Science.gov

Sample records for analytic network process

  1. Analytically solvable processes on networks.

    PubMed

    Smilkov, Daniel; Kocarev, Ljupco

    2011-07-01

    We introduce a broad class of analytically solvable processes on networks. In the special case, they reduce to random walk and consensus process, the two most basic processes on networks. Our class differs from previous models of interactions (such as the stochastic Ising model, cellular automata, infinite particle systems, and the voter model) in several ways, the two most important being (i) the model is analytically solvable even when the dynamical equation for each node may be different and the network may have an arbitrary finite graph and influence structure and (ii) when local dynamics is described by the same evolution equation, the model is decomposable, with the equilibrium behavior of the system expressed as an explicit function of network topology and node dynamics. PMID:21867254

  2. Choosing a municipal landfill site by analytic network process

    NASA Astrophysics Data System (ADS)

    Banar, Mufide; Kose, Barbaros Murat; Ozkan, Aysun; Poyraz Acar, Ilgin

    2007-04-01

    In this study, analytic network process (ANP), one of the multi-criteria decision making (MCDM) tools has been used to choose one of the four alternative landfill sites for the city of Eskisehir, Turkey. For this purpose, Super Decision Software has been used and benefit opportunity cost and risk (BOCR) analysis has been done to apply ANP. In BOCR analysis, each alternative site has been evaluated in terms of its benefits, costs and risks; the opportunity cluster has been examined under the benefit cluster. In this context, technical, economical and social assessments have been done for the site selection of sanitary landfill. Also, results have been compared with analytic hierarchy process (AHP) which is another MCDM technique used in the study conducted before. Finally, the current site has been determined as the most appropriate site in both methods. These methods have not been commonly used in the discipline of environmental engineering but it is believed to be an important contribution for decision makers.

  3. Selecting public relations personnel of hospitals by analytic network process.

    PubMed

    Liao, Sen-Kuei; Chang, Kuei-Lun

    2009-01-01

    This study describes the use of analytic network process (ANP) in the Taiwanese hospital public relations personnel selection process. Starting with interviewing 48 practitioners and executives in north Taiwan, we collected selection criteria. Then, we retained the 12 critical criteria that were mentioned above 40 times by theses respondents, including: interpersonal skill, experience, negotiation, language, ability to follow orders, cognitive ability, adaptation to environment, adaptation to company, emotion, loyalty, attitude, and Response. Finally, we discussed with the 20 executives to take these important criteria into three perspectives to structure the hierarchy for hospital public relations personnel selection. After discussing with practitioners and executives, we find that selecting criteria are interrelated. The ANP, which incorporates interdependence relationships, is a new approach for multi-criteria decision-making. Thus, we apply ANP to select the most optimal public relations personnel of hospitals. An empirical study of public relations personnel selection problems in Taiwan hospitals is conducted to illustrate how the selection procedure works. PMID:19197656

  4. Using analytic network process for evaluating mobile text entry methods.

    PubMed

    Ocampo, Lanndon A; Seva, Rosemary R

    2016-01-01

    This paper highlights a preference evaluation methodology for text entry methods in a touch keyboard smartphone using analytic network process (ANP). Evaluation of text entry methods in literature mainly considers speed and accuracy. This study presents an alternative means for selecting text entry method that considers user preference. A case study was carried out with a group of experts who were asked to develop a selection decision model of five text entry methods. The decision problem is flexible enough to reflect interdependencies of decision elements that are necessary in describing real-life conditions. Results showed that QWERTY method is more preferred than other text entry methods while arrangement of keys is the most preferred criterion in characterizing a sound method. Sensitivity analysis using simulation of normally distributed random numbers under fairly large perturbation reported the foregoing results reliable enough to reflect robust judgment. The main contribution of this paper is the introduction of a multi-criteria decision approach in the preference evaluation of text entry methods. PMID:26360215

  5. INTEGRATED ENVIRONMENTAL ASSESSMENT OF THE MID-ATLANTIC REGION WITH ANALYTICAL NETWORK PROCESS

    EPA Science Inventory

    A decision analysis method for integrating environmental indicators was developed. This was a combination of Principal Component Analysis (PCA) and the Analytic Network Process (ANP). Being able to take into account interdependency among variables, the method was capable of ran...

  6. An analytic network process model for municipal solid waste disposal options

    SciTech Connect

    Khan, Sheeba Faisal, Mohd Nishat

    2008-07-01

    The aim of this paper is to present an evaluation method that can aid decision makers in a local civic body to prioritize and select appropriate municipal solid waste disposal methods. We introduce a hierarchical network (hiernet) decision structure and apply the analytic network process (ANP) super-matrix approach to measure the relative desirability of disposal alternatives using value judgments as the input of the various stakeholders. ANP is a flexible analytical program that enables decision makers to find the best possible solution to complex problems by breaking down a problem into a systematic network of inter-relationships among the various levels and attributes. This method therefore may not only aid in selecting the best alternative but also helps decision makers to understand why an alternative is preferred over the other options.

  7. Assessment of wastewater treatment alternatives for small communities: An analytic network process approach.

    PubMed

    Molinos-Senante, María; Gómez, Trinidad; Caballero, Rafael; Hernández-Sancho, Francesc; Sala-Garrido, Ramón

    2015-11-01

    The selection of the most appropriate wastewater treatment (WWT) technology is a complex problem since many alternatives are available and many criteria are involved in the decision-making process. To deal with this challenge, the analytic network process (ANP) is applied for the first time to rank a set of seven WWT technology set-ups for secondary treatment in small communities. A major advantage of ANP is that it incorporates interdependent relationships between elements. Results illustrated that extensive technologies, constructed wetlands and pond systems are the most preferred alternatives by WWT experts. The sensitivity analysis performed verified that the ranking of WWT alternatives is very stable since constructed wetlands are almost always placed in the first position. This paper showed that ANP analysis is suitable to deal with complex decision-making problems, such as the selection of the most appropriate WWT system contributing to better understand the multiple interdependences among elements involved in the assessment. PMID:26119382

  8. Analytic network process model for sustainable lean and green manufacturing performance indicator

    NASA Astrophysics Data System (ADS)

    Aminuddin, Adam Shariff Adli; Nawawi, Mohd Kamal Mohd; Mohamed, Nik Mohd Zuki Nik

    2014-09-01

    Sustainable manufacturing is regarded as the most complex manufacturing paradigm to date as it holds the widest scope of requirements. In addition, its three major pillars of economic, environment and society though distinct, have some overlapping among each of its elements. Even though the concept of sustainability is not new, the development of the performance indicator still needs a lot of improvement due to its multifaceted nature, which requires integrated approach to solve the problem. This paper proposed the best combination of criteria en route a robust sustainable manufacturing performance indicator formation via Analytic Network Process (ANP). The integrated lean, green and sustainable ANP model can be used to comprehend the complex decision system of the sustainability assessment. The finding shows that green manufacturing is more sustainable than lean manufacturing. It also illustrates that procurement practice is the most important criteria in the sustainable manufacturing performance indicator.

  9. An environmental pressure index proposal for urban development planning based on the analytic network process

    SciTech Connect

    Gomez-Navarro, Tomas; Diaz-Martin, Diego

    2009-09-15

    This paper introduces a new approach to prioritize urban planning projects according to their environmental pressure in an efficient and reliable way. It is based on the combination of three procedures: (i) the use of environmental pressure indicators, (ii) the aggregation of the indicators in an Environmental Pressure Index by means of the Analytic Network Process method (ANP) and (iii) the interpretation of the information obtained from the experts during the decision-making process. The method has been applied to a proposal for urban development of La Carlota airport in Caracas (Venezuela). There are three options which are currently under evaluation. They include a Health Club, a Residential Area and a Theme Park. After a selection process the experts chose the following environmental pressure indicators as ANP criteria for the project life cycle: used land area, population density, energy consumption, water consumption and waste generation. By using goal-oriented questionnaires designed by the authors, the experts determined the importance of the criteria, the relationships among criteria, and the relationships between the criteria and the urban development alternatives. The resulting data showed that water consumption is the most important environmental pressure factor, and the Theme Park project is by far the urban development alternative which exerts the least environmental pressure on the area. The participating experts coincided in appreciating the technique proposed in this paper is useful and, for ranking ordering these alternatives, an improvement from traditional techniques such as environmental impact studies, life-cycle analysis, etc.

  10. Evaluating water management strategies in watersheds by new hybrid Fuzzy Analytical Network Process (FANP) methods

    NASA Astrophysics Data System (ADS)

    RazaviToosi, S. L.; Samani, J. M. V.

    2016-03-01

    Watersheds are considered as hydrological units. Their other important aspects such as economic, social and environmental functions play crucial roles in sustainable development. The objective of this work is to develop methodologies to prioritize watersheds by considering different development strategies in environmental, social and economic sectors. This ranking could play a significant role in management to assign the most critical watersheds where by employing water management strategies, best condition changes are expected to be accomplished. Due to complex relations among different criteria, two new hybrid fuzzy ANP (Analytical Network Process) algorithms, fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and fuzzy max-min set methods are used to provide more flexible and accurate decision model. Five watersheds in Iran named Oroomeyeh, Atrak, Sefidrood, Namak and Zayandehrood are considered as alternatives. Based on long term development goals, 38 water management strategies are defined as subcriteria in 10 clusters. The main advantage of the proposed methods is its ability to overcome uncertainty. This task is accomplished by using fuzzy numbers in all steps of the algorithms. To validate the proposed method, the final results were compared with those obtained from the ANP algorithm and the Spearman rank correlation coefficient is applied to find the similarity in the different ranking methods. Finally, the sensitivity analysis was conducted to investigate the influence of cluster weights on the final ranking.

  11. Process Analytical Chemistry.

    ERIC Educational Resources Information Center

    Callis, James B.; And Others

    1987-01-01

    Discusses process analytical chemistry as a discipline designed to supply quantitative and qualitative information about a chemical process. Encourages academic institutions to examine this field for employment opportunities for students. Describes the five areas of process analytical chemistry, including off-line, at-line, on-line, in-line, and…

  12. Analytic Networks in Music Task Definition.

    ERIC Educational Resources Information Center

    Piper, Richard M.

    For a student to acquire the conceptual systems of a discipline, the designer must reflect that structure or analytic network in his curriculum. The four networks identified for music and used in the development of the Southwest Regional Laboratory (SWRL) Music Program are the variable-value, the whole-part, the process-stage, and the class-member…

  13. Visual Analytics of Brain Networks

    PubMed Central

    Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

    2014-01-01

    Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. PMID:22414991

  14. Visual analytics of brain networks.

    PubMed

    Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

    2012-05-15

    Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. PMID:22414991

  15. An Analytic Network Process approach for the environmental aspect selection problem — A case study for a hand blender

    SciTech Connect

    Bereketli Zafeirakopoulos, Ilke Erol Genevois, Mujde

    2015-09-15

    Life Cycle Assessment is a tool to assess, in a systematic way, the environmental aspects and its potential environmental impacts and resources used throughout a product's life cycle. It is widely accepted and considered as one of the most powerful tools to support decision-making processes used in ecodesign and sustainable production in order to learn about the most problematic parts and life cycle phases of a product and to have a projection for future improvements. However, since Life Cycle Assessment is a cost and time intensive method, companies do not intend to carry out a full version of it, except for large corporate ones. Especially for small and medium sized enterprises, which do not have enough budget for and knowledge on sustainable production and ecodesign approaches, focusing only on the most important possible environmental aspect is unavoidable. In this direction, finding the right environmental aspect to work on is crucial for the companies. In this study, a multi-criteria decision-making methodology, Analytic Network Process is proposed to select the most relevant environmental aspect. The proposed methodology aims at providing a simplified environmental assessment to producers. It is applied for a hand blender, which is a member of the Electrical and Electronic Equipment family. The decision criteria for the environmental aspects and relations of dependence are defined. The evaluation is made by the Analytic Network Process in order to create a realistic approach to inter-dependencies among the criteria. The results are computed via the Super Decisions software. Finally, it is observed that the procedure is completed in less time, with less data, with less cost and in a less subjective way than conventional approaches. - Highlights: • We present a simplified environmental assessment methodology to support LCA. • ANP is proposed to select the most relevant environmental aspect. • ANP deals well with the interdependencies between aspects and

  16. Disaster risk management in prospect mining area Blitar district, East Java, using microtremor analysis and ANP (analytical network processing) approach

    NASA Astrophysics Data System (ADS)

    Parwatiningtyas, Diyan; Ambarsari, Erlin Windia; Marlina, Dwi; Wiratomo, Yogi

    2014-03-01

    Indonesia has a wealth of natural assets is so large to be managed and utilized, either from its own local government and local communities, especially in the mining sector. However, mining activities can change the state of the surface layer of the earth that have a high impact disaster risk. This could threaten the safety and disrupt human life, environmental damage, loss of property, and the psychological impact, sulking to the rule of law no 24 of 2007. That's why we strive to manage and minimize the risk of mine disasters in the region, how to use the method of calculation of Amplification Factor (AF) from the analysis based microtremor sulking Kanai and Nakamura, and decision systems were tested by analysis of ANP. Based on the amplification factor and Analytical Network Processing (ANP) obtained, some points showed instability in the surface layer of a mining area include the site of the TP-7, TP-8, TP-9, TP-10, (Birowo2). If in terms of structure, location indicated unstable due to have a sloping surface layer, resulting in the occurrence of landslides and earthquake risk is high. In the meantime, other areas of the mine site can be said to be a stable area.

  17. Disaster risk management in prospect mining area Blitar district, East Java, using microtremor analysis and ANP (analytical network processing) approach

    SciTech Connect

    Parwatiningtyas, Diyan E-mail: erlinunindra@gmail.com; Ambarsari, Erlin Windia E-mail: erlinunindra@gmail.com; Marlina, Dwi E-mail: erlinunindra@gmail.com; Wiratomo, Yogi E-mail: erlinunindra@gmail.com

    2014-03-24

    Indonesia has a wealth of natural assets is so large to be managed and utilized, either from its own local government and local communities, especially in the mining sector. However, mining activities can change the state of the surface layer of the earth that have a high impact disaster risk. This could threaten the safety and disrupt human life, environmental damage, loss of property, and the psychological impact, sulking to the rule of law no 24 of 2007. That's why we strive to manage and minimize the risk of mine disasters in the region, how to use the method of calculation of Amplification Factor (AF) from the analysis based microtremor sulking Kanai and Nakamura, and decision systems were tested by analysis of ANP. Based on the amplification factor and Analytical Network Processing (ANP) obtained, some points showed instability in the surface layer of a mining area include the site of the TP-7, TP-8, TP-9, TP-10, (Birowo2). If in terms of structure, location indicated unstable due to have a sloping surface layer, resulting in the occurrence of landslides and earthquake risk is high. In the meantime, other areas of the mine site can be said to be a stable area.

  18. Networked analytical sample management system

    SciTech Connect

    Kerrigan, W.J.; Spencer, W.A.

    1986-01-01

    Since 1982, the Savannah River Laboratory (SRL) has operated a computer-controlled analytical sample management system. The system, pogrammed in COBOL, runs on the site IBM 3081 mainframe computer. The system provides for the following subtasks: sample logging, analytical method assignment, worklist generation, cost accounting, and results reporting. Within these subtasks the system functions in a time-sharing mode. Communications between subtasks are done overnight in a batch mode. The system currently supports management of up to 3000 samples a month. Each sample requires, on average, three independent methods. Approximately 100 different analytical techniques are available for customized input of data. The laboratory has implemented extensive computer networking using Ethernet. Electronic mail, RS/1, and online literature searches are in place. Based on our experience with the existing sample management system, we have begun a project to develop a second generation system. The new system will utilize the panel designs developed for the present LIMS, incorporate more realtime features, and take advantage of the many commercial LIMS systems.

  19. An Introduction to Social Network Data Analytics

    NASA Astrophysics Data System (ADS)

    Aggarwal, Charu C.

    The advent of online social networks has been one of the most exciting events in this decade. Many popular online social networks such as Twitter, LinkedIn, and Facebook have become increasingly popular. In addition, a number of multimedia networks such as Flickr have also seen an increasing level of popularity in recent years. Many such social networks are extremely rich in content, and they typically contain a tremendous amount of content and linkage data which can be leveraged for analysis. The linkage data is essentially the graph structure of the social network and the communications between entities; whereas the content data contains the text, images and other multimedia data in the network. The richness of this network provides unprecedented opportunities for data analytics in the context of social networks. This book provides a data-centric view of online social networks; a topic which has been missing from much of the literature. This chapter provides an overview of the key topics in this field, and their coverage in this book.

  20. Optimization of the scheme for natural ecology planning of urban rivers based on ANP (analytic network process) model.

    PubMed

    Zhang, Yichuan; Wang, Jiangping

    2015-07-01

    Rivers serve as a highly valued component in ecosystem and urban infrastructures. River planning should follow basic principles of maintaining or reconstructing the natural landscape and ecological functions of rivers. Optimization of planning scheme is a prerequisite for successful construction of urban rivers. Therefore, relevant studies on optimization of scheme for natural ecology planning of rivers is crucial. In the present study, four planning schemes for Zhaodingpal River in Xinxiang City, Henan Province were included as the objects for optimization. Fourteen factors that influenced the natural ecology planning of urban rivers were selected from five aspects so as to establish the ANP model. The data processing was done using Super Decisions software. The results showed that important degree of scheme 3 was highest. A scientific, reasonable and accurate evaluation of schemes could be made by ANP method on natural ecology planning of urban rivers. This method could be used to provide references for sustainable development and construction of urban rivers. ANP method is also suitable for optimization of schemes for urban green space planning and design. PMID:26387349

  1. Analysis of land suitability for urban development in Ahwaz County in southwestern Iran using fuzzy logic and analytic network process (ANP).

    PubMed

    Malmir, Maryam; Zarkesh, Mir Masoud Kheirkhah; Monavari, Seyed Masoud; Jozi, Seyed Ali; Sharifi, Esmail

    2016-08-01

    The ever-increasing development of cities due to population growth and migration has led to unplanned constructions and great changes in urban spatial structure, especially the physical development of cities in unsuitable places, which requires conscious guidance and fundamental organization. It is therefore necessary to identify suitable sites for future development of cities and prevent urban sprawl as one of the main concerns of urban managers and planners. In this study, to determine the suitable sites for urban development in the county of Ahwaz, the effective biophysical and socioeconomic criteria (including 27 sub-criteria) were initially determined based on literature review and interviews with certified experts. In the next step, a database of criteria and sub-criteria was prepared. Standardization of values and unification of scales in map layers were done using fuzzy logic. The criteria and sub-criteria were weighted by analytic network process (ANP) in the Super Decision software. Next, the map layers were overlaid using weighted linear combination (WLC) in the GIS software. According to the research findings, the final land suitability map was prepared with five suitability classes of very high (5.86 %), high (31.93 %), medium (38.61 %), low (17.65 %), and very low (5.95 %). Also, in terms of spatial distribution, suitable lands for urban development are mainly located in the central and southern parts of the Ahwaz County. It is expected that integration of fuzzy logic and ANP model will provide a better decision support tool compared with other models. The developed model can also be used in the land suitability analysis of other cities. PMID:27376847

  2. Analytical Computation of the Epidemic Threshold on Temporal Networks

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  3. Analytical reasoning task reveals limits of social learning in networks

    PubMed Central

    Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

    2014-01-01

    Social learning—by observing and copying others—is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an ‘unreflective copying bias’, which limits their social learning to the output, rather than the process, of their peers’ reasoning—even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning. PMID:24501275

  4. Analytical reasoning task reveals limits of social learning in networks.

    PubMed

    Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

    2014-04-01

    Social learning-by observing and copying others-is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias', which limits their social learning to the output, rather than the process, of their peers' reasoning-even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning. PMID:24501275

  5. Extremal dynamics on complex networks: Analytic solutions

    NASA Astrophysics Data System (ADS)

    Masuda, N.; Goh, K.-I.; Kahng, B.

    2005-12-01

    The Bak-Sneppen model displaying punctuated equilibria in biological evolution is studied on random complex networks. By using the rate equation and the random walk approaches, we obtain the analytic solution of the fitness threshold xc to be 1/(⟨k⟩f+1) , where ⟨k⟩f=⟨k2⟩/⟨k⟩ (=⟨k⟩) in the quenched (annealed) updating case, where ⟨kn⟩ is the nth moment of the degree distribution. Thus, the threshold is zero (finite) for the degree exponent γ<3 (γ>3) for the quenched case in the thermodynamic limit. The theoretical value xc fits well to the numerical simulation data in the annealed case only. Avalanche size, defined as the duration of successive mutations below the threshold, exhibits a critical behavior as its distribution follows a power law, Pa(s)˜s-3/2 .

  6. Microsystem process networks

    DOEpatents

    Wegeng, Robert S [Richland, WA; TeGrotenhuis, Ward E [Kennewick, WA; Whyatt, Greg A [West Richland, WA

    2010-01-26

    Various aspects and applications or microsystem process networks are described. The design of many types of microsystems can be improved by ortho-cascading mass, heat, or other unit process operations. Microsystems having energetically efficient microchannel heat exchangers are also described. Detailed descriptions of numerous design features in microcomponent systems are also provided.

  7. Microsystem process networks

    DOEpatents

    Wegeng, Robert S.; TeGrotenhuis, Ward E.; Whyatt, Greg A.

    2007-09-18

    Various aspects and applications of microsystem process networks are described. The design of many types of Microsystems can be improved by ortho-cascading mass, heat, or other unit process operations. Microsystems having energetically efficient microchannel heat exchangers are also described. Detailed descriptions of numerous design features in microcomponent systems are also provided.

  8. Microsystem process networks

    DOEpatents

    Wegeng, Robert S.; TeGrotenhuis, Ward E.; Whyatt, Greg A.

    2006-10-24

    Various aspects and applications of microsystem process networks are described. The design of many types of microsystems can be improved by ortho-cascading mass, heat, or other unit process operations. Microsystems having exergetically efficient microchannel heat exchangers are also described. Detailed descriptions of numerous design features in microcomponent systems are also provided.

  9. Epidemic processes in complex networks

    NASA Astrophysics Data System (ADS)

    Pastor-Satorras, Romualdo; Castellano, Claudio; Van Mieghem, Piet; Vespignani, Alessandro

    2015-07-01

    In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.

  10. Analytical investigation of self-organized criticality in neural networks

    PubMed Central

    Droste, Felix; Do, Anne-Ly; Gross, Thilo

    2013-01-01

    Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity-dependent synaptic plasticity. Here, we model neurons as discrete-state nodes on an adaptive network following stochastic dynamics. At a threshold connectivity, this system undergoes a dynamical phase transition at which persistent activity sets in. In a low-dimensional representation of the macroscopic dynamics, this corresponds to a transcritical bifurcation. We show analytically that adding activity-dependent rewiring rules, inspired by homeostatic plasticity, leads to the emergence of an attractive steady state at criticality and present numerical evidence for the system's evolution to such a state. PMID:22977096

  11. Using Analytic Hierarchy Process in Textbook Evaluation

    ERIC Educational Resources Information Center

    Kato, Shigeo

    2014-01-01

    This study demonstrates the application of the analytic hierarchy process (AHP) in English language teaching materials evaluation, focusing in particular on its potential for systematically integrating different components of evaluation criteria in a variety of teaching contexts. AHP is a measurement procedure wherein pairwise comparisons are made…

  12. An analytical framework for local feedforward networks.

    PubMed

    Weaver, S; Baird, L; Polycarpou, M

    1998-01-01

    Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To obtain a better understanding of these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed. These measures incorporate not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal, multilayer perceptron (MLP) networks that employ the backpropagation learning algorithm on the quadratic cost function, we address a familiar misconception that single-hidden-layer sigmoidal networks are inherently nonlocal by demonstrating that given a sufficiently large number of adjustable weights, single-hidden-layer sigmoidal MLP's exist that are arbitrarily local and retain the ability to approximate any continuous function on a compact domain. PMID:18252471

  13. Controlling Contagion Processes in Activity Driven Networks

    NASA Astrophysics Data System (ADS)

    Liu, Suyu; Perra, Nicola; Karsai, Márton; Vespignani, Alessandro

    2014-03-01

    The vast majority of strategies aimed at controlling contagion processes on networks consider the connectivity pattern of the system either quenched or annealed. However, in the real world, many networks are highly dynamical and evolve, in time, concurrently with the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for a class of time-varying networks, namely activity-driven networks. We develop a block variable mean-field approach that allows the derivation of the equations describing the coevolution of the contagion process and the network dynamic. We derive the critical immunization threshold and assess the effectiveness of three different control strategies. Finally, we validate the theoretical picture by simulating numerically the spreading process and control strategies in both synthetic networks and a large-scale, real-world, mobile telephone call data set.

  14. Statistically Qualified Neuro-Analytic system and Method for Process Monitoring

    SciTech Connect

    Vilim, Richard B.; Garcia, Humberto E.; Chen, Frederick W.

    1998-11-04

    An apparatus and method for monitoring a process involves development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two steps: deterministic model adaption and stochastic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics,augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation emor minimization technique. Stochastic model adaptation involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system.

  15. Analytic sequential methods for detecting network intrusions

    NASA Astrophysics Data System (ADS)

    Chen, Xinjia; Walker, Ernest

    2014-05-01

    In this paper, we propose an analytic sequential methods for detecting port-scan attackers which routinely perform random "portscans" of IP addresses to find vulnerable servers to compromise. In addition to rigorously control the probability of falsely implicating benign remote hosts as malicious, our method performs significantly faster than other current solutions. We have developed explicit formulae for quick determination of the parameters of the new detection algorithm.

  16. Exploring the Analytical Processes of Intelligence Analysts

    SciTech Connect

    Chin, George; Kuchar, Olga A.; Wolf, Katherine E.

    2009-04-04

    We present an observational case study in which we investigate and analyze the analytical processes of intelligence analysts. Participating analysts in the study carry out two scenarios where they organize and triage information, conduct intelligence analysis, report results, and collaborate with one another. Through a combination of artifact analyses, group interviews, and participant observations, we explore the space and boundaries in which intelligence analysts work and operate. We also assess the implications of our findings on the use and application of relevant information technologies.

  17. Network Analytical Tool for Monitoring Global Food Safety Highlights China

    PubMed Central

    Nepusz, Tamás; Petróczi, Andrea; Naughton, Declan P.

    2009-01-01

    Background The Beijing Declaration on food safety and security was signed by over fifty countries with the aim of developing comprehensive programs for monitoring food safety and security on behalf of their citizens. Currently, comprehensive systems for food safety and security are absent in many countries, and the systems that are in place have been developed on different principles allowing poor opportunities for integration. Methodology/Principal Findings We have developed a user-friendly analytical tool based on network approaches for instant customized analysis of food alert patterns in the European dataset from the Rapid Alert System for Food and Feed. Data taken from alert logs between January 2003 – August 2008 were processed using network analysis to i) capture complexity, ii) analyze trends, and iii) predict possible effects of interventions by identifying patterns of reporting activities between countries. The detector and transgressor relationships are readily identifiable between countries which are ranked using i) Google's PageRank algorithm and ii) the HITS algorithm of Kleinberg. The program identifies Iran, China and Turkey as the transgressors with the largest number of alerts. However, when characterized by impact, counting the transgressor index and the number of countries involved, China predominates as a transgressor country. Conclusions/Significance This study reports the first development of a network analysis approach to inform countries on their transgressor and detector profiles as a user-friendly aid for the adoption of the Beijing Declaration. The ability to instantly access the country-specific components of the several thousand annual reports will enable each country to identify the major transgressors and detectors within its trading network. Moreover, the tool can be used to monitor trading countries for improved detector/transgressor ratios. PMID:19688088

  18. Process-in-Network: A Comprehensive Network Processing Approach

    PubMed Central

    Urzaiz, Gabriel; Villa, David; Villanueva, Felix; Lopez, Juan Carlos

    2012-01-01

    A solid and versatile communications platform is very important in modern Ambient Intelligence (AmI) applications, which usually require the transmission of large amounts of multimedia information over a highly heterogeneous network. This article focuses on the concept of Process-in-Network (PIN), which is defined as the possibility that the network processes information as it is being transmitted, and introduces a more comprehensive approach than current network processing technologies. PIN can take advantage of waiting times in queues of routers, idle processing capacity in intermediate nodes, and the information that passes through the network. PMID:22969390

  19. Risk prioritisation using the analytic hierarchy process

    NASA Astrophysics Data System (ADS)

    Sum, Rabihah Md.

    2015-12-01

    This study demonstrated how to use the Analytic Hierarchy Process (AHP) to prioritise risks of an insurance company. AHP is a technique to structure complex problems by arranging elements of the problems in a hierarchy, assigning numerical values to subjective judgements on the relative importance of the elements and synthesizing the judgements to determine which elements have the highest priority. The study is motivated by wide application of AHP as a prioritisation technique in complex problems. It aims to show AHP is able to minimise some limitations of risk assessment technique using likelihood and impact. The study shows AHP is able to provide consistency check on subjective judgements, organise a large number of risks into a structured framework, assist risk managers to make explicit risk trade-offs, and provide an easy to understand and systematic risk assessment process.

  20. Parallel processing in immune networks

    NASA Astrophysics Data System (ADS)

    Agliari, Elena; Barra, Adriano; Bartolucci, Silvia; Galluzzi, Andrea; Guerra, Francesco; Moauro, Francesco

    2013-04-01

    In this work, we adopt a statistical-mechanics approach to investigate basic, systemic features exhibited by adaptive immune systems. The lymphocyte network made by B cells and T cells is modeled by a bipartite spin glass, where, following biological prescriptions, links connecting B cells and T cells are sparse. Interestingly, the dilution performed on links is shown to make the system able to orchestrate parallel strategies to fight several pathogens at the same time; this multitasking capability constitutes a remarkable, key property of immune systems as multiple antigens are always present within the host. We also define the stochastic process ruling the temporal evolution of lymphocyte activity and show its relaxation toward an equilibrium measure allowing statistical-mechanics investigations. Analytical results are compared with Monte Carlo simulations and signal-to-noise outcomes showing overall excellent agreement. Finally, within our model, a rationale for the experimentally well-evidenced correlation between lymphocytosis and autoimmunity is achieved; this sheds further light on the systemic features exhibited by immune networks.

  1. Is Analytic Information Processing a Feature of Expertise in Medicine?

    ERIC Educational Resources Information Center

    McLaughlin, Kevin; Rikers, Remy M.; Schmidt, Henk G.

    2008-01-01

    Diagnosing begins by generating an initial diagnostic hypothesis by automatic information processing. Information processing may stop here if the hypothesis is accepted, or analytical processing may be used to refine the hypothesis. This description portrays analytic processing as an optional extra in information processing, leading us to…

  2. Controlling Contagion Processes in Time Varying Networks

    NASA Astrophysics Data System (ADS)

    Liu, Suyu; Perra, Nicola; Karsai, Marton; Vespignani, Alessandro

    2013-03-01

    The vast majority of strategies aimed at controlling contagion and spreading processes on networks consider the connectivity pattern of the system as quenched. In this paper, we consider the class of activity driven networks to analytically evaluate how different control strategies perform in time-varying networks. We consider the limit in which the evolution of the structure of the network and the spreading process are simultaneous yet independent. We analyze three control strategies based on node's activity patterns to decide the removal/immunization of nodes. We find that targeted strategies aimed at the removal of active nodes outperform by orders of magnitude the widely used random strategies. In time-varying networks however any finite time observation of the network dynamics provides only incomplete information on the nodes' activity and does not allow the precise ranking of the most active nodes as needed to implement targeted strategies. Here we develop a control strategy that focuses on targeting the egocentric time-aggregated network of a small control group of nodes.The presented strategy allows the control of spreading processes by removing a fraction of nodes much smaller than the random strategy while at the same time limiting the observation time on the system.

  3. Discovery of Information Diffusion Process in Social Networks

    NASA Astrophysics Data System (ADS)

    Kim, Kwanho; Jung, Jae-Yoon; Park, Jonghun

    Information diffusion analysis in social networks is of significance since it enables us to deeply understand dynamic social interactions among users. In this paper, we introduce approaches to discovering information diffusion process in social networks based on process mining. Process mining techniques are applied from three perspectives: social network analysis, process discovery and community recognition. We then present experimental results by using a real-life social network data. The proposed techniques are expected to employ as new analytical tools in online social networks such as blog and wikis for company marketers, politicians, news reporters and online writers.

  4. Parallel processing neural networks

    SciTech Connect

    Zargham, M.

    1988-09-01

    A model for Neural Network which is based on a particular kind of Petri Net has been introduced. The model has been implemented in C and runs on the Sequent Balance 8000 multiprocessor, however it can be directly ported to different multiprocessor environments. The potential advantages of using Petri Nets include: (1) the overall system is often easier to understand due to the graphical and precise nature of the representation scheme, (2) the behavior of the system can be analyzed using Petri Net theory. Though, the Petri Net is an obvious choice as a basis for the model, the basic Petri Net definition is not adequate to represent the neuronal system. To eliminate certain inadequacies more information has been added to the Petri Net model. In the model, a token represents either a processor or a post synaptic potential. Progress through a particular Neural Network is thus graphically depicted in the movement of the processor tokens through the Petri Net.

  5. Analytical framework for recurrence network analysis of time series

    NASA Astrophysics Data System (ADS)

    Donges, Jonathan F.; Heitzig, Jobst; Donner, Reik V.; Kurths, Jürgen

    2012-04-01

    Recurrence networks are a powerful nonlinear tool for time series analysis of complex dynamical systems. While there are already many successful applications ranging from medicine to paleoclimatology, a solid theoretical foundation of the method has still been missing so far. Here, we interpret an ɛ-recurrence network as a discrete subnetwork of a “continuous” graph with uncountably many vertices and edges corresponding to the system's attractor. This step allows us to show that various statistical measures commonly used in complex network analysis can be seen as discrete estimators of newly defined continuous measures of certain complex geometric properties of the attractor on the scale given by ɛ. In particular, we introduce local measures such as the ɛ-clustering coefficient, mesoscopic measures such as ɛ-motif density, path-based measures such as ɛ-betweennesses, and global measures such as ɛ-efficiency. This new analytical basis for the so far heuristically motivated network measures also provides an objective criterion for the choice of ɛ via a percolation threshold, and it shows that estimation can be improved by so-called node splitting invariant versions of the measures. We finally illustrate the framework for a number of archetypical chaotic attractors such as those of the Bernoulli and logistic maps, periodic and two-dimensional quasiperiodic motions, and for hyperballs and hypercubes by deriving analytical expressions for the novel measures and comparing them with data from numerical experiments. More generally, the theoretical framework put forward in this work describes random geometric graphs and other networks with spatial constraints, which appear frequently in disciplines ranging from biology to climate science.

  6. Accelerating Network Traffic Analytics Using Query-DrivenVisualization

    SciTech Connect

    Bethel, E. Wes; Campbell, Scott; Dart, Eli; Stockinger, Kurt; Wu,Kesheng

    2006-07-29

    Realizing operational analytics solutions where large and complex data must be analyzed in a time-critical fashion entails integrating many different types of technology. This paper focuses on an interdisciplinary combination of scientific data management and visualization/analysis technologies targeted at reducing the time required for data filtering, querying, hypothesis testing and knowledge discovery in the domain of network connection data analysis. We show that use of compressed bitmap indexing can quickly answer queries in an interactive visual data analysis application, and compare its performance with two alternatives for serial and parallel filtering/querying on 2.5 billion records worth of network connection data collected over a period of 42 weeks. Our approach to visual network connection data exploration centers on two primary factors: interactive ad-hoc and multiresolution query formulation and execution over n dimensions and visual display of then-dimensional histogram results. This combination is applied in a case study to detect a distributed network scan and to then identify the set of remote hosts participating in the attack. Our approach is sufficiently general to be applied to a diverse set of data understanding problems as well as used in conjunction with a diverse set of analysis and visualization tools.

  7. Entropy-based heavy tailed distribution transformation and visual analytics for monitoring massive network traffic

    NASA Astrophysics Data System (ADS)

    Han, Keesook J.; Hodge, Matthew; Ross, Virginia W.

    2011-06-01

    For monitoring network traffic, there is an enormous cost in collecting, storing, and analyzing network traffic datasets. Data mining based network traffic analysis has a growing interest in the cyber security community, but is computationally expensive for finding correlations between attributes in massive network traffic datasets. To lower the cost and reduce computational complexity, it is desirable to perform feasible statistical processing on effective reduced datasets instead of on the original full datasets. Because of the dynamic behavior of network traffic, traffic traces exhibit mixtures of heavy tailed statistical distributions or overdispersion. Heavy tailed network traffic characterization and visualization are important and essential tasks to measure network performance for the Quality of Services. However, heavy tailed distributions are limited in their ability to characterize real-time network traffic due to the difficulty of parameter estimation. The Entropy-Based Heavy Tailed Distribution Transformation (EHTDT) was developed to convert the heavy tailed distribution into a transformed distribution to find the linear approximation. The EHTDT linearization has the advantage of being amenable to characterize and aggregate overdispersion of network traffic in realtime. Results of applying the EHTDT for innovative visual analytics to real network traffic data are presented.

  8. Evaluation methodology for comparing memory and communication of analytic processes in visual analytics

    SciTech Connect

    Ragan, Eric D; Goodall, John R

    2014-01-01

    Provenance tools can help capture and represent the history of analytic processes. In addition to supporting analytic performance, provenance tools can be used to support memory of the process and communication of the steps to others. Objective evaluation methods are needed to evaluate how well provenance tools support analyst s memory and communication of analytic processes. In this paper, we present several methods for the evaluation of process memory, and we discuss the advantages and limitations of each. We discuss methods for determining a baseline process for comparison, and we describe various methods that can be used to elicit process recall, step ordering, and time estimations. Additionally, we discuss methods for conducting quantitative and qualitative analyses of process memory. By organizing possible memory evaluation methods and providing a meta-analysis of the potential benefits and drawbacks of different approaches, this paper can inform study design and encourage objective evaluation of process memory and communication.

  9. Diffusive capture process on complex networks

    NASA Astrophysics Data System (ADS)

    Lee, Sungmin; Yook, Soon-Hyung; Kim, Yup

    2006-10-01

    We study the dynamical properties of a diffusing lamb captured by a diffusing lion on the complex networks with various sizes of N . We find that the lifetime ⟨T⟩ of a lamb scales as ⟨T⟩˜N and the survival probability S(N→∞,t) becomes finite on scale-free networks with degree exponent γ>3 . However, S(N,t) for γ<3 has a long-living tail on tree-structured scale-free networks and decays exponentially on looped scale-free networks. This suggests that the second moment of degree distribution ⟨k2⟩ is the relevant factor for the dynamical properties in the diffusive capture process. We numerically find that the normalized number of capture events at a node with degree k , n(k) , decreases as n(k)˜k-σ . When γ<3 , n(k) still increases anomalously for k≈kmax , where kmax is the maximum value of k of given networks with size N . We analytically show that n(k) satisfies the relation n(k)˜k2P(k) for any degree distribution P(k) and the total number of capture events Ntot is proportional to ⟨k2⟩ , which causes the γ -dependent behavior of S(N,t) and ⟨T⟩ .

  10. The influence of retrieval practice on metacognition: The contribution of analytic and non-analytic processes.

    PubMed

    Miller, Tyler M; Geraci, Lisa

    2016-05-01

    People may change their memory predictions after retrieval practice using naïve theories of memory and/or by using subjective experience - analytic and non-analytic processes respectively. The current studies disentangled contributions of each process. In one condition, learners studied paired-associates, made a memory prediction, completed a short-run of retrieval practice and made a second prediction. In another condition, judges read about a yoked learners' retrieval practice performance but did not participate in retrieval practice and therefore, could not use non-analytic processes for the second prediction. In Study 1, learners reduced their predictions following moderately difficult retrieval practice whereas judges increased their predictions. In Study 2, learners made lower adjusted predictions than judges following both easy and difficult retrieval practice. In Study 3, judge-like participants used analytic processes to report adjusted predictions. Overall, the results suggested non-analytic processes play a key role for participants to reduce their predictions after retrieval practice. PMID:26985881

  11. Controlling Contagion Processes in Time-Varying Networks

    NASA Astrophysics Data System (ADS)

    Perra, Nicola; Liu, Suyu; Karsai, Marton; Vespignani, Alessandro

    2014-03-01

    The vast majority of strategies aimed at controlling contagion processes on networks considers the connectivity pattern of the system as either quenched or annealed. However, in the real world many networks are highly dynamical and evolve in time concurrently to the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for time-varying networks. We consider the removal/immunization of individual nodes according the their activity in the network and develop a block variable mean-field approach that allows the derivation of the equations describing the evolution of the contagion process concurrently to the network dynamic. We derive the critical immunization threshold and assess the effectiveness of the control strategies. Finally, we validate the theoretical picture by simulating numerically the information spreading process and control strategies in both synthetic networks and a large-scale, real-world mobile telephone call dataset.

  12. Visual analytics for multimodal social network analysis: a design study with social scientists.

    PubMed

    Ghani, Sohaib; Kwon, Bum Chul; Lee, Seungyoon; Yi, Ji Soo; Elmqvist, Niklas

    2013-12-01

    Social network analysis (SNA) is becoming increasingly concerned not only with actors and their relations, but also with distinguishing between different types of such entities. For example, social scientists may want to investigate asymmetric relations in organizations with strict chains of command, or incorporate non-actors such as conferences and projects when analyzing coauthorship patterns. Multimodal social networks are those where actors and relations belong to different types, or modes, and multimodal social network analysis (mSNA) is accordingly SNA for such networks. In this paper, we present a design study that we conducted with several social scientist collaborators on how to support mSNA using visual analytics tools. Based on an openended, formative design process, we devised a visual representation called parallel node-link bands (PNLBs) that splits modes into separate bands and renders connections between adjacent ones, similar to the list view in Jigsaw. We then used the tool in a qualitative evaluation involving five social scientists whose feedback informed a second design phase that incorporated additional network metrics. Finally, we conducted a second qualitative evaluation with our social scientist collaborators that provided further insights on the utility of the PNLBs representation and the potential of visual analytics for mSNA. PMID:24051769

  13. Information Network Model Query Processing

    NASA Astrophysics Data System (ADS)

    Song, Xiaopu

    Information Networking Model (INM) [31] is a novel database model for real world objects and relationships management. It naturally and directly supports various kinds of static and dynamic relationships between objects. In INM, objects are networked through various natural and complex relationships. INM Query Language (INM-QL) [30] is designed to explore such information network, retrieve information about schema, instance, their attributes, relationships, and context-dependent information, and process query results in the user specified form. INM database management system has been implemented using Berkeley DB, and it supports INM-QL. This thesis is mainly focused on the implementation of the subsystem that is able to effectively and efficiently process INM-QL. The subsystem provides a lexical and syntactical analyzer of INM-QL, and it is able to choose appropriate evaluation strategies and index mechanism to process queries in INM-QL without the user's intervention. It also uses intermediate result structure to hold intermediate query result and other helping structures to reduce complexity of query processing.

  14. Network command processing system overview

    NASA Technical Reports Server (NTRS)

    Nam, Yon-Woo; Murphy, Lisa D.

    1993-01-01

    The Network Command Processing System (NCPS) developed for the National Aeronautics and Space Administration (NASA) Ground Network (GN) stations is a spacecraft command system utilizing a MULTIBUS I/68030 microprocessor. This system was developed and implemented at ground stations worldwide to provide a Project Operations Control Center (POCC) with command capability for support of spacecraft operations such as the LANDSAT, Shuttle, Tracking and Data Relay Satellite, and Nimbus-7. The NCPS consolidates multiple modulation schemes for supporting various manned/unmanned orbital platforms. The NCPS interacts with the POCC and a local operator to process configuration requests, generate modulated uplink sequences, and inform users of the ground command link status. This paper presents the system functional description, hardware description, and the software design.

  15. Intersubjectivity and the creation of meaning in the analytic process.

    PubMed

    Maier, Christian

    2014-11-01

    By means of a clinical illustration, the author describes how the intersubjective exchanges involved in an analytic process facilitate the representation of affects and memories which have been buried in the unconscious or indeed have never been available to consciousness. As a result of projective identificatory processes in the analytic relationship, in this example the analyst falls into a situation of helplessness which connects with his own traumatic experiences. Then he gets into a formal regression of the ego and responds with a so-to-speak hallucinatory reaction-an internal image which enables him to keep the analytic process on track and, later on, to construct an early traumatic experience of the analysand. PMID:25331503

  16. An analytical study of various telecomminication networks using markov models

    NASA Astrophysics Data System (ADS)

    Ramakrishnan, M.; Jayamani, E.; Ezhumalai, P.

    2015-04-01

    The main aim of this paper is to examine issues relating to the performance of various Telecommunication networks, and applied queuing theory for better design and improved efficiency. Firstly, giving an analytical study of queues deals with quantifying the phenomenon of waiting lines using representative measures of performances, such as average queue length (on average number of customers in the queue), average waiting time in queue (on average time to wait) and average facility utilization (proportion of time the service facility is in use). In the second, using Matlab simulator, summarizes the finding of the investigations, from which and where we obtain results and describing methodology for a) compare the waiting time and average number of messages in the queue in M/M/1 and M/M/2 queues b) Compare the performance of M/M/1 and M/D/1 queues and study the effect of increasing the number of servers on the blocking probability M/M/k/k queue model.

  17. Coupling entropy of co-processing model on social networks

    NASA Astrophysics Data System (ADS)

    Zhang, Zhanli

    2015-08-01

    Coupling entropy of co-processing model on social networks is investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. In order to understand the evolution of the co-processing and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the coupling entropy comprehending the structural characteristics and information propagation on social network. Based on the analysis of the co-processing model, we analyze the coupling impact of the structural factor and information propagating factor on the coupling entropy, where the analytical results fit well with the numerical ones on scale-free social networks.

  18. Analytical advantages of multivariate data processing. One, two, three, infinity?

    PubMed

    Olivieri, Alejandro C

    2008-08-01

    Multidimensional data are being abundantly produced by modern analytical instrumentation, calling for new and powerful data-processing techniques. Research in the last two decades has resulted in the development of a multitude of different processing algorithms, each equipped with its own sophisticated artillery. Analysts have slowly discovered that this body of knowledge can be appropriately classified, and that common aspects pervade all these seemingly different ways of analyzing data. As a result, going from univariate data (a single datum per sample, employed in the well-known classical univariate calibration) to multivariate data (data arrays per sample of increasingly complex structure and number of dimensions) is known to provide a gain in sensitivity and selectivity, combined with analytical advantages which cannot be overestimated. The first-order advantage, achieved using vector sample data, allows analysts to flag new samples which cannot be adequately modeled with the current calibration set. The second-order advantage, achieved with second- (or higher-) order sample data, allows one not only to mark new samples containing components which do not occur in the calibration phase but also to model their contribution to the overall signal, and most importantly, to accurately quantitate the calibrated analyte(s). No additional analytical advantages appear to be known for third-order data processing. Future research may permit, among other interesting issues, to assess if this "1, 2, 3, infinity" situation of multivariate calibration is really true. PMID:18613646

  19. On the propagation of diel signals in river networks using analytic solutions of flow equations

    NASA Astrophysics Data System (ADS)

    Fonley, Morgan; Mantilla, Ricardo; Small, Scott J.; Curtu, Rodica

    2016-07-01

    Several authors have reported diel oscillations in streamflow records and have hypothesized that these oscillations are linked to evapotranspiration cycles in the watershed. The timing of oscillations in rivers, however, lags behind those of temperature and evapotranspiration in hillslopes. Two hypotheses have been put forth to explain the magnitude and timing of diel streamflow oscillations during low-flow conditions. The first suggests that delays between the peaks and troughs of streamflow and daily evapotranspiration are due to processes occurring in the soil as water moves toward the channels in the river network. The second posits that they are due to the propagation of the signal through the channels as water makes its way to the outlet of the basin. In this paper, we design and implement a theoretical model to test these hypotheses. We impose a baseflow signal entering the river network and use a linear transport equation to represent flow along the network. We develop analytic streamflow solutions for the case of uniform velocities in space over all river links. We then use our analytic solution to simulate streamflows along a self-similar river network for different flow velocities. Our results show that the amplitude and time delay of the streamflow solution are heavily influenced by transport in the river network. Moreover, our equations show that the geomorphology and topology of the river network play important roles in determining how amplitude and signal delay are reflected in streamflow signals. Finally, we have tested our theoretical formulation in the Dry Creek Experimental Watershed, where oscillations are clearly observed in streamflow records. We find that our solution produces streamflow values and fluctuations that are similar to those observed in the summer of 2011.

  20. Quantitative high throughput analytics to support polysaccharide production process development.

    PubMed

    Noyes, Aaron; Godavarti, Ranga; Titchener-Hooker, Nigel; Coffman, Jonathan; Mukhopadhyay, Tarit

    2014-05-19

    The rapid development of purification processes for polysaccharide vaccines is constrained by a lack of analytical tools current technologies for the measurement of polysaccharide recovery and process-related impurity clearance are complex, time-consuming, and generally not amenable to high throughput process development (HTPD). HTPD is envisioned to be central to the improvement of existing polysaccharide manufacturing processes through the identification of critical process parameters that potentially impact the quality attributes of the vaccine and to the development of de novo processes for clinical candidates, across the spectrum of downstream processing. The availability of a fast and automated analytics platform will expand the scope, robustness, and evolution of Design of Experiment (DOE) studies. This paper details recent advances in improving the speed, throughput, and success of in-process analytics at the micro-scale. Two methods, based on modifications of existing procedures, are described for the rapid measurement of polysaccharide titre in microplates without the need for heating steps. A simplification of a commercial endotoxin assay is also described that features a single measurement at room temperature. These assays, along with existing assays for protein and nucleic acids are qualified for deployment in the high throughput screening of polysaccharide feedstreams. Assay accuracy, precision, robustness, interference, and ease of use are assessed and described. In combination, these assays are capable of measuring the product concentration and impurity profile of a microplate of 96 samples in less than one day. This body of work relies on the evaluation of a combination of commercially available and clinically relevant polysaccharides to ensure maximum versatility and reactivity of the final assay suite. Together, these advancements reduce overall process time by up to 30-fold and significantly reduce sample volume over current practices. The

  1. Functional Analytic Psychotherapy for Interpersonal Process Groups: A Behavioral Application

    ERIC Educational Resources Information Center

    Hoekstra, Renee

    2008-01-01

    This paper is an adaptation of Kohlenberg and Tsai's work, Functional Analytical Psychotherapy (1991), or FAP, to group psychotherapy. This author applied a behavioral rationale for interpersonal process groups by illustrating key points with a hypothetical client. Suggestions are also provided for starting groups, identifying goals, educating…

  2. Optimizing an Immersion ESL Curriculum Using Analytic Hierarchy Process

    ERIC Educational Resources Information Center

    Tang, Hui-Wen Vivian

    2011-01-01

    The main purpose of this study is to fill a substantial knowledge gap regarding reaching a uniform group decision in English curriculum design and planning. A comprehensive content-based course criterion model extracted from existing literature and expert opinions was developed. Analytical hierarchy process (AHP) was used to identify the relative…

  3. Interpersonal Processes in Psychoanalytic, Cognitive Analytical and Cognitive Behavioural Therapy.

    ERIC Educational Resources Information Center

    Habicht, Manuela H.

    The aim of the review was to compare interpersonal processes in psychoanalytic therapy, cognitive analytical therapy, and cognitive-behavioral therapy. Since the emphasis is on psychodynamic therapy, Freud's conceptualization of the phenomenon of transference is discussed. Countertransference as an unconscious and defensive reaction to the…

  4. Using the Analytic Hierarchy Process to Analyze Multiattribute Decisions.

    ERIC Educational Resources Information Center

    Spires, Eric E.

    1991-01-01

    The use of the Analytic Hierarchy Process (AHP) in assisting researchers to analyze decisions is discussed. The AHP is compared with other decision-analysis techniques, including multiattribute utility measurement, conjoint analysis, and general linear models. Insights that AHP can provide are illustrated with data gathered in an auditing context.…

  5. Neural Networks for Signal Processing and Control

    NASA Astrophysics Data System (ADS)

    Hesselroth, Ted Daniel

    cortex by the application of lateral interactions during the learning phase. The organization of the mature network is compared to that found in the macaque monkey by several analytical tests. The capacity of the network to process images is investigated. By a method of reconstructing the input images in terms of V1 activities, the simulations show that images can be faithfully represented in V1 by the proposed network. The signal-to-noise ratio of the image is improved by the representation, and compression ratios of well over two-hundred are possible. Lateral interactions between V1 neurons sharpen their orientational tuning. We further study the dynamics of the processing, showing that the rate of decrease of the error of the reconstruction is maximized for the receptive fields used. Lastly, we employ a Fokker-Planck equation for a more detailed prediction of the error value vs. time. The Fokker-Planck equation for an underdamped system with a driving force is derived, yielding an energy-dependent diffusion coefficient which is the integral of the spectral densities of the force and the velocity of the system. The theory is applied to correlated noise activation and resonant activation. Simulation results for the error of the network vs time are compared to the solution of the Fokker-Planck equation.

  6. An analytical approach to photonic reservoir computing - a network of SOA's - for noisy speech recognition

    NASA Astrophysics Data System (ADS)

    Salehi, Mohammad Reza; Abiri, Ebrahim; Dehyadegari, Louiza

    2013-10-01

    This paper seeks to investigate an approach of photonic reservoir computing for optical speech recognition on an examination isolated digit recognition task. An analytical approach in photonic reservoir computing is further drawn on to decrease time consumption, compared to numerical methods; which is very important in processing large signals such as speech recognition. It is also observed that adjusting reservoir parameters along with a good nonlinear mapping of the input signal into the reservoir, analytical approach, would boost recognition accuracy performance. Perfect recognition accuracy (i.e. 100%) can be achieved for noiseless speech signals. For noisy signals with 0-10 db of signal to noise ratios, however, the accuracy ranges observed varied between 92% and 98%. In fact, photonic reservoir application demonstrated 9-18% improvement compared to classical reservoir networks with hyperbolic tangent nodes.

  7. Automated refinement and inference of analytical models for metabolic networks

    PubMed Central

    Schmidt, Michael D; Vallabhajosyula, Ravishankar R; Jenkins, Jerry W; Hood, Jonathan E; Soni, Abhishek S; Wikswo, John P; Lipson, Hod

    2013-01-01

    The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model – suggesting nonlinear terms and structural modifications – or even constructing a new model that agrees with the system’s time-series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real-time. PMID:21832805

  8. Automated refinement and inference of analytical models for metabolic networks

    NASA Astrophysics Data System (ADS)

    Schmidt, Michael D.; Vallabhajosyula, Ravishankar R.; Jenkins, Jerry W.; Hood, Jonathan E.; Soni, Abhishek S.; Wikswo, John P.; Lipson, Hod

    2011-10-01

    The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model--suggesting nonlinear terms and structural modifications--or even constructing a new model that agrees with the system's time series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real time.

  9. Cooperative spreading processes in multiplex networks

    NASA Astrophysics Data System (ADS)

    Wei, Xiang; Chen, Shihua; Wu, Xiaoqun; Ning, Di; Lu, Jun-an

    2016-06-01

    This study is concerned with the dynamic behaviors of epidemic spreading in multiplex networks. A model composed of two interacting complex networks is proposed to describe cooperative spreading processes, wherein the virus spreading in one layer can penetrate into the other to promote the spreading process. The global epidemic threshold of the model is smaller than the epidemic thresholds of the corresponding isolated networks. Thus, global epidemic onset arises in the interacting networks even though an epidemic onset does not arise in each isolated network. Simulations verify the analysis results and indicate that cooperative spreading processes in multiplex networks enhance the final infection fraction.

  10. Heuristic and analytic processing in online sports betting.

    PubMed

    d'Astous, Alain; Di Gaspero, Marc

    2015-06-01

    This article presents the results of two studies that examine the occurrence of heuristic (i.e., intuitive and fast) and analytic (i.e., deliberate and slow) processes among people who engage in online sports betting on a regular basis. The first study was qualitative and was conducted with a convenience sample of 12 regular online sports gamblers who described the processes by which they arrive at a sports betting decision. The results of this study showed that betting online on sports events involves a mix of heuristic and analytic processes. The second study consisted in a survey of 161 online sports gamblers where performance in terms of monetary gains, experience in online sports betting, propensity to collect and analyze relevant information prior to betting, and use of bookmaker odds were measured. This study showed that heuristic and analytic processes act as mediators of the relationship between experience and performance. The findings stemming of these two studies give some insights into gamblers' modes of thinking and behaviors in an online sports betting context and show the value of the dual mediation process model for research that looks at gambling activities from a judgment and decision making perspective. PMID:24390714

  11. Inferring network topology via the propagation process

    NASA Astrophysics Data System (ADS)

    Zeng, An

    2013-11-01

    Inferring the network topology from the dynamics is a fundamental problem, with wide applications in geology, biology, and even counter-terrorism. Based on the propagation process, we present a simple method to uncover the network topology. A numerical simulation on artificial networks shows that our method enjoys a high accuracy in inferring the network topology. We find that the infection rate in the propagation process significantly influences the accuracy, and that each network corresponds to an optimal infection rate. Moreover, the method generally works better in large networks. These finding are confirmed in both real social and nonsocial networks. Finally, the method is extended to directed networks, and a similarity measure specific for directed networks is designed.

  12. "Violent Intent Modeling: Incorporating Cultural Knowledge into the Analytical Process

    SciTech Connect

    Sanfilippo, Antonio P.; Nibbs, Faith G.

    2007-08-24

    While culture has a significant effect on the appropriate interpretation of textual data, the incorporation of cultural considerations into data transformations has not been systematic. Recognizing that the successful prevention of terrorist activities could hinge on the knowledge of the subcultures, Anthropologist and DHS intern Faith Nibbs has been addressing the need to incorporate cultural knowledge into the analytical process. In this Brown Bag she will present how cultural ideology is being used to understand how the rhetoric of group leaders influences the likelihood of their constituents to engage in violent or radicalized behavior, and how violent intent modeling can benefit from understanding that process.

  13. Analytic hierarchy process (AHP) as a tool in asset allocation

    NASA Astrophysics Data System (ADS)

    Zainol Abidin, Siti Nazifah; Mohd Jaffar, Maheran

    2013-04-01

    Allocation capital investment into different assets is the best way to balance the risk and reward. This can prevent from losing big amount of money. Thus, the aim of this paper is to help investors in making wise investment decision in asset allocation. This paper proposes modifying and adapting Analytic Hierarchy Process (AHP) model. The AHP model is widely used in various fields of study that are related in decision making. The results of the case studies show that the proposed model can categorize stocks and determine the portion of capital investment. Hence, it can assist investors in decision making process and reduce the risk of loss in stock market investment.

  14. Analytical and experimental study on complex compressed air pipe network

    NASA Astrophysics Data System (ADS)

    Gai, Yushou; Cai, Maolin; Shi, Yan

    2015-09-01

    To analyze the working characteristics of complex compressed air networks, numerical methods are widely used which are based on finite element technology or intelligent algorithms. However, the effectiveness of the numerical methods is limited. In this paper, to provide a new method to optimize the design and the air supply strategy of the complex compressed air pipe network, firstly, a novel method to analyze the topology structure of the compressed air flow in the pipe network is initially proposed. A matrix is used to describe the topology structure of the compressed air flow. Moreover, based on the analysis of the pressure loss of the pipe network, the relationship between the pressure and the flow of the compressed air is derived, and a prediction method of pressure fluctuation and air flow in a segment in a complex pipe network is proposed. Finally, to inspect the effectiveness of the method, an experiment with a complex network is designed. The pressure and the flow of airflow in the network are measured and studied. The results of the study show that, the predicted results with the proposed method have a good consistency with the experimental results, and that verifies the air flow prediction method of the complex pipe network. This research proposes a new method to analyze the compressed air network and a prediction method of pressure fluctuation and air flow in a segment, which can predicate the fluctuation of the pressure according to the flow of compressed air, and predicate the fluctuation of the flow according to the pressure in a segment of a complex pipe network.

  15. Analytic prediction of sidelobe statistics for matched-field processing

    NASA Astrophysics Data System (ADS)

    Tracey, Brian; Lee, Nigel; Zurk, Lisa

    2002-05-01

    Underwater source localization using matched-field processing (MFP) is complicated by the relatively high sidelobe levels characteristic of MFP ambiguity surfaces. An understanding of sidelobe statistics is expected to aid in designing robust detection and localization algorithms. MFP sidelobe levels are influenced by the underwater channel, array design, and mismatch between assumed and actual environmental parameters. In earlier work [J. Acoust. Soc. Am. 108, 2645 (2000)], a statistical approach was used to derive analytic expressions for the probability distribution function of the Bartlett ambiguity surface. The distribution was shown to depend on the orthogonality of the mode shapes as sampled by the array. Extensions to a wider class of array geometries and to broadband processing will be shown. Numerical results demonstrating the accuracy of the analytic results and exploring their range of validity will be presented. Finally, analytic predictions will be compared to data from the Santa Barbara Channel experiment. [Work sponsored by DARPA under Air Force Contract F19628-00-C0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the Department of Defense.

  16. Ku-band signal design study. [space shuttle orbiter data processing network

    NASA Technical Reports Server (NTRS)

    Rubin, I.

    1978-01-01

    Analytical tools, methods and techniques for assessing the design and performance of the space shuttle orbiter data processing system (DPS) are provided. The computer data processing network is evaluated in the key areas of queueing behavior synchronization and network reliability. The structure of the data processing network is described as well as the system operation principles and the network configuration. The characteristics of the computer systems are indicated. System reliability measures are defined and studied. System and network invulnerability measures are computed. Communication path and network failure analysis techniques are included.

  17. H CANYON PROCESSING IN CORRELATION WITH FH ANALYTICAL LABS

    SciTech Connect

    Weinheimer, E.

    2012-08-06

    Management of radioactive chemical waste can be a complicated business. H Canyon and F/H Analytical Labs are two facilities present at the Savannah River Site in Aiken, SC that are at the forefront. In fact H Canyon is the only large-scale radiochemical processing facility in the United States and this processing is only enhanced by the aid given from F/H Analytical Labs. As H Canyon processes incoming materials, F/H Labs provide support through a variety of chemical analyses. Necessary checks of the chemical makeup, processing, and accountability of the samples taken from H Canyon process tanks are performed at the labs along with further checks on waste leaving the canyon after processing. Used nuclear material taken in by the canyon is actually not waste. Only a small portion of the radioactive material itself is actually consumed in nuclear reactors. As a result various radioactive elements such as Uranium, Plutonium and Neptunium are commonly found in waste and may be useful to recover. Specific processing is needed to allow for separation of these products from the waste. This is H Canyon's specialty. Furthermore, H Canyon has the capacity to initiate the process for weapons-grade nuclear material to be converted into nuclear fuel. This is one of the main campaigns being set up for the fall of 2012. Once usable material is separated and purified of impurities such as fission products, it can be converted to an oxide and ultimately turned into commercial fuel. The processing of weapons-grade material for commercial fuel is important in the necessary disposition of plutonium. Another processing campaign to start in the fall in H Canyon involves the reprocessing of used nuclear fuel for disposal in improved containment units. The importance of this campaign involves the proper disposal of nuclear waste in order to ensure the safety and well-being of future generations and the environment. As processing proceeds in the fall, H Canyon will have a substantial

  18. Analyte species and concentration identification using differentially functionalized microcantilever arrays and artificial neural networks

    SciTech Connect

    Senesac, Larry R; Datskos, Panos G; Sepaniak, Michael J

    2006-01-01

    In the present work, we have performed analyte species and concentration identification using an array of ten differentially functionalized microcantilevers coupled with a back-propagation artificial neural network pattern recognition algorithm. The array consists of ten nanostructured silicon microcantilevers functionalized by polymeric and gas chromatography phases and macrocyclic receptors as spatially dense, differentially responding sensing layers for identification and quantitation of individual analyte(s) and their binary mixtures. The array response (i.e. cantilever bending) to analyte vapor was measured by an optical readout scheme and the responses were recorded for a selection of individual analytes as well as several binary mixtures. An artificial neural network (ANN) was designed and trained to recognize not only the individual analytes and binary mixtures, but also to determine the concentration of individual components in a mixture. To the best of our knowledge, ANNs have not been applied to microcantilever array responses previously to determine concentrations of individual analytes. The trained ANN correctly identified the eleven test analyte(s) as individual components, most with probabilities greater than 97%, whereas it did not misidentify an unknown (untrained) analyte. Demonstrated unique aspects of this work include an ability to measure binary mixtures and provide both qualitative (identification) and quantitative (concentration) information with array-ANN-based sensor methodologies.

  19. The developmental approach to 'working through' in the analytic process.

    PubMed

    Shane, M

    1979-01-01

    The developmental orientation and approach has been utilized in this paper as a paradigm to understand some of the phenomena of working through in the analytic process. A case is presented of a patient who was arrested along several developmental lines and had suffered from a wool fetish. Many changes in the working through process could be attributed not only to meliorative effects of interpretation but to developmental progression as well. Furthermore, this developmental progression occurred within the analysis not only in relation to the analyst's interpretations but to the developmental impact on the patient of experience with the analyst and with significant others. The patient attained increasing capacities to utilize insight in actions that themselves led to new experience of developmental import, and in a spiral process, further structural developmental change was achieved which consolidated its dominance through further capacity for new insights. PMID:533738

  20. Laser induced breakdown spectroscopy inside liquids: Processes and analytical aspects

    NASA Astrophysics Data System (ADS)

    Lazic, V.; Jovićević, S.

    2014-11-01

    This paper provides an overview of the laser induced breakdown spectroscopy (LIBS) inside liquids, applied for detection of the elements present in the media itself or in the submerged samples. The processes inherent to the laser induced plasma formation and evolution inside liquids are discussed, including shockwave generation, vapor cavitation, and ablation of solids. Types of the laser excitation considered here are single pulse, dual pulse and multi-pulse. The literature relative to the LIBS measurements and applications inside liquids is reviewed and the most relevant results are summarized. Finally, we discuss the analytical aspects and release some suggestions for improving the LIBS sensitivity and accuracy in liquid environment.

  1. SensePath: Understanding the Sensemaking Process Through Analytic Provenance.

    PubMed

    Nguyen, Phong H; Xu, Kai; Wheat, Ashley; Wong, B L William; Attfield, Simon; Fields, Bob

    2016-01-01

    Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures user's sensemaking actions, i.e., analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow and non-intrusive for participants. The tool was used by an experienced HCI researcher to analyze two sensemaking sessions. The researcher found the tool intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process. PMID:26357398

  2. Meta-Analytically Informed Network Analysis of Resting State fMRI Reveals Hyperconnectivity in an Introspective Socio-Affective Network in Depression

    PubMed Central

    Schilbach, Leonhard; Müller, Veronika I.; Hoffstaedter, Felix; Clos, Mareike; Goya-Maldonado, Roberto

    2014-01-01

    Alterations of social cognition and dysfunctional interpersonal expectations are thought to play an important role in the etiology of depression and have, thus, become a key target of psychotherapeutic interventions. The underlying neurobiology, however, remains elusive. Based upon the idea of a close link between affective and introspective processes relevant for social interactions and alterations thereof in states of depression, we used a meta-analytically informed network analysis to investigate resting-state functional connectivity in an introspective socio-affective (ISA) network in individuals with and without depression. Results of our analysis demonstrate significant differences between the groups with depressed individuals showing hyperconnectivity of the ISA network. These findings demonstrate that neurofunctional alterations exist in individuals with depression in a neural network relevant for introspection and socio-affective processing, which may contribute to the interpersonal difficulties that are linked to depressive symptomatology. PMID:24759619

  3. How multiple social networks affect user awareness: The information diffusion process in multiplex networks

    NASA Astrophysics Data System (ADS)

    Li, Weihua; Tang, Shaoting; Fang, Wenyi; Guo, Quantong; Zhang, Xiao; Zheng, Zhiming

    2015-10-01

    The information diffusion process in single complex networks has been extensively studied, especially for modeling the spreading activities in online social networks. However, individuals usually use multiple social networks at the same time, and can share the information they have learned from one social network to another. This phenomenon gives rise to a new diffusion process on multiplex networks with more than one network layer. In this paper we account for this multiplex network spreading by proposing a model of information diffusion in two-layer multiplex networks. We develop a theoretical framework using bond percolation and cascading failure to describe the intralayer and interlayer diffusion. This allows us to obtain analytical solutions for the fraction of informed individuals as a function of transmissibility T and the interlayer transmission rate θ . Simulation results show that interaction between layers can greatly enhance the information diffusion process. And explosive diffusion can occur even if the transmissibility of the focal layer is under the critical threshold, due to interlayer transmission.

  4. Analytical solution of average path length for Apollonian networks

    NASA Astrophysics Data System (ADS)

    Zhang, Zhongzhi; Chen, Lichao; Zhou, Shuigeng; Fang, Lujun; Guan, Jihong; Zou, Tao

    2008-01-01

    With the help of recursion relations derived from the self-similar structure, we obtain the solution of average path length, dmacr t , for Apollonian networks. In contrast to the well-known numerical result dmacr t∝(lnNt)3/4 [J. S. Andrade, Jr. , Phys. Rev. Lett. 94, 018702 (2005)], our rigorous solution shows that the average path length grows logarithmically as dmacr t∝lnNt in the infinite limit of network size Nt . The extensive numerical calculations completely agree with our closed-form solution.

  5. In-Database Raster Analytics: Map Algebra and Parallel Processing in Oracle Spatial Georaster

    NASA Astrophysics Data System (ADS)

    Xie, Q. J.; Zhang, Z. Z.; Ravada, S.

    2012-07-01

    Over the past decade several products have been using enterprise database technology to store and manage geospatial imagery and raster data inside RDBMS, which in turn provides the best manageability and security. With the data volume growing exponentially, real-time or near real-time processing and analysis of such big data becomes more challenging. Oracle Spatial GeoRaster, different from most other products, takes the enterprise database-centric approach for both data management and data processing. This paper describes one of the central components of this database-centric approach: the processing engine built completely inside the database. Part of this processing engine is raster algebra, which we call the In-database Raster Analytics. This paper discusses the three key characteristics of this in-database analytics engine and the benefits. First, it moves the data processing closer to the data instead of moving the data to the processing, which helps achieve greater performance by overcoming the bottleneck of computer networks. Second, we designed and implemented a new raster algebra expression language. This language is based on PL/SQL and is currently focused on the "local" function type of map algebra. This language includes general arithmetic, logical and relational operators and any combination of them, which dramatically improves the analytical capability of the GeoRaster database. The third feature is the implementation of parallel processing of such operations to further improve performance. This paper also presents some sample use cases. The testing results demonstrate that this in-database approach for raster analytics can effectively help solve the biggest performance challenges we are facing today with big raster and image data.

  6. Model and Analytic Processes for Export License Assessments

    SciTech Connect

    Thompson, Sandra E.; Whitney, Paul D.; Weimar, Mark R.; Wood, Thomas W.; Daly, Don S.; Brothers, Alan J.; Sanfilippo, Antonio P.; Cook, Diane; Holder, Larry

    2011-09-29

    This paper represents the Department of Energy Office of Nonproliferation Research and Development (NA-22) Simulations, Algorithms and Modeling (SAM) Program's first effort to identify and frame analytical methods and tools to aid export control professionals in effectively predicting proliferation intent; a complex, multi-step and multi-agency process. The report focuses on analytical modeling methodologies that alone, or combined, may improve the proliferation export control license approval process. It is a follow-up to an earlier paper describing information sources and environments related to international nuclear technology transfer. This report describes the decision criteria used to evaluate modeling techniques and tools to determine which approaches will be investigated during the final 2 years of the project. The report also details the motivation for why new modeling techniques and tools are needed. The analytical modeling methodologies will enable analysts to evaluate the information environment for relevance to detecting proliferation intent, with specific focus on assessing risks associated with transferring dual-use technologies. Dual-use technologies can be used in both weapons and commercial enterprises. A decision-framework was developed to evaluate which of the different analytical modeling methodologies would be most appropriate conditional on the uniqueness of the approach, data availability, laboratory capabilities, relevance to NA-22 and Office of Arms Control and Nonproliferation (NA-24) research needs and the impact if successful. Modeling methodologies were divided into whether they could help micro-level assessments (e.g., help improve individual license assessments) or macro-level assessment. Macro-level assessment focuses on suppliers, technology, consumers, economies, and proliferation context. Macro-level assessment technologies scored higher in the area of uniqueness because less work has been done at the macro level. An approach to

  7. SIRS Dynamics on Random Networks: Simulations and Analytical Models

    NASA Astrophysics Data System (ADS)

    Rozhnova, Ganna; Nunes, Ana

    The standard pair approximation equations (PA) for the Susceptible-Infective-Recovered-Susceptible (SIRS) model of infection spread on a network of homogeneous degree k predict a thin phase of sustained oscillations for parameter values that correspond to diseases that confer long lasting immunity. Here we present a study of the dependence of this oscillatory phase on the parameter k and of its relevance to understand the behaviour of simulations on networks. For k = 4, we compare the phase diagram of the PA model with the results of simulations on regular random graphs (RRG) of the same degree. We show that for parameter values in the oscillatory phase, and even for large system sizes, the simulations either die out or exhibit damped oscillations, depending on the initial conditions. This failure of the standard PA model to capture the qualitative behaviour of the simulations on large RRGs is currently being investigated.

  8. Analytical controllability of deterministic scale-free networks and Cayley trees

    NASA Astrophysics Data System (ADS)

    Xu, Ming; Xu, Chuan-Yun; Wang, Huan; Deng, Cong-Zheng; Cao, Ke-Fei

    2015-07-01

    According to the exact controllability theory, the controllability is investigated analytically for two typical types of self-similar bipartite networks, i.e., the classic deterministic scale-free networks and Cayley trees. Due to their self-similarity, the analytical results of the exact controllability are obtained, and the minimum sets of driver nodes (drivers) are also identified by elementary transformations on adjacency matrices. For these two types of undirected networks, no matter their links are unweighted or (nonzero) weighted, the controllability of networks and the configuration of drivers remain the same, showing a robustness to the link weights. These results have implications for the control of real networked systems with self-similarity.

  9. CT Image Processing Using Public Digital Networks

    PubMed Central

    Rhodes, Michael L.; Azzawi, Yu-Ming; Quinn, John F.; Glenn, William V.; Rothman, Stephen L.G.

    1984-01-01

    Nationwide commercial computer communication is now commonplace for those applications where digital dialogues are generally short and widely distributed, and where bandwidth does not exceed that of dial-up telephone lines. Image processing using such networks is prohibitive because of the large volume of data inherent to digital pictures. With a blend of increasing bandwidth and distributed processing, network image processing becomes possible. This paper examines characteristics of a digital image processing service for a nationwide network of CT scanner installations. Issues of image transmission, data compression, distributed processing, software maintenance, and interfacility communication are also discussed. Included are results that show the volume and type of processing experienced by a network of over 50 CT scanners for the last 32 months.

  10. TRUEX processing of plutonium analytical solutions at Argonne National Laboratory

    SciTech Connect

    Chamberlain, D.B.; Conner, C.; Hutter, J.C.; Leonard, R.A.; Wygmans, D.G.; Vandegrift, G.F.

    1995-12-31

    The TRUEX (TRansUranic EXtraction) solvent extraction process was developed at Argonne National Laboratory (ANL) for the Department of Energy. A TRUEX demonstration completed at ANL involved the processing of analytical and experimental waste generated there and at the New Brunswick Laboratory. A 20-stage centrifugal contactor was used to recover plutonium, americium, and uranium from the waste. Approximately 84 g of plutonium, 18 g of uranium, and 0.2 g of americium were recovered from about 118 liters of solution during four process runs. Alpha decontamination factors as high as 65,000 were attained, which was especially important because it allowed the disposal of the process raffinate as a low-level waste. The recovered plutonium and uranium were converted to oxide; the recovered americium solution was concentrated by evaporation to approximately 100 ml. The flowsheet and operational procedures were modified to overcome process difficulties. These difficulties included the presence of complexants in the feed, solvent degradation, plutonium precipitation, and inadequate decontamination factors during startup. This paper will discuss details of the experimental effort.

  11. Statistical signal processing in sensor networks

    NASA Astrophysics Data System (ADS)

    Guerriero, Marco

    In this dissertation we focus on decentralized signal processing in Sensor Networks (SN). Four topics are studied: (i) Direction of Arrival (DOA) estimation using a Wireless Sensor network (WSN), (ii) multiple target tracking in large SN, (iii) decentralized target detection in SN and (iv) decentralized sequential detection in SN with communication constraints. The first topic of this thesis addresses the problem of estimating the DOA of an acoustic wavefront using a a WSN made of isotropic (hence individually useless) sensors. The WSN was designed according to the SENMA (SEnsor Network with Mobile Agents) architecture with a mobile agent (MA) that successively queries the sensors lying inside its field of view. We propose both fast/simple and optimal DOA-estimation schemes, and an optimization of the MAs observation management is also carried out, with the surprising finding that the MA ought to orient itself at an oblique angle to the expected DOA, rather than directly toward it. We also consider the extension to multiple sources; intriguingly, per-source DOA accuracy is higher when there is more than one source. In all cases, performance is investigated by simulation and compared, when appropriate, with asymptotic bounds; these latter are usually met after a moderate number of MA dwells. In the second topic, we study the problem of tracking multiple targets in large SN. While these networks hold significant potential for surveillance, it is of interest to address fundamental limitations in large-scale implementations. We first introduce a simple analytical tracker performance model. Analysis of this model suggests that scan-based tracking performance improves with increasing numbers of sensors, but only to a certain point beyond which degradation is observed. Correspondingly, we address model-based optimization of the local sensor detection threshold and the number of sensors. Next, we propose a two-stage tracking approach (fuse-before-track) as a possible

  12. Evaluation of Analytical Modeling Functions for the Phonation Onset Process.

    PubMed

    Petermann, Simon; Kniesburges, Stefan; Ziethe, Anke; Schützenberger, Anne; Döllinger, Michael

    2016-01-01

    The human voice originates from oscillations of the vocal folds in the larynx. The duration of the voice onset (VO), called the voice onset time (VOT), is currently under investigation as a clinical indicator for correct laryngeal functionality. Different analytical approaches for computing the VOT based on endoscopic imaging were compared to determine the most reliable method to quantify automatically the transient vocal fold oscillations during VO. Transnasal endoscopic imaging in combination with a high-speed camera (8000 fps) was applied to visualize the phonation onset process. Two different definitions of VO interval were investigated. Six analytical functions were tested that approximate the envelope of the filtered or unfiltered glottal area waveform (GAW) during phonation onset. A total of 126 recordings from nine healthy males and 210 recordings from 15 healthy females were evaluated. Three criteria were analyzed to determine the most appropriate computation approach: (1) reliability of the fit function for a correct approximation of VO; (2) consistency represented by the standard deviation of VOT; and (3) accuracy of the approximation of VO. The results suggest the computation of VOT by a fourth-order polynomial approximation in the interval between 32.2 and 67.8% of the saturation amplitude of the filtered GAW. PMID:27066108

  13. Analytical model of reactive transport processes with spatially variable coefficients.

    PubMed

    Simpson, Matthew J; Morrow, Liam C

    2015-05-01

    Analytical solutions of partial differential equation (PDE) models describing reactive transport phenomena in saturated porous media are often used as screening tools to provide insight into contaminant fate and transport processes. While many practical modelling scenarios involve spatially variable coefficients, such as spatially variable flow velocity, v(x), or spatially variable decay rate, k(x), most analytical models deal with constant coefficients. Here we present a framework for constructing exact solutions of PDE models of reactive transport. Our approach is relevant for advection-dominant problems, and is based on a regular perturbation technique. We present a description of the solution technique for a range of one-dimensional scenarios involving constant and variable coefficients, and we show that the solutions compare well with numerical approximations. Our general approach applies to a range of initial conditions and various forms of v(x) and k(x). Instead of simply documenting specific solutions for particular cases, we present a symbolic worksheet, as supplementary material, which enables the solution to be evaluated for different choices of the initial condition, v(x) and k(x). We also discuss how the technique generalizes to apply to models of coupled multispecies reactive transport as well as higher dimensional problems. PMID:26064648

  14. Evaluation of Analytical Modeling Functions for the Phonation Onset Process

    PubMed Central

    Petermann, Simon; Kniesburges, Stefan; Ziethe, Anke; Schützenberger, Anne; Döllinger, Michael

    2016-01-01

    The human voice originates from oscillations of the vocal folds in the larynx. The duration of the voice onset (VO), called the voice onset time (VOT), is currently under investigation as a clinical indicator for correct laryngeal functionality. Different analytical approaches for computing the VOT based on endoscopic imaging were compared to determine the most reliable method to quantify automatically the transient vocal fold oscillations during VO. Transnasal endoscopic imaging in combination with a high-speed camera (8000 fps) was applied to visualize the phonation onset process. Two different definitions of VO interval were investigated. Six analytical functions were tested that approximate the envelope of the filtered or unfiltered glottal area waveform (GAW) during phonation onset. A total of 126 recordings from nine healthy males and 210 recordings from 15 healthy females were evaluated. Three criteria were analyzed to determine the most appropriate computation approach: (1) reliability of the fit function for a correct approximation of VO; (2) consistency represented by the standard deviation of VOT; and (3) accuracy of the approximation of VO. The results suggest the computation of VOT by a fourth-order polynomial approximation in the interval between 32.2 and 67.8% of the saturation amplitude of the filtered GAW. PMID:27066108

  15. Analytical model of reactive transport processes with spatially variable coefficients

    PubMed Central

    Simpson, Matthew J.; Morrow, Liam C.

    2015-01-01

    Analytical solutions of partial differential equation (PDE) models describing reactive transport phenomena in saturated porous media are often used as screening tools to provide insight into contaminant fate and transport processes. While many practical modelling scenarios involve spatially variable coefficients, such as spatially variable flow velocity, v(x), or spatially variable decay rate, k(x), most analytical models deal with constant coefficients. Here we present a framework for constructing exact solutions of PDE models of reactive transport. Our approach is relevant for advection-dominant problems, and is based on a regular perturbation technique. We present a description of the solution technique for a range of one-dimensional scenarios involving constant and variable coefficients, and we show that the solutions compare well with numerical approximations. Our general approach applies to a range of initial conditions and various forms of v(x) and k(x). Instead of simply documenting specific solutions for particular cases, we present a symbolic worksheet, as supplementary material, which enables the solution to be evaluated for different choices of the initial condition, v(x) and k(x). We also discuss how the technique generalizes to apply to models of coupled multispecies reactive transport as well as higher dimensional problems. PMID:26064648

  16. Electrogenerated thin films of microporous polymer networks with remarkably increased electrochemical response to nitroaromatic analytes.

    PubMed

    Palma-Cando, Alex; Scherf, Ullrich

    2015-06-01

    Thin films of microporous polymer networks (MPNs) have been generated by electrochemical polymerization of a series of multifunctional carbazole-based monomers. The microporous films show high Brunauer-Emmett-Teller (BET) surface areas up to 1300 m2 g(-1) as directly measured by krypton sorption experiments. A correlation between the number of polymerizable carbazole units of the monomer and the resulting surface area is observed. Electrochemical sensing experiments with 1,3,5-trinitrobenzene as prototypical nitroaromatic analyte demonstrate an up to 180 times increased current response of MPN-modified glassy carbon electrodes in relation to the nonmodified electrode. The phenomenon probably involves intermolecular interactions between the electron-poor nitroaromatic analytes and the electron-rich, high surface area microporous deposits, with the electrochemical reduction at the MPN-modified electrodes being an adsorption-controlled process for low scan rates. We expect a high application potential of such MPN-modified electrodes for boosting the sensitivity of electrochemical sensor devices. PMID:25946727

  17. Evaluating supplier quality performance using fuzzy analytical hierarchy process

    NASA Astrophysics Data System (ADS)

    Ahmad, Nazihah; Kasim, Maznah Mat; Rajoo, Shanmugam Sundram Kalimuthu

    2014-12-01

    Evaluating supplier quality performance is vital in ensuring continuous supply chain improvement, reducing the operational costs and risks towards meeting customer's expectation. This paper aims to illustrate an application of Fuzzy Analytical Hierarchy Process to prioritize the evaluation criteria in a context of automotive manufacturing in Malaysia. Five main criteria were identified which were quality, cost, delivery, customer serviceand technology support. These criteria had been arranged into hierarchical structure and evaluated by an expert. The relative importance of each criteria was determined by using linguistic variables which were represented as triangular fuzzy numbers. The Center of Gravity defuzzification method was used to convert the fuzzy evaluations into their corresponding crisps values. Such fuzzy evaluation can be used as a systematic tool to overcome the uncertainty evaluation of suppliers' performance which usually associated with human being subjective judgments.

  18. A Task Analytic Process to Define Future Concepts in Aviation

    NASA Technical Reports Server (NTRS)

    Gore, Brian Francis; Wolter, Cynthia A.

    2014-01-01

    A necessary step when developing next generation systems is to understand the tasks that operators will perform. One NextGen concept under evaluation termed Single Pilot Operations (SPO) is designed to improve the efficiency of airline operations. One SPO concept includes a Pilot on Board (PoB), a Ground Station Operator (GSO), and automation. A number of procedural changes are likely to result when such changes in roles and responsibilities are undertaken. Automation is expected to relieve the PoB and GSO of some tasks (e.g. radio frequency changes, loading expected arrival information). A major difference in the SPO environment is the shift to communication-cued crosschecks (verbal / automated) rather than movement-cued crosschecks that occur in a shared cockpit. The current article highlights a task analytic process of the roles and responsibilities between a PoB, an approach-phase GSO, and automation.

  19. Capital budgeting in hospital management using the analytic hierarchy process.

    PubMed

    Tarimcilar, M M; Khaksari, S Z

    1991-01-01

    In recent years, the health care industry has been experiencing change to a degree unprecedented since the inception of the Medicare program. With traditional in-hospital care on the decline, hospitals are being forced to compete for business. They must identify within their own systems feasible alternatives for dealing with these changes and then determine which ones will best accomplish the goals of the organization. This paper offers a procedure that utilizes the analytic hierarchy process--a multicriteria decision-making tool that helps arrange the possible alternatives in hierarchical order given the priorities of relevant decision makers. An application of the method to a mid-sized hospital is presented. Although the procedure is structured, it is flexible enough to be updated for the realities of any health care institution. PMID:10111675

  20. Special concrete shield selection using the analytic hierarchy process

    SciTech Connect

    Abulfaraj, W.H. . Nuclear Engineering Dept.)

    1994-08-01

    Special types of concrete radiation shields that depend on locally available materials and have improved properties for both neutron and gamma-ray attenuation were developed by using plastic materials and heavy ores. The analytic hierarchy process (AHP) is implemented to evaluate these types for selecting the best biological radiation shield for nuclear reactors. Factors affecting the selection decision are degree of protection against neutrons, degree of protection against gamma rays, suitability of the concrete as building material, and economic considerations. The seven concrete alternatives are barite-polyethylene concrete, barite-polyvinyl chloride (PVC) concrete, barite-portland cement concrete, pyrite-polyethylene concrete, pyrite-PVC concrete, pyrite-portland cement concrete, and ordinary concrete. The AHP analysis shows the superiority of pyrite-polyethylene concrete over the others.

  1. Analytical chemistry of the citrate process for flue gas desulfurization

    SciTech Connect

    Marchant, W.N.; May, S.L.; Simpson, W.W.; Winter, J.K.; Beard, H.R.

    1980-01-01

    The citrate process for flue gas desulfurization (FGD) is a product of continuing research by the US Bureau of Mines to meet the goal of minimizing the objectionable effects of minerals industry operations upon the environment. The reduction of SO/sub 2/ in solution by H/sub 2/S to produce elemental sulfur by the citrate process is extremely complex and results in solutions that contain at least nine different sulfur species. Process solution analysis is essential to a clear understanding of process chemistry and its safe, efficient operation. The various chemical species, the approximate ranges of their concentrations in citrate process solutions, and the analytical methods evolved to determine them are hydrogen sulfide (approx. 0M to 0.06M) by specific ion electrode, polysulfides (unknown) by ultraviolet (uv) spectrophotometry, elemental sulfur (approx. 0M to approx. 0.001M dissolved, approx. 0M to approx. 0.1M suspended) by uv spectrophotometry, thiosulfate (approx. 0M to approx. 0.25M) by iodometry or high performance liquid chromatography (HPLC), polythionates (approx. 0M to approx. 0.01M) by thin layer chromatography (TLC), dithionite (searched for but not detected in process solutions) by polarography or TLC, bisulfite (approx. 0M to 0.2M) by iodometry, sulfate (approx. 0M to 1M) by a Bureau-developed gravimetric procedure, citric acid (approx. 0M to 0.5M) by titration or visible colorimetry, glycolic acid (approx. 0M to 1M) by HPLC, sodium (approx. 1.5M) by flame photometry, and chloride by argentometric titration.

  2. The European Network of Analytical and Experimental Laboratories for Geosciences

    NASA Astrophysics Data System (ADS)

    Freda, Carmela; Funiciello, Francesca; Meredith, Phil; Sagnotti, Leonardo; Scarlato, Piergiorgio; Troll, Valentin R.; Willingshofer, Ernst

    2013-04-01

    Integrating Earth Sciences infrastructures in Europe is the mission of the European Plate Observing System (EPOS).The integration of European analytical, experimental, and analogue laboratories plays a key role in this context and is the task of the EPOS Working Group 6 (WG6). Despite the presence in Europe of high performance infrastructures dedicated to geosciences, there is still limited collaboration in sharing facilities and best practices. The EPOS WG6 aims to overcome this limitation by pushing towards national and trans-national coordination, efficient use of current laboratory infrastructures, and future aggregation of facilities not yet included. This will be attained through the creation of common access and interoperability policies to foster and simplify personnel mobility. The EPOS ambition is to orchestrate European laboratory infrastructures with diverse, complementary tasks and competences into a single, but geographically distributed, infrastructure for rock physics, palaeomagnetism, analytical and experimental petrology and volcanology, and tectonic modeling. The WG6 is presently organizing its thematic core services within the EPOS distributed research infrastructure with the goal of joining the other EPOS communities (geologists, seismologists, volcanologists, etc...) and stakeholders (engineers, risk managers and other geosciences investigators) to: 1) develop tools and services to enhance visitor programs that will mutually benefit visitors and hosts (transnational access); 2) improve support and training activities to make facilities equally accessible to students, young researchers, and experienced users (training and dissemination); 3) collaborate in sharing technological and scientific know-how (transfer of knowledge); 4) optimize interoperability of distributed instrumentation by standardizing data collection, archive, and quality control standards (data preservation and interoperability); 5) implement a unified e-Infrastructure for data

  3. Optimization of analytical laboratory work using computer networking and databasing

    SciTech Connect

    Upp, D.L.; Metcalf, R.A.

    1996-06-01

    The Health Physics Analysis Laboratory (HPAL) performs around 600,000 analyses for radioactive nuclides each year at Los Alamos National Laboratory (LANL). Analysis matrices vary from nasal swipes, air filters, work area swipes, liquids, to the bottoms of shoes and cat litter. HPAL uses 8 liquid scintillation counters, 8 gas proportional counters, and 9 high purity germanium detectors in 5 laboratories to perform these analyses. HPAL has developed a computer network between the labs and software to produce analysis results. The software and hardware package includes barcode sample tracking, log-in, chain of custody, analysis calculations, analysis result printing, and utility programs. All data are written to a database, mirrored on a central server, and eventually written to CD-ROM to provide for online historical results. This system has greatly reduced the work required to provide for analysis results as well as improving the quality of the work performed.

  4. Are EU networks anticipatory systems? An empirical and analytical approach

    NASA Astrophysics Data System (ADS)

    Leydesdorff, Loet

    2000-05-01

    A social system can be considered as distributed by its very nature. Social communication among humans can be expected to be reflexive. Thus, this system contains uncertainty and the uncertainty is provided with meaning. This dual-layeredness enables the network to organize itself ("autopoietically") into an anticipatory mode. The extent to which anticipatory functions have been developed can be observed, notably in the case of intentional constructions of reflexive layers of organization. In this study, the "Self-Organization of the European Information Society" is analyzed from this angle. Using empirical data, I argue that the increasing unification in representations at the European level allows for another differentiation in terms of the substantive communications that are represented. Insofar as the reflexive layers are differently codified, the anticipatory functions of the system can be strengthened.

  5. Networks for image acquisition, processing and display

    NASA Technical Reports Server (NTRS)

    Ahumada, Albert J., Jr.

    1990-01-01

    The human visual system comprises layers of networks which sample, process, and code images. Understanding these networks is a valuable means of understanding human vision and of designing autonomous vision systems based on network processing. Ames Research Center has an ongoing program to develop computational models of such networks. The models predict human performance in detection of targets and in discrimination of displayed information. In addition, the models are artificial vision systems sharing properties with biological vision that has been tuned by evolution for high performance. Properties include variable density sampling, noise immunity, multi-resolution coding, and fault-tolerance. The research stresses analysis of noise in visual networks, including sampling, photon, and processing unit noises. Specific accomplishments include: models of sampling array growth with variable density and irregularity comparable to that of the retinal cone mosaic; noise models of networks with signal-dependent and independent noise; models of network connection development for preserving spatial registration and interpolation; multi-resolution encoding models based on hexagonal arrays (HOP transform); and mathematical procedures for simplifying analysis of large networks.

  6. Controlled English to facilitate human/machine analytical processing

    NASA Astrophysics Data System (ADS)

    Braines, Dave; Mott, David; Laws, Simon; de Mel, Geeth; Pham, Tien

    2013-06-01

    Controlled English is a human-readable information representation format that is implemented using a restricted subset of the English language, but which is unambiguous and directly accessible by simple machine processes. We have been researching the capabilities of CE in a number of contexts, and exploring the degree to which a flexible and more human-friendly information representation format could aid the intelligence analyst in a multi-agent collaborative operational environment; especially in cases where the agents are a mixture of other human users and machine processes aimed at assisting the human users. CE itself is built upon a formal logic basis, but allows users to easily specify models for a domain of interest in a human-friendly language. In our research we have been developing an experimental component known as the "CE Store" in which CE information can be quickly and flexibly processed and shared between human and machine agents. The CE Store environment contains a number of specialized machine agents for common processing tasks and also supports execution of logical inference rules that can be defined in the same CE language. This paper outlines the basic architecture of this approach, discusses some of the example machine agents that have been developed, and provides some typical examples of the CE language and the way in which it has been used to support complex analytical tasks on synthetic data sources. We highlight the fusion of human and machine processing supported through the use of the CE language and CE Store environment, and show this environment with examples of highly dynamic extensions to the model(s) and integration between different user-defined models in a collaborative setting.

  7. Wavelet networks for face processing

    NASA Astrophysics Data System (ADS)

    Krüger, V.; Sommer, G.

    2002-06-01

    Wavelet networks (WNs) were introduced in 1992 as a combination of artificial neural radial basis function (RBF) networks and wavelet decomposition. Since then, however, WNs have received only a little attention. We believe that the potential of WNs has been generally underestimated. WNs have the advantage that the wavelet coefficients are directly related to the image data through the wavelet transform. In addition, the parameters of the wavelets in the WNs are subject to optimization, which results in a direct relation between the represented function and the optimized wavelets, leading to considerable data reduction (thus making subsequent algorithms much more efficient) as well as to wavelets that can be used as an optimized filter bank. In our study we analyze some WN properties and highlight their advantages for object representation purposes. We then present a series of results of experiments in which we used WNs for face tracking. We exploit the efficiency that is due to data reduction for face recognition and face-pose estimation by applying the optimized-filter-bank principle of the WNs.

  8. A Geovisual Analytic Approach to Understanding Geo-Social Relationships in the International Trade Network

    PubMed Central

    Luo, Wei; Yin, Peifeng; Di, Qian; Hardisty, Frank; MacEachren, Alan M.

    2014-01-01

    The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly ‘balkanized’ (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above. PMID:24558409

  9. A geovisual analytic approach to understanding geo-social relationships in the international trade network.

    PubMed

    Luo, Wei; Yin, Peifeng; Di, Qian; Hardisty, Frank; MacEachren, Alan M

    2014-01-01

    The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly 'balkanized' (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above. PMID:24558409

  10. Explicit solutions to analytical models of cross-layer protocol optimization in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Hortos, William S.

    2009-05-01

    The work is based on the interactions among the nodes of a wireless sensor network (WSN) to cooperatively process data from multiple sensors. Quality-of-service (QoS) metrics are associated with the quality of fused information: throughput, delay, packet error rate, etc. A multivariate point process (MVPP) model of discrete random events in WSNs establishes stochastic characteristics of optimal cross-layer protocols. In previous work by the author, discreteevent, cross-layer interactions in the MANET protocol are modeled in very general analytical terms with a set of concatenated design parameters and associated resource levels by multivariate point processes (MVPPs). Characterization of the "best" cross-layer designs for the MANET is formulated by applying the general theory of martingale representations to controlled MVPPs. Performance is described in terms of concatenated protocol parameters and controlled through conditional rates of the MVPPs. Assumptions on WSN characteristics simplify the dynamic programming conditions to yield mathematically tractable descriptions for the optimal routing protocols. Modeling limitations on the determination of closed-form solutions versus iterative explicit solutions for ad hoc WSN controls are presented.

  11. Using neural networks for process planning

    NASA Astrophysics Data System (ADS)

    Huang, Samuel H.; Zhang, HongChao

    1995-08-01

    Process planning has been recognized as an interface between computer-aided design and computer-aided manufacturing. Since the late 1960s, computer techniques have been used to automate process planning activities. AI-based techniques are designed for capturing, representing, organizing, and utilizing knowledge by computers, and are extremely useful for automated process planning. To date, most of the AI-based approaches used in automated process planning are some variations of knowledge-based expert systems. Due to their knowledge acquisition bottleneck, expert systems are not sufficient in solving process planning problems. Fortunately, AI has developed other techniques that are useful for knowledge acquisition, e.g., neural networks. Neural networks have several advantages over expert systems that are desired in today's manufacturing practice. However, very few neural network applications in process planning have been reported. We present this paper in order to stimulate the research on using neural networks for process planning. This paper also identifies the problems with neural networks and suggests some possible solutions, which will provide some guidelines for research and implementation.

  12. Assessment of Learning in Digital Interactive Social Networks: A Learning Analytics Approach

    ERIC Educational Resources Information Center

    Wilson, Mark; Gochyyev, Perman; Scalise, Kathleen

    2016-01-01

    This paper summarizes initial field-test results from data analytics used in the work of the Assessment and Teaching of 21st Century Skills (ATC21S) project, on the "ICT Literacy--Learning in digital networks" learning progression. This project, sponsored by Cisco, Intel and Microsoft, aims to help educators around the world enable…

  13. Analytical solution for a class of network dynamics with mechanical and financial applications

    NASA Astrophysics Data System (ADS)

    Krejčí, P.; Lamba, H.; Melnik, S.; Rachinskii, D.

    2014-09-01

    We show that for a certain class of dynamics at the nodes the response of a network of any topology to arbitrary inputs is defined in a simple way by its response to a monotone input. The nodes may have either a discrete or continuous set of states and there is no limit on the complexity of the network. The results provide both an efficient numerical method and the potential for accurate analytic approximation of the dynamics on such networks. As illustrative applications, we introduce a quasistatic mechanical model with objects interacting via frictional forces and a financial market model with avalanches and critical behavior that are generated by momentum trading strategies.

  14. Analytic Concepts and the Relation Between Content and Process in Science Curricula.

    ERIC Educational Resources Information Center

    Smith, Edward L.

    The interrelation of science content and process is discussed in terms of analytic and systemic concepts. Analytic concepts identify the type or form of systemic concepts found in particular disciplines. In terms of analytic concepts, science processes such as observation, deduction, and prediction can be identified and defined as operations…

  15. Analytical approach to cross-layer protocol optimization in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Hortos, William S.

    2008-04-01

    In the distributed operations of route discovery and maintenance, strong interaction occurs across mobile ad hoc network (MANET) protocol layers. Quality of service (QoS) requirements of multimedia service classes must be satisfied by the cross-layer protocol, along with minimization of the distributed power consumption at nodes and along routes to battery-limited energy constraints. In previous work by the author, cross-layer interactions in the MANET protocol are modeled in terms of a set of concatenated design parameters and associated resource levels by multivariate point processes (MVPPs). Determination of the "best" cross-layer design is carried out using the optimal control of martingale representations of the MVPPs. In contrast to the competitive interaction among nodes in a MANET for multimedia services using limited resources, the interaction among the nodes of a wireless sensor network (WSN) is distributed and collaborative, based on the processing of data from a variety of sensors at nodes to satisfy common mission objectives. Sensor data originates at the nodes at the periphery of the WSN, is successively transported to other nodes for aggregation based on information-theoretic measures of correlation and ultimately sent as information to one or more destination (decision) nodes. The "multimedia services" in the MANET model are replaced by multiple types of sensors, e.g., audio, seismic, imaging, thermal, etc., at the nodes; the QoS metrics associated with MANETs become those associated with the quality of fused information flow, i.e., throughput, delay, packet error rate, data correlation, etc. Significantly, the essential analytical approach to MANET cross-layer optimization, now based on the MVPPs for discrete random events occurring in the WSN, can be applied to develop the stochastic characteristics and optimality conditions for cross-layer designs of sensor network protocols. Functional dependencies of WSN performance metrics are described in

  16. Model choice considerations and information integration using analytical hierarchy process

    SciTech Connect

    Langenbrunner, James R; Hemez, Francois M; Booker, Jane M; Ross, Timothy J.

    2010-10-15

    Using the theory of information-gap for decision-making under severe uncertainty, it has been shown that model output compared to experimental data contains irrevocable trade-offs between fidelity-to-data, robustness-to-uncertainty and confidence-in-prediction. We illustrate a strategy for information integration by gathering and aggregating all available data, knowledge, theory, experience, similar applications. Such integration of information becomes important when the physics is difficult to model, when observational data are sparse or difficult to measure, or both. To aggregate the available information, we take an inference perspective. Models are not rejected, nor wasted, but can be integrated into a final result. We show an example of information integration using Saaty's Analytic Hierarchy Process (AHP), integrating theory, simulation output and experimental data. We used expert elicitation to determine weights for two models and two experimental data sets, by forming pair-wise comparisons between model output and experimental data. In this way we transform epistemic and/or statistical strength from one field of study into another branch of physical application. The price to pay for utilizing all available knowledge is that inferences drawn for the integrated information must be accounted for and the costs can be considerable. Focusing on inferences and inference uncertainty (IU) is one way to understand complex information.

  17. Evaluating supplier quality performance using analytical hierarchy process

    NASA Astrophysics Data System (ADS)

    Kalimuthu Rajoo, Shanmugam Sundram; Kasim, Maznah Mat; Ahmad, Nazihah

    2013-09-01

    This paper elaborates the importance of evaluating supplier quality performance to an organization. Supplier quality performance evaluation reflects the actual performance of the supplier exhibited at customer's end. It is critical in enabling the organization to determine the area of improvement and thereafter works with supplier to close the gaps. Success of the customer partly depends on supplier's quality performance. Key criteria as quality, cost, delivery, technology support and customer service are categorized as main factors in contributing to supplier's quality performance. 18 suppliers' who were manufacturing automotive application parts evaluated in year 2010 using weight point system. There were few suppliers with common rating which led to common ranking observed by few suppliers'. Analytical Hierarchy Process (AHP), a user friendly decision making tool for complex and multi criteria problems was used to evaluate the supplier's quality performance challenging the weight point system that was used for 18 suppliers'. The consistency ratio was checked for criteria and sub-criteria. Final results of AHP obtained with no overlap ratings, therefore yielded a better decision making methodology as compared to weight point rating system.

  18. Consistent analytic approach to the efficiency of collisional Penrose process

    NASA Astrophysics Data System (ADS)

    Harada, Tomohiro; Ogasawara, Kota; Miyamoto, Umpei

    2016-07-01

    We propose a consistent analytic approach to the efficiency of collisional Penrose process in the vicinity of a maximally rotating Kerr black hole. We focus on a collision with arbitrarily high center-of-mass energy, which occurs if either of the colliding particles has its angular momentum fine-tuned to the critical value to enter the horizon. We show that if the fine-tuned particle is ingoing on the collision, the upper limit of the efficiency is (2 +√{3 })(2 -√{2 })≃2.186 , while if the fine-tuned particle is bounced back before the collision, the upper limit is (2 +√{3 })2≃13.93 . Despite earlier claims, the former can be attained for inverse Compton scattering if the fine-tuned particle is massive and starts at rest at infinity, while the latter can be attained for various particle reactions, such as inverse Compton scattering and pair annihilation, if the fine-tuned particle is either massless or highly relativistic at infinity. We discuss the difference between the present and earlier analyses.

  19. The best motivator priorities parents choose via analytical hierarchy process

    NASA Astrophysics Data System (ADS)

    Farah, R. N.; Latha, P.

    2015-05-01

    Motivation is probably the most important factor that educators can target in order to improve learning. Numerous cross-disciplinary theories have been postulated to explain motivation. While each of these theories has some truth, no single theory seems to adequately explain all human motivation. The fact is that human beings in general and pupils in particular are complex creatures with complex needs and desires. In this paper, Analytic Hierarchy Process (AHP) has been proposed as an emerging solution to move towards too large, dynamic and complex real world multi-criteria decision making problems in selecting the most suitable motivator when choosing school for their children. Data were analyzed using SPSS 17.0 ("Statistical Package for Social Science") software. Statistic testing used are descriptive and inferential statistic. Descriptive statistic used to identify respondent pupils and parents demographic factors. The statistical testing used to determine the pupils and parents highest motivator priorities and parents' best priorities using AHP to determine the criteria chosen by parents such as school principals, teachers, pupils and parents. The moderating factors are selected schools based on "Standard Kualiti Pendidikan Malaysia" (SKPM) in Ampang. Inferential statistics such as One-way ANOVA used to get the significant and data used to calculate the weightage of AHP. School principals is found to be the best motivator for parents in choosing school for their pupils followed by teachers, parents and pupils.

  20. Dynamic Graph Analytic Framework (DYGRAF): greater situation awareness through layered multi-modal network analysis

    NASA Astrophysics Data System (ADS)

    Margitus, Michael R.; Tagliaferri, William A., Jr.; Sudit, Moises; LaMonica, Peter M.

    2012-06-01

    Understanding the structure and dynamics of networks are of vital importance to winning the global war on terror. To fully comprehend the network environment, analysts must be able to investigate interconnected relationships of many diverse network types simultaneously as they evolve both spatially and temporally. To remove the burden from the analyst of making mental correlations of observations and conclusions from multiple domains, we introduce the Dynamic Graph Analytic Framework (DYGRAF). DYGRAF provides the infrastructure which facilitates a layered multi-modal network analysis (LMMNA) approach that enables analysts to assemble previously disconnected, yet related, networks in a common battle space picture. In doing so, DYGRAF provides the analyst with timely situation awareness, understanding and anticipation of threats, and support for effective decision-making in diverse environments.

  1. Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration.

    PubMed

    van den Elzen, Stef; Holten, Danny; Blaas, Jorik; van Wijk, Jarke J

    2016-01-01

    We propose a visual analytics approach for the exploration and analysis of dynamic networks. We consider snapshots of the network as points in high-dimensional space and project these to two dimensions for visualization and interaction using two juxtaposed views: one for showing a snapshot and one for showing the evolution of the network. With this approach users are enabled to detect stable states, recurring states, outlier topologies, and gain knowledge about the transitions between states and the network evolution in general. The components of our approach are discretization, vectorization and normalization, dimensionality reduction, and visualization and interaction, which are discussed in detail. The effectiveness of the approach is shown by applying it to artificial and real-world dynamic networks. PMID:26529683

  2. Testing the accuracy of analytical estimates of spare capacity in protected-mesh networks

    NASA Astrophysics Data System (ADS)

    Forst, Brian; Grover, Wayne D.

    2006-10-01

    Recently, two different investigators published analytical models to predict the spare capacity requirements of shared-mesh survivable networks. If accurate, such estimators could be used in network planning and technology-selection applications in network-operating companies, displacing or reducing the need for detailed design studies. However, relatively few test-case results involving irregular topology and demands were provided, and some possibly significant idealizations were involved. We have therefore conducted a further series of tests of the equations to more widely assess the general accuracy of the results and to be aware of the possible limitations to their use. We review and implement the equations in question and compare their predictions, along with two well-known simple estimators, to the properties of integer linear programming (ILP)-based network design solutions for three families of protected-mesh networks. In all, 1464 detailed network designs are used as 'truth' tests for the equations over a systematically varying range of network topologies and demand patterns. On this set of trials the new mathematical models were rarely within 10% accuracy and typically had up to 30% error. By dissecting some specific cases we gain insights as to why average-case mathematical models of such a network-dependent phenomenon are unlikely to be reliable. Insights into the effects of network nodal degree, demand variance, hop and distance topologies, and topology dependence are also given.

  3. An analytic explanation of the stellar initial mass function from the theory of spatial networks

    NASA Astrophysics Data System (ADS)

    Klishin, Andrei; Chilingarian, Igor

    2015-08-01

    The distribution of stars by mass or the stellar initial mass function (IMF) that has been intensively studied in the Milky Way and other galaxies is the key property regulating star formation and galaxy evolution. The mass function of prestellar dense cores (DCMF) is an IMF precursor that has a similar shape, a broken power law with a sharp decline at low masses, but offset to higher masses. Results from numerical simulations of star formation qualitatively resemble an observed IMF/DCMF, however, most analytic IMF theories critically depend on the empirically chosen input spectrum of mass fluctuations which evolve into dense cores and, subsequently, stars. Here we propose an analytic approach by representing a system of dense cores accreting gas from the surrounding diffuse interstellar medium (ISM) as a spatial network growing by preferential attachment and assuming that the ISM density has a self-similar fractal distribution following the Kolmogorov turbulence theory. We obtain a scale free power law with the exponent that is not related to the input fluctuation mass spectrum but depends only on the fractal distribution dimensionalities of infalling gas (Dp) and turbulent ISM (Dm=2.35). It can be as steep as -3.24 (uniform volume density Dp=3) and becomes Salpeter (α=-2.35) for Dp=2.5 that corresponds to a variety of Brownian processes in physics. Our theory reproduces the observed DCMF shape over three orders of magnitude in mass, and it rules out a low mass star dominated "bottom-heavy" IMF shape unless the same steep slope holds at the higher masses.

  4. Nonlinear signal processing using neural networks: Prediction and system modelling

    SciTech Connect

    Lapedes, A.; Farber, R.

    1987-06-01

    The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

  5. Weighted networks as randomly reinforced urn processes

    NASA Astrophysics Data System (ADS)

    Caldarelli, Guido; Chessa, Alessandro; Crimaldi, Irene; Pammolli, Fabio

    2013-02-01

    We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights are determined by a reinforcement mechanism. We develop a statistical test and a procedure based on it to study the evolution of networks over time, detecting the “dominance” of some edges with respect to the others and then assessing if a given instance of the network is taken at its steady state or not. Distance from the steady state can be considered as a measure of the relevance of the observed properties of the network. Our results are quite general, in the sense that they are not based on a particular probability distribution or functional form of the random weights. Moreover, the proposed tool can be applied also to dense networks, which have received little attention by the network community so far, since they are often problematic. We apply our procedure in the context of the International Trade Network, determining a core of “dominant edges.”

  6. Resilient networked sensor-processing implementation

    NASA Astrophysics Data System (ADS)

    Wada, Glen; Hansen, J. S.

    1996-05-01

    The spatial infrared imaging telescope (SPIRIT) III sensor data processing requirement for the calibrated conversion of data to engineering units at a rate of 8 gigabytes of input data per day necessitated a distributed processing solution. As the sensor's five-band scanning radiometer and six- channel Fourier-transform spectrometer characteristics became fully understood, the processing requirements were enhanced. Hardware and schedule constraints compounded the need for a simple and resilient distributed implementation. Sensor data processing was implemented as a loosely coupled, fiber distributed data interface network of Silicon Graphics computers under the IRIX Operating Systems. The software was written in ANSI C and incorporated exception processing. Interprocessor communications and control were done both by the native capabilities of the network and Parallel Virtual Machine (PVM) software. The implementation was limited to four software components. The data reformatter component reduced the data coupling among sensor data processing components by providing self-contained data sets. The distributed processing control and graphical user interface components encased the PVM aspect of the implementation and lessened the concern of the sensor data processing component developers for the distributed model. A loosely coupled solution that dissociated the sensor data processing from the distributed processing environment, a simplified error processing scheme using exception processing, and a limited software configuration have proven resilient and compatible with the dynamics of sensor data processing.

  7. Focused analyte spray emission apparatus and process for mass spectrometric analysis

    DOEpatents

    Roach, Patrick J.; Laskin, Julia; Laskin, Alexander

    2012-01-17

    An apparatus and process are disclosed that deliver an analyte deposited on a substrate to a mass spectrometer that provides for trace analysis of complex organic analytes. Analytes are probed using a small droplet of solvent that is formed at the junction between two capillaries. A supply capillary maintains the droplet of solvent on the substrate; a collection capillary collects analyte desorbed from the surface and emits analyte ions as a focused spray to the inlet of a mass spectrometer for analysis. The invention enables efficient separation of desorption and ionization events, providing enhanced control over transport and ionization of the analyte.

  8. Analytical modelling of no-vent fill process

    NASA Technical Reports Server (NTRS)

    Vaughan, David A.; Schmidt, George R.

    1990-01-01

    An analytical model called FILL is presented which represents the first step in attaining the capability for no-vent fill of cryogens in space. The model's analytical structure is described, including the equations used to calculate transient thermodynamic behavior in different regions of the tank. The code predictions are compared with data from recent no-vent fill ground tests using Freon-114. The results are used to validate the FILL model to evaluate the viability of universal submerged jet theory in predicting system-level condensation effects.

  9. Inferring sparse networks for noisy transient processes

    NASA Astrophysics Data System (ADS)

    Tran, Hoang M.; Bukkapatnam, Satish T. S.

    2016-02-01

    Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the -min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of -min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues.

  10. Inferring sparse networks for noisy transient processes.

    PubMed

    Tran, Hoang M; Bukkapatnam, Satish T S

    2016-01-01

    Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the l1-min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of l1-min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues. PMID:26916813

  11. Inferring sparse networks for noisy transient processes

    PubMed Central

    Tran, Hoang M.; Bukkapatnam, Satish T.S.

    2016-01-01

    Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the -min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of -min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues. PMID:26916813

  12. Stationary and integrated autoregressive neural network processes.

    PubMed

    Trapletti, A; Leisch, F; Hornik, K

    2000-10-01

    We consider autoregressive neural network (AR-NN) processes driven by additive noise and demonstrate that the characteristic roots of the shortcuts-the standard conditions from linear time-series analysis-determine the stochastic behavior of the overall AR-NN process. If all the characteristic roots are outside the unit circle, then the process is ergodic and stationary. If at least one characteristic root lies inside the unit circle, then the process is transient. AR-NN processes with characteristic roots lying on the unit circle exhibit either ergodic, random walk, or transient behavior. We also analyze the class of integrated AR-NN (ARI-NN) processes and show that a standardized ARI-NN process "converges" to a Wiener process. Finally, least-squares estimation (training) of the stationary models and testing for nonstationarity is discussed. The estimators are shown to be consistent, and expressions on the limiting distributions are given. PMID:11032041

  13. Multipass optical device and process for gas and analyte determination

    DOEpatents

    Bernacki, Bruce E.

    2011-01-25

    A torus multipass optical device and method are described that provide for trace level determination of gases and gas-phase analytes. The torus device includes an optical cavity defined by at least one ring mirror. The mirror delivers optical power in at least a radial and axial direction and propagates light in a multipass optical path of a predefined path length.

  14. Neural networks in the process industries

    SciTech Connect

    Ben, L.R.; Heavner, L.

    1996-12-01

    Neural networks, or more precisely, artificial neural networks (ANNs), are rapidly gaining in popularity. They first began to appear on the process-control scene in the early 1990s, but have been a research focus for more than 30 years. Neural networks are really empirical models that approximate the way man thinks neurons in the human brain work. Neural-net technology is not trying to produce computerized clones, but to model nature in an effort to mimic some of the brain`s capabilities. Modeling, for the purposes of this article, means developing a mathematical description of physical phenomena. The physics and chemistry of industrial processes are usually quite complex and sometimes poorly understood. Our process understanding, and our imperfect ability to describe complexity in mathematical terms, limit fidelity of first-principle models. Computational requirements for executing these complex models are a further limitation. It is often not possible to execute first-principle model algorithms at the high rate required for online control. Nevertheless, rigorous first principle models are commonplace design tools. Process control is another matter. Important model inputs are often not available as process measurements, making real-time application difficult. In fact, engineers often use models to infer unavailable measurements. 5 figs.

  15. Experimental and Analytical Research on Fracture Processes in ROck

    SciTech Connect

    Herbert H.. Einstein; Jay Miller; Bruno Silva

    2009-02-27

    Experimental studies on fracture propagation and coalescence were conducted which together with previous tests by this group on gypsum and marble, provide information on fracturing. Specifically, different fracture geometries wsere tested, which together with the different material properties will provide the basis for analytical/numerical modeling. INitial steps on the models were made as were initial investigations on the effect of pressurized water on fracture coalescence.

  16. Analytically tractable studies of traveling waves of activity in integrate-and-fire neural networks

    NASA Astrophysics Data System (ADS)

    Zhang, Jie; Osan, Remus

    2016-05-01

    In contrast to other large-scale network models for propagation of electrical activity in neural tissue that have no analytical solutions for their dynamics, we show that for a specific class of integrate and fire neural networks the acceleration depends quadratically on the instantaneous speed of the activity propagation. We use this property to analytically compute the network spike dynamics and to highlight the emergence of a natural time scale for the evolution of the traveling waves. These results allow us to examine other applications of this model such as the effect that a nonconductive gap of tissue has on further activity propagation. Furthermore we show that activity propagation also depends on local conditions for other more general connectivity functions, by converting the evolution equations for network dynamics into a low-dimensional system of ordinary differential equations. This approach greatly enhances our intuition into the mechanisms of the traveling waves evolution and significantly reduces the simulation time for this class of models.

  17. Analytically tractable studies of traveling waves of activity in integrate-and-fire neural networks.

    PubMed

    Zhang, Jie; Osan, Remus

    2016-05-01

    In contrast to other large-scale network models for propagation of electrical activity in neural tissue that have no analytical solutions for their dynamics, we show that for a specific class of integrate and fire neural networks the acceleration depends quadratically on the instantaneous speed of the activity propagation. We use this property to analytically compute the network spike dynamics and to highlight the emergence of a natural time scale for the evolution of the traveling waves. These results allow us to examine other applications of this model such as the effect that a nonconductive gap of tissue has on further activity propagation. Furthermore we show that activity propagation also depends on local conditions for other more general connectivity functions, by converting the evolution equations for network dynamics into a low-dimensional system of ordinary differential equations. This approach greatly enhances our intuition into the mechanisms of the traveling waves evolution and significantly reduces the simulation time for this class of models. PMID:27300901

  18. Bosonic reaction-diffusion processes on scale-free networks

    NASA Astrophysics Data System (ADS)

    Baronchelli, Andrea; Catanzaro, Michele; Pastor-Satorras, Romualdo

    2008-07-01

    Reaction-diffusion processes can be adopted to model a large number of dynamics on complex networks, such as transport processes or epidemic outbreaks. In most cases, however, they have been studied from a fermionic perspective, in which each vertex can be occupied by at most one particle. While still useful, this approach suffers from some drawbacks, the most important probably being the difficulty to implement reactions involving more than two particles simultaneously. Here we develop a general framework for the study of bosonic reaction-diffusion processes on complex networks, in which there is no restriction on the number of interacting particles that a vertex can host. We describe these processes theoretically by means of continuous-time heterogeneous mean-field theory and divide them into two main classes: steady-state and monotonously decaying processes. We analyze specific examples of both behaviors within the class of one-species processes, comparing the results (whenever possible) with the corresponding fermionic counterparts. We find that the time evolution and critical properties of the particle density are independent of the fermionic or bosonic nature of the process, while differences exist in the functional form of the density of occupied vertices in a given degree class k . We implement a continuous-time Monte Carlo algorithm, well suited for general bosonic simulations, which allows us to confirm the analytical predictions formulated within mean-field theory. Our results, at both the theoretical and numerical levels, can be easily generalized to tackle more complex, multispecies, reaction-diffusion processes and open a promising path for a general study and classification of this kind of dynamical systems on complex networks.

  19. Analytical solution for a class of network dynamics with mechanical and financial applications.

    PubMed

    Krejčí, P; Lamba, H; Melnik, S; Rachinskii, D

    2014-09-01

    We show that for a certain class of dynamics at the nodes the response of a network of any topology to arbitrary inputs is defined in a simple way by its response to a monotone input. The nodes may have either a discrete or continuous set of states and there is no limit on the complexity of the network. The results provide both an efficient numerical method and the potential for accurate analytic approximation of the dynamics on such networks. As illustrative applications, we introduce a quasistatic mechanical model with objects interacting via frictional forces and a financial market model with avalanches and critical behavior that are generated by momentum trading strategies. PMID:25314496

  20. GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

    SciTech Connect

    Harshaw, Chris R; Bridges, Robert A; Iannacone, Michael D; Reed, Joel W; Goodall, John R

    2016-01-01

    This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \\textit{GraphPrints}. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\\textemdash small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84\\% at the time-interval level, and 0.05\\% at the IP-level with 100\\% true positive rates at both.

  1. Speed of synchronization in complex networks of neural oscillators: Analytic results based on Random Matrix Theory

    NASA Astrophysics Data System (ADS)

    Timme, Marc; Geisel, Theo; Wolf, Fred

    2006-03-01

    We analyze the dynamics of networks of spiking neural oscillators. First, we present an exact linear stability theory of the synchronous state for networks of arbitrary connectivity. For general neuron rise functions, stability is determined by multiple operators, for which standard analysis is not suitable. We describe a general nonstandard solution to the multioperator problem. Subsequently, we derive a class of neuronal rise functions for which all stability operators become degenerate and standard eigenvalue analysis becomes a suitable tool. Interestingly, this class is found to consist of networks of leaky integrate-and-fire neurons. For random networks of inhibitory integrate-and-fire neurons, we then develop an analytical approach, based on the theory of random matrices, to precisely determine the eigenvalue distributions of the stability operators. This yields the asymptotic relaxation time for perturbations to the synchronous state which provides the characteristic time scale on which neurons can coordinate their activity in such networks. For networks with finite in-degree, i.e., finite number of presynaptic inputs per neuron, we find a speed limit to coordinating spiking activity. Even with arbitrarily strong interaction strengths neurons cannot synchronize faster than at a certain maximal speed determined by the typical in-degree.

  2. Bias and precision of selected analytes reported by the National Atmospheric Deposition Program and National Trends Network, 1984

    USGS Publications Warehouse

    Brooks, M.H.; Schroder, L.J.; Willoughby, T.C.

    1987-01-01

    The U.S. Geological Survey operated a blind audit sample program during 1974 to test the effects of the sample handling and shipping procedures used by the National Atmospheric Deposition Program and National Trends Network on the quality of wet deposition data produced by the combined networks. Blind audit samples, which were dilutions of standard reference water samples, were submitted by network site operators to the central analytical laboratory disguised as actual wet deposition samples. Results from the analyses of blind audit samples were used to calculate estimates of analyte bias associated with all network wet deposition samples analyzed in 1984 and to estimate analyte precision. Concentration differences between double blind samples that were submitted to the central analytical laboratory and separate analyses of aliquots of those blind audit samples that had not undergone network sample handling and shipping were used to calculate analyte masses that apparently were added to each blind audit sample by routine network handling and shipping procedures. These calculated masses indicated statistically significant biases for magnesium, sodium , potassium, chloride, and sulfate. Median calculated masses were 41.4 micrograms (ug) for calcium, 14.9 ug for magnesium, 23.3 ug for sodium, 0.7 ug for potassium, 16.5 ug for chloride and 55.3 ug for sulfate. Analyte precision was estimated using two different sets of replicate measures performed by the central analytical laboratory. Estimated standard deviations were similar to those previously reported. (Author 's abstract)

  3. The application of the analytic hierarchy process when choosing layout schemes for a geokhod pumping station

    NASA Astrophysics Data System (ADS)

    Chernukhin, R. V.; Dronov, A. A.; Blashchuk, M. Y.

    2015-09-01

    The article describes one possibility of choosing layout schemes for geokhod systems which is the analytic hierarchy process. There is the essence of the method summarized therein. The usage of the method is considered for the analysis and the choice of layout schemes for a geokhod pumping station. Keywords: geokhod, analytic hierarchy process, pumping station, layout scheme.

  4. Tensegrity II. How structural networks influence cellular information processing networks

    NASA Technical Reports Server (NTRS)

    Ingber, Donald E.

    2003-01-01

    The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.

  5. Diagnosing process faults using neural network models

    SciTech Connect

    Buescher, K.L.; Jones, R.D.; Messina, M.J.

    1993-11-01

    In order to be of use for realistic problems, a fault diagnosis method should have the following three features. First, it should apply to nonlinear processes. Second, it should not rely on extensive amounts of data regarding previous faults. Lastly, it should detect faults promptly. The authors present such a scheme for static (i.e., non-dynamic) systems. It involves using a neural network to create an associative memory whose fixed points represent the normal behavior of the system.

  6. Evaluation of feeds for melt and dilute process using an analytical hierarchy process

    SciTech Connect

    Krupa, J.F.

    2000-03-22

    Westinghouse Savannah River Company was requested to evaluate whether nuclear materials other than aluminum-clad spent nuclear fuel should be considered for treatment to prepare them for disposal in the melt and dilute facility as part of the Treatment and Storage Facility currently projected for construction in the L-Reactor process area. The decision analysis process used to develop this analysis considered many variables and uncertainties, including repository requirements that are not yet finalized. The Analytical Hierarchy Process using a ratings methodology was used to rank potential feed candidates for disposition through the Melt and Dilute facility proposed for disposition of Savannah River Site aluminum-clad spent nuclear fuel. Because of the scoping nature of this analysis, the expert team convened for this purpose concentrated on technical feasibility and potential cost impacts associated with using melt and dilute versus the current disposition option. This report documents results of the decision analysis.

  7. The Rondonia Lightning Detection Network: Network Description, Science Objectives, Data Processing Archival/Methodology, and Results

    NASA Technical Reports Server (NTRS)

    Blakeslee, R. J.; Bailey, J. C.; Pinto, O.; Athayde, A.; Renno, N.; Weidman, C. D.

    2003-01-01

    A four station Advanced Lightning Direction Finder (ALDF) network was established in the state of Rondonia in western Brazil in 1999 through a collaboration of U.S. and Brazilian participants from NASA, INPE, INMET, and various universities. The network utilizes ALDF IMPACT (Improved Accuracy from Combined Technology) sensors to provide cloud-to-ground lightning observations (i.e., stroke/flash locations, signal amplitude, and polarity) using both time-of- arrival and magnetic direction finding techniques. The observations are collected, processed and archived at a central site in Brasilia and at the NASA/Marshall Space Flight Center in Huntsville, Alabama. Initial, non-quality assured quick-look results are made available in near real-time over the Internet. The network, which is still operational, was deployed to provide ground truth data for the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite that was launched in November 1997. The measurements are also being used to investigate the relationship between the electrical, microphysical and kinematic properties of tropical convection. In addition, the long-time series observations produced by this network will help establish a regional lightning climatological database, supplementing other databases in Brazil that already exist or may soon be implemented. Analytic inversion algorithms developed at the NASA/Marshall Space Flight Center have been applied to the Rondonian ALDF lightning observations to obtain site error corrections and improved location retrievals. The data will also be corrected for the network detection efficiency. The processing methodology and the results from the analysis of four years of network operations will be presented.

  8. Materials, Processes, and Environmental Engineering Network

    NASA Technical Reports Server (NTRS)

    White, Margo M.

    1993-01-01

    Attention is given to the Materials, Processes, and Environmental Engineering Network (MPEEN), which was developed as a central holding facility for materials testing information generated by the Materials and Processes Laboratory of NASA-Marshall. It contains information from other NASA centers and outside agencies, and also includes the NASA Environmental Information System (NEIS) and Failure Analysis Information System (FAIS) data. The data base is NEIS, which is accessible through MPEEN. Environmental concerns are addressed regarding materials identified by the NASA Operational Environment Team (NOET) to be hazardous to the environment. The data base also contains the usage and performance characteristics of these materials.

  9. Time-to-event analysis with artificial neural networks: an integrated analytical and rule-based study for breast cancer.

    PubMed

    Lisboa, Paulo J G; Etchells, Terence A; Jarman, Ian H; Hane Aung, M S; Chabaud, Sylvie; Bachelot, Thomas; Perol, David; Gargi, Thérèse; Bourdès, Valérie; Bonnevay, Stéphane; Négrier, Sylvie

    2008-01-01

    This paper presents an analysis of censored survival data for breast cancer specific mortality and disease-free survival. There are three stages to the process, namely time-to-event modelling, risk stratification by predicted outcome and model interpretation using rule extraction. Model selection was carried out using the benchmark linear model, Cox regression but risk staging was derived with Cox regression and with Partial Logistic Regression Artificial Neural Networks regularised with Automatic Relevance Determination (PLANN-ARD). This analysis compares the two approaches showing the benefit of using the neural network framework especially for patients at high risk. The neural network model also has results in a smooth model of the hazard without the need for limiting assumptions of proportionality. The model predictions were verified using out-of-sample testing with the mortality model also compared with two other prognostic models called TNG and the NPI rule model. Further verification was carried out by comparing marginal estimates of the predicted and actual cumulative hazards. It was also observed that doctors seem to treat mortality and disease-free models as equivalent, so a further analysis was performed to observe if this was the case. The analysis was extended with automatic rule generation using Orthogonal Search Rule Extraction (OSRE). This methodology translates analytical risk scores into the language of the clinical domain, enabling direct validation of the operation of the Cox or neural network model. This paper extends the existing OSRE methodology to data sets that include continuous-valued variables. PMID:18304780

  10. Resting-brain functional connectivity predicted by analytic measures of network communication.

    PubMed

    Goñi, Joaquín; van den Heuvel, Martijn P; Avena-Koenigsberger, Andrea; Velez de Mendizabal, Nieves; Betzel, Richard F; Griffa, Alessandra; Hagmann, Patric; Corominas-Murtra, Bernat; Thiran, Jean-Philippe; Sporns, Olaf

    2014-01-14

    The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures--search information and path transitivity--which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways. PMID:24379387

  11. Exact performance analytical model for spectrum allocation in flexible grid optical networks

    NASA Astrophysics Data System (ADS)

    Yu, Yiming; Zhang, Jie; Zhao, Yongli; Li, Hui; Ji, Yuefeng; Gu, Wanyi

    2014-03-01

    Dynamic flexible grid optical networks have gained much attention because of the advantages of high spectrum efficiency and flexibility, while the performance analysis will be more complex compared with fixed grid optical networks. An analytical Markov model is first presented in the paper, which can exactly describe the stochastic characteristics of the spectrum allocation in flexible grid optical networks considering both random-fit and first-fit resource assignment policies. We focus on the effect of spectrum contiguous constraint which has not been systematically studied in respect of mathematical modeling, and three major properties of the model are presented and analyzed. The model can expose key performance features and act as the foundation of modeling the Routing and Spectrum Assignment (RSA) problem with diverse topologies. Two heuristic algorithms are also proposed to make it more tractable. Finally, several key parameters, such as blocking probability, resource utilization rate and fragmentation rate are presented and computed, and the corresponding Monte Carlo simulation results match closely with analytical results, which prove the correctness of this mathematical model.

  12. Analytical Model of Large Data Transactions in CoAP Networks

    PubMed Central

    Ludovici, Alessandro; Di Marco, Piergiuseppe; Calveras, Anna; Johansson, Karl H.

    2014-01-01

    We propose a novel analytical model to study fragmentation methods in wireless sensor networks adopting the Constrained Application Protocol (CoAP) and the IEEE 802.15.4 standard for medium access control (MAC). The blockwise transfer technique proposed in CoAP and the 6LoWPAN fragmentation are included in the analysis. The two techniques are compared in terms of reliability and delay, depending on the traffic, the number of nodes and the parameters of the IEEE 802.15.4 MAC. The results are validated trough Monte Carlo simulations. To the best of our knowledge this is the first study that evaluates and compares analytically the performance of CoAP blockwise transfer and 6LoWPAN fragmentation. A major contribution is the possibility to understand the behavior of both techniques with different network conditions. Our results show that 6LoWPAN fragmentation is preferable for delay-constrained applications. For highly congested networks, the blockwise transfer slightly outperforms 6LoWPAN fragmentation in terms of reliability. PMID:25153143

  13. Multi-analyte assay for triazines using cross-reactive antibodies and neural networks.

    PubMed

    Reder, Sabine; Dieterle, Frank; Jansen, Hendrikus; Alcock, Susan; Gauglitz, Günter

    2003-12-30

    A biosensor system based on total internal reflectance fluorescence (TIRF) was used to discriminate a mixture of the triazines atrazine and simazine. Only cross-reactive antibodies were available for these two analytes. The biosensor is fully automated and can be regenerated allowing several hundreds of measurements without any user input. Even a remote control for online monitoring in the field is possible. The multivariate calibration of the sensor signal was performed using artificial neural networks, as the relationship between the sensor signals and the concentration of the analytes is highly non-linear. For the development of a multi-analyte immunoassay consisting of two polyclonal antibodies with cross-reactivity to atrazine and simazine and different derivatives immobilised on the transducer surface, the binding characteristics between these substances like binding capacity and cross-reactivity were characterised. The examination of three different measurement procedures showed that a two-step measurement using only one antibody per step allows a quantification of both analytes in a mixture with limits of detection of 0.2 microg/l for atrazine and 0.3 microg/l for simazine. The biosensor is suitable for online monitoring in the field and remote control is possible. PMID:14623469

  14. Leveraging Big-Data for Business Process Analytics

    ERIC Educational Resources Information Center

    Vera-Baquero, Alejandro; Colomo Palacios, Ricardo; Stantchev, Vladimir; Molloy, Owen

    2015-01-01

    Purpose: This paper aims to present a solution that enables organizations to monitor and analyse the performance of their business processes by means of Big Data technology. Business process improvement can drastically influence in the profit of corporations and helps them to remain viable. However, the use of traditional Business Intelligence…

  15. Process models: analytical tools for managing industrial energy systems

    SciTech Connect

    Howe, S O; Pilati, D A; Balzer, C; Sparrow, F T

    1980-01-01

    How the process models developed at BNL are used to analyze industrial energy systems is described and illustrated. Following a brief overview of the industry modeling program, the general methodology of process modeling is discussed. The discussion highlights the important concepts, contents, inputs, and outputs of a typical process model. A model of the US pulp and paper industry is then discussed as a specific application of process modeling methodology. Applications addressed with the case study results include projections of energy demand, conservation technology assessment, energy-related tax policies, and sensitivity analysis. A subsequent discussion of these results supports the conclusion that industry process models are versatile and powerful tools for managing industrial energy systems.

  16. Transient stability assessment for network topology changes: Application of energy margin analytical sensitivity

    SciTech Connect

    Chadalavada, V.; Vittal, V. . Dept. of Electrical Engineering and Computer Engineering)

    1994-08-01

    Recent developments in direct transient stability assessment using the Transient Energy Function (TEF) method have included the exit point technique to determine the controlling unstable equilibrium point (uep). In this paper, analytical sensitivity of the energy margin is coupled with the exit point based TEF method to assess system stability when there is a change in system parameters: plant generation or network configuration. The principal features of this paper include: introduction of a very fast sensitivity technique to account for network configuration changes, elimination of the assumption that the mode of disturbance of the controlling uep does not change, correlation of the sensitivity results with time simulation through swing curves. The technique is tested on the 50-generator IEEE test system and the 161-generator Northern States Power (NSP) system.

  17. Direct, physically motivated derivation of the contagion condition for spreading processes on generalized random networks

    NASA Astrophysics Data System (ADS)

    Dodds, Peter Sheridan; Harris, Kameron Decker; Payne, Joshua L.

    2011-05-01

    For a broad range of single-seed contagion processes acting on generalized random networks, we derive a unifying analytic expression for the possibility of global spreading events in a straightforward, physically intuitive fashion. Our reasoning lays bare a direct mechanical understanding of an archetypal spreading phenomena that is not evident in circuitous extant mathematical approaches.

  18. A comprehensive Network Security Risk Model for process control networks.

    PubMed

    Henry, Matthew H; Haimes, Yacov Y

    2009-02-01

    The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example. PMID:19000078

  19. An Analytic Hierarchy Process for School Quality and Inspection: Model Development and Application

    ERIC Educational Resources Information Center

    Al Qubaisi, Amal; Badri, Masood; Mohaidat, Jihad; Al Dhaheri, Hamad; Yang, Guang; Al Rashedi, Asma; Greer, Kenneth

    2016-01-01

    Purpose: The purpose of this paper is to develop an analytic hierarchy planning-based framework to establish criteria weights and to develop a school performance system commonly called school inspections. Design/methodology/approach: The analytic hierarchy process (AHP) model uses pairwise comparisons and a measurement scale to generate the…

  20. An analytical approach to customer requirement information processing

    NASA Astrophysics Data System (ADS)

    Zhou, Zude; Xiao, Zheng; Liu, Quan; Ai, Qingsong

    2013-11-01

    'Customer requirements' (CRs) management is a key component of customer relationship management (CRM). By processing customer-focused information, CRs management plays an important role in enterprise systems (ESs). Although two main CRs analysis methods, quality function deployment (QFD) and Kano model, have been applied to many fields by many enterprises in the past several decades, the limitations such as complex processes and operations make them unsuitable for online businesses among small- and medium-sized enterprises (SMEs). Currently, most SMEs do not have the resources to implement QFD or Kano model. In this article, we propose a method named customer requirement information (CRI), which provides a simpler and easier way for SMEs to run CRs analysis. The proposed method analyses CRs from the perspective of information and applies mathematical methods to the analysis process. A detailed description of CRI's acquisition, classification and processing is provided.

  1. Materials, processes, and environmental engineering network

    NASA Technical Reports Server (NTRS)

    White, Margo M.

    1993-01-01

    The Materials, Processes, and Environmental Engineering Network (MPEEN) was developed as a central holding facility for materials testing information generated by the Materials and Processes Laboratory. It contains information from other NASA centers and outside agencies, and also includes the NASA Environmental Information System (NEIS) and Failure Analysis Information System (FAIS) data. Environmental replacement materials information is a newly developed focus of MPEEN. This database is the NASA Environmental Information System, NEIS, which is accessible through MPEEN. Environmental concerns are addressed regarding materials identified by the NASA Operational Environment Team, NOET, to be hazardous to the environment. An environmental replacement technology database is contained within NEIS. Environmental concerns about materials are identified by NOET, and control or replacement strategies are formed. This database also contains the usage and performance characteristics of these hazardous materials. In addition to addressing environmental concerns, MPEEN contains one of the largest materials databases in the world. Over 600 users access this network on a daily basis. There is information available on failure analysis, metals and nonmetals testing, materials properties, standard and commercial parts, foreign alloy cross-reference, Long Duration Exposure Facility (LDEF) data, and Materials and Processes Selection List data.

  2. Analytical theory of polymer-network-mediated interaction between colloidal particles

    PubMed Central

    Di Michele, Lorenzo; Zaccone, Alessio; Eiser, Erika

    2012-01-01

    Nanostructured materials based on colloidal particles embedded in a polymer network are used in a variety of applications ranging from nanocomposite rubbers to organic-inorganic hybrid solar cells. Further, polymer-network-mediated colloidal interactions are highly relevant to biological studies whereby polymer hydrogels are commonly employed to probe the mechanical response of living cells, which can determine their biological function in physiological environments. The performance of nanomaterials crucially relies upon the spatial organization of the colloidal particles within the polymer network that depends, in turn, on the effective interactions between the particles in the medium. Existing models based on nonlocal equilibrium thermodynamics fail to clarify the nature of these interactions, precluding the way toward the rational design of polymer-composite materials. In this article, we present a predictive analytical theory of these interactions based on a coarse-grained model for polymer networks. We apply the theory to the case of colloids partially embedded in cross-linked polymer substrates and clarify the origin of attractive interactions recently observed experimentally. Monte Carlo simulation results that quantitatively confirm the theoretical predictions are also presented. PMID:22679289

  3. Neural network training as a dissipative process.

    PubMed

    Gori, Marco; Maggini, Marco; Rossi, Alessandro

    2016-09-01

    This paper analyzes the practical issues and reports some results on a theory in which learning is modeled as a continuous temporal process driven by laws describing the interactions of intelligent agents with their own environment. The classic regularization framework is paired with the idea of temporal manifolds by introducing the principle of least cognitive action, which is inspired by the related principle of mechanics. The introduction of the counterparts of the kinetic and potential energy leads to an interpretation of learning as a dissipative process. As an example, we apply the theory to supervised learning in neural networks and show that the corresponding Euler-Lagrange differential equations can be connected to the classic gradient descent algorithm on the supervised pairs. We give preliminary experiments to confirm the soundness of the theory. PMID:27389569

  4. Rhodobase, a meta-analytical tool for reconstructing gene regulatory networks in a model photosynthetic bacterium.

    PubMed

    Moskvin, Oleg V; Bolotin, Dmitry; Wang, Andrew; Ivanov, Pavel S; Gomelsky, Mark

    2011-02-01

    We present Rhodobase, a web-based meta-analytical tool for analysis of transcriptional regulation in a model anoxygenic photosynthetic bacterium, Rhodobacter sphaeroides. The gene association meta-analysis is based on the pooled data from 100 of R. sphaeroides whole-genome DNA microarrays. Gene-centric regulatory networks were visualized using the StarNet approach (Jupiter, D.C., VanBuren, V., 2008. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS ONE 3, e1717) with several modifications. We developed a means to identify and visualize operons and superoperons. We designed a framework for the cross-genome search for transcription factor binding sites that takes into account high GC-content and oligonucleotide usage profile characteristic of the R. sphaeroides genome. To facilitate reconstruction of directional relationships between co-regulated genes, we screened upstream sequences (-400 to +20bp from start codons) of all genes for putative binding sites of bacterial transcription factors using a self-optimizing search method developed here. To test performance of the meta-analysis tools and transcription factor site predictions, we reconstructed selected nodes of the R. sphaeroides transcription factor-centric regulatory matrix. The test revealed regulatory relationships that correlate well with the experimentally derived data. The database of transcriptional profile correlations, the network visualization engine and the optimized search engine for transcription factor binding sites analysis are available at http://rhodobase.org. PMID:21070832

  5. Testing and Analytical Modeling for Purging Process of a Cryogenic Line

    NASA Technical Reports Server (NTRS)

    Hedayat, A.; Mazurkivich, P. V.; Nelson, M. A.; Majumdar, A. K.

    2013-01-01

    The purging operations for cryogenic main propulsion systems of upper stage are usually carried out for the following cases: 1) Purging of the Fill/Drain line after completion of propellant loading. This operation allows the removal of residual propellant mass; and 2) Purging of the Feed/Drain line if the mission is scrubbed. The lines would be purged by connections to a ground high-pressure gas storage source. The flowrate of purge gas should be regulated such that the pressure in the line will not exceed the required maximum allowable value. Exceeding the maximum allowable pressure may lead to structural damage in the line. To gain confidence in analytical models of the purge process, a test series was conducted. The test article, a 20-cm incline line, was filled with liquid hydrogen and then purged with gaseous helium (GHe). The influences of GHe flowrates and initial temperatures were evaluated. The Generalized Fluid System Simulation Program, an in-house general-purpose computer program for flow network analysis, was utilized to model and simulate the testing. The test procedures, modeling descriptions, and the results will be presented in the final paper.

  6. Testing and Analytical Modeling for Purging Process of a Cryogenic Line

    NASA Technical Reports Server (NTRS)

    Hedayat, A.; Mazurkivich, P. V.; Nelson, M. A.; Majumdar, A. K.

    2015-01-01

    The purging operations for cryogenic main propulsion systems of upper stage are usually carried out for the following cases: 1) Purging of the Fill/Drain line after completion of propellant loading. This operation allows the removal of residual propellant mass; and 2) Purging of the Feed/Drain line if the mission is scrubbed. The lines would be purged by connections to a ground high-pressure gas storage source. The flow-rate of purge gas should be regulated such that the pressure in the line will not exceed the required maximum allowable value. Exceeding the maximum allowable pressure may lead to structural damage in the line. To gain confidence in analytical models of the purge process, a test series was conducted. The test article, a 20-cm incline line, was filled with liquid hydrogen and then purged with gaseous helium (GHe). The influences of GHe flow-rates and initial temperatures were evaluated. The Generalized Fluid System Simulation Program, an in-house general-purpose computer program for flow network analysis, was utilized to model and simulate the testing. The test procedures, modeling descriptions, and the results will be presented in the final paper.

  7. Analytical and experimental studies for thermal plasma processing of materials

    NASA Astrophysics Data System (ADS)

    Work continued on thermal plasma processing of materials. This quarter, ceramic powders of carbides, aluminum nitride, oxides, solids solutions, magnetic and non magnetic spinels, superconductors, and composites have been successfully synthesized in a Triple DC Torch Plasma Jet Reactor (TTPR) and in a single DC Plasma Jet Reactor. All the ceramic powders with the exception of AIN were synthesized using a novel injection method developed to overcome the problems associated with solid injection, in particular for the single DC plasma jet reactor, and to realize the benefits of gas phase reactions. Also, initial experiments have been performed for the deposition of diamond coatings on Si wafers using the TTPR with methane as the carbon source. Well faceted diamond crystallites were deposited on the surface of the wafers, forming a continuous one particle thick coating. For measuring temperature and velocity fields in plasma systems, enthalpy probes have been developed and tested. The validity has been checked by performing energy and mass flux balances in an argon plasma jet operated in argon atmosphere. Total Gibbs free energy minimization calculations using a quasi-equilibrium modification have been applied to simulate several chemical reactions. Plasma reactor modelling has been performed for the counter-flow liquid injection plasma synthesis experiment. Plasma diagnostics has been initiated to determine the pressure gradient in the coalesced part of the plasma jet. The pressure gradient drives the diffusion of chemical species which ultimately controls the chemical reactions.

  8. Default network activation during episodic and semantic memory retrieval: A selective meta-analytic comparison.

    PubMed

    Kim, Hongkeun

    2016-01-01

    It remains unclear whether and to what extent the default network subregions involved in episodic memory (EM) and semantic memory (SM) processes overlap or are separated from one another. This study addresses this issue through a controlled meta-analysis of functional neuroimaging studies involving healthy participants. Various EM and SM task paradigms differ widely in the extent of default network involvement. Therefore, the issue at hand cannot be properly addressed without some control for this factor. In this regard, this study employs a two-stage analysis: a preliminary meta-analysis to select EM and SM task paradigms that recruit relatively extensive default network regions and a main analysis to compare the selected task paradigms. Based on a within-EM comparison, the default network contributed more to recollection/familiarity effects than to old/new effects, and based on a within-SM comparison, it contributed more to word/pseudoword effects than to semantic/phonological effects. According to a direct comparison of recollection/familiarity and word/pseudoword effects, each involving a range of default network regions, there were more overlaps than separations in default network subregions involved in these two effects. More specifically, overlaps included the bilateral posterior cingulate/retrosplenial cortex, left inferior parietal lobule, and left anteromedial prefrontal regions, whereas separations included only the hippocampal formation and the parahippocampal cortex region, which was unique to recollection/familiarity effects. These results indicate that EM and SM retrieval processes involving strong memory signals recruit extensive and largely overlapping default network regions and differ mainly in distinct contributions of hippocampus and parahippocampal regions to EM retrieval. PMID:26562053

  9. Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling

    PubMed Central

    Laird, Angela R.; Eickhoff, Simon B.; Li, Karl; Robin, Donald A.; Glahn, David C.; Fox, Peter T.

    2010-01-01

    The default mode network (DMN) comprises a set of regions that exhibit ongoing, intrinsic activity in the resting state and task-related decreases in activity across a range of paradigms. However, DMN regions have also been reported as task-related increases, either independently or coactivated with other regions in the network. Cognitive subtractions and the use of low-level baseline conditions have generally masked the functional nature of these regions. Using a combination of activation likelihood estimation, which assesses statistically significant convergence of neuroimaging results, and tools distributed with the BrainMap database, we identified core regions in the DMN and examined their functional heterogeneity. Meta-analytic coactivation maps of task-related increases were independently generated for each region, which included both within-DMN and non-DMN connections. Their functional properties were assessed using behavioral domain metadata in BrainMap. These results were integrated to determine a DMN connectivity model that represents the patterns of interactions observed in task-related increases in activity across diverse tasks. Sub-network components of this model were identified, and behavioral domain analysis of these cliques yielded discrete functional properties, demonstrating that components of the DMN are differentially specialized. Affective and perceptual cliques of the DMN were identified, as well as the cliques associated with a reduced preference for motor processing. In summary, we used advanced coordinate-based meta-analysis techniques to explicate behavior and connectivity in the default mode network; future work will involve applying this analysis strategy to other modes of brain function, such as executive function or sensorimotor systems. PMID:19923283

  10. Technical and analytical support to the ARPA Artificial Neural Network Technology Program

    SciTech Connect

    1995-09-16

    Strategic Analysis (SA) has provided ongoing work for the Advanced Research Projects Agency (ARPA) Artificial Neural Network (ANN) technology program. This effort provides technical and analytical support to the ARPA ANN technology program in support of the following information areas of interest: (1) Alternative approaches for application of ANN technology, hardware approaches that utilize the inherent massive parallelism of ANN technology, and novel ANN theory and modeling analyses. (2) Promising military applications for ANN technology. (3) Measures to use in judging success of ANN technology research and development. (4) Alternative strategies for ARPA involvement in ANN technology R&D. These objectives were accomplished through the development of novel information management tools, strong SA knowledge base, and effective communication with contractors, agents, and other program participants. These goals have been realized. Through enhanced tracking and coordination of research, the ANN program is healthy and recharged for future technological breakthroughs.

  11. Near-infrared spectroscopic measurements of blood analytes using multi-layer perceptron neural networks.

    PubMed

    Kalamatianos, Dimitrios; Liatsis, Panos; Wellstead, Peter E

    2006-01-01

    Near-infrared (NIR) spectroscopy is being applied to the solution of problems in many areas of biomedical and pharmaceutical research. In this paper we investigate the use of NIR spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose and oxyhemoglobin (HbO2). Measurements have been made in vitro with a portable spectrometer developed in our labs that consists of a two beam interferometer operating in the range of 800-2300 nm. For the data analysis a pattern recognition philosophy was used with a preprocessing stage and a multi-layer perceptron (MLP) neural network for the measurement stage. Results show that the interferogram signatures of the above compounds are sufficiently strong in that spectral range. Measurements of three different concentrations were possible with mean squared error (MSE) of the order of 10(-6). PMID:17947035

  12. Analytical approach to the dynamics of facilitated spin models on random networks

    NASA Astrophysics Data System (ADS)

    Fennell, Peter G.; Gleeson, James P.; Cellai, Davide

    2014-09-01

    Facilitated spin models were introduced some decades ago to mimic systems characterized by a glass transition. Recent developments have shown that a class of facilitated spin models is also able to reproduce characteristic signatures of the structural relaxation properties of glass-forming liquids. While the equilibrium phase diagram of these models can be calculated analytically, the dynamics are usually investigated numerically. Here we propose a network-based approach, called approximate master equation (AME), to the dynamics of the Fredrickson-Andersen model. The approach correctly predicts the critical temperature at which the glass transition occurs. We also find excellent agreement between the theory and the numerical simulations for the transient regime, except in close proximity of the liquid-glass transition. Finally, we analytically characterize the critical clusters of the model and show that the departures between our AME approach and the Monte Carlo can be related to the large interface between blocked and unblocked spins at temperatures close to the glass transition.

  13. Downstream processing and chromatography based analytical methods for production of vaccines, gene therapy vectors, and bacteriophages

    PubMed Central

    Kramberger, Petra; Urbas, Lidija; Štrancar, Aleš

    2015-01-01

    Downstream processing of nanoplexes (viruses, virus-like particles, bacteriophages) is characterized by complexity of the starting material, number of purification methods to choose from, regulations that are setting the frame for the final product and analytical methods for upstream and downstream monitoring. This review gives an overview on the nanoplex downstream challenges and chromatography based analytical methods for efficient monitoring of the nanoplex production. PMID:25751122

  14. An analytically resolved model of a potato's thermal processing using Heun functions

    NASA Astrophysics Data System (ADS)

    Vargas Toro, Agustín.

    2014-05-01

    A potato's thermal processing model is solved analytically. The model is formulated using the equation of heat diffusion in the case of a spherical potato processed in a furnace, and assuming that the potato's thermal conductivity is radially modulated. The model is solved using the method of the Laplace transform, applying Bromwich Integral and Residue Theorem. The temperatures' profile in the potato is presented as an infinite series of Heun functions. All computations are performed with computer algebra software, specifically Maple. Using the numerical values of the thermal parameters of the potato and geometric and thermal parameters of the processing furnace, the time evolution of the temperatures in different regions inside the potato are presented analytically and graphically. The duration of thermal processing in order to achieve a specified effect on the potato is computed. It is expected that the obtained analytical results will be important in food engineering and cooking engineering.

  15. Possibilities of Utilizing the Method of Analytical Hierarchy Process Within the Strategy of Corporate Social Business

    NASA Astrophysics Data System (ADS)

    Drieniková, Katarína; Hrdinová, Gabriela; Naňo, Tomáš; Sakál, Peter

    2010-01-01

    The paper deals with the analysis of the theory of corporate social responsibility, risk management and the exact method of analytic hierarchic process that is used in the decision-making processes. The Chapters 2 and 3 focus on presentation of the experience with the application of the method in formulating the stakeholders' strategic goals within the Corporate Social Responsibility (CSR) and simultaneously its utilization in minimizing the environmental risks. The major benefit of this paper is the application of Analytical Hierarchy Process (AHP).

  16. Analytical investigation of torque and flux ripple in induction motor control scheme using wavelet network

    NASA Astrophysics Data System (ADS)

    Liu, Hua; Zhang, Hong; Qin, Aili

    2008-10-01

    By combining wavelet analysis and neural network, a new approach for condition monitoring is presented for rotating machine fault. The wavelet analysis can accurately localize the features of transient signal in time-frequency domains. The wavelet transform technology is appropriate for processing of fault signals consisting of short-lived, high-frequency components closely located in time as well as long duration components closely spaced in frequency. In a view of the inter relationship of wavelet decomposition theory, the crucial components as features are inputted into radial basis function for fault pattern recognition. In order to acquire the network parameters, the improved Levenberg-Marquardt optimization technique is used for training process. By choosing enough samples to train wavelet network, the fault pattern can be determined according to the output results. Also, the robustness of wavelet network for fault diagnosis is discussed. The applied results show that the proposed method can improve the performance for real-time monitoring of vibration fault.

  17. A uniform method for analytically modeling mulit-target acquisition with independent networked imaging sensors

    NASA Astrophysics Data System (ADS)

    Friedman, Melvin

    2014-05-01

    The problem solved in this paper is easily stated: for a scenario with 𝑛 networked and moving imaging sensors, 𝑚 moving targets and 𝑘 independent observers searching imagery produced by the 𝑛 moving sensors, analytically model system target acquisition probability for each target as a function of time. Information input into the model is the time dependence of 𝘗∞ and 𝜏, two parameters that describe observer-sensor-atmosphere-range-target properties of the target acquisition system for the case where neither the sensor nor target is moving. The parameter 𝘗∞ can be calculated by the NV-IPM model and 𝜏 is estimated empirically from 𝘗∞. In this model 𝑛, 𝑚 and 𝑘 are integers and 𝑘 can be less than, equal to or greater than 𝑛. Increasing 𝑛 and 𝑘 results in a substantial increase in target acquisition probabilities. Because the sensors are networked, a target is said to be detected the moment the first of the 𝑘 observers declares the target. The model applies to time-limited or time-unlimited search, and applies to any imaging sensors operating in any wavelength band provided each sensor can be described by 𝘗∞ and 𝜏 parameters.

  18. Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights

    PubMed Central

    Nicola, Wilten; Tripp, Bryan; Scott, Matthew

    2016-01-01

    A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks. PMID:26973503

  19. Competition and cooperation between active intra-network and passive extra-network transport processes

    PubMed Central

    Maruyama, Dan; Zochowski, Michal

    2014-01-01

    Many networks are embedded in physical space and often interact with it. This interaction can be exemplified through constraints exerted on network topology, or through interactions of processes defined on a network with those that are linked to the space that the network is embedded within, leading to complex dynamics. Here we discuss an example of such an interaction in which a signaling agent is actively transported through the network edges and, at the same time, spreads passively through space due to diffusion. We show that these two processes cooperate or compete depending on the network topology leading to complex dynamics. PMID:24920178

  20. Quality Measures in Pre-Analytical Phase of Tissue Processing: Understanding Its Value in Histopathology

    PubMed Central

    Masilamani, Suresh; Sundaram, Sandhya; Duvuru, Prathiba; Swaminathan, Rajendiran

    2016-01-01

    Introduction Quality monitoring in histopathology unit is categorized into three phases, pre-analytical, analytical and post-analytical, to cover various steps in the entire test cycle. Review of literature on quality evaluation studies pertaining to histopathology revealed that earlier reports were mainly focused on analytical aspects with limited studies on assessment of pre-analytical phase. Pre-analytical phase encompasses several processing steps and handling of specimen/sample by multiple individuals, thus allowing enough scope for errors. Due to its critical nature and limited studies in the past to assess quality in pre-analytical phase, it deserves more attention. Aim This study was undertaken to analyse and assess the quality parameters in pre-analytical phase in a histopathology laboratory. Materials and Methods This was a retrospective study done on pre-analytical parameters in histopathology laboratory of a tertiary care centre on 18,626 tissue specimens received in 34 months. Registers and records were checked for efficiency and errors for pre-analytical quality variables: specimen identification, specimen in appropriate fixatives, lost specimens, daily internal quality control performance on staining, performance in inter-laboratory quality assessment program {External quality assurance program (EQAS)} and evaluation of internal non-conformities (NC) for other errors. Results The study revealed incorrect specimen labelling in 0.04%, 0.01% and 0.01% in 2007, 2008 and 2009 respectively. About 0.04%, 0.07% and 0.18% specimens were not sent in fixatives in 2007, 2008 and 2009 respectively. There was no incidence of specimen lost. A total of 113 non-conformities were identified out of which 92.9% belonged to the pre-analytical phase. The predominant NC (any deviation from normal standard which may generate an error and result in compromising with quality standards) identified was wrong labelling of slides. Performance in EQAS for pre-analytical phase was

  1. Comparison of the Analytic Hierarchy Process and Incomplete Analytic Hierarchy Process for identifying customer preferences in the Texas retail energy provider market

    NASA Astrophysics Data System (ADS)

    Davis, Christopher

    The competitive market for retail energy providers in Texas has been in existence for 10 years. When the market opened in 2002, 5 energy providers existed, offering, on average, 20 residential product plans in total. As of January 2012, there are now 115 energy providers in Texas offering over 300 residential product plans for customers. With the increase in providers and product plans, customers can be bombarded with information and suffer from the "too much choice" effect. The goal of this praxis is to aid customers in the decision making process of identifying an energy provider and product plan. Using the Analytic Hierarchy Process (AHP), a hierarchical decomposition decision making tool, and the Incomplete Analytic Hierarchy Process (IAHP), a modified version of AHP, customers can prioritize criteria such as price, rate type, customer service, and green energy products to identify the provider and plan that best meets their needs. To gather customer data, a survey tool has been developed for customers to complete the pairwise comparison process. Results are compared for the Incomplete AHP and AHP method to determine if the Incomplete AHP method is just as accurate, but more efficient, than the traditional AHP method.

  2. Fast radiative transfer of dust reprocessing in semi-analytic models with artificial neural networks

    NASA Astrophysics Data System (ADS)

    Silva, Laura; Fontanot, Fabio; Granato, Gian Luigi

    2012-06-01

    A serious concern for semi-analytical galaxy formation models, aiming to simulate multiwavelength surveys and to thoroughly explore the model parameter space, is the extremely time-consuming numerical solution of the radiative transfer of stellar radiation through dusty media. To overcome this problem, we have implemented an artificial neural network (ANN) algorithm in the radiative transfer code GRASIL, in order to significantly speed up the computation of the infrared (IR) spectral energy distribution (SED). The ANN we have implemented is of general use, in that its input neurons are defined as those quantities effectively determining the shape of the IR SED. Therefore, the training of the ANN can be performed with any model and then applied to other models. We made a blind test to check the algorithm, by applying a net trained with a standard chemical evolution model (i.e. CHE_EVO) to a mock catalogue extracted from the semi-analytic model MORGANA, and compared galaxy counts and evolution of the luminosity functions in several near-IR to sub-millimetre (sub-mm) bands, and also the spectral differences for a large subset of randomly extracted models. The ANN is able to excellently approximate the full computation, but with a gain in CPU time by ˜2 orders of magnitude. It is only advisable that the training covers reasonably well the range of values of the input neurons in the application. Indeed in the sub-mm at high redshift, a tiny fraction of models with some sensible input neurons out of the range of the trained net cause wrong answer by the ANN. These are extreme starbursting models with high optical depths, favourably selected by sub-mm observations, and are difficult to predict a priori.

  3. Recovery of Magnesium from Seawaters and Development of Analytical Techniques for Eco-Friendly Materials Processing.

    NASA Astrophysics Data System (ADS)

    Yoon, H.; Yoon, C.; Chung, K.

    2008-12-01

    Nevertheless other resources such as fossil fuel, oils, mineral resources, drive continued interest in developing fundamental techniques for recovering valuable metals like seawater origin. A process for recovery of magnesium from brine and bittern have been described in achieving low-level detection limits as well as reliability of analytical technique. The choice of analytical technique to meet the most stringent analytical needs of our fields is ICP-OES and XRF for commercial purposes in high solid waters like bittern. This study contains the results of a study of processes for seawater reverse osmosis with enhanced precipitation yield such as NaCl, Mg(OH)2, and Br2. The original bittern composition supplied from Hanjoo Co. Ltd. was pretreated for microbial matter and additional NaOH, NH4OH, or Na2CO3. Adding NaOH at pH 9.0 to pH 9.9 yield precipitation of Na2CO3.

  4. A semi-analytical model for the flow behavior of naturally fractured formations with multi-scale fracture networks

    NASA Astrophysics Data System (ADS)

    Jia, Pin; Cheng, Linsong; Huang, Shijun; Wu, Yonghui

    2016-06-01

    This paper presents a semi-analytical model for the flow behavior of naturally fractured formations with multi-scale fracture networks. The model dynamically couples an analytical dual-porosity model with a numerical discrete fracture model. The small-scale fractures with the matrix are idealized as a dual-porosity continuum and an analytical flow solution is derived based on source functions in Laplace domain. The large-scale fractures are represented explicitly as the major fluid conduits and the flow is numerically modeled, also in Laplace domain. This approach allows us to include finer details of the fracture network characteristics while keeping the computational work manageable. For example, the large-scale fracture network may have complex geometry and varying conductivity, and the computations can be done at predetermined, discrete times, without any grids in the dual-porosity continuum. The validation of the semi-analytical model is demonstrated in comparison to the solution of ECLIPSE reservoir simulator. The simulation is fast, gridless and enables rapid model setup. On the basis of the model, we provide detailed analysis of the flow behavior of a horizontal production well in fractured reservoir with multi-scale fracture networks. The study has shown that the system may exhibit six flow regimes: large-scale fracture network linear flow, bilinear flow, small-scale fracture network linear flow, pseudosteady-state flow, interporosity flow and pseudoradial flow. During the first four flow periods, the large-scale fracture network behaves as if it only drains in the small-scale fracture network; that is, the effect of the matrix is negligibly small. The characteristics of the bilinear flow and the small-scale fracture network linear flow are predominantly determined by the dimensionless large-scale fracture conductivity. And low dimensionless fracture conductivity will generate large pressure drops in the large-scale fractures surrounding the wellbore. With

  5. Optical processing for future computer networks

    NASA Technical Reports Server (NTRS)

    Husain, A.; Haugen, P. R.; Hutcheson, L. D.; Warrior, J.; Murray, N.; Beatty, M.

    1986-01-01

    In the development of future data management systems, such as the NASA Space Station, a major problem represents the design and implementation of a high performance communication network which is self-correcting and repairing, flexible, and evolvable. To obtain the goal of designing such a network, it will be essential to incorporate distributed adaptive network control techniques. The present paper provides an outline of the functional and communication network requirements for the Space Station data management system. Attention is given to the mathematical representation of the operations being carried out to provide the required functionality at each layer of communication protocol on the model. The possible implementation of specific communication functions in optics is also considered.

  6. Evaluating the Effectiveness of the Chemistry Education by Using the Analytic Hierarchy Process

    ERIC Educational Resources Information Center

    Yüksel, Mehmet

    2012-01-01

    In this study, an attempt was made to develop a method of measurement and evaluation aimed at overcoming the difficulties encountered in the determination of the effectiveness of chemistry education based on the goals of chemistry education. An Analytic Hierarchy Process (AHP), which is a multi-criteria decision technique, is used in the present…

  7. 75 FR 13766 - Food and Drug Administration and Process Analytical Technology for Pharma Manufacturing: Food and...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-03-23

    ... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration Food and Drug Administration and Process Analytical Technology for Pharma Manufacturing: Food and Drug Administration--Partnering With Industry; Public Conference AGENCY: Food and Drug Administration,...

  8. Discontinuous phase transition in a core contact process on complex networks

    NASA Astrophysics Data System (ADS)

    Chae, Huiseung; Yook, Soon-Hyung; Kim, Yup

    2015-02-01

    To understand the effect of generalized infection processes, we suggest and study the core contact process (CCP) on complex networks. In CCP an uninfected node is infected when at least k different infected neighbors of the node select the node for the infection. The healing process is the same as that of the normal CP. It is analytically and numerically shown that discontinuous transitions occur in CCP on random networks and scale-free networks depending on infection rate and initial density of infected nodes. The discontinuous transitions include hybrid transitions with β = 1/2 and β = 1. The asymptotic behavior of the phase boundary related to the initial density is found analytically and numerically. The mapping between CCP with k and static (k+1)-core percolation is supposed from the (k+1)-core structure in the active phase and the hybrid transition with β = 1/2. From these properties of CCP one can see that CCP is one of the dynamical processes for the k-core structure on real networks.

  9. Analytical Solutions for Rumor Spreading Dynamical Model in a Social Network

    NASA Astrophysics Data System (ADS)

    Fallahpour, R.; Chakouvari, S.; Askari, H.

    2015-03-01

    In this paper, Laplace Adomian decomposition method is utilized for evaluating of spreading model of rumor. Firstly, a succinct review is constructed on the subject of using analytical methods such as Adomian decomposion method, Variational iteration method and Homotopy Analysis method for epidemic models and biomathematics. In continue a spreading model of rumor with consideration of forgetting mechanism is assumed and subsequently LADM is exerted for solving of it. By means of the aforementioned method, a general solution is achieved for this problem which can be readily employed for assessing of rumor model without exerting any computer program. In addition, obtained consequences for this problem are discussed for different cases and parameters. Furthermore, it is shown the method is so straightforward and fruitful for analyzing equations which have complicated terms same as rumor model. By employing numerical methods, it is revealed LADM is so powerful and accurate for eliciting solutions of this model. Eventually, it is concluded that this method is so appropriate for this problem and it can provide researchers a very powerful vehicle for scrutinizing rumor models in diverse kinds of social networks such as Facebook, YouTube, Flickr, LinkedIn and Tuitor.

  10. Network Traffic Analysis With Query Driven VisualizationSC 2005HPC Analytics Results

    SciTech Connect

    Stockinger, Kurt; Wu, Kesheng; Campbell, Scott; Lau, Stephen; Fisk, Mike; Gavrilov, Eugene; Kent, Alex; Davis, Christopher E.; Olinger,Rick; Young, Rob; Prewett, Jim; Weber, Paul; Caudell, Thomas P.; Bethel,E. Wes; Smith, Steve

    2005-09-01

    Our analytics challenge is to identify, characterize, and visualize anomalous subsets of large collections of network connection data. We use a combination of HPC resources, advanced algorithms, and visualization techniques. To effectively and efficiently identify the salient portions of the data, we rely on a multi-stage workflow that includes data acquisition, summarization (feature extraction), novelty detection, and classification. Once these subsets of interest have been identified and automatically characterized, we use a state-of-the-art-high-dimensional query system to extract data subsets for interactive visualization. Our approach is equally useful for other large-data analysis problems where it is more practical to identify interesting subsets of the data for visualization than to render all data elements. By reducing the size of the rendering workload, we enable highly interactive and useful visualizations. As a result of this work we were able to analyze six months worth of data interactively with response times two orders of magnitude shorter than with conventional methods.

  11. Gaussian process regression for sensor networks under localization uncertainty

    USGS Publications Warehouse

    Jadaliha, M.; Xu, Yunfei; Choi, Jongeun; Johnson, N.S.; Li, Weiming

    2013-01-01

    In this paper, we formulate Gaussian process regression with observations under the localization uncertainty due to the resource-constrained sensor networks. In our formulation, effects of observations, measurement noise, localization uncertainty, and prior distributions are all correctly incorporated in the posterior predictive statistics. The analytically intractable posterior predictive statistics are proposed to be approximated by two techniques, viz., Monte Carlo sampling and Laplace's method. Such approximation techniques have been carefully tailored to our problems and their approximation error and complexity are analyzed. Simulation study demonstrates that the proposed approaches perform much better than approaches without considering the localization uncertainty properly. Finally, we have applied the proposed approaches on the experimentally collected real data from a dye concentration field over a section of a river and a temperature field of an outdoor swimming pool to provide proof of concept tests and evaluate the proposed schemes in real situations. In both simulation and experimental results, the proposed methods outperform the quick-and-dirty solutions often used in practice.

  12. What makes us think? A three-stage dual-process model of analytic engagement.

    PubMed

    Pennycook, Gordon; Fugelsang, Jonathan A; Koehler, Derek J

    2015-08-01

    The distinction between intuitive and analytic thinking is common in psychology. However, while often being quite clear on the characteristics of the two processes ('Type 1' processes are fast, autonomous, intuitive, etc. and 'Type 2' processes are slow, deliberative, analytic, etc.), dual-process theorists have been heavily criticized for being unclear on the factors that determine when an individual will think analytically or rely on their intuition. We address this issue by introducing a three-stage model that elucidates the bottom-up factors that cause individuals to engage Type 2 processing. According to the model, multiple Type 1 processes may be cued by a stimulus (Stage 1), leading to the potential for conflict detection (Stage 2). If successful, conflict detection leads to Type 2 processing (Stage 3), which may take the form of rationalization (i.e., the Type 1 output is verified post hoc) or decoupling (i.e., the Type 1 output is falsified). We tested key aspects of the model using a novel base-rate task where stereotypes and base-rate probabilities cued the same (non-conflict problems) or different (conflict problems) responses about group membership. Our results support two key predictions derived from the model: (1) conflict detection and decoupling are dissociable sources of Type 2 processing and (2) conflict detection sometimes fails. We argue that considering the potential stages of reasoning allows us to distinguish early (conflict detection) and late (decoupling) sources of analytic thought. Errors may occur at both stages and, as a consequence, bias arises from both conflict monitoring and decoupling failures. PMID:26091582

  13. Coupling centrality and authority of co-processing model on complex networks

    NASA Astrophysics Data System (ADS)

    Zhang, Zhanli; Li, Huibin

    2016-04-01

    Coupling centrality and authority of co-processing model on complex networks are investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are obtained to disclose the formation. Considering the influence of a node to the global dynamical behavior, coupling centrality and authority are introduced for each node, which determine the relative importance and authority of nodes in the diffusion process. Furthermore, the experimental results on large-scale complex networks confirm our analytical prediction.

  14. Regulatory gene networks and the properties of the developmental process

    NASA Technical Reports Server (NTRS)

    Davidson, Eric H.; McClay, David R.; Hood, Leroy

    2003-01-01

    Genomic instructions for development are encoded in arrays of regulatory DNA. These specify large networks of interactions among genes producing transcription factors and signaling components. The architecture of such networks both explains and predicts developmental phenomenology. Although network analysis is yet in its early stages, some fundamental commonalities are already emerging. Two such are the use of multigenic feedback loops to ensure the progressivity of developmental regulatory states and the prevalence of repressive regulatory interactions in spatial control processes. Gene regulatory networks make it possible to explain the process of development in causal terms and eventually will enable the redesign of developmental regulatory circuitry to achieve different outcomes.

  15. Analogue implementation of analytic signal processing for pulse-echo systems

    NASA Technical Reports Server (NTRS)

    Gammell, P. M.

    1981-01-01

    An alternative to rectification is proposed for detection of an ultrasonic signal. This method is especially useful in medical and non-destructive evaluation (nde) applications. With this method, the magnitude of the complex analytic signal is used to define the envelope of the ultrasonic waveform. The square of this quantity has been shown elsewhere to be equal to the true rate-of-arrival of energy. An earlier study, using digital data processing, has already demonstrated the superior resolvability of closely spaced interfaces obtained with the analytic signal magnitude, as compared to conventional rectification. Here, an analogue implementation is presented which utilizes single-sideband techniques to obtain both quadrature components of the analytic signal and its magnitude. A conventional transducer, pulser, and receiver are used.

  16. Network cloning unfolds the effect of clustering on dynamical processes

    NASA Astrophysics Data System (ADS)

    Faqeeh, Ali; Melnik, Sergey; Gleeson, James P.

    2015-05-01

    We introduce network L -cloning, a technique for creating ensembles of random networks from any given real-world or artificial network. Each member of the ensemble is an L -cloned network constructed from L copies of the original network. The degree distribution of an L -cloned network and, more importantly, the degree-degree correlation between and beyond nearest neighbors are identical to those of the original network. The density of triangles in an L -cloned network, and hence its clustering coefficient, is reduced by a factor of L compared to those of the original network. Furthermore, the density of loops of any fixed length approaches zero for sufficiently large values of L . Other variants of L -cloning allow us to keep intact the short loops of certain lengths. As an application, we employ these network cloning methods to investigate the effect of short loops on dynamical processes running on networks and to inspect the accuracy of corresponding tree-based theories. We demonstrate that dynamics on L -cloned networks (with sufficiently large L ) are accurately described by the so-called adjacency tree-based theories, examples of which include the message passing technique, some pair approximation methods, and the belief propagation algorithm used respectively to study bond percolation, SI epidemics, and the Ising model.

  17. Reasoning about anomalies: a study of the analytical process of detecting and identifying anomalous behavior in maritime traffic data

    NASA Astrophysics Data System (ADS)

    Riveiro, Maria; Falkman, Göran; Ziemke, Tom; Kronhamn, Thomas

    2009-05-01

    The goal of visual analytical tools is to support the analytical reasoning process, maximizing human perceptual, understanding and reasoning capabilities in complex and dynamic situations. Visual analytics software must be built upon an understanding of the reasoning process, since it must provide appropriate interactions that allow a true discourse with the information. In order to deepen our understanding of the human analytical process and guide developers in the creation of more efficient anomaly detection systems, this paper investigates how is the human analytical process of detecting and identifying anomalous behavior in maritime traffic data. The main focus of this work is to capture the entire analysis process that an analyst goes through, from the raw data to the detection and identification of anomalous behavior. Three different sources are used in this study: a literature survey of the science of analytical reasoning, requirements specified by experts from organizations with interest in port security and user field studies conducted in different marine surveillance control centers. Furthermore, this study elaborates on how to support the human analytical process using data mining, visualization and interaction methods. The contribution of this paper is twofold: (1) within visual analytics, contribute to the science of analytical reasoning with practical understanding of users tasks in order to develop a taxonomy of interactions that support the analytical reasoning process and (2) within anomaly detection, facilitate the design of future anomaly detector systems when fully automatic approaches are not viable and human participation is needed.

  18. Curvature-processing network in macaque visual cortex

    PubMed Central

    Yue, Xiaomin; Pourladian, Irene S.; Tootell, Roger B. H.; Ungerleider, Leslie G.

    2014-01-01

    Our visual environment abounds with curved features. Thus, the goal of understanding visual processing should include the processing of curved features. Using functional magnetic resonance imaging in behaving monkeys, we demonstrated a network of cortical areas selective for the processing of curved features. This network includes three distinct hierarchically organized regions within the ventral visual pathway: a posterior curvature-biased patch (PCP) located in the near-foveal representation of dorsal V4, a middle curvature-biased patch (MCP) located on the ventral lip of the posterior superior temporal sulcus (STS) in area TEO, and an anterior curvature-biased patch (ACP) located just below the STS in anterior area TE. Our results further indicate that the processing of curvature becomes increasingly complex from PCP to ACP. The proximity of the curvature-processing network to the well-known face-processing network suggests a possible functional link between them. PMID:25092328

  19. Detecting link failures in complex network processes using remote monitoring

    NASA Astrophysics Data System (ADS)

    Dhal, R.; Abad Torres, J.; Roy, S.

    2015-11-01

    We study whether local structural changes in a complex network can be distinguished from passive remote time-course measurements of the network's dynamics. Specifically the detection of link failures in a network synchronization process from noisy measurements at a single network component is considered. By phrasing the detection task as a Maximum A Posteriori Probability hypothesis testing problem, we are able to obtain conditions under which the detection is (1) improved over the a priori and (2) asymptotically perfect, in terms of the network spectrum and graph. We find that, in the case where the detector has knowledge of the network's state, perfect detection is possible under general connectivity conditions regardless of the measurement location. When the detector does not have state knowledge, a remote signature permits improved but not perfect detection, under the same connectivity conditions. At its essence, detectability is achieved because of the close connection between a network's topology, its eigenvalues and local response characteristics.

  20. Reliability theory for diffusion processes on interconnected networks

    NASA Astrophysics Data System (ADS)

    Khorramzadeh, Yasamin; Youssef, Mina; Eubank, Stephen

    2014-03-01

    We present the concept of network reliability as a framework to study diffusion dynamics in interdependent networks. We illustrate how different outcomes of diffusion processes, such as cascading failure, can be studied by estimating the reliability polynomial under different reliability rules. As an example, we investigate the effect of structural properties on diffusion dynamics for a few different topologies of two coupled networks. We evaluate the effect of varying the probability of failure propagating along the edges, both within a single network as well as between the networks. We exhibit the sensitivity of interdependent network reliability and connectivity to edge failures in each topology. Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia 24061, USA.

  1. Reducing neural network training time with parallel processing

    NASA Technical Reports Server (NTRS)

    Rogers, James L., Jr.; Lamarsh, William J., II

    1995-01-01

    Obtaining optimal solutions for engineering design problems is often expensive because the process typically requires numerous iterations involving analysis and optimization programs. Previous research has shown that a near optimum solution can be obtained in less time by simulating a slow, expensive analysis with a fast, inexpensive neural network. A new approach has been developed to further reduce this time. This approach decomposes a large neural network into many smaller neural networks that can be trained in parallel. Guidelines are developed to avoid some of the pitfalls when training smaller neural networks in parallel. These guidelines allow the engineer: to determine the number of nodes on the hidden layer of the smaller neural networks; to choose the initial training weights; and to select a network configuration that will capture the interactions among the smaller neural networks. This paper presents results describing how these guidelines are developed.

  2. Contagion processes on the static and activity-driven coupling networks

    NASA Astrophysics Data System (ADS)

    Lei, Yanjun; Jiang, Xin; Guo, Quantong; Ma, Yifang; Li, Meng; Zheng, Zhiming

    2016-03-01

    The evolution of network structure and the spreading of epidemic are common coexistent dynamical processes. In most cases, network structure is treated as either static or time-varying, supposing the whole network is observed in the same time window. In this paper, we consider the epidemics spreading on a network which has both static and time-varying structures. Meanwhile, the time-varying part and the epidemic spreading are supposed to be of the same time scale. We introduce a static and activity-driven coupling (SADC) network model to characterize the coupling between the static ("strong") structure and the dynamic ("weak") structure. Epidemic thresholds of the SIS and SIR models are studied using the SADC model both analytically and numerically under various coupling strategies, where the strong structure is of homogeneous or heterogeneous degree distribution. Theoretical thresholds obtained from the SADC model can both recover and generalize the classical results in static and time-varying networks. It is demonstrated that a weak structure might make the epidemic threshold low in homogeneous networks but high in heterogeneous cases. Furthermore, we show that the weak structure has a substantive effect on the outbreak of the epidemics. This result might be useful in designing some efficient control strategies for epidemics spreading in networks.

  3. A Sensemaking Approach to Visual Analytics of Attribute-Rich Social Networks

    ERIC Educational Resources Information Center

    Gou, Liang

    2012-01-01

    Social networks have become more complex, in particular considering the fact that elements in social networks are not only abstract topological nodes and links, but contain rich social attributes and reflecting diverse social relationships. For example, in a co-authorship social network in a scientific community, nodes in the social network, which…

  4. Advanced information processing system: Input/output network management software

    NASA Technical Reports Server (NTRS)

    Nagle, Gail; Alger, Linda; Kemp, Alexander

    1988-01-01

    The purpose of this document is to provide the software requirements and specifications for the Input/Output Network Management Services for the Advanced Information Processing System. This introduction and overview section is provided to briefly outline the overall architecture and software requirements of the AIPS system before discussing the details of the design requirements and specifications of the AIPS I/O Network Management software. A brief overview of the AIPS architecture followed by a more detailed description of the network architecture.

  5. Identifying and tracking dynamic processes in social networks

    NASA Astrophysics Data System (ADS)

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

    2006-05-01

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

  6. Can neural networks compete with process calculations

    SciTech Connect

    Blaesi, J.; Jensen, B.

    1992-12-01

    Neural networks have been called a real alternative to rigorous theoretical models. A theoretical model for the calculation of refinery coker naphtha end point and coker furnace oil 90% point already was in place on the combination tower of a coking unit. Considerable data had been collected on the theoretical model during the commissioning phase and benefit analysis of the project. A neural net developed for the coker fractionator has equalled the accuracy of theoretical models, and shown the capability to handle normal operating conditions. One disadvantage of a neural network is the amount of data needed to create a good model. Anywhere from 100 to thousands of cases are needed to create a neural network model. Overall, the correlation between theoretical and neural net models for both the coker naphtha end point and the coker furnace oil 90% point was about .80; the average deviation was about 4 degrees. This indicates that the neural net model was at least as capable as the theoretical model in calculating inferred properties. 3 figs.

  7. Load Shedding Scheme in Large Pulp Mill by Using Analytic Hierarchy Process

    NASA Astrophysics Data System (ADS)

    Goh, H. H.; Kok, B. C.; Lee, S. W.; Zin, A. A. Mohd.

    2011-06-01

    Pulp mill is one of the heavy industries that consumes large amount of electricity in its production. In particular, the breakdown of the generator would cause other generators to be overloaded. Thus, load shedding scheme is the best way in handling such condition. Selected load will be shed under this scheme in order to protect the generators from being damaged. In the meantime, the subsequence loads will be shed until the generators are sufficient to provide the power to other loads. In order to determine the sequences of load shedding scheme, analytic hierarchy process (AHP) is introduced. Analytic Hierarchy Process is one of the multi-criteria decision making methods. By using this method, the priority of the load can be determined. This paper presents the theory of the alternative methods to choose the load priority in load shedding scheme for a large pulp mill.

  8. Prioritizing factors influencing nurses' satisfaction with hospital information systems: a fuzzy analytic hierarchy process approach.

    PubMed

    Kimiafar, Khalil; Sadoughi, Farahnaz; Sheikhtaheri, Abbas; Sarbaz, Masoumeh

    2014-04-01

    Our aim was to use the fuzzy analytic hierarchy process approach to prioritize the factors that influence nurses' satisfaction with a hospital information system. First, we reviewed the related literature to identify and select possible factors. Second, we developed an analytic hierarchy process framework with three main factors (quality of services, of systems, and of information) and 22 subfactors. Third, we developed a questionnaire based on pairwise comparisons and invited 10 experienced nurses who were identified through snowball sampling to rate these factors. Finally, we used Chang's fuzzy extent analysis method to compute the weights of these factors and prioritize them. We found that information quality was the most important factor (58%), followed by service quality (22%) and then system quality (19%). In conclusion, although their weights were not similar, all factors were important and should be considered in evaluating nurses' satisfaction. PMID:24469556

  9. Large-scale analytical Fourier transform of photomask layouts using graphics processing units

    NASA Astrophysics Data System (ADS)

    Sakamoto, Julia A.

    2015-10-01

    Compensation of lens-heating effects during the exposure scan in an optical lithographic system requires knowledge of the heating profile in the pupil of the projection lens. A necessary component in the accurate estimation of this profile is the total integrated distribution of light, relying on the squared modulus of the Fourier transform (FT) of the photomask layout for individual process layers. Requiring a layout representation in pixelated image format, the most common approach is to compute the FT numerically via the fast Fourier transform (FFT). However, the file size for a standard 26- mm×33-mm mask with 5-nm pixels is an overwhelming 137 TB in single precision; the data importing process alone, prior to FFT computation, can render this method highly impractical. A more feasible solution is to handle layout data in a highly compact format with vertex locations of mask features (polygons), which correspond to elements in an integrated circuit, as well as pattern symmetries and repetitions (e.g., GDSII format). Provided the polygons can decompose into shapes for which analytical FT expressions are possible, the analytical approach dramatically reduces computation time and alleviates the burden of importing extensive mask data. Algorithms have been developed for importing and interpreting hierarchical layout data and computing the analytical FT on a graphics processing unit (GPU) for rapid parallel processing, not assuming incoherent imaging. Testing was performed on the active layer of a 392- μm×297-μm virtual chip test structure with 43 substructures distributed over six hierarchical levels. The factor of improvement in the analytical versus numerical approach for importing layout data, performing CPU-GPU memory transfers, and executing the FT on a single NVIDIA Tesla K20X GPU was 1.6×104, 4.9×103, and 3.8×103, respectively. Various ideas for algorithm enhancements will be discussed.

  10. Analytical modeling and sensor monitoring for optimal processing of polymeric composite material systems

    NASA Technical Reports Server (NTRS)

    Loos, Alfred C.; Weideman, Mark H.; Kranbuehl, David E.; Long, Edward R., Jr.

    1991-01-01

    Process simulation models and cure monitoring sensors are discussed for use in optimal processing of fiber-reinforced composites. Analytical models relate the specified temperature and pressure cure cycle to the thermal, chemical, and physical processes occurring in the composite during consolidation and cure. Frequency-dependent electromagnetic sensing (FDEMS) is described as an in situ sensor for monitoring the composite curing process and for verification of process simulation models. A model for resin transfer molding of textile composites is used to illustrate the predictive capabilities of a process simulation model. The model is used to calculate the resin infiltration time, fiber volume fraction, resin viscosity, and resin degree of cure. Results of the model are compared with in situ FDEMS measurements.

  11. Optical Multiple Access Network (OMAN) for advanced processing satellite applications

    NASA Technical Reports Server (NTRS)

    Mendez, Antonio J.; Gagliardi, Robert M.; Park, Eugene; Ivancic, William D.; Sherman, Bradley D.

    1991-01-01

    An OMAN breadboard for exploring advanced processing satellite circuit switch applications is introduced. Network architecture, hardware trade offs, and multiple user interference issues are presented. The breadboard test set up and experimental results are discussed.

  12. Hardware and networks for Gaia data processing

    NASA Astrophysics Data System (ADS)

    O'Mullane, W.; Beck, M.; de Angeli, F.; Hoar, J.; Martino, M.; Passot, X.; Portell, J.

    2011-02-01

    A considerable amount of computing power is needed for Gaia data processing during the mission. A pan European system of six data centres are working together to perform different parts of the processing and combine the results. Data processing estimates suggest around 1020 FLOP total processing is required. Data will be transferred daily around Europe and with a final raw data volume approaching 100 TB. With these needs in mind the centres are already gearing up for Gaia. We present the status and plans of the Gaia Data Processing Centres.

  13. Relative frequencies of constrained events in stochastic processes: An analytical approach

    NASA Astrophysics Data System (ADS)

    Rusconi, S.; Akhmatskaya, E.; Sokolovski, D.; Ballard, N.; de la Cal, J. C.

    2015-10-01

    The stochastic simulation algorithm (SSA) and the corresponding Monte Carlo (MC) method are among the most common approaches for studying stochastic processes. They relies on knowledge of interevent probability density functions (PDFs) and on information about dependencies between all possible events. Analytical representations of a PDF are difficult to specify in advance, in many real life applications. Knowing the shapes of PDFs, and using experimental data, different optimization schemes can be applied in order to evaluate probability density functions and, therefore, the properties of the studied system. Such methods, however, are computationally demanding, and often not feasible. We show that, in the case where experimentally accessed properties are directly related to the frequencies of events involved, it may be possible to replace the heavy Monte Carlo core of optimization schemes with an analytical solution. Such a replacement not only provides a more accurate estimation of the properties of the process, but also reduces the simulation time by a factor of order of the sample size (at least ≈104 ). The proposed analytical approach is valid for any choice of PDF. The accuracy, computational efficiency, and advantages of the method over MC procedures are demonstrated in the exactly solvable case and in the evaluation of branching fractions in controlled radical polymerization (CRP) of acrylic monomers. This polymerization can be modeled by a constrained stochastic process. Constrained systems are quite common, and this makes the method useful for various applications.

  14. Relative frequencies of constrained events in stochastic processes: An analytical approach.

    PubMed

    Rusconi, S; Akhmatskaya, E; Sokolovski, D; Ballard, N; de la Cal, J C

    2015-10-01

    The stochastic simulation algorithm (SSA) and the corresponding Monte Carlo (MC) method are among the most common approaches for studying stochastic processes. They relies on knowledge of interevent probability density functions (PDFs) and on information about dependencies between all possible events. Analytical representations of a PDF are difficult to specify in advance, in many real life applications. Knowing the shapes of PDFs, and using experimental data, different optimization schemes can be applied in order to evaluate probability density functions and, therefore, the properties of the studied system. Such methods, however, are computationally demanding, and often not feasible. We show that, in the case where experimentally accessed properties are directly related to the frequencies of events involved, it may be possible to replace the heavy Monte Carlo core of optimization schemes with an analytical solution. Such a replacement not only provides a more accurate estimation of the properties of the process, but also reduces the simulation time by a factor of order of the sample size (at least ≈10(4)). The proposed analytical approach is valid for any choice of PDF. The accuracy, computational efficiency, and advantages of the method over MC procedures are demonstrated in the exactly solvable case and in the evaluation of branching fractions in controlled radical polymerization (CRP) of acrylic monomers. This polymerization can be modeled by a constrained stochastic process. Constrained systems are quite common, and this makes the method useful for various applications. PMID:26565363

  15. Effects of pre-analytical processes on blood samples used in metabolomics studies.

    PubMed

    Yin, Peiyuan; Lehmann, Rainer; Xu, Guowang

    2015-07-01

    Every day, analytical and bio-analytical chemists make sustained efforts to improve the sensitivity, specificity, robustness, and reproducibility of their methods. Especially in targeted and non-targeted profiling approaches, including metabolomics analysis, these objectives are not easy to achieve; however, robust and reproducible measurements and low coefficients of variation (CV) are crucial for successful metabolomics approaches. Nevertheless, all efforts from the analysts are in vain if the sample quality is poor, i.e. if preanalytical errors are made by the partner during sample collection. Preanalytical risks and errors are more common than expected, even when standard operating procedures (SOP) are used. This risk is particularly high in clinical studies, and poor sample quality may heavily bias the CV of the final analytical results, leading to disappointing outcomes of the study and consequently, although unjustified, to critical questions about the analytical performance of the approach from the partner who provided the samples. This review focuses on the preanalytical phase of liquid chromatography-mass spectrometry-driven metabolomics analysis of body fluids. Several important preanalytical factors that may seriously affect the profile of the investigated metabolome in body fluids, including factors before sample collection, blood drawing, subsequent handling of the whole blood (transportation), processing of plasma and serum, and inadequate conditions for sample storage, will be discussed. In addition, a detailed description of latent effects on the stability of the blood metabolome and a suggestion for a practical procedure to circumvent risks in the preanalytical phase will be given. PMID:25736245

  16. Bipartite memory network architectures for parallel processing

    SciTech Connect

    Smith, W.; Kale, L.V. . Dept. of Computer Science)

    1990-01-01

    Parallel architectures are boradly classified as either shared memory or distributed memory architectures. In this paper, the authors propose a third family of architectures, called bipartite memory network architectures. In this architecture, processors and memory modules constitute a bipartite graph, where each processor is allowed to access a small subset of the memory modules, and each memory module allows access from a small set of processors. The architecture is particularly suitable for computations requiring dynamic load balancing. The authors explore the properties of this architecture by examining the Perfect Difference set based topology for the graph. Extensions of this topology are also suggested.

  17. Large-Scale Neural Network for Sentence Processing

    ERIC Educational Resources Information Center

    Cooke, Ayanna; Grossman, Murray; DeVita, Christian; Gonzalez-Atavales, Julio; Moore, Peachie; Chen, Willis; Gee, James; Detre, John

    2006-01-01

    Our model of sentence comprehension includes at least grammatical processes important for structure-building, and executive resources such as working memory that support these grammatical processes. We hypothesized that a core network of brain regions supports grammatical processes, and that additional brain regions are activated depending on the…

  18. On-board processing satellite network architectures for broadband ISDN

    NASA Technical Reports Server (NTRS)

    Inukai, Thomas; Faris, Faris; Shyy, Dong-Jye

    1992-01-01

    Onboard baseband processing architectures for future satellite broadband integrated services digital networks (B-ISDN's) are addressed. To assess the feasibility of implementing satellite B-ISDN services, critical design issues, such as B-ISDN traffic characteristics, transmission link design, and a trade-off between onboard circuit and fast packet switching, are analyzed. Examples of the two types of switching mechanisms and potential onboard network control functions are presented. A sample network architecture is also included to illustrate a potential onboard processing system.

  19. Toward an Analytic Framework of Interdisciplinary Reasoning and Communication (IRC) Processes in Science

    NASA Astrophysics Data System (ADS)

    Shen, Ji; Sung, Shannon; Zhang, Dongmei

    2015-11-01

    Students need to think and work across disciplinary boundaries in the twenty-first century. However, it is unclear what interdisciplinary thinking means and how to analyze interdisciplinary interactions in teamwork. In this paper, drawing on multiple theoretical perspectives and empirical analysis of discourse contents, we formulate a theoretical framework that helps analyze interdisciplinary reasoning and communication (IRC) processes in interdisciplinary collaboration. Specifically, we propose four interrelated IRC processes-integration, translation, transfer, and transformation, and develop a corresponding analytic framework. We apply the framework to analyze two meetings of a project that aims to develop interdisciplinary science assessment items. The results illustrate that the framework can help interpret the interdisciplinary meeting dynamics and patterns. Our coding process and results also suggest that these IRC processes can be further examined in terms of interconnected sub-processes. We also discuss the implications of using the framework in conceptualizing, practicing, and researching interdisciplinary learning and teaching in science education.

  20. Electro-spun organic nanofibers elaboration process investigations using comparative analytical solutions.

    PubMed

    Colantoni, A; Boubaker, K

    2014-01-30

    In this paper Enhanced Variational Iteration Method, EVIM is proposed, along with the BPES, for solving Bratu equation which appears in the particular elecotrospun nanofibers fabrication process framework. Elecotrospun organic nanofibers, with diameters less than 1/4 microns have been used in non-wovens and filtration industries for a broad range of filtration applications in the last decade. Electro-spinning process has been associated to Bratu equation through thermo-electro-hydrodynamics balance equations. Analytical solutions have been proposed, discussed and compared. PMID:24299778

  1. An analytical hierarchy process for decision making of high-level-waste management

    SciTech Connect

    Wang, J.H.C.; Jang, W.

    1995-12-01

    To prove the existence value of nuclear technology for the world of post cold war, demonstration of safe rad-waste disposal is essential. High-level-waste (HLW) certainly is the key issue to be resolved. To assist a rational and persuasive process on various disposal options, an Analytical Hierarchy Process (AHP) for the decision making of HLW management is presented. The basic theory and rationale are discussed, and applications are shown to illustrate the usefulness of the AHP. The authors wish that the AHP can provide a better direction for the current doomed situations of Taiwan nuclear industry, and to exchange with other countries for sharing experiences on the HLW management.

  2. Solving a layout design problem by analytic hierarchy process (AHP) and data envelopment analysis (DEA) approach

    NASA Astrophysics Data System (ADS)

    Tuzkaya, Umut R.; Eser, Arzum; Argon, Goner

    2004-02-01

    Today, growing amounts of waste due to fast consumption rate of products started an irreversible environmental pollution and damage. A considerable part of this waste is caused by packaging material. With the realization of this fact, various waste policies have taken important steps. Here we considered a firm, where waste Aluminum constitutes majority of raw materials for this fir0m. In order to achieve a profitable recycling process, plant layout should be well designed. In this study, we propose a two-step approach involving Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) to solve facility layout design problems. A case example is considered to demonstrate the results achieved.

  3. Parallel processing data network of master and slave transputers controlled by a serial control network

    DOEpatents

    Crosetto, D.B.

    1996-12-31

    The present device provides for a dynamically configurable communication network having a multi-processor parallel processing system having a serial communication network and a high speed parallel communication network. The serial communication network is used to disseminate commands from a master processor to a plurality of slave processors to effect communication protocol, to control transmission of high density data among nodes and to monitor each slave processor`s status. The high speed parallel processing network is used to effect the transmission of high density data among nodes in the parallel processing system. Each node comprises a transputer, a digital signal processor, a parallel transfer controller, and two three-port memory devices. A communication switch within each node connects it to a fast parallel hardware channel through which all high density data arrives or leaves the node. 6 figs.

  4. Parallel processing data network of master and slave transputers controlled by a serial control network

    DOEpatents

    Crosetto, Dario B.

    1996-01-01

    The present device provides for a dynamically configurable communication network having a multi-processor parallel processing system having a serial communication network and a high speed parallel communication network. The serial communication network is used to disseminate commands from a master processor (100) to a plurality of slave processors (200) to effect communication protocol, to control transmission of high density data among nodes and to monitor each slave processor's status. The high speed parallel processing network is used to effect the transmission of high density data among nodes in the parallel processing system. Each node comprises a transputer (104), a digital signal processor (114), a parallel transfer controller (106), and two three-port memory devices. A communication switch (108) within each node (100) connects it to a fast parallel hardware channel (70) through which all high density data arrives or leaves the node.

  5. Understanding wax screen-printing: a novel patterning process for microfluidic cloth-based analytical devices.

    PubMed

    Liu, Min; Zhang, Chunsun; Liu, Feifei

    2015-09-01

    In this work, we first introduce the fabrication of microfluidic cloth-based analytical devices (μCADs) using a wax screen-printing approach that is suitable for simple, inexpensive, rapid, low-energy-consumption and high-throughput preparation of cloth-based analytical devices. We have carried out a detailed study on the wax screen-printing of μCADs and have obtained some interesting results. Firstly, an analytical model is established for the spreading of molten wax in cloth. Secondly, a new wax screen-printing process has been proposed for fabricating μCADs, where the melting of wax into the cloth is much faster (∼5 s) and the heating temperature is much lower (75 °C). Thirdly, the experimental results show that the patterning effects of the proposed wax screen-printing method depend to a certain extent on types of screens, wax melting temperatures and melting time. Under optimized conditions, the minimum printing width of hydrophobic wax barrier and hydrophilic channel is 100 μm and 1.9 mm, respectively. Importantly, the developed analytical model is also well validated by these experiments. Fourthly, the μCADs fabricated by the presented wax screen-printing method are used to perform a proof-of-concept assay of glucose or protein in artificial urine with rapid high-throughput detection taking place on a 48-chamber cloth-based device and being performed by a visual readout. Overall, the developed cloth-based wax screen-printing and arrayed μCADs should provide a new research direction in the development of advanced sensor arrays for detection of a series of analytes relevant to many diverse applications. PMID:26388382

  6. Basic emotion processing and the adolescent brain: Task demands, analytic approaches, and trajectories of changes.

    PubMed

    Del Piero, Larissa B; Saxbe, Darby E; Margolin, Gayla

    2016-06-01

    Early neuroimaging studies suggested that adolescents show initial development in brain regions linked with emotional reactivity, but slower development in brain structures linked with emotion regulation. However, the increased sophistication of adolescent brain research has made this picture more complex. This review examines functional neuroimaging studies that test for differences in basic emotion processing (reactivity and regulation) between adolescents and either children or adults. We delineated different emotional processing demands across the experimental paradigms in the reviewed studies to synthesize the diverse results. The methods for assessing change (i.e., analytical approach) and cohort characteristics (e.g., age range) were also explored as potential factors influencing study results. Few unifying dimensions were found to successfully distill the results of the reviewed studies. However, this review highlights the potential impact of subtle methodological and analytic differences between studies, need for standardized and theory-driven experimental paradigms, and necessity of analytic approaches that are can adequately test the trajectories of developmental change that have recently been proposed. Recommendations for future research highlight connectivity analyses and non-linear developmental trajectories, which appear to be promising approaches for measuring change across adolescence. Recommendations are made for evaluating gender and biological markers of development beyond chronological age. PMID:27038840

  7. Global tree network for computing structures enabling global processing operations

    DOEpatents

    Blumrich; Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Hoenicke, Dirk; Steinmacher-Burow, Burkhard D.; Takken, Todd E.; Vranas, Pavlos M.

    2010-01-19

    A system and method for enabling high-speed, low-latency global tree network communications among processing nodes interconnected according to a tree network structure. The global tree network enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices are included that interconnect the nodes of the tree via links to facilitate performance of low-latency global processing operations at nodes of the virtual tree and sub-tree structures. The global operations performed include one or more of: broadcast operations downstream from a root node to leaf nodes of a virtual tree, reduction operations upstream from leaf nodes to the root node in the virtual tree, and point-to-point message passing from any node to the root node. The global tree network is configurable to provide global barrier and interrupt functionality in asynchronous or synchronized manner, and, is physically and logically partitionable.

  8. Machine learning and predictive data analytics enabling metrology and process control in IC fabrication

    NASA Astrophysics Data System (ADS)

    Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.

    2015-03-01

    Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

  9. Laser processes and analytics for high power 3D battery materials

    NASA Astrophysics Data System (ADS)

    Pfleging, W.; Zheng, Y.; Mangang, M.; Bruns, M.; Smyrek, P.

    2016-03-01

    Laser processes for cutting, modification and structuring of energy storage materials such as electrodes, separator materials and current collectors have a great potential in order to minimize the fabrication costs and to increase the performance and operational lifetime of high power lithium-ion-batteries applicable for stand-alone electric energy storage devices and electric vehicles. Laser direct patterning of battery materials enable a rather new technical approach in order to adjust 3D surface architectures and porosity of composite electrode materials such as LiCoO2, LiMn2O4, LiFePO4, Li(NiMnCo)O2, and Silicon. The architecture design, the increase of active surface area, and the porosity of electrodes or separator layers can be controlled by laser processes and it was shown that a huge impact on electrolyte wetting, lithium-ion diffusion kinetics, cell life-time and cycling stability can be achieved. In general, the ultrafast laser processing can be used for precise surface texturing of battery materials. Nevertheless, regarding cost-efficient production also nanosecond laser material processing can be successfully applied for selected types of energy storage materials. A new concept for an advanced battery manufacturing including laser materials processing is presented. For developing an optimized 3D architecture for high power composite thick film electrodes electrochemical analytics and post mortem analytics using laser-induced breakdown spectroscopy were performed. Based on mapping of lithium in composite electrodes, an analytical approach for studying chemical degradation in structured and unstructured lithium-ion batteries will be presented.

  10. Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy.

    PubMed

    Wirsich, Jonathan; Perry, Alistair; Ridley, Ben; Proix, Timothée; Golos, Mathieu; Bénar, Christian; Ranjeva, Jean-Philippe; Bartolomei, Fabrice; Breakspear, Michael; Jirsa, Viktor; Guye, Maxime

    2016-01-01

    The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures. PMID:27330970

  11. Developing an intelligence analysis process through social network analysis

    NASA Astrophysics Data System (ADS)

    Waskiewicz, Todd; LaMonica, Peter

    2008-04-01

    Intelligence analysts are tasked with making sense of enormous amounts of data and gaining an awareness of a situation that can be acted upon. This process can be extremely difficult and time consuming. Trying to differentiate between important pieces of information and extraneous data only complicates the problem. When dealing with data containing entities and relationships, social network analysis (SNA) techniques can be employed to make this job easier. Applying network measures to social network graphs can identify the most significant nodes (entities) and edges (relationships) and help the analyst further focus on key areas of concern. Strange developed a model that identifies high value targets such as centers of gravity and critical vulnerabilities. SNA lends itself to the discovery of these high value targets and the Air Force Research Laboratory (AFRL) has investigated several network measures such as centrality, betweenness, and grouping to identify centers of gravity and critical vulnerabilities. Using these network measures, a process for the intelligence analyst has been developed to aid analysts in identifying points of tactical emphasis. Organizational Risk Analyzer (ORA) and Terrorist Modus Operandi Discovery System (TMODS) are the two applications used to compute the network measures and identify the points to be acted upon. Therefore, the result of leveraging social network analysis techniques and applications will provide the analyst and the intelligence community with more focused and concentrated analysis results allowing them to more easily exploit key attributes of a network, thus saving time, money, and manpower.

  12. Letter of Intent for RPP Characterization Program Process Engineering and Hanford Analytical Services and Characterization Project

    SciTech Connect

    ADAMS, M.R.

    2000-02-25

    The Characterization Project level of success achieved by the River Protection Project (RPP) is determined by the effectiveness of several organizations across RPP working together. The requirements, expectations, interrelationships, and performance criteria for each of these organizations were examined in order to understand the performances necessary to achieve characterization objectives. This Letter of Intent documents the results of the above examination. It formalizes the details of interfaces, working agreements, and requirements for obtaining and transferring tank waste samples from the Tank Farm System (RPP Process Engineering, Characterization Project Operations, and RPP Quality Assurance) to the characterization laboratory complex (222-S Laboratory, Waste Sampling and Characterization Facility, and the Hanford Analytical Service Program) and for the laboratory complex analysis and reporting of analytical results.

  13. A performance data network for solar process heat systems

    SciTech Connect

    Barker, G.; Hale, M.J.

    1996-03-01

    A solar process heat (SPH) data network has been developed to access remote-site performance data from operational solar heat systems. Each SPH system in the data network is outfitted with monitoring equipment and a datalogger. The datalogger is accessed via modem from the data network computer at the National Renewable Energy Laboratory (NREL). The dataloggers collect both ten-minute and hourly data and download it to the data network every 24-hours for archiving, processing, and plotting. The system data collected includes energy delivered (fluid temperatures and flow rates) and site meteorological conditions, such as solar insolation and ambient temperature. The SPH performance data network was created for collecting performance data from SPH systems that are serving in industrial applications or from systems using technologies that show promise for industrial applications. The network will be used to identify areas of SPH technology needing further development, to correlate computer models with actual performance, and to improve the credibility of SPH technology. The SPH data network also provides a centralized bank of user-friendly performance data that will give prospective SPH users an indication of how actual systems perform. There are currently three systems being monitored and archived under the SPH data network: two are parabolic trough systems and the third is a flat-plate system. The two trough systems both heat water for prisons; the hot water is used for personal hygiene, kitchen operations, and laundry. The flat plate system heats water for meat processing at a slaughter house. We plan to connect another parabolic trough system to the network during the first months of 1996. We continue to look for good examples of systems using other types of collector technologies and systems serving new applications (such as absorption chilling) to include in the SPH performance data network.

  14. Recovery processes and dynamics in single and interdependent networks

    NASA Astrophysics Data System (ADS)

    Majdandzic, Antonio

    Systems composed of dynamical networks --- such as the human body with its biological networks or the global economic network consisting of regional clusters --- often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread, and recovery. Here we develop a model for such systems and find phase diagrams for single and interacting networks. By investigating networks with a small number of nodes, where finite-size effects are pronounced, we describe the spontaneous recovery phenomenon present in these systems. In the case of interacting networks the phase diagram is very rich and becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions, and two forbidden transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyze an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model.

  15. IJA: an efficient algorithm for query processing in sensor networks.

    PubMed

    Lee, Hyun Chang; Lee, Young Jae; Lim, Ji Hyang; Kim, Dong Hwa

    2011-01-01

    One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm. PMID:22319375

  16. Simulation of dynamic processes with adaptive neural networks.

    SciTech Connect

    Tzanos, C. P.

    1998-02-03

    Many industrial processes are highly non-linear and complex. Their simulation with first-principle or conventional input-output correlation models is not satisfactory, either because the process physics is not well understood, or it is so complex that direct simulation is either not adequately accurate, or it requires excessive computation time, especially for on-line applications. Artificial intelligence techniques (neural networks, expert systems, fuzzy logic) or their combination with simple process-physics models can be effectively used for the simulation of such processes. Feedforward (static) neural networks (FNNs) can be used effectively to model steady-state processes. They have also been used to model dynamic (time-varying) processes by adding to the network input layer input nodes that represent values of input variables at previous time steps. The number of previous time steps is problem dependent and, in general, can be determined after extensive testing. This work demonstrates that for dynamic processes that do not vary fast with respect to the retraining time of the neural network, an adaptive feedforward neural network can be an effective simulator that is free of the complexities introduced by the use of input values at previous time steps.

  17. Scalable Networked Information Processing Environment (SNIPE)

    SciTech Connect

    Fagg, G.E.; Moore, K.; Dongarra, J.J. |; Geist, A.

    1997-11-01

    SNIPE is a metacomputing system that aims to provide a reliable, secure, fault tolerant environment for long term distributed computing applications and data stores across the global Internet. This system combines global naming and replication of both processing and data to support large scale information processing applications leading to better availability and reliability than currently available with typical cluster computing and/or distributed computer environments.

  18. Competing Contact Processes on Homogeneous Networks with Tunable Clusterization

    NASA Astrophysics Data System (ADS)

    Rybak, Marcin; Kułakowski, Krzysztof

    2013-03-01

    We investigate two homogeneous networks: the Watts-Strogatz network with mean degree ⟨k⟩ = 4 and the Erdös-Rényi network with ⟨k⟩ = 10. In both kinds of networks, the clustering coefficient C is a tunable control parameter. The network is an area of two competing contact processes, where nodes can be in two states, S or D. A node S becomes D with probability 1 if at least two its mutually linked neighbors are D. A node D becomes S with a given probability p if at least one of its neighbors is S. The competition between the processes is described by a phase diagram, where the critical probability pc depends on the clustering coefficient C. For p > pc the rate of state S increases in time, seemingly to dominate in the whole system. Below pc, the majority of nodes is in the D-state. The numerical results indicate that for the Watts-Strogatz network the D-process is activated at the finite value of the clustering coefficient C, close to 0.3. On the contrary, for the Erdös-Rényi network the transition is observed at the whole investigated range of C.

  19. Quantum Information Processing with Modular Networks

    NASA Astrophysics Data System (ADS)

    Crocker, Clayton; Inlek, Ismail V.; Hucul, David; Sosnova, Ksenia; Vittorini, Grahame; Monroe, Chris

    2015-05-01

    Trapped atomic ions are qubit standards for the production of entangled states in quantum information science and metrology applications. Trapped ions can exhibit very long coherence times, external fields can drive strong local interactions via phonons, and remote qubits can be entangled via photons. Transferring quantum information across spatially separated ion trap modules for a scalable quantum network architecture relies on the juxtaposition of both phononic and photonic buses. We report the successful combination of these protocols within and between two ion trap modules on a unit structure of this architecture where the remote entanglement generation rate exceeds the experimentally measured decoherence rate. Additionally, we report an experimental implementation of a technique to maintain phase coherence between spatially and temporally distributed quantum gate operations, a crucial prerequisite for scalability. Finally, we discuss our progress towards addressing the issue of uncontrolled cross-talk between photonic qubits and memory qubits by implementing a second ion species, Barium, to generate the photonic link. This work is supported by the ARO with funding from the IARPA MQCO program, the DARPA Quiness Program, the ARO MURI on Hybrid Quantum Circuits, the AFOSR MURI on Quantum Transduction, and the NSF Physics Frontier Center at JQI.

  20. The Rondonia Lightning Detection Network: Network Description, Science Objectives, Data Processing/Archival Methodology, and First Results

    NASA Technical Reports Server (NTRS)

    Blakelee, Richard

    1999-01-01

    A four station Advanced Lightning Direction Finder (ALDF) network was recently established in the state of Rondonia in western Brazil through a collaboration of U.S. and Brazilian participants from NASA, INPE, INMET, and various universities. The network utilizes ALDF IMPACT (Improved Accuracy from Combined Technology) sensors to provide cloud-to-ground lightning observations (i.e., stroke/flash locations, signal amplitude, and polarity) using both time-of-arrival and magnetic direction finding techniques. The observations are collected, processed and archived at a central site in Brasilia and at the NASA/Marshall Space Flight Center (MSFC) in Huntsville, Alabama. Initial, non-quality assured quick-look results are made available in near real-time over the internet. The network will remain deployed for several years to provide ground truth data for the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measurement Mission (TRMM) satellite which was launched in November 1997. The measurements will also be used to investigate the relationship between the electrical, microphysical and kinematic properties of tropical convection. In addition, the long-term observations from this network will contribute in establishing a regional lightning climatological data base, supplementing other data bases in Brazil that already exist or may soon be implemented. Analytic inversion algorithms developed at NASA/MSFC are now being applied to the Rondonian ALDF lightning observations to obtain site error corrections and improved location retrievals. The processing methodology and the initial results from an analysis of the first 6 months of network operations will be presented.

  1. The Rondonia Lightning Detection Network: Network Description, Science Objectives, Data Processing/Archival Methodology, and First Results

    NASA Technical Reports Server (NTRS)

    Blakeslee, Rich; Bailey, Jeff; Koshak, Bill

    1999-01-01

    A four station Advanced Lightning Direction Finder (ALDF) network was recently established in the state of Rondonia in western Brazil through a collaboration of U.S. and Brazilian participants from NASA, INPE, INMET, and various universities. The network utilizes ALDF IMPACT (Improved Accuracy from Combined Technology) sensors to provide cloud-to-ground lightning observations (i.e., stroke/flash locations, signal amplitude, and polarity) using both time-of-arrival and magnetic direction finding techniques. The observations are collected, processed and archived at a central site in Brasilia and at the NASA/ Marshall Space Flight Center (MSFC) in Huntsville, Alabama. Initial, non-quality assured quick-look results are made available in near real-time over the internet. The network will remain deployed for several years to provide ground truth data for the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite which was launched in November 1997. The measurements will also be used to investigate the relationship between the electrical, microphysical and kinematic properties of tropical convection. In addition, the long-term observations from this network will contribute in establishing a regional lightning climatological data base, supplementing other data bases in Brazil that already exist or may soon be implemented. Analytic inversion algorithms developed at NASA/Marshall Space Flight Center (MSFC) are now being applied to the Rondonian ALDF lightning observations to obtain site error corrections and improved location retrievals. The processing methodology and the initial results from an analysis of the first 6 months of network operations will be presented.

  2. Towards the understanding of network information processing in biology

    NASA Astrophysics Data System (ADS)

    Singh, Vijay

    Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.

  3. Experimental and analytical investigation of the seizure process in aluminum-silicon alloy/steel tribocontacts

    NASA Astrophysics Data System (ADS)

    He, Xiaozhou

    1998-12-01

    This research is an experimental and analytical investigation of the scuffing/seizure mechanism in Al-Si alloy/steel tribocontacts. An analytical model is developed based on analyses and experiments to predict scuffing/seizure failure in Al-Si alloy/steel tribocontacts, which can be applied to tribo-components in engines, refrigerators and air conditioners. The wear and scuffing/seizure experiments have been conducted through a block-on-ring tester for 339 and ESE-M2A137 Al-Si alloys under the dry and boundary lubrication conditions. The experimental research consists of: (a) wear debris generation and EDX analysis, (b) wear surface morphological analysis, (c) scuffing/seizure mechanism and process analysis, (d) scuffing/seizure PV curves under the dry contact and boundary lubrication, and (e) effects of several main factors on scuffing/seizure. The analytical research includes the following: (a) the investigation of the scuffing/seizure mechanisms in the Al-Si alloy/steel tribocontacts, (b) 3-D asperity contact pressures for longitudinal, transverse and isotropic surface roughness profiles, (c) 3-D surface asperity contact temperature rise due to the friction, (d) failure analyses of the various lubricating films, (e) analyses of the temperature dependence of surface tangential traction and shear strength in a surface layer of Al-Si alloy, (f) the scuffing/seizure failure analytical model under dry contact and boundary lubrication. The analytical model is based on the new hypothesis of three defense lines against scuffing/seizure failure: the adsorbed oil film, oxide film and the ratio of surface tangential traction with the shear strength in a surface layer. These two films together with a surface layer itself form three defense lines against scuffing/seizure. The surface tangential traction exceeds the bulk shear strength in a surface layer of Al-Si alloy is the necessary and sufficient condition for the scuffing/seizure occurrence. The analytical model has a

  4. Analytical Solution of Steady State Equations for Chemical Reaction Networks with Bilinear Rate Laws

    PubMed Central

    Halász, Ádám M.; Lai, Hong-Jian; McCabe, Meghan M.; Radhakrishnan, Krishnan; Edwards, Jeremy S.

    2014-01-01

    True steady states are a rare occurrence in living organisms, yet their knowledge is essential for quasi-steady state approximations, multistability analysis, and other important tools in the investigation of chemical reaction networks (CRN) used to describe molecular processes on the cellular level. Here we present an approach that can provide closed form steady-state solutions to complex systems, resulting from CRN with binary reactions and mass-action rate laws. We map the nonlinear algebraic problem of finding steady states onto a linear problem in a higher dimensional space. We show that the linearized version of the steady state equations obeys the linear conservation laws of the original CRN. We identify two classes of problems for which complete, minimally parameterized solutions may be obtained using only the machinery of linear systems and a judicious choice of the variables used as free parameters. We exemplify our method, providing explicit formulae, on CRN describing signal initiation of two important types of RTK receptor-ligand systems, VEGF and EGF-ErbB1. PMID:24334389

  5. A DNA network as an information processing system.

    PubMed

    Santini, Cristina Costa; Bath, Jonathan; Turberfield, Andrew J; Tyrrell, Andy M

    2012-01-01

    Biomolecular systems that can process information are sought for computational applications, because of their potential for parallelism and miniaturization and because their biocompatibility also makes them suitable for future biomedical applications. DNA has been used to design machines, motors, finite automata, logic gates, reaction networks and logic programs, amongst many other structures and dynamic behaviours. Here we design and program a synthetic DNA network to implement computational paradigms abstracted from cellular regulatory networks. These show information processing properties that are desirable in artificial, engineered molecular systems, including robustness of the output in relation to different sources of variation. We show the results of numerical simulations of the dynamic behaviour of the network and preliminary experimental analysis of its main components. PMID:22606034

  6. Cortical network architecture for context processing in primate brain

    PubMed Central

    Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka

    2015-01-01

    Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition. DOI: http://dx.doi.org/10.7554/eLife.06121.001 PMID:26416139

  7. Prediction and control of chaotic processes using nonlinear adaptive networks

    SciTech Connect

    Jones, R.D.; Barnes, C.W.; Flake, G.W.; Lee, K.; Lewis, P.S.; O'Rouke, M.K.; Qian, S.

    1990-01-01

    We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We then present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series, tidal prediction in Venice lagoon, finite differencing, sonar transient detection, control of nonlinear processes, control of a negative ion source, balancing a double inverted pendulum and design advice for free electron lasers and laser fusion targets.

  8. Information processing in neural networks with the complex dynamic thresholds

    NASA Astrophysics Data System (ADS)

    Kirillov, S. Yu.; Nekorkin, V. I.

    2016-06-01

    A control mechanism of the information processing in neural networks is investigated, based on the complex dynamic threshold of the neural excitation. The threshold properties are controlled by the slowly varying synaptic current. The dynamic threshold shows high sensitivity to the rate of the synaptic current variation. It allows both to realize flexible selective tuning of the network elements and to provide nontrivial regimes of neural coding.

  9. A results-based process for evaluation of diverse visual analytics tools

    NASA Astrophysics Data System (ADS)

    Rubin, Gary; Berger, David H.

    2013-05-01

    With the pervasiveness of still and full-motion imagery in commercial and military applications, the need to ingest and analyze these media has grown rapidly in recent years. Additionally, video hosting and live camera websites provide a near real-time view of our changing world with unprecedented spatial coverage. To take advantage of these controlled and crowd-sourced opportunities, sophisticated visual analytics (VA) tools are required to accurately and efficiently convert raw imagery into usable information. Whether investing in VA products or evaluating algorithms for potential development, it is important for stakeholders to understand the capabilities and limitations of visual analytics tools. Visual analytics algorithms are being applied to problems related to Intelligence, Surveillance, and Reconnaissance (ISR), facility security, and public safety monitoring, to name a few. The diversity of requirements means that a onesize- fits-all approach to performance assessment will not work. We present a process for evaluating the efficacy of algorithms in real-world conditions, thereby allowing users and developers of video analytics software to understand software capabilities and identify potential shortcomings. The results-based approach described in this paper uses an analysis of end-user requirements and Concept of Operations (CONOPS) to define Measures of Effectiveness (MOEs), test data requirements, and evaluation strategies. We define metrics that individually do not fully characterize a system, but when used together, are a powerful way to reveal both strengths and weaknesses. We provide examples of data products, such as heatmaps, performance maps, detection timelines, and rank-based probability-of-detection curves.

  10. Development of balanced key performance indicators for emergency departments strategic dashboards following analytic hierarchical process.

    PubMed

    Safdari, Reza; Ghazisaeedi, Marjan; Mirzaee, Mahboobeh; Farzi, Jebrail; Goodini, Azadeh

    2014-01-01

    Dynamic reporting tools, such as dashboards, should be developed to measure emergency department (ED) performance. However, choosing an effective balanced set of performance measures and key performance indicators (KPIs) is a main challenge to accomplish this. The aim of this study was to develop a balanced set of KPIs for use in ED strategic dashboards following an analytic hierarchical process. The study was carried out in 2 phases: constructing ED performance measures based on balanced scorecard perspectives and incorporating them into analytic hierarchical process framework to select the final KPIs. The respondents placed most importance on ED internal processes perspective especially on measures related to timeliness and accessibility of care in ED. Some measures from financial, customer, and learning and growth perspectives were also selected as other top KPIs. Measures of care effectiveness and care safety were placed as the next priorities too. The respondents placed least importance on disease-/condition-specific "time to" measures. The methodology can be presented as a reference model for development of KPIs in various performance related areas based on a consistent and fair approach. Dashboards that are designed based on such a balanced set of KPIs will help to establish comprehensive performance measurements and fair benchmarks and comparisons. PMID:25350022