Lake. The cost of the whole system is approximately 1,500 yuan (RMB). PMID:22319377

Design and implementation of a wireless sensor network-based remote water-level monitoring system.

PubMed

Li, Xiuhong; Cheng, Xiao; Gong, Peng; Yan, Ke

2011-01-01

The proposed remote water-level monitoring system (RWMS) consists of a field sensor module, a base station module, a data center module and a WEB releasing module. It has advantages in real time and synchronized remote control, expandability, and anti-jamming capabilities. The RWMS can realize real-time remote monitoring, providing early warning of events and protection of the safety of monitoring personnel under certain dangerous circumstances. This system has been successfully applied in Poyanghu Lake. The cost of the whole system is approximately 1,500 yuan (RMB).

Improved representation of situational awareness within a dismounted small combat unit constructive simulation

NASA Astrophysics Data System (ADS)

Lee, K. David; Colony, Mike

2011-06-01

Modeling and simulation has been established as a cost-effective means of supporting the development of requirements, exploring doctrinal alternatives, assessing system performance, and performing design trade-off analysis. The Army's constructive simulation for the evaluation of equipment effectiveness in small combat unit operations is currently limited to representation of situation awareness without inclusion of the many uncertainties associated with real world combat environments. The goal of this research is to provide an ability to model situation awareness and decision process uncertainties in order to improve evaluation of the impact of battlefield equipment on ground soldier and small combat unit decision processes. Our Army Probabilistic Inference and Decision Engine (Army-PRIDE) system provides this required uncertainty modeling through the application of two critical techniques that allow Bayesian network technology to be applied to real-time applications. (Object-Oriented Bayesian Network methodology and Object-Oriented Inference technique). In this research, we implement decision process and situation awareness models for a reference scenario using Army-PRIDE and demonstrate its ability to model a variety of uncertainty elements, including: confidence of source, information completeness, and information loss. We also demonstrate that Army-PRIDE improves the realism of the current constructive simulation's decision processes through Monte Carlo simulation.

Decision-making in irrigation networks: Selecting appropriate canal structures using multi-attribute decision analysis.

PubMed

Hosseinzade, Zeinab; Pagsuyoin, Sheree A; Ponnambalam, Kumaraswamy; Monem, Mohammad J

2017-12-01

The stiff competition for water between agriculture and non-agricultural production sectors makes it necessary to have effective management of irrigation networks in farms. However, the process of selecting flow control structures in irrigation networks is highly complex and involves different levels of decision makers. In this paper, we apply multi-attribute decision making (MADM) methodology to develop a decision analysis (DA) framework for evaluating, ranking and selecting check and intake structures for irrigation canals. The DA framework consists of identifying relevant attributes for canal structures, developing a robust scoring system for alternatives, identifying a procedure for data quality control, and identifying a MADM model for the decision analysis. An application is illustrated through an analysis for automation purposes of the Qazvin irrigation network, one of the oldest and most complex irrigation networks in Iran. A survey questionnaire designed based on the decision framework was distributed to experts, managers, and operators of the Qazvin network and to experts from the Ministry of Power in Iran. Five check structures and four intake structures were evaluated. A decision matrix was generated from the average scores collected from the survey, and was subsequently solved using TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method. To identify the most critical structure attributes for the selection process, optimal attribute weights were calculated using Entropy method. For check structures, results show that the duckbill weir is the preferred structure while the pivot weir is the least preferred. Use of the duckbill weir can potentially address the problem with existing Amil gates where manual intervention is required to regulate water levels during periods of flow extremes. For intake structures, the Neyrpic® gate and constant head orifice are the most and least preferred alternatives, respectively. Some advantages of the Neyrpic® gate are ease of operation and capacity to measure discharge flows. Overall, the application to the Qazvin irrigation network demonstrates the utility of the proposed DA framework in selecting appropriate structures for regulating water flows in irrigation canals. This framework systematically aids the decision process by capturing decisions made at various levels (individual farmers to high-level management). It can be applied to other cases where a new irrigation network is being designed, or where changes in irrigation structures need to be identified to improve flow control in existing networks. Copyright © 2017 Elsevier B.V. All rights reserved.

Update on the activities of the GGOS Bureau of Networks and Observations

NASA Technical Reports Server (NTRS)

Pearlman, Michael R.; Pavlis, Erricos C.; Ma, Chopo; Noll, Carey; Thaller, Daniela; Richter, Bernd; Gross, Richard; Neilan, Ruth; Mueller, Juergen; Barzaghi, Ricardo;

A network-based approach to disturbance transmission through microbial interactions

PubMed Central

Hunt, Dana E.; Ward, Christopher S.

2015-01-01

Microbes numerically dominate aquatic ecosystems and play key roles in the biogeochemistry and the health of these environments. Due to their short generations times and high diversity, microbial communities are among the first responders to environmental changes, including natural and anthropogenic disturbances such as storms, pollutant releases, and upwelling. These disturbances affect members of the microbial communities both directly and indirectly through interactions with impacted community members. Thus, interactions can influence disturbance propagation through the microbial community by either expanding the range of organisms affected or buffering the influence of disturbance. For example, interactions may expand the number of disturbance-affected taxa by favoring a competitor or buffer the impacts of disturbance when a potentially disturbance-responsive clade’s growth is limited by an essential microbial partner. Here, we discuss the potential to use inferred ecological association networks to examine how disturbances propagate through microbial communities focusing on a case study of a coastal community’s response to a storm. This approach will offer greater insight into how disturbances can produce community-wide impacts on aquatic environments following transient changes in environmental parameters. PMID:26579091

Self-Organizing Distributed Architecture Supporting Dynamic Space Expanding and Reducing in Indoor LBS Environment

PubMed Central

Jeong, Seol Young; Jo, Hyeong Gon; Kang, Soon Ju

2015-01-01

Indoor location-based services (iLBS) are extremely dynamic and changeable, and include numerous resources and mobile devices. In particular, the network infrastructure requires support for high scalability in the indoor environment, and various resource lookups are requested concurrently and frequently from several locations based on the dynamic network environment. A traditional map-based centralized approach for iLBSs has several disadvantages: it requires global knowledge to maintain a complete geographic indoor map; the central server is a single point of failure; it can also cause low scalability and traffic congestion; and it is hard to adapt to a change of service area in real time. This paper proposes a self-organizing and fully distributed platform for iLBSs. The proposed self-organizing distributed platform provides a dynamic reconfiguration of locality accuracy and service coverage by expanding and contracting dynamically. In order to verify the suggested platform, scalability performance according to the number of inserted or deleted nodes composing the dynamic infrastructure was evaluated through a simulation similar to the real environment. PMID:26016908

Transfer of Patients in a Telestroke Network: What Are the Relevant Factors for Making This Decision?

PubMed

Klingner, Carsten M; Brodoehl, Stefan; Funck, Laura; Klingner, Caroline C; Berrouschot, Jörg; Witte, Otto W; Günther, Albrecht

2018-02-01

Background/Introduction: Current telestroke network consultations are focused on decision-making in the hyperacute stage of stroke management. The two main questions in telestroke consultations are whether thrombolysis should be initiated and whether the patient should be transferred to a hub hospital. Although guidelines exist for initiating intravenous thrombolytic therapy, the question of whether patients should be transferred is far more elusive. In this study, we investigated the factors involved in the decision to transfer stroke patients to a hub hospital. We were particularly interested in identifying factors that promote or impede the transfer of patients. We enrolled 1,615 cases of telestroke consultation of the University Hospital Jena. The two main factors that independently influenced the probability of transferring a patient were the patient's age and the identification of a proximal vessel occlusion. Interestingly, factors such as the severity of symptoms and the time elapsed from symptom onset were not found to have an independent influence on the decision to transfer a patient. The transfer of most patients was justified by the possibility of performing interventional reperfusion therapy. We discuss the effectiveness of the current decision-making process and possible ways to improve decision-making for a more effective selection of patients who would benefit from transfer. The decision-making process to a transfer patient is not standardized and constitutes a trade-off between the intention to treat all possible patients while avoiding the transfer of patients without treatment options.

Determining the Impact of Personal Mobility Carbon Allowance Schemes in Transportation Networks

DOE PAGES

Aziz, H. M. Abdul; Ukkusuri, Satish V.; Zhan, Xianyuan

2016-10-17

We know that personal mobility carbon allowance (PMCA) schemes are designed to reduce carbon consumption from transportation networks. PMCA schemes influence the travel decision process of users and accordingly impact the system metrics including travel time and greenhouse gas (GHG) emissions. Here, we develop a multi-user class dynamic user equilibrium model to evaluate the transportation system performance when PMCA scheme is implemented. The results using Sioux-Falls test network indicate that PMCA schemes can achieve the emissions reduction goals for transportation networks. Further, users characterized by high value of travel time are found to be less sensitive to carbon budget inmore » the context of work trips. Results also show that PMCA scheme can lead to higher emissions for a path compared with the case without PMCA because of flow redistribution. The developed network equilibrium model allows us to examine the change in system states at different carbon allocation levels and to design parameters of PMCA schemes accounting for population heterogeneity.« less

Determining the Impact of Personal Mobility Carbon Allowance Schemes in Transportation Networks

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

Aziz, H. M. Abdul; Ukkusuri, Satish V.; Zhan, Xianyuan

We know that personal mobility carbon allowance (PMCA) schemes are designed to reduce carbon consumption from transportation networks. PMCA schemes influence the travel decision process of users and accordingly impact the system metrics including travel time and greenhouse gas (GHG) emissions. Here, we develop a multi-user class dynamic user equilibrium model to evaluate the transportation system performance when PMCA scheme is implemented. The results using Sioux-Falls test network indicate that PMCA schemes can achieve the emissions reduction goals for transportation networks. Further, users characterized by high value of travel time are found to be less sensitive to carbon budget inmore » the context of work trips. Results also show that PMCA scheme can lead to higher emissions for a path compared with the case without PMCA because of flow redistribution. The developed network equilibrium model allows us to examine the change in system states at different carbon allocation levels and to design parameters of PMCA schemes accounting for population heterogeneity.« less

Mapping dynamic social networks in real life using participants' own smartphones.

PubMed

Boonstra, Tjeerd W; E Larsen, Mark; Christensen, Helen

2015-11-01

Interpersonal relationships are vital for our daily functioning and wellbeing. Social networks may form the primary means by which environmental influences determine individual traits. Several studies have shown the influence of social networks on decision-making, behaviors and wellbeing. Smartphones have great potential for measuring social networks in a real world setting. Here we tested the feasibility of using people's own smartphones as a data collection platform for face-to-face interactions. We developed an application for iOS and Android to collect Bluetooth data and acquired one week of data from 14 participants in our organization. The Bluetooth scanning statistics were used to quantify the time-resolved connection strength between participants and define the weights of a dynamic social network. We used network metrics to quantify changes in network topology over time and non-negative matrix factorization to identify cliques or subgroups that reoccurred during the week. The scanning rate varied considerably between smartphones running Android and iOS and egocentric networks metrics were correlated with the scanning rate. The time courses of two identified subgroups matched with two meetings that took place that week. These findings demonstrate the feasibility of using participants' own smartphones to map social network, whilst identifying current limitations of using generic smartphones. The bias introduced by variations in scanning rate and missing data is an important limitation that needs to be addressed in future studies.

Software Defined Networking challenges and future direction: A case study of implementing SDN features on OpenStack private cloud

NASA Astrophysics Data System (ADS)

Shamugam, Veeramani; Murray, I.; Leong, J. A.; Sidhu, Amandeep S.

2016-03-01

Cloud computing provides services on demand instantly, such as access to network infrastructure consisting of computing hardware, operating systems, network storage, database and applications. Network usage and demands are growing at a very fast rate and to meet the current requirements, there is a need for automatic infrastructure scaling. Traditional networks are difficult to automate because of the distributed nature of their decision making process for switching or routing which are collocated on the same device. Managing complex environments using traditional networks is time-consuming and expensive, especially in the case of generating virtual machines, migration and network configuration. To mitigate the challenges, network operations require efficient, flexible, agile and scalable software defined networks (SDN). This paper discuss various issues in SDN and suggests how to mitigate the network management related issues. A private cloud prototype test bed was setup to implement the SDN on the OpenStack platform to test and evaluate the various network performances provided by the various configurations.

Differential neural circuitry and self-interest in real vs hypothetical moral decisions

PubMed Central

Dalgleish, Tim; Thompson, Russell; Evans, Davy; Schweizer, Susanne; Mobbs, Dean

2012-01-01

Classic social psychology studies demonstrate that people can behave in ways that contradict their intentions—especially within the moral domain. We measured brain activity while subjects decided between financial self-benefit (earning money) and preventing physical harm (applying an electric shock) to a confederate under both real and hypothetical conditions. We found a shared neural network associated with empathic concern for both types of decisions. However, hypothetical and real moral decisions also recruited distinct neural circuitry: hypothetical moral decisions mapped closely onto the imagination network, while real moral decisions elicited activity in the bilateral amygdala and anterior cingulate—areas essential for social and affective processes. Moreover, during real moral decision-making, distinct regions of the prefrontal cortex (PFC) determined whether subjects make selfish or pro-social moral choices. Together, these results reveal not only differential neural mechanisms for real and hypothetical moral decisions but also that the nature of real moral decisions can be predicted by dissociable networks within the PFC. PMID:22711879

Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: methods of a decision-maker-researcher partnership systematic review.

PubMed

Haynes, R Brian; Wilczynski, Nancy L

2010-02-05

Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. As with any healthcare intervention with claims to improve process of care or patient outcomes, decision support systems should be rigorously evaluated before widespread dissemination into clinical practice. Engaging healthcare providers and managers in the review process may facilitate knowledge translation and uptake. The objective of this research was to form a partnership of healthcare providers, managers, and researchers to review randomized controlled trials assessing the effects of computerized decision support for six clinical application areas: primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease management, diagnostic test ordering and interpretation, and acute care management; and to identify study characteristics that predict benefit. The review was undertaken by the Health Information Research Unit, McMaster University, in partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local Health Integration Network, and pertinent healthcare service teams. Following agreement on information needs and interests with decision-makers, our earlier systematic review was updated by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference lists through 6 January 2010. Data extraction items were expanded according to input from decision-makers. Authors of primary studies were contacted to confirm data and to provide additional information. Eligible trials were organized according to clinical area of application. We included randomized controlled trials that evaluated the effect on practitioner performance or patient outcomes of patient care provided with a computerized clinical decision support system compared with patient care without such a system. Data will be summarized using descriptive summary measures, including proportions for categorical variables and means for continuous variables. Univariable and multivariable logistic regression models will be used to investigate associations between outcomes of interest and study specific covariates. When reporting results from individual studies, we will cite the measures of association and p-values reported in the studies. If appropriate for groups of studies with similar features, we will conduct meta-analyses. A decision-maker-researcher partnership provides a model for systematic reviews that may foster knowledge translation and uptake.

Characterizing the orthodontic patient's purchase decision: A novel approach using netnography.

PubMed

Pittman, Joseph W; Bennett, M Elizabeth; Koroluk, Lorne D; Robinson, Stacey G; Phillips, Ceib L

2017-06-01

A deeper and more thorough characterization of why patients do or do not seek orthodontic treatment is needed for effective shared decision making about receiving treatment. Previous orthodontic qualitative research has identified important dimensions that influence treatment decisions, but our understanding of patients' decisions and how they interpret benefits and barriers of treatment are lacking. The objectives of this study were to expand our current list of decision-making dimensions and to create a conceptual framework to describe the decision-making process. Discussion boards, rich in orthodontic decision-making data, were identified and analyzed with qualitative methods. An iterative process of data collection, dimension identification, and dimension refinement were performed to saturation. A conceptual framework was created to describe the decision-making process. Fifty-four dimensions captured the ideas discussed in regard to a patient's decision to receive orthodontic treatment. Ten domains were identified: function, esthetics, psychosocial benefits, diagnosis, finances, inconveniences, risks of treatment, individual aspects, societal attitudes, and child-specific influences, each containing specific descriptive and conceptual dimensions. A person's desires, self-perceptions, and viewpoints, the public's views on esthetics and orthodontics, and parenting philosophies impacted perceptions of benefits and barriers associated with orthodontic treatment. We identified an expanded list of dimensions, created a conceptual framework describing the orthodontic patient's decision-making process, and identified dimensions associated with yes and no decisions, giving doctors a better understanding of patient attitudes and expectations. Copyright © 2017 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Nationwide online social networking for cardiovascular care in Korea using Facebook.

PubMed

Kim, Changsun; Kang, Bo Seung; Choi, Hyuk Joong; Lee, Young Joo; Kang, Gu Hyun; Choi, Wook Jin; Kwon, In Ho

2014-01-01

To examine the use of online social networking for cardiovascular care using Facebook. All posts and comments in a Facebook group between June 2011 and May 2012 were reviewed, and a survey was conducted. A total of 298 members participated. Of the 277 wall posts, 26.7% were question posts requesting rapid replies, and 50.5% were interesting cases shared with other members. The median response time for the question posts was 16 min (IQR 8-47), which tended to decrease as more members joined the group. Many members (37.4%) accessed the group more than once a day, and more than half (64%) monitored the group posts in real time with automatic notifications of new posts. Most members expressed confidence in the content posted. Facebook enables online social networking between physicians in near-real time and appears to be a useful tool for physicians to share clinical experience and request assistance in decision-making.

Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

PubMed

Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

2014-01-01

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

PubMed Central

Banik, Shipra; Khodadad Khan, A. F. M.; Anwer, Mohammad

2014-01-01

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange. PMID:24701205

Outcomes of an International Workshop on Preconception Expanded Carrier Screening: Some Considerations for Governments.

PubMed

Molster, Caron M; Lister, Karla; Metternick-Jones, Selina; Baynam, Gareth; Clarke, Angus John; Straub, Volker; Dawkins, Hugh J S; Laing, Nigel

2017-01-01

Consideration of expanded carrier screening has become an emerging issue for governments. However, traditional criteria for decision-making regarding screening programs do not incorporate all the issues relevant to expanded carrier screening. Further, there is a lack of consistent guidance in the literature regarding the development of appropriate criteria for government assessment of expanded carrier screening. Given this, a workshop was held to identify key public policy issues related to preconception expanded carrier screening, which governments should consider when deciding whether to publicly fund such programs. In June 2015, a satellite workshop was held at the European Society of Human Genetics Conference. It was structured around two design features: (1) the provision of information from a range of perspectives and (2) small group deliberations on the key issues that governments need to consider and the benefits, risks, and challenges of implementing publicly funded whole-population preconception carrier screening. Forty-one international experts attended the workshop. The deliberations centered primarily on the conditions to be tested and the elements of the screening program itself. Participants expected only severe conditions to be screened but were concerned about the lack of a consensus definition of "severe." Issues raised regarding the screening program included the purpose, benefits, harms, target population, program acceptability, components of a program, and economic evaluation. Participants also made arguments for consideration of the accuracy of screening tests. A wide range of issues require careful consideration by governments that want to assess expanded carrier screening. Traditional criteria for government decision-making regarding screening programs are not a "best fit" for expanded carrier screening and new models of decision-making with appropriate criteria are required. There is a need to define what a "severe" condition is, to build evidence regarding the reliability and accuracy of screening tests, to consider the equitable availability and downstream effects on and costs of follow-up interventions for those identified as carriers, and to explore the ways in which the components of a screening program would be impacted by unique features of expanded carrier screening.

Fall and Recovery of the Murrili Meteorite, and an Update on the Desert Fireball Network

NASA Astrophysics Data System (ADS)

Bland, P. A.; Towner, M. C.; Sansom, E. K.; Devillepoix, H.; Howie, R. M.; Paxman, J. P.; Cupak, M.; Benedix, G. K.; Cox, M. A.; Jansen-Sturgeon, T.; Stuart, D.; Strangway, D.

2016-08-01

The Murrili meteorite was recovered from Lake Eyre, South Australia, on 31 December 2015. It is the third meteorite recovered by the Desert Fireball Network, and the first since the network was upgraded and expanded.

Social Networking Media: An Approach for the Teaching of International Business

ERIC Educational Resources Information Center

Barczyk, Casimir C.; Duncan, Doris G.

2012-01-01

Internet technology and Web 2.0 applications have enabled social networking media to expand in ways that link people globally. By fostering communication, social networks hold immense potential for the enhancement of teaching, especially in the business arena. This article defines social networking and provides a framework for understanding the…

A Framework for Achieving Situational Awareness during Crisis based on Twitter Analysis

NASA Astrophysics Data System (ADS)

Zielinski, Andrea; Tokarchuk, Laurissa; Middleton, Stuart; Chaves, Fernando

2013-04-01

Decision Support Systems for Natural Crisis Management increasingly employ Web 2.0 and 3.0 technologies for future collaborative decision making, including the use of social networks like Twitter. However, human sensor data is not readily accessible and interpretable, since the texts are unstructured, noisy and available in various languages. The present work focusses on the detection of crisis events in a multilingual setting as part of the FP7-funded EU project TRIDEC and is motivated by the goal to establish a Tsunami warning system for the Mediterranean. It is integrated into a dynamic spatial-temporal decision making component with a command and control unit's graphical user interface that presents all relevant information to the human operator to support critical decision-support. To this end, a tool for the interactive visualization of geospatial data is implemented: All tweets with an exact timestamp or geo-location are monitored on the map in real-time so that the operator on duty can get an overall picture of the situation. Apart from the human sensor data, the seismic sensor data will appear also on the same screen. Signs of abnormal activity from twitter usage in social networks as well as in sensor networks devices can then be used to trigger official warning alerts according to the CAP message standard. Whenever a certain threshold of relevant tweets in a HASC region (Hierarchical Administrative Subdivision Code) is exceeded, the twitter activity in this administrative region will be shown on a map. We believe that the following functionalities are crucial for monitoring crisis, making use of text mining and network analysis techniques: Focussed crawling, trustworthyness analysis geo-parsing, and multilingual tweet classification. In the first step, the Twitter Streaming API accesses the social data, using an adaptive keyword list (focussed crawling). Then, tweets are filtered and aggregated to form counts for a certain time-span (e.g., an interval of 1-2 minutes). Particularly, we investigate the following novel techniques that help to fulfill this task: trustworthyness analysis (linkage analysis and user network analysis), geo-parsing (locating the event in space), and multilingual tweet classification (filtering out of noisy tweets for various Mediterranean languages). Lastly, an aberration algorithm looks for spikes in the temporal stream of twitter data.

Informing climate change adaptation with insights from famine early warning (Invited)

NASA Astrophysics Data System (ADS)

Funk, C. C.; Verdin, J. P.

2010-12-01

Famine early warning systems provide a unique viewpoint for understanding the implications of climate change on food security, identifying the locations and seasons where millions of food insecure people are dependent upon climate-sensitive agricultural systems. The Famine Early Warning Systems Network (FEWS NET) is a decision support system sponsored by the Office of Food for Peace of the U.S. Agency for International Development (USAID), which distributes over two billion dollars of food aid to more than 40 countries each year. FEWS NET identifies the times and places where food aid is required by the most climatically sensitive and consequently food insecure populations of the developing world. As result, FEWS NET has developed its own "climate service", implemented by USGS, NOAA, and NASA, to support its decision making processes. The foundation of this climate service is the monitoring of current growing conditions for early identification of agricultural drought that might impact food security. Since station networks are sparse in the countries monitored, FEWS NET has a tradition (dating back to 1985) of reliance on satellite remote sensing of vegetation and rainfall. In the last ten years, climate forecasts have become an additional tool for food security assessment, extending the early warning perspective to include expected agricultural outcomes for the season ahead. More recently, research has expanded to include detailed analyses of recent observed climate trends, combined with diagnostic ocean-atmosphere studies. These studies are then used to develop interpretations of GCM scenarios and their implications for future patterns of precipitation and temperature, revealing trends towards warmer/drier climate conditions and increases in the relative frequency of drought. In some regions, like Eastern Africa, such changes seem to be already occurring, with an associated increase in food insecurity. Sub-national analyses for Kenya, for example, point to the need for adaptation through improved agricultural practices, so that increased yields can offset the impacts of rising temperatures and declining rainfall. Future work will focus on assessing temperature-PET linkages, and evaluating pathways for agricultural development.

IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion.

PubMed

Dehzangi, Omid; Taherisadr, Mojtaba; ChangalVala, Raghvendar

2017-11-27

The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF) expansion of human gait cycles in order to capture joint 2 dimensional (2D) spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN) learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level) and late (decision score level) multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF) method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class identification task. Based on our experimental results, 91% subject identification accuracy was achieved using the best individual IMU and 2DTF-DCNN. We then investigated our proposed early and late sensor fusion approaches, which improved the gait identification accuracy of the system to 93.36% and 97.06%, respectively.

Information technology feasibility study for the Washington State commercial vehicle information systems and networks (CVISN) pilot project

DOT National Transportation Integrated Search

1998-01-08

The CVISN Pilot Project will prototype the use of a comprehensive interface to state and federal motor carrier data systems and will deliver real-time, decision-making information to weigh stations and commercial vehicle enforcement officers. In addi...

Acquisition and management of continuous data streams for crop water management

USDA-ARS?s Scientific Manuscript database

Wireless sensor network systems for decision support in crop water management offer many advantages including larger spatial coverage and multiple types of data input. However, collection and management of multiple and continuous data streams for near real-time post analysis can be problematic. Thi...

Time-Ordered Networks Reveal Limitations to Information Flow in Ant Colonies

PubMed Central

Blonder, Benjamin; Dornhaus, Anna

2011-01-01

Background An important function of many complex networks is to inhibit or promote the transmission of disease, resources, or information between individuals. However, little is known about how the temporal dynamics of individual-level interactions affect these networks and constrain their function. Ant colonies are a model comparative system for understanding general principles linking individual-level interactions to network-level functions because interactions among individuals enable integration of multiple sources of information to collectively make decisions, and allocate tasks and resources. Methodology/Findings Here we show how the temporal and spatial dynamics of such individual interactions provide upper bounds to rates of colony-level information flow in the ant Temnothorax rugatulus. We develop a general framework for analyzing dynamic networks and a mathematical model that predicts how information flow scales with individual mobility and group size. Conclusions/Significance Using thousands of time-stamped interactions between uniquely marked ants in four colonies of a range of sizes, we demonstrate that observed maximum rates of information flow are always slower than predicted, and are constrained by regulation of individual mobility and contact rate. By accounting for the ordering and timing of interactions, we can resolve important difficulties with network sampling frequency and duration, enabling a broader understanding of interaction network functioning across systems and scales. PMID:21625450

Integrating pro-environmental behavior with transportation network modeling: User and system level strategies, implementation, and evaluation

NASA Astrophysics Data System (ADS)

Aziz, H. M. Abdul

Personal transport is a leading contributor to fossil fuel consumption and greenhouse (GHG) emissions in the U.S. The U.S. Energy Information Administration (EIA) reports that light-duty vehicles (LDV) are responsible for 61% of all transportation related energy consumption in 2012, which is equivalent to 8.4 million barrels of oil (fossil fuel) per day. The carbon content in fossil fuels is the primary source of GHG emissions that links to the challenge associated with climate change. Evidently, it is high time to develop actionable and innovative strategies to reduce fuel consumption and GHG emissions from the road transportation networks. This dissertation integrates the broader goal of minimizing energy and emissions into the transportation planning process using novel systems modeling approaches. This research aims to find, investigate, and evaluate strategies that minimize carbon-based fuel consumption and emissions for a transportation network. We propose user and system level strategies that can influence travel decisions and can reinforce pro-environmental attitudes of road users. Further, we develop strategies that system operators can implement to optimize traffic operations with emissions minimization goal. To complete the framework we develop an integrated traffic-emissions (EPA-MOVES) simulation framework that can assess the effectiveness of the strategies with computational efficiency and reasonable accuracy. The dissertation begins with exploring the trade-off between emissions and travel time in context of daily travel decisions and its heterogeneous nature. Data are collected from a web-based survey and the trade-off values indicating the average additional travel minutes a person is willing to consider for reducing a lb. of GHG emissions are estimated from random parameter models. Results indicate that different trade-off values for male and female groups. Further, participants from high-income households are found to have higher trade-off values compared with other groups. Next, we propose personal mobility carbon allowance (PMCA) scheme to reduce emissions from personal travel. PMCA is a market-based scheme that allocates carbon credits to users at no cost based on the emissions reduction goal of the system. Users can spend carbon credits for travel and a market place exists where users can buy or sell credits. This dissertation addresses two primary dimensions: the change in travel behavior of the users and the impact at network level in terms of travel time and emissions when PMCA is implemented. To understand this process, a real-time experimental game tool is developed where players are asked to make travel decisions within the carbon budget set by PMCA and they are allowed to trade carbon credits in a market modeled as a double auction game. Random parameter models are estimated to examine the impact of PMCA on short-term travel decisions. Further, to assess the impact at system level, a multi-class dynamic user equilibrium model is formulated that captures the travel behavior under PMCA scheme. The equivalent variational inequality problem is solved using projection method. Results indicate that PMCA scheme is able to reduce GHG emissions from transportation networks. Individuals with high value of travel time (VOTT) are less sensitive to PMCA scheme in context of work trips. High and medium income users are more likely to have non-work trips with lower carbon cost (higher travel time) to save carbon credits for work trips. Next, we focus on the strategies from the perspectives of system operators in transportation networks. Learning based signal control schemes are developed that can reduce emissions from signalized urban networks. The algorithms are implemented and tested in VISSIM micro simulator. Finally, an integrated emissions-traffic simulator framework is outlined that can be used to evaluate the effectiveness of the strategies. The integrated framework uses MOVES2010b as the emissions simulator. To estimate the emissions efficiently we propose a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the link driving schedules for MOVES2010b. Test results using the data from a five-intersection corridor show that HC-DTW technique can significantly reduce emissions estimation time without compromising the accuracy. The benefits are found to be most significant when the level of congestion variation is high. In addition to finding novel strategies for reducing emissions from transportation networks, this dissertation has broader impacts on behavior based energy policy design and transportation network modeling research. The trade-off values can be a useful indicator to identify which policies are most effective to reinforce pro-environmental travel choices. For instance, the model can estimate the distribution of trade-off between emissions and travel time, and provide insights on the effectiveness of policies for New York City if we are able to collect data to construct a representative sample. The probability of route choice decisions vary across population groups and trip contexts. The probability as a function of travel and demographic attributes can be used as behavior rules for agents in an agent-based traffic simulation. Finally, the dynamic user equilibrium based network model provides a general framework for energy policies such carbon tax, tradable permit, and emissions credits system.

Dissociable contributions of anterior cingulate cortex and basolateral amygdala on a rodent cost/benefit decision-making task of cognitive effort.

PubMed

Hosking, Jay G; Cocker, Paul J; Winstanley, Catharine A

2014-06-01

Personal success often requires the choice to expend greater effort for larger rewards, and deficits in such effortful decision making accompany a number of illnesses including depression, schizophrenia, and attention-deficit/hyperactivity disorder. Animal models have implicated brain regions such as the basolateral amygdala (BLA) and anterior cingulate cortex (ACC) in physical effort-based choice, but disentangling the unique contributions of these two regions has proven difficult, and effort demands in industrialized society are predominantly cognitive in nature. Here we utilize the rodent cognitive effort task (rCET), a modification of the five-choice serial reaction-time task, wherein animals can choose to expend greater visuospatial attention to obtain larger sucrose rewards. Temporary inactivation (via baclofen-muscimol) of BLA and ACC showed dissociable effects: BLA inactivation caused hard-working rats to 'slack off' and 'slacker' rats to work harder, whereas ACC inactivation caused all animals to reduce willingness to expend mental effort. Furthermore, BLA inactivation increased the time needed to make choices, whereas ACC inactivation increased motor impulsivity. These data illuminate unique contributions of BLA and ACC to effort-based decision making, and imply overlapping yet distinct circuitry for cognitive vs physical effort. Our understanding of effortful decision making may therefore require expanding our models beyond purely physical costs.

Networks and the Epidemiology of Infectious Disease

PubMed Central

Danon, Leon; Ford, Ashley P.; House, Thomas; Jewell, Chris P.; Keeling, Matt J.; Roberts, Gareth O.; Ross, Joshua V.; Vernon, Matthew C.

2011-01-01

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues. PMID:21437001

Brain mechanisms for perceptual and reward-related decision-making.

PubMed

Deco, Gustavo; Rolls, Edmund T; Albantakis, Larissa; Romo, Ranulfo

2013-04-01

Phenomenological models of decision-making, including the drift-diffusion and race models, are compared with mechanistic, biologically plausible models, such as integrate-and-fire attractor neuronal network models. The attractor network models show how decision confidence is an emergent property; and make testable predictions about the neural processes (including neuronal activity and fMRI signals) involved in decision-making which indicate that the medial prefrontal cortex is involved in reward value-based decision-making. Synaptic facilitation in these models can help to account for sequential vibrotactile decision-making, and for how postponed decision-related responses are made. The randomness in the neuronal spiking-related noise that makes the decision-making probabilistic is shown to be increased by the graded firing rate representations found in the brain, to be decreased by the diluted connectivity, and still to be significant in biologically large networks with thousands of synapses onto each neuron. The stability of these systems is shown to be influenced in different ways by glutamatergic and GABAergic efficacy, leading to a new field of dynamical neuropsychiatry with applications to understanding schizophrenia and obsessive-compulsive disorder. The noise in these systems is shown to be advantageous, and to apply to similar attractor networks involved in short-term memory, long-term memory, attention, and associative thought processes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Network Coded Cooperative Communication in a Real-Time Wireless Hospital Sensor Network.

PubMed

Prakash, R; Balaji Ganesh, A; Sivabalan, Somu

2017-05-01

The paper presents a network coded cooperative communication (NC-CC) enabled wireless hospital sensor network architecture for monitoring health as well as postural activities of a patient. A wearable device, referred as a smartband is interfaced with pulse rate, body temperature sensors and an accelerometer along with wireless protocol services, such as Bluetooth and Radio-Frequency transceiver and Wi-Fi. The energy efficiency of wearable device is improved by embedding a linear acceleration based transmission duty cycling algorithm (NC-DRDC). The real-time demonstration is carried-out in a hospital environment to evaluate the performance characteristics, such as power spectral density, energy consumption, signal to noise ratio, packet delivery ratio and transmission offset. The resource sharing and energy efficiency features of network coding technique are improved by proposing an algorithm referred as network coding based dynamic retransmit/rebroadcast decision control (LA-TDC). From the experimental results, it is observed that the proposed LA-TDC algorithm reduces network traffic and end-to-end delay by an average of 27.8% and 21.6%, respectively than traditional network coded wireless transmission. The wireless architecture is deployed in a hospital environment and results are then successfully validated.

A Reinforcement Sensor Embedded Vertical Handoff Controller for Vehicular Heterogeneous Wireless Networks

PubMed Central

Li, Limin; Xu, Yubin; Soong, Boon-Hee; Ma, Lin

2013-01-01

Vehicular communication platforms that provide real-time access to wireless networks have drawn more and more attention in recent years. IEEE 802.11p is the main radio access technology that supports communication for high mobility terminals, however, due to its limited coverage, IEEE 802.11p is usually deployed by coupling with cellular networks to achieve seamless mobility. In a heterogeneous cellular/802.11p network, vehicular communication is characterized by its short time span in association with a wireless local area network (WLAN). Moreover, for the media access control (MAC) scheme used for WLAN, the network throughput dramatically decreases with increasing user quantity. In response to these compelling problems, we propose a reinforcement sensor (RFS) embedded vertical handoff control strategy to support mobility management. The RFS has online learning capability and can provide optimal handoff decisions in an adaptive fashion without prior knowledge. The algorithm integrates considerations including vehicular mobility, traffic load, handoff latency, and network status. Simulation results verify that the proposed algorithm can adaptively adjust the handoff strategy, allowing users to stay connected to the best network. Furthermore, the algorithm can ensure that RSUs are adequate, thereby guaranteeing a high quality user experience. PMID:24193101

From trees to forest: relational complexity network and workload of air traffic controllers.

PubMed

Zhang, Jingyu; Yang, Jiazhong; Wu, Changxu

2015-01-01

In this paper, we propose a relational complexity (RC) network framework based on RC metric and network theory to model controllers' workload in conflict detection and resolution. We suggest that, at the sector level, air traffic showing a centralised network pattern can provide cognitive benefits in visual search and resolution decision which will in turn result in lower workload. We found that the network centralisation index can account for more variance in predicting perceived workload and task completion time in both a static conflict detection task (Study 1) and a dynamic one (Study 2) in addition to other aircraft-level and pair-level factors. This finding suggests that linear combination of aircraft-level or dyad-level information may not be adequate and the global-pattern-based index is necessary. Theoretical and practical implications of using this framework to improve future workload modelling and management are discussed. We propose a RC network framework to model the workload of air traffic controllers. The effect of network centralisation was examined in both a static conflict detection task and a dynamic one. Network centralisation was predictive of perceived workload and task completion time over and above other control variables.

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

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

NASA Astrophysics Data System (ADS)

Lieb, Anna; Wilkening, Jon; Rycroft, Chris

2015-11-01

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

Degradable transportation network with the addition of electric vehicles: Network equilibrium analysis

PubMed Central

Zhang, Rui; Yao, Enjian; Yang, Yang

2017-01-01

Introducing electric vehicles (EVs) into urban transportation network brings higher requirement on travel time reliability and charging reliability. Specifically, it is believed that travel time reliability is a key factor influencing travelers’ route choice. Meanwhile, due to the limited cruising range, EV drivers need to better learn about the required energy for the whole trip to make decisions about whether charging or not and where to charge (i.e., charging reliability). Since EV energy consumption is highly related to travel speed, network uncertainty affects travel time and charging demand estimation significantly. Considering the network uncertainty resulted from link degradation, which influences the distribution of travel demand on transportation network and the energy demand on power network, this paper aims to develop a reliability-based network equilibrium framework for accommodating degradable road conditions with the addition of EVs. First, based on the link travel time distribution, the mean and variance of route travel time and monetary expenses related to energy consumption are deduced, respectively. And the charging time distribution of EVs with charging demand is also estimated. Then, a nested structure is considered to deal with the difference of route choice behavior derived by the different uncertainty degrees between the routes with and without degradable links. Given the expected generalized travel cost and a psychological safety margin, a traffic assignment model with the addition of EVs is formulated. Subsequently, a heuristic solution algorithm is developed to solve the proposed model. Finally, the effects of travelers’ risk attitude, network degradation degree, and EV penetration rate on network performance are illustrated through an example network. The numerical results show that the difference of travelers’ risk attitudes does have impact on the route choice, and the widespread adoption of EVs can cut down the total system travel cost effectively when the transportation network is more reliable. PMID:28886167

Degradable transportation network with the addition of electric vehicles: Network equilibrium analysis.

PubMed

Zhang, Rui; Yao, Enjian; Yang, Yang

2017-01-01

Introducing electric vehicles (EVs) into urban transportation network brings higher requirement on travel time reliability and charging reliability. Specifically, it is believed that travel time reliability is a key factor influencing travelers' route choice. Meanwhile, due to the limited cruising range, EV drivers need to better learn about the required energy for the whole trip to make decisions about whether charging or not and where to charge (i.e., charging reliability). Since EV energy consumption is highly related to travel speed, network uncertainty affects travel time and charging demand estimation significantly. Considering the network uncertainty resulted from link degradation, which influences the distribution of travel demand on transportation network and the energy demand on power network, this paper aims to develop a reliability-based network equilibrium framework for accommodating degradable road conditions with the addition of EVs. First, based on the link travel time distribution, the mean and variance of route travel time and monetary expenses related to energy consumption are deduced, respectively. And the charging time distribution of EVs with charging demand is also estimated. Then, a nested structure is considered to deal with the difference of route choice behavior derived by the different uncertainty degrees between the routes with and without degradable links. Given the expected generalized travel cost and a psychological safety margin, a traffic assignment model with the addition of EVs is formulated. Subsequently, a heuristic solution algorithm is developed to solve the proposed model. Finally, the effects of travelers' risk attitude, network degradation degree, and EV penetration rate on network performance are illustrated through an example network. The numerical results show that the difference of travelers' risk attitudes does have impact on the route choice, and the widespread adoption of EVs can cut down the total system travel cost effectively when the transportation network is more reliable.

BRIDGE: A Simulation Model for Comparing the Costs of Expanding a Campus Using Distributed Instruction versus Classroom Instruction. Documentation and Instructions.

ERIC Educational Resources Information Center

Jewett, Frank

These instructions describe the use of BRIDGE, a computer software simulation model that is designed to compare the costs of expanding a college campus using distributed instruction (television or asynchronous network courses) versus the costs of expanding using lecture/lab type instruction. The model compares the projected operating and capital…

Stochastic Simulation of Biomolecular Networks in Dynamic Environments

PubMed Central

Voliotis, Margaritis; Thomas, Philipp; Grima, Ramon; Bowsher, Clive G.

2016-01-01

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits. PMID:27248512

Online social network response to studies on antidepressant use in pregnancy.

PubMed

Vigod, Simone N; Bagheri, Ebrahim; Zarrinkalam, Fattane; Brown, Hilary K; Mamdani, Muhammad; Ray, Joel G

2018-03-01

About 8% of U.S women are prescribed antidepressant medications around the time of pregnancy. Decisions about medication use in pregnancy can be swayed by the opinion of family, friends and online media, sometimes beyond the advice offered by healthcare providers. Exploration of the online social network response to research on antidepressant use in pregnancy could provide insight about how to optimize decision-making in this complex area. For all 17 research articles published on the safety of antidepressant use in pregnancy in 2012, we sought to explore online social network activity regarding antidepressant use in pregnancy, via Twitter, in the 48h after a study was published, compared to the social network activity in the same period 1week prior to each article's publication. Online social network activity about antidepressants in pregnancy quickly doubled upon study publication. The increased activity was driven by studies demonstrating harm associated with antidepressants, lower-quality studies, and studies where abstracts presented relative versus absolute risks. These findings support a call for leadership from medical journals to consider how to best incentivize and support a balanced and clear translation of knowledge around antidepressant safety in pregnancy to their readership and the public. Copyright © 2018 Elsevier Inc. All rights reserved.

Remote Internet access to advanced analytical facilities: a new approach with Web-based services.

PubMed

Sherry, N; Qin, J; Fuller, M Suominen; Xie, Y; Mola, O; Bauer, M; McIntyre, N S; Maxwell, D; Liu, D; Matias, E; Armstrong, C

2012-09-04

Over the past decade, the increasing availability of the World Wide Web has held out the possibility that the efficiency of scientific measurements could be enhanced in cases where experiments were being conducted at distant facilities. Examples of early successes have included X-ray diffraction (XRD) experimental measurements of protein crystal structures at synchrotrons and access to scanning electron microscopy (SEM) and NMR facilities by users from institutions that do not possess such advanced capabilities. Experimental control, visual contact, and receipt of results has used some form of X forwarding and/or VNC (virtual network computing) software that transfers the screen image of a server at the experimental site to that of the users' home site. A more recent development is a web services platform called Science Studio that provides teams of scientists with secure links to experiments at one or more advanced research facilities. The software provides a widely distributed team with a set of controls and screens to operate, observe, and record essential parts of the experiment. As well, Science Studio provides high speed network access to computing resources to process the large data sets that are often involved in complex experiments. The simple web browser and the rapid transfer of experimental data to a processing site allow efficient use of the facility and assist decision making during the acquisition of the experimental results. The software provides users with a comprehensive overview and record of all parts of the experimental process. A prototype network is described involving X-ray beamlines at two different synchrotrons and an SEM facility. An online parallel processing facility has been developed that analyzes the data in near-real time using stream processing. Science Studio and can be expanded to include many other analytical applications, providing teams of users with rapid access to processed results along with the means for detailed discussion of their significance.

EUROSCAN INTERNATIONAL NETWORK MEMBER AGENCIES: THEIR STRUCTURE, PROCESSES, AND OUTPUTS.

PubMed

Packer, Claire; Simpson, Sue; de Almeida, Rosimary Terezinha

2015-01-01

The EuroScan International Network is a global network of publicly funded early awareness and alert (EAA) systems for health technologies. We describe the EuroScan member agency systems and methods, and highlight the potential for increased collaboration. EuroScan members completed postal questionnaires supplemented with telephone interviews in 2012 to elicit additional information and check equivalence of responses. Information was updated between March and May 2013. Fifteen of the seventeen member agencies responded. The principal purpose of agencies is to inform decisions on coverage or reimbursement of health services and decisions on undertaking secondary research. The main users of information are national governments; health professionals; health services purchasers, commissioners, and decision makers; and healthcare providers. Most EuroScan agencies are small with almost half having fewer than two whole time equivalent staff. Ten agencies use both active and passive identification approaches, four use only active approaches. Most start identification in the experimental or investigational stages of the technology life cycle. All agencies assessed technologies when they are between the investigational and established, but under diffusion stages. Barriers to collaboration revolve around different system aims, purposes, and requirements; a lack of staff, finance, or opportunity; language differences; and restrictions on dissemination. Although many barriers to collaboration were identified, the majority of agencies were supportive of increased collaboration either involving the whole EuroScan Network or between individual agencies. Despite differences in the detailed identification processes, members thought that this was the most feasible phase to develop additional collaboration.

Parents, adolescents, and consent for research participation.

PubMed

Iltis, Ana S

2013-06-01

Decisions concerning children in the health care setting have engendered significant controversy and sparked ethics policies and statements, legal action, and guidelines regarding who ought to make decisions involving children and how such decisions ought to be made. Traditionally, parents have been the default decision-makers for children not only with regard to health care but with regard to other matters, such as religious practice and education. In recent decades, there has been a steady trend away from the view that parents are in authority over their children and toward the view that children are rights-bearers who should be granted greater authority over themselves. The mature minor doctrine refers to the decision to grant mature minors the authority to make decisions traditionally reserved for their parents. This essay (1) documents the trend towards expanding the understanding of some minors as "mature" and hence as having the right and authority to give informed consent, (2) examines the reasons for which some commentators have a special interest in expanding the mature minor doctrine to the research setting and allowing minors to enroll in research without parental permission, and (3) defends the view that the mature minor doctrine, regardless of its application to clinical health care decisions, ought to be set aside in the research setting in favor of greater parental involvement.

How Coke Added Life to its Video Network.

ERIC Educational Resources Information Center

Curran, Patrick D.

1981-01-01

Describes how Coca-Cola's training department identified problems in the use of a video training package and made improvements that expanded the potential of the corporation-wide training network. (SK)

Developing A Large-Scale, Collaborative, Productive Geoscience Education Network

NASA Astrophysics Data System (ADS)

Manduca, C. A.; Bralower, T. J.; Egger, A. E.; Fox, S.; Ledley, T. S.; Macdonald, H.; Mcconnell, D. A.; Mogk, D. W.; Tewksbury, B. J.

2012-12-01

Over the past 15 years, the geoscience education community has grown substantially and developed broad and deep capacity for collaboration and dissemination of ideas. While this community is best viewed as emergent from complex interactions among changing educational needs and opportunities, we highlight the role of several large projects in the development of a network within this community. In the 1990s, three NSF projects came together to build a robust web infrastructure to support the production and dissemination of on-line resources: On The Cutting Edge (OTCE), Earth Exploration Toolbook, and Starting Point: Teaching Introductory Geoscience. Along with the contemporaneous Digital Library for Earth System Education, these projects engaged geoscience educators nationwide in exploring professional development experiences that produced lasting on-line resources, collaborative authoring of resources, and models for web-based support for geoscience teaching. As a result, a culture developed in the 2000s in which geoscience educators anticipated that resources for geoscience teaching would be shared broadly and that collaborative authoring would be productive and engaging. By this time, a diverse set of examples demonstrated the power of the web infrastructure in supporting collaboration, dissemination and professional development . Building on this foundation, more recent work has expanded both the size of the network and the scope of its work. Many large research projects initiated collaborations to disseminate resources supporting educational use of their data. Research results from the rapidly expanding geoscience education research community were integrated into the Pedagogies in Action website and OTCE. Projects engaged faculty across the nation in large-scale data collection and educational research. The Climate Literacy and Energy Awareness Network and OTCE engaged community members in reviewing the expanding body of on-line resources. Building Strong Geoscience Departments sought to create the same type of shared information base that was supporting individual faculty for departments. The Teach the Earth portal and its underlying web development tools were used by NSF-funded projects in education to disseminate their results. Leveraging these funded efforts, the Climate Literacy Network has expanded this geoscience education community to include individuals broadly interested in fostering climate literacy. Most recently, the InTeGrate project is implementing inter-institutional collaborative authoring, testing and evaluation of curricular materials. While these projects represent only a fraction of the activity in geoscience education, they are important drivers in the development of a large, national, coherent geoscience education network with the ability to collaborate and disseminate information effectively. Importantly, the community is open and defined by active participation. Key mechanisms for engagement have included alignment of project activities with participants needs and goals; productive face-to-face and virtual workshops, events, and series; stipends for completion of large products; and strong supporting staff to keep projects moving and assist with product production. One measure of its success is the adoption and adaptation of resources and models by emerging projects, which results in the continued growth of the network.

Safety validation of decision trees for hepatocellular carcinoma.

PubMed

Wang, Xian-Qiang; Liu, Zhe; Lv, Wen-Ping; Luo, Ying; Yang, Guang-Yun; Li, Chong-Hui; Meng, Xiang-Fei; Liu, Yang; Xu, Ke-Sen; Dong, Jia-Hong

2015-08-21

To evaluate a different decision tree for safe liver resection and verify its efficiency. A total of 2457 patients underwent hepatic resection between January 2004 and December 2010 at the Chinese PLA General Hospital, and 634 hepatocellular carcinoma (HCC) patients were eligible for the final analyses. Post-hepatectomy liver failure (PHLF) was identified by the association of prothrombin time < 50% and serum bilirubin > 50 μmol/L (the "50-50" criteria), which were assessed at day 5 postoperatively or later. The Swiss-Clavien decision tree, Tokyo University-Makuuchi decision tree, and Chinese consensus decision tree were adopted to divide patients into two groups based on those decision trees in sequence, and the PHLF rates were recorded. The overall mortality and PHLF rate were 0.16% and 3.0%. A total of 19 patients experienced PHLF. The numbers of patients to whom the Swiss-Clavien, Tokyo University-Makuuchi, and Chinese consensus decision trees were applied were 581, 573, and 622, and the PHLF rates were 2.75%, 2.62%, and 2.73%, respectively. Significantly more cases satisfied the Chinese consensus decision tree than the Swiss-Clavien decision tree and Tokyo University-Makuuchi decision tree (P < 0.01，P < 0.01); nevertheless, the latter two shared no difference (P = 0.147). The PHLF rate exhibited no significant difference with respect to the three decision trees. The Chinese consensus decision tree expands the indications for hepatic resection for HCC patients and does not increase the PHLF rate compared to the Swiss-Clavien and Tokyo University-Makuuchi decision trees. It would be a safe and effective algorithm for hepatectomy in patients with hepatocellular carcinoma.

Proceedings of the Mobile Satellite System Architectures and Multiple Access Techniques Workshop

NASA Technical Reports Server (NTRS)

Dessouky, Khaled

1989-01-01

The Mobile Satellite System Architectures and Multiple Access Techniques Workshop served as a forum for the debate of system and network architecture issues. Particular emphasis was on those issues relating to the choice of multiple access technique(s) for the Mobile Satellite Service (MSS). These proceedings contain articles that expand upon the 12 presentations given in the workshop. Contrasting views on Frequency Division Multiple Access (FDMA), Code Division Multiple Access (CDMA), and Time Division Multiple Access (TDMA)-based architectures are presented, and system issues relating to signaling, spacecraft design, and network management constraints are addressed. An overview article that summarizes the issues raised in the numerous discussion periods of the workshop is also included.

FIR: An Effective Scheme for Extracting Useful Metadata from Social Media.

PubMed

Chen, Long-Sheng; Lin, Zue-Cheng; Chang, Jing-Rong

2015-11-01

Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information for their own care and the care of others. Researchers paid much attention on social media in medical educations, patient-pharmacist communications, adverse drug reactions detection, impacts of social media on medicine and healthcare, and so on. However, relatively few papers discuss how to extract useful knowledge from a huge amount of textual comments in social media effectively. Therefore, this study aims to propose a Fuzzy adaptive resonance theory network based Information Retrieval (FIR) scheme by combining Fuzzy adaptive resonance theory (ART) network, Latent Semantic Indexing (LSI), and association rules (AR) discovery to extract knowledge from social media. In our FIR scheme, Fuzzy ART network firstly has been employed to segment comments. Next, for each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, association rules mining is presented to organize these extracted keywords to build metadata. These extracted useful voices of customers will be transformed into design needs by using Quality Function Deployment (QFD) for further decision making. Unlike conventional information retrieval techniques which acquire too many keywords to get key points, our FIR scheme can extract understandable metadata from social media.

Neural basis of economic bubble behavior.

PubMed

Ogawa, A; Onozaki, T; Mizuno, T; Asamizuya, T; Ueno, K; Cheng, K; Iriki, A

2014-04-18

Throughout human history, economic bubbles have formed and burst. As a bubble grows, microeconomic behavior ceases to be constrained by realistic predictions. This contradicts the basic assumption of economics that agents have rational expectations. To examine the neural basis of behavior during bubbles, we performed functional magnetic resonance imaging while participants traded shares in a virtual stock exchange with two non-bubble stocks and one bubble stock. The price was largely deflected from the fair price in one of the non-bubble stocks, but not in the other. Their fair prices were specified. The price of the bubble stock showed a large increase and battering, as based on a real stock-market bust. The imaging results revealed modulation of the brain circuits that regulate trade behavior under different market conditions. The premotor cortex was activated only under a market condition in which the price was largely deflected from the fair price specified. During the bubble, brain regions associated with the cognitive processing that supports order decisions were identified. The asset preference that might bias the decision was associated with the ventrolateral prefrontal cortex and the dorsolateral prefrontal cortex (DLPFC). The activity of the inferior parietal lobule (IPL) was correlated with the score of future time perspective, which would bias the estimation of future price. These regions were deemed to form a distinctive network during the bubble. A functional connectivity analysis showed that the connectivity between the DLPFC and the IPL was predominant compared with other connectivities only during the bubble. These findings indicate that uncertain and unstable market conditions changed brain modes in traders. These brain mechanisms might lead to a loss of control caused by wishful thinking, and to microeconomic bubbles that expand, on the macroscopic scale, toward bust. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Knowledge Management

NASA Technical Reports Server (NTRS)

Shariq, Syed Z.; Kutler, Paul (Technical Monitor)

1997-01-01

The emergence of rapidly expanding technologies for distribution and dissemination of information and knowledge has brought to focus the opportunities for development of knowledge-based networks, knowledge dissemination and knowledge management technologies and their potential applications for enhancing productivity of knowledge work. The challenging and complex problems of the future can be best addressed by developing the knowledge management as a new discipline based on an integrative synthesis of hard and soft sciences. A knowledge management professional society can provide a framework for catalyzing the development of proposed synthesis as well as serve as a focal point for coordination of professional activities in the strategic areas of education, research and technology development. Preliminary concepts for the development of the knowledge management discipline and the professional society are explored. Within this context of knowledge management discipline and the professional society, potential opportunities for application of information technologies for more effectively delivering or transferring information and knowledge (i.e., resulting from the NASA's Mission to Planet Earth) for the development of policy options in critical areas of national and global importance (i.e., policy decisions in economic and environmental areas) can be explored, particularly for those policy areas where a global collaborative knowledge network is likely to be critical to the acceptance of the policies.

Immunizing Children: A Qualitative Analysis of Future Parental Decision Making.

PubMed

Espeleta, Hannah C; Beasley, Lana O; Ridings, Leigh E; Smith, Tyler J; Shields, Jennifer D

2017-10-01

Vaccinations are considered one of public health's greatest accomplishments. Despite evidence for vaccine effectiveness, uptake levels are still well below the Centers for Disease Control and Prevention's guidelines. The immunization decision-making process for parents is complex and depends on factors associated with knowledge and experiences. This qualitative study sought to expand on a previous decision-making model for immunizations by examining how individuals receive vaccination information, determining the role of experience in influencing decisions, and understanding how young adults might locate vaccination information in the future. Three focus groups were conducted with 29 undergraduate students without children. Results suggest that young adults exhibit an awareness of information regarding vaccine use and effectiveness, value doctor opinions and recommendations, and desire more robust research on vaccinations. Implications of these results include the importance of (1) disseminating vaccination education to young adults, (2) enhancing consistency/trust between medical professionals and youth, and (3) expanding public policy to increase vaccine uptake.

Intelligent manipulation technique for multi-branch robotic systems

NASA Technical Reports Server (NTRS)

Chen, Alexander Y. K.; Chen, Eugene Y. S.

1990-01-01

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system.

Wireless Wide Area Networks for School Districts.

ERIC Educational Resources Information Center

Nair, Prakash

This paper considers a basic question that many schools districts face in attempting to develop affordable, expandable district-wide computer networks that are resistant to obsolescence: Should these wide area networks (WANs) employ wireless technology, stick to venerable hard-wired solutions, or combine both. This publication explores the…

Potential travel cost saving in urban public-transport networks using smartphone guidance.

PubMed

Song, Cuiying; Guan, Wei; Ma, Jihui

2018-01-01

Public transport (PT) is a key element in most major cities around the world. With the development of smartphones, available journey planning information is becoming an integral part of the PT system. Each traveler has specific preferences when undertaking a trip, and these preferences can also be reflected on the smartphone. This paper considers transit assignment in urban public-transport networks in which the passengers receive smartphone-based information containing elements that might influence the travel decisions in relation to line loads, as well as passenger benefits, and the paper discusses the transition from the current widespread choosing approach to a personalized decision-making approach based on smartphone information. The approach associated with smartphone guidance that considers passengers' preference on travel time, waiting time and transfer is proposed in the process of obtaining his/her preferred route from the potential travel routes generated by the Deep First Search (DFS) method. Two other approaches, based on the scenarios reflecting reality, include passengers with access to no real time information, and passengers that only have access to the arrival time at the platform are used as comparisons. For illustration, the same network proposed by Spiess and Florian is utilized on the experiments in an agent-based model. Two experiments are conducted respectively according to whether each passenger's choosing method is consistent. As expected, the results in the first experiment showed that the travel for consistent passengers with smartphone guidance was clearly shorter and that it can reduce travel time exceeding 15% and weighted cost exceeding 20%, and the average saved time approximated 3.88 minutes per passenger. The second experiment presented that travel cost, as well as cost savings, gradually decreased by employing smartphone guidance, and the maximum cost savings accounted for 14.2% of the total weighted cost.

The GOES-R Geostationary Lightning Mapper (GLM) and the Global Observing System for Total Lightning

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.; Buechler, D.; Carey, L.; Chronis, T.; Mach, D.; Bateman, M.; Peterson, H.; McCaul, E. W., Jr.;

The rise of politics and the decline of vulnerability as criteria in disaster decisions of the United States, 1953-2009.

PubMed

Daniels, R Steven

2013-10-01

This paper examines the shift from vulnerability to political responsiveness in presidential and gubernatorial disaster decisions in the United States from 1953-2009 (President Dwight D. Eisenhower to President Barack Obama) using annual request, declaration, and approval data from multiple sources. It makes three key conclusions: first, the 1988 Stafford Act expanded federal coverage to all categories of disasters, added a significant range of individual types of assistance, and provided extensive funding for recovery planning. Second, the election effects on disaster decisions increased over time whereas the impact of social and economic vulnerability (measured by scope of disaster) declined. Third, the changes affected governors more than presidents, and the choices of governors drove those of presidents. The analysis underscores the increasingly political nature of the disaster decision-making process, as well as the difficulty in emphasising mitigation and preparedness as intensively as response and recovery. Proactive intervention yields fewer political rewards than responsiveness. © 2013 The Author(s). Disasters © Overseas Development Institute, 2013.

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

Facing Climate Change: Connecting Coastal Communities with Place-Based Ocean Science

NASA Astrophysics Data System (ADS)

Pelz, M.; Dewey, R. K.; Hoeberechts, M.; McLean, M. A.; Brown, J. C.; Ewing, N.; Riddell, D. J.

2016-12-01

As coastal communities face a wide range of environmental changes, including threats from climate change, real-time data from cabled observatories can be used to support community members in making informed decisions about their coast and marine resources. Ocean Networks Canada (ONC) deploys and operates an expanding network of community observatories in the Arctic and coastal British Columbia, which enable communities to monitor real-time and historical data from the local marine environment. Community observatories comprise an underwater cabled seafloor platform and shore station equipped with a variety of sensors that collect environmental data 24/7. It is essential that data being collected by ONC instruments are relevant to community members and can contribute to priorities identified within the community. Using a community-based science approach, ONC is engaging local parties at all stages of each project from location planning, to instrument deployment, to data analysis. Alongside the science objectives, place-based educational programming is being developed with local educators and students. As coastal populations continue to grow and our use of and impacts on the ocean increase, it is vital that global citizens develop an understanding that the health of the ocean reflects the health of the planet. This presentation will focus on programs developed by ONC emphasizing the connection to place and local relevance with an emphasis on Indigenous knowledge. Building programs which embrace multiple perspectives is effective both in making ocean science more relevant to Indigenous students and in linking place-based knowledge to ocean science. The inclusion of Indigenous Knowledge into science-based monitoring programs also helps develop a more complete understanding of local conditions. We present a case study from the Canadian Arctic, in which ONC is working with Inuit community members to develop a snow and ice monitoring program to assist with predictions and modelling of sea-ice.

Life-Cycle Assessment of a Distributed-Scale Thermochemical Bioenergy Conversion System

Treesearch

Hongmei Gu; Richard Bergman

2016-01-01

Expanding bioenergy production from woody biomass has the potential to decrease net greenhouse gas (GHG) emissions and improve the energy security of the United States. Science-based and internationally accepted life-cycle assessment (LCA) is an effective tool for policy makers to make scientifically informed decisions on expanding renewable energy production from...

Good Governance Connects Science and Society

ERIC Educational Resources Information Center

Hurlbut, J. Benjamin; Robert, Jason Scott

2012-01-01

Owen-Smith et al. (this issue) answer the question about expanding funding for human pluripotent stem cell (hPSC) research decisively and emphatically. They conclude that the U.S. federal government should expand funding in volume and scope, and stabilize it through regularity. According to Hurlbut and Robert, If the clear goal of policy should…

Strengthening and Expanding Prekindergarten in the Children First Reorganization

ERIC Educational Resources Information Center

Boressoff, Todd

2012-01-01

This policy brief examines the infrastructure needed to support early education at the Department of Education in the coming years. The Department's newly announced reform agenda will reshape how prekindergarten is managed. It's goal is to help inform the decisions the City must make to integrate an expanding prekindergarten program into Children…

Enhancement Of Reading Accuracy By Multiple Data Integration

NASA Astrophysics Data System (ADS)

Lee, Kangsuk

1989-07-01

In this paper, a multiple sensor integration technique with neural network learning algorithms is presented which can enhance the reading accuracy of the hand-written numerals. Many document reading applications involve hand-written numerals in a predetermined location on a form, and in many cases, critical data is redundantly described. The amount of a personal check is one such case which is written redundantly in numerals and in alphabetical form. Information from two optical character recognition modules, one specialized for digits and one for words, is combined to yield an enhanced recognition of the amount. The combination can be accomplished by a decision tree with "if-then" rules, but by simply fusing two or more sets of sensor data in a single expanded neural net, the same functionality can be expected with a much reduced system cost. Experimental results of fusing two neural nets to enhance overall recognition performance using a controlled data set are presented.

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

Christoph, G.G; Jackson, K.A.; Neuman, M.C.

An effective method for detecting computer misuse is the automatic auditing and analysis of on-line user activity. This activity is reflected in the system audit record, by changes in the vulnerability posture of the system configuration, and in other evidence found through active testing of the system. In 1989 we started developing an automatic misuse detection system for the Integrated Computing Network (ICN) at Los Alamos National Laboratory. Since 1990 this system has been operational, monitoring a variety of network systems and services. We call it the Network Anomaly Detection and Intrusion Reporter, or NADIR. During the last year andmore » a half, we expanded NADIR to include processing of audit and activity records for the Cray UNICOS operating system. This new component is called the UNICOS Real-time NADIR, or UNICORN. UNICORN summarizes user activity and system configuration information in statistical profiles. In near real-time, it can compare current activity to historical profiles and test activity against expert rules that express our security policy and define improper or suspicious behavior. It reports suspicious behavior to security auditors and provides tools to aid in follow-up investigations. UNICORN is currently operational on four Crays in Los Alamos` main computing network, the ICN.« less

Using Anticipative Malware Analysis to Support Decision Making

DTIC Science & Technology

2010-11-01

specifically, we have designed and implemented a network sandbox, i.e. a sandbox that allows us to study malware behaviour from the network perspective. We...plan to use this sandbox to generate malware-sample profiles that can be used by decision making algorithms to help network administrators and security...also allows the user to specify the network topology to be used. 1 INTRODUCTION Once the presence of a malicious software (malware) threat has been

Identifying and tracking attacks on networks: C3I displays and related technologies

NASA Astrophysics Data System (ADS)

Manes, Gavin W.; Dawkins, J.; Shenoi, Sujeet; Hale, John C.

2003-09-01

Converged network security is extremely challenging for several reasons; expanded system and technology perimeters, unexpected feature interaction, and complex interfaces all conspire to provide hackers with greater opportunities for compromising large networks. Preventive security services and architectures are essential, but in and of themselves do not eliminate all threat of compromise. Attack management systems mitigate this residual risk by facilitating incident detection, analysis and response. There are a wealth of attack detection and response tools for IP networks, but a dearth of such tools for wireless and public telephone networks. Moreover, methodologies and formalisms have yet to be identified that can yield a common model for vulnerabilities and attacks in converged networks. A comprehensive attack management system must coordinate detection tools for converged networks, derive fully-integrated attack and network models, perform vulnerability and multi-stage attack analysis, support large-scale attack visualization, and orchestrate strategic responses to cyber attacks that cross network boundaries. We present an architecture that embodies these principles for attack management. The attack management system described engages a suite of detection tools for various networking domains, feeding real-time attack data to a comprehensive modeling, analysis and visualization subsystem. The resulting early warning system not only provides network administrators with a heads-up cockpit display of their entire network, it also supports guided response and predictive capabilities for multi-stage attacks in converged networks.

Effect of a large gaming neighborhood and a strategy adaptation neighborhood for bolstering network reciprocity in a prisoner's dilemma game

NASA Astrophysics Data System (ADS)

Ogasawara, Takashi; Tanimoto, Jun; Fukuda, Eriko; Hagishima, Aya; Ikegaya, Naoki

2014-12-01

In 2 × 2 prisoner's dilemma (PD) games, network reciprocity is one mechanism for adding social viscosity, leading to a cooperative equilibrium. In this paper, we explain how gaming neighborhoods and strategy-adaptation neighborhoods affect network reciprocity independently in spatial PD games. We explore an appropriate range of strategy adaptation neighborhoods as opposed to the conventional method of making the gaming and strategy adaptation neighborhoods coincide to enhance the level of cooperation. In cases of expanding gaming neighborhoods, network reciprocity falls to a low level relative to the conventional setting. In the discussion below, which is based on the results of our simulation, we explore how these enhancements come about. Essentially, varying the range of the neighborhoods influences how cooperative clusters form and expand in the evolutionary process.

A review of cost measures for the economic impact of domestic violence.

PubMed

Chan, Ko Ling; Cho, Esther Yin-Nei

2010-07-01

Although economic analyses of domestic violence typically guide decisions concerning resource allocation, allowing policy makers to make better informed decisions on how to prioritize and allocate scarce resources, the methods adopted to calculate domestic violence costs have varied widely from study to study. In particular, only a few studies have reviewed the cost measures of the economic impact of domestic violence. This article reviews and compares these measures by covering approaches to categorizing costs, the cost components, and ways to estimate them and recommends an integrated framework that brings the various approaches together. Some issues still need to be addressed when further developing measures such as including omitted but significant measures and expanding the time horizons of others. The implications for future study of domestic violence costs are discussed.

A Strategy for Integrating a Large Finite Element Model: X-33 Lessons Learned

NASA Technical Reports Server (NTRS)

McGhee, David S.

2000-01-01

The X-33 vehicle is an advanced technology demonstrator sponsored by NASA. For the past three years the Structural Dynamics & Loads Group of NASA's Marshall Space Flight Center has had the task of integrating the X-33 vehicle structural finite element model. In that time, five versions of the integrated vehicle model have been produced and a strategy has evolved that would benefit anyone given the task of integrating structural finite element models that have been generated by various modelers and companies. The strategy that has been presented here consists of six decisions that need to be made. These six decisions are: purpose of model, units, common material list, model numbering, interface control, and archive format. This strategy has been proved and expanded from experience on the X-33 vehicle.