Controlling extreme events on complex networks

NASA Astrophysics Data System (ADS)

Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

2014-08-01

Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network ``mobile'' can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.

WebDB Component Builder - Lessons Learned

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

Macedo, C.

2000-02-15

Oracle WebDB is the easiest way to produce web enabled lightweight and enterprise-centric applications. This concept from Oracle has tantalized our taste for simplistic web development by using a purely web based tool that lives nowhere else but in the database. The use of online wizards, templates, and query builders, which produces PL/SQL behind the curtains, can be used straight ''out of the box'' by both novice and seasoned developers. The topic of this presentation will introduce lessons learned by developing and deploying applications built using the WebDB Component Builder in conjunction with custom PL/SQL code to empower a hybridmore » application. There are two kinds of WebDB components: those that display data to end users via reporting, and those that let end users update data in the database via entry forms. The presentation will also discuss various methods within the Component Builder to enhance the applications pushed to the desktop. The demonstrated example is an application entitled HOME (Helping Other's More Effectively) that was built to manage a yearly United Way Campaign effort. Our task was to build an end to end application which could manage approximately 900 non-profit agencies, an average of 4,100 individual contributions, and $1.2 million dollars. Using WebDB, the shell of the application was put together in a matter of a few weeks. However, we did encounter some hurdles that WebDB, in it's stage of infancy (v2.0), could not solve for us directly. Together with custom PL/SQL, WebDB's Component Builder became a powerful tool that enabled us to produce a very flexible hybrid application.« less

Practices and Processes of Leading High Performance Home Builders in the Upper Midwest

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

Von Thoma, E.; Ojczyk, C.

2012-12-01

The NorthernSTAR Building America Partnership team proposed this study to gain insight into the business, sales, and construction processes of successful high performance builders. The knowledge gained by understanding the high performance strategies used by individual builders, as well as the process each followed to move from traditional builder to high performance builder, will be beneficial in proposing more in-depth research to yield specific action items to assist the industry at large transform to high performance new home construction. This investigation identified the best practices of three successful high performance builders in the upper Midwest. In-depth field analysis of themore » performance levels of their homes, their business models, and their strategies for market acceptance were explored. All three builders commonly seek ENERGY STAR certification on their homes and implement strategies that would allow them to meet the requirements for the Building America Builders Challenge program. Their desire for continuous improvement, willingness to seek outside assistance, and ambition to be leaders in their field are common themes. Problem solving to overcome challenges was accepted as part of doing business. It was concluded that crossing the gap from code-based building to high performance based building was a natural evolution for these leading builders.« less

Detecting event-related changes in organizational networks using optimized neural network models.

PubMed

Li, Ze; Sun, Duoyong; Zhu, Renqi; Lin, Zihan

2017-01-01

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques.

Detecting event-related changes in organizational networks using optimized neural network models

PubMed Central

Sun, Duoyong; Zhu, Renqi; Lin, Zihan

2017-01-01

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques. PMID:29190799

Extreme events and event size fluctuations in biased random walks on networks.

PubMed

Kishore, Vimal; Santhanam, M S; Amritkar, R E

2012-05-01

Random walk on discrete lattice models is important to understand various types of transport processes. The extreme events, defined as exceedences of the flux of walkers above a prescribed threshold, have been studied recently in the context of complex networks. This was motivated by the occurrence of rare events such as traffic jams, floods, and power blackouts which take place on networks. In this work, we study extreme events in a generalized random walk model in which the walk is preferentially biased by the network topology. The walkers preferentially choose to hop toward the hubs or small degree nodes. In this setting, we show that extremely large fluctuations in event sizes are possible on small degree nodes when the walkers are biased toward the hubs. In particular, we obtain the distribution of event sizes on the network. Further, the probability for the occurrence of extreme events on any node in the network depends on its "generalized strength," a measure of the ability of a node to attract walkers. The generalized strength is a function of the degree of the node and that of its nearest neighbors. We obtain analytical and simulation results for the probability of occurrence of extreme events on the nodes of a network using a generalized random walk model. The result reveals that the nodes with a larger value of generalized strength, on average, display lower probability for the occurrence of extreme events compared to the nodes with lower values of generalized strength.

Counselors as Esteem Builders.

ERIC Educational Resources Information Center

Santa Rita, Emilio, Jr.

This paper contains an instrument for evaluating college counselors as esteem-builders, using Borba's Analysis of Self-Esteem. The instrument is divided into five components, each of which should be fostered in a student by the counselor: (1) Security, a feeling of strong assuredness; (2) Selfhood, a feeling of individuality; (3) Affiliation, a…

USS Cal Builders, Inc. Information Sheet

EPA Pesticide Factsheets

USS Cal Builders, Inc. (the Company) is located in Stanton, California. The settlement involves renovation activities conducted at property constructed prior to 1978, located in San Francisco, California.

ABodyBuilder: Automated antibody structure prediction with data–driven accuracy estimation

PubMed Central

Leem, Jinwoo; Dunbar, James; Georges, Guy; Shi, Jiye; Deane, Charlotte M.

2016-01-01

ABSTRACT Computational modeling of antibody structures plays a critical role in therapeutic antibody design. Several antibody modeling pipelines exist, but no freely available methods currently model nanobodies, provide estimates of expected model accuracy, or highlight potential issues with the antibody's experimental development. Here, we describe our automated antibody modeling pipeline, ABodyBuilder, designed to overcome these issues. The algorithm itself follows the standard 4 steps of template selection, orientation prediction, complementarity-determining region (CDR) loop modeling, and side chain prediction. ABodyBuilder then annotates the ‘confidence’ of the model as a probability that a component of the antibody (e.g., CDRL3 loop) will be modeled within a root–mean square deviation threshold. It also flags structural motifs on the model that are known to cause issues during in vitro development. ABodyBuilder was tested on 4 separate datasets, including the 11 antibodies from the Antibody Modeling Assessment–II competition. ABodyBuilder builds models that are of similar quality to other methodologies, with sub–Angstrom predictions for the ‘canonical’ CDR loops. Its ability to model nanobodies, and rapidly generate models (∼30 seconds per model) widens its potential usage. ABodyBuilder can also help users in decision–making for the development of novel antibodies because it provides model confidence and potential sequence liabilities. ABodyBuilder is freely available at http://opig.stats.ox.ac.uk/webapps/abodybuilder. PMID:27392298

Transitioning to High Performance Homes: Successes and Lessons Learned From Seven Builders

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

Widder, Sarah H.; Kora, Angela R.; Baechler, Michael C.

2013-03-01

As homebuyers are becoming increasingly concerned about rising energy costs and the impact of fossil fuels as a major source of greenhouse gases, the returning new home market is beginning to demand energy-efficient and comfortable high-performance homes. In response to this, some innovative builders are gaining market share because they are able to market their homes’ comfort, better indoor air quality, and aesthetics, in addition to energy efficiency. The success and marketability of these high-performance homes is creating a builder demand for house plans and information about how to design, build, and sell their own low-energy homes. To help makemore » these and other builders more successful in the transition to high-performance construction techniques, Pacific Northwest National Laboratory (PNNL) partnered with seven interested builders in the hot humid and mixed humid climates to provide technical and design assistance through two building science firms, Florida Home Energy and Resources Organization (FL HERO) and Calcs-Plus, and a designer that offers a line of stock plans designed specifically for energy efficiency, called Energy Smart Home Plans (ESHP). This report summarizes the findings of research on cost-effective high-performance whole-house solutions, focusing on real-world implementation and challenges and identifying effective solutions. The ensuing sections provide project background, profile each of the builders who participated in the program, and describe their houses’ construction characteristics, key challenges the builders encountered during the construction and transaction process); and present primary lessons learned to be applied to future projects. As a result of this technical assistance, 17 homes have been built featuring climate-appropriate efficient envelopes, ducts in conditioned space, and correctly sized and controlled heating, ventilation, and air-conditioning systems. In addition, most builders intend to integrate high

Building America Top Innovations 2012: Reduced Call-Backs with High-Performance Production Builders

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

none,

2013-01-01

This Building America Top Innovations profile describes ways Building America teams have helped builders cut call-backs. Harvard University study found builders who worked with Building America had a 50% drop in call-backs. One builder reported a 50-fold reduction in the incidence of pipe freezing, a 50% reduction in drywall cracking, and a 60% decline in call-backs.

Wildlife Scenario Builder and User's Guide (Version 1.0, Beta Test)

EPA Science Inventory

Builder User's Manual" vspace = "5" hspace="5" align="right" border="1" /> The Wildlife Scenario Builder (WSB) was developed to improve the quality of wildlif...

Evaluation of the U.S. Department of Energy Challenge Home Program Certification of Production Builders

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

Kerrigan, P.; Loomis, H.

2014-09-01

The purpose of this project was to evaluate integrated packages of advanced measures in individual test homes to assess their performance with respect to Building America Program goals, specifically compliance with the DOE Challenge Home Program. BSC consulted on the construction of five test houses by three Cold Climate production builders in three separate US cities. BSC worked with the builders to develop a design package tailored to the cost-related impacts for each builder. Therefore, the resulting design packages do vary from builder to builder. BSC provided support through this research project on the design, construction and performance testing ofmore » the five test homes. Overall, the builders have concluded that the energy related upgrades (either through the prescriptive or performance path) represent reasonable upgrades. The builders commented that while not every improvement in specification was cost effective (as in a reasonable payback period), many were improvements that could improve the marketability of the homes and serve to attract more energy efficiency discerning prospective homeowners. However, the builders did express reservations on the associated checklists and added certifications. An increase in administrative time was observed with all builders. The checklists and certifications also inherently increase cost due to: 1. Adding services to the scope of work for various trades, such as HERS Rater, HVAC contractor; 2. Increased material costs related to the checklists, especially the EPA Indoor airPLUS and EPA WaterSense(R) Efficient Hot Water Distribution requirement.« less

JOB BUILDER remote batch processing subsystem

NASA Technical Reports Server (NTRS)

Orlov, I. G.; Orlova, T. L.

1980-01-01

The functions of the JOB BUILDER remote batch processing subsystem are described. Instructions are given for using it as a component of a display system developed by personnel of the System Programming Laboratory, Institute of Space Research, USSR Academy of Sciences.

Energy Conservation for the Home Builder: A Course for Residential Builders. Course Outline and Instructional Materials.

ERIC Educational Resources Information Center

Koenigshofer, Daniel R.

Background information, handouts and related instructional materials comprise this manual for conducting a course on energy conservation for home builders. Information presented in the five- and ten-hour course is intended to help residential contractors make appropriate and cost-effective decisions in constructing energy-efficient dwellings.…

TrustBuilder2: A Reconfigurable Framework for Trust Negotiation

NASA Astrophysics Data System (ADS)

Lee, Adam J.; Winslett, Marianne; Perano, Kenneth J.

To date, research in trust negotiation has focused mainly on the theoretical aspects of the trust negotiation process, and the development of proof of concept implementations. These theoretical works and proofs of concept have been quite successful from a research perspective, and thus researchers must now begin to address the systems constraints that act as barriers to the deployment of these systems. To this end, we present TrustBuilder2, a fully-configurable and extensible framework for prototyping and evaluating trust negotiation systems. TrustBuilder2 leverages a plug-in based architecture, extensible data type hierarchy, and flexible communication protocol to provide a framework within which numerous trust negotiation protocols and system configurations can be quantitatively analyzed. In this paper, we discuss the design and implementation of TrustBuilder2, study its performance, examine the costs associated with flexible authorization systems, and leverage this knowledge to identify potential topics for future research, as well as a novel method for attacking trust negotiation systems.

Knowledge transfer to builders in post-disaster housing reconstruction in West-Sumatra of Indonesia

NASA Astrophysics Data System (ADS)

Hidayat, Benny; Afif, Zal

2017-11-01

Housing is the most affected sector by disasters as can be observed after the 2009 earthquake in West Sumatra province in Indonesia. As in Indonesian construction industry, the housing post-disaster reconstruction is influenced by knowledge and skills of builders or laborers, or locally known as `tukang'. After the earthquake there were trainings to transfer knowledge about earthquake-safe house structure for the builders in the post-disaster reconstruction. This study examined the effectiveness of the training in term of understanding of the builders and application of the new knowledge. Ten semi-structured interviews with the builders were conducted in this study. The results indicate that the builders with prior housing construction experience can absorb and understand the new knowledge about earthquake-safe house structure. Combination of lecturing and practice sessions also help the builders to understand the knowledge. However, findings of this research also suggest there is a problem in implementation of the new knowledge. Utilization of earthquake-safe house structure may leads to a rise in house cost. As a result, some house owners prefer to save money than to adopt the new knowledge.

Coronary calcification in body builders using anabolic steroids.

PubMed

Santora, Lawrence J; Marin, Jairo; Vangrow, Jack; Minegar, Craig; Robinson, Mary; Mora, Janet; Friede, Gerald

2006-01-01

The authors measured coronary artery calcification as a means of examining the impact of anabolic steroids on the development of atherosclerotic disease in body builders using anabolic steroids over an extended period of time. Fourteen male professional body builders with no history of cardiovascular disease were evaluated for coronary artery calcium, serum lipids, left ventricular function, and exercise-induced myocardial ischemia. Seven subjects had coronary artery calcium, with a much higher than expected mean score of 98. Six of the 7 calcium scores were >90th percentile. Mean total cholesterol was 192 mg/dL, while mean high-density lipoprotein was 23 mg/dL and the mean ratio of total cholesterol to high-density lipoprotein was 8.3. Left ventricular ejection fraction ranged between 49% and 68%, with a mean of 59%. No subject had evidence of myocardial ischemia. This small group of professional body builders with a long history of steroid abuse had high levels of coronary artery calcium for age. The authors conclude that in this small pilot study there is an association between early coronary artery calcium and long-term steroid abuse. Large-scale studies are warranted to further explore this association.

Kotai Antibody Builder: automated high-resolution structural modeling of antibodies.

PubMed

Yamashita, Kazuo; Ikeda, Kazuyoshi; Amada, Karlou; Liang, Shide; Tsuchiya, Yuko; Nakamura, Haruki; Shirai, Hiroki; Standley, Daron M

2014-11-15

Kotai Antibody Builder is a Web service for tertiary structural modeling of antibody variable regions. It consists of three main steps: hybrid template selection by sequence alignment and canonical rules, 3D rendering of alignments and CDR-H3 loop modeling. For the last step, in addition to rule-based heuristics used to build the initial model, a refinement option is available that uses fragment assembly followed by knowledge-based scoring. Using targets from the Second Antibody Modeling Assessment, we demonstrate that Kotai Antibody Builder generates models with an overall accuracy equal to that of the best-performing semi-automated predictors using expert knowledge. Kotai Antibody Builder is available at http://kotaiab.org standley@ifrec.osaka-u.ac.jp. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Emergence of event cascades in inhomogeneous networks

NASA Astrophysics Data System (ADS)

Onaga, Tomokatsu; Shinomoto, Shigeru

2016-09-01

There is a commonality among contagious diseases, tweets, and neuronal firings that past events facilitate the future occurrence of events. The spread of events has been extensively studied such that the systems exhibit catastrophic chain reactions if the interaction represented by the ratio of reproduction exceeds unity; however, their subthreshold states are not fully understood. Here, we report that these systems are possessed by nonstationary cascades of event-occurrences already in the subthreshold regime. Event cascades can be harmful in some contexts, when the peak-demand causes vaccine shortages, heavy traffic on communication lines, but may be beneficial in other contexts, such that spontaneous activity in neural networks may be used to generate motion or store memory. Thus it is important to comprehend the mechanism by which such cascades appear, and consider controlling a system to tame or facilitate fluctuations in the event-occurrences. The critical interaction for the emergence of cascades depends greatly on the network structure in which individuals are connected. We demonstrate that we can predict whether cascades may emerge, given information about the interactions between individuals. Furthermore, we develop a method of reallocating connections among individuals so that event cascades may be either impeded or impelled in a network.

Evaluation of the U.S. Department of Energy Challenge Home Program Certification of Production Builders

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

Kerrigan, P.; Loomis, H.

2014-09-01

The purpose of this project was to evaluate integrated packages of advanced measures in individual test homes to assess their performance with respect to Building America program goals, specifically compliance with the DOE Challenge Home Program. BSC consulted on the construction of five test houses by three cold climate production builders in three U.S. cities and worked with the builders to develop a design package tailored to the cost-related impacts for each builder. Also, BSC provided support through performance testing of the five test homes. Overall, the builders have concluded that the energy related upgrades (either through the prescriptive ormore » performance path) represent reasonable upgrades. The builders commented that while not every improvement in specification was cost effective (as in a reasonable payback period), many were improvements that could improve the marketability of the homes and serve to attract more energy efficiency discerning prospective homeowners. However, the builders did express reservations on the associated checklists and added certifications. An increase in administrative time was observed with all builders. The checklists and certifications also inherently increase cost due to: adding services to the scope of work for various trades, such as HERS Rater, HVAC contractor; and increased material costs related to the checklists, especially the EPA Indoor airPLUS and EPA WaterSense® Efficient Hot Water Distribution requirement.« less

Muscular development and lean body weight in body builders and weight lifters.

PubMed

Katch, V L; Katch, F I; Moffatt, R; Gittleson, M

1980-01-01

The extent of extreme muscular development in 39 males identified as body builders (N = 18), power weight lifters (N = 13), and Olympic weight lifters (N = 8) were studied. Body composition and anthropometric data, including calculations of pre-excess muscle body weight (scale weight minus excess muscle) were obtained. The lean body weight and percent fats of the subjects were: body builders = 74.6 kg, 9.3%; power weight lifters = 73.3 kg, 9.1%; and Olympic weight lifters = 68.2 kg, 10.8%. No group differences were present in frame size, percent fat, lean body weight, skinfolds, and diameter measurements. The only group differences were for the shoulders, chest, biceps relaxed and flexed, and forearm girths. In each case the body builders were larger. Calculations of excess muscle by the Behnke method revealed that the body builders had 15.6 kg excess muscle, power weight lifters 14.8 kg, and Olympic weight lifters 13.1 kg. Somatographic comparisons revealed only slight differences between the groups, while differences with reference man were substantial.

Building America Case Study: New Town Builders' Power of Zero Energy Center, Denver, Colorado (Brochure)

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

Not Available

New Town Builders, a builder of energy efficient homes in Denver, Colorado, offers a zero energy option for all the homes it builds. To attract a wide range of potential homebuyers to its energy efficient homes, New Town Builders created a 'Power of Zero Energy Center' linked to its model home in the Stapleton community of Denver. This case study presents New Town Builders' marketing approach, which is targeted to appeal to homebuyers' emotions rather than overwhelming homebuyers with scientific details about the technology. The exhibits in the Power of Zero Energy Center focus on reduced energy expenses for themore » homeowner, improved occupant comfort, the reputation of the builder, and the lack of sacrificing the homebuyers' desired design features to achieve zero net energy in the home. The case study also contains customer and realtor testimonials related to the effectiveness of the Center in influencing homebuyers to purchase a zero energy home.« less

GeneBuilder: interactive in silico prediction of gene structure.

PubMed

Milanesi, L; D'Angelo, D; Rogozin, I B

1999-01-01

Prediction of gene structure in newly sequenced DNA becomes very important in large genome sequencing projects. This problem is complicated due to the exon-intron structure of eukaryotic genes and because gene expression is regulated by many different short nucleotide domains. In order to be able to analyse the full gene structure in different organisms, it is necessary to combine information about potential functional signals (promoter region, splice sites, start and stop codons, 3' untranslated region) together with the statistical properties of coding sequences (coding potential), information about homologous proteins, ESTs and repeated elements. We have developed the GeneBuilder system which is based on prediction of functional signals and coding regions by different approaches in combination with similarity searches in proteins and EST databases. The potential gene structure models are obtained by using a dynamic programming method. The program permits the use of several parameters for gene structure prediction and refinement. During gene model construction, selecting different exon homology levels with a protein sequence selected from a list of homologous proteins can improve the accuracy of the gene structure prediction. In the case of low homology, GeneBuilder is still able to predict the gene structure. The GeneBuilder system has been tested by using the standard set (Burset and Guigo, Genomics, 34, 353-367, 1996) and the performances are: 0.89 sensitivity and 0.91 specificity at the nucleotide level. The total correlation coefficient is 0.88. The GeneBuilder system is implemented as a part of the WebGene a the URL: http://www.itba.mi. cnr.it/webgene and TRADAT (TRAncription Database and Analysis Tools) launcher URL: http://www.itba.mi.cnr.it/tradat.

Exponential Synchronization of Networked Chaotic Delayed Neural Network by a Hybrid Event Trigger Scheme.

PubMed

Fei, Zhongyang; Guan, Chaoxu; Gao, Huijun; Zhongyang Fei; Chaoxu Guan; Huijun Gao; Fei, Zhongyang; Guan, Chaoxu; Gao, Huijun

2018-06-01

This paper is concerned with the exponential synchronization for master-slave chaotic delayed neural network with event trigger control scheme. The model is established on a network control framework, where both external disturbance and network-induced delay are taken into consideration. The desired aim is to synchronize the master and slave systems with limited communication capacity and network bandwidth. In order to save the network resource, we adopt a hybrid event trigger approach, which not only reduces the data package sending out, but also gets rid of the Zeno phenomenon. By using an appropriate Lyapunov functional, a sufficient criterion for the stability is proposed for the error system with extended ( , , )-dissipativity performance index. Moreover, hybrid event trigger scheme and controller are codesigned for network-based delayed neural network to guarantee the exponential synchronization between the master and slave systems. The effectiveness and potential of the proposed results are demonstrated through a numerical example.

High Performance Builder Spotlight: Treasure Homes Inc.

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

None

2011-01-01

Treasure Homes, Inc., achieved a HERS rating of 46 without PV on its prototype “Gem” home, located on the shores of Lake Michigan in northern Indiana, thanks in part to training received from a Building America partner, the National Association of Home Builders Research Center.

Spiking neural network simulation: memory-optimal synaptic event scheduling.

PubMed

Stewart, Robert D; Gurney, Kevin N

2011-06-01

Spiking neural network simulations incorporating variable transmission delays require synaptic events to be scheduled prior to delivery. Conventional methods have memory requirements that scale with the total number of synapses in a network. We introduce novel scheduling algorithms for both discrete and continuous event delivery, where the memory requirement scales instead with the number of neurons. Superior algorithmic performance is demonstrated using large-scale, benchmarking network simulations.

Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks.

PubMed

Zhou, Zhangbing; Xing, Riliang; Duan, Yucong; Zhu, Yueqin; Xiang, Jianming

2015-12-15

With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady.

Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

PubMed Central

Zhou, Zhangbing; Xing, Riliang; Duan, Yucong; Zhu, Yueqin; Xiang, Jianming

2015-01-01

With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady. PMID:26694394

New Whole-House Solutions Case Study: New Town Builders' Power of Zero Energy Center - Denver, Colorado

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

None

New Town Builders, a builder of energy efficient homes in Denver, Colorado, offers a zero energy option for all the homes it builds. To attract a wide range of potential homebuyers to its energy efficient homes, New Town Builders created a "Power of Zero Energy Center" linked to its model home in the Stapleton community. This case study presents New Town Builders' marketing approach, which is targeted to appeal to homebuyers' emotions rather than overwhelming homebuyers with scientific details about the technology. The exhibits in the Power of Zero Energy Center focus on reduced energy expenses for the homeowner, improvedmore » occupant comfort, the reputation of the builder, and the lack of sacrificing the homebuyers' desired design features to achieve zero net energy in the home. This case study also contains customer and realtor testimonials related to the effectiveness of the Center in influencing homebuyers to purchase a zero energy home.« less

Forecasting solar proton event with artificial neural network

NASA Astrophysics Data System (ADS)

Gong, J.; Wang, J.; Xue, B.; Liu, S.; Zou, Z.

Solar proton event (SPE), relatively rare but popular in solar maximum, can bring hazard situation to spacecraft. As a special event, SPE always accompanies flare, which is also called proton flare. To produce such an eruptive event, large amount energy must be accumulated within the active region. So we can investigate the character of the active region and its evolving trend, together with other such as cm radio emission and soft X-ray background to evaluate the potential of SEP in chosen area. In order to summarize the omen of SPEs in the active regions behind the observed parameters, we employed AI technology. Full connecting neural network was chosen to fulfil this job. After constructing the network, we train it with 13 parameters that was able to exhibit the character of active regions and their evolution trend. More than 80 sets of event parameter were defined to teach the neural network to identify whether an active region was potential of SPE. Then we test this model with a data base consisting SPE and non-SPE cases that was not used to train the neural network. The result showed that 75% of the choice by the model was right.

Constructing regional climate networks in the Amazonia during recent drought events.

PubMed

Guo, Heng; Ramos, Antônio M T; Macau, Elbert E N; Zou, Yong; Guan, Shuguang

2017-01-01

Climate networks are powerful approaches to disclose tele-connections in climate systems and to predict severe climate events. Here we construct regional climate networks from precipitation data in the Amazonian region and focus on network properties under the recent drought events in 2005 and 2010. Both the networks of the entire Amazon region and the extreme networks resulted from locations severely affected by drought events suggest that network characteristics show slight difference between the two drought events. Based on network degrees of extreme drought events and that without drought conditions, we identify regions of interest that are correlated to longer expected drought period length. Moreover, we show that the spatial correlation length to the regions of interest decayed much faster in 2010 than in 2005, which is because of the dual roles played by both the Pacific and Atlantic oceans. The results suggest that hub nodes in the regional climate network of Amazonia have fewer long-range connections when more severe drought conditions appeared in 2010 than that in 2005.

Subsurface event detection and classification using Wireless Signal Networks.

PubMed

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

2012-11-05

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

Conditioning of sewage sludge by Fenton's reagent combined with skeleton builders.

PubMed

Liu, Huan; Yang, Jiakuan; Shi, Yafei; Li, Ye; He, Shu; Yang, Changzhu; Yao, Hong

2012-06-01

Physical conditioners, often known as skeleton builders, are commonly used to improve the dewaterability of sewage sludge. This study evaluated a novel joint usage of Fenton's reagent and skeleton builders, referred to as the F-S inorganic composite conditioner, focusing on their efficacies and the optimization of the major operational parameters. The results demonstrate that the F-S composite conditioner for conditioning sewage sludge is a viable alternative to conventional organic polymers, especially when ordinary Portland cement (OPC) and lime are used as the skeleton builders. Experimental investigations confirmed that Fenton reaction required sufficient time (80 min in this study) to degrade organics in the sludge. The optimal condition of this process was at pH=5, Fe(2+)=40 mg g(-1) (dry solids), H(2)O(2)=32 mg g(-1), OPC=300 mg g(-1) and lime=400 mg g(-1), in which the specific resistance to filtration reduction efficiency of 95% was achieved. Copyright © 2012 Elsevier Ltd. All rights reserved.

Subsurface Event Detection and Classification Using Wireless Signal Networks

PubMed Central

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

2012-01-01

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

CHARMM-GUI Membrane Builder toward realistic biological membrane simulations.

PubMed

Wu, Emilia L; Cheng, Xi; Jo, Sunhwan; Rui, Huan; Song, Kevin C; Dávila-Contreras, Eder M; Qi, Yifei; Lee, Jumin; Monje-Galvan, Viviana; Venable, Richard M; Klauda, Jeffery B; Im, Wonpil

2014-10-15

CHARMM-GUI Membrane Builder, http://www.charmm-gui.org/input/membrane, is a web-based user interface designed to interactively build all-atom protein/membrane or membrane-only systems for molecular dynamics simulations through an automated optimized process. In this work, we describe the new features and major improvements in Membrane Builder that allow users to robustly build realistic biological membrane systems, including (1) addition of new lipid types, such as phosphoinositides, cardiolipin (CL), sphingolipids, bacterial lipids, and ergosterol, yielding more than 180 lipid types, (2) enhanced building procedure for lipid packing around protein, (3) reliable algorithm to detect lipid tail penetration to ring structures and protein surface, (4) distance-based algorithm for faster initial ion displacement, (5) CHARMM inputs for P21 image transformation, and (6) NAMD equilibration and production inputs. The robustness of these new features is illustrated by building and simulating a membrane model of the polar and septal regions of E. coli membrane, which contains five lipid types: CL lipids with two types of acyl chains and phosphatidylethanolamine lipids with three types of acyl chains. It is our hope that CHARMM-GUI Membrane Builder becomes a useful tool for simulation studies to better understand the structure and dynamics of proteins and lipids in realistic biological membrane environments. Copyright © 2014 Wiley Periodicals, Inc.

Rare events in networks with internal and external noise

NASA Astrophysics Data System (ADS)

Hindes, J.; Schwartz, I. B.

2017-12-01

We study rare events in networks with both internal and external noise, and develop a general formalism for analyzing rare events that combines pair-quenched techniques and large-deviation theory. The probability distribution, shape, and time scale of rare events are considered in detail for extinction in the Susceptible-Infected-Susceptible model as an illustration. We find that when both types of noise are present, there is a crossover region as the network size is increased, where the probability exponent for large deviations no longer increases linearly with the network size. We demonstrate that the form of the crossover depends on whether the endemic state is localized near the epidemic threshold or not.

78 FR 42122 - Bridge Builder Trust and Olive Street Investment Advisers, LLC; Notice of Application

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-15

... SECURITIES AND EXCHANGE COMMISSION [Investment Company Act Release No. 30592; 812-14118] Bridge...: Bridge Builder Trust (the ``Trust'') and Olive Street Investment Advisers (the ``Adviser'') (collectively... the requested order as a Fund is the Bridge Builder Bond Fund. For purposes of the requested order...

Correction of gynecomastia in body builders and patients with good physique.

PubMed

Blau, Mordcai; Hazani, Ron

2015-02-01

Temporary gynecomastia in the form of breast buds is a common finding in young male subjects. In adults, permanent gynecomastia is an aesthetic impairment that may result in interest in surgical correction. Gynecomastia in body builders creates an even greater distress for patients seeking surgical treatment because of the demands of professional competition. The authors present their experience with gynecomastia in body builders as the largest study of such a group in the literature. Between the years 1980 and 2013, 1574 body builders were treated surgically for gynecomastia. Of those, 1073 were followed up for a period of 1 to 5 years. Ages ranged from 18 to 51 years. Subtotal excision in the form of subcutaneous mastectomy with removal of at least 95 percent of the glandular tissue was used in virtually all cases. In cases where body fat was extremely low, liposuction was performed in fewer than 2 percent of the cases. Aesthetically pleasing results were achieved in 98 percent of the cases based on the authors' patient satisfaction survey. The overall rate of hematomas was 9 percent in the first 15 years of the series and 3 percent in the final 15 years. There were no infections, contour deformities, or recurrences. This study demonstrates the importance of direct excision of the glandular tissue over any other surgical technique when correcting gynecomastia deformities in body builders. The novice surgeon is advised to proceed with cases that are less challenging, primarily with patients that require excision of small to medium glandular tissue. Therapeutic, IV.

Automated 3D Damaged Cavity Model Builder for Lower Surface Acreage Tile on Orbiter

NASA Technical Reports Server (NTRS)

Belknap, Shannon; Zhang, Michael

2013-01-01

The 3D Automated Thermal Tool for Damaged Acreage Tile Math Model builder was developed to perform quickly and accurately 3D thermal analyses on damaged lower surface acreage tiles and structures beneath the damaged locations on a Space Shuttle Orbiter. The 3D model builder created both TRASYS geometric math models (GMMs) and SINDA thermal math models (TMMs) to simulate an idealized damaged cavity in the damaged tile(s). The GMMs are processed in TRASYS to generate radiation conductors between the surfaces in the cavity. The radiation conductors are inserted into the TMMs, which are processed in SINDA to generate temperature histories for all of the nodes on each layer of the TMM. The invention allows a thermal analyst to create quickly and accurately a 3D model of a damaged lower surface tile on the orbiter. The 3D model builder can generate a GMM and the correspond ing TMM in one or two minutes, with the damaged cavity included in the tile material. A separate program creates a configuration file, which would take a couple of minutes to edit. This configuration file is read by the model builder program to determine the location of the damage, the correct tile type, tile thickness, structure thickness, and SIP thickness of the damage, so that the model builder program can build an accurate model at the specified location. Once the models are built, they are processed by the TRASYS and SINDA.

NREL: News - 15 Home Builders Earn Energyvalue Awards

Science.gov Websites

builders who voluntarily integrate energy and resource efficiency into the design, construction and successful approaches to resource-efficient construction. The 2002 EVHA winners are: Tierra Concrete Homes , Sioux City, IA Artistic Homes, Albuquerque, NM Chuck Miller Construction, Hidden Springs ID Enviro

Development and application of CATIA-GDML geometry builder

NASA Astrophysics Data System (ADS)

Belogurov, S.; Berchun, Yu; Chernogorov, A.; Malzacher, P.; Ovcharenko, E.; Schetinin, V.

2014-06-01

Due to conceptual difference between geometry descriptions in Computer-Aided Design (CAD) systems and particle transport Monte Carlo (MC) codes direct conversion of detector geometry in either direction is not feasible. The paper presents an update on functionality and application practice of the CATIA-GDML geometry builder first introduced at CHEP2010. This set of CATIAv5 tools has been developed for building a MC optimized GEANT4/ROOT compatible geometry based on the existing CAD model. The model can be exported via Geometry Description Markup Language (GDML). The builder allows also import and visualization of GEANT4/ROOT geometries in CATIA. The structure of a GDML file, including replicated volumes, volume assemblies and variables, is mapped into a part specification tree. A dedicated file template, a wide range of primitives, tools for measurement and implicit calculation of parameters, different types of multiple volume instantiation, mirroring, positioning and quality check have been implemented. Several use cases are discussed.

Adaptively Adjusted Event-Triggering Mechanism on Fault Detection for Networked Control Systems.

PubMed

Wang, Yu-Long; Lim, Cheng-Chew; Shi, Peng

2016-12-08

This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

15. Detail view of Boston Bridge Works builders plate, north ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

15. Detail view of Boston Bridge Works builders plate, north portal brace, looking southwest - India Point Railroad Bridge, Spanning Seekonk River between Providence & East Providence, Providence, Providence County, RI

Polyol and Amino Acid-Based Biosurfactants, Builders, and Hydrogels

USDA-ARS?s Scientific Manuscript database

This chapter reviews different detergent materials which have been synthesized from natural agricultural commodities. Background information, which gives reasons why the use of biobased materials may be advantageous, is presented. Detergent builders from L-aspartic acid, citric acid and D-sorbitol...

Network Science Research Laboratory (NSRL) Discrete Event Toolkit

DTIC Science & Technology

2016-01-01

ARL-TR-7579 ● JAN 2016 US Army Research Laboratory Network Science Research Laboratory (NSRL) Discrete Event Toolkit by...Laboratory (NSRL) Discrete Event Toolkit by Theron Trout and Andrew J Toth Computational and Information Sciences Directorate, ARL...Research Laboratory (NSRL) Discrete Event Toolkit 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Theron Trout

James Williamson d/b/a Golden Triangle Builders Information Sheet

EPA Pesticide Factsheets

James Williamson d/b/a Golden Triangle Builders (the Company) is located in Pittsburgh, Pennsylvania. The settlement involves renovation activities conducted at property constructed prior to 1978, located in Pittsburgh, Pennsylvania.

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

PubMed

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

2014-09-01

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

Characterizing interactions in online social networks during exceptional events

NASA Astrophysics Data System (ADS)

Omodei, Elisa; De Domenico, Manlio; Arenas, Alex

2015-08-01

Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.

Contribution of past and future self-defining event networks to personal identity.

PubMed

Demblon, Julie; D'Argembeau, Arnaud

2017-05-01

Personal identity is nourished by memories of significant past experiences and by the imagination of meaningful events that one anticipates to happen in the future. The organisation of such self-defining memories and prospective thoughts in the cognitive system has received little empirical attention, however. In the present study, our aims were to investigate to what extent self-defining memories and future projections are organised in networks of related events, and to determine the nature of the connections linking these events. Our results reveal the existence of self-defining event networks, composed of both memories and future events of similar centrality for identity and characterised by similar identity motives. These self-defining networks expressed a strong internal coherence and frequently organised events in meaningful themes and sequences (i.e., event clusters). Finally, we found that the satisfaction of identity motives in represented events and the presence of clustering across events both contributed to increase in the perceived centrality of events for the sense of identity. Overall, these findings suggest that personal identity is not only nourished by representations of significant past and future events, but also depends on the formation of coherent networks of related events that provide an overarching meaning to specific life experiences.

46 CFR 308.409 - Standard form of War Risk Builder's Risk Insurance Policy, Form MA-283.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 8 2011-10-01 2011-10-01 false Standard form of War Risk Builder's Risk Insurance Policy, Form MA-283. 308.409 Section 308.409 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF TRANSPORTATION EMERGENCY OPERATIONS WAR RISK INSURANCE War Risk Builder's Risk Insurance § 308.409 Standard form...

46 CFR 308.409 - Standard form of War Risk Builder's Risk Insurance Policy, Form MA-283.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 8 2013-10-01 2013-10-01 false Standard form of War Risk Builder's Risk Insurance Policy, Form MA-283. 308.409 Section 308.409 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF TRANSPORTATION EMERGENCY OPERATIONS WAR RISK INSURANCE War Risk Builder's Risk Insurance § 308.409 Standard form...

46 CFR 308.409 - Standard form of War Risk Builder's Risk Insurance Policy, Form MA-283.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 8 2010-10-01 2010-10-01 false Standard form of War Risk Builder's Risk Insurance Policy, Form MA-283. 308.409 Section 308.409 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF TRANSPORTATION EMERGENCY OPERATIONS WAR RISK INSURANCE War Risk Builder's Risk Insurance § 308.409 Standard form...

46 CFR 308.409 - Standard form of War Risk Builder's Risk Insurance Policy, Form MA-283.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 8 2012-10-01 2012-10-01 false Standard form of War Risk Builder's Risk Insurance Policy, Form MA-283. 308.409 Section 308.409 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF TRANSPORTATION EMERGENCY OPERATIONS WAR RISK INSURANCE War Risk Builder's Risk Insurance § 308.409 Standard form...

46 CFR 308.409 - Standard form of War Risk Builder's Risk Insurance Policy, Form MA-283.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 8 2014-10-01 2014-10-01 false Standard form of War Risk Builder's Risk Insurance Policy, Form MA-283. 308.409 Section 308.409 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF TRANSPORTATION EMERGENCY OPERATIONS WAR RISK INSURANCE War Risk Builder's Risk Insurance § 308.409 Standard form...

6. BUILDER'S PLATE ON WEST TRUSS: 'MOSELEY IRON BUILDING WORKS, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

6. BUILDER'S PLATE ON WEST TRUSS: 'MOSELEY IRON BUILDING WORKS, BOSTON 1888, PATENTED 1881 TO T.W.E. MOSELEY' - Upper Pacific Mills Bridge, Moved to Merrimack College, North Andover, MA, Lawrence, Essex County, MA

XAL Application Framework and Bricks GUI Builder

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

Pelaia II, Tom

2007-01-01

The XAL [1] Application Framework is a framework for rapidly developing document based Java applications with a common look and feel along with many built-in user interface behaviors. The Bricks GUI builder consists of a modern application and framework for rapidly building user interfaces in support of true Model-View-Controller (MVC) compliant Java applications. Bricks and the XAL Application Framework allow developers to rapidly create quality applications.

SDMProjectBuilder: SWAT Setup for Nutrient Fate and Transport

EPA Science Inventory

This tutorial reviews some of the screens, icons, and basic functions of the SDMProjectBuilder (SDMPB) and explains how one uses SDMPB output to populate the Soil and Water Assessment Tool (SWAT) input files for nutrient fate and transport modeling in the Salt River Basin. It dem...

HVAC Design Strategy for a Hot-Humid Production Builder, Houston, Texas (Fact Sheet)

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

None, None

BSC worked directly with the David Weekley Homes - Houston division to redesign three floor plans in order to locate the HVAC system in conditioned space. The purpose of this project is to develop a cost effective design for moving the HVAC system into conditioned space. In addition, BSC conducted energy analysis to calculate the most economical strategy for increasing the energy performance of future production houses. This is in preparation for the upcoming code changes in 2015. The builder wishes to develop an upgrade package that will allow for a seamless transition to the new code mandate. The followingmore » research questions were addressed by this research project: 1. What is the most cost effective, best performing and most easily replicable method of locating ducts inside conditioned space for a hot-humid production home builder that constructs one and two story single family detached residences? 2. What is a cost effective and practical method of achieving 50% source energy savings vs. the 2006 International Energy Conservation Code for a hot-humid production builder? 3. How accurate are the pre-construction whole house cost estimates compared to confirmed post construction actual cost? BSC and the builder developed a duct design strategy that employs a system of dropped ceilings and attic coffers for moving the ductwork from the vented attic to conditioned space. The furnace has been moved to either a mechanical closet in the conditioned living space or a coffered space in the attic.« less

Heating, Ventilation, and Air Conditioning Design Strategy for a Hot-Humid Production Builder

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

Kerrigan, P.

2014-03-01

BSC worked directly with the David Weekley Homes - Houston division to redesign three floor plans in order to locate the HVAC system in conditioned space. The purpose of this project is to develop a cost effective design for moving the HVAC system into conditioned space. In addition, BSC conducted energy analysis to calculate the most economical strategy for increasing the energy performance of future production houses. This is in preparation for the upcoming code changes in 2015. The builder wishes to develop an upgrade package that will allow for a seamless transition to the new code mandate. The followingmore » research questions were addressed by this research project: 1. What is the most cost effective, best performing and most easily replicable method of locating ducts inside conditioned space for a hot-humid production home builder that constructs one and two story single family detached residences? 2. What is a cost effective and practical method of achieving 50% source energy savings vs. the 2006 International Energy Conservation Code for a hot-humid production builder? 3. How accurate are the pre-construction whole house cost estimates compared to confirmed post construction actual cost? BSC and the builder developed a duct design strategy that employs a system of dropped ceilings and attic coffers for moving the ductwork from the vented attic to conditioned space. The furnace has been moved to either a mechanical closet in the conditioned living space or a coffered space in the attic.« less

18. Castiron bridge plate citing builder, date, and names of ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

18. Cast-iron bridge plate citing builder, date, and names of bridge committee members. Jack Boucher, photographer, 1977 - Neshanic Station Lenticular Truss Bridge, State Route 567, spanning South Branch of Raritan River, Neshanic Station, Somerset County, NJ

nmsBuilder: Freeware to create subject-specific musculoskeletal models for OpenSim.

PubMed

Valente, Giordano; Crimi, Gianluigi; Vanella, Nicola; Schileo, Enrico; Taddei, Fulvia

2017-12-01

Musculoskeletal modeling and simulations of movement have been increasingly used in orthopedic and neurological scenarios, with increased attention to subject-specific applications. In general, musculoskeletal modeling applications have been facilitated by the development of dedicated software tools; however, subject-specific studies have been limited also by time-consuming modeling workflows and high skilled expertise required. In addition, no reference tools exist to standardize the process of musculoskeletal model creation and make it more efficient. Here we present a freely available software application, nmsBuilder 2.0, to create musculoskeletal models in the file format of OpenSim, a widely-used open-source platform for musculoskeletal modeling and simulation. nmsBuilder 2.0 is the result of a major refactoring of a previous implementation that moved a first step toward an efficient workflow for subject-specific model creation. nmsBuilder includes a graphical user interface that provides access to all functionalities, based on a framework for computer-aided medicine written in C++. The operations implemented can be used in a workflow to create OpenSim musculoskeletal models from 3D surfaces. A first step includes data processing to create supporting objects necessary to create models, e.g. surfaces, anatomical landmarks, reference systems; and a second step includes the creation of OpenSim objects, e.g. bodies, joints, muscles, and the corresponding model. We present a case study using nmsBuilder 2.0: the creation of an MRI-based musculoskeletal model of the lower limb. The model included four rigid bodies, five degrees of freedom and 43 musculotendon actuators, and was created from 3D surfaces of the segmented images of a healthy subject through the modeling workflow implemented in the software application. We have presented nmsBuilder 2.0 for the creation of musculoskeletal OpenSim models from image-based data, and made it freely available via nmsbuilder

Automatic Seismic-Event Classification with Convolutional Neural Networks.

NASA Astrophysics Data System (ADS)

Bueno Rodriguez, A.; Titos Luzón, M.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

2017-12-01

Active volcanoes exhibit a wide range of seismic signals, providing vast amounts of unlabelled volcano-seismic data that can be analyzed through the lens of artificial intelligence. However, obtaining high-quality labelled data is time-consuming and expensive. Deep neural networks can process data in their raw form, compute high-level features and provide a better representation of the input data distribution. These systems can be deployed to classify seismic data at scale, enhance current early-warning systems and build extensive seismic catalogs. In this research, we aim to classify spectrograms from seven different seismic events registered at "Volcán de Fuego" (Colima, Mexico), during four eruptive periods. Our approach is based on convolutional neural networks (CNNs), a sub-type of deep neural networks that can exploit grid structure from the data. Volcano-seismic signals can be mapped into a grid-like structure using the spectrogram: a representation of the temporal evolution in terms of time and frequency. Spectrograms were computed from the data using Hamming windows with 4 seconds length, 2.5 seconds overlapping and 128 points FFT resolution. Results are compared to deep neural networks, random forest and SVMs. Experiments show that CNNs can exploit temporal and frequency information, attaining a classification accuracy of 93%, similar to deep networks 91% but outperforming SVM and random forest. These results empirically show that CNNs are powerful models to classify a wide range of volcano-seismic signals, and achieve good generalization. Furthermore, volcano-seismic spectrograms contains useful discriminative information for the CNN, as higher layers of the network combine high-level features computed for each frequency band, helping to detect simultaneous events in time. Being at the intersection of deep learning and geophysics, this research enables future studies of how CNNs can be used in volcano monitoring to accurately determine the detection and

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

PubMed

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

2018-05-01

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

[Imhotep--builder, physician, god].

PubMed

Mikić, Zelimir

2008-01-01

The medicine had been practiced in ancient Egypt since the earliest, prehistoric days, many millenia before Christ, and was quite developed in later periods. This is evident from the sceletal findings, surgical instruments found in tombs, wall printings, the reliefs and inscriptions, and most of all, from the sparse written material known as medical papyri. However, there were not many physicians from that time whose names had been recorded. The earliest physician in ancient Egypt known by name was Imhotep. WHO WAS IMHOTEP?: Imhotep lived and worked during the time of the 3rd Dynasty of Old Kingdom and served under the pharaoh Djoser (reigned 2667-2648 BC) as his vizier or chief minister, high priest, chief builder and carpenter. He obviously was an Egyptian polymath, a learned man and scribe and was credited with many inventions. As one of the highest officials of the pharaoh Djoser Imhotep is credited with designing and building of the famous Step Pyramid of Djoser at Saqqarah, near the old Egyptian capital of Memphis. Imhotep is also credited with inventing the method of stone-dressed building and using of columns in architecture and is considered to be the first architect in history known by name. It is believed that, as the high priest, Imhotel also served as the nation's chief physician in his time. As the builder of the Step Pyramid, and as a physician, he also had to take medical care of thousands of workers engaged in that great project. He is also credited with being the founder of Egyptian medicine and with being the author of the so-called Smith papirus containing a collection of 48 specimen clinical records with detailed accurate record of the features and treatment of various injuries. As such he emerges as the first physician of ancient Egypt known by name and, at the same time, as the first physician known by name in written history of the world. GOD: As Imhotep was considered by Egyptian people as the "inventor of healing", soon after the death, he

The Columbia-Willamette Skill Builders Consortium. Final Performance Report.

ERIC Educational Resources Information Center

Portland Community Coll., OR.

The Columbia-Willamette Skill Builders Consortium was formed in early 1988 in response to a growing awareness of the need for improved workplace literacy training and coordinated service delivery in Northwest Oregon. In June 1990, the consortium received a National Workplace Literacy Program grant to develop and demonstrate such training. The…

E-Classical Fairy Tales: Multimedia Builder as a Tool

ERIC Educational Resources Information Center

Eteokleous, Nikleia; Ktoridou, Despo; Tsolakidis, Symeon

2011-01-01

The study examines pre-service teachers' experiences in delivering a traditional-classical fairy tale using the Multimedia Builder software, in other words an e-fairy tale. A case study approach was employed, collecting qualitative data through classroom observations and focus groups. The results focus on pre-service teachers' reactions, opinions,…

Best Practices Case Study: Devoted Builders, LLC, Mediterrtanean Villas, Pasco,WA

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

None

2010-12-01

Devoted Builders of Kennewick, WA worked with Building America's BIRA team to achieve the 50% Federal tax credit level energy savings on 81 homes at its Mediterranean Villas community in eastern Washington.

Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control.

PubMed

Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen

2017-10-01

This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.

Identifying Typhoon Tracks based on Event Synchronization derived Spatially Embedded Climate Networks

NASA Astrophysics Data System (ADS)

Ozturk, Ugur; Marwan, Norbert; Kurths, Jürgen

2017-04-01

Complex networks are commonly used for investigating spatiotemporal dynamics of complex systems, e.g. extreme rainfall. Especially directed networks are very effective tools in identifying climatic patterns on spatially embedded networks. They can capture the network flux, so as the principal dynamics of spreading significant phenomena. Network measures, such as network divergence, bare the source-receptor relation of the directed networks. However, it is still a challenge how to catch fast evolving atmospheric events, i.e. typhoons. In this study, we propose a new technique, namely Radial Ranks, to detect the general pattern of typhoons forward direction based on the strength parameter of the event synchronization over Japan. We suggest to subset a circular zone of high correlation around the selected grid based on the strength parameter. Radial sums of the strength parameter along vectors within this zone, radial ranks are measured for potential directions, which allows us to trace the network flux over long distances. We employed also the delay parameter of event synchronization to identify and separate the frontal storms' and typhoons' individual behaviors.

Percolation Features on Climate Network under Attacks of El Niño Events

NASA Astrophysics Data System (ADS)

Lu, Z.

2015-12-01

Percolation theory under different attacks is one of the main research areas in complex networks but never be applied to investigate climate network. In this study, for the first time we construct a climate network of surface air temperature field to analyze its percolation features. Here, we regard El Niño event as a kind of naturally attacks generated from Pacific Ocean to attack its upper climate network. We find that El Niño event leads an abrupt percolation phase transition to the climate network which makes it splitting and unstable suddenly. Comparing the results of the climate network under three different forms of attacks, including most connected attack (MA), localized attack (LA) and random attack (RA) respectively, it is found that both MA and LA lead first-order transition and RA leads second-order transition to the climate network. Furthermore, we find that most real attacks consist of all these three forms of attacks. With El Niño event emerging, the ratios of LA and MA increase and dominate the style of attack while RA decreasing. It means the percolation phase transition due to El Niño events is close to first-order transition mostly affected by LA and MA. Our research may help us further understand two questions from perspective of percolation on network: (1) Why not all warming in Pacific Ocean but El Niño events could affect the climate. (2) Why the climate affected by El Niño events changes abruptly.

Solving a real-world problem using an evolving heuristically driven schedule builder.

PubMed

Hart, E; Ross, P; Nelson, J

1998-01-01

This work addresses the real-life scheduling problem of a Scottish company that must produce daily schedules for the catching and transportation of large numbers of live chickens. The problem is complex and highly constrained. We show that it can be successfully solved by division into two subproblems and solving each using a separate genetic algorithm (GA). We address the problem of whether this produces locally optimal solutions and how to overcome this. We extend the traditional approach of evolving a "permutation + schedule builder" by concentrating on evolving the schedule builder itself. This results in a unique schedule builder being built for each daily scheduling problem, each individually tailored to deal with the particular features of that problem. This results in a robust, fast, and flexible system that can cope with most of the circumstances imaginable at the factory. We also compare the performance of a GA approach to several other evolutionary methods and show that population-based methods are superior to both hill-climbing and simulated annealing in the quality of solutions produced. Population-based methods also have the distinct advantage of producing multiple, equally fit solutions, which is of particular importance when considering the practical aspects of the problem.

Neural network classification of questionable EGRET events

NASA Astrophysics Data System (ADS)

Meetre, C. A.; Norris, J. P.

1992-02-01

High energy gamma rays (greater than 20 MeV) pair producing in the spark chamber of the Energetic Gamma Ray Telescope Experiment (EGRET) give rise to a characteristic but highly variable 3-D locus of spark sites, which must be processed to decide whether the event is to be included in the database. A significant fraction (about 15 percent or 104 events/day) of the candidate events cannot be categorized (accept/reject) by an automated rule-based procedure; they are therefore tagged, and must be examined and classified manually by a team of expert analysts. We describe a feedforward, back-propagation neural network approach to the classification of the questionable events. The algorithm computes a set of coefficients using representative exemplars drawn from the preclassified set of questionable events. These coefficients map a given input event into a decision vector that, ideally, describes the correct disposition of the event. The net's accuracy is then tested using a different subset of preclassified events. Preliminary results demonstrate the net's ability to correctly classify a large proportion of the events for some categories of questionables. Current work includes the use of much larger training sets to improve the accuracy of the net.

Event management for large scale event-driven digital hardware spiking neural networks.

PubMed

Caron, Louis-Charles; D'Haene, Michiel; Mailhot, Frédéric; Schrauwen, Benjamin; Rouat, Jean

2013-09-01

The interest in brain-like computation has led to the design of a plethora of innovative neuromorphic systems. Individually, spiking neural networks (SNNs), event-driven simulation and digital hardware neuromorphic systems get a lot of attention. Despite the popularity of event-driven SNNs in software, very few digital hardware architectures are found. This is because existing hardware solutions for event management scale badly with the number of events. This paper introduces the structured heap queue, a pipelined digital hardware data structure, and demonstrates its suitability for event management. The structured heap queue scales gracefully with the number of events, allowing the efficient implementation of large scale digital hardware event-driven SNNs. The scaling is linear for memory, logarithmic for logic resources and constant for processing time. The use of the structured heap queue is demonstrated on a field-programmable gate array (FPGA) with an image segmentation experiment and a SNN of 65,536 neurons and 513,184 synapses. Events can be processed at the rate of 1 every 7 clock cycles and a 406×158 pixel image is segmented in 200 ms. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comparison of Event Detection Methods for Centralized Sensor Networks

NASA Technical Reports Server (NTRS)

Sauvageon, Julien; Agogiono, Alice M.; Farhang, Ali; Tumer, Irem Y.

2006-01-01

The development of an Integrated Vehicle Health Management (IVHM) for space vehicles has become a great concern. Smart Sensor Networks is one of the promising technologies that are catching a lot of attention. In this paper, we propose to a qualitative comparison of several local event (hot spot) detection algorithms in centralized redundant sensor networks. The algorithms are compared regarding their ability to locate and evaluate the event under noise and sensor failures. The purpose of this study is to check if the ratio performance/computational power of the Mote Fuzzy Validation and Fusion algorithm is relevant compare to simpler methods.

Compound Event Barrier Coverage in Wireless Sensor Networks under Multi-Constraint Conditions.

PubMed

Zhuang, Yaoming; Wu, Chengdong; Zhang, Yunzhou; Jia, Zixi

2016-12-24

It is important to monitor compound event by barrier coverage issues in wireless sensor networks (WSNs). Compound event barrier coverage (CEBC) is a novel coverage problem. Unlike traditional ones, the data of compound event barrier coverage comes from different types of sensors. It will be subject to multiple constraints under complex conditions in real-world applications. The main objective of this paper is to design an efficient algorithm for complex conditions that can combine the compound event confidence. Moreover, a multiplier method based on an active-set strategy (ASMP) is proposed to optimize the multiple constraints in compound event barrier coverage. The algorithm can calculate the coverage ratio efficiently and allocate the sensor resources reasonably in compound event barrier coverage. The proposed algorithm can simplify complex problems to reduce the computational load of the network and improve the network efficiency. The simulation results demonstrate that the proposed algorithm is more effective and efficient than existing methods, especially in the allocation of sensor resources.

Compound Event Barrier Coverage in Wireless Sensor Networks under Multi-Constraint Conditions

PubMed Central

Zhuang, Yaoming; Wu, Chengdong; Zhang, Yunzhou; Jia, Zixi

2016-01-01

It is important to monitor compound event by barrier coverage issues in wireless sensor networks (WSNs). Compound event barrier coverage (CEBC) is a novel coverage problem. Unlike traditional ones, the data of compound event barrier coverage comes from different types of sensors. It will be subject to multiple constraints under complex conditions in real-world applications. The main objective of this paper is to design an efficient algorithm for complex conditions that can combine the compound event confidence. Moreover, a multiplier method based on an active-set strategy (ASMP) is proposed to optimize the multiple constraints in compound event barrier coverage. The algorithm can calculate the coverage ratio efficiently and allocate the sensor resources reasonably in compound event barrier coverage. The proposed algorithm can simplify complex problems to reduce the computational load of the network and improve the network efficiency. The simulation results demonstrate that the proposed algorithm is more effective and efficient than existing methods, especially in the allocation of sensor resources. PMID:28029118

Children's E-Book Technology: Devices, Books, and Book Builder

ERIC Educational Resources Information Center

Shiratuddin, Norshuhada; Landoni, Monica

2003-01-01

This article describes a study of children's electronic books (e-books) technology. In particular, the focus is on devices used to access children's e-books, current available e-books and an e-book builder specifically for children. Three small case studies were conducted: two to evaluate how children accept the devices and one to test the ease of…

Network Systems Technician.

ERIC Educational Resources Information Center

Ohio State Univ., Columbus. Center on Education and Training for Employment.

This publication contains 17 subjects appropriate for use in a competency list for the occupation of network systems technician, 1 of 12 occupations within the business/computer technologies cluster. Each unit consists of a number of competencies; a list of competency builders is provided for each competency. Titles of the 17 units are as follows:…

DETAIL OF CORNERSTONE, WHICH STATES "J.J. DANIELS, BUILDER 1861." NOTE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

DETAIL OF CORNERSTONE, WHICH STATES "J.J. DANIELS, BUILDER 1861." NOTE ALSO IRON STRAP AT EAST CORNER OF ABUTMENT. - Jackson Covered Bridge, Spanning Sugar Creek, CR 775N (Changed from Spanning Sugar Creek), Bloomingdale, Parke County, IN

Modeling a Million-Node Slim Fly Network Using Parallel Discrete-Event Simulation

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

Wolfe, Noah; Carothers, Christopher; Mubarak, Misbah

As supercomputers close in on exascale performance, the increased number of processors and processing power translates to an increased demand on the underlying network interconnect. The Slim Fly network topology, a new lowdiameter and low-latency interconnection network, is gaining interest as one possible solution for next-generation supercomputing interconnect systems. In this paper, we present a high-fidelity Slim Fly it-level model leveraging the Rensselaer Optimistic Simulation System (ROSS) and Co-Design of Exascale Storage (CODES) frameworks. We validate our Slim Fly model with the Kathareios et al. Slim Fly model results provided at moderately sized network scales. We further scale the modelmore » size up to n unprecedented 1 million compute nodes; and through visualization of network simulation metrics such as link bandwidth, packet latency, and port occupancy, we get an insight into the network behavior at the million-node scale. We also show linear strong scaling of the Slim Fly model on an Intel cluster achieving a peak event rate of 36 million events per second using 128 MPI tasks to process 7 billion events. Detailed analysis of the underlying discrete-event simulation performance shows that a million-node Slim Fly model simulation can execute in 198 seconds on the Intel cluster.« less

BRIDGE BUILDER WILLIAM FLINN’S “CAMP & BRIDGE BUILDING OUTFIT”. INTERIOR ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

BRIDGE BUILDER WILLIAM FLINN’S “CAMP & BRIDGE BUILDING OUTFIT”. INTERIOR VIEW SHOWING LABORERS AT MEAL TIME. - Clear Fork of Brazos River Suspension Bridge, Spanning Clear Fork of Brazos River at County Route 179, Albany, Shackelford County, TX

A Measurement Plane for Optical Networks to Manage Emergency Events

NASA Astrophysics Data System (ADS)

Tego, E.; Carciofi, C.; Grazioso, P.; Petrini, V.; Pompei, S.; Matera, F.; Attanasio, V.; Nastri, E.; Restuccia, E.

2017-11-01

In this work, we show a wide geographical area optical network test bed, adopting the mPlane measurement plane for monitoring its performance and to manage software defined network approaches, with some specific tests and procedures dedicated to respond to disaster events and to support emergency networks. Such a test bed includes FTTX accesses, and it is currently implemented to support future 5G wireless services with slicing procedures based on Carrier Ethernet. The characteristics of this platform have been experimentally tested in the case of a damage-causing link failure and traffic congestion, showing a fast reactions to these disastrous events, allowing the user to recharge the initial QoS parameters.

A classification of event sequences in the influence network

NASA Astrophysics Data System (ADS)

Walsh, James Lyons; Knuth, Kevin H.

2017-06-01

We build on the classification in [1] of event sequences in the influence network as respecting collinearity or not, so as to determine in future work what phenomena arise in each case. Collinearity enables each observer to uniquely associate each particle event of influencing with one of the observer's own events, even in the case of events of influencing the other observer. We further classify events as to whether they are spacetime events that obey in the fine-grained case the coarse-grained conditions of [2], finding that Newton's First and Second Laws of motion are obeyed at spacetime events. A proof of Newton's Third Law under particular circumstances is also presented.

Automatic classification of seismic events within a regional seismograph network

NASA Astrophysics Data System (ADS)

Tiira, Timo; Kortström, Jari; Uski, Marja

2015-04-01

A fully automatic method for seismic event classification within a sparse regional seismograph network is presented. The tool is based on a supervised pattern recognition technique, Support Vector Machine (SVM), trained here to distinguish weak local earthquakes from a bulk of human-made or spurious seismic events. The classification rules rely on differences in signal energy distribution between natural and artificial seismic sources. Seismic records are divided into four windows, P, P coda, S, and S coda. For each signal window STA is computed in 20 narrow frequency bands between 1 and 41 Hz. The 80 discrimination parameters are used as a training data for the SVM. The SVM models are calculated for 19 on-line seismic stations in Finland. The event data are compiled mainly from fully automatic event solutions that are manually classified after automatic location process. The station-specific SVM training events include 11-302 positive (earthquake) and 227-1048 negative (non-earthquake) examples. The best voting rules for combining results from different stations are determined during an independent testing period. Finally, the network processing rules are applied to an independent evaluation period comprising 4681 fully automatic event determinations, of which 98 % have been manually identified as explosions or noise and 2 % as earthquakes. The SVM method correctly identifies 94 % of the non-earthquakes and all the earthquakes. The results imply that the SVM tool can identify and filter out blasts and spurious events from fully automatic event solutions with a high level of confidence. The tool helps to reduce work-load in manual seismic analysis by leaving only ~5 % of the automatic event determinations, i.e. the probable earthquakes for more detailed seismological analysis. The approach presented is easy to adjust to requirements of a denser or wider high-frequency network, once enough training examples for building a station-specific data set are available.

Neural network classification of questionable EGRET events

NASA Technical Reports Server (NTRS)

Meetre, C. A.; Norris, J. P.

1992-01-01

High energy gamma rays (greater than 20 MeV) pair producing in the spark chamber of the Energetic Gamma Ray Telescope Experiment (EGRET) give rise to a characteristic but highly variable 3-D locus of spark sites, which must be processed to decide whether the event is to be included in the database. A significant fraction (about 15 percent or 10(exp 4) events/day) of the candidate events cannot be categorized (accept/reject) by an automated rule-based procedure; they are therefore tagged, and must be examined and classified manually by a team of expert analysts. We describe a feedforward, back-propagation neural network approach to the classification of the questionable events. The algorithm computes a set of coefficients using representative exemplars drawn from the preclassified set of questionable events. These coefficients map a given input event into a decision vector that, ideally, describes the correct disposition of the event. The net's accuracy is then tested using a different subset of preclassified events. Preliminary results demonstrate the net's ability to correctly classify a large proportion of the events for some categories of questionables. Current work includes the use of much larger training sets to improve the accuracy of the net.

Lost in translation? A multilingual Query Builder improves the quality of PubMed queries: a randomised controlled trial.

PubMed

Schuers, Matthieu; Joulakian, Mher; Kerdelhué, Gaetan; Segas, Léa; Grosjean, Julien; Darmoni, Stéfan J; Griffon, Nicolas

2017-07-03

MEDLINE is the most widely used medical bibliographic database in the world. Most of its citations are in English and this can be an obstacle for some researchers to access the information the database contains. We created a multilingual query builder to facilitate access to the PubMed subset using a language other than English. The aim of our study was to assess the impact of this multilingual query builder on the quality of PubMed queries for non-native English speaking physicians and medical researchers. A randomised controlled study was conducted among French speaking general practice residents. We designed a multi-lingual query builder to facilitate information retrieval, based on available MeSH translations and providing users with both an interface and a controlled vocabulary in their own language. Participating residents were randomly allocated either the French or the English version of the query builder. They were asked to translate 12 short medical questions into MeSH queries. The main outcome was the quality of the query. Two librarians blind to the arm independently evaluated each query, using a modified published classification that differentiated eight types of errors. Twenty residents used the French version of the query builder and 22 used the English version. 492 queries were analysed. There were significantly more perfect queries in the French group vs. the English group (respectively 37.9% vs. 17.9%; p < 0.01). It took significantly more time for the members of the English group than the members of the French group to build each query, respectively 194 sec vs. 128 sec; p < 0.01. This multi-lingual query builder is an effective tool to improve the quality of PubMed queries in particular for researchers whose first language is not English.

13 CFR 120.391 - What is the Builders Loan Program?

Code of Federal Regulations, 2010 CFR

2010-01-01

... 13 Business Credit and Assistance 1 2010-01-01 2010-01-01 false What is the Builders Loan Program? 120.391 Section 120.391 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS...)(9) of the Act, SBA may make or guarantee loans to finance small general contractors to construct or...

13 CFR 120.391 - What is the Builders Loan Program?

Code of Federal Regulations, 2014 CFR

2014-01-01

... 13 Business Credit and Assistance 1 2014-01-01 2014-01-01 false What is the Builders Loan Program? 120.391 Section 120.391 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS...)(9) of the Act, SBA may make or guarantee loans to finance small general contractors to construct or...

13 CFR 120.391 - What is the Builders Loan Program?

Code of Federal Regulations, 2012 CFR

2012-01-01

... 13 Business Credit and Assistance 1 2012-01-01 2012-01-01 false What is the Builders Loan Program? 120.391 Section 120.391 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS...)(9) of the Act, SBA may make or guarantee loans to finance small general contractors to construct or...

13 CFR 120.391 - What is the Builders Loan Program?

Code of Federal Regulations, 2013 CFR

2013-01-01

... 13 Business Credit and Assistance 1 2013-01-01 2013-01-01 false What is the Builders Loan Program? 120.391 Section 120.391 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS...)(9) of the Act, SBA may make or guarantee loans to finance small general contractors to construct or...

13 CFR 120.391 - What is the Builders Loan Program?

Code of Federal Regulations, 2011 CFR

2011-01-01

... 13 Business Credit and Assistance 1 2011-01-01 2011-01-01 false What is the Builders Loan Program? 120.391 Section 120.391 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS...)(9) of the Act, SBA may make or guarantee loans to finance small general contractors to construct or...

Validation of bioelectrical impedance analysis to hydrostatic weighing in male body builders.

PubMed

Volpe, Stella Lucia; Melanson, Edward L; Kline, Gregory

2010-03-01

The purpose of this study was to compare bioelectrical impedance analysis (BIA) to hydrostatic weighing (HW) in male weight lifters and body builders. Twenty-two male body builders and weight lifters, 23 +/- 3 years of age (mean +/- SD), were studied to determine the efficacy of BIA to HW in this population. Subjects were measured on two separate occasions, 6 weeks apart, for test-retest reliability purposes. Participants recorded 3-day dietary intakes and average work-out times and regimens between the two testing periods. Subjects were, on average, 75 +/- 8 kg of body weight and 175 +/- 7 cm tall. Validation results were as follows: constant error for HW-BIA = 0.128 +/- 3.7%, r for HW versus BIA = -0.294. Standard error of the estimate for BIA = 2.32% and the total error for BIA = 3.6%. Percent body fat was 7.8 +/- 1% from BIA and 8.5 +/- 2% from HW (P > 0.05). Subjects consumed 3,217 +/- 1,027 kcals; 1,848 +/- 768 kcals from carbohydrates; 604 +/- 300 kcals from protein; and 783 +/- 369 kcals from fat. Although work-outs differed among one another, within subject training did not vary. These results suggest that measurement of percent body fat in male body builders and weight trainers is equally as accurate using BIA or HW.

Computer (PC/Network) Coordinator.

ERIC Educational Resources Information Center

Ohio State Univ., Columbus. Center on Education and Training for Employment.

This publication contains 22 subjects appropriate for use in a competency list for the occupation of computer (PC/network) coordinator, 1 of 12 occupations within the business/computer technologies cluster. Each unit consists of a number of competencies; a list of competency builders is provided for each competency. Titles of the 22 units are as…

Networked event-triggered control: an introduction and research trends

NASA Astrophysics Data System (ADS)

Mahmoud, Magdi S.; Sabih, Muhammad

2014-11-01

A physical system can be studied as either continuous time or discrete-time system depending upon the control objectives. Discrete-time control systems can be further classified into two categories based on the sampling: (1) time-triggered control systems and (2) event-triggered control systems. Time-triggered systems sample states and calculate controls at every sampling instant in a periodic fashion, even in cases when states and calculated control do not change much. This indicates unnecessary and useless data transmission and computation efforts of a time-triggered system, thus inefficiency. For networked systems, the transmission of measurement and control signals, thus, cause unnecessary network traffic. Event-triggered systems, on the other hand, have potential to reduce the communication burden in addition to reducing the computation of control signals. This paper provides an up-to-date survey on the event-triggered methods for control systems and highlights the potential research directions.

DISCRETE EVENT SIMULATION OF OPTICAL SWITCH MATRIX PERFORMANCE IN COMPUTER NETWORKS

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

Imam, Neena; Poole, Stephen W

2013-01-01

In this paper, we present application of a Discrete Event Simulator (DES) for performance modeling of optical switching devices in computer networks. Network simulators are valuable tools in situations where one cannot investigate the system directly. This situation may arise if the system under study does not exist yet or the cost of studying the system directly is prohibitive. Most available network simulators are based on the paradigm of discrete-event-based simulation. As computer networks become increasingly larger and more complex, sophisticated DES tool chains have become available for both commercial and academic research. Some well-known simulators are NS2, NS3, OPNET,more » and OMNEST. For this research, we have applied OMNEST for the purpose of simulating multi-wavelength performance of optical switch matrices in computer interconnection networks. Our results suggest that the application of DES to computer interconnection networks provides valuable insight in device performance and aids in topology and system optimization.« less

Captivate MenuBuilder: Creating an Online Tutorial for Teaching Software

ERIC Educational Resources Information Center

Yelinek, Kathryn; Tarnowski, Lynn; Hannon, Patricia; Oliver, Susan

2008-01-01

In this article, the authors, students in an instructional technology graduate course, describe a process to create an online tutorial for teaching software. They created the tutorial for a cyber school's use. Five tutorial modules were linked together through one menu screen using the MenuBuilder feature in the Adobe Captivate program. The…

Communication: Analysing kinetic transition networks for rare events.

PubMed

Stevenson, Jacob D; Wales, David J

2014-07-28

The graph transformation approach is a recently proposed method for computing mean first passage times, rates, and committor probabilities for kinetic transition networks. Here we compare the performance to existing linear algebra methods, focusing on large, sparse networks. We show that graph transformation provides a much more robust framework, succeeding when numerical precision issues cause the other methods to fail completely. These are precisely the situations that correspond to rare event dynamics for which the graph transformation was introduced.

A Database of Tornado Events as Perceived by the USArray Transportable Array Network

NASA Astrophysics Data System (ADS)

Tytell, J. E.; Vernon, F.; Reyes, J. C.

2015-12-01

Over the course of the deployment of Earthscope's USArray Transportable Array (TA) network there have numerous tornado events that have occurred within the changing footprint of its network. The Array Network Facility based in San Diego, California, has compiled a database of these tornado events based on data provided by the NOAA Storm Prediction Center (SPC). The SPC data itself consists of parameters such as start-end point track data for each event, maximum EF intensities, and maximum track widths. Our database is Antelope driven and combines these data from the SPC with detailed station information from the TA network. We are now able to list all available TA stations during any specific tornado event date and also provide a single calculated "nearest" TA station per individual tornado event. We aim to provide this database as a starting resource for those with an interest in investigating tornado signatures within surface pressure and seismic response data. On a larger scale, the database may be of particular interest to the infrasound research community

A network of discrete events for the representation and analysis of diffusion dynamics.

PubMed

Pintus, Alberto M; Pazzona, Federico G; Demontis, Pierfranco; Suffritti, Giuseppe B

2015-11-14

We developed a coarse-grained description of the phenomenology of diffusive processes, in terms of a space of discrete events and its representation as a network. Once a proper classification of the discrete events underlying the diffusive process is carried out, their transition matrix is calculated on the basis of molecular dynamics data. This matrix can be represented as a directed, weighted network where nodes represent discrete events, and the weight of edges is given by the probability that one follows the other. The structure of this network reflects dynamical properties of the process of interest in such features as its modularity and the entropy rate of nodes. As an example of the applicability of this conceptual framework, we discuss here the physics of diffusion of small non-polar molecules in a microporous material, in terms of the structure of the corresponding network of events, and explain on this basis the diffusivity trends observed. A quantitative account of these trends is obtained by considering the contribution of the various events to the displacement autocorrelation function.

A Markovian event-based framework for stochastic spiking neural networks.

PubMed

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

Social network changes and life events across the life span: a meta-analysis.

PubMed

Wrzus, Cornelia; Hänel, Martha; Wagner, Jenny; Neyer, Franz J

2013-01-01

For researchers and practitioners interested in social relationships, the question remains as to how large social networks typically are, and how their size and composition change across adulthood. On the basis of predictions of socioemotional selectivity theory and social convoy theory, we conducted a meta-analysis on age-related social network changes and the effects of life events on social networks using 277 studies with 177,635 participants from adolescence to old age. Cross-sectional as well as longitudinal studies consistently showed that (a) the global social network increased up until young adulthood and then decreased steadily, (b) both the personal network and the friendship network decreased throughout adulthood, (c) the family network was stable in size from adolescence to old age, and (d) other networks with coworkers or neighbors were important only in specific age ranges. Studies focusing on life events that occur at specific ages, such as transition to parenthood, job entry, or widowhood, demonstrated network changes similar to such age-related network changes. Moderator analyses detected that the type of network assessment affected the reported size of global, personal, and family networks. Period effects on network sizes occurred for personal and friendship networks, which have decreased in size over the last 35 years. Together the findings are consistent with the view that a portion of normative, age-related social network changes are due to normative, age-related life events. We discuss how these patterns of normative social network development inform research in social, evolutionary, cultural, and personality psychology. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Marketing Career Speed Networking: A Classroom Event to Foster Career Awareness

ERIC Educational Resources Information Center

Buff, Cheryl L.; O'Connor, Suzanne

2012-01-01

This paper describes the design, implementation, and evaluation of a marketing career speed networking event held during class time in two sections of the consumer behavior class. The event was coordinated through a partnering effort with marketing faculty and the college's Career Center. A total of 57 students participated in the event, providing…

AGU Hosts Networking Event for Female Scientists

NASA Astrophysics Data System (ADS)

McEntee, Chris

2013-01-01

At Fall Meeting this year I had the pleasure of cohosting a new event, a Networking Reception for Early Career Female Scientists and Students, with Jane Lubchenco, under secretary of Commerce for Oceans and Atmosphere and National Oceanic and Atmospheric Administration administrator, and Marcia McNutt, director of the U.S. Geological Survey. AGU recognizes the importance of having a diverse pool of new researchers who can enrich Earth and space sciences with their skills and innovation. That's why one of our four strategic goals is to help build the global talent pool and provide early-career scientists with networking opportunities like this one.

Network hydraulics inclusion in water quality event detection using multiple sensor stations data.

PubMed

Oliker, Nurit; Ostfeld, Avi

2015-09-01

Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

PubMed Central

Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.

2009-01-01

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

A hybrid adaptive routing algorithm for event-driven wireless sensor networks.

PubMed

Figueiredo, Carlos M S; Nakamura, Eduardo F; Loureiro, Antonio A F

2009-01-01

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption.

The Misuse of Anabolic-Androgenic Steroids among Iranian Recreational Male Body-Builders and Their Related Psycho-Socio-Demographic factors

PubMed Central

ANGOORANI, Hooman; HALABCHI, Farzin

2015-01-01

Background: The high prevalence and potential side effects of anabolic-androgenic steroids (AAS) misuse by athletes has made it a major public health concern. Epidemiological studies on the abuse of such drugs are mandatory for developing effective preventive drug control programs in sports community. This study aimed to investigate the prevalence of AAS abuse and their association with some psycho-socio-demographic factors in Iranian male recreational body-builders. Methods: Between March and October 2011; 906 recreational male body-builders from 103 randomly selected bodybuilding clubs in Tehran, Iran were participated in this study. Some psycho-socio- demographic factors including age, job, average family income, family size, sport experience (months), weekly duration of the sporting activity (h), purpose of participation in sporting activity, mental health as well as body image (via General Health Questionnaire and Multidimensional Body-Self Relations Questionnaire, respectively), and history of AAS use were obtained by interviews using questionnaires. Results: Participants were all recreational male body-builders [mean age (SD): 25.7 (7.1), ranging 14–56 yr]. Self-report of AAS abuse was registered in 150 body-builders (16.6%). Among different psycho-socio-demographic factors, only family income and sport experience were inversely associated with AAS abuse. Conclusion: Lifetime prevalence of AAS abuse is relatively high among recreational body-builders based on their self-report. Some psycho-socio-demographic factors including family income and sport experience may influence the prevalence of AAS abuse. PMID:26811817

The Misuse of Anabolic-Androgenic Steroids among Iranian Recreational Male Body-Builders and Their Related Psycho-Socio-Demographic factors.

PubMed

Angoorani, Hooman; Halabchi, Farzin

2015-12-01

The high prevalence and potential side effects of anabolic-androgenic steroids (AAS) misuse by athletes has made it a major public health concern. Epidemiological studies on the abuse of such drugs are mandatory for developing effective preventive drug control programs in sports community. This study aimed to investigate the prevalence of AAS abuse and their association with some psycho-socio-demographic factors in Iranian male recreational body-builders. Between March and October 2011; 906 recreational male body-builders from 103 randomly selected bodybuilding clubs in Tehran, Iran were participated in this study. Some psycho-socio- demographic factors including age, job, average family income, family size, sport experience (months), weekly duration of the sporting activity (h), purpose of participation in sporting activity, mental health as well as body image (via General Health Questionnaire and Multidimensional Body-Self Relations Questionnaire, respectively), and history of AAS use were obtained by interviews using questionnaires. Participants were all recreational male body-builders [mean age (SD): 25.7 (7.1), ranging 14-56 yr]. Self-report of AAS abuse was registered in 150 body-builders (16.6%). Among different psycho-socio-demographic factors, only family income and sport experience were inversely associated with AAS abuse. Lifetime prevalence of AAS abuse is relatively high among recreational body-builders based on their self-report. Some psycho-socio-demographic factors including family income and sport experience may influence the prevalence of AAS abuse.

Automatic Classification of volcano-seismic events based on Deep Neural Networks.

NASA Astrophysics Data System (ADS)

Titos Luzón, M.; Bueno Rodriguez, A.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

2017-12-01

Seismic monitoring of active volcanoes is a popular remote sensing technique to detect seismic activity, often associated to energy exchanges between the volcano and the environment. As a result, seismographs register a wide range of volcano-seismic signals that reflect the nature and underlying physics of volcanic processes. Machine learning and signal processing techniques provide an appropriate framework to analyze such data. In this research, we propose a new classification framework for seismic events based on deep neural networks. Deep neural networks are composed by multiple processing layers, and can discover intrinsic patterns from the data itself. Internal parameters can be initialized using a greedy unsupervised pre-training stage, leading to an efficient training of fully connected architectures. We aim to determine the robustness of these architectures as classifiers of seven different types of seismic events recorded at "Volcán de Fuego" (Colima, Mexico). Two deep neural networks with different pre-training strategies are studied: stacked denoising autoencoder and deep belief networks. Results are compared to existing machine learning algorithms (SVM, Random Forest, Multilayer Perceptron). We used 5 LPC coefficients over three non-overlapping segments as training features in order to characterize temporal evolution, avoid redundancy and encode the signal, regardless of its duration. Experimental results show that deep architectures can classify seismic events with higher accuracy than classical algorithms, attaining up to 92% recognition accuracy. Pre-training initialization helps these models to detect events that occur simultaneously in time (such explosions and rockfalls), increase robustness against noisy inputs, and provide better generalization. These results demonstrate deep neural networks are robust classifiers, and can be deployed in real-environments to monitor the seismicity of restless volcanoes.

5. DETAIL OF BUILDER'S PLATE, WHICH READS '1898, THE SANITARY ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

5. DETAIL OF BUILDER'S PLATE, WHICH READS '1898, THE SANITARY DISTRICT OF CHICAGO, BOARD OF TRUSTEES, WILLIAM BOLDENWECK, JOSEPH C. BRADEN, ZINA R. CARTER, BERNARD A. ECKART, ALEXANDER J. JONES, THOMAS KELLY, JAMES P. MALLETTE, THOMAS SMYTHE, FRANK WINTER; ISHAM RANDOLPH, CHIEF ENGINEER.' - Santa Fe Railroad, Sanitary & Ship Canal Bridge, Spanning Sanitary & Ship Canal east of Harlem Avenue, Chicago, Cook County, IL

A Probabilistic Approach to Network Event Formation from Pre-Processed Waveform Data

NASA Astrophysics Data System (ADS)

Kohl, B. C.; Given, J.

2017-12-01

The current state of the art for seismic event detection still largely depends on signal detection at individual sensor stations, including picking accurate arrivals times and correctly identifying phases, and relying on fusion algorithms to associate individual signal detections to form event hypotheses. But increasing computational capability has enabled progress toward the objective of fully utilizing body-wave recordings in an integrated manner to detect events without the necessity of previously recorded ground truth events. In 2011-2012 Leidos (then SAIC) operated a seismic network to monitor activity associated with geothermal field operations in western Nevada. We developed a new association approach for detecting and quantifying events by probabilistically combining pre-processed waveform data to deal with noisy data and clutter at local distance ranges. The ProbDet algorithm maps continuous waveform data into continuous conditional probability traces using a source model (e.g. Brune earthquake or Mueller-Murphy explosion) to map frequency content and an attenuation model to map amplitudes. Event detection and classification is accomplished by combining the conditional probabilities from the entire network using a Bayesian formulation. This approach was successful in producing a high-Pd, low-Pfa automated bulletin for a local network and preliminary tests with regional and teleseismic data show that it has promise for global seismic and nuclear monitoring applications. The approach highlights several features that we believe are essential to achieving low-threshold automated event detection: Minimizes the utilization of individual seismic phase detections - in traditional techniques, errors in signal detection, timing, feature measurement and initial phase ID compound and propagate into errors in event formation, Has a formalized framework that utilizes information from non-detecting stations, Has a formalized framework that utilizes source information, in

Mining the key predictors for event outbreaks in social networks

NASA Astrophysics Data System (ADS)

Yi, Chengqi; Bao, Yuanyuan; Xue, Yibo

2016-04-01

It will be beneficial to devise a method to predict a so-called event outbreak. Existing works mainly focus on exploring effective methods for improving the accuracy of predictions, while ignoring the underlying causes: What makes event go viral? What factors that significantly influence the prediction of an event outbreak in social networks? In this paper, we proposed a novel definition for an event outbreak, taking into account the structural changes to a network during the propagation of content. In addition, we investigated features that were sensitive to predicting an event outbreak. In order to investigate the universality of these features at different stages of an event, we split the entire lifecycle of an event into 20 equal segments according to the proportion of the propagation time. We extracted 44 features, including features related to content, users, structure, and time, from each segment of the event. Based on these features, we proposed a prediction method using supervised classification algorithms to predict event outbreaks. Experimental results indicate that, as time goes by, our method is highly accurate, with a precision rate ranging from 79% to 97% and a recall rate ranging from 74% to 97%. In addition, after applying a feature-selection algorithm, the top five selected features can considerably improve the accuracy of the prediction. Data-driven experimental results show that the entropy of the eigenvector centrality, the entropy of the PageRank, the standard deviation of the betweenness centrality, the proportion of re-shares without content, and the average path length are the key predictors for an event outbreak. Our findings are especially useful for further exploring the intrinsic characteristics of outbreak prediction.

Event-Triggered Distributed Average Consensus Over Directed Digital Networks With Limited Communication Bandwidth.

PubMed

Li, Huaqing; Chen, Guo; Huang, Tingwen; Dong, Zhaoyang; Zhu, Wei; Gao, Lan

2016-12-01

In this paper, we consider the event-triggered distributed average-consensus of discrete-time first-order multiagent systems with limited communication data rate and general directed network topology. In the framework of digital communication network, each agent has a real-valued state but can only exchange finite-bit binary symbolic data sequence with its neighborhood agents at each time step due to the digital communication channels with energy constraints. Novel event-triggered dynamic encoder and decoder for each agent are designed, based on which a distributed control algorithm is proposed. A scheme that selects the number of channel quantization level (number of bits) at each time step is developed, under which all the quantizers in the network are never saturated. The convergence rate of consensus is explicitly characterized, which is related to the scale of network, the maximum degree of nodes, the network structure, the scaling function, the quantization interval, the initial states of agents, the control gain and the event gain. It is also found that under the designed event-triggered protocol, by selecting suitable parameters, for any directed digital network containing a spanning tree, the distributed average consensus can be always achieved with an exponential convergence rate based on merely one bit information exchange between each pair of adjacent agents at each time step. Two simulation examples are provided to illustrate the feasibility of presented protocol and the correctness of the theoretical results.

Supervised machine learning on a network scale: application to seismic event classification and detection

NASA Astrophysics Data System (ADS)

Reynen, Andrew; Audet, Pascal

2017-09-01

A new method using a machine learning technique is applied to event classification and detection at seismic networks. This method is applicable to a variety of network sizes and settings. The algorithm makes use of a small catalogue of known observations across the entire network. Two attributes, the polarization and frequency content, are used as input to regression. These attributes are extracted at predicted arrival times for P and S waves using only an approximate velocity model, as attributes are calculated over large time spans. This method of waveform characterization is shown to be able to distinguish between blasts and earthquakes with 99 per cent accuracy using a network of 13 stations located in Southern California. The combination of machine learning with generalized waveform features is further applied to event detection in Oklahoma, United States. The event detection algorithm makes use of a pair of unique seismic phases to locate events, with a precision directly related to the sampling rate of the generalized waveform features. Over a week of data from 30 stations in Oklahoma, United States are used to automatically detect 25 times more events than the catalogue of the local geological survey, with a false detection rate of less than 2 per cent. This method provides a highly confident way of detecting and locating events. Furthermore, a large number of seismic events can be automatically detected with low false alarm, allowing for a larger automatic event catalogue with a high degree of trust.

ARACHNE: A neural-neuroglial network builder with remotely controlled parallel computing

PubMed Central

Rusakov, Dmitri A.; Savtchenko, Leonid P.

2017-01-01

Creating and running realistic models of neural networks has hitherto been a task for computing professionals rather than experimental neuroscientists. This is mainly because such networks usually engage substantial computational resources, the handling of which requires specific programing skills. Here we put forward a newly developed simulation environment ARACHNE: it enables an investigator to build and explore cellular networks of arbitrary biophysical and architectural complexity using the logic of NEURON and a simple interface on a local computer or a mobile device. The interface can control, through the internet, an optimized computational kernel installed on a remote computer cluster. ARACHNE can combine neuronal (wired) and astroglial (extracellular volume-transmission driven) network types and adopt realistic cell models from the NEURON library. The program and documentation (current version) are available at GitHub repository https://github.com/LeonidSavtchenko/Arachne under the MIT License (MIT). PMID:28362877

A Distributed and Energy-Efficient Algorithm for Event K-Coverage in Underwater Sensor Networks.

PubMed

Jiang, Peng; Xu, Yiming; Liu, Jun

2017-01-19

For event dynamic K-coverage algorithms, each management node selects its assistant node by using a greedy algorithm without considering the residual energy and situations in which a node is selected by several events. This approach affects network energy consumption and balance. Therefore, this study proposes a distributed and energy-efficient event K-coverage algorithm (DEEKA). After the network achieves 1-coverage, the nodes that detect the same event compete for the event management node with the number of candidate nodes and the average residual energy, as well as the distance to the event. Second, each management node estimates the probability of its neighbor nodes' being selected by the event it manages with the distance level, the residual energy level, and the number of dynamic coverage event of these nodes. Third, each management node establishes an optimization model that uses expectation energy consumption and the residual energy variance of its neighbor nodes and detects the performance of the events it manages as targets. Finally, each management node uses a constrained non-dominated sorting genetic algorithm (NSGA-II) to obtain the Pareto set of the model and the best strategy via technique for order preference by similarity to an ideal solution (TOPSIS). The algorithm first considers the effect of harsh underwater environments on information collection and transmission. It also considers the residual energy of a node and a situation in which the node is selected by several other events. Simulation results show that, unlike the on-demand variable sensing K-coverage algorithm, DEEKA balances and reduces network energy consumption, thereby prolonging the network's best service quality and lifetime.

Changes in event-related potential functional networks predict traumatic brain injury in piglets.

PubMed

Atlan, Lorre S; Lan, Ingrid S; Smith, Colin; Margulies, Susan S

2018-06-01

Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species. Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured. Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy. This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

MLM Builder: An Integrated Suite for Development and Maintenance of Arden Syntax Medical Logic Modules

PubMed Central

Sailors, R. Matthew

1997-01-01

The Arden Syntax specification for sharable computerized medical knowledge bases has not been widely utilized in the medical informatics community because of a lack of tools for developing Arden Syntax knowledge bases (Medical Logic Modules). The MLM Builder is a Microsoft Windows-hosted CASE (Computer Aided Software Engineering) tool designed to aid in the development and maintenance of Arden Syntax Medical Logic Modules (MLMs). The MLM Builder consists of the MLM Writer (an MLM generation tool), OSCAR (an anagram of Object-oriented ARden Syntax Compiler), a test database, and the MLManager (an MLM management information system). Working together, these components form a self-contained, unified development environment for the creation, testing, and maintenance of Arden Syntax Medical Logic Modules.

Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model

PubMed Central

Gigante, Guido; Deco, Gustavo; Marom, Shimon; Del Giudice, Paolo

2015-01-01

Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural) is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed ‘quasi-orbits’, which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network’s firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms. PMID:26558616

The GlycanBuilder: a fast, intuitive and flexible software tool for building and displaying glycan structures.

PubMed

Ceroni, Alessio; Dell, Anne; Haslam, Stuart M

2007-08-07

Carbohydrates play a critical role in human diseases and their potential utility as biomarkers for pathological conditions is a major driver for characterization of the glycome. However, the additional complexity of glycans compared to proteins and nucleic acids has slowed the advancement of glycomics in comparison to genomics and proteomics. The branched nature of carbohydrates, the great diversity of their constituents and the numerous alternative symbolic notations, make the input and display of glycans not as straightforward as for example the amino-acid sequence of a protein. Every glycoinformatic tool providing a user interface would benefit from a fast, intuitive, appealing mechanism for input and output of glycan structures in a computer readable format. A software tool for building and displaying glycan structures using a chosen symbolic notation is described here. The "GlycanBuilder" uses an automatic rendering algorithm to draw the saccharide symbols and to place them on the drawing board. The information about the symbolic notation is derived from a configurable graphical model as a set of rules governing the aspect and placement of residues and linkages. The algorithm is able to represent a structure using only few traversals of the tree and is inherently fast. The tool uses an XML format for import and export of encoded structures. The rendering algorithm described here is able to produce high-quality representations of glycan structures in a chosen symbolic notation. The automated rendering process enables the "GlycanBuilder" to be used both as a user-independent component for displaying glycans and as an easy-to-use drawing tool. The "GlycanBuilder" can be integrated in web pages as a Java applet for the visual editing of glycans. The same component is available as a web service to render an encoded structure into a graphical format. Finally, the "GlycanBuilder" can be integrated into other applications to create intuitive and appealing user

Korea Microlensing Telescope Network Microlensing Events from 2015: Event-finding Algorithm, Vetting, and Photometry

NASA Astrophysics Data System (ADS)

Kim, D.-J.; Kim, H.-W.; Hwang, K.-H.; Albrow, M. D.; Chung, S.-J.; Gould, A.; Han, C.; Jung, Y. K.; Ryu, Y.-H.; Shin, I.-G.; Yee, J. C.; Zhu, W.; Cha, S.-M.; Kim, S.-L.; Lee, C.-U.; Lee, D.-J.; Lee, Y.; Park, B.-G.; Pogge, R. W.; The KMTNet Collaboration

2018-02-01

We present microlensing events in the 2015 Korea Microlensing Telescope Network (KMTNet) data and our procedure for identifying these events. In particular, candidates were detected with a novel “completed-event” microlensing event-finder algorithm. The algorithm works by making linear fits to a ({t}0,{t}{eff},{u}0) grid of point-lens microlensing models. This approach is rendered computationally efficient by restricting u 0 to just two values (0 and 1), which we show is quite adequate. The implementation presented here is specifically tailored to the commission-year character of the 2015 data, but the algorithm is quite general and has already been applied to a completely different (non-KMTNet) data set. We outline expected improvements for 2016 and future KMTNet data. The light curves of the 660 “clear microlensing” and 182 “possible microlensing” events that were found in 2015 are presented along with our policy for their public release.

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

PubMed

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

2017-06-30

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

Separation of Benign and Malicious Network Events for Accurate Malware Family Classification

DTIC Science & Technology

2015-09-28

use Kullback - Leibler (KL) divergence [15] to measure the information ...related work in an important aspect concerning the order of events. We use n-grams to capture the order of events, which exposes richer information about...DISCUSSION Using n-grams on higher level network events helps under- stand the underlying operation of the malware, and provides a good feature set

Associative nature of event participation dynamics: A network theory approach

PubMed Central

Smiljanić, Jelena; Mitrović Dankulov, Marija

2017-01-01

The affiliation with various social groups can be a critical factor when it comes to quality of life of each individual, making such groups an essential element of every society. The group dynamics, longevity and effectiveness strongly depend on group’s ability to attract new members and keep them engaged in group activities. It was shown that high heterogeneity of scientist’s engagement in conference activities of the specific scientific community depends on the balance between the numbers of previous attendances and non-attendances and is directly related to scientist’s association with that community. Here we show that the same holds for leisure groups of the Meetup website and further quantify individual members’ association with the group. We examine how structure of personal social networks is evolving with the event attendance. Our results show that member’s increasing engagement in the group activities is primarily associated with the strengthening of already existing ties and increase in the bonding social capital. We also show that Meetup social networks mostly grow trough big events, while small events contribute to the groups cohesiveness. PMID:28166305

Climate network analysis of regional precipitation extremes: The true story told by event synchronization

NASA Astrophysics Data System (ADS)

Odenweller, Adrian; Donner, Reik V.

2017-04-01

Over the last decade, complex network methods have been frequently used for characterizing spatio-temporal patterns of climate variability from a complex systems perspective, yielding new insights into time-dependent teleconnectivity patterns and couplings between different components of the Earth climate. Among the foremost results reported, network analyses of the synchronicity of extreme events as captured by the so-called event synchronization have been proposed to be powerful tools for disentangling the spatio-temporal organization of particularly extreme rainfall events and anticipating the timing of monsoon onsets or extreme floodings. Rooted in the analysis of spike train synchrony analysis in the neurosciences, event synchronization has the great advantage of automatically classifying pairs of events arising at two distinct spatial locations as temporally close (and, thus, possibly statistically - or even dynamically - interrelated) or not without the necessity of selecting an additional parameter in terms of a maximally tolerable delay between these events. This consideration is conceptually justified in case of the original application to spike trains in electroencephalogram (EEG) recordings, where the inter-spike intervals show relatively narrow distributions at high temporal sampling rates. However, in case of climate studies, precipitation extremes defined by daily precipitation sums exceeding a certain empirical percentile of their local distribution exhibit a distinctively different type of distribution of waiting times between subsequent events. This raises conceptual concerns if event synchronization is still appropriate for detecting interlinkages between spatially distributed precipitation extremes. In order to study this problem in more detail, we employ event synchronization together with an alternative similarity measure for event sequences, event coincidence rates, which requires a manual setting of the tolerable maximum delay between two

Time-related patient data retrieval for the case studies from the pharmacogenomics research network

PubMed Central

Zhu, Qian; Tao, Cui; Ding, Ying; Chute, Christopher G.

2012-01-01

There are lots of question-based data elements from the pharmacogenomics research network (PGRN) studies. Many data elements contain temporal information. To semantically represent these elements so that they can be machine processiable is a challenging problem for the following reasons: (1) the designers of these studies usually do not have the knowledge of any computer modeling and query languages, so that the original data elements usually are represented in spreadsheets in human languages; and (2) the time aspects in these data elements can be too complex to be represented faithfully in a machine-understandable way. In this paper, we introduce our efforts on representing these data elements using semantic web technologies. We have developed an ontology, CNTRO, for representing clinical events and their temporal relations in the web ontology language (OWL). Here we use CNTRO to represent the time aspects in the data elements. We have evaluated 720 time-related data elements from PGRN studies. We adapted and extended the knowledge representation requirements for EliXR-TIME to categorize our data elements. A CNTRO-based SPARQL query builder has been developed to customize users’ own SPARQL queries for each knowledge representation requirement. The SPARQL query builder has been evaluated with a simulated EHR triple store to ensure its functionalities. PMID:23076712

Time-related patient data retrieval for the case studies from the pharmacogenomics research network.

PubMed

Zhu, Qian; Tao, Cui; Ding, Ying; Chute, Christopher G

2012-11-01

There are lots of question-based data elements from the pharmacogenomics research network (PGRN) studies. Many data elements contain temporal information. To semantically represent these elements so that they can be machine processiable is a challenging problem for the following reasons: (1) the designers of these studies usually do not have the knowledge of any computer modeling and query languages, so that the original data elements usually are represented in spreadsheets in human languages; and (2) the time aspects in these data elements can be too complex to be represented faithfully in a machine-understandable way. In this paper, we introduce our efforts on representing these data elements using semantic web technologies. We have developed an ontology, CNTRO, for representing clinical events and their temporal relations in the web ontology language (OWL). Here we use CNTRO to represent the time aspects in the data elements. We have evaluated 720 time-related data elements from PGRN studies. We adapted and extended the knowledge representation requirements for EliXR-TIME to categorize our data elements. A CNTRO-based SPARQL query builder has been developed to customize users' own SPARQL queries for each knowledge representation requirement. The SPARQL query builder has been evaluated with a simulated EHR triple store to ensure its functionalities.

A community-based event delivery protocol in publish/subscribe systems for delay tolerant sensor networks.

PubMed

Liu, Nianbo; Liu, Ming; Zhu, Jinqi; Gong, Haigang

2009-01-01

The basic operation of a Delay Tolerant Sensor Network (DTSN) is to finish pervasive data gathering in networks with intermittent connectivity, while the publish/subscribe (Pub/Sub for short) paradigm is used to deliver events from a source to interested clients in an asynchronous way. Recently, extension of Pub/Sub systems in DTSNs has become a promising research topic. However, due to the unique frequent partitioning characteristic of DTSNs, extension of a Pub/Sub system in a DTSN is a considerably difficult and challenging problem, and there are no good solutions to this problem in published works. To ad apt Pub/Sub systems to DTSNs, we propose CED, a community-based event delivery protocol. In our design, event delivery is based on several unchanged communities, which are formed by sensor nodes in the network according to their connectivity. CED consists of two components: event delivery and queue management. In event delivery, events in a community are delivered to mobile subscribers once a subscriber comes into the community, for improving the data delivery ratio. The queue management employs both the event successful delivery time and the event survival time to decide whether an event should be delivered or dropped for minimizing the transmission overhead. The effectiveness of CED is demonstrated through comprehensive simulation studies.

Heating, Ventilation, and Air Conditioning Design Strategy for a Hot-Humid Production Builder

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

Kerrigan, P.

2014-03-01

Building Science Corporation (BSC) worked directly with the David Weekley Homes - Houston division to develop a cost-effective design for moving the HVAC system into conditioned space. In addition, BSC conducted energy analysis to calculate the most economical strategy for increasing the energy performance of future production houses in preparation for the upcoming code changes in 2015. This research project addressed the following questions: 1. What is the most cost effective, best performing and most easily replicable method of locating ducts inside conditioned space for a hot-humid production home builder that constructs one and two story single family detached residences?more » 2. What is a cost effective and practical method of achieving 50% source energy savings vs. the 2006 International Energy Conservation Code for a hot-humid production builder? 3. How accurate are the pre-construction whole house cost estimates compared to confirmed post construction actual cost?« less

BioPartsBuilder: a synthetic biology tool for combinatorial assembly of biological parts.

PubMed

Yang, Kun; Stracquadanio, Giovanni; Luo, Jingchuan; Boeke, Jef D; Bader, Joel S

2016-03-15

Combinatorial assembly of DNA elements is an efficient method for building large-scale synthetic pathways from standardized, reusable components. These methods are particularly useful because they enable assembly of multiple DNA fragments in one reaction, at the cost of requiring that each fragment satisfies design constraints. We developed BioPartsBuilder as a biologist-friendly web tool to design biological parts that are compatible with DNA combinatorial assembly methods, such as Golden Gate and related methods. It retrieves biological sequences, enforces compliance with assembly design standards and provides a fabrication plan for each fragment. BioPartsBuilder is accessible at http://public.biopartsbuilder.org and an Amazon Web Services image is available from the AWS Market Place (AMI ID: ami-508acf38). Source code is released under the MIT license, and available for download at https://github.com/baderzone/biopartsbuilder joel.bader@jhu.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

PubMed Central

Zhang, Shukui; Chen, Hao; Zhu, Qiaoming

2014-01-01

The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic. PMID:25136690

DOE Zero Energy Ready Home Case Study: Alliance Green Builders, Casa Aguila

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

Pacific Northwest National Laboratory

Alliance Green Builders built this 3,129-ft2 home in the hills above Ramona, California, to the high-performance criteria of the DOE Zero Energy Ready Home (ZERH) program. The home should perform far better than net zero thanks to a super-efficient building shell, a wind turbine, three suntracking solar photovoltaic arrays, and solar thermal water heating.

Information Spread of Emergency Events: Path Searching on Social Networks

PubMed Central

Hu, Hongzhi; Wu, Tunan

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323

Information spread of emergency events: path searching on social networks.

PubMed

Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.

[Character accentuations as a criterion for psychological risks in the professional activity of the builders of main gas pipelines in the conditions of arctic].

PubMed

Korneeva, Ia A; Simonova, N N

2015-01-01

The article is devoted to the study of character accentuations as a criterion for psychological risks in the professional activity of builders of main gas pipelines in the conditions of Arctic. to study the severity of character accentuations in rotation-employed builders of main gas pipelines, stipulated by their professional activities, as well as personal resources to overcome these destructions. The study involved 70 rotation-employed builders of trunk pipelines, working in the Tyumen Region (duration of the shift-in--52 days), aged from 23 to 59 (mean age 34,9 ± 8.1) years, with the experience of work from 0.5 years to 14 years (the average length of 4.42 ± 3.1). Methods of the study: questionnaires, psychological testing, participant observation. One-Sample t-test of Student, multiple regression analysis, incremental analysis. In the work there were revealed differences of expression of character accentuations in builders of trunk pipelines with experience in work on rotation less and more than five years. There was determined that builders of the main gas pipelines, working on the rotation in Arctic, with more pronounced accentuation ofthe character use mainly psychological defenses of compensation, substitution and denial, and have an average level of expression of flexibility as the regulatory process.

Event Networks and the Identification of Crime Pattern Motifs

PubMed Central

2015-01-01

In this paper we demonstrate the use of network analysis to characterise patterns of clustering in spatio-temporal events. Such clustering is of both theoretical and practical importance in the study of crime, and forms the basis for a number of preventative strategies. However, existing analytical methods show only that clustering is present in data, while offering little insight into the nature of the patterns present. Here, we show how the classification of pairs of events as close in space and time can be used to define a network, thereby generalising previous approaches. The application of graph-theoretic techniques to these networks can then offer significantly deeper insight into the structure of the data than previously possible. In particular, we focus on the identification of network motifs, which have clear interpretation in terms of spatio-temporal behaviour. Statistical analysis is complicated by the nature of the underlying data, and we provide a method by which appropriate randomised graphs can be generated. Two datasets are used as case studies: maritime piracy at the global scale, and residential burglary in an urban area. In both cases, the same significant 3-vertex motif is found; this result suggests that incidents tend to occur not just in pairs, but in fact in larger groups within a restricted spatio-temporal domain. In the 4-vertex case, different motifs are found to be significant in each case, suggesting that this technique is capable of discriminating between clustering patterns at a finer granularity than previously possible. PMID:26605544

A Distributed and Energy-Efficient Algorithm for Event K-Coverage in Underwater Sensor Networks

PubMed Central

Jiang, Peng; Xu, Yiming; Liu, Jun

2017-01-01

For event dynamic K-coverage algorithms, each management node selects its assistant node by using a greedy algorithm without considering the residual energy and situations in which a node is selected by several events. This approach affects network energy consumption and balance. Therefore, this study proposes a distributed and energy-efficient event K-coverage algorithm (DEEKA). After the network achieves 1-coverage, the nodes that detect the same event compete for the event management node with the number of candidate nodes and the average residual energy, as well as the distance to the event. Second, each management node estimates the probability of its neighbor nodes’ being selected by the event it manages with the distance level, the residual energy level, and the number of dynamic coverage event of these nodes. Third, each management node establishes an optimization model that uses expectation energy consumption and the residual energy variance of its neighbor nodes and detects the performance of the events it manages as targets. Finally, each management node uses a constrained non-dominated sorting genetic algorithm (NSGA-II) to obtain the Pareto set of the model and the best strategy via technique for order preference by similarity to an ideal solution (TOPSIS). The algorithm first considers the effect of harsh underwater environments on information collection and transmission. It also considers the residual energy of a node and a situation in which the node is selected by several other events. Simulation results show that, unlike the on-demand variable sensing K-coverage algorithm, DEEKA balances and reduces network energy consumption, thereby prolonging the network’s best service quality and lifetime. PMID:28106837

Event-Triggered Fault Detection of Nonlinear Networked Systems.

PubMed

Li, Hongyi; Chen, Ziran; Wu, Ligang; Lam, Hak-Keung; Du, Haiping

2017-04-01

This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

"Repeating Events" as Estimator of Location Precision: The China National Seismograph Network

NASA Astrophysics Data System (ADS)

Jiang, Changsheng; Wu, Zhongliang; Li, Yutong; Ma, Tengfei

2014-03-01

"Repeating earthquakes" identified by waveform cross-correlation, with inter-event separation of no more than 1 km, can be used for assessment of location precision. Assuming that the network-measured apparent inter-epicenter distance X of the "repeating doublets" indicates the location precision, we estimated the regionalized location quality of the China National Seismograph Network by comparing the "repeating events" in and around China by S chaff and R ichards (Science 303: 1176-1178, 2004; J Geophys Res 116: B03309, 2011) and the monthly catalogue of the China Earthquake Networks Center. The comparison shows that the average X value of the China National Seismograph Network is approximately 10 km. The mis-location is larger for the Tibetan Plateau, west and north of Xinjiang, and east of Inner Mongolia, as indicated by larger X values. Mis-location is correlated with the completeness magnitude of the earthquake catalogue. Using the data from the Beijing Capital Circle Region, the dependence of the mis-location on the distribution of seismic stations can be further confirmed.

Dynamical Networks Characterization of Space Weather Events

NASA Astrophysics Data System (ADS)

Orr, L.; Chapman, S. C.; Dods, J.; Gjerloev, J. W.

2017-12-01

Space weather can cause disturbances to satellite systems, impacting navigation technology and telecommunications; it can cause power loss and aviation disruption. A central aspect of the earth's magnetospheric response to space weather events are large scale and rapid changes in ionospheric current patterns. Space weather is highly dynamic and there are still many controversies about how the current system evolves in time. The recent SuperMAG initiative, collates ground-based vector magnetic field time series from over 200 magnetometers with 1-minute temporal resolution. In principle this combined dataset is an ideal candidate for quantification using dynamical networks. Network properties and parameters allow us to characterize the time dynamics of the full spatiotemporal pattern of the ionospheric current system. However, applying network methodologies to physical data presents new challenges. We establish whether a given pair of magnetometers are connected in the network by calculating their canonical cross correlation. The magnetometers are connected if their cross correlation exceeds a threshold. In our physical time series this threshold needs to be both station specific, as it varies with (non-linear) individual station sensitivity and location, and able to vary with season, which affects ground conductivity. Additionally, the earth rotates and therefore the ground stations move significantly on the timescales of geomagnetic disturbances. The magnetometers are non-uniformly spatially distributed. We will present new methodology which addresses these problems and in particular achieves dynamic normalization of the physical time series in order to form the network. Correlated disturbances across the magnetometers capture transient currents. Once the dynamical network has been obtained [1][2] from the full magnetometer data set it can be used to directly identify detailed inferred transient ionospheric current patterns and track their dynamics. We will show

Supplement consumption in body builder athletes

PubMed Central

Karimian, Jahangir; Esfahani, Parivash Shekarchizadeh

2011-01-01

BACKGROUND: Widespread use of supplements is observed among world athletes in different fields. The aim of this study was to estimate the prevalence and determinants of using supplements among body builder athletes. METHODS: This cross-sectional study was conducted on 250 men and 250 women from 30 different bodybuilding clubs. Participants were asked to complete a self-administered standardized anonymous check-list. RESULTS: Forty nine percent of the respondents declared supplement use. Men were more likely to take supplements than women (86.8% vs. 11.2%, p = 0.001). Reasons for using supplements were reported to be for health (45%), enhancing the immune system (40%) and improving athletic performance (25%). Most athletes (72%) had access to a nutritionist but underused this resource. Coaches (65%) had the greatest influence on supplementation practices followed by nutritionists (30%) and doctors (25%) after them. CONCLUSIONS: The prevalence of supplement use among bodybuilders was high. Sex, health-related issues and sport experts were determinant factors of supplement use. PMID:22973330

Event-triggered output feedback control for distributed networked systems.

PubMed

Mahmoud, Magdi S; Sabih, Muhammad; Elshafei, Moustafa

2016-01-01

This paper addresses the problem of output-feedback communication and control with event-triggered framework in the context of distributed networked control systems. The design problem of the event-triggered output-feedback control is proposed as a linear matrix inequality (LMI) feasibility problem. The scheme is developed for the distributed system where only partial states are available. In this scheme, a subsystem uses local observers and share its information to its neighbors only when the subsystem's local error exceeds a specified threshold. The developed method is illustrated by using a coupled cart example from the literature. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

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

PubMed

Su, Housheng; Li, Zhenghao; Ye, Yanyan

2017-11-01

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

New Whole-House Solutions Case Study: HVAC Design Strategy for a Hot-Humid Production Builder, Houston, Texas

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

None

Building Science Corporation (BSC) worked directly with the David Weekley Homes - Houston division to develop a cost-effective design for moving the HVAC system into conditioned space. In addition, BSC conducted energy analysis to calculate the most economical strategy for increasing the energy performance of future production houses in preparation for the upcoming code changes in 2015. The following research questions were addressed by this research project: 1. What is the most cost effective, best performing and most easily replicable method of locating ducts inside conditioned space for a hot-humid production home builder that constructs one and two story singlemore » family detached residences? 2. What is a cost effective and practical method of achieving 50% source energy savings vs. the 2006 International Energy Conservation Code for a hot-humid production builder? 3. How accurate are the pre-construction whole house cost estimates compared to confirmed post construction actual cost? BSC and the builder developed a duct design strategy that employs a system of dropped ceilings and attic coffers for moving the ductwork from the vented attic to conditioned space. The furnace has been moved to either a mechanical closet in the conditioned living space or a coffered space in the attic.« less

76 FR 13226 - Amended Certification Regarding Eligibility To Apply for Worker Adjustment Assistance

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-10

... BUILDER CABINET GROUP INCLUDING ON-SITE LEASED WORKERS FROM RESERVES NETWORK AND RELIABLE STAFFING WAVERLY, OHIO. TA-W-71,287B MASCO BUILDER CABINET GROUP INCLUDING ON-SITE LEASED WORKERS FROM RESERVES NETWORK... Builder Cabinet Group, including on-site leased workers from Reserves Network and Reliable Staffing...

Event Detection in Aerospace Systems using Centralized Sensor Networks: A Comparative Study of Several Methodologies

NASA Technical Reports Server (NTRS)

Mehr, Ali Farhang; Sauvageon, Julien; Agogino, Alice M.; Tumer, Irem Y.

2006-01-01

Recent advances in micro electromechanical systems technology, digital electronics, and wireless communications have enabled development of low-cost, low-power, multifunctional miniature smart sensors. These sensors can be deployed throughout a region in an aerospace vehicle to build a network for measurement, detection and surveillance applications. Event detection using such centralized sensor networks is often regarded as one of the most promising health management technologies in aerospace applications where timely detection of local anomalies has a great impact on the safety of the mission. In this paper, we propose to conduct a qualitative comparison of several local event detection algorithms for centralized redundant sensor networks. The algorithms are compared with respect to their ability to locate and evaluate an event in the presence of noise and sensor failures for various node geometries and densities.

ScholarshipBuilder Program, Second Annual Report 1989-1990. Report No. 10, Vol. 25.

ERIC Educational Resources Information Center

Ruffin, Minnie R.; And Others

The ScholarshipBuilder Program, an adopt-a-class program, began in November 1988 to encourage selected first-grade students to stay in school until graduation in the year 2000. Scholarships will be provided by Merrill Lynch, Incorporated, for students who go on to college, and a one-time stipend will be provided to students who enter the military…

VQone MATLAB toolbox: A graphical experiment builder for image and video quality evaluations: VQone MATLAB toolbox.

PubMed

Nuutinen, Mikko; Virtanen, Toni; Rummukainen, Olli; Häkkinen, Jukka

2016-03-01

This article presents VQone, a graphical experiment builder, written as a MATLAB toolbox, developed for image and video quality ratings. VQone contains the main elements needed for the subjective image and video quality rating process. This includes building and conducting experiments and data analysis. All functions can be controlled through graphical user interfaces. The experiment builder includes many standardized image and video quality rating methods. Moreover, it enables the creation of new methods or modified versions from standard methods. VQone is distributed free of charge under the terms of the GNU general public license and allows code modifications to be made so that the program's functions can be adjusted according to a user's requirements. VQone is available for download from the project page (http://www.helsinki.fi/psychology/groups/visualcognition/).

Building America Best Practices Series Volume 11. Builders Challenge Guide to 40% Whole-House Energy Savings in the Marine Climate

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

Baechler, Michael C.; Gilbride, Theresa L.; Hefty, Marye G.

2010-09-01

This best practices guide is the eleventh in a series of guides for builders produced by the U.S. Department of Energy’s Building America Program. This guide book is a resource to help builders design and construct homes that are among the most energy-efficient available, while addressing issues such as building durability, indoor air quality, and occupant health, safety, and comfort. With the measures described in this guide, builders in the marine climate (portions of Washington, Oregon, and California) can achieve homes that have whole house energy savings of 40% over the Building America benchmark (a home built to mid-1990s buildingmore » practices roughly equivalent to the 1993 Model Energy Code) with no added overall costs for consumers. These best practices are based on the results of research and demonstration projects conducted by Building America’s research teams. The guide includes information for managers, designers, marketers, site supervisors, and subcontractors, as well as case studies of builders who are successfully building homes that cut energy use by 40% in the marine climate. This document is available on the web at www.buildingamerica.gov. This report was originally cleared 06-29-2010. This version is Rev 1 cleared in Nov 2010. The only change is the reference to the Energy Star Windows critieria shown on pg 8.25 was updated to match the criteria - Version 5.0, 04/07/2009, effective 01/04/2010.« less

Influence of Network Model Detail on Estimated Health Effects of Drinking Water Contamination Events

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

Davis, Michael J.; Janke, Robert

Network model detail can influence the accuracy of results from analyses of water distribution systems. Some previous work has shown the limitations of skeletonized network models when considering water quality and hydraulic effects. Loss of model detail is potentially less important for aggregated effects such as the systemwide health effects associated with a contamination event, but has received limited attention. The influence of model detail on such effects is examined here by comparing results obtained for contamination events using three large network models and several skeletonized versions of the models. Loss of model detail decreases the accuracy of estimated aggregatedmore » adverse effects related to contamination events. It has the potential to have a large negative influence on the results of consequence assessments and the design of contamination warning systems. But, the adverse influence on analysis results can be minimized by restricting attention to high percentile effects (i.e., 95th percentile or higher).« less

Influence of Network Model Detail on Estimated Health Effects of Drinking Water Contamination Events

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

Davis, Michael J.; Janke, Robert

Network model detail can influence the accuracy of results from analyses of water distribution systems. Previous work has shown the limitations of skeletonized network models when considering water quality and hydraulic effects. Loss of model detail is potentially less important for aggregated effects such as the systemwide health effects associated with a contamination event, but has received limited attention. The influence of model detail on such effects is examined here by comparing results obtained for contamination events using three large network models and several skeletonized versions of the models. Loss of model detail decreases the accuracy of estimated aggregated adversemore » effects related to contamination events. It has the potential to have a large negative influence on the results of consequence assessments and the design of contamination warning systems. However, the adverse influence on analysis results can be minimized by restricting attention to high percentile effects (i.e., 95th percentile or higher).« less

Influence of Network Model Detail on Estimated Health Effects of Drinking Water Contamination Events

DOE PAGES

Davis, Michael J.; Janke, Robert

2015-01-01

Network model detail can influence the accuracy of results from analyses of water distribution systems. Some previous work has shown the limitations of skeletonized network models when considering water quality and hydraulic effects. Loss of model detail is potentially less important for aggregated effects such as the systemwide health effects associated with a contamination event, but has received limited attention. The influence of model detail on such effects is examined here by comparing results obtained for contamination events using three large network models and several skeletonized versions of the models. Loss of model detail decreases the accuracy of estimated aggregatedmore » adverse effects related to contamination events. It has the potential to have a large negative influence on the results of consequence assessments and the design of contamination warning systems. But, the adverse influence on analysis results can be minimized by restricting attention to high percentile effects (i.e., 95th percentile or higher).« less

Building America Best Practices Series Volume 12: Builders Challenge Guide to 40% Whole-House Energy Savings in the Cold and Very Cold Climates

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

Baechler, Michael C.; Gilbride, Theresa L.; Hefty, Marye G.

2011-02-01

This best practices guide is the twelfth in a series of guides for builders produced by PNNL for the U.S. Department of Energy’s Building America program. This guide book is a resource to help builders design and construct homes that are among the most energy-efficient available, while addressing issues such as building durability, indoor air quality, and occupant health, safety, and comfort. With the measures described in this guide, builders in the cold and very cold climates can build homes that have whole-house energy savings of 40% over the Building America benchmark with no added overall costs for consumers. Themore » best practices described in this document are based on the results of research and demonstration projects conducted by Building America’s research teams. Building America brings together the nation’s leading building scientists with over 300 production builders to develop, test, and apply innovative, energy-efficient construction practices. Building America builders have found they can build homes that meet these aggressive energy-efficiency goals at no net increased costs to the homeowners. Currently, Building America homes achieve energy savings of 40% greater than the Building America benchmark home (a home built to mid-1990s building practices roughly equivalent to the 1993 Model Energy Code). The recommendations in this document meet or exceed the requirements of the 2009 IECC and 2009 IRC and thos erequirements are highlighted in the text. This document will be distributed via the DOE Building America website: www.buildingamerica.gov.« less

System diagnostic builder

NASA Technical Reports Server (NTRS)

Nieten, Joseph L.; Burke, Roger

1992-01-01

The System Diagnostic Builder (SDB) is an automated software verification and validation tool using state-of-the-art Artificial Intelligence (AI) technologies. The SDB is used extensively by project BURKE at NASA-JSC as one component of a software re-engineering toolkit. The SDB is applicable to any government or commercial organization which performs verification and validation tasks. The SDB has an X-window interface, which allows the user to 'train' a set of rules for use in a rule-based evaluator. The interface has a window that allows the user to plot up to five data parameters (attributes) at a time. Using these plots and a mouse, the user can identify and classify a particular behavior of the subject software. Once the user has identified the general behavior patterns of the software, he can train a set of rules to represent his knowledge of that behavior. The training process builds rules and fuzzy sets to use in the evaluator. The fuzzy sets classify those data points not clearly identified as a particular classification. Once an initial set of rules is trained, each additional data set given to the SDB will be used by a machine learning mechanism to refine the rules and fuzzy sets. This is a passive process and, therefore, it does not require any additional operator time. The evaluation component of the SDB can be used to validate a single software system using some number of different data sets, such as a simulator. Moreover, it can be used to validate software systems which have been re-engineered from one language and design methodology to a totally new implementation.

OpenSesame: an open-source, graphical experiment builder for the social sciences.

PubMed

Mathôt, Sebastiaan; Schreij, Daniel; Theeuwes, Jan

2012-06-01

In the present article, we introduce OpenSesame, a graphical experiment builder for the social sciences. OpenSesame is free, open-source, and cross-platform. It features a comprehensive and intuitive graphical user interface and supports Python scripting for complex tasks. Additional functionality, such as support for eyetrackers, input devices, and video playback, is available through plug-ins. OpenSesame can be used in combination with existing software for creating experiments.

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.

PubMed

Rueckauer, Bodo; Lungu, Iulia-Alexandra; Hu, Yuhuang; Pfeiffer, Michael; Liu, Shih-Chii

2017-01-01

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications.

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification

PubMed Central

Rueckauer, Bodo; Lungu, Iulia-Alexandra; Hu, Yuhuang; Pfeiffer, Michael; Liu, Shih-Chii

2017-01-01

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications. PMID:29375284

Sequence-of-events-driven automation of the deep space network

NASA Technical Reports Server (NTRS)

Hill, R., Jr.; Fayyad, K.; Smyth, C.; Santos, T.; Chen, R.; Chien, S.; Bevan, R.

1996-01-01

In February 1995, sequence-of-events (SOE)-driven automation technology was demonstrated for a Voyager telemetry downlink track at DSS 13. This demonstration entailed automated generation of an operations procedure (in the form of a temporal dependency network) from project SOE information using artificial intelligence planning technology and automated execution of the temporal dependency network using the link monitor and control operator assistant system. This article describes the overall approach to SOE-driven automation that was demonstrated, identifies gaps in SOE definitions and project profiles that hamper automation, and provides detailed measurements of the knowledge engineering effort required for automation.

Sequence-of-Events-Driven Automation of the Deep Space Network

NASA Technical Reports Server (NTRS)

Hill, R., Jr.; Fayyad, K.; Smyth, C.; Santos, T.; Chen, R.; Chien, S.; Bevan, R.

1996-01-01

In February 1995, sequence-of-events (SOE)-driven automation technology was demonstrated for a Voyager telemetry downlink track at DSS 13. This demonstration entailed automated generation of an operations procedure (in the form of a temporal dependency network) from project SOE information using artificial intelligence planning technology and automated execution of the temporal dependency network using the link monitor and control operator assistant system. This article describes the overall approach to SOE-driven automation that was demonstrated, identifies gaps in SOE definitions and project profiles that hamper automation, and provides detailed measurements of the knowledge engineering effort required for automation.

Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks

PubMed Central

Yang, Fan; Su, Jinsong; Zhou, Qifeng; Wang, Tian; Zhang, Lu; Xu, Yifan

2017-01-01

Vehicular nodes are equipped with more and more sensing units, and a large amount of sensing data is generated. Recently, more and more research considers cooperative urban sensing as the heart of intelligent and green city traffic management. The key components of the platform will be a combination of a pervasive vehicular sensing system, as well as a central control and analysis system, where data-gathering is a fundamental component. However, the data-gathering and monitoring are also challenging issues in vehicular sensor networks because of the large amount of data and the dynamic nature of the network. In this paper, we propose an efficient continuous event-monitoring and data-gathering framework based on fog nodes in vehicular sensor networks. A fog-based two-level threshold strategy is adopted to suppress unnecessary data upload and transmissions. In the monitoring phase, nodes sense the environment in low cost sensing mode and generate sensed data. When the probability of the event is high and exceeds some threshold, nodes transfer to the event-checking phase, and some nodes would be selected to transfer to the deep sensing mode to generate more accurate data of the environment. Furthermore, it adaptively adjusts the threshold to upload a suitable amount of data for decision making, while at the same time suppressing unnecessary message transmissions. Simulation results showed that the proposed scheme could reduce more than 84 percent of the data transmissions compared with other existing algorithms, while it detects the events and gathers the event data. PMID:29286320

The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

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

Psihas, Fernanda

In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearancemore » signal by 40% and studies show potential impact to the vμ disappearance analysis.« less

Safe, Seen, and Celebrated with AHA! Peace Builders: Putting Youth in Charge of Change

ERIC Educational Resources Information Center

Freed, Jennifer; Lowenstein, Melissa

2017-01-01

Since 2013, AHA! (Attitude. Harmony. Achievement) has trained over 300 AHA! Peace Builder youth at six area schools in Santa Barbara, California. These young people have conducted outreach to more than 5,000 additional peers, family members, and community members via Connection Circles, which they led during class, between classes, at AHA! Peace…

Parallel discrete-event simulation of FCFS stochastic queueing networks

NASA Technical Reports Server (NTRS)

Nicol, David M.

1988-01-01

Physical systems are inherently parallel. Intuition suggests that simulations of these systems may be amenable to parallel execution. The parallel execution of a discrete-event simulation requires careful synchronization of processes in order to ensure the execution's correctness; this synchronization can degrade performance. Largely negative results were recently reported in a study which used a well-known synchronization method on queueing network simulations. Discussed here is a synchronization method (appointments), which has proven itself to be effective on simulations of FCFS queueing networks. The key concept behind appointments is the provision of lookahead. Lookahead is a prediction on a processor's future behavior, based on an analysis of the processor's simulation state. It is shown how lookahead can be computed for FCFS queueing network simulations, give performance data that demonstrates the method's effectiveness under moderate to heavy loads, and discuss performance tradeoffs between the quality of lookahead, and the cost of computing lookahead.

Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier

PubMed Central

Akram, M. Usman; Khan, Shoab A.; Javed, Muhammad Younus

2014-01-01

National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network. PMID:25136674

A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation.

PubMed

Huang, Dongmei; Xu, Chenyixuan; Zhao, Danfeng; Song, Wei; He, Qi

2017-09-21

Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

Event-based cluster synchronization of coupled genetic regulatory networks

NASA Astrophysics Data System (ADS)

Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

2017-09-01

In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

Landscape Builder: software for the creation of initial landscapes for LANDIS from FIA data

Treesearch

William Dijak

2013-01-01

I developed Landscape Builder to create spatially explicit landscapes as starting conditions for LANDIS Pro 7.0 and LANDIS II landscape forest simulation models from classified satellite imagery and Forest Inventory and Analysis (FIA) data collected over multiple years. LANDIS Pro and LANDIS II models project future landscapes by simulating tree growth, tree species...

The Real Time Display Builder (RTDB)

NASA Technical Reports Server (NTRS)

Kindred, Erick D.; Bailey, Samuel A., Jr.

1989-01-01

The Real Time Display Builder (RTDB) is a prototype interactive graphics tool that builds logic-driven displays. These displays reflect current system status, implement fault detection algorithms in real time, and incorporate the operational knowledge of experienced flight controllers. RTDB utilizes an object-oriented approach that integrates the display symbols with the underlying operational logic. This approach allows the user to specify the screen layout and the driving logic as the display is being built. RTDB is being developed under UNIX in C utilizing the MASSCOMP graphics environment with appropriate functional separation to ease portability to other graphics environments. RTDB grew from the need to develop customized real-time data-driven Space Shuttle systems displays. One display, using initial functionality of the tool, was operational during the orbit phase of STS-26 Discovery. RTDB is being used to produce subsequent displays for the Real Time Data System project currently under development within the Mission Operations Directorate at NASA/JSC. The features of the tool, its current state of development, and its applications are discussed.

Resilient filtering for time-varying stochastic coupling networks under the event-triggering scheduling

NASA Astrophysics Data System (ADS)

Wang, Fan; Liang, Jinling; Dobaie, Abdullah M.

2018-07-01

The resilient filtering problem is considered for a class of time-varying networks with stochastic coupling strengths. An event-triggered strategy is adopted to save the network resources by scheduling the signal transmission from the sensors to the filters based on certain prescribed rules. Moreover, the filter parameters to be designed are subject to gain perturbations. The primary aim of the addressed problem is to determine a resilient filter that ensures an acceptable filtering performance for the considered network with event-triggering scheduling. To handle such an issue, an upper bound on the estimation error variance is established for each node according to the stochastic analysis. Subsequently, the resilient filter is designed by locally minimizing the derived upper bound at each iteration. Moreover, rigorous analysis shows the monotonicity of the minimal upper bound regarding the triggering threshold. Finally, a simulation example is presented to show effectiveness of the established filter scheme.

The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

PubMed Central

Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

2010-01-01

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

Decentralized event-triggered consensus control strategy for leader-follower networked systems

NASA Astrophysics Data System (ADS)

Zhang, Shouxu; Xie, Duosi; Yan, Weisheng

2017-08-01

In this paper, the consensus problem of leader-follower networked systems is addressed. At first, a centralized and a decentralized event-triggered control strategy are proposed, which make the control actuators of followers update at aperiodic invent interval. In particular, the latter one makes each follower requires the local information only. After that, an improved triggering function that only uses the follower's own information and the neighbors' states at their latest event instants is developed to relax the requirement of the continuous state of the neighbors. In addition, the strategy does not require the information of the topology, nor the eigenvalues of the Laplacian matrix. And if the follower does not have direct connection to the leader, the leader's information is not required either. It is analytically shown that by using the proposed strategy the leader-follower networked system is able to reach consensus without continuous communication among followers. Simulation examples are given to show effectiveness of the proposed control strategy.

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

Andre, J.M.; et al.

The data acquisition system (DAQ) of the CMS experiment at the CERN Large Hadron Collider assembles events at a rate of 100 kHz, transporting event data at an aggregate throughput of to the high-level trigger farm. The DAQ architecture is based on state-of-the-art network technologies for the event building. For the data concentration, 10/40 Gbit/s Ethernet technologies are used together with a reduced TCP/IP protocol implemented in FPGA for a reliable transport between custom electronics and commercial computing hardware. A 56 Gbit/s Infiniband FDR Clos network has been chosen for the event builder. This paper presents the implementation and performancemore » of the event-building system.« less

A Hierarchical Convolutional Neural Network for vesicle fusion event classification.

PubMed

Li, Haohan; Mao, Yunxiang; Yin, Zhaozheng; Xu, Yingke

2017-09-01

Quantitative analysis of vesicle exocytosis and classification of different modes of vesicle fusion from the fluorescence microscopy are of primary importance for biomedical researches. In this paper, we propose a novel Hierarchical Convolutional Neural Network (HCNN) method to automatically identify vesicle fusion events in time-lapse Total Internal Reflection Fluorescence Microscopy (TIRFM) image sequences. Firstly, a detection and tracking method is developed to extract image patch sequences containing potential fusion events. Then, a Gaussian Mixture Model (GMM) is applied on each image patch of the patch sequence with outliers rejected for robust Gaussian fitting. By utilizing the high-level time-series intensity change features introduced by GMM and the visual appearance features embedded in some key moments of the fusion process, the proposed HCNN architecture is able to classify each candidate patch sequence into three classes: full fusion event, partial fusion event and non-fusion event. Finally, we validate the performance of our method on 9 challenging datasets that have been annotated by cell biologists, and our method achieves better performances when comparing with three previous methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Secret Forwarding of Events over Distributed Publish/Subscribe Overlay Network.

PubMed

Yoon, Young; Kim, Beom Heyn

2016-01-01

Publish/subscribe is a communication paradigm where loosely-coupled clients communicate in an asynchronous fashion. Publish/subscribe supports the flexible development of large-scale, event-driven and ubiquitous systems. Publish/subscribe is prevalent in a number of application domains such as social networking, distributed business processes and real-time mission-critical systems. Many publish/subscribe applications are sensitive to message loss and violation of privacy. To overcome such issues, we propose a novel method of using secret sharing and replication techniques. This is to reliably and confidentially deliver decryption keys along with encrypted publications even under the presence of several Byzantine brokers across publish/subscribe overlay networks. We also propose a framework for dynamically and strategically allocating broker replicas based on flexibly definable criteria for reliability and performance. Moreover, a thorough evaluation is done through a case study on social networks using the real trace of interactions among Facebook users.

The design and implementation of signal decomposition system of CL multi-wavelet transform based on DSP builder

NASA Astrophysics Data System (ADS)

Huang, Yan; Wang, Zhihui

2015-12-01

With the development of FPGA, DSP Builder is widely applied to design system-level algorithms. The algorithm of CL multi-wavelet is more advanced and effective than scalar wavelets in processing signal decomposition. Thus, a system of CL multi-wavelet based on DSP Builder is designed for the first time in this paper. The system mainly contains three parts: a pre-filtering subsystem, a one-level decomposition subsystem and a two-level decomposition subsystem. It can be converted into hardware language VHDL by the Signal Complier block that can be used in Quartus II. After analyzing the energy indicator, it shows that this system outperforms Daubenchies wavelet in signal decomposition. Furthermore, it has proved to be suitable for the implementation of signal fusion based on SoPC hardware, and it will become a solid foundation in this new field.

Sampled-data consensus in switching networks of integrators based on edge events

NASA Astrophysics Data System (ADS)

Xiao, Feng; Meng, Xiangyu; Chen, Tongwen

2015-02-01

This paper investigates the event-driven sampled-data consensus in switching networks of multiple integrators and studies both the bidirectional interaction and leader-following passive reaction topologies in a unified framework. In these topologies, each information link is modelled by an edge of the information graph and assigned a sequence of edge events, which activate the mutual data sampling and controller updates of the two linked agents. Two kinds of edge-event-detecting rules are proposed for the general asynchronous data-sampling case and the synchronous periodic event-detecting case. They are implemented in a distributed fashion, and their effectiveness in reducing communication costs and solving consensus problems under a jointly connected topology condition is shown by both theoretical analysis and simulation examples.

An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks.

PubMed

Raza, Waseem; Arshad, Farzana; Ahmed, Imran; Abdul, Wadood; Ghouzali, Sanaa; Niaz, Iftikhar Azim; Javaid, Nadeem

2016-11-04

In this paper, a novel routing strategy to cater the energy consumption and delay sensitivity issues in deep underwater wireless sensor networks is proposed. This strategy is named as ESDR: Event Segregation based Delay sensitive Routing. In this strategy sensed events are segregated on the basis of their criticality and, are forwarded to their respective destinations based on forwarding functions. These functions depend on different routing metrics like: Signal Quality Index, Localization free Signal to Noise Ratio, Energy Cost Function and Depth Dependent Function. The problem of incomparable values of previously defined forwarding functions causes uneven delays in forwarding process. Hence forwarding functions are redefined to ensure their comparable values in different depth regions. Packet forwarding strategy is based on the event segregation approach which forwards one third of the generated events (delay sensitive) to surface sinks and two third events (normal events) are forwarded to mobile sinks. Motion of mobile sinks is influenced by the relative distribution of normal nodes. We have also incorporated two different mobility patterns named as; adaptive mobility and uniform mobility for mobile sinks. The later one is implemented for collecting the packets generated by the normal nodes. These improvements ensure optimum holding time, uniform delay and in-time reporting of delay sensitive events. This scheme is compared with the existing ones and outperforms the existing schemes in terms of network lifetime, delay and throughput.

Intelligent Support System of Steel Technical Preparation in an Arc Furnace: Functional Scheme of Interactive Builder of the Multi Objective Optimization Problem

NASA Astrophysics Data System (ADS)

Logunova, O. S.; Sibileva, N. S.

2017-12-01

The purpose of the study is to increase the efficiency of the steelmaking process in large capacity arc furnace on the basis of implementation a new decision-making system about the composition of charge materials. The authors proposed an interactive builder for the formation of the optimization problem, taking into account the requirements of the customer, normative documents and stocks of charge materials in the warehouse. To implement the interactive builder, the sets of deterministic and stochastic model components are developed, as well as a list of preferences of criteria and constraints.

Discrete-event simulation of a wide-area health care network.

PubMed Central

McDaniel, J G

1995-01-01

OBJECTIVE: Predict the behavior and estimate the telecommunication cost of a wide-area message store-and-forward network for health care providers that uses the telephone system. DESIGN: A tool with which to perform large-scale discrete-event simulations was developed. Network models for star and mesh topologies were constructed to analyze the differences in performances and telecommunication costs. The distribution of nodes in the network models approximates the distribution of physicians, hospitals, medical labs, and insurers in the Province of Saskatchewan, Canada. Modeling parameters were based on measurements taken from a prototype telephone network and a survey conducted at two medical clinics. Simulation studies were conducted for both topologies. RESULTS: For either topology, the telecommunication cost of a network in Saskatchewan is projected to be less than $100 (Canadian) per month per node. The estimated telecommunication cost of the star topology is approximately half that of the mesh. Simulations predict that a mean end-to-end message delivery time of two hours or less is achievable at this cost. A doubling of the data volume results in an increase of less than 50% in the mean end-to-end message transfer time. CONCLUSION: The simulation models provided an estimate of network performance and telecommunication cost in a specific Canadian province. At the expected operating point, network performance appeared to be relatively insensitive to increases in data volume. Similar results might be anticipated in other rural states and provinces in North America where a telephone-based network is desired. PMID:7583646

Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

NASA Astrophysics Data System (ADS)

Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.; Bagby, L.; Baller, B.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Bugel, L.; Camilleri, L.; Caratelli, D.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anadón, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Esquivel, J.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; James, C.; de Vries, J. Jan; Jen, C.-M.; Jiang, L.; Johnson, R. A.; Jones, B. J. P.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Martinez Caicedo, D. A.; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; von Rohr, C. Rudolf; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Van de Water, R. G.; Viren, B.; Weber, M.; Weston, J.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Zeller, G. P.; Zennamo, J.; Zhang, C.

2017-03-01

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

SME Innovation and Learning: The Role of Networks and Crisis Events

ERIC Educational Resources Information Center

Saunders, Mark N. K.; Gray, David E; Goregaokar, Harshita

2014-01-01

Purpose: The purpose of this paper is to contribute to the literature on innovation and entrepreneurial learning by exploring how SMEs learn and innovate, how they use both formal and informal learning and in particular the role of networks and crisis events within their learning experience. Design/methodology/approach: Mixed method study,…

A PCIe Gen3 based readout for the LHCb upgrade

NASA Astrophysics Data System (ADS)

Bellato, M.; Collazuol, G.; D'Antone, I.; Durante, P.; Galli, D.; Jost, B.; Lax, I.; Liu, G.; Marconi, U.; Neufeld, N.; Schwemmer, R.; Vagnoni, V.

2014-06-01

The architecture of the data acquisition system foreseen for the LHCb upgrade, to be installed by 2018, is devised to readout events trigger-less, synchronously with the LHC bunch crossing rate at 40 MHz. Within this approach the readout boards act as a bridge between the front-end electronics and the High Level Trigger (HLT) computing farm. The baseline design for the LHCb readout is an ATCA board requiring dedicated crates. A local area standard network protocol is implemented in the on-board FPGAs to read out the data. The alternative solution proposed here consists in building the readout boards as PCIe peripherals of the event-builder servers. The main architectural advantage is that protocol and link-technology of the event-builder can be left open until very late, to profit from the most cost-effective industry technology available at the time of the LHC LS2.

Builder 3 & 2. Naval Education and Training Command Rate Training Manual and Nonresident Career Course. Revised.

ERIC Educational Resources Information Center

Countryman, Gene L.

This Rate Training Manual (Textbook) and Nonresident Career Course form a correspondence, self-study package to provide information related to tasks assigned to Builders Third and Second Class. Focus is on constructing, maintaining, and repairing wooden, concrete, and masonry structures, concrete pavement, and waterfront and underwater structures;…

Secret Forwarding of Events over Distributed Publish/Subscribe Overlay Network

PubMed Central

Kim, Beom Heyn

2016-01-01

Publish/subscribe is a communication paradigm where loosely-coupled clients communicate in an asynchronous fashion. Publish/subscribe supports the flexible development of large-scale, event-driven and ubiquitous systems. Publish/subscribe is prevalent in a number of application domains such as social networking, distributed business processes and real-time mission-critical systems. Many publish/subscribe applications are sensitive to message loss and violation of privacy. To overcome such issues, we propose a novel method of using secret sharing and replication techniques. This is to reliably and confidentially deliver decryption keys along with encrypted publications even under the presence of several Byzantine brokers across publish/subscribe overlay networks. We also propose a framework for dynamically and strategically allocating broker replicas based on flexibly definable criteria for reliability and performance. Moreover, a thorough evaluation is done through a case study on social networks using the real trace of interactions among Facebook users. PMID:27367610

Predictability of Extreme Climate Events via a Complex Network Approach

NASA Astrophysics Data System (ADS)

Muhkin, D.; Kurths, J.

2017-12-01

We analyse climate dynamics from a complex network approach. This leads to an inverse problem: Is there a backbone-like structure underlying the climate system? For this we propose a method to reconstruct and analyze a complex network from data generated by a spatio-temporal dynamical system. This approach enables us to uncover relations to global circulation patterns in oceans and atmosphere. This concept is then applied to Monsoon data; in particular, we develop a general framework to predict extreme events by combining a non-linear synchronization technique with complex networks. Applying this method, we uncover a new mechanism of extreme floods in the eastern Central Andes which could be used for operational forecasts. Moreover, we analyze the Indian Summer Monsoon (ISM) and identify two regions of high importance. By estimating an underlying critical point, this leads to an improved prediction of the onset of the ISM; this scheme was successful in 2016 and 2017.

Virtualization of Event Sources in Wireless Sensor Networks for the Internet of Things

PubMed Central

Martínez, Néstor Lucas; Martínez, José-Fernán; Díaz, Vicente Hernández

2014-01-01

Wireless Sensor Networks (WSNs) are generally used to collect information from the environment. The gathered data are delivered mainly to sinks or gateways that become the endpoints where applications can retrieve and process such data. However, applications would also expect from a WSN an event-driven operational model, so that they can be notified whenever occur some specific environmental changes instead of continuously analyzing the data provided periodically. In either operational model, WSNs represent a collection of interconnected objects, as outlined by the Internet of Things. Additionally, in order to fulfill the Internet of Things principles, Wireless Sensor Networks must have a virtual representation that allows indirect access to their resources, a model that should also include the virtualization of event sources in a WSN. Thus, in this paper a model for a virtual representation of event sources in a WSN is proposed. They are modeled as internet resources that are accessible by any internet application, following an Internet of Things approach. The model has been tested in a real implementation where a WSN has been deployed in an open neighborhood environment. Different event sources have been identified in the proposed scenario, and they have been represented following the proposed model. PMID:25470489

Virtualization of event sources in wireless sensor networks for the internet of things.

PubMed

Lucas Martínez, Néstor; Martínez, José-Fernán; Hernández Díaz, Vicente

2014-12-01

Wireless Sensor Networks (WSNs) are generally used to collect information from the environment. The gathered data are delivered mainly to sinks or gateways that become the endpoints where applications can retrieve and process such data. However, applications would also expect from a WSN an event-driven operational model, so that they can be notified whenever occur some specific environmental changes instead of continuously analyzing the data provided periodically. In either operational model, WSNs represent a collection of interconnected objects, as outlined by the Internet of Things. Additionally, in order to fulfill the Internet of Things principles, Wireless Sensor Networks must have a virtual representation that allows indirect access to their resources, a model that should also include the virtualization of event sources in a WSN. Thus, in this paper a model for a virtual representation of event sources in a WSN is proposed. They are modeled as internet resources that are accessible by any internet application, following an Internet of Things approach. The model has been tested in a real implementation where a WSN has been deployed in an open neighborhood environment. Different event sources have been identified in the proposed scenario, and they have been represented following the proposed model.

Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

DOE PAGES

Acciarri, R.; Adams, C.; An, R.; ...

2017-03-14

Here, we present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. Lastly, we also address technical issues that arise when applying this technique to data from a large LArTPCmore » at or near ground level.« less

Mining induced seismic event on an inactive fault in view of local surface and in mine underground networksS

NASA Astrophysics Data System (ADS)

Rudzinski, Lukasz; Lizurek, Grzegorz; Plesiewicz, Beata

2014-05-01

On 19th March 2013 tremor shook the surface of Polkowice town were "Rudna" mine is located. This event of ML=4.2 was third most powerful seismic event recorded in Legnica Głogów Copper District (LGCD). Citizens of the area reported that felt tremors were bigger and last longer than any other ones felt in last couple years. The event was studied with use of two different networks: underground network of "Rudna" mine and surface local network run by IGF PAS (LUMINEOS network). The first one is composed of 32 vertical seismometers at mining level, except 5 sensors placed in elevator shafts, seismometers location depth varies from 300 down to 1000 meters below surface. The seismometers used in this network are vertical short period Willmore MkII and MkIII sensors, with the frequency band from 1Hz to 100Hz. At the beginning of 2013th the local surface network of the Institute of Geophysics Polish Academy of Sciences (IGF PAS) with acronym LUMINEOS was installed under agreement with KGHM SA and "Rudna" mine officials. This network at the moment of the March 19th 2013 event was composed of 4 short-period one-second triaxial seismometers LE-3D/1s manufactured by Lenartz Electronics. Analysis of spectral parameters of the records from in mine seismic system and surface LUMINEOS network along with broadband station KSP record were carried out. Location of the event was close to the Rudna Główna fault zone, the nodal planes orientations determined with two different approaches were almost parallel to the strike of the fault. The mechanism solutions were also obtained in form of Full Moment Tensor inversion from P wave amplitude pulses of underground records and waveform inversion of surface network seismograms. Final results of the seismic analysis along with macroseismic survey and observed effects from the destroyed part of the mining panel indicate that the mechanism of the event was thrust faulting on inactive tectonic fault. The results confirm that the fault zones

Space fabrication demonstration system. [beam builder and induction fastening

NASA Technical Reports Server (NTRS)

1980-01-01

The development effort on the composite beam cap fabricator was completed within cost and close to abbreviated goals. The design and analysis of flight weight primary and secondary beam builder structures proceeded satisfactorily but remains curtailed until further funding is made available to complete the work. The induction fastening effort remains within cost and schedule constraints. Tests of the LARC prototype induction welder is continuing in an instrumented test stand comprised of a Dumore drill press (air over oil feed for variable applied loads) and a dynamometer to measure actual welding loads. Continued testing shows that the interface screening must be well impregnated with resin to ensure proper flow when bonding graphite-acrylic lap shear samples. Specimens prepared from 0.030 inch thick graphite-polyethersulfone are also available for future induction fastening evaluation.

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

PubMed

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

2018-05-17

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

Discrete event command and control for networked teams with multiple missions

NASA Astrophysics Data System (ADS)

Lewis, Frank L.; Hudas, Greg R.; Pang, Chee Khiang; Middleton, Matthew B.; McMurrough, Christopher

2009-05-01

During mission execution in military applications, the TRADOC Pamphlet 525-66 Battle Command and Battle Space Awareness capabilities prescribe expectations that networked teams will perform in a reliable manner under changing mission requirements, varying resource availability and reliability, and resource faults. In this paper, a Command and Control (C2) structure is presented that allows for computer-aided execution of the networked team decision-making process, control of force resources, shared resource dispatching, and adaptability to change based on battlefield conditions. A mathematically justified networked computing environment is provided called the Discrete Event Control (DEC) Framework. DEC has the ability to provide the logical connectivity among all team participants including mission planners, field commanders, war-fighters, and robotic platforms. The proposed data management tools are developed and demonstrated on a simulation study and an implementation on a distributed wireless sensor network. The results show that the tasks of multiple missions are correctly sequenced in real-time, and that shared resources are suitably assigned to competing tasks under dynamically changing conditions without conflicts and bottlenecks.

Group Sex Events and HIV/STI Risk in an Urban Network

PubMed Central

Friedman, Samuel R.; Bolyard, Melissa; Khan, Maria; Maslow, Carey; Sandoval, Milagros; Mateu-Gelabert, Pedro; Krauss, Beatrice; Aral, Sevgi O.

2012-01-01

Objectives To describe: a. the prevalence and individual and network characteristics of group sex events (GSE) and GSE attendees; and b. HIV/STI discordance among respondents who said they went to a GSE together. Methods and Design In a sociometric network study of risk partners (defined as sexual partners, persons with whom respondents attended a GSE, or drug-injection partners) in Brooklyn, NY, we recruited a high-risk sample of 465 adults. Respondents reported on GSE attendance, the characteristics of GSEs, and their own and others’ behaviors at GSEs. Sera and urines were collected and STI prevalence was assayed. Results Of the 465 participants, 36% had attended a GSE in the last year, 26% had sex during the most recent of these GSEs, and 13% had unprotected sex there. Certain subgroups (hard drug users, men who have sex with men, women who have sex with women, and sex workers) were more likely to attend and more likely to engage in risk behaviors at these events. Among 90 GSE dyads in which at least one partner named the other as someone with whom they attended a GSE in the previous three months, STI/HIV discordance was common (HSV-2: 45% of dyads, HIV: 12% of dyads, Chlamydia: 21% of dyads). Many GSEs had 10 or more participants, and multiple partnerships at GSEs were common. High attendance rates at GSEs among members of large networks may increase community vulnerability to STI/HIV, particularly since network data show that almost all members of a large sociometric risk network either had sex with a GSE attendee or had sex with someone who had sex with a GSE attended. Conclusions Self-reported GSE attendance and participation was common among this high-risk sample. STI/HIV discordance among GSE attendees was high, highlighting the potential transmission risk associated with GSEs. Research on sexual behaviors should incorporate measures of GSE behaviors as standard research protocol. Interventions should be developed to reduce transmission at GSEs. PMID

Neural network approach in multichannel auditory event-related potential analysis.

PubMed

Wu, F Y; Slater, J D; Ramsay, R E

1994-04-01

Even though there are presently no clearly defined criteria for the assessment of P300 event-related potential (ERP) abnormality, it is strongly indicated through statistical analysis that such criteria exist for classifying control subjects and patients with diseases resulting in neuropsychological impairment such as multiple sclerosis (MS). We have demonstrated the feasibility of artificial neural network (ANN) methods in classifying ERP waveforms measured at a single channel (Cz) from control subjects and MS patients. In this paper, we report the results of multichannel ERP analysis and a modified network analysis methodology to enhance automation of the classification rule extraction process. The proposed methodology significantly reduces the work of statistical analysis. It also helps to standardize the criteria of P300 ERP assessment and facilitate the computer-aided analysis on neuropsychological functions.

Ocean acidification causes bleaching and productivity loss in coral reef builders

PubMed Central

Anthony, K. R. N.; Kline, D. I.; Diaz-Pulido, G.; Dove, S.; Hoegh-Guldberg, O.

2008-01-01

Ocean acidification represents a key threat to coral reefs by reducing the calcification rate of framework builders. In addition, acidification is likely to affect the relationship between corals and their symbiotic dinoflagellates and the productivity of this association. However, little is known about how acidification impacts on the physiology of reef builders and how acidification interacts with warming. Here, we report on an 8-week study that compared bleaching, productivity, and calcification responses of crustose coralline algae (CCA) and branching (Acropora) and massive (Porites) coral species in response to acidification and warming. Using a 30-tank experimental system, we manipulated CO2 levels to simulate doubling and three- to fourfold increases [Intergovernmental Panel on Climate Change (IPCC) projection categories IV and VI] relative to present-day levels under cool and warm scenarios. Results indicated that high CO2 is a bleaching agent for corals and CCA under high irradiance, acting synergistically with warming to lower thermal bleaching thresholds. We propose that CO2 induces bleaching via its impact on photoprotective mechanisms of the photosystems. Overall, acidification impacted more strongly on bleaching and productivity than on calcification. Interestingly, the intermediate, warm CO2 scenario led to a 30% increase in productivity in Acropora, whereas high CO2 lead to zero productivity in both corals. CCA were most sensitive to acidification, with high CO2 leading to negative productivity and high rates of net dissolution. Our findings suggest that sensitive reef-building species such as CCA may be pushed beyond their thresholds for growth and survival within the next few decades whereas corals will show delayed and mixed responses. PMID:18988740

Ocean acidification causes bleaching and productivity loss in coral reef builders.

PubMed

Anthony, K R N; Kline, D I; Diaz-Pulido, G; Dove, S; Hoegh-Guldberg, O

2008-11-11

Ocean acidification represents a key threat to coral reefs by reducing the calcification rate of framework builders. In addition, acidification is likely to affect the relationship between corals and their symbiotic dinoflagellates and the productivity of this association. However, little is known about how acidification impacts on the physiology of reef builders and how acidification interacts with warming. Here, we report on an 8-week study that compared bleaching, productivity, and calcification responses of crustose coralline algae (CCA) and branching (Acropora) and massive (Porites) coral species in response to acidification and warming. Using a 30-tank experimental system, we manipulated CO(2) levels to simulate doubling and three- to fourfold increases [Intergovernmental Panel on Climate Change (IPCC) projection categories IV and VI] relative to present-day levels under cool and warm scenarios. Results indicated that high CO(2) is a bleaching agent for corals and CCA under high irradiance, acting synergistically with warming to lower thermal bleaching thresholds. We propose that CO(2) induces bleaching via its impact on photoprotective mechanisms of the photosystems. Overall, acidification impacted more strongly on bleaching and productivity than on calcification. Interestingly, the intermediate, warm CO(2) scenario led to a 30% increase in productivity in Acropora, whereas high CO(2) lead to zero productivity in both corals. CCA were most sensitive to acidification, with high CO(2) leading to negative productivity and high rates of net dissolution. Our findings suggest that sensitive reef-building species such as CCA may be pushed beyond their thresholds for growth and survival within the next few decades whereas corals will show delayed and mixed responses.

Development of a beam builder for automatic fabrication of large composite space structures

NASA Technical Reports Server (NTRS)

Bodle, J. G.

1979-01-01

The composite material beam builder which will produce triangular beams from pre-consolidated graphite/glass/thermoplastic composite material through automated mechanical processes is presented, side member storage, feed and positioning, ultrasonic welding, and beam cutoff are formed. Each process lends itself to modular subsystem development. Initial development is concentrated on the key processes for roll forming and ultrasonic welding composite thermoplastic materials. The construction and test of an experimental roll forming machine and ultrasonic welding process control techniques are described.

TwitterSensing: An Event-Based Approach for Wireless Sensor Networks Optimization Exploiting Social Media in Smart City Applications

PubMed Central

2018-01-01

Modern cities are subject to periodic or unexpected critical events, which may bring economic losses or even put people in danger. When some monitoring systems based on wireless sensor networks are deployed, sensing and transmission configurations of sensor nodes may be adjusted exploiting the relevance of the considered events, but efficient detection and classification of events of interest may be hard to achieve. In Smart City environments, several people spontaneously post information in social media about some event that is being observed and such information may be mined and processed for detection and classification of critical events. This article proposes an integrated approach to detect and classify events of interest posted in social media, notably in Twitter, and the assignment of sensing priorities to source nodes. By doing so, wireless sensor networks deployed in Smart City scenarios can be optimized for higher efficiency when monitoring areas under the influence of the detected events. PMID:29614060

TwitterSensing: An Event-Based Approach for Wireless Sensor Networks Optimization Exploiting Social Media in Smart City Applications.

PubMed

Costa, Daniel G; Duran-Faundez, Cristian; Andrade, Daniel C; Rocha-Junior, João B; Peixoto, João Paulo Just

2018-04-03

Modern cities are subject to periodic or unexpected critical events, which may bring economic losses or even put people in danger. When some monitoring systems based on wireless sensor networks are deployed, sensing and transmission configurations of sensor nodes may be adjusted exploiting the relevance of the considered events, but efficient detection and classification of events of interest may be hard to achieve. In Smart City environments, several people spontaneously post information in social media about some event that is being observed and such information may be mined and processed for detection and classification of critical events. This article proposes an integrated approach to detect and classify events of interest posted in social media, notably in Twitter , and the assignment of sensing priorities to source nodes. By doing so, wireless sensor networks deployed in Smart City scenarios can be optimized for higher efficiency when monitoring areas under the influence of the detected events.

Energy efficient data representation and aggregation with event region detection in wireless sensor networks

NASA Astrophysics Data System (ADS)

Banerjee, Torsha

Unlike conventional networks, wireless sensor networks (WSNs) are limited in power, have much smaller memory buffers, and possess relatively slower processing speeds. These characteristics necessitate minimum transfer and storage of information in order to prolong the network lifetime. In this dissertation, we exploit the spatio-temporal nature of sensor data to approximate the current values of the sensors based on readings obtained from neighboring sensors and itself. We propose a Tree based polynomial REGression algorithm, (TREG) that addresses the problem of data compression in wireless sensor networks. Instead of aggregated data, a polynomial function (P) is computed by the regression function, TREG. The coefficients of P are then passed to achieve the following goals: (i) The sink can get attribute values in the regions devoid of sensor nodes, and (ii) Readings over any portion of the region can be obtained at one time by querying the root of the tree. As the size of the data packet from each tree node to its parent remains constant, the proposed scheme scales very well with growing network density or increased coverage area. Since physical attributes exhibit a gradual change over time, we propose an iterative scheme, UPDATE_COEFF, which obviates the need to perform the regression function repeatedly and uses approximations based on previous readings. Extensive simulations are performed on real world data to demonstrate the effectiveness of our proposed aggregation algorithm, TREG. Results reveal that for a network density of 0.0025 nodes/m2, a complete binary tree of depth 4 could provide the absolute error to be less than 6%. A data compression ratio of about 0.02 is achieved using our proposed algorithm, which is almost independent of the tree depth. In addition, our proposed updating scheme makes the aggregation process faster while maintaining the desired error bounds. We also propose a Polynomial-based scheme that addresses the problem of Event Region

Source space analysis of event-related dynamic reorganization of brain networks.

PubMed

Ioannides, Andreas A; Dimitriadis, Stavros I; Saridis, George A; Voultsidou, Marotesa; Poghosyan, Vahe; Liu, Lichan; Laskaris, Nikolaos A

2012-01-01

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

Car Builder: Design, Construct and Test Your Own Cars. School Version with Lesson Plans. [CD-ROM].

ERIC Educational Resources Information Center

Highsmith, Joni Bitman

Car Builder is a scientific CD-ROM-based simulation program that lets students design, construct, modify, test, and compare their own cars. Students can design sedans, four-wheel-drive vehicles, vans, sport cars, and hot rods. They may select for aerodynamics, power, and racing ability, or economic and fuel efficiency. It is a program that teaches…

Trail-Based Search for Efficient Event Report to Mobile Actors in Wireless Sensor and Actor Networks.

PubMed

Xu, Zhezhuang; Liu, Guanglun; Yan, Haotian; Cheng, Bin; Lin, Feilong

2017-10-27

In wireless sensor and actor networks, when an event is detected, the sensor node needs to transmit an event report to inform the actor. Since the actor moves in the network to execute missions, its location is always unavailable to the sensor nodes. A popular solution is the search strategy that can forward the data to a node without its location information. However, most existing works have not considered the mobility of the node, and thus generate significant energy consumption or transmission delay. In this paper, we propose the trail-based search (TS) strategy that takes advantage of actor's mobility to improve the search efficiency. The main idea of TS is that, when the actor moves in the network, it can leave its trail composed of continuous footprints. The search packet with the event report is transmitted in the network to search the actor or its footprints. Once an effective footprint is discovered, the packet will be forwarded along the trail until it is received by the actor. Moreover, we derive the condition to guarantee the trail connectivity, and propose the redundancy reduction scheme based on TS (TS-R) to reduce nontrivial transmission redundancy that is generated by the trail. The theoretical and numerical analysis is provided to prove the efficiency of TS. Compared with the well-known expanding ring search (ERS), TS significantly reduces the energy consumption and search delay.

Earth Science Data Fusion with Event Building Approach

NASA Technical Reports Server (NTRS)

Lukashin, C.; Bartle, Ar.; Callaway, E.; Gyurjyan, V.; Mancilla, S.; Oyarzun, R.; Vakhnin, A.

2015-01-01

Objectives of the NASA Information And Data System (NAIADS) project are to develop a prototype of a conceptually new middleware framework to modernize and significantly improve efficiency of the Earth Science data fusion, big data processing and analytics. The key components of the NAIADS include: Service Oriented Architecture (SOA) multi-lingual framework, multi-sensor coincident data Predictor, fast into-memory data Staging, multi-sensor data-Event Builder, complete data-Event streaming (a work flow with minimized IO), on-line data processing control and analytics services. The NAIADS project is leveraging CLARA framework, developed in Jefferson Lab, and integrated with the ZeroMQ messaging library. The science services are prototyped and incorporated into the system. Merging the SCIAMACHY Level-1 observations and MODIS/Terra Level-2 (Clouds and Aerosols) data products, and ECMWF re- analysis will be used for NAIADS demonstration and performance tests in compute Cloud and Cluster environments.

HOS network-based classification of power quality events via regression algorithms

NASA Astrophysics Data System (ADS)

Palomares Salas, José Carlos; González de la Rosa, Juan José; Sierra Fernández, José María; Pérez, Agustín Agüera

2015-12-01

This work compares seven regression algorithms implemented in artificial neural networks (ANNs) supported by 14 power-quality features, which are based in higher-order statistics. Combining time and frequency domain estimators to deal with non-stationary measurement sequences, the final goal of the system is the implementation in the future smart grid to guarantee compatibility between all equipment connected. The principal results are based in spectral kurtosis measurements, which easily adapt to the impulsive nature of the power quality events. These results verify that the proposed technique is capable of offering interesting results for power quality (PQ) disturbance classification. The best results are obtained using radial basis networks, generalized regression, and multilayer perceptron, mainly due to the non-linear nature of data.

Event-triggered synchronization for reaction-diffusion complex networks via random sampling

NASA Astrophysics Data System (ADS)

Dong, Tao; Wang, Aijuan; Zhu, Huiyun; Liao, Xiaofeng

2018-04-01

In this paper, the synchronization problem of the reaction-diffusion complex networks (RDCNs) with Dirichlet boundary conditions is considered, where the data is sampled randomly. An event-triggered controller based on the sampled data is proposed, which can reduce the number of controller and the communication load. Under this strategy, the synchronization problem of the diffusion complex network is equivalently converted to the stability of a of reaction-diffusion complex dynamical systems with time delay. By using the matrix inequality technique and Lyapunov method, the synchronization conditions of the RDCNs are derived, which are dependent on the diffusion term. Moreover, it is found the proposed control strategy can get rid of the Zeno behavior naturally. Finally, a numerical example is given to verify the obtained results.

New Whole-House Solutions Case Study: A Production Builder's Passive House - Denver, Colorado

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

None

Brookfield Home’s first project is in a community called Midtown in Denver, Colorado, in which the builder took on the challenge of increased energy efficiency by creating a Passive House (PH)-certified model home. Brookfield worked with the U.S. Department of Energy’s Building America research team IBACOS to create the home, evaluate advanced building technologies, and use the home as a marketing tool for potential homebuyers. Brookfield also worked with KGA studio architects to create a new floor plan that would be constructed to the PH standard as an upgrade option.

Long-Term Responses of the Endemic Reef-Builder Cladocora caespitosa to Mediterranean Warming

PubMed Central

Kersting, Diego K.; Bensoussan, Nathaniel; Linares, Cristina

2013-01-01

Recurrent climate-induced mass-mortalities have been recorded in the Mediterranean Sea over the past 15 years. Cladocora caespitosa, the sole zooxanthellate scleractinian reef-builder in the Mediterranean, is among the organisms affected by these episodes. Extensive bioconstructions of this endemic coral are very rare at the present time and are threatened by several stressors. In this study, we assessed the long-term response of this temperate coral to warming sea-water in the Columbretes Islands (NW Mediterranean) and described, for the first time, the relationship between recurrent mortality events and local sea surface temperature (SST) regimes in the Mediterranean Sea. A water temperature series spanning more than 20 years showed a summer warming trend of 0.06°C per year and an increased frequency of positive thermal anomalies. Mortality resulted from tissue necrosis without massive zooxanthellae loss and during the 11-year study, necrosis was recorded during nine summers separated into two mortality periods (2003–2006 and 2008–2012). The highest necrosis rates were registered during the first mortality period, after the exceptionally hot summer of 2003. Although necrosis and temperature were significantly associated, the variability in necrosis rates during summers with similar thermal anomalies pointed to other acting factors. In this sense, our results showed that these differences were more closely related to the interannual temperature context and delayed thermal stress after extreme summers, rather than to acclimatisation and adaption processes. PMID:23951016

Using the relational event model (REM) to investigate the temporal dynamics of animal social networks.

PubMed

Tranmer, Mark; Marcum, Christopher Steven; Morton, F Blake; Croft, Darren P; de Kort, Selvino R

2015-03-01

Social dynamics are of fundamental importance in animal societies. Studies on nonhuman animal social systems often aggregate social interaction event data into a single network within a particular time frame. Analysis of the resulting network can provide a useful insight into the overall extent of interaction. However, through aggregation, information is lost about the order in which interactions occurred, and hence the sequences of actions over time. Many research hypotheses relate directly to the sequence of actions, such as the recency or rate of action, rather than to their overall volume or presence. Here, we demonstrate how the temporal structure of social interaction sequences can be quantified from disaggregated event data using the relational event model (REM). We first outline the REM, explaining why it is different from other models for longitudinal data, and how it can be used to model sequences of events unfolding in a network. We then discuss a case study on the European jackdaw, Corvus monedula , in which temporal patterns of persistence and reciprocity of action are of interest, and present and discuss the results of a REM analysis of these data. One of the strengths of a REM analysis is its ability to take into account different ways in which data are collected. Having explained how to take into account the way in which the data were collected for the jackdaw study, we briefly discuss the application of the model to other studies. We provide details of how the models may be fitted in the R statistical software environment and outline some recent extensions to the REM framework.

A computational approach to extinction events in chemical reaction networks with discrete state spaces.

PubMed

Johnston, Matthew D

2017-12-01

Recent work of Johnston et al. has produced sufficient conditions on the structure of a chemical reaction network which guarantee that the corresponding discrete state space system exhibits an extinction event. The conditions consist of a series of systems of equalities and inequalities on the edges of a modified reaction network called a domination-expanded reaction network. In this paper, we present a computational implementation of these conditions written in Python and apply the program on examples drawn from the biochemical literature. We also run the program on 458 models from the European Bioinformatics Institute's BioModels Database and report our results. Copyright © 2017 Elsevier Inc. All rights reserved.

The effect of social networks and social support on common mental disorders following specific life events.

PubMed

Maulik, P K; Eaton, W W; Bradshaw, C P

2010-08-01

This study examined the association between life events and common mental disorders while accounting for social networks and social supports. Participants included 1920 adults in the Baltimore Epidemiologic Catchment Area Cohort who were interviewed in 1993-1996, of whom 1071 were re-interviewed in 2004-2005. Generalized estimating equations were used to analyze the data. Social support from friends, spouse or relatives was associated with significantly reduced odds of panic disorder and psychological distress, after experiencing specific life events. Social networks or social support had no significant stress-buffering effect. Social networks and social support had almost no direct or buffering effect on major depressive disorder, and no effect on generalized anxiety disorder and alcohol abuse or dependence disorder. The significant association between social support and psychological distress, rather than diagnosable mental disorders, highlights the importance of social support, especially when the severity of a mental health related problem is low.

DOE Zero Energy Ready Home Case Study: BPC Green Builders — Trolle Residence, Danbury, CT

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

none,

2014-09-01

The builder of this 1,650-ft2 cabin won a Custom Home honor in the 2014 Housing Innovations Awards. The home meets Passive House Standards with 5.5-in. of foil-faced polysiocyanurate foam boards lining the outside walls, R-55 of rigid EPS foam under the slab, R-86 of blown cellulose in the attic, triple-pane windows, and a single ductless heat pump to heat and cool the entire home.

DOE Zero Energy Ready Home Case Study: Thrive Home Builders, Lowry Plan

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

Pacific Northwest National Laboratory

Thrive Home Builders built this 4,119-ft2 home at the Lowry development in Denver, Colorado, to the high-performance criteria of the U.S. Department of Energy’s Zero Energy Ready Home Program. Despite the dense positioning of the homes, mono-plane roof designs afforded plenty of space for the 8.68 kW of photovoltaic panels. With the PV, the home achieves a Home Energy Rating System (HERS) score of 4 and the home owners should enjoy energy bills of about $-11 a year. Without the PV, the home would score a HERS 38 (far lower than the HERS 80 to 100 of typical new homes).

Pickless event detection and location: The waveform correlation event detection system (WCEDS) revisited

DOE PAGES

Arrowsmith, Stephen John; Young, Christopher J.; Ballard, Sanford; ...

2016-01-01

The standard paradigm for seismic event monitoring breaks the event detection problem down into a series of processing stages that can be categorized at the highest level into station-level processing and network-level processing algorithms (e.g., Le Bras and Wuster (2002)). At the station-level, waveforms are typically processed to detect signals and identify phases, which may subsequently be updated based on network processing. At the network-level, phase picks are associated to form events, which are subsequently located. Furthermore, waveforms are typically directly exploited only at the station-level, while network-level operations rely on earth models to associate and locate the events thatmore » generated the phase picks.« less

Not so secret agents: Event-related potentials to semantic roles in visual event comprehension.

PubMed

Cohn, Neil; Paczynski, Martin; Kutas, Marta

2017-12-01

Research across domains has suggested that agents, the doers of actions, have a processing advantage over patients, the receivers of actions. We hypothesized that agents as "event builders" for discrete actions (e.g., throwing a ball, punching) build on cues embedded in their preparatory postures (e.g., reaching back an arm to throw or punch) that lead to (predictable) culminating actions, and that these cues afford frontloading of event structure processing. To test this hypothesis, we compared event-related brain potentials (ERPs) to averbal comic panels depicting preparatory agents (ex. reaching back an arm to punch) that cued specific actions with those to non-preparatory agents (ex. arm to the side) and patients that did not cue any specific actions. We also compared subsequent completed action panels (ex. agent punching patient) across conditions, where we expected an inverse pattern of ERPs indexing the differential costs of processing completed actions asa function of preparatory cues. Preparatory agents evoked a greater frontal positivity (600-900ms) relative to non-preparatory agents and patients, while subsequent completed actions panels following non-preparatory agents elicited a smaller frontal positivity (600-900ms). These results suggest that preparatory (vs. non-) postures may differentially impact the processing of agents and subsequent actions in real time. Copyright © 2017 Elsevier Inc. All rights reserved.

Proton Single Event Effects (SEE) Testing of the Myrinet Crossbar Switch and Network Interface Card

NASA Technical Reports Server (NTRS)

Howard, James W., Jr.; LaBel, Kenneth A.; Carts, Martin A.; Stattel, Ronald; Irwin, Timothy L.; Day, John H. (Technical Monitor)

2002-01-01

As part of the Remote Exploration and Experimentation Project (REE), work was performed to do a proton SEE (Single Event Effect) evaluation of the Myricom network protocol system (Myrinet). This testing included the evaluation of the Myrinet crossbar switch and the Network Interface Card (NIC). To this end, two crossbar switch devices and five components in the NIC were exposed to the proton beam at the University of California at Davis Crocker Nuclear Laboratory (CNL).

Detection of rain events in radiological early warning networks with spectro-dosimetric systems

NASA Astrophysics Data System (ADS)

Dąbrowski, R.; Dombrowski, H.; Kessler, P.; Röttger, A.; Neumaier, S.

2017-10-01

Short-term pronounced increases of the ambient dose equivalent rate, due to rainfall are a well-known phenomenon. Increases in the same order of magnitude or even below may also be caused by a nuclear or radiological event, i.e. by artificial radiation. Hence, it is important to be able to identify natural rain events in dosimetric early warning networks and to distinguish them from radiological events. Novel spectrometric systems based on scintillators may be used to differentiate between the two scenarios, because the measured gamma spectra provide significant nuclide-specific information. This paper describes three simple, automatic methods to check whether an dot H*(10) increase is caused by a rain event or by artificial radiation. These methods were applied to measurements of three spectrometric systems based on CeBr3, LaBr3 and SrI2 scintillation crystals, investigated and tested for their practicability at a free-field reference site of PTB.

An event- and network-level analysis of college students' maximum drinking day.

PubMed

Meisel, Matthew K; DiBello, Angelo M; Balestrieri, Sara G; Ott, Miles Q; DiGuiseppi, Graham T; Clark, Melissa A; Barnett, Nancy P

2018-04-01

Heavy episodic drinking is common among college students and remains a serious public health issue. Previous event-level research among college students has examined behaviors and individual-level characteristics that drive consumption and related consequences but often ignores the social network of people with whom these heavy drinking episodes occur. The main aim of the current study was to investigate the network of social connections between drinkers on their heaviest drinking occasions. Sociocentric network methods were used to collect information from individuals in the first-year class (N=1342) at one university. Past-month drinkers (N=972) reported on the characteristics of their heaviest drinking occasion in the past month and indicated who else among their network connections was present during this occasion. Average max drinking day indegree, or the total number of times a participant was nominated as being present on another students' heaviest drinking occasion, was 2.50 (SD=2.05). Network autocorrelation models indicated that max drinking day indegree (e.g., popularity on heaviest drinking occassions) and peers' number of drinks on their own maximum drinking occasions were significantly associated with participant maximum number of drinks, after controlling for demographic variables, pregaming, and global network indegree (e.g., popularity in the entire first-year class). Being present at other peers' heaviest drinking occasions is associated with greater drinking quantities on one's own heaviest drinking occasion. These findings suggest the potential for interventions that target peer influences within close social networks of drinkers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Trail-Based Search for Efficient Event Report to Mobile Actors in Wireless Sensor and Actor Networks †

PubMed Central

Xu, Zhezhuang; Liu, Guanglun; Yan, Haotian; Cheng, Bin; Lin, Feilong

2017-01-01

In wireless sensor and actor networks, when an event is detected, the sensor node needs to transmit an event report to inform the actor. Since the actor moves in the network to execute missions, its location is always unavailable to the sensor nodes. A popular solution is the search strategy that can forward the data to a node without its location information. However, most existing works have not considered the mobility of the node, and thus generate significant energy consumption or transmission delay. In this paper, we propose the trail-based search (TS) strategy that takes advantage of actor’s mobility to improve the search efficiency. The main idea of TS is that, when the actor moves in the network, it can leave its trail composed of continuous footprints. The search packet with the event report is transmitted in the network to search the actor or its footprints. Once an effective footprint is discovered, the packet will be forwarded along the trail until it is received by the actor. Moreover, we derive the condition to guarantee the trail connectivity, and propose the redundancy reduction scheme based on TS (TS-R) to reduce nontrivial transmission redundancy that is generated by the trail. The theoretical and numerical analysis is provided to prove the efficiency of TS. Compared with the well-known expanding ring search (ERS), TS significantly reduces the energy consumption and search delay. PMID:29077017

Experimental evidence of the synergistic effects of warming and invasive algae on a temperate reef-builder coral.

PubMed

Kersting, Diego K; Cebrian, Emma; Casado, Clara; Teixidó, Núria; Garrabou, Joaquim; Linares, Cristina

2015-12-22

In the current global climate change scenario, stressors overlap in space and time, and knowledge on the effects of their interaction is highly needed to understand and predict the response and resilience of organisms. Corals, among many other benthic organisms, are affected by an increasing number of global change-related stressors including warming and invasive species. In this study, the cumulative effects between warming and invasive algae were experimentally assessed on the temperate reef-builder coral Cladocora caespitosa. We first investigated the potential local adaptation to thermal stress in two distant populations subjected to contrasting thermal and necrosis histories. No significant differences were found between populations. Colonies from both populations suffered no necrosis after long-term exposure to temperatures up to 29 °C. Second, we tested the effects of the interaction of both warming and the presence of invasive algae. The combined exposure triggered critical synergistic effects on photosynthetic efficiency and tissue necrosis. At the end of the experiment, over 90% of the colonies subjected to warming and invasive algae showed signs of necrosis. The results are of particular concern when considering the predicted increase of extreme climatic events and the spread of invasive species in the Mediterranean and other seas in the future.

Quantitative Microbial Risk Assessment Tutorial – SDMProjectBuilder: Import Local Data Files to Identify and Modify Contamination Sources and Input Parameters

EPA Science Inventory

Twelve example local data support files are automatically downloaded when the SDMProjectBuilder is installed on a computer. They allow the user to modify values to parameters that impact the release, migration, fate, and transport of microbes within a watershed, and control delin...

Digital Learning Network Education Events of NASA's Extreme Environments Mission Operations

NASA Technical Reports Server (NTRS)

Paul, Heather; Guillory, Erika

2007-01-01

NASA's Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and web casting. The DLN has created a series of live education videoconferences connecting NASA s Extreme Environment Missions Operations (NEEMO) team to students across the United States. The programs are also extended to students around the world live web casting. The primary focus of the events is the vision for space exploration. During the programs, NEEMO Crewmembers including NASA astronauts, engineers and scientists inform and inspire students about the importance of exploration and share the impact of the project as it correlates with plans to return to the moon and explore the planet Mars. These events highlight interactivity. Students talk live with the aquanauts in Aquarius, the National Oceanic and Atmospheric Administration s underwater laboratory. With this program, NASA continues the Agency s tradition of investing in the nation's education programs. It is directly tied to the Agency's major education goal of attracting and retaining students in science, technology, and engineering disciplines. Before connecting with the aquanauts, the students conduct experiments of their own designed to coincide with mission objectives. This paper describes the events that took place in September 2006.

Rome: sinkhole events and network of underground cavities (Italy)

NASA Astrophysics Data System (ADS)

Nisio, Stefania; Ciotoli, Giancarlo

2016-04-01

The anthropogenic sinkholes in the city of Rome are closely linked to the network of underground cavities produced by human activities in more than two thousand years of history. Over the past fifteen years the increased frequency of intense rainfall events, favors sinkhole formation. The risk assessment induced by anthropogenic sinkhole is really difficult. However, a susceptibility of the territory to sinkholes can be more easily determined as the probability that an event may occur in a given space, with unique geological-morphological characteristics, and in an infinite time. A sinkhole susceptibility map of the Rome territory, up to the ring road, has been constructed by using Geographically Weighted Regression technique and geostatistics. The spatial regression model includes the analysis of more than 2700 anthropogenic sinkholes (recorded from 1875 to 2015), as well as geological, morphological, hydrological and predisposing anthropogenic characteristics of the study area. The numerous available data (underground cavities, the ancient entrances to the quarry, bunkers, etc.) facilitate the creation of a series of maps. The density map of the cavity, updated to 2015, showed that more than 20 km2 of the Roman territory are affected by underground cavities. The census of sinkholes (over 2700) shows that over 30 km2 has been affected by sinkholes. The final susceptibility map highlights that inside the Ring Road about 40 km2 of the territory (about 11%) have a very high probability of triggering a sinkhole event. The susceptibility map was also compared with the data of ground subsidence (InSAR) to obtain a predictive model.

A framework for incorporating DTI Atlas Builder registration into Tract-Based Spatial Statistics and a simulated comparison to standard TBSS.

PubMed

Leming, Matthew; Steiner, Rachel; Styner, Martin

2016-02-27

Tract-based spatial statistics (TBSS) 6 is a software pipeline widely employed in comparative analysis of the white matter integrity from diffusion tensor imaging (DTI) datasets. In this study, we seek to evaluate the relationship between different methods of atlas registration for use with TBSS and different measurements of DTI (fractional anisotropy, FA, axial diffusivity, AD, radial diffusivity, RD, and medial diffusivity, MD). To do so, we have developed a novel tool that builds on existing diffusion atlas building software, integrating it into an adapted version of TBSS called DAB-TBSS (DTI Atlas Builder-Tract-Based Spatial Statistics) by using the advanced registration offered in DTI Atlas Builder 7 . To compare the effectiveness of these two versions of TBSS, we also propose a framework for simulating population differences for diffusion tensor imaging data, providing a more substantive means of empirically comparing DTI group analysis programs such as TBSS. In this study, we used 33 diffusion tensor imaging datasets and simulated group-wise changes in this data by increasing, in three different simulations, the principal eigenvalue (directly altering AD), the second and third eigenvalues (RD), and all three eigenvalues (MD) in the genu, the right uncinate fasciculus, and the left IFO. Additionally, we assessed the benefits of comparing the tensors directly using a functional analysis of diffusion tensor tract statistics (FADTTS 10 ). Our results indicate comparable levels of FA-based detection between DAB-TBSS and TBSS, with standard TBSS registration reporting a higher rate of false positives in other measurements of DTI. Within the simulated changes investigated here, this study suggests that the use of DTI Atlas Builder's registration enhances TBSS group-based studies.

Distributed convex optimisation with event-triggered communication in networked systems

NASA Astrophysics Data System (ADS)

Liu, Jiayun; Chen, Weisheng

2016-12-01

This paper studies the distributed convex optimisation problem over directed networks. Motivated by practical considerations, we propose a novel distributed zero-gradient-sum optimisation algorithm with event-triggered communication. Therefore, communication and control updates just occur at discrete instants when some predefined condition satisfies. Thus, compared with the time-driven distributed optimisation algorithms, the proposed algorithm has the advantages of less energy consumption and less communication cost. Based on Lyapunov approaches, we show that the proposed algorithm makes the system states asymptotically converge to the solution of the problem exponentially fast and the Zeno behaviour is excluded. Finally, simulation example is given to illustrate the effectiveness of the proposed algorithm.

Convolutional neural networks for event-related potential detection: impact of the architecture.

PubMed

Cecotti, H

2017-07-01

The detection of brain responses at the single-trial level in the electroencephalogram (EEG) such as event-related potentials (ERPs) is a difficult problem that requires different processing steps to extract relevant discriminant features. While most of the signal and classification techniques for the detection of brain responses are based on linear algebra, different pattern recognition techniques such as convolutional neural network (CNN), as a type of deep learning technique, have shown some interests as they are able to process the signal after limited pre-processing. In this study, we propose to investigate the performance of CNNs in relation of their architecture and in relation to how they are evaluated: a single system for each subject, or a system for all the subjects. More particularly, we want to address the change of performance that can be observed between specifying a neural network to a subject, or by considering a neural network for a group of subjects, taking advantage of a larger number of trials from different subjects. The results support the conclusion that a convolutional neural network trained on different subjects can lead to an AUC above 0.9 by using an appropriate architecture using spatial filtering and shift invariant layers.

Words analysis of online Chinese news headlines about trending events: a complex network perspective.

PubMed

Li, Huajiao; Fang, Wei; An, Haizhong; Huang, Xuan

2015-01-01

Because the volume of information available online is growing at breakneck speed, keeping up with meaning and information communicated by the media and netizens is a new challenge both for scholars and for companies who must address public relations crises. Most current theories and tools are directed at identifying one website or one piece of online news and do not attempt to develop a rapid understanding of all websites and all news covering one topic. This paper represents an effort to integrate statistics, word segmentation, complex networks and visualization to analyze headlines' keywords and words relationships in online Chinese news using two samples: the 2011 Bohai Bay oil spill and the 2010 Gulf of Mexico oil spill. We gathered all the news headlines concerning the two trending events in the search results from Baidu, the most popular Chinese search engine. We used Simple Chinese Word Segmentation to segment all the headlines into words and then took words as nodes and considered adjacent relations as edges to construct word networks both using the whole sample and at the monthly level. Finally, we develop an integrated mechanism to analyze the features of words' networks based on news headlines that can account for all the keywords in the news about a particular event and therefore track the evolution of news deeply and rapidly.

Words Analysis of Online Chinese News Headlines about Trending Events: A Complex Network Perspective

PubMed Central

Li, Huajiao; Fang, Wei; An, Haizhong; Huang, Xuan

2015-01-01

Because the volume of information available online is growing at breakneck speed, keeping up with meaning and information communicated by the media and netizens is a new challenge both for scholars and for companies who must address public relations crises. Most current theories and tools are directed at identifying one website or one piece of online news and do not attempt to develop a rapid understanding of all websites and all news covering one topic. This paper represents an effort to integrate statistics, word segmentation, complex networks and visualization to analyze headlines’ keywords and words relationships in online Chinese news using two samples: the 2011 Bohai Bay oil spill and the 2010 Gulf of Mexico oil spill. We gathered all the news headlines concerning the two trending events in the search results from Baidu, the most popular Chinese search engine. We used Simple Chinese Word Segmentation to segment all the headlines into words and then took words as nodes and considered adjacent relations as edges to construct word networks both using the whole sample and at the monthly level. Finally, we develop an integrated mechanism to analyze the features of words’ networks based on news headlines that can account for all the keywords in the news about a particular event and therefore track the evolution of news deeply and rapidly. PMID:25807376

Life-Course Events, Social Networks, and the Emergence of Violence among Female Gang Members

ERIC Educational Resources Information Center

Fleisher, Mark S.; Krienert, Jessie L.

2004-01-01

Using data gathered from a multi-year field study, this article identifies specific life-course events shared by gang-affiliated women. Gangs emerge as a cultural adaptation or pro-social community response to poverty and racial isolation. Through the use of a social-network approach, data show that violence dramatically increases in the period…

A method of network topology optimization design considering application process characteristic

NASA Astrophysics Data System (ADS)

Wang, Chunlin; Huang, Ning; Bai, Yanan; Zhang, Shuo

2018-03-01

Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.

Decentralized event-triggered synchronization of uncertain Markovian jumping neutral-type neural networks with mixed delays.

PubMed

Senan, Sibel; Syed Ali, M; Vadivel, R; Arik, Sabri

2017-02-01

In this study, we present an approach for the decentralized event-triggered synchronization of Markovian jumping neutral-type neural networks with mixed delays. We present a method for designing decentralized event-triggered synchronization, which only utilizes locally available information, in order to determine the time instants for transmission from sensors to a central controller. By applying a novel Lyapunov-Krasovskii functional, as well as using the reciprocal convex combination method and some inequality techniques such as Jensen's inequality, we obtain several sufficient conditions in terms of a set of linear matrix inequalities (LMIs) under which the delayed neural networks are stochastically stable in terms of the error systems. Finally, we conclude that the drive systems synchronize stochastically with the response systems. We show that the proposed stability criteria can be verified easily using the numerically efficient Matlab LMI toolbox. The effectiveness and feasibility of the results obtained are verified by numerical examples. Copyright © 2016 Elsevier Ltd. All rights reserved.

AgBufferBuilder: A geographic information system (GIS) tool for precision design and performance assessment of filter strips

Treesearch

M. G. Dosskey; S. Neelakantan; T. G. Mueller; T. Kellerman; M. J. Helmers; E. Rienzi

2015-01-01

Spatially nonuniform runoif reduces the water qua1iry perfortnance of constant- width filter strips. A geographic inlormation system (Gls)-based tool was developed and tested that ernploys terrain analysis to account lor spatially nonuniform runoffand produce more ellbctive filter strip designs.The computer program,AgBufTerBuilder, runs with ATcGIS versions 10.0 and 10...

CONNJUR Workflow Builder: A software integration environment for spectral reconstruction

PubMed Central

Fenwick, Matthew; Weatherby, Gerard; Vyas, Jay; Sesanker, Colbert; Martyn, Timothy O.; Ellis, Heidi J.C.; Gryk, Michael R.

2015-01-01

CONNJUR Workflow Builder (WB) is an open-source software integration environment that leverages existing spectral reconstruction tools to create a synergistic, coherent platform for converting biomolecular NMR data from the time domain to the frequency domain. WB provides data integration of primary data and metadata using a relational database, and includes a library of pre-built workflows for processing time domain data. WB simplifies maximum entropy reconstruction, facilitating the processing of non-uniformly sampled time domain data. As will be shown in the paper, the unique features of WB provide it with novel abilities to enhance the quality, accuracy, and fidelity of the spectral reconstruction process. WB also provides features which promote collaboration, education, parameterization, and non-uniform data sets along with processing integrated with the Rowland NMR Toolkit (RNMRTK) and NMRPipe software packages. WB is available free of charge in perpetuity, dual-licensed under the MIT and GPL open source licenses. PMID:26066803

CONNJUR Workflow Builder: a software integration environment for spectral reconstruction.

PubMed

Fenwick, Matthew; Weatherby, Gerard; Vyas, Jay; Sesanker, Colbert; Martyn, Timothy O; Ellis, Heidi J C; Gryk, Michael R

2015-07-01

CONNJUR Workflow Builder (WB) is an open-source software integration environment that leverages existing spectral reconstruction tools to create a synergistic, coherent platform for converting biomolecular NMR data from the time domain to the frequency domain. WB provides data integration of primary data and metadata using a relational database, and includes a library of pre-built workflows for processing time domain data. WB simplifies maximum entropy reconstruction, facilitating the processing of non-uniformly sampled time domain data. As will be shown in the paper, the unique features of WB provide it with novel abilities to enhance the quality, accuracy, and fidelity of the spectral reconstruction process. WB also provides features which promote collaboration, education, parameterization, and non-uniform data sets along with processing integrated with the Rowland NMR Toolkit (RNMRTK) and NMRPipe software packages. WB is available free of charge in perpetuity, dual-licensed under the MIT and GPL open source licenses.

Using wireless sensor networks to improve understanding of rain-on-snow events across the Sierra Nevada

NASA Astrophysics Data System (ADS)

Maurer, T.; Avanzi, F.; Oroza, C.; Malek, S. A.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2017-12-01

We use data gathered from Wireless Sensor Networks (WSNs) between 2008 and 2017 to investigate the temporal/spatial patterns of rain-on-snow events in three river basins of California's Sierra Nevada. Rain-on-snow transitions occur across a broad elevation range (several hundred meters), both between storms and within a given storm, creating an opportunity to use spatially and temporally dense data to forecast and study them. WSNs collect snow depth; meteorological data; and soil moisture and temperature data across relatively dense sensor clusters. Ten to twelve measurement nodes per cluster are placed across 1-km2 areas in locations representative of snow patterns at larger scales. Combining precipitation and snow data from snow-pillow and climate stations with an estimation of dew-point temperature from WSNs, we determine the frequency, timing, and geographic extent of rain-on-snow events. We compare these results to WSN data to evaluate the impact of rain-on-snow events on snowpack energy balance, density, and depth as well as on soil moisture. Rain-on-snow events are compared to dry warm-weather days to identify the relative importance of rain and radiation as the primary energy input to the snowpack for snowmelt generation. An intercomparison of rain-on-snow events for the WSNs in the Feather, American, and Kings River basins captures the behavior across a 2° latitudinal range of the Sierra Nevada. Rain-on-snow events are potentially a more important streamflow generation mechanism in the lower-elevation Feather River basin. Snowmelt response to rain-on-snow events changes throughout the wet season, with later events resulting in more melt due to snow isothermal conditions, coarser grain size, and more-homogeneous snow stratigraphy. Regardless of snowmelt response, rain-on-snow events tend to result in decreasing snow depth and a corresponding increase in snow density. Our results demonstrate that strategically placed WSNs can provide the necessary data at

Quantitative Microbial Risk Assessment Tutorial - SDMProjectBuilder: Import Local Data Files to Identify and Modify Contamination Sources and Input ParametersUpdated 2017

EPA Science Inventory

Twelve example local data support files are automatically downloaded when the SDMProjectBuilder is installed on a computer. They allow the user to modify values to parameters that impact the release, migration, fate, and transport of microbes within a watershed, and control delin...

Identification of interactive gene networks: a novel approach in gene array profiling of myometrial events during guinea pig pregnancy.

PubMed

Mason, Clifford W; Swaan, Peter W; Weiner, Carl P

2006-06-01

The transition from myometrial quiescence to activation is poorly understood, and the analysis of array data is limited by the available data mining tools. We applied functional analysis and logical operations along regulatory gene networks to identify molecular processes and pathways underlying quiescence and activation. We analyzed some 18,400 transcripts and variants in guinea pig myometrium at stages corresponding to quiescence and activation, and compared them to the nonpregnant (control) counterpart using a functional mapping tool, MetaCore (GeneGo, St Joseph, MI) to identify novel gene networks composed of biological pathways during mid (MP) and late (LP) pregnancy. Genes altered during quiescence and or activation were identified following gene specific comparisons with myometrium from nonpregnant animals, and then linked to curated pathways and formulated networks. The MP and LP networks were subtracted from each other to identify unique genomic events during those periods. For example, changes 2-fold or greater in genes mediating protein biosynthesis, programmed cell death, microtubule polymerization, and microtubule based movement were noted during the transition to LP. We describe a novel approach combining microarrays and genetic data to identify networks associated with normal myometrial events. The resulting insights help identify potential biomarkers and permit future targeted investigations of these pathways or networks to confirm or refute their importance.

Dynamic sensing model for accurate delectability of environmental phenomena using event wireless sensor network

NASA Astrophysics Data System (ADS)

Missif, Lial Raja; Kadhum, Mohammad M.

2017-09-01

Wireless Sensor Network (WSN) has been widely used for monitoring where sensors are deployed to operate independently to sense abnormal phenomena. Most of the proposed environmental monitoring systems are designed based on a predetermined sensing range which does not reflect the sensor reliability, event characteristics, and the environment conditions. Measuring of the capability of a sensor node to accurately detect an event within a sensing field is of great important for monitoring applications. This paper presents an efficient mechanism for even detection based on probabilistic sensing model. Different models have been presented theoretically in this paper to examine their adaptability and applicability to the real environment applications. The numerical results of the experimental evaluation have showed that the probabilistic sensing model provides accurate observation and delectability of an event, and it can be utilized for different environment scenarios.

The Gene Set Builder: collation, curation, and distribution of sets of genes

PubMed Central

Yusuf, Dimas; Lim, Jonathan S; Wasserman, Wyeth W

2005-01-01

Background In bioinformatics and genomics, there are many applications designed to investigate the common properties for a set of genes. Often, these multi-gene analysis tools attempt to reveal sequential, functional, and expressional ties. However, while tremendous effort has been invested in developing tools that can analyze a set of genes, minimal effort has been invested in developing tools that can help researchers compile, store, and annotate gene sets in the first place. As a result, the process of making or accessing a set often involves tedious and time consuming steps such as finding identifiers for each individual gene. These steps are often repeated extensively to shift from one identifier type to another; or to recreate a published set. In this paper, we present a simple online tool which – with the help of the gene catalogs Ensembl and GeneLynx – can help researchers build and annotate sets of genes quickly and easily. Description The Gene Set Builder is a database-driven, web-based tool designed to help researchers compile, store, export, and share sets of genes. This application supports the 17 eukaryotic genomes found in version 32 of the Ensembl database, which includes species from yeast to human. User-created information such as sets and customized annotations are stored to facilitate easy access. Gene sets stored in the system can be "exported" in a variety of output formats – as lists of identifiers, in tables, or as sequences. In addition, gene sets can be "shared" with specific users to facilitate collaborations or fully released to provide access to published results. The application also features a Perl API (Application Programming Interface) for direct connectivity to custom analysis tools. A downloadable Quick Reference guide and an online tutorial are available to help new users learn its functionalities. Conclusion The Gene Set Builder is an Ensembl-facilitated online tool designed to help researchers compile and manage sets of

Exact event-driven implementation for recurrent networks of stochastic perfect integrate-and-fire neurons.

PubMed

Taillefumier, Thibaud; Touboul, Jonathan; Magnasco, Marcelo

2012-12-01

In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models delineate a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision. However, there are inherent difficulties in simulating their networks' dynamics in silico with standard numerical discretization schemes. Indeed, the well-posedness of the evolution of such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. Here, we answer these issues for perfect stochastic integrate-and-fire neurons by designing an exact event-driven algorithm for the simulation of recurrent networks, with delayed Dirac-like interactions. In addition to being exact from the mathematical standpoint, our proposed method is highly efficient numerically. We envision that our algorithm is especially indicated for studying the emergence of polychronized motifs in networks evolving under spike-timing-dependent plasticity with intrinsic noise.

How activation, entanglement, and searching a semantic network contribute to event memory.

PubMed

Nelson, Douglas L; Kitto, Kirsty; Galea, David; McEvoy, Cathy L; Bruza, Peter D

2013-08-01

Free-association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long-lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist-cuing, primed free-association, intralist-cuing, and single-item recognition tasks. The findings also show that when a related word is presented in order to cue the recall of a studied word, the cue activates the target in an array of related words that distract and reduce the probability of the target's selection. The activation of the semantic network produces priming benefits during encoding, and search costs during retrieval. In extralist cuing, recall is a negative function of cue-to-distractor strength, and a positive function of neighborhood density, cue-to-target strength, and target-to-cue strength. We show how these four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks, indicating that the contribution of the semantic network varies with the context provided by the task. Finally, we evaluate spreading-activation and quantum-like entanglement explanations for the priming effects produced by neighborhood density.

Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems.

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-01-01

This paper presents a novel adaptive neural network (NN) control of single-input and single-output uncertain nonlinear discrete-time systems under event sampled NN inputs. In this control scheme, the feedback signals are transmitted, and the NN weights are tuned in an aperiodic manner at the event sampled instants. After reviewing the NN approximation property with event sampled inputs, an adaptive state estimator (SE), consisting of linearly parameterized NNs, is utilized to approximate the unknown system dynamics in an event sampled context. The SE is viewed as a model and its approximated dynamics and the state vector, during any two events, are utilized for the event-triggered controller design. An adaptive event-trigger condition is derived by using both the estimated NN weights and a dead-zone operator to determine the event sampling instants. This condition both facilitates the NN approximation and reduces the transmission of feedback signals. The ultimate boundedness of both the NN weight estimation error and the system state vector is demonstrated through the Lyapunov approach. As expected, during an initial online learning phase, events are observed more frequently. Over time with the convergence of the NN weights, the inter-event times increase, thereby lowering the number of triggered events. These claims are illustrated through the simulation results.

Stepwise construction of a metabolic network in Event-B: The heat shock response.

PubMed

Sanwal, Usman; Petre, Luigia; Petre, Ion

2017-12-01

There is a high interest in constructing large, detailed computational models for biological processes. This is often done by putting together existing submodels and adding to them extra details/knowledge. The result of such approaches is usually a model that can only answer questions on a very specific level of detail, and thus, ultimately, is of limited use. We focus instead on an approach to systematically add details to a model, with formal verification of its consistency at each step. In this way, one obtains a set of reusable models, at different levels of abstraction, to be used for different purposes depending on the question to address. We demonstrate this approach using Event-B, a computational framework introduced to develop formal specifications of distributed software systems. We first describe how to model generic metabolic networks in Event-B. Then, we apply this method for modeling the biological heat shock response in eukaryotic cells, using Event-B refinement techniques. The advantage of using Event-B consists in having refinement as an intrinsic feature; this provides as a final result not only a correct model, but a chain of models automatically linked by refinement, each of which is provably correct and reusable. This is a proof-of-concept that refinement in Event-B is suitable for biomodeling, serving for mastering biological complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Simulating adverse event spontaneous reporting systems as preferential attachment networks: application to the Vaccine Adverse Event Reporting System.

PubMed

Scott, J; Botsis, T; Ball, R

2014-01-01

Spontaneous Reporting Systems [SRS] are critical tools in the post-licensure evaluation of medical product safety. Regulatory authorities use a variety of data mining techniques to detect potential safety signals in SRS databases. Assessing the performance of such signal detection procedures requires simulated SRS databases, but simulation strategies proposed to date each have limitations. We sought to develop a novel SRS simulation strategy based on plausible mechanisms for the growth of databases over time. We developed a simulation strategy based on the network principle of preferential attachment. We demonstrated how this strategy can be used to create simulations based on specific databases of interest, and provided an example of using such simulations to compare signal detection thresholds for a popular data mining algorithm. The preferential attachment simulations were generally structurally similar to our targeted SRS database, although they had fewer nodes of very high degree. The approach was able to generate signal-free SRS simulations, as well as mimicking specific known true signals. Explorations of different reporting thresholds for the FDA Vaccine Adverse Event Reporting System suggested that using proportional reporting ratio [PRR] > 3.0 may yield better signal detection operating characteristics than the more commonly used PRR > 2.0 threshold. The network analytic approach to SRS simulation based on the principle of preferential attachment provides an attractive framework for exploring the performance of safety signal detection algorithms. This approach is potentially more principled and versatile than existing simulation approaches. The utility of network-based SRS simulations needs to be further explored by evaluating other types of simulated signals with a broader range of data mining approaches, and comparing network-based simulations with other simulation strategies where applicable.

MCPB.py: A Python Based Metal Center Parameter Builder.

PubMed

Li, Pengfei; Merz, Kenneth M

2016-04-25

MCPB.py, a python based metal center parameter builder, has been developed to build force fields for the simulation of metal complexes employing the bonded model approach. It has an optimized code structure, with far fewer required steps than the previous developed MCPB program. It supports various AMBER force fields and more than 80 metal ions. A series of parametrization schemes to derive force constants and charge parameters are available within the program. We give two examples (one metalloprotein example and one organometallic compound example), indicating the program's ability to build reliable force fields for different metal ion containing complexes. The original version was released with AmberTools15. It is provided via the GNU General Public License v3.0 (GNU_GPL_v3) agreement and is free to download and distribute. MCPB.py provides a bridge between quantum mechanical calculations and molecular dynamics simulation software packages thereby enabling the modeling of metal ion centers. It offers an entry into simulating metal ions in a number of situations by providing an efficient way for researchers to handle the vagaries and difficulties associated with metal ion modeling.

A Distributed Compressive Sensing Scheme for Event Capture in Wireless Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Hou, Meng; Xu, Sen; Wu, Weiling; Lin, Fei

2018-01-01

Image signals which acquired by wireless visual sensor network can be used for specific event capture. This event capture is realized by image processing at the sink node. A distributed compressive sensing scheme is used for the transmission of these image signals from the camera nodes to the sink node. A measurement and joint reconstruction algorithm for these image signals are proposed in this paper. Make advantage of spatial correlation between images within a sensing area, the cluster head node which as the image decoder can accurately co-reconstruct these image signals. The subjective visual quality and the reconstruction error rate are used for the evaluation of reconstructed image quality. Simulation results show that the joint reconstruction algorithm achieves higher image quality at the same image compressive rate than the independent reconstruction algorithm.

Management of a Complex Open Channel Network During Flood Events

NASA Astrophysics Data System (ADS)

Franchini, M.; Valiani, A.; Schippa, L.; Mascellani, G.

2003-04-01

Most part of the area around Ferrara (Italy) is below the mean sea level and an extensive drainage system combined with several pump stations allows the use of this area for both urban development and industrial and agricultural activities. The three main channels of this hydraulic system constitute the Ferrara Inland Waterway (total length approximately 70 km), which connects the Po river near Ferrara to the sea. Because of the level difference between the upstream and dowstream ends of the waterway, three locks are located along it, each of them combined with a set of gates to control the water levels. During rainfall events, most of the water of the basin flows into the waterway and heavy precipitations sometimes cause flooding in several areas. This is due to the insufficiency of the channel network dimensions and an inadequate manual operation of the gates. This study presents a hydrological-hydraulic model for the entire Ferrara basin and a system of rules in order to operate the gates. In particular, their opening is designed to be regulated in real time by monitoring the water level in several sections along the channels. Besides flood peak attenuation, this operation strategy contributes also to the maintenance of a constant water level for irrigation and fluvial navigation during the dry periods. With reference to the flood event of May 1996, it is shown that this floodgate operation policy, unlike that which was actually adopted during that event, would lead to a significant flood peak attenuation, avoiding flooding in the area upstream of Ferrara.

Multi-voxel pattern classification differentiates personally experienced event memories from secondhand event knowledge.

PubMed

Chow, Tiffany E; Westphal, Andrew J; Rissman, Jesse

2018-04-11

Studies of autobiographical memory retrieval often use photographs to probe participants' memories for past events. Recent neuroimaging work has shown that viewing photographs depicting events from one's own life evokes a characteristic pattern of brain activity across a network of frontal, parietal, and medial temporal lobe regions that can be readily distinguished from brain activity associated with viewing photographs from someone else's life (Rissman, Chow, Reggente, and Wagner, 2016). However, it is unclear whether the neural signatures associated with remembering a personally experienced event are distinct from those associated with recognizing previously encountered photographs of an event. The present experiment used a novel functional magnetic resonance imaging (fMRI) paradigm to investigate putative differences in brain activity patterns associated with these distinct expressions of memory retrieval. Eighteen participants wore necklace-mounted digital cameras to capture events from their everyday lives over the course of three weeks. One week later, participants underwent fMRI scanning, where on each trial they viewed a sequence of photographs depicting either an event from their own life or from another participant's life and judged their memory for this event. Importantly, half of the trials featured photographic sequences that had been shown to participants during a laboratory session administered the previous day. Multi-voxel pattern analyses assessed the sensitivity of two brain networks of interest-as identified by a meta-analysis of prior autobiographical and laboratory-based memory retrieval studies-to the original source of the photographs (own life or other's life) and their experiential history as stimuli (previewed or non-previewed). The classification analyses revealed a striking dissociation: activity patterns within the autobiographical memory network were significantly more diagnostic than those within the laboratory-based network as to

Markov logic network based complex event detection under uncertainty

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Jia, Bin; Chen, Genshe; Chen, Hua-mei; Sullivan, Nichole; Pham, Khanh; Blasch, Erik

2018-05-01

In a cognitive reasoning system, the four-stage Observe-Orient-Decision-Act (OODA) reasoning loop is of interest. The OODA loop is essential for the situational awareness especially in heterogeneous data fusion. Cognitive reasoning for making decisions can take advantage of different formats of information such as symbolic observations, various real-world sensor readings, or the relationship between intelligent modalities. Markov Logic Network (MLN) provides mathematically sound technique in presenting and fusing data at multiple levels of abstraction, and across multiple intelligent sensors to conduct complex decision-making tasks. In this paper, a scenario about vehicle interaction is investigated, in which uncertainty is taken into consideration as no systematic approaches can perfectly characterize the complex event scenario. MLNs are applied to the terrestrial domain where the dynamic features and relationships among vehicles are captured through multiple sensors and information sources regarding the data uncertainty.

Development of an event search and download system for analyzing waveform data observed at seafloor seismic network, DONET

NASA Astrophysics Data System (ADS)

Takaesu, M.; Horikawa, H.; Sueki, K.; Kamiya, S.; Nakamura, T.; Nakano, M.; Takahashi, N.; Sonoda, A.; Tsuboi, S.

2014-12-01

Mega-thrust earthquakes are anticipated to occur in the Nankai Trough in southwest Japan. In the source areas, we installed seafloor seismic network, DONET (Dense Ocean-floor Network System for Earthquake and Tsunamis), in 2010 in order to monitor seismicity, crustal deformations, and tsunamis. DONET system consists of totally 20 stations, which is composed of six kinds of sensors; strong-motion and broadband seismometers, quartz and differential pressure gauges, hydrophone, and thermometer. The stations are densely distributed with an average spatial interval of 15-20 km and cover near coastal areas to the trench axis. Observed data are transferred to a land station through a fiber-optical cable and then to JAMSTEC (Japan Agency for Marine-Earth Science and Technology) data management center through a private network in real time. The data are based on WIN32 format in the private network and finally archived in SEED format in the management center to combine waveform data with related metadata. We are developing a web-based application system to easily download seismic waveform data of DONET. In this system, users can select 20 Hz broadband (BH type) and 200 Hz strong-motion (EH type) data and download them in SEED. Users can also search events from the options of time periods, magnitude, source area and depth in a GUI platform. Event data are produced referring to event catalogues from USGS and JMA (Japan Meteorological Agency). The thresholds of magnitudes for the production are M6 for far-field and M4 for local events using the USGS and JMA lists, respectively. Available data lengths depend on magnitudes and epicentral distances. In this presentation, we briefly introduce DONET stations and then show our developed application system. We open DONET data through the system and want them to be widely recognized so that many users analyze. We also discuss next plans for further developments of the system.

Digital Learning Network Education Events for the Desert Research and Technology Studies

NASA Technical Reports Server (NTRS)

Paul, Heather L.; Guillory, Erika R.

2007-01-01

NASA s Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and webcasting. As part of NASA s Strategic Plan to reach the next generation of space explorers, the DLN develops and delivers educational programs that reinforce principles in the areas of science, technology, engineering and mathematics. The DLN has created a series of live education videoconferences connecting the Desert Research and Technology Studies (RATS) field test to students across the United States. The programs are also extended to students around the world via live webcasting. The primary focus of the events is the Vision for Space Exploration. During the programs, Desert RATS engineers and scientists inform and inspire students about the importance of exploration and share the importance of the field test as it correlates with plans to return to the Moon and explore Mars. This paper describes the events that took place in September 2006.

Promoting and developing a trail network across suburban, rural, and urban communities.

PubMed

Schasberger, Michele G; Hussa, Carol S; Polgar, Michael F; McMonagle, Julie A; Burke, Sharon J; Gegaris, Andrew J

2009-12-01

The Wyoming Valley Wellness Trails Partnership received an Active Living by Design grant late in 2003 for a project centered on a growing trail network linking urban, suburban, and rural communities in northeast Pennsylvania, a former coal region, in order to increase physical activity among residents. The partnership conducted research, collected information, created promotional documents, worked with partners on events and programs, and participated in trail planning. Local trail organizations continued planning and construction toward developing a trail network. Other partners spearheaded policy change in schools and worksites and worked toward downtown revitalization. The partnership assisted these efforts by providing a forum in which organizations could meet. The partnership became a central resource for information about local parks, trails, and outdoor recreational activities. The partnership increased awareness and use of recreational facilities. Trail partners constructed 22 miles of walking and biking trails. The partnership took advantage of an allied effort that created organizational capacity for wellness in schools and worksites. Messages promoting social and entertainment benefits of physical activity were more successful than those promoting health benefits. The existence of multiple small, independent trail organizations can help advance trail development through concurrent development efforts. Urban, suburban, and rural residents' conceptions of walkability may differ. Trails provide options for recreational and transportation-related physical activity across urban, suburban, and rural landscapes that are supported by all constituents. Trail builders can be strong allies in bringing active living to suburban and rural places.

Social networks and inference about unknown events: A case of the match between Google's AlphaGo and Sedol Lee.

PubMed

Bae, Jonghoon; Cha, Young-Jae; Lee, Hyungsuk; Lee, Boyun; Baek, Sojung; Choi, Semin; Jang, Dayk

2017-01-01

This study examines whether the way that a person makes inferences about unknown events is associated with his or her social relations, more precisely, those characterized by ego network density that reflects the structure of a person's immediate social relation. From the analysis of individual predictions over the Go match between AlphaGo and Sedol Lee in March 2016 in Seoul, Korea, this study shows that the low-density group scored higher than the high-density group in the accuracy of the prediction over a future state of a social event, i.e., the outcome of the first game. We corroborated this finding with three replication tests that asked the participants to predict the following: film awards, President Park's impeachment in Korea, and the counterfactual assessment of the US presidential election. Taken together, this study suggests that network density is negatively associated with vision advantage, i.e., the ability to discover and forecast an unknown aspect of a social event.

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

PubMed

Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2017-01-01

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

A neural network model to predict the wastewater inflow incorporating rainfall events.

PubMed

El-Din, Ahmed Gamal; Smith, Daniel W

2002-03-01

Under steady-state conditions, a wastewater treatment plant usually has a satisfactory performance because these conditions are similar to design conditions. However, load variations constitute a large portion of the operating life of a treatment facility and most of the observed problems in complying with permit requirements occur during these load transients. During storm events upsets to the different physical and biological processes may take place in a wastewater treatment plant, and therefore, the ability to predict the hydraulic load to a treatment facility during such events is very beneficial for the optimization of the treatment process. Most of the hydrologic and hydraulic models describing sewage collection systems are deterministic. Such models require detailed knowledge of the system and usually rely on a large number of parameters, some of which are uncertain or difficult to determine. Presented in this paper, an artificial neural network (ANN) model that is used to make short-term predictions of wastewater inflow rate that enters the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest plant in the Edmonton area (Alberta, Canada). The neural model uses rainfall data, observed in the collection system discharging to the plant, as inputs. The building process of the model was conducted in a systematic way that allowed the identification of a parsimonious model that is able to learn (and not memorize) from past data and generalize very well to unseen data that was used to validate the model. The neural network model gave excellent results. The potential of using the model as part of a real-time process control system is also discussed.

Transforming the U.S. Immigration System After 9/11: The Impact of Organizational Change and Collaboration in the Context of Homeland Security

DTIC Science & Technology

2008-12-01

disqualifying criminal conviction from an application to become a Lawful Permanent Resident of the U.S.10 10...developing Collaborative Capacity Builders (CCBs) is another method to sustain and continue to build an effective USCIS and ICE network into the 21st...Weber and Anne M. Khademian, “Wicked Problems, Knowledge Challenges, and Collaborative Capacity Builders in Network Settings,” Public Administration

WGISS-45 International Directory Network (IDN) Report

NASA Technical Reports Server (NTRS)

Morahan, Michael

2018-01-01

The objective of this presentation is to provide IDN (International Directory Network) updates on features and activities to the Committee on Earth Observation Satellites (CEOS) Working Group on Information Systems and Services (WGISS) and provider community. The following topics will be will be discussed during the presentation: Transition of Providers DIF-9 (Directory Interchange Format-9) to DIF-10 Metadata Records in the Common Metadata Repository (CMR); GCMD (Global Change Master Directory) Keyword Update; DIF-10 and UMM-C (Unified Metadata Model-Collections) Schema Changes; Metadata Validation of Provider Metadata; docBUILDER for Submitting IDN Metadata to the CMR (i.e. Registration); and Mapping WGClimate Essential Climate Variable (ECV) Inventory to IDN Records.

Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network.

PubMed

Zhao, Bo; Ding, Ruoxi; Chen, Shoushun; Linares-Barranco, Bernabe; Tang, Huajin

2015-09-01

This paper introduces an event-driven feedforward categorization system, which takes data from a temporal contrast address event representation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different patterns using an AER based tempotron classifier (a network of leaky integrate-and-fire spiking neurons). One of the system's most appealing characteristics is its event-driven processing, with both input and features taking the form of address events (spikes). The system was evaluated on an AER posture dataset and compared with two recently developed bio-inspired models. Experimental results have shown that it consumes much less simulation time while still maintaining comparable performance. In addition, experiments on the Mixed National Institute of Standards and Technology (MNIST) image dataset have demonstrated that the proposed system can work not only on raw AER data but also on images (with a preprocessing step to convert images into AER events) and that it can maintain competitive accuracy even when noise is added. The system was further evaluated on the MNIST dynamic vision sensor dataset (in which data is recorded using an AER dynamic vision sensor), with testing accuracy of 88.14%.

Observer-Based Non-PDC Control for Networked T-S Fuzzy Systems With an Event-Triggered Communication.

PubMed

Peng, Chen; Ma, Shaodong; Xie, Xiangpeng

2017-02-07

This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi-Sugeno (T-S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T-S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources. Different from the traditional PDC method, the synchronous premise between the fuzzy observer and the T-S fuzzy system are no longer needed in this paper. Second, by use of Lyapunov theory, a stability criterion and a stabilization condition are obtained for ensuring asymptotically stable of the studied system. On account of the imperfect premise matching conditions are well considered in the derivation of the above criteria, less conservation can be expected to enhance the design flexibility. Compared with some existing emulation-based methods, the controller gains are no longer required to be known a priori. Finally, the availability of proposed non-PDC design scheme is illustrated by the backing-up control of a truck-trailer system.

Macrophage Polarization in Chronic Inflammatory Diseases: Killers or Builders?

PubMed Central

Baci, Denisa; Tremolati, Marco; Fanuli, Matteo; Farronato, Giampietro; Mortara, Lorenzo

2018-01-01

Macrophages are key cellular components of the innate immunity, acting as the main player in the first-line defence against the pathogens and modulating homeostatic and inflammatory responses. Plasticity is a major feature of macrophages resulting in extreme heterogeneity both in normal and in pathological conditions. Macrophages are not homogenous, and they are generally categorized into two broad but distinct subsets as either classically activated (M1) or alternatively activated (M2). However, macrophages represent a continuum of highly plastic effector cells, resembling a spectrum of diverse phenotype states. Induction of specific macrophage functions is closely related to the surrounding environment that acts as a relevant orchestrator of macrophage functions. This phenomenon, termed polarization, results from cell/cell, cell/molecule interaction, governing macrophage functionality within the hosting tissues. Here, we summarized relevant cellular and molecular mechanisms driving macrophage polarization in “distant” pathological conditions, such as cancer, type 2 diabetes, atherosclerosis, and periodontitis that share macrophage-driven inflammation as a key feature, playing their dual role as killers (M1-like) and/or builders (M2-like). We also dissect the physio/pathological consequences related to macrophage polarization within selected chronic inflammatory diseases, placing polarized macrophages as a relevant hallmark, putative biomarkers, and possible target for prevention/therapy. PMID:29507865

Sources of Infrasound events listed in IDC Reviewed Event Bulletin

NASA Astrophysics Data System (ADS)

Bittner, Paulina; Polich, Paul; Gore, Jane; Ali, Sherif; Medinskaya, Tatiana; Mialle, Pierrick

2017-04-01

Until 2003 two waveform technologies, i.e. seismic and hydroacoustic were used to detect and locate events included in the International Data Centre (IDC) Reviewed Event Bulletin (REB). The first atmospheric event was published in the REB in 2003, however automatic processing required significant improvements to reduce the number of false events. In the beginning of 2010 the infrasound technology was reintroduced to the IDC operations and has contributed to both automatic and reviewed IDC bulletins. The primary contribution of infrasound technology is to detect atmospheric events. These events may also be observed at seismic stations, which will significantly improve event location. Examples sources of REB events, which were detected by the International Monitoring System (IMS) infrasound network were fireballs (e.g. Bangkok fireball, 2015), volcanic eruptions (e.g. Calbuco, Chile 2015) and large surface explosions (e.g. Tjanjin, China 2015). Query blasts (e.g. Zheleznogorsk) and large earthquakes (e.g. Italy 2016) belong to events primarily recorded at seismic stations of the IMS network but often detected at the infrasound stations. In case of earthquakes analysis of infrasound signals may help to estimate the area affected by ground vibration. Infrasound associations to query blast events may help to obtain better source location. The role of IDC analysts is to verify and improve location of events detected by the automatic system and to add events which were missed in the automatic process. Open source materials may help to identify nature of some events. Well recorded examples may be added to the Reference Infrasound Event Database to help in analysis process. This presentation will provide examples of events generated by different sources which were included in the IDC bulletins.

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

PubMed Central

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

2014-01-01

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

Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients.

PubMed

Kusy, Maciej; Obrzut, Bogdan; Kluska, Jacek

2013-12-01

The aim of this article was to compare gene expression programming (GEP) method with three types of neural networks in the prediction of adverse events of radical hysterectomy in cervical cancer patients. One-hundred and seven patients treated by radical hysterectomy were analyzed. Each record representing a single patient consisted of 10 parameters. The occurrence and lack of perioperative complications imposed a two-class classification problem. In the simulations, GEP algorithm was compared to a multilayer perceptron (MLP), a radial basis function network neural, and a probabilistic neural network. The generalization ability of the models was assessed on the basis of their accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve (AUROC). The GEP classifier provided best results in the prediction of the adverse events with the accuracy of 71.96 %. Comparable but slightly worse outcomes were obtained using MLP, i.e., 71.87 %. For each of measured indices: accuracy, sensitivity, specificity, and the AUROC, the standard deviation was the smallest for the models generated by GEP classifier.

Social networks and inference about unknown events: A case of the match between Google’s AlphaGo and Sedol Lee

PubMed Central

Bae, Jonghoon; Cha, Young-Jae; Lee, Hyungsuk; Lee, Boyun; Baek, Sojung; Choi, Semin

2017-01-01

This study examines whether the way that a person makes inferences about unknown events is associated with his or her social relations, more precisely, those characterized by ego network density that reflects the structure of a person’s immediate social relation. From the analysis of individual predictions over the Go match between AlphaGo and Sedol Lee in March 2016 in Seoul, Korea, this study shows that the low-density group scored higher than the high-density group in the accuracy of the prediction over a future state of a social event, i.e., the outcome of the first game. We corroborated this finding with three replication tests that asked the participants to predict the following: film awards, President Park’s impeachment in Korea, and the counterfactual assessment of the US presidential election. Taken together, this study suggests that network density is negatively associated with vision advantage, i.e., the ability to discover and forecast an unknown aspect of a social event. PMID:28222114

Parallel discrete event simulation using shared memory

NASA Technical Reports Server (NTRS)

Reed, Daniel A.; Malony, Allen D.; Mccredie, Bradley D.

1988-01-01

With traditional event-list techniques, evaluating a detailed discrete-event simulation-model can often require hours or even days of computation time. By eliminating the event list and maintaining only sufficient synchronization to ensure causality, parallel simulation can potentially provide speedups that are linear in the numbers of processors. A set of shared-memory experiments, using the Chandy-Misra distributed-simulation algorithm, to simulate networks of queues is presented. Parameters of the study include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential-simulation of most queueing network models.

Seismic event classification system

DOEpatents

Dowla, Farid U.; Jarpe, Stephen P.; Maurer, William

1994-01-01

In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.

Fault Isolation Filter for Networked Control System with Event-Triggered Sampling Scheme

PubMed Central

Li, Shanbin; Sauter, Dominique; Xu, Bugong

2011-01-01

In this paper, the sensor data is transmitted only when the absolute value of difference between the current sensor value and the previously transmitted one is greater than the given threshold value. Based on this send-on-delta scheme which is one of the event-triggered sampling strategies, a modified fault isolation filter for a discrete-time networked control system with multiple faults is then implemented by a particular form of the Kalman filter. The proposed fault isolation filter improves the resource utilization with graceful fault estimation performance degradation. An illustrative example is given to show the efficiency of the proposed method. PMID:22346590

Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network

NASA Technical Reports Server (NTRS)

Kuhn, D. Richard; Kacker, Raghu; Lei, Yu

2010-01-01

This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.

How events determine spreading patterns: information transmission via internal and external influences on social networks

NASA Astrophysics Data System (ADS)

Liu, Chuang; Zhan, Xiu-Xiu; Zhang, Zi-Ke; Sun, Gui-Quan; Hui, Pak Ming

2015-11-01

Recently, information transmission models motivated by the classical epidemic propagation, have been applied to a wide-range of social systems, generally assume that information mainly transmits among individuals via peer-to-peer interactions on social networks. In this paper, we consider one more approach for users to get information: the out-of-social-network influence. Empirical analyzes of eight typical events’ diffusion on a very large micro-blogging system, Sina Weibo, show that the external influence has significant impact on information spreading along with social activities. In addition, we propose a theoretical model to interpret the spreading process via both internal and external channels, considering three essential properties: (i) memory effect; (ii) role of spreaders; and (iii) non-redundancy of contacts. Experimental and mathematical results indicate that the information indeed spreads much quicker and broader with mutual effects of the internal and external influences. More importantly, the present model reveals that the event characteristic would highly determine the essential spreading patterns once the network structure is established. The results may shed some light on the in-depth understanding of the underlying dynamics of information transmission on real social networks.

Qualitative Exploration of the Potential for Adverse Events When Using an Online Peer Support Network for Mental Health: Cross-Sectional Survey.

PubMed

Easton, Katherine; Diggle, Jacob; Ruethi-Davis, Mabel; Holmes, Megan; Byron-Parker, Darian; Nuttall, Jessica; Blackmore, Chris

2017-10-30

Online peer support networks are a growing area of mental health support for offering social connection, identity, and support. However, it has been reported that not all individuals have a positive experience on such networks. The potential for adverse events within a moderated online peer support network is a new area of research exploration. The objective of the study was to determine if use of an online moderated peer networks leads to adverse events for users. Four biannual online surveys (October 2014 to March 2016) were conducted by a large national UK mental health charity, with users of their online peer support network exploring personal safety, moderation, experiences on the site, and how the site could be improved. Data were analyzed using thematic analysis by 2 independent researchers using a priori themes: negative experiences of moderation, social exclusion, contagion, negative interactions with other users, online relationships, co-rumination and collusion, and other. In total, 2353 survey responses were logged with 197 (8.37%) documenting an adverse event of negative experience. A dominant theme of negative experiences of moderation emerged (73/197, 37.1%) with evidence of social exclusion (50/197, 25.4%). Reading user posts was shown to be a cause of worry and distress for a few users, and analysis highlighted several instances of depressogenic and emotional contagion as well as some limited evidence of behavioral contagion (46/197, 23.4%). Very limited evidence of co-rumination (1/197, 0.5%) and no evidence of collusion were identified. Evidence of adverse events was identified at low levels in the sample of respondents, although we have no comparison data to indicate if levels are low compared with comparable platforms. Not all users of online peer support networks find them wholly beneficial. Research must explore what works for whom. The next stage of service development should consider which users may be likely to receive no benefit, or even

Morphogenesis in sea urchin embryos: linking cellular events to gene regulatory network states

PubMed Central

Lyons, Deidre; Kaltenbach, Stacy; McClay, David R.

2013-01-01

Gastrulation in the sea urchin begins with ingression of the primary mesenchyme cells (PMCs) at the vegetal pole of the embryo. After entering the blastocoel the PMCs migrate, form a syncitium, and synthesize the skeleton of the embryo. Several hours after the PMCs ingress the vegetal plate buckles to initiate invagination of the archenteron. That morphogenetic process occurs in several steps. The non-skeletogenic cells produce the initial inbending of the vegetal plate. Endoderm cells then rearrange and extend the length of the gut across the blastocoel to a target near the animal pole. Finally, cells that will form part of the midgut and hindgut are added to complete gastrulation. Later, the stomodeum invaginates from the oral ectoderm and fuses with the foregut to complete the archenteron. In advance of, and during these morphogenetic events an increasingly complex gene regulatory network controls the specification and the cell biological events that conduct the gastrulation movements. PMID:23801438

REVEAL: An Extensible Reduced Order Model Builder for Simulation and Modeling

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

Agarwal, Khushbu; Sharma, Poorva; Ma, Jinliang

2013-04-30

Many science domains need to build computationally efficient and accurate representations of high fidelity, computationally expensive simulations. These computationally efficient versions are known as reduced-order models. This paper presents the design and implementation of a novel reduced-order model (ROM) builder, the REVEAL toolset. This toolset generates ROMs based on science- and engineering-domain specific simulations executed on high performance computing (HPC) platforms. The toolset encompasses a range of sampling and regression methods that can be used to generate a ROM, automatically quantifies the ROM accuracy, and provides support for an iterative approach to improve ROM accuracy. REVEAL is designed to bemore » extensible in order to utilize the core functionality with any simulator that has published input and output formats. It also defines programmatic interfaces to include new sampling and regression techniques so that users can ‘mix and match’ mathematical techniques to best suit the characteristics of their model. In this paper, we describe the architecture of REVEAL and demonstrate its usage with a computational fluid dynamics model used in carbon capture.« less

Systematic observations of long-range transport events and climatological backscatter profiles with the DWD ceilometer network

NASA Astrophysics Data System (ADS)

Mattis, Ina; Müller, Gerhard; Wagner, Frank; Hervo, Maxime

2015-04-01

The German Meteorological Service (DWD) operates a network of about 60 CHM15K-Nimbus ceilometers for cloud base height observations. Those very powerful ceilometers allow for the detection and characterization of aerosol layers. Raw data of all network ceilometers are transferred online to DWD's data analysis center at the Hohenpeißenberg Meteorological Observatory. There, the occurrence of aerosol layers from long-range transport events in the free troposphere is systematically monitored on daily basis for each single station. If possible, the origin of the aerosol layers is determined manually from the analysis of the meteorological situation and model output. We use backward trajectories as well as the output of the MACC and DREAM models for the decision, whether the observed layer originated in the Sahara region, from forest fires in North America or from another, unknown source. Further, the magnitude of the observed layers is qualitatively estimated taking into account the geometrical layer depth, signal intensity, model output and nearby sun photometer or lidar observations (where available). All observed layers are attributed to one of the categories 'faint', 'weak', 'medium', 'strong', or 'extreme'. We started this kind of analysis in August 2013 and plan to continue this systematic documentation of long-range transport events of aerosol layers to Germany on long-term base in the framework of our GAW activities. Most of the observed aerosol layers have been advected from the Sahara region to Germany. In the 15 months between August 2013 and November 2014 we observed on average 46 days with Sahara dust layers per station, but only 16 days with aerosol layers from forest fires. The occurrence of Sahara dust layers vary with latitude. We observed only 28 dusty days in the north, close to the coasts of North Sea and Baltic Sea. In contrast, in southern Germany, in Bavarian Pre-Alps and in the Black Forest mountains, we observed up to 59 days with dust. At

Neuroticism, social network, stressful life events: association with mood disorders, depressive symptoms and suicidal ideation in a community sample of women.

PubMed

Mandelli, Laura; Nearchou, Finiki A; Vaiopoulos, Chrysostomos; Stefanis, Costas N; Vitoratou, Silia; Serretti, Alessandro; Stefanis, Nicholas C

2015-03-30

According to the stress-diathesis hypothesis, depression and suicidal behavior may be precipitated by psychosocial stressors in vulnerable individuals. However, risk factors for mental health are often gender-specific. In the present study, we evaluated common risk factors for female depression in association with depressive symptoms and suicidal ideation in a community sample of women. The sample was composed by 415 women evaluated for mood disorders (MDs), depressive symptoms and suicidal ideation by structured interviews and the Beck depression inventory II (BDI II). All women also filled in the Eysenck personality questionnaire to evaluate neuroticism and were interviewed for social contact frequency and stressful life events (SLEs). In the whole sample, 19% of the women satisfied criteria for MD and suicidal ideation was reported by 12% of the women. Though stressful life events, especially personal and interpersonal problems, and poor social network were associated with all the outcome variables (mood disorder, depressive symptomatology and suicidal ideation), neuroticism survived to all multivariate analyses. Social network, together with neuroticism, also showed strong association with depressive severity, independently from current depressive state. Though we were unable to compare women and men, data obtained from the present study suggest that in women neurotic traits are strongly related to depression and suicidal ideation, and potentially mediate reporting of stressful life events and impaired social network. Independently from a current diagnosis of depression, impaired social network increases depressive symptoms in the women. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Event-Based $H_\\infty $ State Estimation for Time-Varying Stochastic Dynamical Networks With State- and Disturbance-Dependent Noises.

PubMed

Sheng, Li; Wang, Zidong; Zou, Lei; Alsaadi, Fuad E

2017-10-01

In this paper, the event-based finite-horizon H ∞ state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with state- and disturbance-dependent noises [also called (x,v) -dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H ∞ performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.

A statistical framework for evaluating neural networks to predict recurrent events in breast cancer

NASA Astrophysics Data System (ADS)

Gorunescu, Florin; Gorunescu, Marina; El-Darzi, Elia; Gorunescu, Smaranda

2010-07-01

Breast cancer is the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often a more challenging task than the initial one. In this paper, we investigate the potential contribution of neural networks (NNs) to support health professionals in diagnosing such events. The NN algorithms are tested and applied to two different datasets. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perceptron and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and, finally, the classification performances of all algorithms are statistically robust. Moreover, we have shown that the best performing algorithm will strongly depend on the features of the datasets, and hence, there is not necessarily a single best classifier.

The Next Generation of NASA Night Sky Network: A Searchable Nationwide Database of Astronomy Events

NASA Astrophysics Data System (ADS)

Ames, Z.; Berendsen, M.; White, V.

2010-08-01

With support from NASA, the Astronomical Society of the Pacific (ASP) first developed the Night Sky Network (NSN) in 2004. The NSN was created in response to research conducted by the Institute for Learning Innovation (ILI) to determine what type of support amateur astronomers could use to increase the efficiency and extent of their educational outreach programs. Since its creation, the NSN has grown to include an online searchable database of toolkit resources, Presentation Skills Videos covering topics such as working with kids and how to answer difficult questions, and a searchable nationwide calendar of astronomy events that supports club organization. The features of the NSN have allowed the ASP to create a template that amateur science organizations might use to create a similar support network for their members and the public.

Seismic event classification system

DOEpatents

Dowla, F.U.; Jarpe, S.P.; Maurer, W.

1994-12-13

In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities. 21 figures.

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks.

PubMed

Vajdi, Ahmadreza; Zhang, Gongxuan; Zhou, Junlong; Wei, Tongquan; Wang, Yongli; Wang, Tianshu

2018-05-04

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.

Markov Logic Networks for Adverse Drug Event Extraction from Text.

PubMed

Natarajan, Sriraam; Bangera, Vishal; Khot, Tushar; Picado, Jose; Wazalwar, Anurag; Costa, Vitor Santos; Page, David; Caldwell, Michael

2017-05-01

Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring, and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text.

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method

PubMed Central

Cao, Youfang; Liang, Jie

2013-01-01

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method

NASA Astrophysics Data System (ADS)

Cao, Youfang; Liang, Jie

2013-07-01

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method.

PubMed

Cao, Youfang; Liang, Jie

2013-07-14

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively

Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis

PubMed Central

2015-01-01

Background Modern methods for mining biomolecular interactions from literature typically make predictions based solely on the immediate textual context, in effect a single sentence. No prior work has been published on extending this context to the information automatically gathered from the whole biomedical literature. Thus, our motivation for this study is to explore whether mutually supporting evidence, aggregated across several documents can be utilized to improve the performance of the state-of-the-art event extraction systems. In this paper, we describe our participation in the latest BioNLP Shared Task using the large-scale text mining resource EVEX. We participated in the Genia Event Extraction (GE) and Gene Regulation Network (GRN) tasks with two separate systems. In the GE task, we implemented a re-ranking approach to improve the precision of an existing event extraction system, incorporating features from the EVEX resource. In the GRN task, our system relied solely on the EVEX resource and utilized a rule-based conversion algorithm between the EVEX and GRN formats. Results In the GE task, our re-ranking approach led to a modest performance increase and resulted in the first rank of the official Shared Task results with 50.97% F-score. Additionally, in this paper we explore and evaluate the usage of distributed vector representations for this challenge. In the GRN task, we ranked fifth in the official results with a strict/relaxed SER score of 0.92/0.81 respectively. To try and improve upon these results, we have implemented a novel machine learning based conversion system and benchmarked its performance against the original rule-based system. Conclusions For the GRN task, we were able to produce a gene regulatory network from the EVEX data, warranting the use of such generic large-scale text mining data in network biology settings. A detailed performance and error analysis provides more insight into the relatively low recall rates. In the GE task we

Characterization of computer network events through simultaneous feature selection and clustering of intrusion alerts

NASA Astrophysics Data System (ADS)

Chen, Siyue; Leung, Henry; Dondo, Maxwell

2014-05-01

As computer network security threats increase, many organizations implement multiple Network Intrusion Detection Systems (NIDS) to maximize the likelihood of intrusion detection and provide a comprehensive understanding of intrusion activities. However, NIDS trigger a massive number of alerts on a daily basis. This can be overwhelming for computer network security analysts since it is a slow and tedious process to manually analyse each alert produced. Thus, automated and intelligent clustering of alerts is important to reveal the structural correlation of events by grouping alerts with common features. As the nature of computer network attacks, and therefore alerts, is not known in advance, unsupervised alert clustering is a promising approach to achieve this goal. We propose a joint optimization technique for feature selection and clustering to aggregate similar alerts and to reduce the number of alerts that analysts have to handle individually. More precisely, each identified feature is assigned a binary value, which reflects the feature's saliency. This value is treated as a hidden variable and incorporated into a likelihood function for clustering. Since computing the optimal solution of the likelihood function directly is analytically intractable, we use the Expectation-Maximisation (EM) algorithm to iteratively update the hidden variable and use it to maximize the expected likelihood. Our empirical results, using a labelled Defense Advanced Research Projects Agency (DARPA) 2000 reference dataset, show that the proposed method gives better results than the EM clustering without feature selection in terms of the clustering accuracy.

Timing and pacing of the Late Devonian mass extinction event regulated by eccentricity and obliquity.

PubMed

De Vleeschouwer, David; Da Silva, Anne-Christine; Sinnesael, Matthias; Chen, Daizhao; Day, James E; Whalen, Michael T; Guo, Zenghui; Claeys, Philippe

2017-12-22

The Late Devonian envelops one of Earth's big five mass extinction events at the Frasnian-Famennian boundary (374 Ma). Environmental change across the extinction severely affected Devonian reef-builders, besides many other forms of marine life. Yet, cause-and-effect chains leading to the extinction remain poorly constrained as Late Devonian stratigraphy is poorly resolved, compared to younger cataclysmic intervals. In this study we present a global orbitally calibrated chronology across this momentous interval, applying cyclostratigraphic techniques. Our timescale stipulates that 600 kyr separate the lower and upper Kellwasser positive δ 13 C excursions. The latter excursion is paced by obliquity and is therein similar to Mesozoic intervals of environmental upheaval, like the Cretaceous Ocean-Anoxic-Event-2 (OAE-2). This obliquity signature implies coincidence with a minimum of the 2.4 Myr eccentricity cycle, during which obliquity prevails over precession, and highlights the decisive role of astronomically forced "Milankovitch" climate change in timing and pacing the Late Devonian mass extinction.

Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

USGS Publications Warehouse

Bolker, Benjamin M.; Gardner, Beth; Maunder, Mark; Berg, Casper W.; Brooks, Mollie; Comita, Liza; Crone, Elizabeth; Cubaynes, Sarah; Davies, Trevor; de Valpine, Perry; Ford, Jessica; Gimenez, Olivier; Kéry, Marc; Kim, Eun Jung; Lennert-Cody, Cleridy; Magunsson, Arni; Martell, Steve; Nash, John; Nielson, Anders; Regentz, Jim; Skaug, Hans; Zipkin, Elise

2013-01-01

1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. 2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. 3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation. 4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data.

Signaling communication events in a computer network

DOEpatents

Bender, Carl A.; DiNicola, Paul D.; Gildea, Kevin J.; Govindaraju, Rama K.; Kim, Chulho; Mirza, Jamshed H.; Shah, Gautam H.; Nieplocha, Jaroslaw

2000-01-01

A method, apparatus and program product for detecting a communication event in a distributed parallel data processing system in which a message is sent from an origin to a target. A low-level application programming interface (LAPI) is provided which has an operation for associating a counter with a communication event to be detected. The LAPI increments the counter upon the occurrence of the communication event. The number in the counter is monitored, and when the number increases, the event is detected. A completion counter in the origin is associated with the completion of a message being sent from the origin to the target. When the message is completed, LAPI increments the completion counter such that monitoring the completion counter detects the completion of the message. The completion counter may be used to insure that a first message has been sent from the origin to the target and completed before a second message is sent.

Differential Network Analyses of Alzheimer’s Disease Identify Early Events in Alzheimer’s Disease Pathology

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

Xia, Jing; Rocke, David M.; Perry, George

In late-onset Alzheimer’s disease (AD), multiple brain regions are not affected simultaneously. Comparing the gene expression of the affected regions to identify the differences in the biological processes perturbed can lead to greater insight into AD pathogenesis and early characteristics. We identified differentially expressed (DE) genes from single cell microarray data of four AD affected brain regions: entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC), and middle temporal gyrus (MTG). We organized the DE genes in the four brain regions into region-specific gene coexpression networks. Differential neighborhood analyses in the coexpression networks were performed to identify genes with lowmore » topological overlap (TO) of their direct neighbors. The low TO genes were used to characterize the biological differences between two regions. Our analyses show that increased oxidative stress, along with alterations in lipid metabolism in neurons, may be some of the very early events occurring in AD pathology. Cellular defense mechanisms try to intervene but fail, finally resulting in AD pathology as the disease progresses. Furthermore, disease annotation of the low TO genes in two independent protein interaction networks has resulted in association between cancer, diabetes, renal diseases, and cardiovascular diseases.« less

Differential Network Analyses of Alzheimer’s Disease Identify Early Events in Alzheimer’s Disease Pathology

DOE PAGES

Xia, Jing; Rocke, David M.; Perry, George; ...

2014-01-01

In late-onset Alzheimer’s disease (AD), multiple brain regions are not affected simultaneously. Comparing the gene expression of the affected regions to identify the differences in the biological processes perturbed can lead to greater insight into AD pathogenesis and early characteristics. We identified differentially expressed (DE) genes from single cell microarray data of four AD affected brain regions: entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC), and middle temporal gyrus (MTG). We organized the DE genes in the four brain regions into region-specific gene coexpression networks. Differential neighborhood analyses in the coexpression networks were performed to identify genes with lowmore » topological overlap (TO) of their direct neighbors. The low TO genes were used to characterize the biological differences between two regions. Our analyses show that increased oxidative stress, along with alterations in lipid metabolism in neurons, may be some of the very early events occurring in AD pathology. Cellular defense mechanisms try to intervene but fail, finally resulting in AD pathology as the disease progresses. Furthermore, disease annotation of the low TO genes in two independent protein interaction networks has resulted in association between cancer, diabetes, renal diseases, and cardiovascular diseases.« less

Parallel discrete event simulation: A shared memory approach

NASA Technical Reports Server (NTRS)

Reed, Daniel A.; Malony, Allen D.; Mccredie, Bradley D.

1987-01-01

With traditional event list techniques, evaluating a detailed discrete event simulation model can often require hours or even days of computation time. Parallel simulation mimics the interacting servers and queues of a real system by assigning each simulated entity to a processor. By eliminating the event list and maintaining only sufficient synchronization to insure causality, parallel simulation can potentially provide speedups that are linear in the number of processors. A set of shared memory experiments is presented using the Chandy-Misra distributed simulation algorithm to simulate networks of queues. Parameters include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential simulation of most queueing network models.

Sentiment Diffusion of Public Opinions about Hot Events: Based on Complex Network

PubMed Central

Hao, Xiaoqing; An, Haizhong; Zhang, Lijia; Li, Huajiao; Wei, Guannan

2015-01-01

To study the sentiment diffusion of online public opinions about hot events, we collected people’s posts through web data mining techniques. We calculated the sentiment value of each post based on a sentiment dictionary. Next, we divided those posts into five different orientations of sentiments: strongly positive (P), weakly positive (p), neutral (o), weakly negative (n), and strongly negative (N). These sentiments are combined into modes through coarse graining. We constructed sentiment mode complex network of online public opinions (SMCOP) with modes as nodes and the conversion relation in chronological order between different types of modes as edges. We calculated the strength, k-plex clique, clustering coefficient and betweenness centrality of the SMCOP. The results show that the strength distribution obeys power law. Most posts’ sentiments are weakly positive and neutral, whereas few are strongly negative. There are weakly positive subgroups and neutral subgroups with ppppp and ooooo as the core mode, respectively. Few modes have larger betweenness centrality values and most modes convert to each other with these higher betweenness centrality modes as mediums. Therefore, the relevant person or institutes can take measures to lead people’s sentiments regarding online hot events according to the sentiment diffusion mechanism. PMID:26462230

The Collaborative Heliophysics Events Knowledgebase

NASA Astrophysics Data System (ADS)

Hurlburt, N. E.; Schuler, D.; Cheung, C.

2010-12-01

The Collaborative Heliophysics Events Knowledgebase (CHEK) leverages and integrates the existing resources developed by HEK for SDO (Hurlburt et al. 2010) to provide a collaborative framework for heliophysics researchers. This framework will enable an environment were researches can not only identify and locate relevant data, but can deploy a social network for sharing and expanding knowledge about heliophysical events. CHEK will expand the HEK and key HEK clients into the heliosphere and geospace, and create a heliophysics social network. We describe our design and goals of the CHEK project and discuss its relation to Citizen Science in the heliosphere. Hurlburt, N et al. 2010, “A Heliophysics Event Knowledgebase for Solar Dynamics Observatory,” Sol Phys., in press

LEONA: Transient Luminous Event and Thunderstorm High Energy Emission Collaborative Network in Latin America

NASA Astrophysics Data System (ADS)

Sao Sabbas, F. T.

2012-12-01

This project has the goal of establishing the Collaborative Network LEONA, to study the electrodynamical coupling of the atmospheric layers signaled by Transient Luminous Events - TLEs and high energy emissions from thunderstorms. We will develop and install a remotely controlled network of cameras to perform TLE observations in different locations in South America and one neutron detector in southern Brazil. The camera network will allow building a continuous data set of the phenomena studied in this continent. The first two trial units of the camera network are already installed, in Brazil and Peru, and two more will be installed until December 2012, in Argentina and Brazil. We expect to determine the TLE geographic distribution, occurrence rate, morphology, and possible coupling with other geophysical phenomena in South America, such as the South Atlantic Magnetic Anomaly - SAMA. We also expect to study thunderstorm neutron emissions in a region of intense electrical activity, measuring neutron fluxes with high time resolution simultaneously with TLEs and lightning for the first time in South America. Using an intensified high-speed camera for TLE observation during 2 campaigns we expect to be able to determine the duration and spatial- temporal development of the TLEs observed, to study the structure and initiation of sprites and to measure the velocity of development of sprite structures and the sprite delay. The camera was acquired via the FAPESP project DEELUMINOS (2005-2010), which also nucleated our research group Atmospheric Electrodynamical Coupling - ACATMOS. LEONA will nucleate this research in other institutions in Brazil and other countries in South America, providing continuity for this important research in our region. The camera network will be an unique tool to perform consistent long term TLE observation, and in fact is the only way to accumulate a data set for a climatological study of South America, since satellite instrumentation turns off in

Event detection in an assisted living environment.

PubMed

Stroiescu, Florin; Daly, Kieran; Kuris, Benjamin

2011-01-01

This paper presents the design of a wireless event detection and in building location awareness system. The systems architecture is based on using a body worn sensor to detect events such as falls where they occur in an assisted living environment. This process involves developing event detection algorithms and transmitting such events wirelessly to an in house network based on the 802.15.4 protocol. The network would then generate alerts both in the assisted living facility and remotely to an offsite monitoring facility. The focus of this paper is on the design of the system architecture and the compliance challenges in applying this technology.

Event-based simulation of networks with pulse delayed coupling

NASA Astrophysics Data System (ADS)

Klinshov, Vladimir; Nekorkin, Vladimir

2017-10-01

Pulse-mediated interactions are common in networks of different nature. Here we develop a general framework for simulation of networks with pulse delayed coupling. We introduce the discrete map governing the dynamics of such networks and describe the computation algorithm for its numerical simulation.

On the impact of RN network coverage on event selection and data fusion during the 2009 National Data Centres Preparedness Exercise

NASA Astrophysics Data System (ADS)

Becker, Andreas; Krysta, Monika; Auer, Matthias; Brachet, Nicolas; Ceranna, Lars; Gestermann, Nicolai; Nikkinen, Mika; Zähringer, Matthias

2010-05-01

The so-called National Data Centres (NDCs) to the Provisional Technical Secretariat of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) Organization are in charge to provide for the final judgement on the CTBT relevance of explosion events encountered in the PTS International Monitoring System (IMS). The latter is a 321 stations network set-up by the PTS (to date completion level: 80%) in order to globally monitor for occurrence of CTBT relevant seismo-acoustic and radionuclide signals. In doing so, NDCs learn about any seismo-acoustic or radionuclide event by active retrieval or subscription to corresponding event lists and products provided by the International Data Centre (IDC) to the PTS. To prepare for their instrumental role in case of a CTBT relevant event, the NDCs jointly conduct annually so-called NDC Preparedness Exercises. In 2009, NDC Germany was in charge to lead the exercise and to choose a seismo-acoustic event out of the list of events provided by the PTS (Gestermann et al., EGU2010-13067). The novelty in this procedure was that also the infrasound readings and the monitoring coverage of existing (certified) radionuclide stations into the area of consideration were taken into account during the event selection process (Coyne et al., EGU2010-12660). Hence, the event finally chosen and examined took place near Kara-Zhyra mine in Eastern Kazakhstan on 28 November 2009 around 07:20:31 UTC (Event-ID 5727516). NDC Austria performed forward atmospheric transport modelling in order to predict RN measurements that should have occurred in the radionuclide IMS. In doing so the fictitious case that there would have been a release of radionuclides taking place at the same location (Wotawa and Schraik, 2010; EGU2010-4907) in a strength being typical for a non-contained nuclear explosion is examined. The stations indicated should then be analysed for their actual radionuclide readings in order to confirm the non nuclear character of

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks

PubMed Central

Zhang, Gongxuan; Wang, Yongli; Wang, Tianshu

2018-01-01

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach. PMID:29734718

Decoding fMRI events in Sensorimotor Motor Network using Sparse Paradigm Free Mapping and Activation Likelihood Estimates

PubMed Central

Tan, Francisca M.; Caballero-Gaudes, César; Mullinger, Karen J.; Cho, Siu-Yeung; Zhang, Yaping; Dryden, Ian L.; Francis, Susan T.; Gowland, Penny A.

2017-01-01

Most fMRI studies map task-driven brain activity using a block or event-related paradigm. Sparse Paradigm Free Mapping (SPFM) can detect the onset and spatial distribution of BOLD events in the brain without prior timing information; but relating the detected events to brain function remains a challenge. In this study, we developed a decoding method for SPFM using a coordinate-based meta-analysis method of Activation Likelihood Estimation (ALE). We defined meta-maps of statistically significant ALE values that correspond to types of events and calculated a summation overlap between the normalized meta-maps and SPFM maps. As a proof of concept, this framework was applied to relate SPFM-detected events in the Sensorimotor Network (SMN) to six motor function (left/right fingers, left/right toes, swallowing and eye blinks). We validated the framework using simultaneous Electromyography-fMRI experiments and motor tasks with short and long duration, and random inter-stimulus interval. The decoding scores were considerably lower for eye movements relative to other movement types tested. The average successful rate for short and long motor events was 77 ± 13% and 74 ± 16% respectively, excluding eye movements. We found good agreement between the decoding results and EMG for most events and subjects, with a range in sensitivity between 55 and 100%, excluding eye movements. The proposed method was then used to classify the movement types of spontaneous single-trial events in the SMN during resting state, which produced an average successful rate of 22 ± 12%. Finally, this paper discusses methodological implications and improvements to increase the decoding performance. PMID:28815863

Assessment of Critical Events Corridors through Multivariate Cascading Outages Analysis

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

Makarov, Yuri V.; Samaan, Nader A.; Diao, Ruisheng

2011-10-17

Massive blackouts of electrical power systems in North America over the past decade has focused increasing attention upon ways to identify and simulate network events that may potentially lead to widespread network collapse. This paper summarizes a method to simulate power-system vulnerability to cascading failures to a supplied set of initiating events synonymously termed as Extreme Events. The implemented simulation method is currently confined to simulating steady state power-system response to a set of extreme events. The outlined method of simulation is meant to augment and provide a new insight into bulk power transmission network planning that at present remainsmore » mainly confined to maintaining power system security for single and double component outages under a number of projected future network operating conditions. Although one of the aims of this paper is to demonstrate the feasibility of simulating network vulnerability to cascading outages, a more important goal has been to determine vulnerable parts of the network that may potentially be strengthened in practice so as to mitigate system susceptibility to cascading failures. This paper proposes to demonstrate a systematic approach to analyze extreme events and identify vulnerable system elements that may be contributing to cascading outages. The hypothesis of critical events corridors is proposed to represent repeating sequential outages that can occur in the system for multiple initiating events. The new concept helps to identify system reinforcements that planners could engineer in order to 'break' the critical events sequences and therefore lessen the likelihood of cascading outages. This hypothesis has been successfully validated with a California power system model.« less

Asynchronous networks: modularization of dynamics theorem

NASA Astrophysics Data System (ADS)

Bick, Christian; Field, Michael

2017-02-01

Building on the first part of this paper, we develop the theory of functional asynchronous networks. We show that a large class of functional asynchronous networks can be (uniquely) represented as feedforward networks connecting events or dynamical modules. For these networks we can give a complete description of the network function in terms of the function of the events comprising the network: the modularization of dynamics theorem. We give examples to illustrate the main results.

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time.

PubMed

Galán, S F; Aguado, F; Díez, F J; Mira, J

2002-07-01

The spread of cancer is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is general enough to be applied to any other type of cancer.

Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates.

PubMed

Tan, Francisca M; Caballero-Gaudes, César; Mullinger, Karen J; Cho, Siu-Yeung; Zhang, Yaping; Dryden, Ian L; Francis, Susan T; Gowland, Penny A

2017-11-01

Most functional MRI (fMRI) studies map task-driven brain activity using a block or event-related paradigm. Sparse paradigm free mapping (SPFM) can detect the onset and spatial distribution of BOLD events in the brain without prior timing information, but relating the detected events to brain function remains a challenge. In this study, we developed a decoding method for SPFM using a coordinate-based meta-analysis method of activation likelihood estimation (ALE). We defined meta-maps of statistically significant ALE values that correspond to types of events and calculated a summation overlap between the normalized meta-maps and SPFM maps. As a proof of concept, this framework was applied to relate SPFM-detected events in the sensorimotor network (SMN) to six motor functions (left/right fingers, left/right toes, swallowing, and eye blinks). We validated the framework using simultaneous electromyography (EMG)-fMRI experiments and motor tasks with short and long duration, and random interstimulus interval. The decoding scores were considerably lower for eye movements relative to other movement types tested. The average successful rate for short and long motor events were 77 ± 13% and 74 ± 16%, respectively, excluding eye movements. We found good agreement between the decoding results and EMG for most events and subjects, with a range in sensitivity between 55% and 100%, excluding eye movements. The proposed method was then used to classify the movement types of spontaneous single-trial events in the SMN during resting state, which produced an average successful rate of 22 ± 12%. Finally, this article discusses methodological implications and improvements to increase the decoding performance. Hum Brain Mapp 38:5778-5794, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Adding run history to CLIPS

NASA Technical Reports Server (NTRS)

Tuttle, Sharon M.; Eick, Christoph F.

1991-01-01

To debug a C Language Integrated Production System (CLIPS) program, certain 'historical' information about a run is needed. It would be convenient for system builders to have the capability to request such information. We will discuss how historical Rete networks can be used for answering questions that help a system builder detect the cause of an error in a CLIPS program. Moreover, the cost of maintaining a historical Rete network is compared with that for a classical Rete network. We will demonstrate that the cost for assertions is only slightly higher for a historical Rete network. The cost for handling retraction could be significantly higher; however, we will show that by using special data structures that rely on hashing, it is also possible to implement retractions efficiently.

Human Rights Event Detection from Heterogeneous Social Media Graphs.

PubMed

Chen, Feng; Neill, Daniel B

2015-03-01

Human rights organizations are increasingly monitoring social media for identification, verification, and documentation of human rights violations. Since manual extraction of events from the massive amount of online social network data is difficult and time-consuming, we propose an approach for automated, large-scale discovery and analysis of human rights-related events. We apply our recently developed Non-Parametric Heterogeneous Graph Scan (NPHGS), which models social media data such as Twitter as a heterogeneous network (with multiple different node types, features, and relationships) and detects emerging patterns in the network, to identify and characterize human rights events. NPHGS efficiently maximizes a nonparametric scan statistic (an aggregate measure of anomalousness) over connected subgraphs of the heterogeneous network to identify the most anomalous network clusters. It summarizes each event with information such as type of event, geographical locations, time, and participants, and provides documentation such as links to videos and news reports. Building on our previous work that demonstrates the utility of NPHGS for civil unrest prediction and rare disease outbreak detection, we present an analysis of human rights events detected by NPHGS using two years of Twitter data from Mexico. NPHGS was able to accurately detect relevant clusters of human rights-related tweets prior to international news sources, and in some cases, prior to local news reports. Analysis of social media using NPHGS could enhance the information-gathering missions of human rights organizations by pinpointing specific abuses, revealing events and details that may be blocked from traditional media sources, and providing evidence of emerging patterns of human rights violations. This could lead to more timely, targeted, and effective advocacy, as well as other potential interventions.

Contribution of Infrasound to IDC Reviewed Event Bulletin

NASA Astrophysics Data System (ADS)

Bittner, Paulina; Polich, Paul; Gore, Jane; Ali, Sherif Mohamed; Medinskaya, Tatiana; Mialle, Pierrick

2016-04-01

Until 2003 two waveform technologies, i.e. seismic and hydroacoustic were used to detect and locate events included in the International Data Centre (IDC) Reviewed Event Bulletin (REB). The first atmospheric event was published in the REB in 2003 but infrasound detections could not be used by the Global Association (GA) Software due to the unmanageable high number of spurious associations. Offline improvements of the automatic processing took place to reduce the number of false detections to a reasonable level. In February 2010 the infrasound technology was reintroduced to the IDC operations and has contributed to both automatic and reviewed IDC bulletins. The primary contribution of infrasound technology is to detect atmospheric events. These events may also be observed at seismic stations, which will significantly improve event location. Examples of REB events, which were detected by the International Monitoring System (IMS) infrasound network were fireballs (e.g. Bangkok fireball, 2015), volcanic eruptions (e.g. Calbuco, Chile 2015) and large surface explosions (e.g. Tjanjin, China 2015). Query blasts and large earthquakes belong to events primarily recorded at seismic stations of the IMS network but often detected at the infrasound stations. Presence of infrasound detection associated to an event from a mining area indicates a surface explosion. Satellite imaging and a database of active mines can be used to confirm the origin of such events. This presentation will summarize the contribution of 6 years of infrasound data to IDC bulletins and provide examples of events recorded at the IMS infrasound network. Results of this study may help to improve location of small events with observations on infrasound stations.

The Functional Measurement Experiment Builder suite: two Java-based programs to generate and run functional measurement experiments.

PubMed

Mairesse, Olivier; Hofmans, Joeri; Theuns, Peter

2008-05-01

We propose a free, easy-to-use computer program that does not requires prior knowledge of computer programming to generate and run experiments using textual or pictorial stimuli. Although the FM Experiment Builder suite was initially programmed for building and conducting FM experiments, it can also be applied for non-FM experiments that necessitate randomized, single, or multifactorial designs. The program is highly configurable, allowing multilingual use and a wide range of different response formats. The outputs of the experiments are Microsoft Excel compatible .xls files that allow easy copy-paste of the results into Weiss's FM CalSTAT program (2006) or any other statistical package. Its Java-based structure is compatible with both Windows and Macintosh operating systems, and its compactness (< 1 MB) makes it easily distributable over the Internet.

Comprehensive, Multi-Source Cyber-Security Events Data Set

DOE Data Explorer

Kent, Alexander D. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2015-05-21

This data set represents 58 consecutive days of de-identified event data collected from five sources within Los Alamos National Laboratory’s corporate, internal computer network. The data sources include Windows-based authentication events from both individual computers and centralized Active Directory domain controller servers; process start and stop events from individual Windows computers; Domain Name Service (DNS) lookups as collected on internal DNS servers; network flow data as collected on at several key router locations; and a set of well-defined red teaming events that present bad behavior within the 58 days. In total, the data set is approximately 12 gigabytes compressed across the five data elements and presents 1,648,275,307 events in total for 12,425 users, 17,684 computers, and 62,974 processes. Specific users that are well known system related (SYSTEM, Local Service) were not de-identified though any well-known administrators account were still de-identified. In the network flow data, well-known ports (e.g. 80, 443, etc) were not de-identified. All other users, computers, process, ports, times, and other details were de-identified as a unified set across all the data elements (e.g. U1 is the same U1 in all of the data). The specific timeframe used is not disclosed for security purposes. In addition, no data that allows association outside of LANL’s network is included. All data starts with a time epoch of 1 using a time resolution of 1 second. In the authentication data, failed authentication events are only included for users that had a successful authentication event somewhere within the data set.

A convolutional neural network neutrino event classifier

DOE PAGES

Aurisano, A.; Radovic, A.; Rocco, D.; ...

2016-09-01

Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology withoutmore » the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.« less

A convolutional neural network neutrino event classifier

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

Aurisano, A.; Radovic, A.; Rocco, D.

Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology withoutmore » the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.« less

Cough event classification by pretrained deep neural network.

PubMed

Liu, Jia-Ming; You, Mingyu; Wang, Zheng; Li, Guo-Zheng; Xu, Xianghuai; Qiu, Zhongmin

2015-01-01

Cough is an essential symptom in respiratory diseases. In the measurement of cough severity, an accurate and objective cough monitor is expected by respiratory disease society. This paper aims to introduce a better performed algorithm, pretrained deep neural network (DNN), to the cough classification problem, which is a key step in the cough monitor. The deep neural network models are built from two steps, pretrain and fine-tuning, followed by a Hidden Markov Model (HMM) decoder to capture tamporal information of the audio signals. By unsupervised pretraining a deep belief network, a good initialization for a deep neural network is learned. Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples. Three cough HMMs and one noncough HMM are employed to model coughs and noncoughs respectively. The final decision is made based on viterbi decoding algorihtm that generates the most likely HMM sequence for each sample. A sample is labeled as cough if a cough HMM is found in the sequence. The experiments were conducted on a dataset that was collected from 22 patients with respiratory diseases. Patient dependent (PD) and patient independent (PI) experimental settings were used to evaluate the models. Five criteria, sensitivity, specificity, F1, macro average and micro average are shown to depict different aspects of the models. From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average. They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI. In this paper, we tried pretrained deep neural network in

Event ambiguity fuels the effective spread of rumors

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Zhang, Yi

2015-08-01

In this paper, a new rumor spreading model which quantifies a specific rumor spreading feature is proposed. The specific feature focused on is the important role the event ambiguity plays in the rumor spreading process. To study the impact of this event ambiguity on the spread of rumors, the probability p(t) that an individual becomes a rumor spreader from an initially unaware person at time t is built. p(t) reflects the extent of event ambiguity, and a parameter c of p(t) is used to measure the speed at which the event moves from ambiguity to confirmation. At the same time, a principle is given to decide on the correct value for parameter c A rumor spreading model is then developed with this function added as a parameter to the traditional model. Then, several rumor spreading model simulations are conducted with different values for c on both regular networks and ER random networks. The simulation results indicate that a rumor spreads faster and more broadly when c is smaller. This shows that if events are ambiguous over a longer time, rumor spreading appears to be more effective, and is influenced more significantly by parameter c in a random network than in a regular network. We then determine parameters of this model through data fitting of the missing Malaysian plane, and apply this model to an analysis of the missing Malaysian plane. The simulation results demonstrate that the most critical time for authorities to control rumor spreading is in the early stages of a critical event.

The XSD-Builder Specification Language—Toward a Semantic View of XML Schema Definition

NASA Astrophysics Data System (ADS)

Fong, Joseph; Cheung, San Kuen

In the present database market, XML database model is a main structure for the forthcoming database system in the Internet environment. As a conceptual schema of XML database, XML Model has its limitation on presenting its data semantics. System analyst has no toolset for modeling and analyzing XML system. We apply XML Tree Model (shown in Figure 2) as a conceptual schema of XML database to model and analyze the structure of an XML database. It is important not only for visualizing, specifying, and documenting structural models, but also for constructing executable systems. The tree model represents inter-relationship among elements inside different logical schema such as XML Schema Definition (XSD), DTD, Schematron, XDR, SOX, and DSD (shown in Figure 1, an explanation of the terms in the figure are shown in Table 1). The XSD-Builder consists of XML Tree Model, source language, translator, and XSD. The source language is called XSD-Source which is mainly for providing an environment with concept of user friendliness while writing an XSD. The source language will consequently be translated by XSD-Translator. Output of XSD-Translator is an XSD which is our target and is called as an object language.

Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field

PubMed Central

Jeong, Myeong-Hun; Duckham, Matt

2015-01-01

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes’ coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks. PMID:26343672

Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field.

PubMed

Jeong, Myeong-Hun; Duckham, Matt

2015-08-28

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes' coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks.

Responding to the Event Deluge

NASA Technical Reports Server (NTRS)

Williams, Roy D.; Barthelmy, Scott D.; Denny, Robert B.; Graham, Matthew J.; Swinbank, John

2012-01-01

We present the VOEventNet infrastructure for large-scale rapid follow-up of astronomical events, including selection, annotation, machine intelligence, and coordination of observations. The VOEvent.standard is central to this vision, with distributed and replicated services rather than centralized facilities. We also describe some of the event brokers, services, and software that .are connected to the network. These technologies will become more important in the coming years, with new event streams from Gaia, LOF AR, LIGO, LSST, and many others

Seismicity in Pennsylvania: Evidence for Anthropogenic Events?

NASA Astrophysics Data System (ADS)

Homman, K.; Nyblade, A.

2015-12-01

The deployment and operation of the USArray Transportable Array (TA) and the PASEIS (XY) seismic networks in Pennsylvania during 2013 and 2014 provide a unique opportunity for investigating the seismicity of Pennsylvania. These networks, along with several permanent stations in Pennsylvania, resulted in a total of 104 seismometers in and around Pennsylvania that have been used in this study. Event locations were first obtained with Antelope Environmental Monitoring Software using P-wave arrival times. Arrival times were hand picked using a 1-5 Hz bandpass filter to within 0.1 seconds. Events were then relocated using a velocity model developed for Pennsylvania and the HYPOELLIPSE location code. In this study, 1593 seismic events occurred between February 2013 and December 2014 in Pennsylvania. These events ranged between magnitude (ML) 1.04 and 2.89 with an average MLof 1.90. Locations of the events occur across the state in many areas where no seismicity has been previously reported. Preliminary results indicate that most of these events are related to mining activity. Additional work using cross-correlation techniques is underway to examine a number of event clusters for evidence of hydraulic fracturing or wastewater injection sources.

Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation.

PubMed

Xie, Jiaheng; Liu, Xiao; Dajun Zeng, Daniel

2018-01-01

Recent years have seen increased worldwide popularity of e-cigarette use. However, the risks of e-cigarettes are underexamined. Most e-cigarette adverse event studies have achieved low detection rates due to limited subject sample sizes in the experiments and surveys. Social media provides a large data repository of consumers' e-cigarette feedback and experiences, which are useful for e-cigarette safety surveillance. However, it is difficult to automatically interpret the informal and nontechnical consumer vocabulary about e-cigarettes in social media. This issue hinders the use of social media content for e-cigarette safety surveillance. Recent developments in deep neural network methods have shown promise for named entity extraction from noisy text. Motivated by these observations, we aimed to design a deep neural network approach to extract e-cigarette safety information in social media. Our deep neural language model utilizes word embedding as the representation of text input and recognizes named entity types with the state-of-the-art Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Network. Our Bi-LSTM model achieved the best performance compared to 3 baseline models, with a precision of 94.10%, a recall of 91.80%, and an F-measure of 92.94%. We identified 1591 unique adverse events and 9930 unique e-cigarette components (ie, chemicals, flavors, and devices) from our research testbed. Although the conditional random field baseline model had slightly better precision than our approach, our Bi-LSTM model achieved much higher recall, resulting in the best F-measure. Our method can be generalized to extract medical concepts from social media for other medical applications. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Impact assessment of extreme storm events using a Bayesian network

USGS Publications Warehouse

den Heijer, C.(Kees); Knipping, Dirk T.J.A.; Plant, Nathaniel G.; van Thiel de Vries, Jaap S. M.; Baart, Fedor; van Gelder, Pieter H. A. J. M.

2012-01-01

This paper describes an investigation on the usefulness of Bayesian Networks in the safety assessment of dune coasts. A network has been created that predicts the erosion volume based on hydraulic boundary conditions and a number of cross-shore profile indicators. Field measurement data along a large part of the Dutch coast has been used to train the network. Corresponding storm impact on the dunes was calculated with an empirical dune erosion model named duros+. Comparison between the Bayesian Network predictions and the original duros+ results, here considered as observations, results in a skill up to 0.88, provided that the training data covers the range of predictions. Hence, the predictions from a deterministic model (duros+) can be captured in a probabilistic model (Bayesian Network) such that both the process knowledge and uncertainties can be included in impact and vulnerability assessments.

Autocorrel I: A Neural Network Based Network Event Correlation Approach

DTIC Science & Technology

2005-05-01

which concern any component of the network. 2.1.1 Existing Intrusion Detection Systems EMERALD [8] is a distributed, scalable, hierarchal, customizable...writing this paper, the updaters of this system had not released their correlation unit to the public. EMERALD ex- plicitly divides statistical analysis... EMERALD , NetSTAT is scalable and composi- ble. QuidSCOR [12] is an open-source IDS, though it requires a subscription from its publisher, Qualys Inc

Multimodal Event Detection in Twitter Hashtag Networks

DOE PAGES

Yilmaz, Yasin; Hero, Alfred O.

2016-07-01

In this study, event detection in a multimodal Twitter dataset is considered. We treat the hashtags in the dataset as instances with two modes: text and geolocation features. The text feature consists of a bag-of-words representation. The geolocation feature consists of geotags (i.e., geographical coordinates) of the tweets. Fusing the multimodal data we aim to detect, in terms of topic and geolocation, the interesting events and the associated hashtags. To this end, a generative latent variable model is assumed, and a generalized expectation-maximization (EM) algorithm is derived to learn the model parameters. The proposed method is computationally efficient, and lendsmore » itself to big datasets. Lastly, experimental results on a Twitter dataset from August 2014 show the efficacy of the proposed method.« less

Event-Based Stereo Depth Estimation Using Belief Propagation.

PubMed

Xie, Zhen; Chen, Shengyong; Orchard, Garrick

2017-01-01

Compared to standard frame-based cameras, biologically-inspired event-based sensors capture visual information with low latency and minimal redundancy. These event-based sensors are also far less prone to motion blur than traditional cameras, and still operate effectively in high dynamic range scenes. However, classical framed-based algorithms are not typically suitable for these event-based data and new processing algorithms are required. This paper focuses on the problem of depth estimation from a stereo pair of event-based sensors. A fully event-based stereo depth estimation algorithm which relies on message passing is proposed. The algorithm not only considers the properties of a single event but also uses a Markov Random Field (MRF) to consider the constraints between the nearby events, such as disparity uniqueness and depth continuity. The method is tested on five different scenes and compared to other state-of-art event-based stereo matching methods. The results show that the method detects more stereo matches than other methods, with each match having a higher accuracy. The method can operate in an event-driven manner where depths are reported for individual events as they are received, or the network can be queried at any time to generate a sparse depth frame which represents the current state of the network.

Clinical Correlates of Surveillance Events Detected by National Healthcare Safety Network Pneumonia and Lower Respiratory Infection Definitions-Pennsylvania, 2011-2012.

PubMed

See, Isaac; Chang, Julia; Gualandi, Nicole; Buser, Genevieve L; Rohrbach, Pamela; Smeltz, Debra A; Bellush, Mary Jo; Coffin, Susan E; Gould, Jane M; Hess, Debra; Hennessey, Patricia; Hubbard, Sydney; Kiernan, Andrea; O'Donnell, Judith; Pegues, David A; Miller, Jeffrey R; Magill, Shelley S

2016-07-01

OBJECTIVE To determine the clinical diagnoses associated with the National Healthcare Safety Network (NHSN) pneumonia (PNEU) or lower respiratory infection (LRI) surveillance events DESIGN Retrospective chart review SETTING A convenience sample of 8 acute-care hospitals in Pennsylvania PATIENTS All patients hospitalized during 2011-2012 METHODS Medical records were reviewed from a random sample of patients reported to the NHSN to have PNEU or LRI, excluding adults with ventilator-associated PNEU. Documented clinical diagnoses corresponding temporally to the PNEU and LRI events were recorded. RESULTS We reviewed 250 (30%) of 838 eligible PNEU and LRI events reported to the NHSN; 29 reported events (12%) fulfilled neither PNEU nor LRI case criteria. Differences interpreting radiology reports accounted for most misclassifications. Of 81 PNEU events in adults not on mechanical ventilation, 84% had clinician-diagnosed pneumonia; of these, 25% were attributed to aspiration. Of 43 adult LRI, 88% were in mechanically ventilated patients and 35% had no corresponding clinical diagnosis (infectious or noninfectious) documented at the time of LRI. Of 36 pediatric PNEU events, 72% were ventilator associated, and 70% corresponded to a clinical pneumonia diagnosis. Of 61 pediatric LRI patients, 84% were mechanically ventilated and 21% had no corresponding clinical diagnosis documented. CONCLUSIONS In adults not on mechanical ventilation and in children, most NHSN-defined PNEU events corresponded with compatible clinical conditions documented in the medical record. In contrast, NHSN LRI events often did not. As a result, substantial modifications to the LRI definitions were implemented in 2015. Infect Control Hosp Epidemiol 2016;37:818-824.

Evolution of Linux operating system network

NASA Astrophysics Data System (ADS)

Xiao, Guanping; Zheng, Zheng; Wang, Haoqin

2017-01-01

Linux operating system (LOS) is a sophisticated man-made system and one of the most ubiquitous operating systems. However, there is little research on the structure and functionality evolution of LOS from the prospective of networks. In this paper, we investigate the evolution of the LOS network. 62 major releases of LOS ranging from versions 1.0 to 4.1 are modeled as directed networks in which functions are denoted by nodes and function calls are denoted by edges. It is found that the size of the LOS network grows almost linearly, while clustering coefficient monotonically decays. The degree distributions are almost the same: the out-degree follows an exponential distribution while both in-degree and undirected degree follow power-law distributions. We further explore the functionality evolution of the LOS network. It is observed that the evolution of functional modules is shown as a sequence of seven events (changes) succeeding each other, including continuing, growth, contraction, birth, splitting, death and merging events. By means of a statistical analysis of these events in the top 4 largest components (i.e., arch, drivers, fs and net), it is shown that continuing, growth and contraction events occupy more than 95% events. Our work exemplifies a better understanding and describing of the dynamics of LOS evolution.

Real-time Monitoring Network to Characterize Anthropogenic and Natural Events Affecting the Hudson River, NY

NASA Astrophysics Data System (ADS)

Islam, M. S.; Bonner, J. S.; Fuller, C.; Kirkey, W.; Ojo, T.

2011-12-01

The Hudson River watershed spans 34,700 km2 predominantly in New York State, including agricultural, wilderness, and urban areas. The Hudson River supports many activities including shipping, supplies water for municipal, commercial, and agricultural uses, and is an important recreational resource. As the population increases within this watershed, so does the anthropogenic impact on this natural system. To address the impacts of anthropogenic and natural activities on this ecosystem, the River and Estuary Observatory Network (REON) is being developed through a joint venture between the Beacon Institute, Clarkson University, General Electric Inc. and IBM Inc. to monitor New York's Hudson and Mohawk Rivers in real-time. REON uses four sensor platform types with multiple nodes within the network to capture environmentally relevant episodic events. Sensor platform types include: 1) fixed robotic vertical profiler (FRVP); 2) mobile robotic undulating platform (MRUP); 3) fixed acoustic Doppler current profiler (FADCP) and 4) Autonomous Underwater Vehicle (AUV). The FRVP periodically generates a vertical profile with respect to water temperature, salinity, dissolved oxygen, particle concentration and size distribution, and fluorescence. The MRUP utilizes an undulating tow-body tethered behind a research vessel to measure the same set of water parameters as the FRVP, but does so 'synchronically' over a highly-resolved spatial regime. The fixed ADCP provides continuous water current profiles. The AUV maps four-dimensional (time, latitude, longitude, depth) variation of water quality, water currents and bathymetry along a pre-determined transect route. REON data can be used to identify episodic events, both anthropogenic and natural, that impact the Hudson River. For example, a strong heat signature associated with cooling water discharge from the Indian Point nuclear power plant was detected with the MRUP. The FRVP monitoring platform at Beacon, NY, located in the

Clinical Correlates of Surveillance Events Detected by National Healthcare Safety Network Pneumonia and Lower Respiratory Infection Definitions—Pennsylvania, 2011–2012

PubMed Central

See, Isaac; Chang, Julia; Gualandi, Nicole; Buser, Genevieve L.; Rohrbach, Pamela; Smeltz, Debra A.; Bellush, Mary Jo; Coffin, Susan E.; Gould, Jane M.; Hess, Debra; Hennessey, Patricia; Hubbard, Sydney; Kiernan, Andrea; O’Donnell, Judith; Pegues, David A.; Miller, Jeffrey R.; Magill, Shelley S.

2017-01-01

Objective To determine the clinical diagnoses associated with National Healthcare Safety Network (NHSN) pneumonia (PNEU) or lower respiratory infection (LRI) surveillance events. Design Retrospective chart review. Setting Eight acute care hospitals in Pennsylvania. Patients All patients hospitalized during 2011–2012. Methods Medical records were reviewed from a random sample of patients reported to NHSN to have PNEU or LRI, excluding adults with ventilator-associated PNEU. Documented clinical diagnoses corresponding temporally to the PNEU/LRI events were recorded. Results We reviewed 250 (30%) of 838 eligible PNEU and LRI events reported to NHSN; 29 (12%) of reported events fulfilled neither PNEU nor LRI case criteria. Differences interpreting radiology reports accounted for most misclassification. Of 81 PNEU events in adults not on mechanical ventilation, 84% had clinician-diagnosed pneumonia, of which 25% were attributed to aspiration. Of 43 adult LRI, 88% were in mechanically ventilated patients and 35% had no corresponding clinical diagnosis (infectious or non-infectious) documented at the time of LRI. Of 36 pediatric PNEU events, 72% were ventilator-associated, and 70% corresponded to a clinical pneumonia diagnosis. Of 61 pediatric LRI, 84% were in mechanically-ventilated patients, and 21% had no corresponding clinical diagnosis documented. Conclusions In adults not on mechanical ventilation and in children, most NHSN-defined PNEU events corresponded to compatible clinical conditions documented in the medical record. In contrast, NHSN LRI events often did not. As a result, substantial modifications to the LRI definitions were implemented in 2015. PMID:27072043

Analysis hierarchical model for discrete event systems

NASA Astrophysics Data System (ADS)

Ciortea, E. M.

2015-11-01

The This paper presents the hierarchical model based on discrete event network for robotic systems. Based on the hierarchical approach, Petri network is analysed as a network of the highest conceptual level and the lowest level of local control. For modelling and control of complex robotic systems using extended Petri nets. Such a system is structured, controlled and analysed in this paper by using Visual Object Net ++ package that is relatively simple and easy to use, and the results are shown as representations easy to interpret. The hierarchical structure of the robotic system is implemented on computers analysed using specialized programs. Implementation of hierarchical model discrete event systems, as a real-time operating system on a computer network connected via a serial bus is possible, where each computer is dedicated to local and Petri model of a subsystem global robotic system. Since Petri models are simplified to apply general computers, analysis, modelling, complex manufacturing systems control can be achieved using Petri nets. Discrete event systems is a pragmatic tool for modelling industrial systems. For system modelling using Petri nets because we have our system where discrete event. To highlight the auxiliary time Petri model using transport stream divided into hierarchical levels and sections are analysed successively. Proposed robotic system simulation using timed Petri, offers the opportunity to view the robotic time. Application of goods or robotic and transmission times obtained by measuring spot is obtained graphics showing the average time for transport activity, using the parameters sets of finished products. individually.

The Air Land Sea Bulletin. Issue No. 2008-2, May 2008

DTIC Science & Technology

2008-05-01

during the meeting. We saw this as a key event because in Iraq the act of breaking bread together is a significant rapport builder and the first...meaning for both parties and were incredible rapport builders —more than that, they created brothers in arms. BUILDING RELATIONSHIPS Once you...possible. Primary disqualifiers for selection as an advisor are medical profiles/issues, lack of security clearances or inability to gain a clearance

Real-time detection and classification of anomalous events in streaming data

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

Ferragut, Erik M.; Goodall, John R.; Iannacone, Michael D.

2016-04-19

A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The events can be displayed to a user in user-defined groupings in an animated fashion. The system can include a plurality of anomaly detectors that together implement an algorithm to identify low probability events and detect atypical traffic patterns. The atypical traffic patterns can then be classified as being of interest or not. In one particular example, in a network environment, the classification can be whether the network traffic is malicious or not.

Nonbinary Tree-Based Phylogenetic Networks.

PubMed

Jetten, Laura; van Iersel, Leo

2018-01-01

Rooted phylogenetic networks are used to describe evolutionary histories that contain non-treelike evolutionary events such as hybridization and horizontal gene transfer. In some cases, such histories can be described by a phylogenetic base-tree with additional linking arcs, which can, for example, represent gene transfer events. Such phylogenetic networks are called tree-based. Here, we consider two possible generalizations of this concept to nonbinary networks, which we call tree-based and strictly-tree-based nonbinary phylogenetic networks. We give simple graph-theoretic characterizations of tree-based and strictly-tree-based nonbinary phylogenetic networks. Moreover, we show for each of these two classes that it can be decided in polynomial time whether a given network is contained in the class. Our approach also provides a new view on tree-based binary phylogenetic networks. Finally, we discuss two examples of nonbinary phylogenetic networks in biology and show how our results can be applied to them.

Improving Seismic Event Characterisation

DTIC Science & Technology

1996-07-22

classificat i,; and further phase identification . 6.4.3 Seismic event interpretation The’ system of event processing is based on an assumption tree ...and is enhanced with usez by a network. 14, SUBJECT TERMSý 15. NUMBER OF PAGES seismic models, travel. timtes phase identification 16 PRICE CODE 17...hesimwinlia’ rati of t lieDl scisillograonis is 2/3 secondIs andI the receiver spaci mi is 1 /3 degreeus. ’lIi iiaiiiii iltdiwic’ ewe ii rayv-the~oret~icaIl

Synchronization Of Parallel Discrete Event Simulations

NASA Technical Reports Server (NTRS)

Steinman, Jeffrey S.

1992-01-01

Adaptive, parallel, discrete-event-simulation-synchronization algorithm, Breathing Time Buckets, developed in Synchronous Parallel Environment for Emulation and Discrete Event Simulation (SPEEDES) operating system. Algorithm allows parallel simulations to process events optimistically in fluctuating time cycles that naturally adapt while simulation in progress. Combines best of optimistic and conservative synchronization strategies while avoiding major disadvantages. Algorithm processes events optimistically in time cycles adapting while simulation in progress. Well suited for modeling communication networks, for large-scale war games, for simulated flights of aircraft, for simulations of computer equipment, for mathematical modeling, for interactive engineering simulations, and for depictions of flows of information.

Acoustic event location and background noise characterization on a free flying infrasound sensor network in the stratosphere

NASA Astrophysics Data System (ADS)

Bowman, Daniel C.; Albert, Sarah A.

2018-06-01

A variety of Earth surface and atmospheric sources generate low-frequency sound waves that can travel great distances. Despite a rich history of ground-based sensor studies, very few experiments have investigated the prospects of free floating microphone arrays at high altitudes. However, recent initiatives have shown that such networks have very low background noise and may sample an acoustic wave field that is fundamentally different than that at Earth's surface. The experiments have been limited to at most two stations at altitude, making acoustic event detection and localization difficult. We describe the deployment of four drifting microphone stations at altitudes between 21 and 24 km above sea level. The stations detected one of two regional ground-based chemical explosions as well as the ocean microbarom while travelling almost 500 km across the American Southwest. The explosion signal consisted of multiple arrivals; signal amplitudes did not correlate with sensor elevation or source range. The waveforms and propagation patterns suggest interactions with gravity waves at 35-45 km altitude. A sparse network method that employed curved wave front corrections was able to determine the backazimuth from the free flying network to the acoustic source. Episodic signals similar to those seen on previous flights in the same region were noted, but their source remains unclear. Background noise levels were commensurate with those on infrasound stations in the International Monitoring System below 2 s.

High Latitude Reefs: A Potential Refuge for Reef Builders

NASA Astrophysics Data System (ADS)

Amat, A.; Bates, N.

2003-04-01

Coral reefs globally show variable signs of deterioration or community structure changes due to a host of anthropogenic and natural factors. In these global scenarios, rates of calcification by reef builders such as Scleractinian corals are predicted to significantly decline in the future due to the increase in atmospheric CO_2. When considering the response of reefs to the present climate change, temperature effects should also be taken into account. Here, we investigate the simultaneous impact of temperature and CO_2 on the high-latitude Bermuda coral reef system (32^oN, 64^oE)through a series of in vitro experiments at different CO_2 levels and seasonally different summer (27^oC) and winter (20^oC) temperature conditions. Four species of Scleractinian corals (Porites astreoides, Diploria labyrinthiformis, Madracis mirabilis and decactis) were acclimated for three months at: 20^oC and 27^oC (both with CO_2 levels at 400 ppm (control) and 700 ppm). Growth was assessed by buoyant weight techniques during the acclimation period. Photosynthesis, respiration and calcification were measured at the end of this period using respirometric chambers. A reproduction experiment was also undertaken under 27^oC. Photosynthesis mainly remains constant or increases under high CO_2 conditions. The results of the integrated calcification measurements confirm the hypothesis that an increase in CO_2 induces a decrease in calcification. However an increase in photosynthesis can be observed when CO_2 is unfavorable for calcification suggesting that a biological control of calcification through photosynthesis could prevent a drop in the calcification potential. Buoyant weight results indicate that the CO_2 impact could be less detrimental under lower temperature. This result will be compared with the instantaneous calcification measurements in the chambers and some in situ coral growth assessments in winter and summer conditions. The consequences for the response of marginal reefs

Detection of anomalous events

DOEpatents

Ferragut, Erik M.; Laska, Jason A.; Bridges, Robert A.

2016-06-07

A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The system can include a plurality of anomaly detectors that together implement an algorithm to identify low-probability events and detect atypical traffic patterns. The anomaly detector provides for comparability of disparate sources of data (e.g., network flow data and firewall logs.) Additionally, the anomaly detector allows for regulatability, meaning that the algorithm can be user configurable to adjust a number of false alerts. The anomaly detector can be used for a variety of probability density functions, including normal Gaussian distributions, irregular distributions, as well as functions associated with continuous or discrete variables.

A networks-based discrete dynamic systems approach to volcanic seismicity

NASA Astrophysics Data System (ADS)

Suteanu, Mirela

2013-04-01

The detection and relevant description of pattern change concerning earthquake events is an important, but challenging task. In this paper, earthquake events related to volcanic activity are considered manifestations of a dynamic system evolving over time. The system dynamics is seen as a succession of events with point-like appearance both in time and in space. Each event is characterized by a position in three-dimensional space, a moment of occurrence, and an event size (magnitude). A weighted directed network is constructed to capture the effects of earthquakes on subsequent events. Each seismic event represents a node. Relations among events represent edges. Edge directions are given by the temporal succession of the events. Edges are also characterized by weights reflecting the strengths of the relation between the nodes. Weights are calculated as a function of (i) the time interval separating the two events, (ii) the spatial distance between the events, (iii) the magnitude of the earliest event among the two. Different ways of addressing weight components are explored, and their implications for the properties of the produced networks are analyzed. The resulting networks are then characterized in terms of degree- and weight distributions. Subsequently, the distribution of system transitions is determined for all the edges connecting related events in the network. Two- and three-dimensional diagrams are constructed to reflect transition distributions for each set of events. Networks are thus generated for successive temporal windows of different size, and the evolution of (a) network properties and (b) system transition distributions are followed over time and compared to the timeline of documented geologic processes. Applications concerning volcanic seismicity on the Big Island of Hawaii show that this approach is capable of revealing novel aspects of change occurring in the volcanic system on different scales in time and in space.

Asynchronous sampled-data approach for event-triggered systems

NASA Astrophysics Data System (ADS)

Mahmoud, Magdi S.; Memon, Azhar M.

2017-11-01

While aperiodically triggered network control systems save a considerable amount of communication bandwidth, they also pose challenges such as coupling between control and event-condition design, optimisation of the available resources such as control, communication and computation power, and time-delays due to computation and communication network. With this motivation, the paper presents separate designs of control and event-triggering mechanism, thus simplifying the overall analysis, asynchronous linear quadratic Gaussian controller which tackles delays and aperiodic nature of transmissions, and a novel event mechanism which compares the cost of the aperiodic system against a reference periodic implementation. The proposed scheme is simulated on a linearised wind turbine model for pitch angle control and the results show significant improvement against the periodic counterpart.

Event-based user classification in Weibo media.

PubMed

Guo, Liang; Wang, Wendong; Cheng, Shiduan; Que, Xirong

2014-01-01

Weibo media, known as the real-time microblogging services, has attracted massive attention and support from social network users. Weibo platform offers an opportunity for people to access information and changes the way people acquire and disseminate information significantly. Meanwhile, it enables people to respond to the social events in a more convenient way. Much of the information in Weibo media is related to some events. Users who post different contents, and exert different behavior or attitude may lead to different contribution to the specific event. Therefore, classifying the large amount of uncategorized social circles generated in Weibo media automatically from the perspective of events has been a promising task. Under this circumstance, in order to effectively organize and manage the huge amounts of users, thereby further managing their contents, we address the task of user classification in a more granular, event-based approach in this paper. By analyzing real data collected from Sina Weibo, we investigate the Weibo properties and utilize both content information and social network information to classify the numerous users into four primary groups: celebrities, organizations/media accounts, grassroots stars, and ordinary individuals. The experiments results show that our method identifies the user categories accurately.

Event Recognition Based on Deep Learning in Chinese Texts

PubMed Central

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%. PMID:27501231

Event Recognition Based on Deep Learning in Chinese Texts.

PubMed

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

United States National seismograph network

USGS Publications Warehouse

Masse, R.P.; Filson, J.R.; Murphy, A.

1989-01-01

The USGS National Earthquake Information Center (NEIC) has planned and is developing a broadband digital seismograph network for the United States. The network will consist of approximately 150 seismograph stations distributed across the contiguous 48 states and across Alaska, Hawaii, Puerto Rico and the Virgin Islands. Data transmission will be via two-way satellite telemetry from the network sites to a central recording facility at the NEIC in Golden, Colorado. The design goal for the network is the on-scale recording by at least five well-distributed stations of any seismic event of magnitude 2.5 or greater in all areas of the United States except possibly part of Alaska. All event data from the network will be distributed to the scientific community on compact disc with read-only memory (CD-ROM). ?? 1989.

Prediction and Characterization of High-Activity Events in Social Media Triggered by Real-World News.

PubMed

Kalyanam, Janani; Quezada, Mauricio; Poblete, Barbara; Lanckriet, Gert

2016-01-01

On-line social networks publish information on a high volume of real-world events almost instantly, becoming a primary source for breaking news. Some of these real-world events can end up having a very strong impact on on-line social networks. The effect of such events can be analyzed from several perspectives, one of them being the intensity and characteristics of the collective activity that it produces in the social platform. We research 5,234 real-world news events encompassing 43 million messages discussed on the Twitter microblogging service for approximately 1 year. We show empirically that exogenous news events naturally create collective patterns of bursty behavior in combination with long periods of inactivity in the network. This type of behavior agrees with other patterns previously observed in other types of natural collective phenomena, as well as in individual human communications. In addition, we propose a methodology to classify news events according to the different levels of intensity in activity that they produce. In particular, we analyze the most highly active events and observe a consistent and strikingly different collective reaction from users when they are exposed to such events. This reaction is independent of an event's reach and scope. We further observe that extremely high-activity events have characteristics that are quite distinguishable at the beginning stages of their outbreak. This allows us to predict with high precision, the top 8% of events that will have the most impact in the social network by just using the first 5% of the information of an event's lifetime evolution. This strongly implies that high-activity events are naturally prioritized collectively by the social network, engaging users early on, way before they are brought to the mainstream audience.

Detecting and Locating Seismic Events Without Phase Picks or Velocity Models

NASA Astrophysics Data System (ADS)

Arrowsmith, S.; Young, C. J.; Ballard, S.; Slinkard, M.

2015-12-01

The standard paradigm for seismic event monitoring is to scan waveforms from a network of stations and identify the arrival time of various seismic phases. A signal association algorithm then groups the picks to form events, which are subsequently located by minimizing residuals between measured travel times and travel times predicted by an Earth model. Many of these steps are prone to significant errors which can lead to erroneous arrival associations and event locations. Here, we revisit a concept for event detection that does not require phase picks or travel time curves and fuses detection, association and location into a single algorithm. Our pickless event detector exploits existing catalog and waveform data to build an empirical stack of the full regional seismic wavefield, which is subsequently used to detect and locate events at a network level using correlation techniques. Because the technique uses more of the information content of the original waveforms, the concept is particularly powerful for detecting weak events that would be missed by conventional methods. We apply our detector to seismic data from the University of Utah Seismograph Stations network and compare our results with the earthquake catalog published by the University of Utah. We demonstrate that the pickless detector can detect and locate significant numbers of events previously missed by standard data processing techniques.

Adaptive Long-Term Monitoring at Environmental Restoration Sites (ER-0629)

DTIC Science & Technology

2009-05-01

Figures Figure 2-1 General Flowchart of Software Application Figure 2-2 Overview of the Genetic Algorithm Approach Figure 2-3 Example of a...and Model Builder) are highlighted on Figure 2-1, which is a general flowchart illustrating the application of the software. The software is applied...monitoring event (e.g., contaminant mass based on interpolation) that modeling is provided by Model Builder. 4 Figure 2-1. General Flowchart of Software

Training Deep Spiking Neural Networks Using Backpropagation.

PubMed

Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

2016-01-01

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

Forecasting ENSO events: A neural network-extended EOF approach

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

Tangang, F.T.; Tang, B.; Monahan, A.H.

The authors constructed neural network models to forecast the sea surface temperature anomalies (SSTA) for three regions: Nino 4. Nino 3.5, and Nino 3, representing the western-central, the central, and the eastern-central parts of the equatorial Pacific Ocean, respectively. The inputs were the extended empirical orthogonal functions (EEOF) of the sea level pressure (SLP) field that covered the tropical Indian and Pacific Oceans and evolved for a duration of 1 yr. The EEOFs greatly reduced the size of the neural networks from those of the authors` earlier papers using EOFs. The Nino 4 region appeared to be the best forecastedmore » region, with useful skills up to a year lead time for the 1982-93 forecast period. By network pruning analysis and spectral analysis, four important inputs were identified: modes 1, 2, and 6 of the SLP EEOFs and the SSTA persistence. Mode 1 characterized the low-frequency oscillation (LFO, with 4-5-yr period), and was seen as the typical ENSO signal, while mode 2, with a period of 2-5 yr, characterized the quasi-biennial oscillation (QBO) plus the LFO. Mode 6 was dominated by decadal and interdecadal variations. Thus, forecasting ENSO required information from the QBO, and the decadal-interdecadal oscillations. The nonlinearity of the networks tended to increase with lead time and to become stronger for the eastern regions of the equatorial Pacific Ocean. 35 refs., 14 figs., 4 tabs.« less

Toward Regional Clusters: Networking Events, Collaborative Research, and the Business Finder

NASA Astrophysics Data System (ADS)

Reichling, Tim; Moos, Benjamin; Rohde, Markus; Wulf, Volker

Networks of regionally collocated organizations improve the competitiveness of their member companies. This is not only a result of lower transportation costs when delivering or purchasing physical goods but also other matters such as mutual trust or a higher diffusion of specialized knowledge among companies that have emerged as important aspects of regional networks. Even increased competition among collocated companies can lead to comparative advantages over externals as a result of an increased pressure for innovation. While the reasons why regional networks of companies offer comparative advantages has been widely investigated, the question arises as to how networks can be developed in terms of higher interconnectedness and deeper connections.

Hierarchical Address Event Routing for Reconfigurable Large-Scale Neuromorphic Systems.

PubMed

Park, Jongkil; Yu, Theodore; Joshi, Siddharth; Maier, Christoph; Cauwenberghs, Gert

2017-10-01

We present a hierarchical address-event routing (HiAER) architecture for scalable communication of neural and synaptic spike events between neuromorphic processors, implemented with five Xilinx Spartan-6 field-programmable gate arrays and four custom analog neuromophic integrated circuits serving 262k neurons and 262M synapses. The architecture extends the single-bus address-event representation protocol to a hierarchy of multiple nested buses, routing events across increasing scales of spatial distance. The HiAER protocol provides individually programmable axonal delay in addition to strength for each synapse, lending itself toward biologically plausible neural network architectures, and scales across a range of hierarchies suitable for multichip and multiboard systems in reconfigurable large-scale neuromorphic systems. We show approximately linear scaling of net global synaptic event throughput with number of routing nodes in the network, at 3.6×10 7 synaptic events per second per 16k-neuron node in the hierarchy.

Response control networks are selectively modulated by attention to rare events and memory load regardless of the need for inhibition.

PubMed

Wijeakumar, Sobanawartiny; Magnotta, Vincent A; Buss, Aaron T; Ambrose, Joseph P; Wifall, Timothy A; Hazeltine, Eliot; Spencer, John P

2015-10-15

Recent evidence has sparked debate about the neural bases of response selection and inhibition. In the current study, we employed two reactive inhibition tasks, the Go/Nogo (GnG) and Simon tasks, to examine questions central to these debates. First, we investigated whether a fronto-cortical-striatal system was sensitive to the need for inhibition per se or the presentation of infrequent stimuli, by manipulating the proportion of trials that do not require inhibition (Go/Compatible trials) relative to trials that require inhibition (Nogo/Incompatible trials). A cortico-subcortical network composed of insula, putamen, and thalamus showed greater activation on salient and infrequent events, regardless of the need for inhibition. Thus, consistent with recent findings, key parts of the fronto-cortical-striatal system are engaged by salient events and do not appear to play a selective role in response inhibition. Second, we examined how the fronto-cortical-striatal system is modulated by working memory demands by varying the number of stimulus-response (SR) mappings. Right inferior parietal lobule showed decreasing activation as the number of SR mappings increased, suggesting that a form of associative memory - rather than working memory - might underlie performance in these tasks. A broad motor planning and control network showed similar trends that were also modulated by the number of motor responses required in each task. Finally, bilateral lingual gyri were more robustly engaged in the Simon task, consistent with the role of this area in shifts of visuo-spatial attention. The current study sheds light on how the fronto-cortical-striatal network is selectively engaged in reactive control tasks and how control is modulated by manipulations of attention and memory load. Copyright © 2015 Elsevier Inc. All rights reserved.

Acoustic Event Location and Background Noise Characterization on a Free Flying Infrasound Sensor Network in the Stratosphere

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

Bowman, Daniel C.; Albert, Sarah A.

We present that a variety of Earth surface and atmospheric sources generate low frequency sound waves that can travel great distances. Despite a rich history of ground-based sensor studies, very few experiments have investigated the prospects of free floating microphone arrays at high altitudes. However, recent initiatives have shown that such networks have very low background noise and may sample an acoustic wave field that is fundamentally different than that at Earth’s surface. The experiments have been limited to at most two stations at altitude, making acoustic event detection and localization difficult. We describe the deployment of four drifting microphonemore » stations at altitudes between 21 and 24 km above sea level. The stations detected one of two regional ground-based chemical explosions as well as the ocean microbarom while traveling almost 500 km across the American Southwest. The explosion signal consisted of multiple arrivals; signal amplitudes did not correlate with sensor elevation or source range. The waveforms and propagation patterns suggest interactions with gravity waves in the 35-45 km altitude. A sparse network method that employed curved wave front corrections was able to determine the backazimuth from the free flying network to the acoustic source. Episodic signals similar to those seen on previous flights in the same region were noted, but their source remains unclear. Lastly, background noise levels were commensurate with those on infrasound stations in the International Monitoring System below 2 seconds.« less

Acoustic Event Location and Background Noise Characterization on a Free Flying Infrasound Sensor Network in the Stratosphere

DOE PAGES

Bowman, Daniel C.; Albert, Sarah A.

2018-02-22

We present that a variety of Earth surface and atmospheric sources generate low frequency sound waves that can travel great distances. Despite a rich history of ground-based sensor studies, very few experiments have investigated the prospects of free floating microphone arrays at high altitudes. However, recent initiatives have shown that such networks have very low background noise and may sample an acoustic wave field that is fundamentally different than that at Earth’s surface. The experiments have been limited to at most two stations at altitude, making acoustic event detection and localization difficult. We describe the deployment of four drifting microphonemore » stations at altitudes between 21 and 24 km above sea level. The stations detected one of two regional ground-based chemical explosions as well as the ocean microbarom while traveling almost 500 km across the American Southwest. The explosion signal consisted of multiple arrivals; signal amplitudes did not correlate with sensor elevation or source range. The waveforms and propagation patterns suggest interactions with gravity waves in the 35-45 km altitude. A sparse network method that employed curved wave front corrections was able to determine the backazimuth from the free flying network to the acoustic source. Episodic signals similar to those seen on previous flights in the same region were noted, but their source remains unclear. Lastly, background noise levels were commensurate with those on infrasound stations in the International Monitoring System below 2 seconds.« less

Changes in Caribbean coral disease prevalence after the 2005 bleaching event.

PubMed

Cróquer, Aldo; Weil, Ernesto

2009-11-16

Bleaching events and disease epizootics have increased during the past decades, suggesting a positive link between these 2 causes in producing coral mortality. However, studies to test this hypothesis, integrating a broad range of hierarchical spatial scales from habitats to distant localities, have not been conducted in the Caribbean. In this study, we examined links between bleaching intensity and disease prevalence collected from 6 countries, 2 reef sites for each country, and 3 habitats within each reef site (N = 6 x 2 x 3 = 36 site-habitat combinations) during the peak of bleaching in 2005 and a year after, in 2006. Patterns of disease prevalence and bleaching were significantly correlated (Rho = 0.58, p = 0.04). Higher variability in disease prevalence after bleaching occurred among habitats at each particular reef site, with a significant increase in prevalence recorded in 4 of the 10 site-habitats where bleaching was intense and a non-significant increase in disease prevalence in 18 out of the 26 site-habitats where bleaching was low to moderate. A significant linear correlation was found (r = 0.89, p = 0.008) between bleaching and the prevalence of 2 virulent diseases (yellow band disease and white plague) affecting the Montastraea species complex. Results of this study suggest that if bleaching events become more intense and frequent, disease-related mortality of Caribbean coral reef builders could increase, with uncertain effects on coral reef resilience.

Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control

PubMed Central

Pawlowski, Andrzej; Guzman, Jose Luis; Rodríguez, Francisco; Berenguel, Manuel; Sánchez, José; Dormido, Sebastián

2009-01-01

Monitoring and control of the greenhouse environment play a decisive role in greenhouse production processes. Assurance of optimal climate conditions has a direct influence on crop growth performance, but it usually increases the required equipment cost. Traditionally, greenhouse installations have required a great effort to connect and distribute all the sensors and data acquisition systems. These installations need many data and power wires to be distributed along the greenhouses, making the system complex and expensive. For this reason, and others such as unavailability of distributed actuators, only individual sensors are usually located in a fixed point that is selected as representative of the overall greenhouse dynamics. On the other hand, the actuation system in greenhouses is usually composed by mechanical devices controlled by relays, being desirable to reduce the number of commutations of the control signals from security and economical point of views. Therefore, and in order to face these drawbacks, this paper describes how the greenhouse climate control can be represented as an event-based system in combination with wireless sensor networks, where low-frequency dynamics variables have to be controlled and control actions are mainly calculated against events produced by external disturbances. The proposed control system allows saving costs related with wear minimization and prolonging the actuator life, but keeping promising performance results. Analysis and conclusions are given by means of simulation results. PMID:22389597

Simulation of greenhouse climate monitoring and control with wireless sensor network and event-based control.

PubMed

Pawlowski, Andrzej; Guzman, Jose Luis; Rodríguez, Francisco; Berenguel, Manuel; Sánchez, José; Dormido, Sebastián

2009-01-01

Monitoring and control of the greenhouse environment play a decisive role in greenhouse production processes. Assurance of optimal climate conditions has a direct influence on crop growth performance, but it usually increases the required equipment cost. Traditionally, greenhouse installations have required a great effort to connect and distribute all the sensors and data acquisition systems. These installations need many data and power wires to be distributed along the greenhouses, making the system complex and expensive. For this reason, and others such as unavailability of distributed actuators, only individual sensors are usually located in a fixed point that is selected as representative of the overall greenhouse dynamics. On the other hand, the actuation system in greenhouses is usually composed by mechanical devices controlled by relays, being desirable to reduce the number of commutations of the control signals from security and economical point of views. Therefore, and in order to face these drawbacks, this paper describes how the greenhouse climate control can be represented as an event-based system in combination with wireless sensor networks, where low-frequency dynamics variables have to be controlled and control actions are mainly calculated against events produced by external disturbances. The proposed control system allows saving costs related with wear minimization and prolonging the actuator life, but keeping promising performance results. Analysis and conclusions are given by means of simulation results.

System Diagnostic Builder - A rule generation tool for expert systems that do intelligent data evaluation. [applied to Shuttle Mission Simulator

NASA Technical Reports Server (NTRS)

Nieten, Joseph; Burke, Roger

1993-01-01

Consideration is given to the System Diagnostic Builder (SDB), an automated knowledge acquisition tool using state-of-the-art AI technologies. The SDB employs an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert. Thus, data are captured from the subject system, classified, and used to drive the rule generation process. These rule bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The knowledge bases captured from the Shuttle Mission Simulator can be used as black box simulations by the Intelligent Computer Aided Training devices. The SDB can also be used to construct knowledge bases for the process control industry, such as chemical production or oil and gas production.

Ontology-based Vaccine and Drug Adverse Event Representation and Theory-guided Systematic Causal Network Analysis toward Integrative Pharmacovigilance Research

PubMed Central

He, Yongqun

2016-01-01

Compared with controlled terminologies (e.g., MedDRA, CTCAE, and WHO-ART), the community-based Ontology of AEs (OAE) has many advantages in adverse event (AE) classifications. The OAE-derived Ontology of Vaccine AEs (OVAE) and Ontology of Drug Neuropathy AEs (ODNAE) serve as AE knowledge bases and support data integration and analysis. The Immune Response Gene Network Theory explains molecular mechanisms of vaccine-related AEs. The OneNet Theory of Life treats the whole process of a life of an organism as a single complex and dynamic network (i.e., OneNet). A new “OneNet effectiveness” tenet is proposed here to expand the OneNet theory. Derived from the OneNet theory, the author hypothesizes that one human uses one single genotype-rooted mechanism to respond to different vaccinations and drug treatments, and experimentally identified mechanisms are manifestations of the OneNet blueprint mechanism under specific conditions. The theories and ontologies interact together as semantic frameworks to support integrative pharmacovigilance research. PMID:27458549

Ontology-based Vaccine and Drug Adverse Event Representation and Theory-guided Systematic Causal Network Analysis toward Integrative Pharmacovigilance Research.

PubMed

He, Yongqun

2016-06-01

Compared with controlled terminologies ( e.g. , MedDRA, CTCAE, and WHO-ART), the community-based Ontology of AEs (OAE) has many advantages in adverse event (AE) classifications. The OAE-derived Ontology of Vaccine AEs (OVAE) and Ontology of Drug Neuropathy AEs (ODNAE) serve as AE knowledge bases and support data integration and analysis. The Immune Response Gene Network Theory explains molecular mechanisms of vaccine-related AEs. The OneNet Theory of Life treats the whole process of a life of an organism as a single complex and dynamic network ( i.e. , OneNet). A new "OneNet effectiveness" tenet is proposed here to expand the OneNet theory. Derived from the OneNet theory, the author hypothesizes that one human uses one single genotype-rooted mechanism to respond to different vaccinations and drug treatments, and experimentally identified mechanisms are manifestations of the OneNet blueprint mechanism under specific conditions. The theories and ontologies interact together as semantic frameworks to support integrative pharmacovigilance research.

Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care.

PubMed

Donald, Rob; Howells, Tim; Piper, Ian; Enblad, P; Nilsson, P; Chambers, I; Gregson, B; Citerio, G; Kiening, K; Neumann, J; Ragauskas, A; Sahuquillo, J; Sinnott, R; Stell, A

2018-05-24

Traumatically brain injured (TBI) patients are at risk from secondary insults. Arterial hypotension, critically low blood pressure, is one of the most dangerous secondary insults and is related to poor outcome in patients. The overall aim of this study was to get proof of the concept that advanced statistical techniques (machine learning) are methods that are able to provide early warning of impending hypotensive events before they occur during neuro-critical care. A Bayesian artificial neural network (BANN) model predicting episodes of hypotension was developed using data from 104 patients selected from the BrainIT multi-center database. Arterial hypotension events were recorded and defined using the Edinburgh University Secondary Insult Grades (EUSIG) physiological adverse event scoring system. The BANN was trained on a random selection of 50% of the available patients (n = 52) and validated on the remaining cohort. A multi-center prospective pilot study (Phase 1, n = 30) was then conducted with the system running live in the clinical environment, followed by a second validation pilot study (Phase 2, n = 49). From these prospectively collected data, a final evaluation study was done on 69 of these patients with 10 patients excluded from the Phase 2 study because of insufficient or invalid data. Each data collection phase was a prospective non-interventional observational study conducted in a live clinical setting to test the data collection systems and the model performance. No prediction information was available to the clinical teams during a patient's stay in the ICU. The final cohort (n = 69), using a decision threshold of 0.4, and including false positive checks, gave a sensitivity of 39.3% (95% CI 32.9-46.1) and a specificity of 91.5% (95% CI 89.0-93.7). Using a decision threshold of 0.3, and false positive correction, gave a sensitivity of 46.6% (95% CI 40.1-53.2) and specificity of 85.6% (95% CI 82.3-88.8). With a decision threshold of 0

Event-Based User Classification in Weibo Media

PubMed Central

Wang, Wendong; Cheng, Shiduan; Que, Xirong

2014-01-01

Weibo media, known as the real-time microblogging services, has attracted massive attention and support from social network users. Weibo platform offers an opportunity for people to access information and changes the way people acquire and disseminate information significantly. Meanwhile, it enables people to respond to the social events in a more convenient way. Much of the information in Weibo media is related to some events. Users who post different contents, and exert different behavior or attitude may lead to different contribution to the specific event. Therefore, classifying the large amount of uncategorized social circles generated in Weibo media automatically from the perspective of events has been a promising task. Under this circumstance, in order to effectively organize and manage the huge amounts of users, thereby further managing their contents, we address the task of user classification in a more granular, event-based approach in this paper. By analyzing real data collected from Sina Weibo, we investigate the Weibo properties and utilize both content information and social network information to classify the numerous users into four primary groups: celebrities, organizations/media accounts, grassroots stars, and ordinary individuals. The experiments results show that our method identifies the user categories accurately. PMID:25133235

47 CFR 25.261 - Procedures for avoidance of in-line interference events for Non Geostationary Satellite Orbit...

Code of Federal Regulations, 2010 CFR

2010-10-01

... interference events for Non Geostationary Satellite Orbit (NGSO) Satellite Network Operations in the Fixed... avoidance of in-line interference events for Non Geostationary Satellite Orbit (NGSO) Satellite Network... procedures in this section apply to non-Federal-Government NGSO FSS satellite networks operating in the...

47 CFR 25.261 - Procedures for avoidance of in-line interference events for Non Geostationary Satellite Orbit...

Code of Federal Regulations, 2011 CFR

2011-10-01

... interference events for Non Geostationary Satellite Orbit (NGSO) Satellite Network Operations in the Fixed... avoidance of in-line interference events for Non Geostationary Satellite Orbit (NGSO) Satellite Network... procedures in this section apply to non-Federal-Government NGSO FSS satellite networks operating in the...

Episodic simulation of future events is impaired in mild Alzheimer's disease

PubMed Central

Addis, Donna Rose; Sacchetti, Daniel C.; Ally, Brandon A.; Budson, Andrew E.; Schacter, Daniel L.

2009-01-01

Recent neuroimaging studies have demonstrated that both remembering the past and simulating the future activate a core neural network including the medial temporal lobes. Regions of this network, in particular the medial temporal lobes, are prime sites for amyloid deposition and are structurally and functionally compromised in Alzheimer's disease (AD). While we know some functions of this core network, specifically episodic autobiographical memory, are impaired in AD, no study has examined whether future episodic simulation is similarly impaired. We tested the ability of sixteen AD patients and sixteen age-matched controls to generate past and future autobiographical events using an adapted version of the Autobiographical Interview. Participants also generated five remote autobiographical memories from across the lifespan. Event transcriptions were segmented into distinct details, classified as either internal (episodic) or external (non-episodic). AD patients exhibited deficits in both remembering past events and simulating future events, generating fewer internal and external episodic details than healthy older controls. The internal and external detail scores were strongly correlated across past and future events, providing further evidence of the close linkages between the mental representations of past and future. PMID:19497331

Background rejection in NEXT using deep neural networks

DOE PAGES

Renner, J.; Farbin, A.; Vidal, J. Muñoz; ...

2017-01-16

Here, we investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the usemore » of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.« less

Effect of densifying the GNSS GBAS network on monitoring the troposphere zenith total delay and precipitable water vapour content during severe weather events

NASA Astrophysics Data System (ADS)

Kapłon, Jan; Stankunavicius, Gintautas

2016-04-01

The dense ground based augmentation networks can provide the important information for monitoring the state of neutral atmosphere. The GNSS&METEO research group at Wroclaw University of Environmental and Life Sciences (WUELS) is operating the self-developed near real-time service estimating the troposphere parameters from GNSS data for the area of Poland. The service is operational since December 2012 and it's results calculated from ASG-EUPOS GBAS network (120 stations) data are supporting the EGVAP (http://egvap.dmi.dk) project. At first the zenith troposphere delays (ZTD) were calculated in hourly intervals, but since September 2015 the service was upgraded to include SmartNet GBAS network (Leica Geosystems Polska - 150 stations). The upgrade included as well: increasing the result interval to 30 minutes, upgrade from Bernese GPS Software v. 5.0 to Bernese GNSS Software v. 5.2 and estimation of the ZTD and it's horizontal gradients. Processing includes nowadays 270 stations. The densification of network from 70 km of mean distance between stations to 40 km created the opportunity to investigate on it's impact on resolution of estimated ZTD and integrated water vapour content (IWV) fields during the weather events of high intensity. Increase in density of ZTD measurements allows to define better the meso-scale features within different synoptic systems (e.g. frontal waves, meso-scale convective systems, squall lines etc). These meso-scale structures, as a rule are short living but fast developing and hardly predictable by numerical models. Even so, such limited size systems can produce very hazardous phenomena - like widespread squalls and thunderstorms, tornadoes, heavy rains, snowfalls, hail etc. because of prevalence of Cb clouds with high concentration of IWV. Study deals with two meteorological events: 2015-09-01 with the devastating squalls and rainfall bringing 2M Euro loss of property in northern Poland and 2015-10-12 with the very active front bringing

docBUILDER - Building Your Useful Metadata for Earth Science Data and Services.

NASA Astrophysics Data System (ADS)

Weir, H. M.; Pollack, J.; Olsen, L. M.; Major, G. R.

2005-12-01

The docBUILDER tool, created by NASA's Global Change Master Directory (GCMD), assists the scientific community in efficiently creating quality data and services metadata. Metadata authors are asked to complete five required fields to ensure enough information is provided for users to discover the data and related services they seek. After the metadata record is submitted to the GCMD, it is reviewed for semantic and syntactic consistency. Currently, two versions are available - a Web-based tool accessible with most browsers (docBUILDERweb) and a stand-alone desktop application (docBUILDERsolo). The Web version is available through the GCMD website, at http://gcmd.nasa.gov/User/authoring.html. This version has been updated and now offers: personalized templates to ease entering similar information for multiple data sets/services; automatic population of Data Center/Service Provider URLs based on the selected center/provider; three-color support to indicate required, recommended, and optional fields; an editable text window containing the XML record, to allow for quick editing; and improved overall performance and presentation. The docBUILDERsolo version offers the ability to create metadata records on a computer wherever you are. Except for installation and the occasional update of keywords, data/service providers are not required to have an Internet connection. This freedom will allow users with portable computers (Windows, Mac, and Linux) to create records in field campaigns, whether in Antarctica or the Australian Outback. This version also offers a spell-checker, in addition to all of the features found in the Web version.

Toward Joint Hypothesis-Tests Seismic Event Screening Analysis: Ms|mb and Event Depth

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

Anderson, Dale; Selby, Neil

2012-08-14

Well established theory can be used to combine single-phenomenology hypothesis tests into a multi-phenomenology event screening hypothesis test (Fisher's and Tippett's tests). Commonly used standard error in Ms:mb event screening hypothesis test is not fully consistent with physical basis. Improved standard error - Better agreement with physical basis, and correctly partitions error to include Model Error as a component of variance, correctly reduces station noise variance through network averaging. For 2009 DPRK test - Commonly used standard error 'rejects' H0 even with better scaling slope ({beta} = 1, Selby et al.), improved standard error 'fails to rejects' H0.

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

PubMed

Urtnasan, Erdenebayar; Park, Jong-Uk; Joo, Eun-Yeon; Lee, Kyoung-Joung

2018-04-23

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F 1 -score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

Distributed Event-Based Set-Membership Filtering for a Class of Nonlinear Systems With Sensor Saturations Over Sensor Networks.

PubMed

Ma, Lifeng; Wang, Zidong; Lam, Hak-Keung; Kyriakoulis, Nikos

2017-11-01

In this paper, the distributed set-membership filtering problem is investigated for a class of discrete time-varying system with an event-based communication mechanism over sensor networks. The system under consideration is subject to sector-bounded nonlinearity, unknown but bounded noises and sensor saturations. Each intelligent sensing node transmits the data to its neighbors only when certain triggering condition is violated. By means of a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired distributed event-based filter which is capable of confining the system state in certain ellipsoidal regions centered at the estimates. Within the established theoretical framework, two additional optimization problems are formulated: one is to seek the minimal ellipsoids (in the sense of matrix trace) for the best filtering performance, and the other is to maximize the triggering threshold so as to reduce the triggering frequency with satisfactory filtering performance. A numerically attractive chaos algorithm is employed to solve the optimization problems. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.

Statistical Model Applied to NetFlow for Network Intrusion Detection

NASA Astrophysics Data System (ADS)

Proto, André; Alexandre, Leandro A.; Batista, Maira L.; Oliveira, Isabela L.; Cansian, Adriano M.

The computers and network services became presence guaranteed in several places. These characteristics resulted in the growth of illicit events and therefore the computers and networks security has become an essential point in any computing environment. Many methodologies were created to identify these events; however, with increasing of users and services on the Internet, many difficulties are found in trying to monitor a large network environment. This paper proposes a methodology for events detection in large-scale networks. The proposal approaches the anomaly detection using the NetFlow protocol, statistical methods and monitoring the environment in a best time for the application.

ST-Elevation myocardial infarction network: systematization in 205 cases reduced clinical events in the public health care system.

PubMed

Caluza, Ana Christina Vellozo; Barbosa, Adriano H; Gonçalves, Iran; Oliveira, Carlos Alexandre L de; Matos, Lívia Nascimento de; Zeefried, Claus; Moreno, Antonio Célio C; Tarkieltaub, Elcio; Alves, Cláudia Maria R; Carvalho, Antonio Carlos

2012-11-01

The major cause of death in the city of São Paulo (SP) is cardiac events. At its periphery, in-hospital mortality in acute myocardial infarction is estimated to range between 15% and 20% due to difficulties inherent in large metropoles. To describe in-hospital mortality in ST-segment elevation acute myocardial infarction (STEMI) of patients admitted via ambulance or peripheral hospitals, which are part of a structured training network (STEMI Network). Health care teams of four emergency services (Ermelino Matarazzo, Campo Limpo, Tatuapé and Saboya) of the periphery of the city of São Paulo and advanced ambulances of the Emergency Mobile Health Care Service (abbreviation in Portuguese, SAMU) were trained to use tenecteplase or to refer for primary angioplasty. A central office for electrocardiogram reading was used. After thrombolysis, the patient was sent to a tertiary reference hospital to undergo cardiac catheterization immediately (in case of failed thrombolysis) or in 6 to 24 hours, if the patient was stable. Quantitative and qualitative variables were assessed by use of uni- and multivariate analysis. From January 2010 to June 2011, 205 consecutive patients used the STEMI Network, and the findings were as follows: 87 anterior wall infarctions; 11 left bundle-branch blocks; 14 complete atrioventricular blocks; and 14 resuscitations after initial cardiorespiratory arrest. In-hospital mortality was 6.8% (14 patients), most of which due to cardiogenic shock, one hemorrhagic cerebrovascular accident, and one bleeding. The organization in the public health care system of a network for the treatment of STEMI, involving diagnosis, reperfusion, immediate transfer, and tertiary reference hospital, resulted in immediate improvement of STEMI outcomes.

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

PubMed Central

Jung, Sungmo; Kim, Jong Hyun; Kim, Seoksoo

2012-01-01

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods. PMID:23202190

NASA Integrated Network COOP

NASA Technical Reports Server (NTRS)

Anderson, Michael L.; Wright, Nathaniel; Tai, Wallace

2012-01-01

Natural disasters, terrorist attacks, civil unrest, and other events have the potential of disrupting mission-essential operations in any space communications network. NASA's Space Communications and Navigation office (SCaN) is in the process of studying options for integrating the three existing NASA network elements, the Deep Space Network, the Near Earth Network, and the Space Network, into a single integrated network with common services and interfaces. The need to maintain Continuity of Operations (COOP) after a disastrous event has a direct impact on the future network design and operations concepts. The SCaN Integrated Network will provide support to a variety of user missions. The missions have diverse requirements and include anything from earth based platforms to planetary missions and rovers. It is presumed that an integrated network, with common interfaces and processes, provides an inherent advantage to COOP in that multiple elements and networks can provide cross-support in a seamless manner. The results of trade studies support this assumption but also show that centralization as a means of achieving integration can result in single points of failure that must be mitigated. The cost to provide this mitigation can be substantial. In support of this effort, the team evaluated the current approaches to COOP, developed multiple potential approaches to COOP in a future integrated network, evaluated the interdependencies of the various approaches to the various network control and operations options, and did a best value assessment of the options. The paper will describe the trade space, the study methods, and results of the study.

Study of Tools for Network Discovery and Network Mapping

DTIC Science & Technology

2003-11-01

connected to the switch. iv. Accessibility of historical data and event data In general, network discovery tools keep a history of the collected...has the following software dependencies: - Java Virtual machine 76 - Perl modules - RRD Tool - TomCat - PostgreSQL STRENGTHS AND...systems - provide a simple view of the current network status - generate alarms on status change - generate history of status change VISUAL MAP

Event-Based Variance-Constrained ${\\mathcal {H}}_{\\infty }$ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements.

PubMed

Wang, Licheng; Wang, Zidong; Han, Qing-Long; Wei, Guoliang

2018-03-01

This paper is concerned with the distributed filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur. The objective of the problem addressed is to design a time-varying filter such that both the requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises. By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy.

Unveiling causal activity of complex networks

NASA Astrophysics Data System (ADS)

Williams-García, Rashid V.; Beggs, John M.; Ortiz, Gerardo

2017-07-01

We introduce a novel tool for analyzing complex network dynamics, allowing for cascades of causally-related events, which we call causal webs (c-webs), to be separated from other non-causally-related events. This tool shows that traditionally-conceived avalanches may contain mixtures of spatially-distinct but temporally-overlapping cascades of events, and dynamical disorder or noise. In contrast, c-webs separate these components, unveiling previously hidden features of the network and dynamics. We apply our method to mouse cortical data with resulting statistics which demonstrate for the first time that neuronal avalanches are not merely composed of causally-related events. The original version of this article was uploaded to the arXiv on March 17th, 2016 [1].

Friendship Dissolution Within Social Networks Modeled Through Multilevel Event History Analysis

PubMed Central

Dean, Danielle O.; Bauer, Daniel J.; Prinstein, Mitchell J.

2018-01-01

A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common—as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network. While the modeling framework is introduced in terms of understanding friendship dissolution, it can be used to understand microlevel dynamics of a social network more generally. These models can be fit with standard generalized linear mixed-model software, after transforming the data to a pair-period data set. An empirical example highlights how the model can be applied to understand the processes leading to friendship dissolution between high school students, and a simulation study is used to test the use of the modeling framework under representative conditions that would be found in social network data. Advantages of the modeling framework are highlighted, and potential limitations and future directions are discussed. PMID:28463022

Operating systems and network protocols for wireless sensor networks.

PubMed

Dutta, Prabal; Dunkels, Adam

2012-01-13

Sensor network protocols exist to satisfy the communication needs of diverse applications, including data collection, event detection, target tracking and control. Network protocols to enable these services are constrained by the extreme resource scarcity of sensor nodes-including energy, computing, communications and storage-which must be carefully managed and multiplexed by the operating system. These challenges have led to new protocols and operating systems that are efficient in their energy consumption, careful in their computational needs and miserly in their memory footprints, all while discovering neighbours, forming networks, delivering data and correcting failures.

Exploring the evolution of node neighborhoods in Dynamic Networks

NASA Astrophysics Data System (ADS)

Orman, Günce Keziban; Labatut, Vincent; Naskali, Ahmet Teoman

2017-09-01

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of neighborhood event, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes.

Comparisons of diabetic retinopathy events associated with glucose-lowering drugs in patients with type 2 diabetes mellitus: A network meta-analysis.

PubMed

Tang, Huilin; Li, Guangyao; Zhao, Ying; Wang, Fei; Gower, Emily W; Shi, Luwen; Wang, Tiansheng

2018-05-01

To assess the comparative effects of glucose-lowering drugs (GLDs) on the risk of diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). We systematically searched Cochrane Central Register of Controlled Trials, PUBMED and EMBASE from inception to January 17, 2017 to identify randomized controlled trials (RCTs) that reported DR events among T2DM patients receiving any GLD. Random-effects pairwise and network meta-analyses were performed to calculate odds ratios (ORs) with 95% confidence intervals (CIs). A total of 37 independent RCTs with 1806 DR events among 100 928 patients with T2DM were included. The mean duration of diabetes was 8.7 years and mean baseline HbA1c was 8.2% (SD, 0.5%). Our network meta-analysis found that DPP-4i (OR, 1.20; 95% CI, 0.87-1.65), GLP-1RA (OR, 1.19; 95% CI, 0.94-1.52) and SGLT2 inhibitors (OR, 0.79; 95% CI, 0.49-1.28) were not associated with a higher risk of DR than placebo; however, a significantly increased risk of DR was associated with DPP-4i in the pairwise meta-analysis (OR, 1.27; 95% CI, 1.05-1.53). Sulfonylureas, on the other hand, were associated with a significantly increased risk of DR compared to placebo (OR, 1.67; 95% CI, 1.01-2.76). Current evidence indicates that the association between DPP-4i, GLP-1RA or SGLT2 inhibitors and risk of DR remains uncertain in patients with T2DM. Some evidence suggests that sulfonylureas may be associated with increased risk of DR. However, given that DR events were not systematically assessed, these effects should be explored further in large-scale, well-designed studies. © 2018 John Wiley & Sons Ltd.

On the globality of motor suppression: unexpected events and their influence on behavior and cognition

PubMed Central

Wessel, Jan R.; Aron, Adam R.

2016-01-01

SUMMARY Unexpected events are part of everyday experience. They come in several varieties – action errors, unexpected action outcomes, and unexpected perceptual events – and they lead to motor slowing and cognitive distraction. While different varieties of unexpected events have been studied largely independently, and many different mechanisms are thought to explain their effects on action and cognition, we suggest a unifying theory. We propose that unexpected events recruit a fronto-basal-ganglia network for stopping. This network includes specific prefrontal cortical nodes and is posited to project to the subthalamic nucleus, with a putative global suppressive effect on basal-ganglia output. We argue that unexpected events interrupt action and impact cognition, partly at least, by recruiting this global suppressive network. This provides a common mechanistic basis for different types of unexpected events, links the literatures on motor inhibition, performance-monitoring, attention, and working memory, and is relevant for understanding clinical symptoms of distractibility and mental inflexibility. PMID:28103476

On the Globality of Motor Suppression: Unexpected Events and Their Influence on Behavior and Cognition.

PubMed

Wessel, Jan R; Aron, Adam R

2017-01-18

Unexpected events are part of everyday experience. They come in several varieties-action errors, unexpected action outcomes, and unexpected perceptual events-and they lead to motor slowing and cognitive distraction. While different varieties of unexpected events have been studied largely independently, and many different mechanisms are thought to explain their effects on action and cognition, we suggest a unifying theory. We propose that unexpected events recruit a fronto-basal-ganglia network for stopping. This network includes specific prefrontal cortical nodes and is posited to project to the subthalamic nucleus, with a putative global suppressive effect on basal-ganglia output. We argue that unexpected events interrupt action and impact cognition, partly at least, by recruiting this global suppressive network. This provides a common mechanistic basis for different types of unexpected events; links the literatures on motor inhibition, performance monitoring, attention, and working memory; and is relevant for understanding clinical symptoms of distractibility and mental inflexibility. Copyright © 2017 Elsevier Inc. All rights reserved.

Collaboration in the School Social Network

ERIC Educational Resources Information Center

Schultz-Jones, Barbara

2009-01-01

Social networks are fundamental to all people. Their social network describes how they are connected to others: close relationships, peripheral relationships, and those relationships that help connect them to other people, events, or things. As information specialists, school librarians develop a multidimensional social network that enables them…

News Competition: Physics Olympiad hits Thailand Report: Institute carries out survey into maths in physics at university Event: A day for everyone teaching physics Conference: Welsh conference celebrates birthday Schools: Researchers in Residence scheme set to close Teachers: A day for new physics teachers Social: Network combines fun and physics Forthcoming events

NASA Astrophysics Data System (ADS)

2011-09-01

Competition: Physics Olympiad hits Thailand Report: Institute carries out survey into maths in physics at university Event: A day for everyone teaching physics Conference: Welsh conference celebrates birthday Schools: Researchers in Residence scheme set to close Teachers: A day for new physics teachers Social: Network combines fun and physics Forthcoming events

Event identification for KM3NeT/ARCA

NASA Astrophysics Data System (ADS)

Heid, Thomas; KM3NeT Collaboration

2017-09-01

KM3NeT is a large research infrastructure consisting of a network of deep-sea neutrino telescopes. KM3NeT/ARCA will be the instrument detecting high-energy neutrinos with energies above 100 TeV. This instrument gives a new opportunity to observe the neutrino sky with very high angular resolution to be able to detect neutrino point sources. Furthermore it will be possible to probe the flavour composition of neutrino fluxes, and hence production mechanisms, with so-far unreached precision. Neutrinos produce different event topologies in the detector according to their flavour, interaction channel and deposited energy. Machine-learning algorithms are able to learn features of topologies to discriminate them. In previous analyses only two event types were regarded, namely the shower and track topology. With good timing resolution and precise reconstruction algorithms it is possible to separate into more event types, for example the double bang topology produced by tau neutrinos. The final goal is to distinguish all three neutrino flavors as much as possible. To resolve this issue the KM3NeT collaboration uses deep neural networks trained with Monte Carlo events of all neutrino types. This contribution shows the ability of KM3NeT/ARCA to classify events in more than two neutrino event topologies. Furthermore, the borders between detectable classes are shown, such as the minimum distance the tau has to travel before decaying into a tau neutrino to be detected as double bang event.

Detecting earthquakes over a seismic network using single-station similarity measures

NASA Astrophysics Data System (ADS)

Bergen, Karianne J.; Beroza, Gregory C.

2018-06-01

New blind waveform-similarity-based detection methods, such as Fingerprint and Similarity Thresholding (FAST), have shown promise for detecting weak signals in long-duration, continuous waveform data. While blind detectors are capable of identifying similar or repeating waveforms without templates, they can also be susceptible to false detections due to local correlated noise. In this work, we present a set of three new methods that allow us to extend single-station similarity-based detection over a seismic network; event-pair extraction, pairwise pseudo-association, and event resolution complete a post-processing pipeline that combines single-station similarity measures (e.g. FAST sparse similarity matrix) from each station in a network into a list of candidate events. The core technique, pairwise pseudo-association, leverages the pairwise structure of event detections in its network detection model, which allows it to identify events observed at multiple stations in the network without modeling the expected moveout. Though our approach is general, we apply it to extend FAST over a sparse seismic network. We demonstrate that our network-based extension of FAST is both sensitive and maintains a low false detection rate. As a test case, we apply our approach to 2 weeks of continuous waveform data from five stations during the foreshock sequence prior to the 2014 Mw 8.2 Iquique earthquake. Our method identifies nearly five times as many events as the local seismicity catalogue (including 95 per cent of the catalogue events), and less than 1 per cent of these candidate events are false detections.

Detecting Earthquakes over a Seismic Network using Single-Station Similarity Measures

NASA Astrophysics Data System (ADS)

Bergen, Karianne J.; Beroza, Gregory C.

2018-03-01

New blind waveform-similarity-based detection methods, such as Fingerprint and Similarity Thresholding (FAST), have shown promise for detecting weak signals in long-duration, continuous waveform data. While blind detectors are capable of identifying similar or repeating waveforms without templates, they can also be susceptible to false detections due to local correlated noise. In this work, we present a set of three new methods that allow us to extend single-station similarity-based detection over a seismic network; event-pair extraction, pairwise pseudo-association, and event resolution complete a post-processing pipeline that combines single-station similarity measures (e.g. FAST sparse similarity matrix) from each station in a network into a list of candidate events. The core technique, pairwise pseudo-association, leverages the pairwise structure of event detections in its network detection model, which allows it to identify events observed at multiple stations in the network without modeling the expected move-out. Though our approach is general, we apply it to extend FAST over a sparse seismic network. We demonstrate that our network-based extension of FAST is both sensitive and maintains a low false detection rate. As a test case, we apply our approach to two weeks of continuous waveform data from five stations during the foreshock sequence prior to the 2014 Mw 8.2 Iquique earthquake. Our method identifies nearly five times as many events as the local seismicity catalog (including 95% of the catalog events), and less than 1% of these candidate events are false detections.

System diagnostic builder: a rule-generation tool for expert systems that do intelligent data evaluation

NASA Astrophysics Data System (ADS)

Nieten, Joseph L.; Burke, Roger

1993-03-01

The system diagnostic builder (SDB) is an automated knowledge acquisition tool using state- of-the-art artificial intelligence (AI) technologies. The SDB uses an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert (SME). Thus, data is captured from the subject system, classified by an expert, and used to drive the rule generation process. These rule-bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The rule-bases can be used in any knowledge based system which monitors or controls a physical system or simulation. The SDB has demonstrated the utility of using inductive machine learning technology to generate reliable knowledge bases. In fact, we have discovered that the knowledge captured by the SDB can be used in any number of applications. For example, the knowledge bases captured from the SMS can be used as black box simulations by intelligent computer aided training devices. We can also use the SDB to construct knowledge bases for the process control industry, such as chemical production, or oil and gas production. These knowledge bases can be used in automated advisory systems to ensure safety, productivity, and consistency.

[The RUTA project (Registro UTIC Triveneto ANMCO). An e-network for the coronary care units for acute myocardial infarction].

PubMed

Di Chiara, Antonio; Zonzin, Pietro; Pavoni, Daisy; Fioretti, Paolo Maria

2003-06-01

In the era of evidence-based medicine, the monitoring of the adherence to the guidelines is fundamental, in order to verify the diagnostic and therapeutic processes. Informatic paperless databases allow a higher data quality, lower costs and timely analysis with overall advantages over the traditional surveys. The RUTA project (acronym of Triveneto Registry of ANMCO CCUs) was designed in 1999, aiming at creating an informatic network among the coronary care units of a large Italian region, for a permanent survey of patients admitted for acute myocardial infarction. Information ranges from the pre-hospital phase to discharge, including all relevant clinical and management variables. The database uses DBMS Personal Oracle and Power-Builder as user interface, on Windows platform. Anonymous data are sent to a central server.

Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks

PubMed Central

Moon, Hankyu; Lu, Tsai-Ching

2015-01-01

Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of—how to interpret the self-organized patterns to know more about the imminent crisis. We start with a very general description — of interacting population giving rise to large-scale emergent behaviors that constitute critical events. Then we pose a key question: is there a quantifiable relation between the network of interactions and the emergent patterns? Our investigation leads to a fundamental understanding to: 1. Detect the system's transition based on the principal mode of the pattern dynamics; 2. Identify its evolving structure based on the observed patterns. The main finding of this study is that while the pattern is distorted by the network of interactions, its principal mode is invariant to the distortion even when the network constantly evolves. Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible. PMID:25822423

Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks

NASA Astrophysics Data System (ADS)

Moon, Hankyu; Lu, Tsai-Ching

2015-03-01

Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of--how to interpret the self-organized patterns to know more about the imminent crisis. We start with a very general description -- of interacting population giving rise to large-scale emergent behaviors that constitute critical events. Then we pose a key question: is there a quantifiable relation between the network of interactions and the emergent patterns? Our investigation leads to a fundamental understanding to: 1. Detect the system's transition based on the principal mode of the pattern dynamics; 2. Identify its evolving structure based on the observed patterns. The main finding of this study is that while the pattern is distorted by the network of interactions, its principal mode is invariant to the distortion even when the network constantly evolves. Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible.

Testing the global capabilities of the Antelope software suite: fast location and Mb determination of teleseismic events using the ASAIN and GSN seismic networks

NASA Astrophysics Data System (ADS)

Pesaresi, D.; Russi, M.; Plasencia, M.; Cravos, C.

2009-04-01

The Italian National Institute for Oceanography and Experimental Geophysics (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, OGS) is running the Antarctic Seismographic Argentinean Italian Network (ASAIN), made of 5 seismic stations located in the Scotia Sea region in Antarctica and in Argentina: data from these stations are transferred in real time to the OGS headquarters in Trieste (Italy) via satellite links. OGS is also running, in close cooperation with the Friuli-Venezia Giulia Civil Defense, the North East (NI) Italy seismic network, making use of the Antelope commercial software suite from BRTT as the main acquisition system. As a test to check the global capabilities of Antelope, we set up an instance of Antelope acquiring data in real time from both the regional ASAIN seismic network in Antarctica and a subset of the Global Seismic Network (GSN) funded by the Incorporated Research Institution for Seismology (IRIS). The facilities of the IRIS Data Management System, and specifically the IRIS Data Management Center, were used for real time access to waveform required in this study. Preliminary results over 1 month period indicated that about 82% of the earthquakes with magnitude M>5.0 listed in the PDE catalogue of the National Earthquake Information Center (NEIC) of the United States Geological Survey (USGS) were also correctly detected by Antelope, with an average location error of 0.05 degrees and average body wave magnitude Mb estimation error below 0.1. The average time difference between event origin time and the actual time of event determination by Antelope was of about 45': the comparison with 20', the IASPEI91 P-wave travel time for 180 degrees distance, and 25', the estimate of our test system data latency, indicate that Antelope is a serious candidate for regional and global early warning systems. Updated figures calculated over a longer period of time will be presented and discussed.

Global Seismic Event Detection Using Surface Waves: 15 Possible Antarctic Glacial Sliding Events

NASA Astrophysics Data System (ADS)

Chen, X.; Shearer, P. M.; Walker, K. T.; Fricker, H. A.

2008-12-01

To identify overlooked or anomalous seismic events not listed in standard catalogs, we have developed an algorithm to detect and locate global seismic events using intermediate-period (35-70s) surface waves. We apply our method to continuous vertical-component seismograms from the global seismic networks as archived in the IRIS UV FARM database from 1997 to 2007. We first bandpass filter the seismograms, apply automatic gain control, and compute envelope functions. We then examine 1654 target event locations defined at 5 degree intervals and stack the seismogram envelopes along the predicted Rayleigh-wave travel times. The resulting function has spatial and temporal peaks that indicate possible seismic events. We visually check these peaks using a graphical user interface to eliminate artifacts and assign an overall reliability grade (A, B or C) to the new events. We detect 78% of events in the Global Centroid Moment Tensor (CMT) catalog. However, we also find 840 new events not listed in the PDE, ISC and REB catalogs. Many of these new events were previously identified by Ekstrom (2006) using a different Rayleigh-wave detection scheme. Most of these new events are located along oceanic ridges and transform faults. Some new events can be associated with volcanic eruptions such as the 2000 Miyakejima sequence near Japan and others with apparent glacial sliding events in Greenland (Ekstrom et al., 2003). We focus our attention on 15 events detected from near the Antarctic coastline and relocate them using a cross-correlation approach. The events occur in 3 groups which are well-separated from areas of cataloged earthquake activity. We speculate that these are iceberg calving and/or glacial sliding events, and hope to test this by inverting for their source mechanisms and examining remote sensing data from their source regions.

Modeling the energy performance of event-driven wireless sensor network by using static sink and mobile sink.

PubMed

Chen, Jiehui; Salim, Mariam B; Matsumoto, Mitsuji

2010-01-01

Wireless Sensor Networks (WSNs) designed for mission-critical applications suffer from limited sensing capacities, particularly fast energy depletion. Regarding this, mobile sinks can be used to balance the energy consumption in WSNs, but the frequent location updates of the mobile sinks can lead to data collisions and rapid energy consumption for some specific sensors. This paper explores an optimal barrier coverage based sensor deployment for event driven WSNs where a dual-sink model was designed to evaluate the energy performance of not only static sensors, but Static Sink (SS) and Mobile Sinks (MSs) simultaneously, based on parameters such as sensor transmission range r and the velocity of the mobile sink v, etc. Moreover, a MS mobility model was developed to enable SS and MSs to effectively collaborate, while achieving spatiotemporal energy performance efficiency by using the knowledge of the cumulative density function (cdf), Poisson process and M/G/1 queue. The simulation results verified that the improved energy performance of the whole network was demonstrated clearly and our eDSA algorithm is more efficient than the static-sink model, reducing energy consumption approximately in half. Moreover, we demonstrate that our results are robust to realistic sensing models and also validate the correctness of our results through extensive simulations.

Modeling the Energy Performance of Event-Driven Wireless Sensor Network by Using Static Sink and Mobile Sink

PubMed Central

Chen, Jiehui; Salim, Mariam B.; Matsumoto, Mitsuji

2010-01-01

Wireless Sensor Networks (WSNs) designed for mission-critical applications suffer from limited sensing capacities, particularly fast energy depletion. Regarding this, mobile sinks can be used to balance the energy consumption in WSNs, but the frequent location updates of the mobile sinks can lead to data collisions and rapid energy consumption for some specific sensors. This paper explores an optimal barrier coverage based sensor deployment for event driven WSNs where a dual-sink model was designed to evaluate the energy performance of not only static sensors, but Static Sink (SS) and Mobile Sinks (MSs) simultaneously, based on parameters such as sensor transmission range r and the velocity of the mobile sink v, etc. Moreover, a MS mobility model was developed to enable SS and MSs to effectively collaborate, while achieving spatiotemporal energy performance efficiency by using the knowledge of the cumulative density function (cdf), Poisson process and M/G/1 queue. The simulation results verified that the improved energy performance of the whole network was demonstrated clearly and our eDSA algorithm is more efficient than the static-sink model, reducing energy consumption approximately in half. Moreover, we demonstrate that our results are robust to realistic sensing models and also validate the correctness of our results through extensive simulations. PMID:22163503

The new CMS DAQ system for run-2 of the LHC

DOE PAGES

Bawej, Tomasz; Behrens, Ulf; Branson, James; ...

2015-05-21

The data acquisition (DAQ) system of the CMS experiment at the CERN Large Hadron Collider assembles events at a rate of 100 kHz, transporting event data at an aggregate throughput of 100 GB/s to the high level trigger (HLT) farm. The HLT farm selects interesting events for storage and offline analysis at a rate of around 1 kHz. The DAQ system has been redesigned during the accelerator shutdown in 2013/14. The motivation is twofold: Firstly, the current compute nodes, networking, and storage infrastructure will have reached the end of their lifetime by the time the LHC restarts. Secondly, in ordermore » to handle higher LHC luminosities and event pileup, a number of sub-detectors will be upgraded, increasing the number of readout channels and replacing the off-detector readout electronics with a μTCA implementation. The new DAQ architecture will take advantage of the latest developments in the computing industry. For data concentration, 10/40 Gb/s Ethernet technologies will be used, as well as an implementation of a reduced TCP/IP in FPGA for a reliable transport between custom electronics and commercial computing hardware. A Clos network based on 56 Gb/s FDR Infiniband has been chosen for the event builder with a throughput of ~ 4 Tb/s. The HLT processing is entirely file based. This allows the DAQ and HLT systems to be independent, and to use the HLT software in the same way as for the offline processing. The fully built events are sent to the HLT with 1/10/40 Gb/s Ethernet via network file systems. Hierarchical collection of HLT accepted events and monitoring meta-data are stored into a global file system. As a result, this paper presents the requirements, technical choices, and performance of the new system.« less

Transient Volcano Deformation Event Detection over Variable Spatial Scales in Alaska

NASA Astrophysics Data System (ADS)

Li, J. D.; Rude, C. M.; Gowanlock, M.; Herring, T.; Pankratius, V.

2016-12-01

Transient deformation events driven by volcanic activity can be monitored using increasingly dense networks of continuous Global Positioning System (GPS) ground stations. The wide spatial extent of GPS networks, the large number of GPS stations, and the spatially and temporally varying scale of deformation events result in the mixing of signals from multiple sources. Typical analysis then necessitates manual identification of times and regions of volcanic activity for further study and the careful tuning of algorithmic parameters to extract possible transient events. Here we present a computer-aided discovery system that facilitates the discovery of potential transient deformation events at volcanoes by providing a framework for selecting varying spatial regions of interest and for tuning the analysis parameters. This site specification step in the framework reduces the spatial mixing of signals from different volcanic sources before applying filters to remove interfering signals originating from other geophysical processes. We analyze GPS data recorded by the Plate Boundary Observatory network and volcanic activity logs from the Alaska Volcano Observatory to search for and characterize transient inflation events in Alaska. We find 3 transient inflation events between 2008 and 2015 at the Akutan, Westdahl, and Shishaldin volcanoes in the Aleutian Islands. The inflation event detected in the first half of 2008 at Akutan is validated other studies, while the inflation events observed in early 2011 at Westdahl and in early 2013 at Shishaldin are previously unreported. Our analysis framework also incorporates modelling of the transient inflation events and enables a comparison of different magma chamber inversion models. Here, we also estimate the magma sources that best describe the deformation observed by the GPS stations at Akutan, Westdahl, and Shishaldin. We acknowledge support from NASA AIST-NNX15AG84G (PI: V. Pankratius).

Does the Type of Event Influence How User Interactions Evolve on Twitter?

PubMed Central

del Val, Elena; Rebollo, Miguel; Botti, Vicente

2015-01-01

The number of people using on-line social networks as a new way of communication is continually increasing. The messages that a user writes in these networks and his/her interactions with other users leave a digital trace that is recorded. Thanks to this fact and the use of network theory, the analysis of messages, user interactions, and the complex structures that emerge is greatly facilitated. In addition, information generated in on-line social networks is labeled temporarily, which makes it possible to go a step further analyzing the dynamics of the interaction patterns. In this article, we present an analysis of the evolution of user interactions that take place in television, socio-political, conference, and keynote events on Twitter. Interactions have been modeled as networks that are annotated with the time markers. We study changes in the structural properties at both the network level and the node level. As a result of this analysis, we have detected patterns of network evolution and common structural features as well as differences among the events. PMID:25961305

Women's History Month Event

NASA Image and Video Library

2018-03-27

JoAnn Morgan, former associate director of NASA Kennedy Space Center, speaks to workers during a Women's History Month event at the center. With the theme "Nevertheless She Persisted," Morgan described her experience as the first female engineer working in the space program in the 1960s. Morgan was the first female in the Launch Control Center firing room during the Apollo 11 launch. The event was hosted by the center's Kennedy Networking Opportunities for Women (KNOW) and Launching Leaders organizations. The purpose of KNOW is to provide focus on issues such as employment, retention, promotion, training, career and personal development, education, and identify and eliminate barriers that hinder the advancement of women in the workforce.

Women's History Month Event

NASA Image and Video Library

2018-03-27

JoAnn Morgan, former associate director of NASA Kennedy Space Center, was the keynote speaker during a Women's History Month event at the center. With the theme "Nevertheless She Persisted," Morgan described her experience as the first female engineer working in the space program in the 1960s. Morgan was the first female in the Launch Control Center firing room during the Apollo 11 launch. The event was hosted by the center's Kennedy Networking Opportunities for Women (KNOW) and Launching Leaders organizations. The purpose of KNOW is to provide focus on issues such as employment, retention, promotion, training, career and personal development, education, and identify and eliminate barriers that hinder the advancement of women in the workforce.

Women's History Month Event

NASA Image and Video Library

2018-03-27

JoAnn Morgan, former associate director of Kennedy Space Center, was the keynote speaker during a Women's History Month event at the center. With the theme "Nevertheless She Persisted," Morgan described her experience as the first female engineer working in the space program in the 1960s. Morgan was the first female in the Launch Control Center firing room during the Apollo 11 launch. The event was hosted by the center's Kennedy Networking Opportunities for Women (KNOW) and Launching Leaders organizations. The purpose of KNOW is to provide focus on issues such as employment, retention, promotion, training, career and personal development, education, and identify and eliminate barriers that hinder the advancement of women in the workforce.

Event-Triggered Adaptive Dynamic Programming for Continuous-Time Systems With Control Constraints.

PubMed

Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

2016-08-31

In this paper, an event-triggered near optimal control structure is developed for nonlinear continuous-time systems with control constraints. Due to the saturating actuators, a nonquadratic cost function is introduced and the Hamilton-Jacobi-Bellman (HJB) equation for constrained nonlinear continuous-time systems is formulated. In order to solve the HJB equation, an actor-critic framework is presented. The critic network is used to approximate the cost function and the action network is used to estimate the optimal control law. In addition, in the proposed method, the control signal is transmitted in an aperiodic manner to reduce the computational and the transmission cost. Both the networks are only updated at the trigger instants decided by the event-triggered condition. Detailed Lyapunov analysis is provided to guarantee that the closed-loop event-triggered system is ultimately bounded. Three case studies are used to demonstrate the effectiveness of the proposed method.

Network analysis applications in hydrology

NASA Astrophysics Data System (ADS)

Price, Katie

2017-04-01

Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain under­explored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five long­term USGS streamflow and water quality gages, allowing network application of long­term flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long­ term and event­based hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwater­surface water interactions.

NASA's Meteoroid Environments Office's Response to Three Significant Bolide Events Over North America

NASA Technical Reports Server (NTRS)

Blaauw, Rhiannon C.; Cooke, William J.; Kingery, Aaron M.

2015-01-01

Being the only U.S. Government entity charged with monitoring the meteor environment, the Meteoroid Environment Office has deployed a network of all sky and wide field meteor cameras, along with the appropriate software tools to quickly analyze data from these systems. However, the coverage of this network is still quite limited, forcing the incorporation of data from other cameras posted to the internet in analyzing many of the fireballs reported by the public and media. A procedure has been developed that determines the analysis process for a given fireball event based on the types and amount of data available. The differences between these analysis process will be explained and outlined by looking at three bolide events, all of which were large enough to produce meteorites. The first example is an ideal event - a bright meteor that occurred over NASA's All Sky Camera Network on August 2, 2014. With clear video of the event from various angles, a high-accuracy trajectory, beginning and end heights, orbit and approximate brightness/size of the event are able to be found very quickly using custom software. The bolide had the potential to have dropped meteorites, so dark flight analysis and modeling was performed, allowing potential fall locations to be mapped as a function of meteorite mass. The second case study was a bright bolide that occurred November 3, 2014 over West Virginia. This was just north of the NASA southeastern all-sky network, and just south of the Ohio-Pennsylvania network. This case study showcases the MEO's ability to use social media and various internet sources to locate videos of the event from obscure sources (including the Washington Monument) for anything that will permit a determination of a basic trajectory and fireball light curve The third case study will highlight the ability to use doppler weather radar in helping locate meteorites, which enable a definitive classification of the impactor. The input data and analysis steps differ for

Artificial Neural Network applied to lightning flashes

NASA Astrophysics Data System (ADS)

Gin, R. B.; Guedes, D.; Bianchi, R.

2013-05-01

The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a

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.

Building energy analysis of Electrical Engineering Building from DesignBuilder tool: calibration and simulations

NASA Astrophysics Data System (ADS)

Cárdenas, J.; Osma, G.; Caicedo, C.; Torres, A.; Sánchez, S.; Ordóñez, G.

2016-07-01

This research shows the energy analysis of the Electrical Engineering Building, located on campus of the Industrial University of Santander in Bucaramanga - Colombia. This building is a green pilot for analysing energy saving strategies such as solar pipes, green roof, daylighting, and automation, among others. Energy analysis was performed by means of DesignBuilder software from virtual model of the building. Several variables were analysed such as air temperature, relative humidity, air velocity, daylighting, and energy consumption. According to two criteria, thermal load and energy consumption, critical areas were defined. The calibration and validation process of the virtual model was done obtaining error below 5% in comparison with measured values. The simulations show that the average indoor temperature in the critical areas of the building was 27°C, whilst relative humidity reached values near to 70% per year. The most critical discomfort conditions were found in the area of the greatest concentration of people, which has an average annual temperature of 30°C. Solar pipes can increase 33% daylight levels into the areas located on the upper floors of the building. In the case of the green roofs, the simulated results show that these reduces of nearly 31% of the internal heat gains through the roof, as well as a decrease in energy consumption related to air conditioning of 5% for some areas on the fourth and fifth floor. The estimated energy consumption of the building was 69 283 kWh per year.

Can Social Networks Assist Analysts Fight Terrorism?

DTIC Science & Technology

2011-06-01

protecting America from terrorism in a 2007 article. He proposed the intelligence community ought to build a social networking database to track... filming the event, mostly with mobile phones (Shirky 2009). BBC and the U.S. Geological Survey agencies learned of the event from Twitter minutes...North America , Facebook has over 500,000 unique users visit its site every month (eBizMBA 2011). Third only to QQ, China’s top social network, and Skype

47 CFR 25.261 - Procedures for avoidance of in-line interference events for Non Geostationary Satellite Orbit...

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 2 2014-10-01 2014-10-01 false Procedures for avoidance of in-line interference events for Non Geostationary Satellite Orbit (NGSO) Satellite Network Operations in the Fixed... avoidance of in-line interference events for Non Geostationary Satellite Orbit (NGSO) Satellite Network...

47 CFR 25.261 - Procedures for avoidance of in-line interference events for Non Geostationary Satellite Orbit...

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 2 2012-10-01 2012-10-01 false Procedures for avoidance of in-line interference events for Non Geostationary Satellite Orbit (NGSO) Satellite Network Operations in the Fixed... avoidance of in-line interference events for Non Geostationary Satellite Orbit (NGSO) Satellite Network...

47 CFR 25.261 - Procedures for avoidance of in-line interference events for Non Geostationary Satellite Orbit...

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 2 2013-10-01 2013-10-01 false Procedures for avoidance of in-line interference events for Non Geostationary Satellite Orbit (NGSO) Satellite Network Operations in the Fixed... avoidance of in-line interference events for Non Geostationary Satellite Orbit (NGSO) Satellite Network...

Distributed network scheduling

NASA Technical Reports Server (NTRS)

Clement, Bradley J.; Schaffer, Steven R.

2004-01-01

Distributed Network Scheduling is the scheduling of future communications of a network by nodes in the network. This report details software for doing this onboard spacecraft in a remote network. While prior work on distributed scheduling has been applied to remote spacecraft networks, the software reported here focuses on modeling communication activities in greater detail and including quality of service constraints. Our main results are based on a Mars network of spacecraft and include identifying a maximum opportunity of improving traverse exploration rate a factor of three; a simulation showing reduction in one-way delivery times from a rover to Earth from as much as 5 to 1.5 hours; simulated response to unexpected events averaging under an hour onboard; and ground schedule generation ranging from seconds to 50 minutes for 15 to 100 communication goals.

Enabling parallel simulation of large-scale HPC network systems

DOE PAGES

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.; ...

2016-04-07

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Enabling parallel simulation of large-scale HPC network systems

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

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Interpreting the Marine Calcium Isotope Record: Influence of Reef Builders

NASA Astrophysics Data System (ADS)

Boehm, F.; Eisenhauer, A.; Farkas, J.; Kiessling, W.; Veizer, J.; Wallmann, K.

2008-12-01

The calcium isotopic composition of seawater as recorded in brachiopod shells varied substantially during the Paleozoic (Farkas et al. 2007, Geochim. Cosmochim. Acta, 71, 5117-5134). The most prominent feature of the record is an excursion to higher 44Ca/40Ca values that started during the Early Carboniferous and lasted until the Permian. The shift occurred shortly after the transition from a calcite-sea to an aragonite-sea (Sandberg 1983, Nature 305, 19-22; Stanley and Hardie 1998, Pal3, 144, 3-19). It therefore has been interpreted to reflect a change in the average calcium isotope fractionation of carbonates produced in the oceans. Aragonite is depleted by about 0.6 permil in 44Ca/40Ca compared to calcite (Gussone et al. 2005, Geochim. Cosmochim. Acta, 69, 4485-4494). Consequently a transient shift from calcite dominated to an aragonite dominated calcium carbonate sedimentation could have caused the observed 0.5 permil isotope shift. We compare the marine calcium isotope record with a new compilation of the Phanerozoic trends in the skeletal mineralogy of marine invertebrates (Kiessling et al. 2008, Nature Geoscience, 1, 527-530). The compilation is based on data collected in the PaleoReef database and the Paleobiology Database, which include information on Phanerozoic reef complexes and taxonomic collection data of Phanerozoic biota, respectively. We find a strong positive correlation between the calcium isotope ratios and the abundance of aragonitic reef builders from the Silurian until the Permian at a sample resolution of about 10 million years. The two records, however, diverge in the Triassic, when reefs were dominated by aragonite but the calcium isotope values remained at a relatively low level. We also find a good correlation between calcium isotopes and the proportion of aragonite in the general record of Phanerozoic biota. However, in this case the records start to diverge already in the latest Carboniferous. The observations suggest that the

Stability of a giant connected component in a complex network

NASA Astrophysics Data System (ADS)

Kitsak, Maksim; Ganin, Alexander A.; Eisenberg, Daniel A.; Krapivsky, Pavel L.; Krioukov, Dmitri; Alderson, David L.; Linkov, Igor

2018-01-01

We analyze the stability of the network's giant connected component under impact of adverse events, which we model through the link percolation. Specifically, we quantify the extent to which the largest connected component of a network consists of the same nodes, regardless of the specific set of deactivated links. Our results are intuitive in the case of single-layered systems: the presence of large degree nodes in a single-layered network ensures both its robustness and stability. In contrast, we find that interdependent networks that are robust to adverse events have unstable connected components. Our results bring novel insights to the design of resilient network topologies and the reinforcement of existing networked systems.

HFT events - Shallow moonquakes. [High-Frequency Teleseismic

NASA Technical Reports Server (NTRS)

Nakamura, Y.

1977-01-01

A few large distant seismic events of distinctly high signal frequency, designated HFT (high-frequency teleseismic) events, are observed yearly by the Apollo lunar seismic network. Their sources are located on or near the surface of the moon, leaving a large gap in seismic activity between the zones of HFT sources and deep moonquakes. No strong regularities are found in either their spatial or temporal distributions. Several working hypotheses for the identity of these sources have advanced, but many characteristics of the events seem to favor a hypothesis that they are shallow moonquakes. Simultaneous observations of other lunar phenomena may eventually enable the determination of their true identity.

Detecting event-related changes of multivariate phase coupling in dynamic brain networks.

PubMed

Canolty, Ryan T; Cadieu, Charles F; Koepsell, Kilian; Ganguly, Karunesh; Knight, Robert T; Carmena, Jose M

2012-04-01

Oscillatory phase coupling within large-scale brain networks is a topic of increasing interest within systems, cognitive, and theoretical neuroscience. Evidence shows that brain rhythms play a role in controlling neuronal excitability and response modulation (Haider B, McCormick D. Neuron 62: 171-189, 2009) and regulate the efficacy of communication between cortical regions (Fries P. Trends Cogn Sci 9: 474-480, 2005) and distinct spatiotemporal scales (Canolty RT, Knight RT. Trends Cogn Sci 14: 506-515, 2010). In this view, anatomically connected brain areas form the scaffolding upon which neuronal oscillations rapidly create and dissolve transient functional networks (Lakatos P, Karmos G, Mehta A, Ulbert I, Schroeder C. Science 320: 110-113, 2008). Importantly, testing these hypotheses requires methods designed to accurately reflect dynamic changes in multivariate phase coupling within brain networks. Unfortunately, phase coupling between neurophysiological signals is commonly investigated using suboptimal techniques. Here we describe how a recently developed probabilistic model, phase coupling estimation (PCE; Cadieu C, Koepsell K Neural Comput 44: 3107-3126, 2010), can be used to investigate changes in multivariate phase coupling, and we detail the advantages of this model over the commonly employed phase-locking value (PLV; Lachaux JP, Rodriguez E, Martinerie J, Varela F. Human Brain Map 8: 194-208, 1999). We show that the N-dimensional PCE is a natural generalization of the inherently bivariate PLV. Using simulations, we show that PCE accurately captures both direct and indirect (network mediated) coupling between network elements in situations where PLV produces erroneous results. We present empirical results on recordings from humans and nonhuman primates and show that the PCE-estimated coupling values are different from those using the bivariate PLV. Critically on these empirical recordings, PCE output tends to be sparser than the PLVs, indicating fewer

Women's History Month Event

NASA Image and Video Library

2018-03-27

NASA Kennedy Space Center's Deputy Director Janet Petro welcomes workers to the center's Women's History Month event, with the theme "Nevertheless She Persisted." Keynote speaker, JoAnn Morgan, former associate director of the center, spoke to the group about her experience as the first female engineer working in the space program in the 1960s. Morgan was the first female in the Launch Control Center firing room during the Apollo 11 launch. The event was hosted by the center's Kennedy Networking Opportunities for Women (KNOW) and Launching Leaders organizations. The purpose of KNOW is to provide focus on issues such as employment, retention, promotion, training, career and personal development, education, and identify and eliminate barriers that hinder the advancement of women in the workforce.

Women's History Month Event

NASA Image and Video Library

2018-03-27

JoAnn Morgan, former associate director of Kennedy Space Center, at left, accepts a special coin from Janet Petro, deputy director of Kennedy, during a Women's History Month event at the center. With the theme "Nevertheless She Persisted," Morgan described her experience as the first female engineer working in the space program in the 1960s. Morgan was the first female in the Launch Control Center firing room during the Apollo 11 launch. The event was hosted by the center's Kennedy Networking Opportunities for Women (KNOW) and Launching Leaders organizations. The purpose of KNOW is to provide focus on issues such as employment, retention, promotion, training, career and personal development, education, and identify and eliminate barriers that hinder the advancement of women in the workforce.

Women's History Month Event

NASA Image and Video Library

2018-03-27

JoAnn Morgan, far left at the podium, former associate director of Kennedy Space Center, was the keynote speaker during a Women's History Month event at the center. With the theme "Nevertheless She Persisted," Morgan described her experience as the first female engineer working in the space program in the 1960s. Morgan was the first female in the Launch Control Center firing room during the Apollo 11 launch. The event was hosted by the center's Kennedy Networking Opportunities for Women (KNOW) and Launching Leaders organizations. The purpose of KNOW is to provide focus on issues such as employment, retention, promotion, training, career and personal development, education, and identify and eliminate barriers that hinder the advancement of women in the workforce.

Adaptive Self-Tuning Networks

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.

2015-12-01

The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.

A model of spreading of sudden events on social networks

NASA Astrophysics Data System (ADS)

Wu, Jiao; Zheng, Muhua; Zhang, Zi-Ke; Wang, Wei; Gu, Changgui; Liu, Zonghua

2018-03-01

Information spreading has been studied for decades, but its underlying mechanism is still under debate, especially for those ones spreading extremely fast through the Internet. By focusing on the information spreading data of six typical events on Sina Weibo, we surprisingly find that the spreading of modern information shows some new features, i.e., either extremely fast or slow, depending on the individual events. To understand its mechanism, we present a susceptible-accepted-recovered model with both information sensitivity and social reinforcement. Numerical simulations show that the model can reproduce the main spreading patterns of the six typical events. By this model, we further reveal that the spreading can be speeded up by increasing either the strength of information sensitivity or social reinforcement. Depending on the transmission probability and information sensitivity, the final accepted size can change from continuous to discontinuous transition when the strength of the social reinforcement is large. Moreover, an edge-based compartmental theory is presented to explain the numerical results. These findings may be of significance on the control of information spreading in modern society.

Protein interaction networks from literature mining

NASA Astrophysics Data System (ADS)