A SPATIALLY EXPLICIT HIERARCHICAL APPROACH TO MODELING COMPLEX ECOLOGICAL SYSTEMS: THEORY AND APPLICATIONS. (R827676)

EPA Science Inventory

Ecological systems are generally considered among the most complex because they are characterized by a large number of diverse components, nonlinear interactions, scale multiplicity, and spatial heterogeneity. Hierarchy theory, as well as empirical evidence, suggests that comp...

Primordial Evolution in the Finitary Process Soup

NASA Astrophysics Data System (ADS)

Görnerup, Olof; Crutchfield, James P.

A general and basic model of primordial evolution—a soup of reacting finitary and discrete processes—is employed to identify and analyze fundamental mechanisms that generate and maintain complex structures in prebiotic systems. The processes—ɛ-machines as defined in computational mechanics—and their interaction networks both provide well defined notions of structure. This enables us to quantitatively demonstrate hierarchical self-organization in the soup in terms of complexity. We found that replicating processes evolve the strategy of successively building higher levels of organization by autocatalysis. Moreover, this is facilitated by local components that have low structural complexity, but high generality. In effect, the finitary process soup spontaneously evolves a selection pressure that favors such components. In light of the finitary process soup's generality, these results suggest a fundamental law of hierarchical systems: global complexity requires local simplicity.

Star formation in a hierarchical model for Cloud Complexes

NASA Astrophysics Data System (ADS)

Sanchez, N.; Parravano, A.

The effects of the external and initial conditions on the star formation processes in Molecular Cloud Complexes are examined in the context of a schematic model. The model considers a hierarchical system with five predefined phases: warm gas, neutral gas, low density molecular gas, high density molecular gas and protostars. The model follows the mass evolution of each substructure by computing its mass exchange with their parent and children. The parent-child mass exchange depends on the radiation density at the interphase, which is produced by the radiation coming from the stars that form at the end of the hierarchical structure, and by the external radiation field. The system is chaotic in the sense that its temporal evolution is very sensitive to small changes in the initial or external conditions. However, global features such as the star formation efficience and the Initial Mass Function are less affected by those variations.

Heterarchies: Reconciling Networks and Hierarchies.

PubMed

Cumming, Graeme S

2016-08-01

Social-ecological systems research suffers from a disconnect between hierarchical (top-down or bottom-up) and network (peer-to-peer) analyses. The concept of the heterarchy unifies these perspectives in a single framework. Here, I review the history and application of 'heterarchy' in neuroscience, ecology, archaeology, multiagent control systems, business and organisational studies, and politics. Recognising complex system architecture as a continuum along vertical and lateral axes ('flat versus hierarchical' and 'individual versus networked') suggests four basic types of heterarchy: reticulated, polycentric, pyramidal, and individualistic. Each has different implications for system functioning and resilience. Systems can also shift predictably and abruptly between architectures. Heterarchies suggest new ways of contextualising and generalising from case studies and new methods for analysing complex structure-function relations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Toward Scalable Fabrication of Hierarchical Silica Capsules with Integrated Micro-, Meso-, and Macropores.

PubMed

Zhou, Weizheng; Tong, Gangsheng; Wang, Dali; Zhu, Bangshang; Ren, Yu; Butler, Michael; Pelan, Eddie; Yan, Deyue; Zhu, Xinyuan; Stoyanov, Simeon D

2016-04-06

Hierarchical porous structures are ubiquitous in biological organisms and inorganic systems. Although such structures have been replicated, designed, and fabricated, they are often inferior to naturally occurring analogues. Apart from the complexity and multiple functionalities developed by the biological systems, the controllable and scalable production of hierarchically porous structures and building blocks remains a technological challenge. Herein, a facile and scalable approach is developed to fabricate hierarchical hollow spheres with integrated micro-, meso-, and macropores ranging from 1 nm to 100 μm (spanning five orders of magnitude). (Macro)molecules, micro-rods (which play a key role for the creation of robust capsules), and emulsion droplets have been successfully employed as multiple length scale templates, allowing the creation of hierarchical porous macrospheres. Thanks to their specific mechanical strength, these hierarchical porous spheres could be incorporated and assembled as higher level building blocks in various novel materials. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A stage is a stage is a stage: a direct comparison of two scoring systems.

PubMed

Dawson, Theo L

2003-09-01

L. Kohlberg (1969) argued that his moral stages captured a developmental sequence specific to the moral domain. To explore that contention, the author compared stage assignments obtained with the Standard Issue Scoring System (A. Colby & L. Kohlberg, 1987a, 1987b) and those obtained with a generalized content-independent stage-scoring system called the Hierarchical Complexity Scoring System (T. L. Dawson, 2002a), on 637 moral judgment interviews (participants' ages ranged from 5 to 86 years). The correlation between stage scores produced with the 2 systems was .88. Although standard issue scoring and hierarchical complexity scoring often awarded different scores up to Kohlberg's Moral Stage 2/3, from his Moral Stage 3 onward, scores awarded with the two systems predominantly agreed. The author explores the implications for developmental research.

Hierarchical organization of macaque and cat cortical sensory systems explored with a novel network processor.

PubMed

Hilgetag, C C; O'Neill, M A; Young, M P

2000-01-29

Neuroanatomists have described a large number of connections between the various structures of monkey and cat cortical sensory systems. Because of the complexity of the connection data, analysis is required to unravel what principles of organization they imply. To date, analysis of laminar origin and termination connection data to reveal hierarchical relationships between the cortical areas has been the most widely acknowledged approach. We programmed a network processor that searches for optimal hierarchical orderings of cortical areas given known hierarchical constraints and rules for their interpretation. For all cortical systems and all cost functions, the processor found a multitude of equally low-cost hierarchies. Laminar hierarchical constraints that are presently available in the anatomical literature were therefore insufficient to constrain a unique ordering for any of the sensory systems we analysed. Hierarchical orderings of the monkey visual system that have been widely reported, but which were derived by hand, were not among the optimal orderings. All the cortical systems we studied displayed a significant degree of hierarchical organization, and the anatomical constraints from the monkey visual and somato-motor systems were satisfied with very few constraint violations in the optimal hierarchies. The visual and somato-motor systems in that animal were therefore surprisingly strictly hierarchical. Most inconsistencies between the constraints and the hierarchical relationships in the optimal structures for the visual system were related to connections of area FST (fundus of superior temporal sulcus). We found that the hierarchical solutions could be further improved by assuming that FST consists of two areas, which differ in the nature of their projections. Indeed, we found that perfect hierarchical arrangements of the primate visual system, without any violation of anatomical constraints, could be obtained under two reasonable conditions, namely the subdivision of FST into two distinct areas, whose connectivity we predict, and the abolition of at least one of the less reliable rule constraints. Our analyses showed that the future collection of the same type of laminar constraints, or the inclusion of new hierarchical constraints from thalamocortical connections, will not resolve the problem of multiple optimal hierarchical representations for the primate visual system. Further data, however, may help to specify the relative ordering of some more areas. This indeterminacy of the visual hierarchy is in part due to the reported absence of some connections between cortical areas. These absences are consistent with limited cross-talk between differentiated processing streams in the system. Hence, hierarchical representation of the visual system is affected by, and must take into account, other organizational features, such as processing streams.

Intelligibility in microbial complex systems: Wittgenstein and the score of life.

PubMed

Baquero, Fernando; Moya, Andrés

2012-01-01

Knowledge in microbiology is reaching an extreme level of diversification and complexity, which paradoxically results in a strong reduction in the intelligibility of microbial life. In our days, the "score of life" metaphor is more accurate to express the complexity of living systems than the classic "book of life." Music and life can be represented at lower hierarchical levels by music scores and genomic sequences, and such representations have a generational influence in the reproduction of music and life. If music can be considered as a representation of life, such representation remains as unthinkable as life itself. The analysis of scores and genomic sequences might provide mechanistic, phylogenetic, and evolutionary insights into music and life, but not about their real dynamics and nature, which is still maintained unthinkable, as was proposed by Wittgenstein. As complex systems, life or music is composed by thinkable and only showable parts, and a strategy of half-thinking, half-seeing is needed to expand knowledge. Complex models for complex systems, based on experiences on trans-hierarchical integrations, should be developed in order to provide a mixture of legibility and imageability of biological processes, which should lead to higher levels of intelligibility of microbial life.

Intelligibility in microbial complex systems: Wittgenstein and the score of life

PubMed Central

Baquero, Fernando; Moya, Andrés

2012-01-01

Knowledge in microbiology is reaching an extreme level of diversification and complexity, which paradoxically results in a strong reduction in the intelligibility of microbial life. In our days, the “score of life” metaphor is more accurate to express the complexity of living systems than the classic “book of life.” Music and life can be represented at lower hierarchical levels by music scores and genomic sequences, and such representations have a generational influence in the reproduction of music and life. If music can be considered as a representation of life, such representation remains as unthinkable as life itself. The analysis of scores and genomic sequences might provide mechanistic, phylogenetic, and evolutionary insights into music and life, but not about their real dynamics and nature, which is still maintained unthinkable, as was proposed by Wittgenstein. As complex systems, life or music is composed by thinkable and only showable parts, and a strategy of half-thinking, half-seeing is needed to expand knowledge. Complex models for complex systems, based on experiences on trans-hierarchical integrations, should be developed in order to provide a mixture of legibility and imageability of biological processes, which should lead to higher levels of intelligibility of microbial life. PMID:22919679

Large-scale systems: Complexity, stability, reliability

NASA Technical Reports Server (NTRS)

Siljak, D. D.

1975-01-01

After showing that a complex dynamic system with a competitive structure has highly reliable stability, a class of noncompetitive dynamic systems for which competitive models can be constructed is defined. It is shown that such a construction is possible in the context of the hierarchic stability analysis. The scheme is based on the comparison principle and vector Liapunov functions.

RAFCON: A Graphical Tool for Engineering Complex, Robotic Tasks

DTIC Science & Technology

2016-10-09

Robotic tasks are becoming increasingly complex, and with this also the robotic systems. This requires new tools to manage this complexity and to...execution of robotic tasks, called RAFCON. These tasks are described in hierarchical state machines supporting concurrency. A formal notation of this concept

Hierarchy Measure for Complex Networks

PubMed Central

Mones, Enys; Vicsek, Lilla; Vicsek, Tamás

2012-01-01

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure. PMID:22470477

Hierarchical Control Using Networks Trained with Higher-Level Forward Models

PubMed Central

Wayne, Greg; Abbott, L.F.

2015-01-01

We propose and develop a hierarchical approach to network control of complex tasks. In this approach, a low-level controller directs the activity of a “plant,” the system that performs the task. However, the low-level controller may only be able to solve fairly simple problems involving the plant. To accomplish more complex tasks, we introduce a higher-level controller that controls the lower-level controller. We use this system to direct an articulated truck to a specified location through an environment filled with static or moving obstacles. The final system consists of networks that have memorized associations between the sensory data they receive and the commands they issue. These networks are trained on a set of optimal associations that are generated by minimizing cost functions. Cost function minimization requires predicting the consequences of sequences of commands, which is achieved by constructing forward models, including a model of the lower-level controller. The forward models and cost minimization are only used during training, allowing the trained networks to respond rapidly. In general, the hierarchical approach can be extended to larger numbers of levels, dividing complex tasks into more manageable sub-tasks. The optimization procedure and the construction of the forward models and controllers can be performed in similar ways at each level of the hierarchy, which allows the system to be modified to perform other tasks, or to be extended for more complex tasks without retraining lower-levels. PMID:25058706

Modes of Interaction between Individuals Dominate the Topologies of Real World Networks

PubMed Central

Lee, Insuk; Kim, Eiru; Marcotte, Edward M.

2015-01-01

We find that the topologies of real world networks, such as those formed within human societies, by the Internet, or among cellular proteins, are dominated by the mode of the interactions considered among the individuals. Specifically, a major dichotomy in previously studied networks arises from modeling networks in terms of pairwise versus group tasks. The former often intrinsically give rise to scale-free, disassortative, hierarchical networks, whereas the latter often give rise to single- or broad-scale, assortative, nonhierarchical networks. These dependencies explain contrasting observations among previous topological analyses of real world complex systems. We also observe this trend in systems with natural hierarchies, in which alternate representations of the same networks, but which capture different levels of the hierarchy, manifest these signature topological differences. For example, in both the Internet and cellular proteomes, networks of lower-level system components (routers within domains or proteins within biological processes) are assortative and nonhierarchical, whereas networks of upper-level system components (internet domains or biological processes) are disassortative and hierarchical. Our results demonstrate that network topologies of complex systems must be interpreted in light of their hierarchical natures and interaction types. PMID:25793969

Modeling urban air pollution with optimized hierarchical fuzzy inference system.

PubMed

Tashayo, Behnam; Alimohammadi, Abbas

2016-10-01

Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.

Observing complex action sequences: The role of the fronto-parietal mirror neuron system.

PubMed

Molnar-Szakacs, Istvan; Kaplan, Jonas; Greenfield, Patricia M; Iacoboni, Marco

2006-11-15

A fronto-parietal mirror neuron network in the human brain supports the ability to represent and understand observed actions allowing us to successfully interact with others and our environment. Using functional magnetic resonance imaging (fMRI), we wanted to investigate the response of this network in adults during observation of hierarchically organized action sequences of varying complexity that emerge at different developmental stages. We hypothesized that fronto-parietal systems may play a role in coding the hierarchical structure of object-directed actions. The observation of all action sequences recruited a common bilateral network including the fronto-parietal mirror neuron system and occipito-temporal visual motion areas. Activity in mirror neuron areas varied according to the motoric complexity of the observed actions, but not according to the developmental sequence of action structures, possibly due to the fact that our subjects were all adults. These results suggest that the mirror neuron system provides a fairly accurate simulation process of observed actions, mimicking internally the level of motoric complexity. We also discuss the results in terms of the links between mirror neurons, language development and evolution.

Stability and structural properties of gene regulation networks with coregulation rules.

PubMed

Warrell, Jonathan; Mhlanga, Musa

2017-05-07

Coregulation of the expression of groups of genes has been extensively demonstrated empirically in bacterial and eukaryotic systems. Such coregulation can arise through the use of shared regulatory motifs, which allow the coordinated expression of modules (and module groups) of functionally related genes across the genome. Coregulation can also arise through the physical association of multi-gene complexes through chromosomal looping, which are then transcribed together. We present a general formalism for modeling coregulation rules in the framework of Random Boolean Networks (RBN), and develop specific models for transcription factor networks with modular structure (including module groups, and multi-input modules (MIM) with autoregulation) and multi-gene complexes (including hierarchical differentiation between multi-gene complex members). We develop a mean-field approach to analyse the dynamical stability of large networks incorporating coregulation, and show that autoregulated MIM and hierarchical gene-complex models can achieve greater stability than networks without coregulation whose rules have matching activation frequency. We provide further analysis of the stability of small networks of both kinds through simulations. We also characterize several general properties of the transients and attractors in the hierarchical coregulation model, and show using simulations that the steady-state distribution factorizes hierarchically as a Bayesian network in a Markov Jump Process analogue of the RBN model. Copyright © 2017. Published by Elsevier Ltd.

Emergence of the interplay between hierarchy and contact splitting in biological adhesion highlighted through a hierarchical shear lag model.

PubMed

Brely, Lucas; Bosia, Federico; Pugno, Nicola M

2018-06-20

Contact unit size reduction is a widely studied mechanism as a means to improve adhesion in natural fibrillar systems, such as those observed in beetles or geckos. However, these animals also display complex structural features in the way the contact is subdivided in a hierarchical manner. Here, we study the influence of hierarchical fibrillar architectures on the load distribution over the contact elements of the adhesive system, and the corresponding delamination behaviour. We present an analytical model to derive the load distribution in a fibrillar system loaded in shear, including hierarchical splitting of contacts, i.e. a "hierarchical shear-lag" model that generalizes the well-known shear-lag model used in mechanics. The influence on the detachment process is investigated introducing a numerical procedure that allows the derivation of the maximum delamination force as a function of the considered geometry, including statistical variability of local adhesive energy. Our study suggests that contact splitting generates improved adhesion only in the ideal case of extremely compliant contacts. In real cases, to produce efficient adhesive performance, contact splitting needs to be coupled with hierarchical architectures to counterbalance high load concentrations resulting from contact unit size reduction, generating multiple delamination fronts and helping to avoid detrimental non-uniform load distributions. We show that these results can be summarized in a generalized adhesion scaling scheme for hierarchical structures, proving the beneficial effect of multiple hierarchical levels. The model can thus be used to predict the adhesive performance of hierarchical adhesive structures, as well as the mechanical behaviour of composite materials with hierarchical reinforcements.

Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part I: System identification and methodology development.

PubMed

Cheng, Guanhui; Huang, Guohe; Dong, Cong; Xu, Ye; Chen, Xiujuan; Chen, Jiapei

2017-03-01

Due to the existence of complexities of heterogeneities, hierarchy, discreteness, and interactions in municipal solid waste management (MSWM) systems such as Beijing, China, a series of socio-economic and eco-environmental problems may emerge or worsen and result in irredeemable damages in the following decades. Meanwhile, existing studies, especially ones focusing on MSWM in Beijing, could hardly reflect these complexities in system simulations and provide reliable decision support for management practices. Thus, a framework of distributed mixed-integer fuzzy hierarchical programming (DMIFHP) is developed in this study for MSWM under these complexities. Beijing is selected as a representative case. The Beijing MSWM system is comprehensively analyzed in many aspects such as socio-economic conditions, natural conditions, spatial heterogeneities, treatment facilities, and system complexities, building a solid foundation for system simulation and optimization. Correspondingly, the MSWM system in Beijing is discretized as 235 grids to reflect spatial heterogeneity. A DMIFHP model which is a nonlinear programming problem is constructed to parameterize the Beijing MSWM system. To enable scientific solving of it, a solution algorithm is proposed based on coupling of fuzzy programming and mixed-integer linear programming. Innovations and advantages of the DMIFHP framework are discussed. The optimal MSWM schemes and mechanism revelations will be discussed in another companion paper due to length limitation.

Settlement-Size Scaling among Prehistoric Hunter-Gatherer Settlement Systems in the New World

PubMed Central

Haas, W. Randall; Klink, Cynthia J.; Maggard, Greg J.; Aldenderfer, Mark S.

2015-01-01

Settlement size predicts extreme variation in the rates and magnitudes of many social and ecological processes in human societies. Yet, the factors that drive human settlement-size variation remain poorly understood. Size variation among economically integrated settlements tends to be heavy tailed such that the smallest settlements are extremely common and the largest settlements extremely large and rare. The upper tail of this size distribution is often formalized mathematically as a power-law function. Explanations for this scaling structure in human settlement systems tend to emphasize complex socioeconomic processes including agriculture, manufacturing, and warfare—behaviors that tend to differentially nucleate and disperse populations hierarchically among settlements. But, the degree to which heavy-tailed settlement-size variation requires such complex behaviors remains unclear. By examining the settlement patterns of eight prehistoric New World hunter-gatherer settlement systems spanning three distinct environmental contexts, this analysis explores the degree to which heavy-tailed settlement-size scaling depends on the aforementioned socioeconomic complexities. Surprisingly, the analysis finds that power-law models offer plausible and parsimonious statistical descriptions of prehistoric hunter-gatherer settlement-size variation. This finding reveals that incipient forms of hierarchical settlement structure may have preceded socioeconomic complexity in human societies and points to a need for additional research to explicate how mobile foragers came to exhibit settlement patterns that are more commonly associated with hierarchical organization. We propose that hunter-gatherer mobility with preferential attachment to previously occupied locations may account for the observed structure in site-size variation. PMID:26536241

Shared neural coding for social hierarchy and reward value in primate amygdala.

PubMed

Munuera, Jérôme; Rigotti, Mattia; Salzman, C Daniel

2018-03-01

The social brain hypothesis posits that dedicated neural systems process social information. In support of this, neurophysiological data have shown that some brain regions are specialized for representing faces. It remains unknown, however, whether distinct anatomical substrates also represent more complex social variables, such as the hierarchical rank of individuals within a social group. Here we show that the primate amygdala encodes the hierarchical rank of individuals in the same neuronal ensembles that encode the rewards associated with nonsocial stimuli. By contrast, orbitofrontal and anterior cingulate cortices lack strong representations of hierarchical rank while still representing reward values. These results challenge the conventional view that dedicated neural systems process social information. Instead, information about hierarchical rank-which contributes to the assessment of the social value of individuals within a group-is linked in the amygdala to representations of rewards associated with nonsocial stimuli.

Methods Used to Support a Life Cycle of Complex Engineering Products

NASA Astrophysics Data System (ADS)

Zakharova, Alexandra A.; Kolegova, Olga A.; Nekrasova, Maria E.; Eremenko, Andrey O.

2016-08-01

Management of companies involved in the design, development and operation of complex engineering products recognize the relevance of creating systems for product lifecycle management. A system of methods is proposed to support life cycles of complex engineering products, based on fuzzy set theory and hierarchical analysis. The system of methods serves to demonstrate the grounds for making strategic decisions in an environment of uncertainty, allows the use of expert knowledge, and provides interconnection of decisions at all phases of strategic management and all stages of a complex engineering product lifecycle.

Coarse-grained simulation of polymer-filler blends

NASA Astrophysics Data System (ADS)

Legters, Gregg; Kuppa, Vikram; Beaucage, Gregory; Univ of Dayton Collaboration; Univ of Cincinnati Collaboration

The practical use of polymers often relies on additives that improve the property of the mixture. Examples of such complex blends include tires, pigments, blowing agents and other reactive additives in thermoplastics, and recycled polymers. Such systems usually exhibit a complex partitioning of the components. Most prior work has either focused on fine-grained details such as molecular modeling of chains at interfaces, or on coarse, heuristic, trial-and-error approaches to compounding (eg: tire industry). Thus, there is a significant gap in our understanding of how complex hierarchical structure (across several decades in length) develops in these multicomponent systems. This research employs dissipative particle thermodynamics in conjunction with a pseudo-thermodynamic parameter derived from scattering experiments to represent polymer-filler interactions. DPD simulations will probe how filler dispersion and hierarchical morphology develops in these complex blends, and are validated against experimental (scattering) data. The outcome of our approach is a practical solution to compounding issues, based on a mutually validating experimental and simulation methodology. Support from the NSF (CMMI-1636036/1635865) is gratefully acknowledged.

Hierarchical analytical and simulation modelling of human-machine systems with interference

NASA Astrophysics Data System (ADS)

Braginsky, M. Ya; Tarakanov, D. V.; Tsapko, S. G.; Tsapko, I. V.; Baglaeva, E. A.

2017-01-01

The article considers the principles of building the analytical and simulation model of the human operator and the industrial control system hardware and software. E-networks as the extension of Petri nets are used as the mathematical apparatus. This approach allows simulating complex parallel distributed processes in human-machine systems. The structural and hierarchical approach is used as the building method for the mathematical model of the human operator. The upper level of the human operator is represented by the logical dynamic model of decision making based on E-networks. The lower level reflects psychophysiological characteristics of the human-operator.

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

PubMed Central

Ren, Jun; Zhou, Wei; Wang, Jianxin

2014-01-01

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

From globally coupled maps to complex-systems biology

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

Kaneko, Kunihiko, E-mail: kaneko@complex.c.u-tokyo.ac.jp

Studies of globally coupled maps, introduced as a network of chaotic dynamics, are briefly reviewed with an emphasis on novel concepts therein, which are universal in high-dimensional dynamical systems. They include clustering of synchronized oscillations, hierarchical clustering, chimera of synchronization and desynchronization, partition complexity, prevalence of Milnor attractors, chaotic itinerancy, and collective chaos. The degrees of freedom necessary for high dimensionality are proposed to equal the number in which the combinatorial exceeds the exponential. Future analysis of high-dimensional dynamical systems with regard to complex-systems biology is briefly discussed.

Hierarchical Ada robot programming system (HARPS)- A complete and working telerobot control system based on the NASREM model

NASA Technical Reports Server (NTRS)

Leake, Stephen; Green, Tom; Cofer, Sue; Sauerwein, Tim

1989-01-01

HARPS is a telerobot control system that can perform some simple but useful tasks. This capability is demonstrated by performing the ORU exchange demonstration. HARPS is based on NASREM (NASA Standard Reference Model). All software is developed in Ada, and the project incorporates a number of different CASE (computer-aided software engineering) tools. NASREM was found to be a valid and useful model for building a telerobot control system. Its hierarchical and distributed structure creates a natural and logical flow for implementing large complex robust control systems. The ability of Ada to create and enforce abstraction enhanced the implementation of such control systems.

An assembly process model based on object-oriented hierarchical time Petri Nets

NASA Astrophysics Data System (ADS)

Wang, Jiapeng; Liu, Shaoli; Liu, Jianhua; Du, Zenghui

2017-04-01

In order to improve the versatility, accuracy and integrity of the assembly process model of complex products, an assembly process model based on object-oriented hierarchical time Petri Nets is presented. A complete assembly process information model including assembly resources, assembly inspection, time, structure and flexible parts is established, and this model describes the static and dynamic data involved in the assembly process. Through the analysis of three-dimensional assembly process information, the assembly information is hierarchically divided from the whole, the local to the details and the subnet model of different levels of object-oriented Petri Nets is established. The communication problem between Petri subnets is solved by using message database, and it reduces the complexity of system modeling effectively. Finally, the modeling process is presented, and a five layer Petri Nets model is established based on the hoisting process of the engine compartment of a wheeled armored vehicle.

Using Generative Representations to Evolve Robots. Chapter 1

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.

2004-01-01

Recent research has demonstrated the ability of evolutionary algorithms to automatically design both the physical structure and software controller of real physical robots. One of the challenges for these automated design systems is to improve their ability to scale to the high complexities found in real-world problems. Here we claim that for automated design systems to scale in complexity they must use a representation which allows for the hierarchical creation and reuse of modules, which we call a generative representation. Not only is the ability to reuse modules necessary for functional scalability, but it is also valuable for improving efficiency in testing and construction. We then describe an evolutionary design system with a generative representation capable of hierarchical modularity and demonstrate it for the design of locomoting robots in simulation. Finally, results from our experiments show that evolution with our generative representation produces better robots than those evolved with a non-generative representation.

Biomimetic wall-shaped hierarchical microstructure for gecko-like attachment.

PubMed

Kasem, Haytam; Tsipenyuk, Alexey; Varenberg, Michael

2015-04-21

Most biological hairy adhesive systems involved in locomotion rely on spatula-shaped terminal elements, whose operation has been actively studied during the last decade. However, though functional principles underlying their amazing performance are now well understood, due to technical difficulties in manufacturing the complex structure of hierarchical spatulate systems, a biomimetic surface structure featuring true shear-induced dynamic attachment still remains elusive. To try bridging this gap, a novel method of manufacturing gecko-like attachment surfaces is devised based on a laser-micromachining technology. This method overcomes the inherent disadvantages of photolithography techniques and opens wide perspectives for future production of gecko-like attachment systems. Advanced smart-performance surfaces featuring thin-film-based hierarchical shear-activated elements are fabricated and found capable of generating friction force of several tens of times the contact load, which makes a significant step forward towards a true gecko-like adhesive.

Directed assembly of bio-inspired hierarchical materials with controlled nanofibrillar architectures

NASA Astrophysics Data System (ADS)

Tseng, Peter; Napier, Bradley; Zhao, Siwei; Mitropoulos, Alexander N.; Applegate, Matthew B.; Marelli, Benedetto; Kaplan, David L.; Omenetto, Fiorenzo G.

2017-05-01

In natural systems, directed self-assembly of structural proteins produces complex, hierarchical materials that exhibit a unique combination of mechanical, chemical and transport properties. This controlled process covers dimensions ranging from the nano- to the macroscale. Such materials are desirable to synthesize integrated and adaptive materials and systems. We describe a bio-inspired process to generate hierarchically defined structures with multiscale morphology by using regenerated silk fibroin. The combination of protein self-assembly and microscale mechanical constraints is used to form oriented, porous nanofibrillar networks within predesigned macroscopic structures. This approach allows us to predefine the mechanical and physical properties of these materials, achieved by the definition of gradients in nano- to macroscale order. We fabricate centimetre-scale material geometries including anchors, cables, lattices and webs, as well as functional materials with structure-dependent strength and anisotropic thermal transport. Finally, multiple three-dimensional geometries and doped nanofibrillar constructs are presented to illustrate the facile integration of synthetic and natural additives to form functional, interactive, hierarchical networks.

Using arborescences to estimate hierarchicalness in directed complex networks

PubMed Central

2018-01-01

Complex networks are a useful tool for the understanding of complex systems. One of the emerging properties of such systems is their tendency to form hierarchies: networks can be organized in levels, with nodes in each level exerting control on the ones beneath them. In this paper, we focus on the problem of estimating how hierarchical a directed network is. We propose a structural argument: a network has a strong top-down organization if we need to delete only few edges to reduce it to a perfect hierarchy—an arborescence. In an arborescence, all edges point away from the root and there are no horizontal connections, both characteristics we desire in our idealization of what a perfect hierarchy requires. We test our arborescence score in synthetic and real-world directed networks against the current state of the art in hierarchy detection: agony, flow hierarchy and global reaching centrality. These tests highlight that our arborescence score is intuitive and we can visualize it; it is able to better distinguish between networks with and without a hierarchical structure; it agrees the most with the literature about the hierarchy of well-studied complex systems; and it is not just a score, but it provides an overall scheme of the underlying hierarchy of any directed complex network. PMID:29381761

Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems

PubMed Central

Choo, Benjamin Y.; Adams, Stephen C.; Weiss, Brian A.; Marvel, Jeremy A.; Beling, Peter A.

2017-01-01

The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. In application, PHM information is not yet fully utilized in higher-level decision-making in manufacturing systems. AM-PHM leverages and integrates lower-level PHM information such as from a machine or component with hierarchical relationships across the component, machine, work cell, and assembly line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are then made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. To overcome the issue of exponential explosion of complexity associated with describing a large manufacturing system, the AM-PHM methodology takes a hierarchical Markov Decision Process (MDP) approach into describing the system and solving for an optimized policy. A description of the AM-PHM methodology is followed by a simulated industry-inspired example to demonstrate the effectiveness of AM-PHM. PMID:28736651

Organization of excitable dynamics in hierarchical biological networks.

PubMed

Müller-Linow, Mark; Hilgetag, Claus C; Hütt, Marc-Thorsten

2008-09-26

This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

A novel diamond micro-/nano-machining process for the generation of hierarchical micro-/nano-structures

NASA Astrophysics Data System (ADS)

Zhu, Zhiwei; To, Suet; Ehmann, Kornel F.; Xiao, Gaobo; Zhu, Wule

2016-03-01

A new mechanical micro-/nano-machining process that combines rotary spatial vibrations (RSV) of a diamond tool and the servo motions of the workpiece is proposed and applied for the generation of multi-tier hierarchical micro-/nano-structures. In the proposed micro-/nano-machining system, the servo motion, as the primary cutting motion generated by a slow-tool-servo, is adopted for the fine generation of the primary surfaces with complex shapes. The RSV, as the tertiary cutting operation, is superimposed on the secondary fundamental rotary cutting motion to construct secondary nano-structures on the primary surface. Since the RSV system generally works at much higher frequencies and motion resolution than the primary and secondary motions, it leads to an inherent hierarchical cutting architecture. To investigate the machining performance, complex micro-/nano-structures were generated and explored by both numerical simulations and actual cutting tests. Rotary vibrations of the diamond tool at a constant rotational distance offer an inherent constant cutting velocity, leading to the ability for the generation of homogeneous micro-/nano-structures with fixed amplitudes and frequencies of the vibrations, even over large-scale surfaces. Furthermore, by deliberately combining the non-resonant three-axial vibrations and the servo motion, the generation of a variety of micro-/nano-structures with complex shapes and with flexibly tunable feature sizes can be achieved.

How to Use the DX SYSTEM of Diagnostic Testing. Methodology Project.

ERIC Educational Resources Information Center

McArthur, David; Cabello, Beverly

The DX SYSTEM of Diagnostic Testing is an easy-to-use computerized system for developing and administering diagnostic tests. A diagnostic test measures a student's mastery of a specific domain (skill or content area). It examines the necessary subskills hierarchically from the most to the least complex. The DX SYSTEM features tailored testing with…

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.

PubMed

Cohen, Mitchell J; Grossman, Adam D; Morabito, Diane; Knudson, M Margaret; Butte, Atul J; Manley, Geoffrey T

2010-01-01

Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. Multivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality. We identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters. Here we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients.

Category Theoretic Analysis of Hierarchical Protein Materials and Social Networks

PubMed Central

Spivak, David I.; Giesa, Tristan; Wood, Elizabeth; Buehler, Markus J.

2011-01-01

Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we describe an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a “concept web” or “semantic network” except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other olog. We consider simple cases of beta-helical and amyloid-like protein filaments subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog for the social network feature identical category-theoretic representations, and we proceed to precisely explicate the analogy or isomorphism between them. The examples presented here demonstrate that the intrinsic nature of a complex system, which in particular includes a precise relationship between structure and function at different hierarchical levels, can be effectively represented by an olog. This, in turn, allows for comparative studies between disparate materials or fields of application, and results in novel approaches to derive functionality in the design of de novo hierarchical systems. We discuss opportunities and challenges associated with the description of complex biological materials by using ologs as a powerful tool for analysis and design in the context of materiomics, and we present the potential impact of this approach for engineering, life sciences, and medicine. PMID:21931622

Vanishing point: Scale independence in geomorphological hierarchies

NASA Astrophysics Data System (ADS)

Phillips, Jonathan D.

2016-08-01

Scale linkage problems in geosciences are often associated with a hierarchy of components. Both dynamical systems perspectives and intuition suggest that processes or relationships operating at fundamentally different scales are independent with respect to influences on system dynamics. But how far apart is ;fundamentally different;-that is, what is the ;vanishing point; at which scales are no longer interdependent? And how do we reconcile that with the idea (again, supported by both theory and intuition) that we can work our way along scale hierarchies from microscale to planetary (and vice-versa)? Graph and network theory are employed here to address these questions. Analysis of two archetypal hierarchical networks shows low algebraic connectivity, indicating low levels of inferential synchronization. This explains the apparent paradox between scale independence and hierarchical linkages. Incorporating more hierarchical levels results in an increase in complexity or entropy of the network as a whole, but at a nonlinear rate. Complexity increases as a power α of the number of levels in the hierarchy, with α < 1 and usually ≤ 0.6. However, algebraic connectivity decreases at a more rapid rate. Thus, the ability to infer one part of the hierarchical network from other level decays rapidly as more levels are added. Relatedness among system components decreases with differences in scale or resolution, analogous to distance decay in the spatial domain. These findings suggest a strategy of identifying and focusing on the most important or interesting scale levels, rather than attempting to identify the smallest or largest scale levels and work top-down or bottom-up from there. Examples are given from soil geomorphology and karst flow networks.

Cooperative action of coherent groups in broadly heterogeneous populations of interacting chemical oscillators

PubMed Central

Mikhailov, A. S.; Zanette, D. H.; Zhai, Y. M.; Kiss, I. Z.; Hudson, J. L.

2004-01-01

We present laboratory experiments on the effects of global coupling in a population of electrochemical oscillators with a multimodal frequency distribution. The experiments show that complex collective signals are generated by this system through spontaneous emergence and joint operation of coherently acting groups representing hierarchically organized resonant clusters. Numerical simulations support these experimental findings. Our results suggest that some forms of internal self-organization, characteristic for complex multiagent systems, are already possible in simple chemical systems. PMID:15263084

Queueing Network Models for Parallel Processing of Task Systems: an Operational Approach

NASA Technical Reports Server (NTRS)

Mak, Victor W. K.

1986-01-01

Computer performance modeling of possibly complex computations running on highly concurrent systems is considered. Earlier works in this area either dealt with a very simple program structure or resulted in methods with exponential complexity. An efficient procedure is developed to compute the performance measures for series-parallel-reducible task systems using queueing network models. The procedure is based on the concept of hierarchical decomposition and a new operational approach. Numerical results for three test cases are presented and compared to those of simulations.

Application of X-ray and neutron small angle scattering techniques to study the hierarchical structure of plant cell walls: a review.

PubMed

Martínez-Sanz, Marta; Gidley, Michael J; Gilbert, Elliot P

2015-07-10

Plant cell walls present an extremely complex structure of hierarchically assembled cellulose microfibrils embedded in a multi-component matrix. The biosynthesis process determines the mechanism of cellulose crystallisation and assembly, as well as the interaction of cellulose with other cell wall components. Thus, a knowledge of cellulose microfibril and bundle architecture, and the structural role of matrix components, is crucial for understanding cell wall functional and technological roles. Small angle scattering techniques, combined with complementary methods, provide an efficient approach to characterise plant cell walls, covering a broad and relevant size range while minimising experimental artefacts derived from sample treatment. Given the system complexity, approaches such as component extraction and the use of plant cell wall analogues are typically employed to enable the interpretation of experimental results. This review summarises the current research status on the characterisation of the hierarchical structure of plant cell walls using small angle scattering techniques. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Developing Conceptions of Authority and Contract across the Lifespan: Two Perspectives.

ERIC Educational Resources Information Center

Dawson, Theo L.; Gabrielian, Sonya

2003-01-01

Compares concepts defining Kohlbergian stages of moral development with those associated with orders of hierarchical complexity determined with a generalized content-independent stage-scoring system. Finds that Kohlberg's sequence generally matches that identified with the scoring system and that contract and authority concepts match the concepts…

Artificial 3D hierarchical and isotropic porous polymeric materials

PubMed Central

Musteata, Valentina-Elena; Behzad, Ali Reza

2018-01-01

Hierarchical porous materials that replicate complex living structures are attractive for a wide variety of applications, ranging from storage and catalysis to biological and artificial systems. However, the preparation of structures with a high level of complexity and long-range order at the mesoscale and microscale is challenging. We report a simple, nonextractive, and nonreactive method used to prepare three-dimensional porous materials that mimic biological systems such as marine skeletons and honeycombs. This method exploits the concurrent occurrence of the self-assembly of block copolymers in solution and macrophase separation by nucleation and growth. We obtained a long-range order of micrometer-sized compartments. These compartments are interconnected by ordered cylindrical nanochannels. The new approach is demonstrated using polystyrene-b-poly(t-butyl acrylate), which can be further explored for a broad range of applications, such as air purification filters for viruses and pollution particle removal or growth of bioinspired materials for bone regeneration.

Artificial 3D hierarchical and isotropic porous polymeric materials.

PubMed

Chisca, Stefan; Musteata, Valentina-Elena; Sougrat, Rachid; Behzad, Ali Reza; Nunes, Suzana P

2018-05-01

Hierarchical porous materials that replicate complex living structures are attractive for a wide variety of applications, ranging from storage and catalysis to biological and artificial systems. However, the preparation of structures with a high level of complexity and long-range order at the mesoscale and microscale is challenging. We report a simple, nonextractive, and nonreactive method used to prepare three-dimensional porous materials that mimic biological systems such as marine skeletons and honeycombs. This method exploits the concurrent occurrence of the self-assembly of block copolymers in solution and macrophase separation by nucleation and growth. We obtained a long-range order of micrometer-sized compartments. These compartments are interconnected by ordered cylindrical nanochannels. The new approach is demonstrated using polystyrene- b -poly( t -butyl acrylate), which can be further explored for a broad range of applications, such as air purification filters for viruses and pollution particle removal or growth of bioinspired materials for bone regeneration.

Universality classes of fluctuation dynamics in hierarchical complex systems

NASA Astrophysics Data System (ADS)

Macêdo, A. M. S.; González, Iván R. Roa; Salazar, D. S. P.; Vasconcelos, G. L.

2017-03-01

A unified approach is proposed to describe the statistics of the short-time dynamics of multiscale complex systems. The probability density function of the relevant time series (signal) is represented as a statistical superposition of a large time-scale distribution weighted by the distribution of certain internal variables that characterize the slowly changing background. The dynamics of the background is formulated as a hierarchical stochastic model whose form is derived from simple physical constraints, which in turn restrict the dynamics to only two possible classes. The probability distributions of both the signal and the background have simple representations in terms of Meijer G functions. The two universality classes for the background dynamics manifest themselves in the signal distribution as two types of tails: power law and stretched exponential, respectively. A detailed analysis of empirical data from classical turbulence and financial markets shows excellent agreement with the theory.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

NASA Astrophysics Data System (ADS)

Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

2015-12-01

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

Hierarchically nanostructured materials for sustainable environmental applications

PubMed Central

Ren, Zheng; Guo, Yanbing; Liu, Cai-Hong; Gao, Pu-Xian

2013-01-01

This review presents a comprehensive overview of the hierarchical nanostructured materials with either geometry or composition complexity in environmental applications. The hierarchical nanostructures offer advantages of high surface area, synergistic interactions, and multiple functionalities toward water remediation, biosensing, environmental gas sensing and monitoring as well as catalytic gas treatment. Recent advances in synthetic strategies for various hierarchical morphologies such as hollow spheres and urchin-shaped architectures have been reviewed. In addition to the chemical synthesis, the physical mechanisms associated with the materials design and device fabrication have been discussed for each specific application. The development and application of hierarchical complex perovskite oxide nanostructures have also been introduced in photocatalytic water remediation, gas sensing, and catalytic converter. Hierarchical nanostructures will open up many possibilities for materials design and device fabrication in environmental chemistry and technology. PMID:24790946

Hierarchically Nanostructured Materials for Sustainable Environmental Applications

NASA Astrophysics Data System (ADS)

Ren, Zheng; Guo, Yanbing; Liu, Cai-Hong; Gao, Pu-Xian

2013-11-01

This article presents a comprehensive overview of the hierarchical nanostructured materials with either geometry or composition complexity in environmental applications. The hierarchical nanostructures offer advantages of high surface area, synergistic interactions and multiple functionalities towards water remediation, environmental gas sensing and monitoring as well as catalytic gas treatment. Recent advances in synthetic strategies for various hierarchical morphologies such as hollow spheres and urchin-shaped architectures have been reviewed. In addition to the chemical synthesis, the physical mechanisms associated with the materials design and device fabrication have been discussed for each specific application. The development and application of hierarchical complex perovskite oxide nanostructures have also been introduced in photocatalytic water remediation, gas sensing and catalytic converter. Hierarchical nanostructures will open up many possibilities for materials design and device fabrication in environmental chemistry and technology.

Hierarchical optimal control of large-scale nonlinear chemical processes.

PubMed

Ramezani, Mohammad Hossein; Sadati, Nasser

2009-01-01

In this paper, a new approach is presented for optimal control of large-scale chemical processes. In this approach, the chemical process is decomposed into smaller sub-systems at the first level, and a coordinator at the second level, for which a two-level hierarchical control strategy is designed. For this purpose, each sub-system in the first level can be solved separately, by using any conventional optimization algorithm. In the second level, the solutions obtained from the first level are coordinated using a new gradient-type strategy, which is updated by the error of the coordination vector. The proposed algorithm is used to solve the optimal control problem of a complex nonlinear chemical stirred tank reactor (CSTR), where its solution is also compared with the ones obtained using the centralized approach. The simulation results show the efficiency and the capability of the proposed hierarchical approach, in finding the optimal solution, over the centralized method.

The hierarchical expert tuning of PID controllers using tools of soft computing.

PubMed

Karray, F; Gueaieb, W; Al-Sharhan, S

2002-01-01

We present soft computing-based results pertaining to the hierarchical tuning process of PID controllers located within the control loop of a class of nonlinear systems. The results are compared with PID controllers implemented either in a stand alone scheme or as a part of conventional gain scheduling structure. This work is motivated by the increasing need in the industry to design highly reliable and efficient controllers for dealing with regulation and tracking capabilities of complex processes characterized by nonlinearities and possibly time varying parameters. The soft computing-based controllers proposed are hybrid in nature in that they integrate within a well-defined hierarchical structure the benefits of hard algorithmic controllers with those having supervisory capabilities. The controllers proposed also have the distinct features of learning and auto-tuning without the need for tedious and computationally extensive online systems identification schemes.

The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

NASA Astrophysics Data System (ADS)

Grasha, K.; Calzetti, D.; Adamo, A.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Dale, D. A.; Fumagalli, M.; Grebel, E. K.; Johnson, K. E.; Kahre, L.; Kennicutt, R. C.; Messa, M.; Pellerin, A.; Ryon, J. E.; Smith, L. J.; Shabani, F.; Thilker, D.; Ubeda, L.

2017-05-01

We present a study of the hierarchical clustering of the young stellar clusters in six local (3-15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ˜40-60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.

Hierarchical socioeconomic fractality: The rich, the poor, and the middle-class

NASA Astrophysics Data System (ADS)

Eliazar, Iddo; Cohen, Morrel H.

2014-05-01

Since the seminal work of the Italian economist Vilfredo Pareto, the study of wealth and income has been a topic of active scientific exploration engaging researches ranging from economics and political science to econophysics and complex systems. This paper investigates the intrinsic fractality of wealth and income. To that end we introduce and characterize three forms of socioeconomic scale-invariance-poor fractality, rich fractality, and middle-class fractality-and construct hierarchical fractal approximations of general wealth and income distributions, based on the stitching of these three forms of fractality. Intertwining the theoretical results with real-world empirical data we then establish that the three forms of socioeconomic fractality-amalgamated into a composite hierarchical structure-underlie the distributions of wealth and income in human societies. We further establish that the hierarchical socioeconomic fractality of wealth and income is also displayed by empirical rank distributions observed across the sciences.

Design for testability and diagnosis at the system-level

NASA Technical Reports Server (NTRS)

Simpson, William R.; Sheppard, John W.

1993-01-01

The growing complexity of full-scale systems has surpassed the capabilities of most simulation software to provide detailed models or gate-level failure analyses. The process of system-level diagnosis approaches the fault-isolation problem in a manner that differs significantly from the traditional and exhaustive failure mode search. System-level diagnosis is based on a functional representation of the system. For example, one can exercise one portion of a radar algorithm (the Fast Fourier Transform (FFT) function) by injecting several standard input patterns and comparing the results to standardized output results. An anomalous output would point to one of several items (including the FFT circuit) without specifying the gate or failure mode. For system-level repair, identifying an anomalous chip is sufficient. We describe here an information theoretic and dependency modeling approach that discards much of the detailed physical knowledge about the system and analyzes its information flow and functional interrelationships. The approach relies on group and flow associations and, as such, is hierarchical. Its hierarchical nature allows the approach to be applicable to any level of complexity and to any repair level. This approach has been incorporated in a product called STAMP (System Testability and Maintenance Program) which was developed and refined through more than 10 years of field-level applications to complex system diagnosis. The results have been outstanding, even spectacular in some cases. In this paper we describe system-level testability, system-level diagnoses, and the STAMP analysis approach, as well as a few STAMP applications.

Cloud Classification in Polar and Desert Regions and Smoke Classification from Biomass Burning Using a Hierarchical Neural Network

NASA Technical Reports Server (NTRS)

Alexander, June; Corwin, Edward; Lloyd, David; Logar, Antonette; Welch, Ronald

1996-01-01

This research focuses on a new neural network scene classification technique. The task is to identify scene elements in Advanced Very High Resolution Radiometry (AVHRR) data from three scene types: polar, desert and smoke from biomass burning in South America (smoke). The ultimate goal of this research is to design and implement a computer system which will identify the clouds present on a whole-Earth satellite view as a means of tracking global climate changes. Previous research has reported results for rule-based systems (Tovinkere et at 1992, 1993) for standard back propagation (Watters et at. 1993) and for a hierarchical approach (Corwin et al 1994) for polar data. This research uses a hierarchical neural network with don't care conditions and applies this technique to complex scenes. A hierarchical neural network consists of a switching network and a collection of leaf networks. The idea of the hierarchical neural network is that it is a simpler task to classify a certain pattern from a subset of patterns than it is to classify a pattern from the entire set. Therefore, the first task is to cluster the classes into groups. The switching, or decision network, performs an initial classification by selecting a leaf network. The leaf networks contain a reduced set of similar classes, and it is in the various leaf networks that the actual classification takes place. The grouping of classes in the various leaf networks is determined by applying an iterative clustering algorithm. Several clustering algorithms were investigated, but due to the size of the data sets, the exhaustive search algorithms were eliminated. A heuristic approach using a confusion matrix from a lightly trained neural network provided the basis for the clustering algorithm. Once the clusters have been identified, the hierarchical network can be trained. The approach of using don't care nodes results from the difficulty in generating extremely complex surfaces in order to separate one class from all of the others. This approach finds pairwise separating surfaces and forms the more complex separating surface from combinations of simpler surfaces. This technique both reduces training time and improves accuracy over the previously reported results. Accuracies of 97.47%, 95.70%, and 99.05% were achieved for the polar, desert and smoke data sets.

Reasoning about Resources and Hierarchical Tasks Using OWL and SWRL

NASA Astrophysics Data System (ADS)

Elenius, Daniel; Martin, David; Ford, Reginald; Denker, Grit

Military training and testing events are highly complex affairs, potentially involving dozens of legacy systems that need to interoperate in a meaningful way. There are superficial interoperability concerns (such as two systems not sharing the same messaging formats), but also substantive problems such as different systems not sharing the same understanding of the terrain, positions of entities, and so forth. We describe our approach to facilitating such events: describe the systems and requirements in great detail using ontologies, and use automated reasoning to automatically find and help resolve problems. The complexity of our problem took us to the limits of what one can do with OWL, and we needed to introduce some innovative techniques of using and extending it. We describe our novel ways of using SWRL and discuss its limitations as well as extensions to it that we found necessary or desirable. Another innovation is our representation of hierarchical tasks in OWL, and an engine that reasons about them. Our task ontology has proved to be a very flexible and expressive framework to describe requirements on resources and their capabilities in order to achieve some purpose.

Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

NASA Astrophysics Data System (ADS)

Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-04-01

One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community detection in complex networks.

Multilevel Model Prediction

ERIC Educational Resources Information Center

Frees, Edward W.; Kim, Jee-Seon

2006-01-01

Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling--model disturbances, random coefficients, and future response…

Credit networks and systemic risk of Chinese local financing platforms: Too central or too big to fail?. -based on different credit correlations using hierarchical methods

NASA Astrophysics Data System (ADS)

He, Fang; Chen, Xi

2016-11-01

The accelerating accumulation and risk concentration of Chinese local financing platforms debts have attracted wide attention throughout the world. Due to the network of financial exposures among institutions, the failure of several platforms or regions of systemic importance will probably trigger systemic risk and destabilize the financial system. However, the complex network of credit relationships in Chinese local financing platforms at the state level remains unknown. To fill this gap, we presented the first complex networks and hierarchical cluster analysis of the credit market of Chinese local financing platforms using the ;bottom up; method from firm-level data. Based on balance-sheet channel, we analyzed the topology and taxonomy by applying the analysis paradigm of subdominant ultra-metric space to an empirical data in 2013. It is remarked that we chose to extract the network of co-financed financing platforms in order to evaluate the effect of risk contagion from platforms to bank system. We used the new credit similarity measure by combining the factor of connectivity and size, to extract minimal spanning trees (MSTs) and hierarchical trees (HTs). We found that: (1) the degree distributions of credit correlation backbone structure of Chinese local financing platforms are fat tailed, and the structure is unstable with respect to targeted failures; (2) the backbone is highly hierarchical, and largely explained by the geographic region; (3) the credit correlation backbone structure based on connectivity and size is significantly heterogeneous; (4) key platforms and regions of systemic importance, and contagion path of systemic risk are obtained, which are contributed to preventing systemic risk and regional risk of Chinese local financing platforms and preserving financial stability under the framework of macro prudential supervision. Our approach of credit similarity measure provides a means of recognizing ;systemically important; institutions and regions for a targeted policy with risk minimization which gives a flexible and comprehensive consideration to both aspects of ;too big to fail; and ;too central to fail;.

Secular dynamics of hierarchical multiple systems composed of nested binaries, with an arbitrary number of bodies and arbitrary hierarchical structure - II. External perturbations: flybys and supernovae

NASA Astrophysics Data System (ADS)

Hamers, Adrian S.

2018-05-01

We extend the formalism of a previous paper to include the effects of flybys and instantaneous perturbations such as supernovae on the long-term secular evolution of hierarchical multiple systems with an arbitrary number of bodies and hierarchy, provided that the system is composed of nested binary orbits. To model secular encounters, we expand the Hamiltonian in terms of the ratio of the separation of the perturber with respect to the barycentre of the multiple system, to the separation of the widest orbit. Subsequently, we integrate over the perturber orbit numerically or analytically. We verify our method for secular encounters and illustrate it with an example. Furthermore, we describe a method to compute instantaneous orbital changes to multiple systems, such as asymmetric supernovae and impulsive encounters. The secular code, with implementation of the extensions described in this paper, is publicly available within AMUSE, and we provide a number of simple example scripts to illustrate its usage for secular and impulsive encounters and asymmetric supernovae. The extensions presented in this paper are a next step towards efficiently modelling the evolution of complex multiple systems embedded in star clusters.

Hierarchical regulation of photosynthesis gene expression by the oxygen-responsive PrrBA and AppA-PpsR systems of Rhodobacter sphaeroides.

PubMed

Gomelsky, Larissa; Moskvin, Oleg V; Stenzel, Rachel A; Jones, Denise F; Donohue, Timothy J; Gomelsky, Mark

2008-12-01

In the facultatively phototrophic proteobacterium Rhodobacter sphaeroides, formation of the photosynthetic apparatus is oxygen dependent. When oxygen tension decreases, the response regulator PrrA of the global two-component PrrBA system is believed to directly activate transcription of the puf, puh, and puc operons, encoding structural proteins of the photosynthetic complexes, and to indirectly upregulate the photopigment biosynthesis genes bch and crt. Decreased oxygen also results in inactivation of the photosynthesis-specific repressor PpsR, bringing about derepression of the puc, bch, and crt operons. We uncovered a hierarchical relationship between these two regulatory systems, earlier thought to function independently. We also more accurately assessed the spectrum of gene targets of the PrrBA system. First, expression of the appA gene, encoding the PpsR antirepressor, is PrrA dependent, which establishes one level of hierarchical dominance of the PrrBA system over AppA-PpsR. Second, restoration of the appA transcript to the wild-type level is insufficient for rescuing phototrophic growth impairment of the prrA mutant, whereas inactivation of ppsR is sufficient. This suggests that in addition to controlling appA transcription, PrrA affects the activity of the AppA-PpsR system via an as yet unidentified mechanism(s). Third, PrrA directly activates several bch and crt genes, traditionally considered to be the PpsR targets. Therefore, in R. sphaeroides, the global PrrBA system regulates photosynthesis gene expression (i) by rigorous control over the photosynthesis-specific AppA-PpsR regulatory system and (ii) by extensive direct transcription activation of genes encoding structural proteins of photosynthetic complexes as well as genes encoding photopigment biosynthesis enzymes.

Data flow modeling techniques

NASA Technical Reports Server (NTRS)

Kavi, K. M.

1984-01-01

There have been a number of simulation packages developed for the purpose of designing, testing and validating computer systems, digital systems and software systems. Complex analytical tools based on Markov and semi-Markov processes have been designed to estimate the reliability and performance of simulated systems. Petri nets have received wide acceptance for modeling complex and highly parallel computers. In this research data flow models for computer systems are investigated. Data flow models can be used to simulate both software and hardware in a uniform manner. Data flow simulation techniques provide the computer systems designer with a CAD environment which enables highly parallel complex systems to be defined, evaluated at all levels and finally implemented in either hardware or software. Inherent in data flow concept is the hierarchical handling of complex systems. In this paper we will describe how data flow can be used to model computer system.

Complexity, Robustness, and Multistability in Network Systems with Switching Topologies: A Hierarchical Hybrid Control Approach

DTIC Science & Technology

2015-05-22

sensor networks for managing power levels of wireless networks ; air and ground transportation systems for air traffic control and payload transport and... network systems, large-scale systems, adaptive control, discontinuous systems 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF...cover a broad spectrum of ap- plications including cooperative control of unmanned air vehicles, autonomous underwater vehicles, distributed sensor

Maximum entropy approach to H -theory: Statistical mechanics of hierarchical systems

NASA Astrophysics Data System (ADS)

Vasconcelos, Giovani L.; Salazar, Domingos S. P.; Macêdo, A. M. S.

2018-02-01

A formalism, called H-theory, is applied to the problem of statistical equilibrium of a hierarchical complex system with multiple time and length scales. In this approach, the system is formally treated as being composed of a small subsystem—representing the region where the measurements are made—in contact with a set of "nested heat reservoirs" corresponding to the hierarchical structure of the system, where the temperatures of the reservoirs are allowed to fluctuate owing to the complex interactions between degrees of freedom at different scales. The probability distribution function (pdf) of the temperature of the reservoir at a given scale, conditioned on the temperature of the reservoir at the next largest scale in the hierarchy, is determined from a maximum entropy principle subject to appropriate constraints that describe the thermal equilibrium properties of the system. The marginal temperature distribution of the innermost reservoir is obtained by integrating over the conditional distributions of all larger scales, and the resulting pdf is written in analytical form in terms of certain special transcendental functions, known as the Fox H functions. The distribution of states of the small subsystem is then computed by averaging the quasiequilibrium Boltzmann distribution over the temperature of the innermost reservoir. This distribution can also be written in terms of H functions. The general family of distributions reported here recovers, as particular cases, the stationary distributions recently obtained by Macêdo et al. [Phys. Rev. E 95, 032315 (2017), 10.1103/PhysRevE.95.032315] from a stochastic dynamical approach to the problem.

Maximum entropy approach to H-theory: Statistical mechanics of hierarchical systems.

PubMed

Vasconcelos, Giovani L; Salazar, Domingos S P; Macêdo, A M S

2018-02-01

A formalism, called H-theory, is applied to the problem of statistical equilibrium of a hierarchical complex system with multiple time and length scales. In this approach, the system is formally treated as being composed of a small subsystem-representing the region where the measurements are made-in contact with a set of "nested heat reservoirs" corresponding to the hierarchical structure of the system, where the temperatures of the reservoirs are allowed to fluctuate owing to the complex interactions between degrees of freedom at different scales. The probability distribution function (pdf) of the temperature of the reservoir at a given scale, conditioned on the temperature of the reservoir at the next largest scale in the hierarchy, is determined from a maximum entropy principle subject to appropriate constraints that describe the thermal equilibrium properties of the system. The marginal temperature distribution of the innermost reservoir is obtained by integrating over the conditional distributions of all larger scales, and the resulting pdf is written in analytical form in terms of certain special transcendental functions, known as the Fox H functions. The distribution of states of the small subsystem is then computed by averaging the quasiequilibrium Boltzmann distribution over the temperature of the innermost reservoir. This distribution can also be written in terms of H functions. The general family of distributions reported here recovers, as particular cases, the stationary distributions recently obtained by Macêdo et al. [Phys. Rev. E 95, 032315 (2017)10.1103/PhysRevE.95.032315] from a stochastic dynamical approach to the problem.

Network structure of subway passenger flows

NASA Astrophysics Data System (ADS)

Xu, Q.; Mao, B. H.; Bai, Y.

2016-03-01

The results of transportation infrastructure network analyses have been used to analyze complex networks in a topological context. However, most modeling approaches, including those based on complex network theory, do not fully account for real-life traffic patterns and may provide an incomplete view of network functions. This study utilizes trip data obtained from the Beijing Subway System to characterize individual passenger movement patterns. A directed weighted passenger flow network was constructed from the subway infrastructure network topology by incorporating trip data. The passenger flow networks exhibit several properties that can be characterized by power-law distributions based on flow size, and log-logistic distributions based on the fraction of boarding and departing passengers. The study also characterizes the temporal patterns of in-transit and waiting passengers and provides a hierarchical clustering structure for passenger flows. This hierarchical flow organization varies in the spatial domain. Ten cluster groups were identified, indicating a hierarchical urban polycentric structure composed of large concentrated flows at urban activity centers. These empirical findings provide insights regarding urban human mobility patterns within a large subway network.

The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

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

Grasha, K.; Calzetti, D.; Adamo, A.

We present a study of the hierarchical clustering of the young stellar clusters in six local (3–15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. Themore » strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ∼40–60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.« less

Efficiently computing and deriving topological relation matrices between complex regions with broad boundaries

NASA Astrophysics Data System (ADS)

Du, Shihong; Guo, Luo; Wang, Qiao; Qin, Qimin

The extended 9-intersection matrix is used to formalize topological relations between uncertain regions while it is designed to satisfy the requirements at a concept level, and to deal with the complex regions with broad boundaries (CBBRs) as a whole without considering their hierarchical structures. In contrast to simple regions with broad boundaries, CBBRs have complex hierarchical structures. Therefore, it is necessary to take into account the complex hierarchical structure and to represent the topological relations between all regions in CBBRs as a relation matrix, rather than using the extended 9-intersection matrix to determine topological relations. In this study, a tree model is first used to represent the intrinsic configuration of CBBRs hierarchically. Then, the reasoning tables are presented for deriving topological relations between child, parent and sibling regions from the relations between two given regions in CBBRs. Finally, based on the reasoning, efficient methods are proposed to compute and derive the topological relation matrix. The proposed methods can be incorporated into spatial databases to facilitate geometric-oriented applications.

High-Dimensional Bayesian Geostatistics

PubMed Central

Banerjee, Sudipto

2017-01-01

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understand the complex nature of spatial and temporal variability. However, fitting hierarchical spatiotemporal models often involves expensive matrix computations with complexity increasing in cubic order for the number of spatial locations and temporal points. This renders such models unfeasible for large data sets. This article offers a focused review of two methods for constructing well-defined highly scalable spatiotemporal stochastic processes. Both these processes can be used as “priors” for spatiotemporal random fields. The first approach constructs a low-rank process operating on a lower-dimensional subspace. The second approach constructs a Nearest-Neighbor Gaussian Process (NNGP) that ensures sparse precision matrices for its finite realizations. Both processes can be exploited as a scalable prior embedded within a rich hierarchical modeling framework to deliver full Bayesian inference. These approaches can be described as model-based solutions for big spatiotemporal datasets. The models ensure that the algorithmic complexity has ~ n floating point operations (flops), where n the number of spatial locations (per iteration). We compare these methods and provide some insight into their methodological underpinnings. PMID:29391920

High-Dimensional Bayesian Geostatistics.

PubMed

Banerjee, Sudipto

2017-06-01

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understand the complex nature of spatial and temporal variability. However, fitting hierarchical spatiotemporal models often involves expensive matrix computations with complexity increasing in cubic order for the number of spatial locations and temporal points. This renders such models unfeasible for large data sets. This article offers a focused review of two methods for constructing well-defined highly scalable spatiotemporal stochastic processes. Both these processes can be used as "priors" for spatiotemporal random fields. The first approach constructs a low-rank process operating on a lower-dimensional subspace. The second approach constructs a Nearest-Neighbor Gaussian Process (NNGP) that ensures sparse precision matrices for its finite realizations. Both processes can be exploited as a scalable prior embedded within a rich hierarchical modeling framework to deliver full Bayesian inference. These approaches can be described as model-based solutions for big spatiotemporal datasets. The models ensure that the algorithmic complexity has ~ n floating point operations (flops), where n the number of spatial locations (per iteration). We compare these methods and provide some insight into their methodological underpinnings.

Ecosystem and immune systems: Hierarchial response provides resilience against invasions

USGS Publications Warehouse

Allen, Craig R.

2001-01-01

Janssen (2001) provides the stimulus for thoughtful comparison and consideration of the ranges of responses exhibited by immune systems and ecological systems in the face of perturbations such as biological invasions. It may indeed be informative to consider the similarities of the responses to invasions exhibited by immune systems and ecological systems. Clearly, both types of systems share a general organizational structure with all other complex hierarchical systems. Their organization provides these systems with resilience. However, when describing the response of ecological-economic systems to invasions, Janssen emphasizes the human-economic response. I would like to expand on his comparison by focusing on how resilience is maintained in complex systems under the threat of invasion.

Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

PubMed Central

Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

2010-01-01

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information processing. PMID:21852971

A hierarchical modeling methodology for the definition and selection of requirements

NASA Astrophysics Data System (ADS)

Dufresne, Stephane

This dissertation describes the development of a requirements analysis methodology that takes into account the concept of operations and the hierarchical decomposition of aerospace systems. At the core of the methodology, the Analytic Network Process (ANP) is used to ensure the traceability between the qualitative and quantitative information present in the hierarchical model. The proposed methodology is implemented to the requirements definition of a hurricane tracker Unmanned Aerial Vehicle. Three research objectives are identified in this work; (1) improve the requirements mapping process by matching the stakeholder expectations with the concept of operations, systems and available resources; (2) reduce the epistemic uncertainty surrounding the requirements and requirements mapping; and (3) improve the requirements down-selection process by taking into account the level of importance of the criteria and the available resources. Several challenges are associated with the identification and definition of requirements. The complexity of the system implies that a large number of requirements are needed to define the systems. These requirements are defined early in the conceptual design, where the level of knowledge is relatively low and the level of uncertainty is large. The proposed methodology intends to increase the level of knowledge and reduce the level of uncertainty by guiding the design team through a structured process. To address these challenges, a new methodology is created to flow-down the requirements from the stakeholder expectations to the systems alternatives. A taxonomy of requirements is created to classify the information gathered during the problem definition. Subsequently, the operational and systems functions and measures of effectiveness are integrated to a hierarchical model to allow the traceability of the information. Monte Carlo methods are used to evaluate the variations of the hierarchical model elements and consequently reduce the epistemic uncertainty. The proposed methodology is applied to the design of a hurricane tracker Unmanned Aerial Vehicles to demonstrate the origin and impact of requirements on the concept of operations and systems alternatives. This research demonstrates that the hierarchical modeling methodology provides a traceable flow-down of the requirements from the problem definition to the systems alternatives phases of conceptual design.

An object-oriented class library for medical software development.

PubMed

O'Kane, K C; McColligan, E E

1996-12-01

The objective of this research is the development of a Medical Object Library (MOL) consisting of reusable, inheritable, portable, extendable C++ classes that facilitate rapid development of medical software at reduced cost and increased functionality. The result of this research is a library of class objects that range in function from string and hierarchical file handling entities to high level, procedural agents that perform increasingly complex, integrated tasks. A system built upon these classes is compatible with any other system similarly constructed with respect to data definitions, semantics, data organization and storage. As new objects are built, they can be added to the class library for subsequent use. The MOL is a toolkit of software objects intended to support a common file access methodology, a unified medical record structure, consistent message processing, standard graphical display facilities and uniform data collection procedures. This work emphasizes the relationship that potentially exists between the structure of a hierarchical medical record and procedural language components by means of a hierarchical class library and tree structured file access facility. In doing so, it attempts to establish interest in and demonstrate the practicality of the hierarchical medical record model in the modern context of object oriented programming.

Stress reaction process-based hierarchical recognition algorithm for continuous intrusion events in optical fiber prewarning system

NASA Astrophysics Data System (ADS)

Qu, Hongquan; Yuan, Shijiao; Wang, Yanping; Yang, Dan

2018-04-01

To improve the recognition performance of optical fiber prewarning system (OFPS), this study proposed a hierarchical recognition algorithm (HRA). Compared with traditional methods, which employ only a complex algorithm that includes multiple extracted features and complex classifiers to increase the recognition rate with a considerable decrease in recognition speed, HRA takes advantage of the continuity of intrusion events, thereby creating a staged recognition flow inspired by stress reaction. HRA is expected to achieve high-level recognition accuracy with less time consumption. First, this work analyzed the continuity of intrusion events and then presented the algorithm based on the mechanism of stress reaction. Finally, it verified the time consumption through theoretical analysis and experiments, and the recognition accuracy was obtained through experiments. Experiment results show that the processing speed of HRA is 3.3 times faster than that of a traditional complicated algorithm and has a similar recognition rate of 98%. The study is of great significance to fast intrusion event recognition in OFPS.

Numerical Modelling of Tertiary Tides

NASA Astrophysics Data System (ADS)

Gao, Yan; Correia, Alexandre C. M.; Eggleton, Peter P.; Han, Zhanwen

2018-06-01

Stellar systems consisting of multiple stars tend to undergo tidal interactions when the separations between the stars are short. While tidal phenomena have been extensively studied, a certain tidal effect exclusive to hierarchical triples (triples in which one component star has a much wider orbit than the others) has hardly received any attention, mainly due to its complexity and consequent resistance to being modelled. This tidal effect is the tidal perturbation of the tertiary by the inner binary, which in turn depletes orbital energy from the inner binary, causing the inner binary separation to shrink. In this paper, we develop a fully numerical simulation of these "tertiary tides" by modifying established tidal models. We also provide general insight as to how close a hierarchical triple needs to be in order for such an effect to take place, and demonstrate that our simulations can effectively retrieve the orbital evolution for such systems. We conclude that tertiary tides are a significant factor in the evolution of close hierarchical triples, and strongly influence at least ˜1% of all multiple star systems.

Analysis and design of algorithm-based fault-tolerant systems

NASA Technical Reports Server (NTRS)

Nair, V. S. Sukumaran

1990-01-01

An important consideration in the design of high performance multiprocessor systems is to ensure the correctness of the results computed in the presence of transient and intermittent failures. Concurrent error detection and correction have been applied to such systems in order to achieve reliability. Algorithm Based Fault Tolerance (ABFT) was suggested as a cost-effective concurrent error detection scheme. The research was motivated by the complexity involved in the analysis and design of ABFT systems. To that end, a matrix-based model was developed and, based on that, algorithms for both the design and analysis of ABFT systems are formulated. These algorithms are less complex than the existing ones. In order to reduce the complexity further, a hierarchical approach is developed for the analysis of large systems.

Hierarchical graphs for rule-based modeling of biochemical systems

PubMed Central

2011-01-01

Background In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal) of an edge represents a class of association (dissociation) reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Results For purposes of model annotation, we propose the use of hierarchical graphs to represent structural relationships among components and subcomponents of molecules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR) complex. We also show that computational methods developed for regular graphs can be applied to hierarchical graphs. In particular, we describe a generalization of Nauty, a graph isomorphism and canonical labeling algorithm. The generalized version of the Nauty procedure, which we call HNauty, can be used to assign canonical labels to hierarchical graphs or more generally to graphs with multiple edge types. The difference between the Nauty and HNauty procedures is minor, but for completeness, we provide an explanation of the entire HNauty algorithm. Conclusions Hierarchical graphs provide more intuitive formal representations of proteins and other structured molecules with multiple functional components than do the regular graphs of current languages for specifying rule-based models, such as the BioNetGen language (BNGL). Thus, the proposed use of hierarchical graphs should promote clarity and better understanding of rule-based models. PMID:21288338

A system architecture for a planetary rover

NASA Technical Reports Server (NTRS)

Smith, D. B.; Matijevic, J. R.

1989-01-01

Each planetary mission requires a complex space vehicle which integrates several functions to accomplish the mission and science objectives. A Mars Rover is one of these vehicles, and extends the normal spacecraft functionality with two additional functions: surface mobility and sample acquisition. All functions are assembled into a hierarchical and structured format to understand the complexities of interactions between functions during different mission times. It can graphically show data flow between functions, and most importantly, the necessary control flow to avoid unambiguous results. Diagrams are presented organizing the functions into a structured, block format where each block represents a major function at the system level. As such, there are six blocks representing telecomm, power, thermal, science, mobility and sampling under a supervisory block called Data Management/Executive. Each block is a simple collection of state machines arranged into a hierarchical order very close to the NASREM model for Telerobotics. Each layer within a block represents a level of control for a set of state machines that do the three primary interface functions: command, telemetry, and fault protection. This latter function is expanded to include automatic reactions to the environment as well as internal faults. Lastly, diagrams are presented that trace the system operations involved in moving from site to site after site selection. The diagrams clearly illustrate both the data and control flows. They also illustrate inter-block data transfers and a hierarchical approach to fault protection. This systems architecture can be used to determine functional requirements, interface specifications and be used as a mechanism for grouping subsystems (i.e., collecting groups of machines, or blocks consistent with good and testable implementations).

Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model.

PubMed

Xiao, Fuyuan; Aritsugi, Masayoshi; Wang, Qing; Zhang, Rong

2016-09-01

For efficient and sophisticated analysis of complex event patterns that appear in streams of big data from health care information systems and support for decision-making, a triaxial hierarchical model is proposed in this paper. Our triaxial hierarchical model is developed by focusing on hierarchies among nested event pattern queries with an event concept hierarchy, thereby allowing us to identify the relationships among the expressions and sub-expressions of the queries extensively. We devise a cost-based heuristic by means of the triaxial hierarchical model to find an optimised query execution plan in terms of the costs of both the operators and the communications between them. According to the triaxial hierarchical model, we can also calculate how to reuse the results of the common sub-expressions in multiple queries. By integrating the optimised query execution plan with the reuse schemes, a multi-query optimisation strategy is developed to accomplish efficient processing of multiple nested event pattern queries. We present empirical studies in which the performance of multi-query optimisation strategy was examined under various stream input rates and workloads. Specifically, the workloads of pattern queries can be used for supporting monitoring patients' conditions. On the other hand, experiments with varying input rates of streams can correspond to changes of the numbers of patients that a system should manage, whereas burst input rates can correspond to changes of rushes of patients to be taken care of. The experimental results have shown that, in Workload 1, our proposal can improve about 4 and 2 times throughput comparing with the relative works, respectively; in Workload 2, our proposal can improve about 3 and 2 times throughput comparing with the relative works, respectively; in Workload 3, our proposal can improve about 6 times throughput comparing with the relative work. The experimental results demonstrated that our proposal was able to process complex queries efficiently which can support health information systems and further decision-making. Copyright © 2016 Elsevier B.V. All rights reserved.

Future of computerised electrocardiography.

PubMed Central

Meijler, F L; Robles de Medina, E O; Helder, J C

1980-01-01

The advent of computerised electrocardiography has been of prime importance for the storage and retrieval of data, but none of the available systems is of universal application for analysis of patterns. Future needs require hierarchical systems of increasing degrees of complexity, depending on the source of requests, and there should be appropriate provision for review by cardiologists before the final report is issued. PMID:7000098

RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations

PubMed Central

2012-01-01

Visualization and analysis of molecular networks are both central to systems biology. However, there still exists a large technological gap between them, especially when assessing multiple network levels or hierarchies. Here we present RedeR, an R/Bioconductor package combined with a Java core engine for representing modular networks. The functionality of RedeR is demonstrated in two different scenarios: hierarchical and modular organization in gene co-expression networks and nested structures in time-course gene expression subnetworks. Our results demonstrate RedeR as a new framework to deal with the multiple network levels that are inherent to complex biological systems. RedeR is available from http://bioconductor.org/packages/release/bioc/html/RedeR.html. PMID:22531049

Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns.

PubMed

Kauppi, Jukka-Pekka; Martikainen, Kalle; Ruotsalainen, Ulla

2010-12-01

The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns. Copyright © 2010 Elsevier Ltd. All rights reserved.

Parameterization of aquatic ecosystem functioning and its natural variation: Hierarchical Bayesian modelling of plankton food web dynamics

NASA Astrophysics Data System (ADS)

Norros, Veera; Laine, Marko; Lignell, Risto; Thingstad, Frede

2017-10-01

Methods for extracting empirically and theoretically sound parameter values are urgently needed in aquatic ecosystem modelling to describe key flows and their variation in the system. Here, we compare three Bayesian formulations for mechanistic model parameterization that differ in their assumptions about the variation in parameter values between various datasets: 1) global analysis - no variation, 2) separate analysis - independent variation and 3) hierarchical analysis - variation arising from a shared distribution defined by hyperparameters. We tested these methods, using computer-generated and empirical data, coupled with simplified and reasonably realistic plankton food web models, respectively. While all methods were adequate, the simulated example demonstrated that a well-designed hierarchical analysis can result in the most accurate and precise parameter estimates and predictions, due to its ability to combine information across datasets. However, our results also highlighted sensitivity to hyperparameter prior distributions as an important caveat of hierarchical analysis. In the more complex empirical example, hierarchical analysis was able to combine precise identification of parameter values with reasonably good predictive performance, although the ranking of the methods was less straightforward. We conclude that hierarchical Bayesian analysis is a promising tool for identifying key ecosystem-functioning parameters and their variation from empirical datasets.

The mathematical relationship between Zipf’s law and the hierarchical scaling law

NASA Astrophysics Data System (ADS)

Chen, Yanguang

2012-06-01

The empirical studies of city-size distribution show that Zipf's law and the hierarchical scaling law are linked in many ways. The rank-size scaling and hierarchical scaling seem to be two different sides of the same coin, but their relationship has never been revealed by strict mathematical proof. In this paper, the Zipf's distribution of cities is abstracted as a q-sequence. Based on this sequence, a self-similar hierarchy consisting of many levels is defined and the numbers of cities in different levels form a geometric sequence. An exponential distribution of the average size of cities is derived from the hierarchy. Thus we have two exponential functions, from which follows a hierarchical scaling equation. The results can be statistically verified by simple mathematical experiments and observational data of cities. A theoretical foundation is then laid for the conversion from Zipf's law to the hierarchical scaling law, and the latter can show more information about city development than the former. Moreover, the self-similar hierarchy provides a new perspective for studying networks of cities as complex systems. A series of mathematical rules applied to cities such as the allometric growth law, the 2n principle and Pareto's law can be associated with one another by the hierarchical organization.

Group Decision Support System to Aid the Process of Design and Maintenance of Large Scale Systems

DTIC Science & Technology

1992-03-23

from a fuzzy set of user requirements. The overall objective of the project is to develop a system combining the characteristics of a compact computer... AHP ) for hierarchical prioritization. 4) Individual Evaluation and Selection of Alternatives - Allows the decision maker to individually evaluate...its concept of outranking relations. The AHP method supports complex decision problems by successively decomposing and synthesizing various elements

Synthesis of Systemic Functional Theory & Dynamical Systems Theory for Socio-Cultural Modeling

DTIC Science & Technology

2011-01-26

is, language and other resources (e.g. images and sound resources) are conceptualised as inter-locking systems of meaning which realise four...hierarchical ranks and strata (e.g. sounds, word groups, clauses, and complex discourse structures in language, and elements, figures and episodes in images ...integrating platform for describing how language and other resources (e.g. images and sound) work together to fulfil particular objectives. While

Hierarchical graphs for better annotations of rule-based models of biochemical systems

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

Hu, Bin; Hlavacek, William

2009-01-01

In the graph-based formalism of the BioNetGen language (BNGL), graphs are used to represent molecules, with a colored vertex representing a component of a molecule, a vertex label representing the internal state of a component, and an edge representing a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions, with a rule that specifies addition (removal) of an edge representing a class of association (dissociation) reactions and with a rule that specifies a change of vertex label representing a class of reactions that affect the internal state of amore » molecular component. A set of rules comprises a mathematical/computational model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Here, for purposes of model annotation, we propose an extension of BNGL that involves the use of hierarchical graphs to represent (1) relationships among components and subcomponents of molecules and (2) relationships among classes of reactions defined by rules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR)/CD3 complex. Likewise, we illustrate how hierarchical graphs can be used to document the similarity of two related rules for kinase-catalyzed phosphorylation of a protein substrate. We also demonstrate how a hierarchical graph representing a protein can be encoded in an XML-based format.« less

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

PubMed

Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald

2011-06-01

Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

PubMed Central

2011-01-01

Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. Conclusions HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems. PMID:21627852

Chimera states in networks of logistic maps with hierarchical connectivities

NASA Astrophysics Data System (ADS)

zur Bonsen, Alexander; Omelchenko, Iryna; Zakharova, Anna; Schöll, Eckehard

2018-04-01

Chimera states are complex spatiotemporal patterns consisting of coexisting domains of coherence and incoherence. We study networks of nonlocally coupled logistic maps and analyze systematically how the dilution of the network links influences the appearance of chimera patterns. The network connectivities are constructed using an iterative Cantor algorithm to generate fractal (hierarchical) connectivities. Increasing the hierarchical level of iteration, we compare the resulting spatiotemporal patterns. We demonstrate that a high clustering coefficient and symmetry of the base pattern promotes chimera states, and asymmetric connectivities result in complex nested chimera patterns.

A novel knowledge-based system for interpreting complex engineering drawings: theory, representation, and implementation.

PubMed

Lu, Tong; Tai, Chiew-Lan; Yang, Huafei; Cai, Shijie

2009-08-01

We present a novel knowledge-based system to automatically convert real-life engineering drawings to content-oriented high-level descriptions. The proposed method essentially turns the complex interpretation process into two parts: knowledge representation and knowledge-based interpretation. We propose a new hierarchical descriptor-based knowledge representation method to organize the various types of engineering objects and their complex high-level relations. The descriptors are defined using an Extended Backus Naur Form (EBNF), facilitating modification and maintenance. When interpreting a set of related engineering drawings, the knowledge-based interpretation system first constructs an EBNF-tree from the knowledge representation file, then searches for potential engineering objects guided by a depth-first order of the nodes in the EBNF-tree. Experimental results and comparisons with other interpretation systems demonstrate that our knowledge-based system is accurate and robust for high-level interpretation of complex real-life engineering projects.

COMPLEX ADAPTIVE HIERARCHICAL SYSTEMS

EPA Science Inventory

One of the most powerful images of our time, an image that has changed the way we think of ourselves and the way we think about our relationship to our environment, is the image of Earth viewed from the surface of the moon. As we view "spaceship Earth" we sense that the complexit...

A Hierarchic System for Information Usage.

ERIC Educational Resources Information Center

Lu, John; Markham, David

This paper demonstrates an approach which enables one to reduce in a systematic way the immense complexity of a large body of knowledge. This approach provides considerable insight into what is known and unknown in a given academic field by systematically and pragmatically ordering the information. As a case study, the authors selected…

Segregating the core computational faculty of human language from working memory.

PubMed

Makuuchi, Michiru; Bahlmann, Jörg; Anwander, Alfred; Friederici, Angela D

2009-05-19

In contrast to simple structures in animal vocal behavior, hierarchical structures such as center-embedded sentences manifest the core computational faculty of human language. Previous artificial grammar learning studies found that the left pars opercularis (LPO) subserves the processing of hierarchical structures. However, it is not clear whether this area is activated by the structural complexity per se or by the increased memory load entailed in processing hierarchical structures. To dissociate the effect of structural complexity from the effect of memory cost, we conducted a functional magnetic resonance imaging study of German sentence processing with a 2-way factorial design tapping structural complexity (with/without hierarchical structure, i.e., center-embedding of clauses) and working memory load (long/short distance between syntactically dependent elements; i.e., subject nouns and their respective verbs). Functional imaging data revealed that the processes for structure and memory operate separately but co-operatively in the left inferior frontal gyrus; activities in the LPO increased as a function of structural complexity, whereas activities in the left inferior frontal sulcus (LIFS) were modulated by the distance over which the syntactic information had to be transferred. Diffusion tensor imaging showed that these 2 regions were interconnected through white matter fibers. Moreover, functional coupling between the 2 regions was found to increase during the processing of complex, hierarchically structured sentences. These results suggest a neuroanatomical segregation of syntax-related aspects represented in the LPO from memory-related aspects reflected in the LIFS, which are, however, highly interconnected functionally and anatomically.

Fluctuation relations between hierarchical kinetically equivalent networks with Arrhenius-type transitions and their roles in systems and structural biology.

PubMed

Deng, De-Ming; Lu, Yi-Ta; Chang, Cheng-Hung

2017-06-01

The legality of using simple kinetic schemes to determine the stochastic properties of a complex system depends on whether the fluctuations generated from hierarchical equivalent schemes are consistent with one another. To analyze this consistency, we perform lumping processes on the stochastic differential equations and the generalized fluctuation-dissipation theorem and apply them to networks with the frequently encountered Arrhenius-type transition rates. The explicit Langevin force derived from those networks enables us to calculate the state fluctuations caused by the intrinsic and extrinsic noises on the free energy surface and deduce their relations between kinetically equivalent networks. In addition to its applicability to wide classes of network related systems, such as those in structural and systems biology, the result sheds light on the fluctuation relations for general physical variables in Keizer's canonical theory.

Efficient steady-state solver for hierarchical quantum master equations

NASA Astrophysics Data System (ADS)

Zhang, Hou-Dao; Qiao, Qin; Xu, Rui-Xue; Zheng, Xiao; Yan, YiJing

2017-07-01

Steady states play pivotal roles in many equilibrium and non-equilibrium open system studies. Their accurate evaluations call for exact theories with rigorous treatment of system-bath interactions. Therein, the hierarchical equations-of-motion (HEOM) formalism is a nonperturbative and non-Markovian quantum dissipation theory, which can faithfully describe the dissipative dynamics and nonlinear response of open systems. Nevertheless, solving the steady states of open quantum systems via HEOM is often a challenging task, due to the vast number of dynamical quantities involved. In this work, we propose a self-consistent iteration approach that quickly solves the HEOM steady states. We demonstrate its high efficiency with accurate and fast evaluations of low-temperature thermal equilibrium of a model Fenna-Matthews-Olson pigment-protein complex. Numerically exact evaluation of thermal equilibrium Rényi entropies and stationary emission line shapes is presented with detailed discussion.

Exploring with PAM: Prospecting ANTS Missions for Solar System Surveys

NASA Technical Reports Server (NTRS)

Clark, P. E.; Rilee, M. L.; Curtis, S. A.

2003-01-01

ANTS (Autonomous Nano-Technology Swarm), a large (1000 member) swarm of nano to picoclass (10 to 1 kg) totally autonomous spacecraft, are being developed as a NASA advanced mission concept. ANTS, based on a hierarchical insect social order, use an evolvable, self-similar, hierarchical neural system in which individual spacecraft represent the highest level nodes. ANTS uses swarm intelligence attained through collective, cooperative interactions of the nodes at all levels of the system. At the highest levels this can take the form of cooperative, collective behavior among the individual spacecraft in a very large constellation. The ANTS neural architecture is designed for totally autonomous operation of complex systems including spacecraft constellations. The ANTS (Autonomous Nano Technology Swarm) concept has a number of possible applications. A version of ANTS designed for surveying and determining the resource potential of the asteroid belt, called PAM (Prospecting ANTS Mission), is examined here.

Recognition and characterization of hierarchical interstellar structure. II - Structure tree statistics

NASA Technical Reports Server (NTRS)

Houlahan, Padraig; Scalo, John

1992-01-01

A new method of image analysis is described, in which images partitioned into 'clouds' are represented by simplified skeleton images, called structure trees, that preserve the spatial relations of the component clouds while disregarding information concerning their sizes and shapes. The method can be used to discriminate between images of projected hierarchical (multiply nested) and random three-dimensional simulated collections of clouds constructed on the basis of observed interstellar properties, and even intermediate systems formed by combining random and hierarchical simulations. For a given structure type, the method can distinguish between different subclasses of models with different parameters and reliably estimate their hierarchical parameters: average number of children per parent, scale reduction factor per level of hierarchy, density contrast, and number of resolved levels. An application to a column density image of the Taurus complex constructed from IRAS data is given. Moderately strong evidence for a hierarchical structural component is found, and parameters of the hierarchy, as well as the average volume filling factor and mass efficiency of fragmentation per level of hierarchy, are estimated. The existence of nested structure contradicts models in which large molecular clouds are supposed to fragment, in a single stage, into roughly stellar-mass cores.

Hierarchically nested river landform sequences

NASA Astrophysics Data System (ADS)

Pasternack, G. B.; Weber, M. D.; Brown, R. A.; Baig, D.

2017-12-01

River corridors exhibit landforms nested within landforms repeatedly down spatial scales. In this study we developed, tested, and implemented a new way to create river classifications by mapping domains of fluvial processes with respect to the hierarchical organization of topographic complexity that drives fluvial dynamism. We tested this approach on flow convergence routing, a morphodynamic mechanism with different states depending on the structure of nondimensional topographic variability. Five nondimensional landform types with unique functionality (nozzle, wide bar, normal channel, constricted pool, and oversized) represent this process at any flow. When this typology is nested at base flow, bankfull, and floodprone scales it creates a system with up to 125 functional types. This shows how a single mechanism produces complex dynamism via nesting. Given the classification, we answered nine specific scientific questions to investigate the abundance, sequencing, and hierarchical nesting of these new landform types using a 35-km gravel/cobble river segment of the Yuba River in California. The nested structure of flow convergence routing landforms found in this study revealed that bankfull landforms are nested within specific floodprone valley landform types, and these types control bankfull morphodynamics during moderate to large floods. As a result, this study calls into question the prevailing theory that the bankfull channel of a gravel/cobble river is controlled by in-channel, bankfull, and/or small flood flows. Such flows are too small to initiate widespread sediment transport in a gravel/cobble river with topographic complexity.

Hierarchical Model for the Evolution of Cloud Complexes

NASA Astrophysics Data System (ADS)

Sánchez D., Néstor M.; Parravano, Antonio

1999-01-01

The structure of cloud complexes appears to be well described by a tree structure (i.e., a simplified ``stick man'') representation when the image is partitioned into ``clouds.'' In this representation, the parent-child relationships are assigned according to containment. Based on this picture, a hierarchical model for the evolution of cloud complexes, including star formation, is constructed. The model follows the mass evolution of each substructure by computing its mass exchange with its parent and children. The parent-child mass exchange (evaporation or condensation) depends on the radiation density at the interphase. At the end of the ``lineage,'' stars may be born or die, so that there is a nonstationary mass flow in the hierarchical structure. For a variety of parameter sets the system follows the same series of steps to transform diffuse gas into stars, and the regulation of the mass flux in the tree by previously formed stars dominates the evolution of the star formation. For the set of parameters used here as a reference model, the system tends to produce initial mass functions (IMFs) that have a maximum at a mass that is too high (~2 Msolar) and the characteristic times for evolution seem too long. We show that these undesired properties can be improved by adjusting the model parameters. The model requires further physics (e.g., allowing for multiple stellar systems and clump collisions) before a definitive comparison with observations can be made. Instead, the emphasis here is to illustrate some general properties of this kind of complex nonlinear model for the star formation process. Notwithstanding the simplifications involved, the model reveals an essential feature that will likely remain if additional physical processes are included, that is, the detailed behavior of the system is very sensitive to the variations on the initial and external conditions, suggesting that a ``universal'' IMF is very unlikely. When an ensemble of IMFs corresponding to a variety of initial or external conditions is examined, the slope of the IMF at high masses shows variations comparable to the range derived from observational data. These facts suggest that the considered physical processes (phase transitions regulated by the radiation field) may play a role in the global evolution of molecular complexes.

Exploring the significance of structural hierarchy in material systems-A review

NASA Astrophysics Data System (ADS)

Pan, Ning

2014-06-01

Structural hierarchy and heterogeneity are inherent features in biological materials, but their significance in affecting the system behaviors is yet to be fully understood. In Sec. I, this article first identifies the major characteristics that manifest, or are resulted from, such hierarchy and heterogeneity in materials. Then in Sec. II, it presents several typical natural material systems including wood, bone, and others from animals to illustrate the proposed views. The paper also discusses a man-made smart material, textiles, to demonstrate that textiles are hierarchal, multifunctional, highly complex, and arguably the engineered material closest on a par with biological materials in complexity, and, more importantly, we can still learn quite a few new things from them in development of novel materials. In Sec. III, the paper summarizes several general approaches in developing a hierarchal material system at various scales, including structure thinning and splitting, laminating and layering, spatial and angular orientation, heterogenization and hybridization, and analyzes the advantages associated with them. It also stresses the adverse consequences once the existing structural hierarchy breaks down due to various mutations in biological systems. It discusses, in particular, the influences of moisture and air on material properties, given the near ubiquitousness of both air and water in materials. It next deals with in Sec. IV, some theoretical issues in material research including packing and ordering, the bi-modular mechanics, the behavior non-affinities due to disparity in hierarchal levels, the importance of system dimensionality in a hierarchal material system, and more philosophically, the issues of Nature's wisdom versus Intelligent Design. Section V then offers some concluding remarks, including a recap of the major issues covered in this article, and some general conclusions derived from the analyses and discussions. The main purpose of this paper is to make an effort to explore, identify, derive, or theorize some generic principles based on the existing results, not to offer another comprehensive review of current research activities in the fields for that there already exist some excellent ones. This paper examines the related topics with several approaches to not only reveal the underlying geometrical and physical mechanisms but also to emphasize the ways in which such mechanisms may be applied to developing engineered material systems with novel properties.

Heuristic decomposition for non-hierarchic systems

NASA Technical Reports Server (NTRS)

Bloebaum, Christina L.; Hajela, P.

1991-01-01

Design and optimization is substantially more complex in multidisciplinary and large-scale engineering applications due to the existing inherently coupled interactions. The paper introduces a quasi-procedural methodology for multidisciplinary optimization that is applicable for nonhierarchic systems. The necessary decision-making support for the design process is provided by means of an embedded expert systems capability. The method employs a decomposition approach whose modularity allows for implementation of specialized methods for analysis and optimization within disciplines.

Architecture of the parallel hierarchical network for fast image recognition

NASA Astrophysics Data System (ADS)

Timchenko, Leonid; Wójcik, Waldemar; Kokriatskaia, Natalia; Kutaev, Yuriy; Ivasyuk, Igor; Kotyra, Andrzej; Smailova, Saule

2016-09-01

Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

Complex networks with scale-free nature and hierarchical modularity

NASA Astrophysics Data System (ADS)

Shekatkar, Snehal M.; Ambika, G.

2015-09-01

Generative mechanisms which lead to empirically observed structure of networked systems from diverse fields like biology, technology and social sciences form a very important part of study of complex networks. The structure of many networked systems like biological cell, human society and World Wide Web markedly deviate from that of completely random networks indicating the presence of underlying processes. Often the main process involved in their evolution is the addition of links between existing nodes having a common neighbor. In this context we introduce an important property of the nodes, which we call mediating capacity, that is generic to many networks. This capacity decreases rapidly with increase in degree, making hubs weak mediators of the process. We show that this property of nodes provides an explanation for the simultaneous occurrence of the observed scale-free structure and hierarchical modularity in many networked systems. This also explains the high clustering and small-path length seen in real networks as well as non-zero degree-correlations. Our study also provides insight into the local process which ultimately leads to emergence of preferential attachment and hence is also important in understanding robustness and control of real networks as well as processes happening on real networks.

Hierarchical scheme for detecting the rotating MIMO transmission of the in-door RGB-LED visible light wireless communications using mobile-phone camera

NASA Astrophysics Data System (ADS)

Chen, Shih-Hao; Chow, Chi-Wai

2015-01-01

Multiple-input and multiple-output (MIMO) scheme can extend the transmission capacity for the light-emitting-diode (LED) based visible light communication (VLC) systems. The MIMO VLC system that uses the mobile-phone camera as the optical receiver (Rx) to receive MIMO signal from the n×n Red-Green-Blue (RGB) LED array is desirable. The key step of decoding this signal is to detect the signal direction. If the LED transmitter (Tx) is rotated, the Rx may not realize the rotation and transmission error can occur. In this work, we propose and demonstrate a novel hierarchical transmission scheme which can reduce the computation complexity of rotation detection in LED array VLC system. We use the n×n RGB LED array as the MIMO Tx. In our study, a novel two dimensional Hadamard coding scheme is proposed. Using the different LED color layers to indicate the rotation, a low complexity rotation detection method can be used for improving the quality of received signal. The detection correction rate is above 95% in the indoor usage distance. Experimental results confirm the feasibility of the proposed scheme.

Role of local assembly in the hierarchical crystallization of associating colloidal hard hemispheres

NASA Astrophysics Data System (ADS)

Lei, Qun-li; Hadinoto, Kunn; Ni, Ran

2017-10-01

Hierarchical self-assembly consisting of local associations of simple building blocks for the formation of complex structures widely exists in nature, while the essential role of local assembly remains unknown. In this work, by using computer simulations, we study a simple model system consisting of associating colloidal hemispheres crystallizing into face-centered-cubic crystals comprised of spherical dimers of hemispheres, focusing on the effect of dimer formation on the hierarchical crystallization. We found that besides assisting the crystal nucleation because of increasing the symmetry of building blocks, the association between hemispheres can also induce both reentrant melting and reentrant crystallization depending on the range of interaction. Especially when the interaction is highly sticky, we observe a novel reentrant crystallization of identical crystals, which melt only in a certain temperature range. This offers another axis in fabricating responsive crystalline materials by tuning the fluctuation of local association.

Segregating the core computational faculty of human language from working memory

PubMed Central

Makuuchi, Michiru; Bahlmann, Jörg; Anwander, Alfred; Friederici, Angela D.

2009-01-01

In contrast to simple structures in animal vocal behavior, hierarchical structures such as center-embedded sentences manifest the core computational faculty of human language. Previous artificial grammar learning studies found that the left pars opercularis (LPO) subserves the processing of hierarchical structures. However, it is not clear whether this area is activated by the structural complexity per se or by the increased memory load entailed in processing hierarchical structures. To dissociate the effect of structural complexity from the effect of memory cost, we conducted a functional magnetic resonance imaging study of German sentence processing with a 2-way factorial design tapping structural complexity (with/without hierarchical structure, i.e., center-embedding of clauses) and working memory load (long/short distance between syntactically dependent elements; i.e., subject nouns and their respective verbs). Functional imaging data revealed that the processes for structure and memory operate separately but co-operatively in the left inferior frontal gyrus; activities in the LPO increased as a function of structural complexity, whereas activities in the left inferior frontal sulcus (LIFS) were modulated by the distance over which the syntactic information had to be transferred. Diffusion tensor imaging showed that these 2 regions were interconnected through white matter fibers. Moreover, functional coupling between the 2 regions was found to increase during the processing of complex, hierarchically structured sentences. These results suggest a neuroanatomical segregation of syntax-related aspects represented in the LPO from memory-related aspects reflected in the LIFS, which are, however, highly interconnected functionally and anatomically. PMID:19416819

LONI visualization environment.

PubMed

Dinov, Ivo D; Valentino, Daniel; Shin, Bae Cheol; Konstantinidis, Fotios; Hu, Guogang; MacKenzie-Graham, Allan; Lee, Erh-Fang; Shattuck, David; Ma, Jeff; Schwartz, Craig; Toga, Arthur W

2006-06-01

Over the past decade, the use of informatics to solve complex neuroscientific problems has increased dramatically. Many of these research endeavors involve examining large amounts of imaging, behavioral, genetic, neurobiological, and neuropsychiatric data. Superimposing, processing, visualizing, or interpreting such a complex cohort of datasets frequently becomes a challenge. We developed a new software environment that allows investigators to integrate multimodal imaging data, hierarchical brain ontology systems, on-line genetic and phylogenic databases, and 3D virtual data reconstruction models. The Laboratory of Neuro Imaging visualization environment (LONI Viz) consists of the following components: a sectional viewer for imaging data, an interactive 3D display for surface and volume rendering of imaging data, a brain ontology viewer, and an external database query system. The synchronization of all components according to stereotaxic coordinates, region name, hierarchical ontology, and genetic labels is achieved via a comprehensive BrainMapper functionality, which directly maps between position, structure name, database, and functional connectivity information. This environment is freely available, portable, and extensible, and may prove very useful for neurobiologists, neurogenetisists, brain mappers, and for other clinical, pedagogical, and research endeavors.

The Evolutionary Origins of Hierarchy

PubMed Central

Huizinga, Joost; Clune, Jeff

2016-01-01

Hierarchical organization—the recursive composition of sub-modules—is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force–the cost of connections–promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics. PMID:27280881

Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model.

PubMed

Damij, Nadja; Boškoski, Pavle; Bohanec, Marko; Mileva Boshkoska, Biljana

2016-01-01

The omnipresent need for optimisation requires constant improvements of companies' business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and "what-if" scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results.

The Evolutionary Origins of Hierarchy.

PubMed

Mengistu, Henok; Huizinga, Joost; Mouret, Jean-Baptiste; Clune, Jeff

2016-06-01

Hierarchical organization-the recursive composition of sub-modules-is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force-the cost of connections-promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.

A Hierarchical Bayesian Model for Crowd Emotions

PubMed Central

Urizar, Oscar J.; Baig, Mirza S.; Barakova, Emilia I.; Regazzoni, Carlo S.; Marcenaro, Lucio; Rauterberg, Matthias

2016-01-01

Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions. This paper proposes a hierarchical Bayesian model to learn in unsupervised manner the behavior of individuals and of the crowd as a single entity, and explore the relation between behavior and emotions to infer emotional states. Information about the motion patterns of individuals are described using a self-organizing map, and a hierarchical Bayesian network builds probabilistic models to identify behaviors and infer the emotional state of individuals and the crowd. This model is trained and tested using data produced from simulated scenarios that resemble real-life environments. The conducted experiments tested the efficiency of our method to learn, detect and associate behaviors with emotional states yielding accuracy levels of 74% for individuals and 81% for the crowd, similar in performance with existing methods for pedestrian behavior detection but with novel concepts regarding the analysis of crowds. PMID:27458366

Hierarchical and coupling model of factors influencing vessel traffic flow.

PubMed

Liu, Zhao; Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

2017-01-01

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

Hierarchical and coupling model of factors influencing vessel traffic flow

PubMed Central

Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

2017-01-01

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system. PMID:28414747

Complex Applications of HLM in Studies of Science and Mathematics Achievement: Cross-Classified Random Effects Models

ERIC Educational Resources Information Center

Moreno, Mario; Harwell, Michael; Guzey, S. Selcen; Phillips, Alison; Moore, Tamara J.

2016-01-01

Hierarchical linear models have become a familiar method for accounting for a hierarchical data structure in studies of science and mathematics achievement. This paper illustrates the use of cross-classified random effects models (CCREMs), which are likely less familiar. The defining characteristic of CCREMs is a hierarchical data structure…

Sleeping of a Complex Brain Networks with Hierarchical Organization

NASA Astrophysics Data System (ADS)

Zhang, Ying-Yue; Yang, Qiu-Ying; Chen, Tian-Lun

2009-01-01

The dynamical behavior in the cortical brain network of macaque is studied by modeling each cortical area with a subnetwork of interacting excitable neurons. We characterize the system by studying how to perform the transition, which is now topology-dependent, from the active state to that with no activity. This could be a naive model for the wakening and sleeping of a brain-like system, i.e., a multi-component system with two different dynamical behavior.

Interpreter composition issues in the formal verification of a processor-memory module

NASA Technical Reports Server (NTRS)

Fura, David A.; Cohen, Gerald C.

1994-01-01

This report describes interpreter composition techniques suitable for the formal specification and verification of a processor-memory module using the HOL theorem proving system. The processor-memory module is a multichip subsystem within a fault-tolerant embedded system under development within the Boeing Defense and Space Group. Modeling and verification methods were developed that permit provably secure composition at the transaction-level of specification, significantly reducing the complexity of the hierarchical verification of the system.

Method and system for knowledge discovery using non-linear statistical analysis and a 1st and 2nd tier computer program

DOEpatents

Hively, Lee M [Philadelphia, TN

2011-07-12

The invention relates to a method and apparatus for simultaneously processing different sources of test data into informational data and then processing different categories of informational data into knowledge-based data. The knowledge-based data can then be communicated between nodes in a system of multiple computers according to rules for a type of complex, hierarchical computer system modeled on a human brain.

Object-based class modelling for multi-scale riparian forest habitat mapping

NASA Astrophysics Data System (ADS)

Strasser, Thomas; Lang, Stefan

2015-05-01

Object-based class modelling allows for mapping complex, hierarchical habitat systems. The riparian zone, including forests, represents such a complex ecosystem. Forests within riparian zones are biologically high productive and characterized by a rich biodiversity; thus considered of high community interest with an imperative to be protected and regularly monitored. Satellite earth observation (EO) provides tools for capturing the current state of forest habitats such as forest composition including intermixture of non-native tree species. Here we present a semi-automated object based image analysis (OBIA) approach for the mapping of riparian forests by applying class modelling of habitats based on the European Nature Information System (EUNIS) habitat classifications and the European Habitats Directive (HabDir) Annex 1. A very high resolution (VHR) WorldView-2 satellite image provided the required spatial and spectral details for a multi-scale image segmentation and rule-base composition to generate a six-level hierarchical representation of riparian forest habitats. Thereby habitats were hierarchically represented within an image object hierarchy as forest stands, stands of homogenous tree species and single trees represented by sunlit tree crowns. 522 EUNIS level 3 (EUNIS-3) habitat patches with a mean patch size (MPS) of 12,349.64 m2 were modelled from 938 forest stand patches (MPS = 6868.20 m2) and 43,742 tree stand patches (MPS = 140.79 m2). The delineation quality of the modelled EUNIS-3 habitats (focal level) was quantitatively assessed to an expert-based visual interpretation showing a mean deviation of 11.71%.

Hierarchical modularization of biochemical pathways using fuzzy-c means clustering.

PubMed

de Luis Balaguer, Maria A; Williams, Cranos M

2014-08-01

Biological systems that are representative of regulatory, metabolic, or signaling pathways can be highly complex. Mathematical models that describe such systems inherit this complexity. As a result, these models can often fail to provide a path toward the intuitive comprehension of these systems. More coarse information that allows a perceptive insight of the system is sometimes needed in combination with the model to understand control hierarchies or lower level functional relationships. In this paper, we present a method to identify relationships between components of dynamic models of biochemical pathways that reside in different functional groups. We find primary relationships and secondary relationships. The secondary relationships reveal connections that are present in the system, which current techniques that only identify primary relationships are unable to show. We also identify how relationships between components dynamically change over time. This results in a method that provides the hierarchy of the relationships among components, which can help us to understand the low level functional structure of the system and to elucidate potential hierarchical control. As a proof of concept, we apply the algorithm to the epidermal growth factor signal transduction pathway, and to the C3 photosynthesis pathway. We identify primary relationships among components that are in agreement with previous computational decomposition studies, and identify secondary relationships that uncover connections among components that current computational approaches were unable to reveal.

General description and understanding of the nonlinear dynamics of mode-locked fiber lasers.

PubMed

Wei, Huai; Li, Bin; Shi, Wei; Zhu, Xiushan; Norwood, Robert A; Peyghambarian, Nasser; Jian, Shuisheng

2017-05-02

As a type of nonlinear system with complexity, mode-locked fiber lasers are known for their complex behaviour. It is a challenging task to understand the fundamental physics behind such complex behaviour, and a unified description for the nonlinear behaviour and the systematic and quantitative analysis of the underlying mechanisms of these lasers have not been developed. Here, we present a complexity science-based theoretical framework for understanding the behaviour of mode-locked fiber lasers by going beyond reductionism. This hierarchically structured framework provides a model with variable dimensionality, resulting in a simple view that can be used to systematically describe complex states. Moreover, research into the attractors' basins reveals the origin of stochasticity, hysteresis and multistability in these systems and presents a new method for quantitative analysis of these nonlinear phenomena. These findings pave the way for dynamics analysis and system designs of mode-locked fiber lasers. We expect that this paradigm will also enable potential applications in diverse research fields related to complex nonlinear phenomena.

A Decision-Oriented Investigation of Air Force Civil Engineering’s Operations Branch and the Implications for a Decision Support System.

DTIC Science & Technology

1984-09-01

information when making a decision [ Szilagyi and Wallace , 1983:3201." Driver and Mock used cognitive complexity ideas to develop this two dimensional...flexible AMOUNT OF INFORMATION USED High hierarchic integrative Figure 6. Cognitive Complexity Model ( Szilagyi and Wallace , 1983:321) Decisive Style. The...large amount of inform- ation. However, he processes this information with a multiple focus approach ( Szilagyi and Wallace , 1983:320-321). 26 McKenney

Conceptual hierarchical modeling to describe wetland plant community organization

USGS Publications Warehouse

Little, A.M.; Guntenspergen, G.R.; Allen, T.F.H.

2010-01-01

Using multivariate analysis, we created a hierarchical modeling process that describes how differently-scaled environmental factors interact to affect wetland-scale plant community organization in a system of small, isolated wetlands on Mount Desert Island, Maine. We followed the procedure: 1) delineate wetland groups using cluster analysis, 2) identify differently scaled environmental gradients using non-metric multidimensional scaling, 3) order gradient hierarchical levels according to spatiotem-poral scale of fluctuation, and 4) assemble hierarchical model using group relationships with ordination axes and post-hoc tests of environmental differences. Using this process, we determined 1) large wetland size and poor surface water chemistry led to the development of shrub fen wetland vegetation, 2) Sphagnum and water chemistry differences affected fen vs. marsh / sedge meadows status within small wetlands, and 3) small-scale hydrologic differences explained transitions between forested vs. non-forested and marsh vs. sedge meadow vegetation. This hierarchical modeling process can help explain how upper level contextual processes constrain biotic community response to lower-level environmental changes. It creates models with more nuanced spatiotemporal complexity than classification and regression tree procedures. Using this process, wetland scientists will be able to generate more generalizable theories of plant community organization, and useful management models. ?? Society of Wetland Scientists 2009.

Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part II: scheme analysis and mechanism revelation.

PubMed

Cheng, Guanhui; Huang, Guohe; Dong, Cong; Xu, Ye; Chen, Jiapei; Chen, Xiujuan; Li, Kailong

2017-03-01

As presented in the first companion paper, distributed mixed-integer fuzzy hierarchical programming (DMIFHP) was developed for municipal solid waste management (MSWM) under complexities of heterogeneities, hierarchy, discreteness, and interactions. Beijing was selected as a representative case. This paper focuses on presenting the obtained schemes and the revealed mechanisms of the Beijing MSWM system. The optimal MSWM schemes for Beijing under various solid waste treatment policies and their differences are deliberated. The impacts of facility expansion, hierarchy, and spatial heterogeneities and potential extensions of DMIFHP are also discussed. A few of findings are revealed from the results and a series of comparisons and analyses. For instance, DMIFHP is capable of robustly reflecting these complexities in MSWM systems, especially for Beijing. The optimal MSWM schemes are of fragmented patterns due to the dominant role of the proximity principle in allocating solid waste treatment resources, and they are closely related to regulated ratios of landfilling, incineration, and composting. Communities without significant differences among distances to different types of treatment facilities are more sensitive to these ratios than others. The complexities of hierarchy and heterogeneities pose significant impacts on MSWM practices. Spatial dislocation of MSW generation rates and facility capacities caused by unreasonable planning in the past may result in insufficient utilization of treatment capacities under substantial influences of transportation costs. The problems of unreasonable MSWM planning, e.g., severe imbalance among different technologies and complete vacancy of ten facilities, should be gained deliberation of the public and the municipal or local governments in Beijing. These findings are helpful for gaining insights into MSWM systems under these complexities, mitigating key challenges in the planning of these systems, improving the related management practices, and eliminating potential socio-economic and eco-environmental issues resulting from unreasonable management.

Recognition of complex human behaviours using 3D imaging for intelligent surveillance applications

NASA Astrophysics Data System (ADS)

Yao, Bo; Lepley, Jason J.; Peall, Robert; Butler, Michael; Hagras, Hani

2016-10-01

We introduce a system that exploits 3-D imaging technology as an enabler for the robust recognition of the human form. We combine this with pose and feature recognition capabilities from which we can recognise high-level human behaviours. We propose a hierarchical methodology for the recognition of complex human behaviours, based on the identification of a set of atomic behaviours, individual and sequential poses (e.g. standing, sitting, walking, drinking and eating) that provides a framework from which we adopt time-based machine learning techniques to recognise complex behaviour patterns.

Hierarchicality of trade flow networks reveals complexity of products.

PubMed

Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei

2014-01-01

With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.

Hierarchicality of Trade Flow Networks Reveals Complexity of Products

PubMed Central

Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei

2014-01-01

With globalization, countries are more connected than before by trading flows, which amounts to at least trillion dollars today. Interestingly, around percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely. PMID:24905753

Development of new smart materials and spinning systems inspired by natural silks and their applications

NASA Astrophysics Data System (ADS)

Cheng, Jie; Lee, Sang-Hoon

2015-12-01

Silks produced by spiders and silkworms are charming natural biological materials with highly optimized hierarchical structures and outstanding physicomechanical properties. The superior performance of silks relies on the integration of a unique protein sequence, a distinctive spinning process, and complex hierarchical structures. Silks have been prepared to form a variety of morphologies and are widely used in diverse applications, for example, in the textile industry, as drug delivery vehicles, and as tissue engineering scaffolds. This review presents an overview of the organization of natural silks, in which chemical and physical functions are optimized, as well as a range of new materials inspired by the desire to mimic natural silk structure and synthesis.

Understanding Diffusion in Hierarchical Zeolites with House-of-Cards Nanosheets.

PubMed

Bai, Peng; Haldoupis, Emmanuel; Dauenhauer, Paul J; Tsapatsis, Michael; Siepmann, J Ilja

2016-08-23

Introducing mesoporosity to conventional microporous sorbents or catalysts is often proposed as a solution to enhance their mass transport rates. Here, we show that diffusion in these hierarchical materials is more complex and exhibits non-monotonic dependence on sorbate loading. Our atomistic simulations of n-hexane in a model system containing microporous nanosheets and mesopore channels indicate that diffusivity can be smaller than in a conventional zeolite with the same micropore structure, and this observation holds true even if we confine the analysis to molecules completely inside the microporous nanosheets. Only at high sorbate loadings or elevated temperatures, when the mesopores begin to be sufficiently populated, does the overall diffusion in the hierarchical material exceed that in conventional microporous zeolites. Our model system is free of structural defects, such as pore blocking or surface disorder, that are typically invoked to explain slower-than-expected diffusion phenomena in experimental measurements. Examination of free energy profiles and visualization of molecular diffusion pathways demonstrates that the large free energy cost (mostly enthalpic in origin) for escaping from the microporous region into the mesopores leads to more tortuous diffusion paths and causes this unusual transport behavior in hierarchical nanoporous materials. This knowledge allows us to re-examine zero-length-column chromatography data and show that these experimental measurements are consistent with the simulation data when the crystallite size instead of the nanosheet thickness is used for the nominal diffusional length.

Application of Nonlinear Systems Inverses to Automatic Flight Control Design: System Concepts and Flight Evaluations

NASA Technical Reports Server (NTRS)

Meyer, G.; Cicolani, L.

1981-01-01

A practical method for the design of automatic flight control systems for aircraft with complex characteristics and operational requirements, such as the powered lift STOL and V/STOL configurations, is presented. The method is effective for a large class of dynamic systems requiring multi-axis control which have highly coupled nonlinearities, redundant controls, and complex multidimensional operational envelopes. It exploits the concept of inverse dynamic systems, and an algorithm for the construction of inverse is given. A hierarchic structure for the total control logic with inverses is presented. The method is illustrated with an application to the Augmentor Wing Jet STOL Research Aircraft equipped with a digital flight control system. Results of flight evaluation of the control concept on this aircraft are presented.

The methodology of multi-viewpoint clustering analysis

NASA Technical Reports Server (NTRS)

Mehrotra, Mala; Wild, Chris

1993-01-01

One of the greatest challenges facing the software engineering community is the ability to produce large and complex computer systems, such as ground support systems for unmanned scientific missions, that are reliable and cost effective. In order to build and maintain these systems, it is important that the knowledge in the system be suitably abstracted, structured, and otherwise clustered in a manner which facilitates its understanding, manipulation, testing, and utilization. Development of complex mission-critical systems will require the ability to abstract overall concepts in the system at various levels of detail and to consider the system from different points of view. Multi-ViewPoint - Clustering Analysis MVP-CA methodology has been developed to provide multiple views of large, complicated systems. MVP-CA provides an ability to discover significant structures by providing an automated mechanism to structure both hierarchically (from detail to abstract) and orthogonally (from different perspectives). We propose to integrate MVP/CA into an overall software engineering life cycle to support the development and evolution of complex mission critical systems.

Current Scientific Approaches to Decision Making in Complex Systems. 3. Volume 2. Conference Position Papers

DTIC Science & Technology

1980-01-01

Search: Traffic on a Multi- dimensional Structure R. i. Atkin, University of Essex, England b. Annex. Volume 3: Decision: Foundation and Practice Brian R...Gaines, University of Essex, England Volume 4: Competing Modes of Cognition and Communication in Simulated and Self-Reflective Systems Stein Braten... University of Oslo, Norway Volume 5: On the Spontaneous Emergence of Decision Making Constraints in Communicating Hierarchical Structure John S

Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model

PubMed Central

2016-01-01

The omnipresent need for optimisation requires constant improvements of companies’ business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and “what-if” scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results. PMID:26871694

Methodological aspects of fuel performance system analysis at raw hydrocarbon processing plants

NASA Astrophysics Data System (ADS)

Kulbjakina, A. V.; Dolotovskij, I. V.

2018-01-01

The article discusses the methodological aspects of fuel performance system analysis at raw hydrocarbon (RH) processing plants. Modern RH processing facilities are the major consumers of energy resources (ER) for their own needs. To reduce ER, including fuel consumption, and to develop rational fuel system structure are complex and relevant scientific tasks that can only be done using system analysis and complex system synthesis. In accordance with the principles of system analysis, the hierarchical structure of the fuel system, the block scheme for the synthesis of the most efficient alternative of the fuel system using mathematical models and the set of performance criteria have been developed on the main stages of the study. The results from the introduction of specific engineering solutions to develop their own energy supply sources for RH processing facilities have been provided.

Enhanced lithium storage performance of hierarchical CuO nanomaterials with surface fractal characteristics

NASA Astrophysics Data System (ADS)

Li, Ang; He, Renyue; Bian, Zhuo; Song, Huaihe; Chen, Xiaohong; Zhou, Jisheng

2018-06-01

Self-assembled hierarchical CuO nanostructures with fractal structures were prepared by a mild method and exhibited excellent lithium storage properties, certain of which even demonstrated a high reversible capacity of 827 mAh g-1 at a rate of 0.1 C. An interesting phenomenon was observed that the electrochemical performance varies along with the structure complexity, and the products with higher surface factal dimensions exhibited larger capability and better cyclability. Structural and electrochemical analysis methods were used to explore the lithiation kinetics of the samples and the reasons for the outstanding electrochemical performances related to the complexities of hierarchical nanostructures and the irregularities of surface and mass distribution.

Adaptive Correction from Virtually Complex Dynamic Libraries: The Role of Noncovalent Interactions in Structural Selection and Folding.

PubMed

Lafuente, Maria; Atcher, Joan; Solà, Jordi; Alfonso, Ignacio

2015-11-16

The hierarchical self-assembling of complex molecular systems is dictated by the chemical and structural information stored in their components. This information can be expressed through an adaptive process that determines the structurally fittest assembly under given environmental conditions. We have set up complex disulfide-based dynamic covalent libraries of chemically and topologically diverse pseudopeptidic compounds. We show how the reaction evolves from very complex mixtures at short reaction times to the almost exclusive formation of a major compound, through the establishment of intramolecular noncovalent interactions. Our experiments demonstrate that the systems evolve through error-check and error-correction processes. The nature of these interactions, the importance of the folding and the effects of the environment are also discussed. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Design of a structural and functional hierarchy for planning and control of telerobotic systems

NASA Technical Reports Server (NTRS)

Acar, Levent; Ozguner, Umit

1989-01-01

Hierarchical structures offer numerous advantages over conventional structures for the control of telerobotic systems. A hierarchically organized system can be controlled via undetailed task assignments and can easily adapt to changing circumstances. The distributed and modular structure of these systems also enables fast response needed in most telerobotic applications. On the other hand, most of the hierarchical structures proposed in the literature are based on functional properties of a system. These structures work best for a few given functions of a large class of systems. In telerobotic applications, all functions of a single system needed to be explored. This approach requires a hierarchical organization based on physical properties of a system and such a hierarchical organization is introduced. The decomposition, organization, and control of the hierarchical structure are considered, and a system with two robot arms and a camera is presented.

Titanate-silica mesostructured nanocables: synthesis, structural analysis and biomedical applications

NASA Astrophysics Data System (ADS)

Su, Yonghua; Qiao, Shizhang; Yang, Huagui; Yang, Chen; Jin, Yonggang; Stahr, Frances; Sheng, Jiayu; Cheng, Lina; Ling, Changquan; Qing Lu, Gao

2010-02-01

1D hierarchical composite mesostructures of titanate and silica were synthesized via an interfacial surfactant templating approach. Such mesostructures have complex core-shell architectures consisting of single-crystalline H2Ti3O7 nanobelts inside the ordered mesoporous SiO2 shell, which are nontoxic and highly biocompatible. The overall diameter of as-prepared 1D hierarchical composite mesostructures is only approx. 34.2 nm with a length over 500 nm on average. A model to explain the formation mechanism of these mesostructures has been proposed; the negatively charged surface of H2Ti3O7 nanobelts controls the formation of the octadecyltrimethylammonium bromide (C18TAB) bilayer, which in turn regulates the cooperative self-assembly of silica and C18TAB complex micelles on the interface to produce a mesoporous silica shell. More importantly, the application of synthesized mesostructured nanocables as anticancer drug reservoirs has also been explored, which indicates that the membranes containing these mesoporous nanocables have a great potential to be used as transdermal drug delivery systems.

A Hyperspherical Adaptive Sparse-Grid Method for High-Dimensional Discontinuity Detection

DOE PAGES

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max D.; ...

2015-06-24

This study proposes and analyzes a hyperspherical adaptive hierarchical sparse-grid method for detecting jump discontinuities of functions in high-dimensional spaces. The method is motivated by the theoretical and computational inefficiencies of well-known adaptive sparse-grid methods for discontinuity detection. Our novel approach constructs a function representation of the discontinuity hypersurface of an N-dimensional discontinuous quantity of interest, by virtue of a hyperspherical transformation. Then, a sparse-grid approximation of the transformed function is built in the hyperspherical coordinate system, whose value at each point is estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of the hypersurface, the newmore » technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. In addition, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. Rigorous complexity analyses of the new method are provided as are several numerical examples that illustrate the effectiveness of the approach.« less

A hyper-spherical adaptive sparse-grid method for high-dimensional discontinuity detection

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

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max D.

This work proposes and analyzes a hyper-spherical adaptive hierarchical sparse-grid method for detecting jump discontinuities of functions in high-dimensional spaces is proposed. The method is motivated by the theoretical and computational inefficiencies of well-known adaptive sparse-grid methods for discontinuity detection. Our novel approach constructs a function representation of the discontinuity hyper-surface of an N-dimensional dis- continuous quantity of interest, by virtue of a hyper-spherical transformation. Then, a sparse-grid approximation of the transformed function is built in the hyper-spherical coordinate system, whose value at each point is estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of themore » hyper-surface, the new technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. Rigorous error estimates and complexity analyses of the new method are provided as are several numerical examples that illustrate the effectiveness of the approach.« less

A Reduced-Complexity Investigation of Blunt Leading-Edge Separation Motivated by UCAV Aerodynamics

NASA Technical Reports Server (NTRS)

Luckring, James M.; Boelens, Okko J.

2015-01-01

A reduced complexity investigation for blunt-leading-edge vortical separation has been undertaken. The overall approach is to design the fundamental work in such a way so that it relates to the aerodynamics of a more complex Uninhabited Combat Air Vehicle (UCAV) concept known as SACCON. Some of the challenges associated with both the vehicle-class aerodynamics and the fundamental vortical flows are reviewed, and principles from a hierarchical complexity approach are used to relate flow fundamentals to system-level interests. The work is part of roughly 6-year research program on blunt-leading-edge separation pertinent to UCAVs, and was conducted under the NATO Science and Technology Organization, Applied Vehicle Technology panel.

Hierarchically Mesoporous o-Hydroxyazobenzene Polymers: Synthesis and Their Applications in CO2 Capture and Conversion.

PubMed

Ji, Guipeng; Yang, Zhenzhen; Zhang, Hongye; Zhao, Yanfei; Yu, Bo; Ma, Zhishuang; Liu, Zhimin

2016-08-08

The synthesis of hierarchically mesoporous polymers with multiple functionalities is challenging. Herein we reported a template-free strategy for synthesis of phenolic azo-polymers with hierarchical porous structures based on diazo-coupling reaction in aqueous solution under mild conditions. The resultant polymers have surface areas up to 593 m(2)  g(-1) with the mesopore ratio of >80 %, and a good ability to complex with metal ions, such as Cu(2+) , Zn(2+) ,Ni(2+) , achieving a metal loading up to 26.24 wt %. Moreover, the polymers complexed with Zn showed excellent performance for catalyzing the reaction of CO2 with epoxide, affording a TOF of 2570 h(-1) in the presence of tetrabutyl ammonium bromide (7.2 mol %). The polymer complexed with Cu could catalyze the oxidation of alcohol with high efficiency. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cities and regions in Britain through hierarchical percolation

NASA Astrophysics Data System (ADS)

Arcaute, Elsa; Molinero, Carlos; Hatna, Erez; Murcio, Roberto; Vargas-Ruiz, Camilo; Masucci, A. Paolo; Batty, Michael

2016-04-01

Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations which are the outcome of complex geographical, political and historical processes which leave almost indelible footprints on infrastructure such as the street network. In this work, we uncover a set of hierarchies in Britain at different scales using percolation theory on the street network and on its intersections which are the primary points of interaction and urban agglomeration. At the larger scales, the observed hierarchical structures can be interpreted as regional fractures of Britain, observed in various forms, from natural boundaries, such as National Parks, to regional divisions based on social class and wealth such as the well-known North-South divide. At smaller scales, cities are generated through recursive percolations on each of the emerging regional clusters. We examine the evolution of the morphology of the system as a whole, by measuring the fractal dimension of the clusters at each distance threshold in the percolation. We observe that this reaches a maximum plateau at a specific distance. The clusters defined at this distance threshold are in excellent correspondence with the boundaries of cities recovered from satellite images, and from previous methods using population density.

Adaptive variable structure hierarchical fuzzy control for a class of high-order nonlinear dynamic systems.

PubMed

Mansouri, Mohammad; Teshnehlab, Mohammad; Aliyari Shoorehdeli, Mahdi

2015-05-01

In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Systems thinking and complexity: considerations for health promoting schools.

PubMed

Rosas, Scott R

2017-04-01

The health promoting schools concept reflects a comprehensive and integrated philosophy to improving student and personnel health and well-being. Conceptualized as a configuration of interacting, interdependent parts connected through a web of relationships that form a whole greater than the sum of its parts, school health promotion initiatives often target several levels (e.g. individual, professional, procedural and policy) simultaneously. Health promoting initiatives, such as those operationalized under the whole school approach, include several interconnected components that are coordinated to improve health outcomes in complex settings. These complex systems interventions are embedded in intricate arrangements of physical, biological, ecological, social, political and organizational relationships. Systems thinking and characteristics of complex adaptive systems are introduced in this article to provide a perspective that emphasizes the patterns of inter-relationships associated with the nonlinear, dynamic and adaptive nature of complex hierarchical systems. Four systems thinking areas: knowledge, networks, models and organizing are explored as a means to further manage the complex nature of the development and sustainability of health promoting schools. Applying systems thinking and insights about complex adaptive systems can illuminate how to address challenges found in settings with both complicated (i.e. multi-level and multisite) and complex aspects (i.e. synergistic processes and emergent outcomes). © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Microfabrication of hierarchical structures for engineered mechanical materials

NASA Astrophysics Data System (ADS)

Vera Canudas, Marc

Materials found in nature present, in some cases, unique properties from their constituents that are of great interest in engineered materials for applications ranging from structural materials for the construction of bridges, canals and buildings to the fabrication of new lightweight composites for airplane and automotive bodies, to protective thin film coatings, amongst other fields. Research in the growing field of biomimetic materials indicates that the micro-architectures present in natural materials are critical to their macroscopic mechanical properties. A better understanding of the effect that structure and hierarchy across scales have on the material properties will enable engineered materials with enhanced properties. At the moment, very few theoretical models predict mechanical properties of simple materials based on their microstructures. Moreover these models are based on observations from complex biological systems. One way to overcome this challenge is through the use of microfabrication techniques to design and fabricate simple materials, more appropriate for the study of hierarchical organizations and microstructured materials. Arrays of structures with controlled geometry and dimension can be designed and fabricated at different length scales, ranging from a few hundred nanometers to centimeters, in order to mimic similar systems found in nature. In this thesis, materials have been fabricated in order to gain fundamental insight into the complex hierarchical materials found in nature and to engineer novel materials with enhanced mechanical properties. The materials fabricated here were mechanically characterized and compared to simple mechanics models to describe their behavior with the goal of applying the knowledge acquired to the design and synthesis of future engineered materials with novel properties.

Self-assembled hierarchically structured organic-inorganic composite systems.

PubMed

Tritschler, Ulrich; Cölfen, Helmut

2016-05-13

Designing bio-inspired, multifunctional organic-inorganic composite materials is one of the most popular current research objectives. Due to the high complexity of biocomposite structures found in nacre and bone, for example, a one-pot scalable and versatile synthesis approach addressing structural key features of biominerals and affording bio-inspired, multifunctional organic-inorganic composites with advanced physical properties is highly challenging. This article reviews recent progress in synthesizing organic-inorganic composite materials via various self-assembly techniques and in this context highlights a recently developed bio-inspired synthesis concept for the fabrication of hierarchically structured, organic-inorganic composite materials. This one-step self-organization concept based on simultaneous liquid crystal formation of anisotropic inorganic nanoparticles and a functional liquid crystalline polymer turned out to be simple, fast, scalable and versatile, leading to various (multi-)functional composite materials, which exhibit hierarchical structuring over several length scales. Consequently, this synthesis approach is relevant for further progress and scientific breakthrough in the research field of bio-inspired and biomimetic materials.

Active Interaction Mapping as a tool to elucidate hierarchical functions of biological processes.

PubMed

Farré, Jean-Claude; Kramer, Michael; Ideker, Trey; Subramani, Suresh

2017-07-03

Increasingly, various 'omics data are contributing significantly to our understanding of novel biological processes, but it has not been possible to iteratively elucidate hierarchical functions in complex phenomena. We describe a general systems biology approach called Active Interaction Mapping (AI-MAP), which elucidates the hierarchy of functions for any biological process. Existing and new 'omics data sets can be iteratively added to create and improve hierarchical models which enhance our understanding of particular biological processes. The best datatypes to further improve an AI-MAP model are predicted computationally. We applied this approach to our understanding of general and selective autophagy, which are conserved in most eukaryotes, setting the stage for the broader application to other cellular processes of interest. In the particular application to autophagy-related processes, we uncovered and validated new autophagy and autophagy-related processes, expanded known autophagy processes with new components, integrated known non-autophagic processes with autophagy and predict other unexplored connections.

Hierarchical and Well-Ordered Porous Copper for Liquid Transport Properties Control.

PubMed

Pham, Quang N; Shao, Bowen; Kim, Yongsung; Won, Yoonjin

2018-05-09

Liquid delivery through interconnected pore network is essential for various interfacial transport applications ranging from energy storage to evaporative cooling. The liquid transport performance in porous media can be significantly improved through the use of hierarchical morphology that leverages transport phenomena at different length scales. Traditional surface engineering techniques using chemical or thermal reactions often show nonuniform surface nanostructuring within three-dimensional pore network due to uncontrollable diffusion and reactivity in geometrically complex porous structures. Here, we demonstrate hierarchical architectures on the basis of crystalline copper inverse opals using an electrochemistry approach, which offers volumetric controllability of structural and surface properties within the complex porous metal. The electrochemical process sequentially combines subtractive and additive steps-electrochemical polishing and electrochemical oxidation-to improve surface wetting properties without sacrificing structural permeability. We report the transport performance of the hierarchical inverse opals by measuring the capillary-driven liquid rise. The capillary performance parameter of hierarchically engineered inverse opal ( K/ R eff = ∼5 × 10 -3 μm) is shown to be higher than that of a typical crystalline inverse opal ( K/ R eff = ∼1 × 10 -3 μm) owing to the enhancement in fluid permeable and hydrophilic pathways. The new surface engineering method presented in this work provides a rational approach in designing hierarchical porous copper for transport performance enhancements.

Efficient calculation of open quantum system dynamics and time-resolved spectroscopy with distributed memory HEOM (DM-HEOM).

PubMed

Kramer, Tobias; Noack, Matthias; Reinefeld, Alexander; Rodríguez, Mirta; Zelinskyy, Yaroslav

2018-06-11

Time- and frequency-resolved optical signals provide insights into the properties of light-harvesting molecular complexes, including excitation energies, dipole strengths and orientations, as well as in the exciton energy flow through the complex. The hierarchical equations of motion (HEOM) provide a unifying theory, which allows one to study the combined effects of system-environment dissipation and non-Markovian memory without making restrictive assumptions about weak or strong couplings or separability of vibrational and electronic degrees of freedom. With increasing system size the exact solution of the open quantum system dynamics requires memory and compute resources beyond a single compute node. To overcome this barrier, we developed a scalable variant of HEOM. Our distributed memory HEOM, DM-HEOM, is a universal tool for open quantum system dynamics. It is used to accurately compute all experimentally accessible time- and frequency-resolved processes in light-harvesting molecular complexes with arbitrary system-environment couplings for a wide range of temperatures and complex sizes. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

[From symmetries to the laws of evolution. I. Chirality as a means of active media stratification].

PubMed

Tverdislov, V A; Sidorova, A É; Iakovenko, L V

2012-01-01

Features of the hypothetical evolution of a hierarchy of chiral objects formed by active media are discussed. On the basis of experimental facts a new synergetic generalization is made: an evolving system can repeatedly broaden the spectrum of its symmetry types within one level of organization which increases its complexity and change the sign of chirality during transition to a higher level. Switching the chirality sign of macroscopic objects provides irreversibility of stratification. The known chirality of biological structures at different levels suggests that the chiral L/D-stratification should be universal and the hierarchical paths are stable and determined. A high level enantiomorph with reciprocal chirality demonstrates a wider spectrum of functionality. A fractal description of natural hierarchical systems is pointed out to be inadequate because it implicates invariance of the chirality sign of the objects at different scales.

DNAzyme-Based Logic Gate-Mediated DNA Self-Assembly.

PubMed

Zhang, Cheng; Yang, Jing; Jiang, Shuoxing; Liu, Yan; Yan, Hao

2016-01-13

Controlling DNA self-assembly processes using rationally designed logic gates is a major goal of DNA-based nanotechnology and programming. Such controls could facilitate the hierarchical engineering of complex nanopatterns responding to various molecular triggers or inputs. Here, we demonstrate the use of a series of DNAzyme-based logic gates to control DNA tile self-assembly onto a prescribed DNA origami frame. Logic systems such as "YES," "OR," "AND," and "logic switch" are implemented based on DNAzyme-mediated tile recognition with the DNA origami frame. DNAzyme is designed to play two roles: (1) as an intermediate messenger to motivate downstream reactions and (2) as a final trigger to report fluorescent signals, enabling information relay between the DNA origami-framed tile assembly and fluorescent signaling. The results of this study demonstrate the plausibility of DNAzyme-mediated hierarchical self-assembly and provide new tools for generating dynamic and responsive self-assembly systems.

Robust Optimization Design for Turbine Blade-Tip Radial Running Clearance using Hierarchically Response Surface Method

NASA Astrophysics Data System (ADS)

Zhiying, Chen; Ping, Zhou

2017-11-01

Considering the robust optimization computational precision and efficiency for complex mechanical assembly relationship like turbine blade-tip radial running clearance, a hierarchically response surface robust optimization algorithm is proposed. The distribute collaborative response surface method is used to generate assembly system level approximation model of overall parameters and blade-tip clearance, and then a set samples of design parameters and objective response mean and/or standard deviation is generated by using system approximation model and design of experiment method. Finally, a new response surface approximation model is constructed by using those samples, and this approximation model is used for robust optimization process. The analyses results demonstrate the proposed method can dramatic reduce the computational cost and ensure the computational precision. The presented research offers an effective way for the robust optimization design of turbine blade-tip radial running clearance.

Cross-regulation among arabinose, xylose and rhamnose utilization systems in E. coli.

PubMed

Choudhury, D; Saini, S

2018-02-01

Bacteria frequently encounter multiple sugars in their natural surroundings. While the dynamics of utilization of glucose-containing sugar mixtures have been well investigated, there are few reports addressing regulation of utilization of glucose-free mixtures particularly pentoses. These sugars comprise a considerable fraction in hemicellulose which can be converted by suitable biocatalysts to biofuels and other value-added products. Hence, understanding of transcriptional cross-regulation among different pentose sugar utilization systems is essential for successful development of industrial strains. In this work, we study mixed-sugar utilization with respect to three secondary carbon sources - arabinose, xylose and rhamnose at single-cell resolution in Escherichia coli. Our results reveal that hierarchical utilization among these systems is not strict but rather can be eliminated or reversed by altering the relative ratios of the preferred and nonpreferred sugars. Since transcriptional cross-regulation among pentose sugar systems operates through competitive binding of noncognate sugar-regulator complex, altering sugar concentrations is thought to eliminate nonspecific binding by affecting concentration of the regulator - sugar complexes. Plant biomass comprises of hexose and pentose sugar mixtures. These sugars are processed by micro-organisms to form products like biofuels, polymers etc. One of the major challenges with mixed-sugar processing by micro-organisms is hierarchical utilization of sugars due to cross-regulation among sugar systems. In this work, we discuss cross-regulation among three secondary carbon sources - arabinose, xylose and rhamnose. Our results show that cross-regulation between pentose sugars is complex with multiple layers of regulation. These aspects need to be addressed for effective design of processes to extract energy from biomass. © 2017 The Society for Applied Microbiology.

Model Error Budgets

NASA Technical Reports Server (NTRS)

Briggs, Hugh C.

2008-01-01

An error budget is a commonly used tool in design of complex aerospace systems. It represents system performance requirements in terms of allowable errors and flows these down through a hierarchical structure to lower assemblies and components. The requirements may simply be 'allocated' based upon heuristics or experience, or they may be designed through use of physics-based models. This paper presents a basis for developing an error budget for models of the system, as opposed to the system itself. The need for model error budgets arises when system models are a principle design agent as is increasingly more common for poorly testable high performance space systems.

Image Information Mining Utilizing Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Marchisio, Giovanni; Koperski, Krzysztof; Datcu, Mihai

2002-01-01

The Hierarchical Segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

MIS - The Human Connection

PubMed Central

Bush, Ian E.

1980-01-01

The lessons of the 70's with MIS were largely painful, often the same as those of the 60's, and were found in different phases on two continents. On examination this turns out to be true for many non-medical fields, true for systems programming, and thus a very general phenomenon. It is related to the functional complexity rather than to the sheer size of the software required, and above all to the relative neglect of human factors at all levels of software and hardware design. Simple hierarchical theory is a useful tool for analyzing complex systems and restoring the necessary dominance of common sense human factors. An example shows the very large effects of neglecting these factors on costs and benefits of MIS and their sub-systems.

An evolving systems-based methodology for healthcare planning.

PubMed

Warwick, Jon; Bell, Gary

2007-01-01

Healthcare planning seems beset with problems at all hierarchical levels. These are caused by the 'soft' nature of many of the issues present in healthcare planning and the high levels of complexity inherent in healthcare services. There has, in recent years, been a move to utilize systems thinking ideas in an effort to gain a better understanding of the forces at work within the healthcare environment and these have had some success. This paper argues that systems-based methodologies can be further enhanced by metrication and modeling which assist in exploring the changed emergent behavior of a system resulting from management intervention. The paper describes the Holon Framework as an evolving systems-based approach that has been used to help clients understand complex systems (in the education domain) that would have application in the analysis of healthcare problems.

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

Hansen, Jacob; Edgar, Thomas W.; Daily, Jeffrey A.

With an ever-evolving power grid, concerns regarding how to maintain system stability, efficiency, and reliability remain constant because of increasing uncertainties and decreasing rotating inertia. To alleviate some of these concerns, demand response represents a viable solution and is virtually an untapped resource in the current power grid. This work describes a hierarchical control framework that allows coordination between distributed energy resources and demand response. This control framework is composed of two control layers: a coordination layer that ensures aggregations of resources are coordinated to achieve system objectives and a device layer that controls individual resources to assure the predeterminedmore » power profile is tracked in real time. Large-scale simulations are executed to study the hierarchical control, requiring advancements in simulation capabilities. Technical advancements necessary to investigate and answer control interaction questions, including the Framework for Network Co-Simulation platform and Arion modeling capability, are detailed. Insights into the interdependencies of controls across a complex system and how they must be tuned, as well as validation of the effectiveness of the proposed control framework, are yielded using a large-scale integrated transmission system model coupled with multiple distribution systems.« less

Hierarchical HMM based learning of navigation primitives for cooperative robotic endovascular catheterization.

PubMed

Rafii-Tari, Hedyeh; Liu, Jindong; Payne, Christopher J; Bicknell, Colin; Yang, Guang-Zhong

2014-01-01

Despite increased use of remote-controlled steerable catheter navigation systems for endovascular intervention, most current designs are based on master configurations which tend to alter natural operator tool interactions. This introduces problems to both ergonomics and shared human-robot control. This paper proposes a novel cooperative robotic catheterization system based on learning-from-demonstration. By encoding the higher-level structure of a catheterization task as a sequence of primitive motions, we demonstrate how to achieve prospective learning for complex tasks whilst incorporating subject-specific variations. A hierarchical Hidden Markov Model is used to model each movement primitive as well as their sequential relationship. This model is applied to generation of motion sequences, recognition of operator input, and prediction of future movements for the robot. The framework is validated by comparing catheter tip motions against the manual approach, showing significant improvements in the quality of catheterization. The results motivate the design of collaborative robotic systems that are intuitive to use, while reducing the cognitive workload of the operator.

Hierarchical drivers of reef-fish metacommunity structure.

PubMed

MacNeil, M Aaron; Graham, Nicholas A J; Polunin, Nicholas V C; Kulbicki, Michel; Galzin, René; Harmelin-Vivien, Mireille; Rushton, Steven P

2009-01-01

Coral reefs are highly complex ecological systems, where multiple processes interact across scales in space and time to create assemblages of exceptionally high biodiversity. Despite the increasing frequency of hierarchically structured sampling programs used in coral-reef science, little progress has been made in quantifying the relative importance of processes operating across multiple scales. The vast majority of reef studies are conducted, or at least analyzed, at a single spatial scale, ignoring the implicitly hierarchical structure of the overall system in favor of small-scale experiments or large-scale observations. Here we demonstrate how alpha (mean local number of species), beta diversity (degree of species dissimilarity among local sites), and gamma diversity (overall species richness) vary with spatial scale, and using a hierarchical, information-theoretic approach, we evaluate the relative importance of site-, reef-, and atoll-level processes driving the fish metacommunity structure among 10 atolls in French Polynesia. Process-based models, representing well-established hypotheses about drivers of reef-fish community structure, were assembled into a candidate set of 12 hierarchical linear models. Variation in fish abundance, biomass, and species richness were unevenly distributed among transect, reef, and atoll levels, establishing the relative contribution of variation at these spatial scales to the structure of the metacommunity. Reef-fish biomass, species richness, and the abundance of most functional-groups corresponded primarily with transect-level habitat diversity and atoll-lagoon size, whereas detritivore and grazer abundances were largely correlated with potential covariates of larval dispersal. Our findings show that (1) within-transect and among-atoll factors primarily drive the relationship between alpha and gamma diversity in this reef-fish metacommunity; (2) habitat is the primary correlate with reef-fish metacommunity structure at multiple spatial scales; and (3) inter-atoll connectedness was poorly correlated with the nonrandom clustering of reef-fish species. These results demonstrate the importance of modeling hierarchical data and processes in understanding reef-fish metacommunity structure.

[The system-oriented model of psychosocial rehabilitation].

PubMed

Iastrebov V S; Mitikhin, V G; Solokhina, T A; Mikhaĭlova, I I

2008-01-01

A model of psychosocial rehabilitation based on the system approach that allows taking into account both the patient-centered approach of the rehabilitation service, the development of its resource basis, the effectiveness of this care system in whole and its patterns as well has been worked out. In the framework of this model, the authors suggest to single out three basic stages of the psychosocial rehabilitation process: evaluation and planning, rehabilitation interventions per se, achievement of the result. In author's opinion, the most successful way for constructing a modern model of psychosocial rehabilitation is a method of hierarchic modeling which can reveal a complex chain of interactions between all participants of the rehabilitation process and factors involved in this process and at the same time specify the multi-level hierarchic character of these interactions and factors. An important advantage of this method is the possibility of obtaining as static as well dynamic evaluations of the rehabilitation service activity that may be used on the following levels: 1) patient; 2) his/her close environment; 3) macrosocial level. The obvious merits of the system-oriented model appear to be the possibility of application of its principles in the organization of specialized care for psychiatric patients on the local, regional and federal levels. The authors emphasize that hierarchic models have universal character and can be implemented in the elaboration of information-analytical systems aimed at solving the problems of monitoring and analysis of social-medical service activity in order to increase its effectiveness.

Strabo: An App and Database for Structural Geology and Tectonics Data

NASA Astrophysics Data System (ADS)

Newman, J.; Williams, R. T.; Tikoff, B.; Walker, J. D.; Good, J.; Michels, Z. D.; Ash, J.

2016-12-01

Strabo is a data system designed to facilitate digital storage and sharing of structural geology and tectonics data. The data system allows researchers to store and share field and laboratory data as well as construct new multi-disciplinary data sets. Strabo is built on graph database technology, as opposed to a relational database, which provides the flexibility to define relationships between objects of any type. This framework allows observations to be linked in a complex and hierarchical manner that is not possible in traditional database topologies. Thus, the advantage of the Strabo data structure is the ability of graph databases to link objects in both numerous and complex ways, in a manner that more accurately reflects the realities of the collecting and organizing of geological data sets. The data system is accessible via a mobile interface (iOS and Android devices) that allows these data to be stored, visualized, and shared during primary collection in the field or the laboratory. The Strabo Data System is underlain by the concept of a "Spot," which we define as any observation that characterizes a specific area. This can be anything from a strike and dip measurement of bedding to cross-cutting relationships between faults in complex dissected terrains. Each of these spots can then contain other Spots and/or measurements (e.g., lithology, slickenlines, displacement magnitude.) Hence, the Spot concept is applicable to all relationships and observation sets. Strabo is therefore capable of quantifying and digitally storing large spatial variations and complex geometries of naturally deformed rocks within hierarchically related maps and images. These approaches provide an observational fidelity comparable to a traditional field book, but with the added benefits of digital data storage, processing, and ease of sharing. This approach allows Strabo to integrate seamlessly into the workflow of most geologists. Future efforts will focus on extending Strabo to other sub-disciplines as well as developing a desktop system for the enhanced collection and organization of microstructural data.

Risk transfer modeling among hierarchically associated stakeholders in development of space systems

NASA Astrophysics Data System (ADS)

Henkle, Thomas Grove, III

Research develops an empirically derived cardinal model that prescribes handling and transfer of risks between organizations with hierarchical relationships. Descriptions of mission risk events, risk attitudes, and conditions for risk transfer are determined for client and underwriting entities associated with acquisition, production, and deployment of space systems. The hypothesis anticipates that large client organizations should be able to assume larger dollar-value risks of a program in comparison to smaller organizations even though many current risk transfer arrangements via space insurance violate this hypothesis. A literature survey covers conventional and current risk assessment methods, current techniques used in the satellite industry for complex system development, cardinal risk modeling, and relevant aspects of utility theory. Data gathered from open literature on demonstrated launch vehicle and satellite in-orbit reliability, annual space insurance premiums and losses, and ground fatalities and range damage associated with satellite launch activities are presented. Empirically derived models are developed for risk attitudes of space system clients and third-party underwriters associated with satellite system development and deployment. Two application topics for risk transfer are examined: the client-underwriter relationship on assumption or transfer of risks associated with first-year mission success, and statutory risk transfer agreements between space insurance underwriters and the US government to promote growth in both commercial client and underwriting industries. Results indicate that client entities with wealth of at least an order of magnitude above satellite project costs should retain risks to first-year mission success despite present trends. Furthermore, large client entities such as the US government should never pursue risk transfer via insurance under previously demonstrated probabilities of mission success; potential savings may reasonably exceed multiple tens of $millions per space project. Additional results indicate that current US government statutory arrangements on risk sharing with underwriting entities appears reasonable with respect to stated objectives. This research combines aspects of multiple disciplines to include risk management, decision theory, utility theory, and systems architecting. It also demonstrates development of a more general theory on prescribing risk transfer criteria between distinct, but hierarchically associated entities involved in complex system development with applicability to a variety of technical domains.

Low temperature oxidative desulfurization with hierarchically mesoporous titaniumsilicate Ti-SBA-2 single crystals.

PubMed

Shi, Chengxiang; Wang, Wenxuan; Liu, Ni; Xu, Xueyan; Wang, Danhong; Zhang, Minghui; Sun, Pingchuan; Chen, Tiehong

2015-07-21

Hierarchically porous Ti-SBA-2 with high framework Ti content (up to 5 wt%) was firstly synthesized by employing organic mesomorphous complexes of a cationic surfactant (CTAB) and an anionic polyelectrolyte (PAA) as templates. The material exhibited excellent performance in oxidative desulfurization of diesel fuel at low temperature (40 °C or 25 °C) due to the unique hierarchically porous structure and high framework Ti content.

Cortical Representations of Speech in a Multitalker Auditory Scene.

PubMed

Puvvada, Krishna C; Simon, Jonathan Z

2017-09-20

The ability to parse a complex auditory scene into perceptual objects is facilitated by a hierarchical auditory system. Successive stages in the hierarchy transform an auditory scene of multiple overlapping sources, from peripheral tonotopically based representations in the auditory nerve, into perceptually distinct auditory-object-based representations in the auditory cortex. Here, using magnetoencephalography recordings from men and women, we investigate how a complex acoustic scene consisting of multiple speech sources is represented in distinct hierarchical stages of the auditory cortex. Using systems-theoretic methods of stimulus reconstruction, we show that the primary-like areas in the auditory cortex contain dominantly spectrotemporal-based representations of the entire auditory scene. Here, both attended and ignored speech streams are represented with almost equal fidelity, and a global representation of the full auditory scene with all its streams is a better candidate neural representation than that of individual streams being represented separately. We also show that higher-order auditory cortical areas, by contrast, represent the attended stream separately and with significantly higher fidelity than unattended streams. Furthermore, the unattended background streams are more faithfully represented as a single unsegregated background object rather than as separated objects. Together, these findings demonstrate the progression of the representations and processing of a complex acoustic scene up through the hierarchy of the human auditory cortex. SIGNIFICANCE STATEMENT Using magnetoencephalography recordings from human listeners in a simulated cocktail party environment, we investigate how a complex acoustic scene consisting of multiple speech sources is represented in separate hierarchical stages of the auditory cortex. We show that the primary-like areas in the auditory cortex use a dominantly spectrotemporal-based representation of the entire auditory scene, with both attended and unattended speech streams represented with almost equal fidelity. We also show that higher-order auditory cortical areas, by contrast, represent an attended speech stream separately from, and with significantly higher fidelity than, unattended speech streams. Furthermore, the unattended background streams are represented as a single undivided background object rather than as distinct background objects. Copyright © 2017 the authors 0270-6474/17/379189-08$15.00/0.

Next-Generation Pathology.

PubMed

Caie, Peter D; Harrison, David J

2016-01-01

The field of pathology is rapidly transforming from a semiquantitative and empirical science toward a big data discipline. Large data sets from across multiple omics fields may now be extracted from a patient's tissue sample. Tissue is, however, complex, heterogeneous, and prone to artifact. A reductionist view of tissue and disease progression, which does not take this complexity into account, may lead to single biomarkers failing in clinical trials. The integration of standardized multi-omics big data and the retention of valuable information on spatial heterogeneity are imperative to model complex disease mechanisms. Mathematical modeling through systems pathology approaches is the ideal medium to distill the significant information from these large, multi-parametric, and hierarchical data sets. Systems pathology may also predict the dynamical response of disease progression or response to therapy regimens from a static tissue sample. Next-generation pathology will incorporate big data with systems medicine in order to personalize clinical practice for both prognostic and predictive patient care.

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models

PubMed Central

Liu, Ziyue; Cappola, Anne R.; Crofford, Leslie J.; Guo, Wensheng

2013-01-01

The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls. PMID:24729646

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models.

PubMed

Liu, Ziyue; Cappola, Anne R; Crofford, Leslie J; Guo, Wensheng

2014-01-01

The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls.

Digit replacement: A generic map for nonlinear dynamical systems.

PubMed

García-Morales, Vladimir

2016-09-01

A simple discontinuous map is proposed as a generic model for nonlinear dynamical systems. The orbit of the map admits exact solutions for wide regions in parameter space and the method employed (digit manipulation) allows the mathematical design of useful signals, such as regular or aperiodic oscillations with specific waveforms, the construction of complex attractors with nontrivial properties as well as the coexistence of different basins of attraction in phase space with different qualitative properties. A detailed analysis of the dynamical behavior of the map suggests how the latter can be used in the modeling of complex nonlinear dynamics including, e.g., aperiodic nonchaotic attractors and the hierarchical deposition of grains of different sizes on a surface.

Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

NASA Astrophysics Data System (ADS)

Seinstra, Frank J.; Maassen, Jason; van Nieuwpoort, Rob V.; Drost, Niels; van Kessel, Timo; van Werkhoven, Ben; Urbani, Jacopo; Jacobs, Ceriel; Kielmann, Thilo; Bal, Henri E.

In recent years, the application of high-performance and distributed computing in scientific practice has become increasingly wide spread. Among the most widely available platforms to scientists are clusters, grids, and cloud systems. Such infrastructures currently are undergoing revolutionary change due to the integration of many-core technologies, providing orders-of-magnitude speed improvements for selected compute kernels. With high-performance and distributed computing systems thus becoming more heterogeneous and hierarchical, programming complexity is vastly increased. Further complexities arise because urgent desire for scalability and issues including data distribution, software heterogeneity, and ad hoc hardware availability commonly force scientists into simultaneous use of multiple platforms (e.g., clusters, grids, and clouds used concurrently). A true computing jungle.

Leadership Behaviors of Management for Complex Adaptive Systems

DTIC Science & Technology

2010-04-01

arson • The Five Dysfunctions of a Team - Patrick M. Lencioni Agile Development Practices • Agile Project Management with Scrum – Ken Schwaber...and down to the individual and team level – Relationships are not defined hierarchically, but rather through interactions across References: Mike...n – Consisting of parts intricately combined © Copyright 2009 Northrop GrummanCopyright 2010 Northrop Grumman 5 Predictable or Adaptive Software/IT

Action detection by double hierarchical multi-structure space-time statistical matching model

NASA Astrophysics Data System (ADS)

Han, Jing; Zhu, Junwei; Cui, Yiyin; Bai, Lianfa; Yue, Jiang

2018-03-01

Aimed at the complex information in videos and low detection efficiency, an actions detection model based on neighboring Gaussian structure and 3D LARK features is put forward. We exploit a double hierarchical multi-structure space-time statistical matching model (DMSM) in temporal action localization. First, a neighboring Gaussian structure is presented to describe the multi-scale structural relationship. Then, a space-time statistical matching method is proposed to achieve two similarity matrices on both large and small scales, which combines double hierarchical structural constraints in model by both the neighboring Gaussian structure and the 3D LARK local structure. Finally, the double hierarchical similarity is fused and analyzed to detect actions. Besides, the multi-scale composite template extends the model application into multi-view. Experimental results of DMSM on the complex visual tracker benchmark data sets and THUMOS 2014 data sets show the promising performance. Compared with other state-of-the-art algorithm, DMSM achieves superior performances.

Action detection by double hierarchical multi-structure space–time statistical matching model

NASA Astrophysics Data System (ADS)

Han, Jing; Zhu, Junwei; Cui, Yiyin; Bai, Lianfa; Yue, Jiang

2018-06-01

Aimed at the complex information in videos and low detection efficiency, an actions detection model based on neighboring Gaussian structure and 3D LARK features is put forward. We exploit a double hierarchical multi-structure space-time statistical matching model (DMSM) in temporal action localization. First, a neighboring Gaussian structure is presented to describe the multi-scale structural relationship. Then, a space-time statistical matching method is proposed to achieve two similarity matrices on both large and small scales, which combines double hierarchical structural constraints in model by both the neighboring Gaussian structure and the 3D LARK local structure. Finally, the double hierarchical similarity is fused and analyzed to detect actions. Besides, the multi-scale composite template extends the model application into multi-view. Experimental results of DMSM on the complex visual tracker benchmark data sets and THUMOS 2014 data sets show the promising performance. Compared with other state-of-the-art algorithm, DMSM achieves superior performances.

GA-optimization for rapid prototype system demonstration

NASA Technical Reports Server (NTRS)

Kim, Jinwoo; Zeigler, Bernard P.

1994-01-01

An application of the Genetic Algorithm (GA) is discussed. A novel scheme of Hierarchical GA was developed to solve complicated engineering problems which require optimization of a large number of parameters with high precision. High level GAs search for few parameters which are much more sensitive to the system performance. Low level GAs search in more detail and employ a greater number of parameters for further optimization. Therefore, the complexity of the search is decreased and the computing resources are used more efficiently.

Pore-scale study of effects of macroscopic pores and their distributions on reactive transport in hierarchical porous media

DOE PAGES

Chen, Li; Zhang, Ruiyuan; Min, Ting; ...

2018-05-19

For applications of reactive transport in porous media, optimal porous structures should possess both high surface area for reactive sites loading and low mass transport resistance. Hierarchical porous media with a combination of pores at different scales are designed for this purpose. In this paper, using the lattice Boltzmann method, pore-scale numerical studies are conducted to investigate diffusion-reaction processes in 2D hierarchical porous media generated by self-developed reconstruction scheme. Complex interactions between porous structures and reactive transport are revealed under different conditions. Simulation results show that adding macropores can greatly enhance the mass transport, but at the same time reducemore » the reactive surface, leading to complex change trend of the total reaction rate. Effects of gradient distribution of macropores within the porous medium are also investigated. It is found that a front-loose, back-tight (FLBT) hierarchical structure is desirable for enhancing mass transport, increasing total reaction rate, and improving catalyst utilization. Finally, on the whole, from the viewpoint of reducing cost and improving material performance, hierarchical porous structures, especially gradient structures with the size of macropores gradually decreasing along the transport direction, are desirable for catalyst application.« less

Pore-scale study of effects of macroscopic pores and their distributions on reactive transport in hierarchical porous media

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

Chen, Li; Zhang, Ruiyuan; Min, Ting

For applications of reactive transport in porous media, optimal porous structures should possess both high surface area for reactive sites loading and low mass transport resistance. Hierarchical porous media with a combination of pores at different scales are designed for this purpose. In this paper, using the lattice Boltzmann method, pore-scale numerical studies are conducted to investigate diffusion-reaction processes in 2D hierarchical porous media generated by self-developed reconstruction scheme. Complex interactions between porous structures and reactive transport are revealed under different conditions. Simulation results show that adding macropores can greatly enhance the mass transport, but at the same time reducemore » the reactive surface, leading to complex change trend of the total reaction rate. Effects of gradient distribution of macropores within the porous medium are also investigated. It is found that a front-loose, back-tight (FLBT) hierarchical structure is desirable for enhancing mass transport, increasing total reaction rate, and improving catalyst utilization. Finally, on the whole, from the viewpoint of reducing cost and improving material performance, hierarchical porous structures, especially gradient structures with the size of macropores gradually decreasing along the transport direction, are desirable for catalyst application.« less

A discrete control model of PLANT

NASA Technical Reports Server (NTRS)

Mitchell, C. M.

1985-01-01

A model of the PLANT system using the discrete control modeling techniques developed by Miller is described. Discrete control models attempt to represent in a mathematical form how a human operator might decompose a complex system into simpler parts and how the control actions and system configuration are coordinated so that acceptable overall system performance is achieved. Basic questions include knowledge representation, information flow, and decision making in complex systems. The structure of the model is a general hierarchical/heterarchical scheme which structurally accounts for coordination and dynamic focus of attention. Mathematically, the discrete control model is defined in terms of a network of finite state systems. Specifically, the discrete control model accounts for how specific control actions are selected from information about the controlled system, the environment, and the context of the situation. The objective is to provide a plausible and empirically testable accounting and, if possible, explanation of control behavior.

Hierarchical star formation across the grand-design spiral NGC 1566

NASA Astrophysics Data System (ADS)

Gouliermis, Dimitrios A.; Elmegreen, Bruce G.; Elmegreen, Debra M.; Calzetti, Daniela; Cignoni, Michele; Gallagher, John S., III; Kennicutt, Robert C.; Klessen, Ralf S.; Sabbi, Elena; Thilker, David; Ubeda, Leonardo; Aloisi, Alessandra; Adamo, Angela; Cook, David O.; Dale, Daniel; Grasha, Kathryn; Grebel, Eva K.; Johnson, Kelsey E.; Sacchi, Elena; Shabani, Fayezeh; Smith, Linda J.; Wofford, Aida

2017-06-01

We investigate how star formation is spatially organized in the grand-design spiral NGC 1566 from deep Hubble Space Telescope photometry with the Legacy ExtraGalactic UV Survey. Our contour-based clustering analysis reveals 890 distinct stellar conglomerations at various levels of significance. These star-forming complexes are organized in a hierarchical fashion with the larger congregations consisting of smaller structures, which themselves fragment into even smaller and more compact stellar groupings. Their size distribution, covering a wide range in length-scales, shows a power law as expected from scale-free processes. We explain this shape with a simple 'fragmentation and enrichment' model. The hierarchical morphology of the complexes is confirmed by their mass-size relation that can be represented by a power law with a fractional exponent, analogous to that determined for fractal molecular clouds. The surface stellar density distribution of the complexes shows a lognormal shape similar to that for supersonic non-gravitating turbulent gas. Between 50 and 65 per cent of the recently formed stars, as well as about 90 per cent of the young star clusters, are found inside the stellar complexes, located along the spiral arms. We find an age difference between young stars inside the complexes and those in their direct vicinity in the arms of at least 10 Myr. This time-scale may relate to the minimum time for stellar evaporation, although we cannot exclude the in situ formation of stars. As expected, star formation preferentially occurs in spiral arms. Our findings reveal turbulent-driven hierarchical star formation along the arms of a grand-design galaxy.

Automated control of hierarchical systems using value-driven methods

NASA Technical Reports Server (NTRS)

Pugh, George E.; Burke, Thomas E.

1990-01-01

An introduction is given to the Value-driven methodology, which has been successfully applied to solve a variety of difficult decision, control, and optimization problems. Many real-world decision processes (e.g., those encountered in scheduling, allocation, and command and control) involve a hierarchy of complex planning considerations. For such problems it is virtually impossible to define a fixed set of rules that will operate satisfactorily over the full range of probable contingencies. Decision Science Applications' value-driven methodology offers a systematic way of automating the intuitive, common-sense approach used by human planners. The inherent responsiveness of value-driven systems to user-controlled priorities makes them particularly suitable for semi-automated applications in which the user must remain in command of the systems operation. Three examples of the practical application of the approach in the automation of hierarchical decision processes are discussed: the TAC Brawler air-to-air combat simulation is a four-level computerized hierarchy; the autonomous underwater vehicle mission planning system is a three-level control system; and the Space Station Freedom electrical power control and scheduling system is designed as a two-level hierarchy. The methodology is compared with rule-based systems and with other more widely-known optimization techniques.

Theoretical aspects of Systems Biology.

PubMed

Bizzarri, Mariano; Palombo, Alessandro; Cucina, Alessandra

2013-05-01

The natural world consists of hierarchical levels of complexity that range from subatomic particles and molecules to ecosystems and beyond. This implies that, in order to explain the features and behavior of a whole system, a theory might be required that would operate at the corresponding hierarchical level, i.e. where self-organization processes take place. In the past, biological research has focused on questions that could be answered by a reductionist program of genetics. The organism (and its development) was considered an epiphenomenona of its genes. However, a profound rethinking of the biological paradigm is now underway and it is likely that such a process will lead to a conceptual revolution emerging from the ashes of reductionism. This revolution implies the search for general principles on which a cogent theory of biology might rely. Because much of the logic of living systems is located at higher levels, it is imperative to focus on them. Indeed, both evolution and physiology work on these levels. Thus, by no means Systems Biology could be considered a 'simple' 'gradual' extension of Molecular Biology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Hierarchy concepts: classification and preparation strategies for zeolite containing materials with hierarchical porosity.

PubMed

Schwieger, Wilhelm; Machoke, Albert Gonche; Weissenberger, Tobias; Inayat, Amer; Selvam, Thangaraj; Klumpp, Michael; Inayat, Alexandra

2016-06-13

'Hierarchy' is a property which can be attributed to a manifold of different immaterial systems, such as ideas, items and organisations or material ones like biological systems within living organisms or artificial, man-made constructions. The property 'hierarchy' is mainly characterised by a certain ordering of individual elements relative to each other, often in combination with a certain degree of branching. Especially mass-flow related systems in the natural environment feature special hierarchically branched patterns. This review is a survey into the world of hierarchical systems with special focus on hierarchically porous zeolite materials. A classification of hierarchical porosity is proposed based on the flow distribution pattern within the respective pore systems. In addition, this review might serve as a toolbox providing several synthetic and post-synthetic strategies to prepare zeolitic or zeolite containing material with tailored hierarchical porosity. Very often, such strategies with their underlying principles were developed for improving the performance of the final materials in different technical applications like adsorptive or catalytic processes. In the present review, besides on the hierarchically porous all-zeolite material, special focus is laid on the preparation of zeolitic composite materials with hierarchical porosity capable to face the demands of industrial application.

Hierarchical cortical transcriptome disorganization in autism.

PubMed

Lombardo, Michael V; Courchesne, Eric; Lewis, Nathan E; Pramparo, Tiziano

2017-01-01

Autism spectrum disorders (ASD) are etiologically heterogeneous and complex. Functional genomics work has begun to identify a diverse array of dysregulated transcriptomic programs (e.g., synaptic, immune, cell cycle, DNA damage, WNT signaling, cortical patterning and differentiation) potentially involved in ASD brain abnormalities during childhood and adulthood. However, it remains unclear whether such diverse dysregulated pathways are independent of each other or instead reflect coordinated hierarchical systems-level pathology. Two ASD cortical transcriptome datasets were re-analyzed using consensus weighted gene co-expression network analysis (WGCNA) to identify common co-expression modules across datasets. Linear mixed-effect models and Bayesian replication statistics were used to identify replicable differentially expressed modules. Eigengene network analysis was then utilized to identify between-group differences in how co-expression modules interact and cluster into hierarchical meta-modular organization. Protein-protein interaction analyses were also used to determine whether dysregulated co-expression modules show enhanced interactions. We find replicable evidence for 10 gene co-expression modules that are differentially expressed in ASD cortex. Rather than being independent non-interacting sources of pathology, these dysregulated co-expression modules work in synergy and physically interact at the protein level. These systems-level transcriptional signals are characterized by downregulation of synaptic processes coordinated with upregulation of immune/inflammation, response to other organism, catabolism, viral processes, translation, protein targeting and localization, cell proliferation, and vasculature development. Hierarchical organization of meta-modules (clusters of highly correlated modules) is also highly affected in ASD. These findings highlight that dysregulation of the ASD cortical transcriptome is characterized by the dysregulation of multiple coordinated transcriptional programs producing synergistic systems-level effects that cannot be fully appreciated by studying the individual component biological processes in isolation.

Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits.

PubMed

Freua, Mateus Castelani; Santana, Miguel Henrique de Almeida; Ventura, Ricardo Vieira; Tedeschi, Luis Orlindo; Ferraz, José Bento Sterman

2017-08-01

The interplay between dynamic models of biological systems and genomics is based on the assumption that genetic variation of the complex trait (i.e., outcome of model behavior) arises from component traits (i.e., model parameters) in lower hierarchical levels. In order to provide a proof of concept of this statement for a cattle growth model, we ask whether model parameters map genomic regions that harbor quantitative trait loci (QTLs) already described for the complex trait. We conducted a genome-wide association study (GWAS) with a Bayesian hierarchical LASSO method in two parameters of the Davis Growth Model, a system of three ordinary differential equations describing DNA accretion, protein synthesis and degradation, and fat synthesis. Phenotypic and genotypic data were available for 893 Nellore (Bos indicus) cattle. Computed values for parameter k 1 (DNA accretion rate) ranged from 0.005 ± 0.003 and for α (constant for energy for maintenance requirement) 0.134 ± 0.024. The expected biological interpretation of the parameters is confirmed by QTLs mapped for k 1 and α. QTLs within genomic regions mapped for k 1 are expected to be correlated with the DNA pool: body size and weight. Single nucleotide polymorphisms (SNPs) which were significant for α mapped QTLs that had already been associated with residual feed intake, feed conversion ratio, average daily gain (ADG), body weight, and also dry matter intake. SNPs identified for k 1 were able to additionally explain 2.2% of the phenotypic variability of the complex ADG, even when SNPs for k 1 did not match the genomic regions associated with ADG. Although improvements are needed, our findings suggest that genomic analysis on component traits may help to uncover the genetic basis of more complex traits, particularly when lower biological hierarchies are mechanistically described by mathematical simulation models.

Hierarchical design of a polymeric nanovehicle for efficient tumor regression and imaging

NASA Astrophysics Data System (ADS)

An, Jinxia; Guo, Qianqian; Zhang, Peng; Sinclair, Andrew; Zhao, Yu; Zhang, Xinge; Wu, Kan; Sun, Fang; Hung, Hsiang-Chieh; Li, Chaoxing; Jiang, Shaoyi

2016-04-01

Effective delivery of therapeutics to disease sites significantly contributes to drug efficacy, toxicity and clearance. Here we designed a hierarchical polymeric nanoparticle structure for anti-cancer chemotherapy delivery by utilizing state-of-the-art polymer chemistry and co-assembly techniques. This novel structural design combines the most desired merits for drug delivery in a single particle, including a long in vivo circulation time, inhibited non-specific cell uptake, enhanced tumor cell internalization, pH-controlled drug release and simultaneous imaging. This co-assembled nanoparticle showed exceptional stability in complex biological media. Benefiting from the synergistic effects of zwitterionic and multivalent galactose polymers, drug-loaded nanoparticles were selectively internalized by cancer cells rather than normal tissue cells. In addition, the pH-responsive core retained their cargo within their polymeric coating through hydrophobic interaction and released it under slightly acidic conditions. In vivo pharmacokinetic studies in mice showed minimal uptake of nanoparticles by the mononuclear phagocyte system and excellent blood circulation half-lives of 14.4 h. As a result, tumor growth was completely inhibited and no damage was observed for normal organ tissues. This newly developed drug nanovehicle has great potential in cancer therapy, and the hierarchical design principle should provide valuable information for the development of the next generation of drug delivery systems.Effective delivery of therapeutics to disease sites significantly contributes to drug efficacy, toxicity and clearance. Here we designed a hierarchical polymeric nanoparticle structure for anti-cancer chemotherapy delivery by utilizing state-of-the-art polymer chemistry and co-assembly techniques. This novel structural design combines the most desired merits for drug delivery in a single particle, including a long in vivo circulation time, inhibited non-specific cell uptake, enhanced tumor cell internalization, pH-controlled drug release and simultaneous imaging. This co-assembled nanoparticle showed exceptional stability in complex biological media. Benefiting from the synergistic effects of zwitterionic and multivalent galactose polymers, drug-loaded nanoparticles were selectively internalized by cancer cells rather than normal tissue cells. In addition, the pH-responsive core retained their cargo within their polymeric coating through hydrophobic interaction and released it under slightly acidic conditions. In vivo pharmacokinetic studies in mice showed minimal uptake of nanoparticles by the mononuclear phagocyte system and excellent blood circulation half-lives of 14.4 h. As a result, tumor growth was completely inhibited and no damage was observed for normal organ tissues. This newly developed drug nanovehicle has great potential in cancer therapy, and the hierarchical design principle should provide valuable information for the development of the next generation of drug delivery systems. Electronic supplementary information (ESI) available: Experimental details, 1H NMR spectra and GPC of polymers. See DOI: 10.1039/c6nr01595f

Hierarchical Medical System Based on Big Data and Mobile Internet: A New Strategic Choice in Health Care

PubMed Central

2017-01-01

China is setting up a hierarchical medical system to solve the problems of biased resource allocation and high patient flows to large hospitals. The development of big data and mobile Internet technology provides a new perspective for the establishment of hierarchical medical system. This viewpoint discusses the challenges with the hierarchical medical system in China and how big data and mobile Internet can be used to mitigate these challenges. PMID:28790024

Space-Time Smoothing of Complex Survey Data: Small Area Estimation for Child Mortality.

PubMed

Mercer, Laina D; Wakefield, Jon; Pantazis, Athena; Lutambi, Angelina M; Masanja, Honorati; Clark, Samuel

2015-12-01

Many people living in low and middle-income countries are not covered by civil registration and vital statistics systems. Consequently, a wide variety of other types of data including many household sample surveys are used to estimate health and population indicators. In this paper we combine data from sample surveys and demographic surveillance systems to produce small area estimates of child mortality through time. Small area estimates are necessary to understand geographical heterogeneity in health indicators when full-coverage vital statistics are not available. For this endeavor spatio-temporal smoothing is beneficial to alleviate problems of data sparsity. The use of conventional hierarchical models requires careful thought since the survey weights may need to be considered to alleviate bias due to non-random sampling and non-response. The application that motivated this work is estimation of child mortality rates in five-year time intervals in regions of Tanzania. Data come from Demographic and Health Surveys conducted over the period 1991-2010 and two demographic surveillance system sites. We derive a variance estimator of under five years child mortality that accounts for the complex survey weighting. For our application, the hierarchical models we consider include random effects for area, time and survey and we compare models using a variety of measures including the conditional predictive ordinate (CPO). The method we propose is implemented via the fast and accurate integrated nested Laplace approximation (INLA).

Scale-dependent intrinsic entropies of complex time series.

PubMed

Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

2016-04-13

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. © 2016 The Author(s).

Hierarchical Task Analysis and Training Decisions.

ERIC Educational Resources Information Center

Shepherd, A.

1985-01-01

Hierarchical task analysis (HTA), which requires description of a task in terms of a hierarchy of operations and plans, is reviewed and examined as a basis for making training decisions. Benefits of HTA in terms of economy of analysis and as a means of accounting for complex performance are outlined. (Author/MBR)

Organizational and Spatial Dynamics of Attentional Focusing in Hierarchically Structured Objects

ERIC Educational Resources Information Center

Yeari, Menahem; Goldsmith, Morris

2011-01-01

Is the focusing of visual attention object-based, space-based, both, or neither? Attentional focusing latencies in hierarchically structured compound-letter objects were examined, orthogonally manipulating global size (larger vs. smaller) and organizational complexity (two-level structure vs. three-level structure). In a dynamic focusing task,…

Dynamic resource allocation in a hierarchical multiprocessor system: A preliminary study

NASA Technical Reports Server (NTRS)

Ngai, Tin-Fook

1986-01-01

An integrated system approach to dynamic resource allocation is proposed. Some of the problems in dynamic resource allocation and the relationship of these problems to system structures are examined. A general dynamic resource allocation scheme is presented. A hierarchial system architecture which dynamically maps between processor structure and programs at multiple levels of instantiations is described. Simulation experiments were conducted to study dynamic resource allocation on the proposed system. Preliminary evaluation based on simple dynamic resource allocation algorithms indicates that with the proposed system approach, the complexity of dynamic resource management could be significantly reduced while achieving reasonable effective dynamic resource allocation.

Coordination of a complex welfare system case: rehabilitation entity in Finland.

PubMed

Miettinen, Sari; Ashorn, Ulla; Lehto, Juhani; Viitanen, Elina

2011-01-01

The main purpose of this article is to analyse the institutional and political structures of the Finnish rehabilitation entity and the governmental efforts to improve the governance of the rehabilitation policy. Rehabilitation in Finland is a complex welfare system which has undergone several coordination attempts during the last two decades. The centrality of the coordination of this welfare system is obvious. Based on the content analysis of three Government's rehabilitation reports from 1994 to 2002 and their background papers, this article provides two main findings. First, the rehabilitation entity seems to be based on different funding strategies, different governing and different coordination models between the rehabilitation subsystems. Second, the governance discourse in the reports seems to be unchanging with a predominantly hierarchical mode. The article concludes with a discussion on the challenges to coordinate this kind of a complex welfare system as an entity and also how to overcome those challenges. Copyright © 2010 John Wiley & Sons, Ltd.

Synthesis and morphogenesis of organic polymer materials with hierarchical structures in biominerals.

PubMed

Oaki, Yuya; Kijima, Misako; Imai, Hiroaki

2011-06-08

Synthesis and morphogenesis of polypyrrole (PPy) with hierarchical structures from nanoscopic to macroscopic scales have been achieved by using hierarchically organized architectures of biominerals. We adopted biominerals, such as a sea urchin spine and nacreous layer, having hierarchical architectures based on mesocrystals as model materials used for synthesis of an organic polymer. A sea urchin spine led to the formation of PPy macroscopic sponge structures consisting of nanosheets less than 100 nm in thickness with the mosaic interior of the nanoparticles. The morphologies of the resultant PPy hierarchical architectures can be tuned by the structural modification of the original biomineral with chemical and thermal treatments. In another case, a nacreous layer provided PPy porous nanosheets consisting of the nanoparticles. Conductive pathways were formed in these PPy hierarchical architectures. The nanoscale interspaces in the mesocrystal structures of biominerals are used for introduction and polymerization of the monomers, leading to the formation of hierarchically organized polymer architectures. These results show that functional organic materials with complex and nanoscale morphologies can be synthesized by using hierarchically organized architectures as observed in biominerals.

Space versus Place in Complex Human-Natural Systems: Spatial and Multi-level Models of Tropical Land Use and Cover Change (LUCC) in Guatemala

PubMed Central

López-Carr, David; Davis, Jason; Jankowska, Marta; Grant, Laura; López-Carr, Anna Carla; Clark, Matthew

2013-01-01

The relative role of space and place has long been debated in geography. Yet modeling efforts applied to coupled human-natural systems seemingly favor models assuming continuous spatial relationships. We examine the relative importance of placebased hierarchical versus spatial clustering influences in tropical land use/cover change (LUCC). Guatemala was chosen as our study site given its high rural population growth and deforestation in recent decades. We test predictors of 2009 forest cover and forest cover change from 2001-2009 across Guatemala's 331 municipalities and 22 departments using spatial and multi-level statistical models. Our results indicate the emergence of several socio-economic predictors of LUCC regardless of model choice. Hierarchical model results suggest that significant differences exist at the municipal and departmental levels but largely maintain the magnitude and direction of single-level model coefficient estimates. They are also intervention-relevant since policies tend to be applicable to distinct political units rather than to continuous space. Spatial models complement hierarchical approaches by indicating where and to what magnitude significant negative and positive clustering associations emerge. Appreciating the comparative advantages and limitations of spatial and nested models enhances a holistic approach to geographical analysis of tropical LUCC and human-environment interactions. PMID:24013908

Hierarchical neural network model of the visual system determining figure/ground relation

NASA Astrophysics Data System (ADS)

Kikuchi, Masayuki

2017-07-01

One of the most important functions of the visual perception in the brain is figure/ground interpretation from input images. Figural region in 2D image corresponding to object in 3D space are distinguished from background region extended behind the object. Previously the author proposed a neural network model of figure/ground separation constructed on the standpoint that local geometric features such as curvatures and outer angles at corners are extracted and propagated along input contour in a single layer network (Kikuchi & Akashi, 2001). However, such a processing principle has the defect that signal propagation requires manyiterations despite the fact that actual visual system determines figure/ground relation within the short period (Zhou et al., 2000). In order to attain speed-up for determining figure/ground, this study incorporates hierarchical architecture into the previous model. This study confirmed the effect of the hierarchization as for the computation time by simulation. As the number of layers increased, the required computation time reduced. However, such speed-up effect was saturatedas the layers increased to some extent. This study attempted to explain this saturation effect by the notion of average distance between vertices in the area of complex network, and succeeded to mimic the saturation effect by computer simulation.

Neuromorphic VLSI vision system for real-time texture segregation.

PubMed

Shimonomura, Kazuhiro; Yagi, Tetsuya

2008-10-01

The visual system of the brain can perceive an external scene in real-time with extremely low power dissipation, although the response speed of an individual neuron is considerably lower than that of semiconductor devices. The neurons in the visual pathway generate their receptive fields using a parallel and hierarchical architecture. This architecture of the visual cortex is interesting and important for designing a novel perception system from an engineering perspective. The aim of this study is to develop a vision system hardware, which is designed inspired by a hierarchical visual processing in V1, for real time texture segregation. The system consists of a silicon retina, orientation chip, and field programmable gate array (FPGA) circuit. The silicon retina emulates the neural circuits of the vertebrate retina and exhibits a Laplacian-Gaussian-like receptive field. The orientation chip selectively aggregates multiple pixels of the silicon retina in order to produce Gabor-like receptive fields that are tuned to various orientations by mimicking the feed-forward model proposed by Hubel and Wiesel. The FPGA circuit receives the output of the orientation chip and computes the responses of the complex cells. Using this system, the neural images of simple cells were computed in real-time for various orientations and spatial frequencies. Using the orientation-selective outputs obtained from the multi-chip system, a real-time texture segregation was conducted based on a computational model inspired by psychophysics and neurophysiology. The texture image was filtered by the two orthogonally oriented receptive fields of the multi-chip system and the filtered images were combined to segregate the area of different texture orientation with the aid of FPGA. The present system is also useful for the investigation of the functions of the higher-order cells that can be obtained by combining the simple and complex cells.

Image understanding in terms of semiotics

NASA Astrophysics Data System (ADS)

Zakharko, E.; Kaminsky, Roman M.; Shpytko, V.

1995-06-01

Human perception of pictorial visual information is investigated from iconical sign view-point and appropriate semiotical model is discussed. Image construction (syntactics) is analyzed as a complex hierarchical system and various types of pictorial objects, their relations, regular configurations are represented, studied, and modeled. Relations between image syntactics, its semantics, and pragmatics is investigated. Research results application to the problems of thematic interpretation of Earth surface remote imgages is illustrated.

PNNL Report on the Development of Bench-scale CFD Simulations for Gas Absorption across a Wetted Wall Column

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

Wang, Chao; Xu, Zhijie; Lai, Canhai

This report is prepared for the demonstration of hierarchical prediction of carbon capture efficiency of a solvent-based absorption column. A computational fluid dynamics (CFD) model is first developed to simulate the core phenomena of solvent-based carbon capture, i.e., the CO2 physical absorption and chemical reaction, on a simplified geometry of wetted wall column (WWC) at bench scale. Aqueous solutions of ethanolamine (MEA) are commonly selected as a CO2 stream scrubbing liquid. CO2 is captured by both physical and chemical absorption using highly CO2 soluble and reactive solvent, MEA, during the scrubbing process. In order to provide confidence bound on themore » computational predictions of this complex engineering system, a hierarchical calibration and validation framework is proposed. The overall goal of this effort is to provide a mechanism-based predictive framework with confidence bound for overall mass transfer coefficient of the wetted wall column (WWC) with statistical analyses of the corresponding WWC experiments with increasing physical complexity.« less

Identifying behaviour patterns of construction safety using system archetypes.

PubMed

Guo, Brian H W; Yiu, Tak Wing; González, Vicente A

2015-07-01

Construction safety management involves complex issues (e.g., different trades, multi-organizational project structure, constantly changing work environment, and transient workforce). Systems thinking is widely considered as an effective approach to understanding and managing the complexity. This paper aims to better understand dynamic complexity of construction safety management by exploring archetypes of construction safety. To achieve this, this paper adopted the ground theory method (GTM) and 22 interviews were conducted with participants in various positions (government safety inspector, client, health and safety manager, safety consultant, safety auditor, and safety researcher). Eight archetypes were emerged from the collected data: (1) safety regulations, (2) incentive programs, (3) procurement and safety, (4) safety management in small businesses (5) production and safety, (6) workers' conflicting goals, (7) blame on workers, and (8) reactive and proactive learning. These archetypes capture the interactions between a wide range of factors within various hierarchical levels and subsystems. As a free-standing tool, they advance the understanding of dynamic complexity of construction safety management and provide systemic insights into dealing with the complexity. They also can facilitate system dynamics modelling of construction safety process. Copyright © 2015 Elsevier Ltd. All rights reserved.

Large-scale model of flow in heterogeneous and hierarchical porous media

NASA Astrophysics Data System (ADS)

Chabanon, Morgan; Valdés-Parada, Francisco J.; Ochoa-Tapia, J. Alberto; Goyeau, Benoît

2017-11-01

Heterogeneous porous structures are very often encountered in natural environments, bioremediation processes among many others. Reliable models for momentum transport are crucial whenever mass transport or convective heat occurs in these systems. In this work, we derive a large-scale average model for incompressible single-phase flow in heterogeneous and hierarchical soil porous media composed of two distinct porous regions embedding a solid impermeable structure. The model, based on the local mechanical equilibrium assumption between the porous regions, results in a unique momentum transport equation where the global effective permeability naturally depends on the permeabilities at the intermediate mesoscopic scales and therefore includes the complex hierarchical structure of the soil. The associated closure problem is numerically solved for various configurations and properties of the heterogeneous medium. The results clearly show that the effective permeability increases with the volume fraction of the most permeable porous region. It is also shown that the effective permeability is sensitive to the dimensionality spatial arrangement of the porous regions and in particular depends on the contact between the impermeable solid and the two porous regions.

A hierarchical approach to cooperativity in macromolecular and self-assembling binding systems.

PubMed

Garcés, Josep Lluís; Acerenza, Luis; Mizraji, Eduardo; Mas, Francesc

2008-04-01

The study of complex macromolecular binding systems reveals that a high number of states and processes are involved in their mechanism of action, as has become more apparent with the sophistication of the experimental techniques used. The resulting information is often difficult to interpret because of the complexity of the scheme (large size and profuse interactions, including cooperative and self-assembling interactions) and the lack of transparency that this complexity introduces into the interpretation of the indexes traditionally used to describe the binding properties. In particular, cooperative behaviour can be attributed to very different causes, such as direct chemical modification of the binding sites, conformational changes in the whole structure of the macromolecule, aggregation processes between different subunits, etc. In this paper, we propose a novel approach for the analysis of the binding properties of complex macromolecular and self-assembling systems. To quantify the binding behaviour, we use the global association quotient defined as K(c) = [occupied sites]/([free sites] L), L being the free ligand concentration. K(c) can be easily related to other measures of cooperativity (such as the Hill number or the Scatchard plot) and to the free energies involved in the binding processes at each ligand concentration. In a previous work, it was shown that K(c) could be decomposed as an average of equilibrium constants in two ways: intrinsic constants for Adair binding systems and elementary constants for the general case. In this study, we show that these two decompositions are particular cases of a more general expression, where the average is over partial association quotients, associated with subsystems from which the system is composed. We also show that if the system is split into different subsystems according to a binding hierarchy that starts from the lower, microscopic level and ends at the higher, aggregation level, the global association quotient can be decomposed following the hierarchical levels of macromolecular organisation. In this process, the partial association quotients of one level are expressed, in a recursive way, as a function of the partial quotients of the level that is immediately below, until the microscopic level is reached. As a result, the binding properties of very complex macromolecular systems can be analysed in detail, making the mechanistic explanation of their behaviour transparent. In addition, our approach provides a model-independent interpretation of the intrinsic equilibrium constants in terms of the elementary ones.

Partitioning degrees of freedom in hierarchical and other richly-parameterized models.

PubMed

Cui, Yue; Hodges, James S; Kong, Xiaoxiao; Carlin, Bradley P

2010-02-01

Hodges & Sargent (2001) developed a measure of a hierarchical model's complexity, degrees of freedom (DF), that is consistent with definitions for scatterplot smoothers, interpretable in terms of simple models, and that enables control of a fit's complexity by means of a prior distribution on complexity. DF describes complexity of the whole fitted model but in general it is unclear how to allocate DF to individual effects. We give a new definition of DF for arbitrary normal-error linear hierarchical models, consistent with Hodges & Sargent's, that naturally partitions the n observations into DF for individual effects and for error. The new conception of an effect's DF is the ratio of the effect's modeled variance matrix to the total variance matrix. This gives a way to describe the sizes of different parts of a model (e.g., spatial clustering vs. heterogeneity), to place DF-based priors on smoothing parameters, and to describe how a smoothed effect competes with other effects. It also avoids difficulties with the most common definition of DF for residuals. We conclude by comparing DF to the effective number of parameters p(D) of Spiegelhalter et al (2002). Technical appendices and a dataset are available online as supplemental materials.

Efficient electronic structure theory via hierarchical scale-adaptive coupled-cluster formalism: I. Theory and computational complexity analysis

NASA Astrophysics Data System (ADS)

Lyakh, Dmitry I.

2018-03-01

A novel reduced-scaling, general-order coupled-cluster approach is formulated by exploiting hierarchical representations of many-body tensors, combined with the recently suggested formalism of scale-adaptive tensor algebra. Inspired by the hierarchical techniques from the renormalisation group approach, H/H2-matrix algebra and fast multipole method, the computational scaling reduction in our formalism is achieved via coarsening of quantum many-body interactions at larger interaction scales, thus imposing a hierarchical structure on many-body tensors of coupled-cluster theory. In our approach, the interaction scale can be defined on any appropriate Euclidean domain (spatial domain, momentum-space domain, energy domain, etc.). We show that the hierarchically resolved many-body tensors can reduce the storage requirements to O(N), where N is the number of simulated quantum particles. Subsequently, we prove that any connected many-body diagram consisting of a finite number of arbitrary-order tensors, e.g. an arbitrary coupled-cluster diagram, can be evaluated in O(NlogN) floating-point operations. On top of that, we suggest an additional approximation to further reduce the computational complexity of higher order coupled-cluster equations, i.e. equations involving higher than double excitations, which otherwise would introduce a large prefactor into formal O(NlogN) scaling.

Knowledge-based approach to system integration

NASA Technical Reports Server (NTRS)

Blokland, W.; Krishnamurthy, C.; Biegl, C.; Sztipanovits, J.

1988-01-01

To solve complex problems one can often use the decomposition principle. However, a problem is seldom decomposable into completely independent subproblems. System integration deals with problem of resolving the interdependencies and the integration of the subsolutions. A natural method of decomposition is the hierarchical one. High-level specifications are broken down into lower level specifications until they can be transformed into solutions relatively easily. By automating the hierarchical decomposition and solution generation an integrated system is obtained in which the declaration of high level specifications is enough to solve the problem. We offer a knowledge-based approach to integrate the development and building of control systems. The process modeling is supported by using graphic editors. The user selects and connects icons that represent subprocesses and might refer to prewritten programs. The graphical editor assists the user in selecting parameters for each subprocess and allows the testing of a specific configuration. Next, from the definitions created by the graphical editor, the actual control program is built. Fault-diagnosis routines are generated automatically as well. Since the user is not required to write program code and knowledge about the process is present in the development system, the user is not required to have expertise in many fields.

Mutual and asynchronous anticipation and action in sports as globally competitive and locally coordinative dynamics

PubMed Central

Fujii, Keisuke; Isaka, Tadao; Kouzaki, Motoki; Yamamoto, Yuji

2015-01-01

Humans interact by changing their actions, perceiving other’s actions and executing solutions in conflicting situations. Using oscillator models, nonlinear dynamics have been considered for describing these complex human movements as an emergence of self-organisation. However, these frameworks cannot explain the hierarchical structures of complex behaviours between conflicting inter-agent and adapting intra-agent systems, especially in sport competitions wherein mutually quick decision making and execution are required. Here we adopt a hybrid multiscale approach to model an attack-and-defend game during which both players predict the opponent’s movement and move with a delay. From both simulated and measured data, one synchronous outcome between two-agent (i.e. successful defence) can be described as one attractor. In contrast, the other coordination-breaking outcome (i.e. successful attack) cannot be explained using gradient dynamics because the asymmetric interaction cannot always assume a conserved physical quantity. Instead, we provide the asymmetric and asynchronous hierarchical dynamical models to discuss two-agent competition. Our framework suggests that possessing information about an opponent and oneself in local-coordinative and global-competitive scale enables us to gain a deeper understanding of sports competitions. We anticipate developments in the scientific fields of complex movement adapting to such uncontrolled environments. PMID:26538452

Mutual and asynchronous anticipation and action in sports as globally competitive and locally coordinative dynamics

NASA Astrophysics Data System (ADS)

Fujii, Keisuke; Isaka, Tadao; Kouzaki, Motoki; Yamamoto, Yuji

2015-11-01

Humans interact by changing their actions, perceiving other’s actions and executing solutions in conflicting situations. Using oscillator models, nonlinear dynamics have been considered for describing these complex human movements as an emergence of self-organisation. However, these frameworks cannot explain the hierarchical structures of complex behaviours between conflicting inter-agent and adapting intra-agent systems, especially in sport competitions wherein mutually quick decision making and execution are required. Here we adopt a hybrid multiscale approach to model an attack-and-defend game during which both players predict the opponent’s movement and move with a delay. From both simulated and measured data, one synchronous outcome between two-agent (i.e. successful defence) can be described as one attractor. In contrast, the other coordination-breaking outcome (i.e. successful attack) cannot be explained using gradient dynamics because the asymmetric interaction cannot always assume a conserved physical quantity. Instead, we provide the asymmetric and asynchronous hierarchical dynamical models to discuss two-agent competition. Our framework suggests that possessing information about an opponent and oneself in local-coordinative and global-competitive scale enables us to gain a deeper understanding of sports competitions. We anticipate developments in the scientific fields of complex movement adapting to such uncontrolled environments.

Bio-inspired Fabrication of Complex Hierarchical Structure in Silicon.

PubMed

Gao, Yang; Peng, Zhengchun; Shi, Tielin; Tan, Xianhua; Zhang, Deqin; Huang, Qiang; Zou, Chuanping; Liao, Guanglan

2015-08-01

In this paper, we developed a top-down method to fabricate complex three dimensional silicon structure, which was inspired by the hierarchical micro/nanostructure of the Morpho butterfly scales. The fabrication procedure includes photolithography, metal masking, and both dry and wet etching techniques. First, microscale photoresist grating pattern was formed on the silicon (111) wafer. Trenches with controllable rippled structures on the sidewalls were etched by inductively coupled plasma reactive ion etching Bosch process. Then, Cr film was angled deposited on the bottom of the ripples by electron beam evaporation, followed by anisotropic wet etching of the silicon. The simple fabrication method results in large scale hierarchical structure on a silicon wafer. The fabricated Si structure has multiple layers with uniform thickness of hundreds nanometers. We conducted both light reflection and heat transfer experiments on this structure. They exhibited excellent antireflection performance for polarized ultraviolet, visible and near infrared wavelengths. And the heat flux of the structure was significantly enhanced. As such, we believe that these bio-inspired hierarchical silicon structure will have promising applications in photovoltaics, sensor technology and photonic crystal devices.

Graphical Methods for Reducing, Visualizing and Analyzing Large Data Sets Using Hierarchical Terminologies

PubMed Central

Jing, Xia; Cimino, James J.

2011-01-01

Objective: To explore new graphical methods for reducing and analyzing large data sets in which the data are coded with a hierarchical terminology. Methods: We use a hierarchical terminology to organize a data set and display it in a graph. We reduce the size and complexity of the data set by considering the terminological structure and the data set itself (using a variety of thresholds) as well as contributions of child level nodes to parent level nodes. Results: We found that our methods can reduce large data sets to manageable size and highlight the differences among graphs. The thresholds used as filters to reduce the data set can be used alone or in combination. We applied our methods to two data sets containing information about how nurses and physicians query online knowledge resources. The reduced graphs make the differences between the two groups readily apparent. Conclusions: This is a new approach to reduce size and complexity of large data sets and to simplify visualization. This approach can be applied to any data sets that are coded with hierarchical terminologies. PMID:22195119

Hierarchical organization of functional connectivity in the mouse brain: a complex network approach.

PubMed

Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

2016-08-18

This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.

Hierarchical organization of functional connectivity in the mouse brain: a complex network approach

NASA Astrophysics Data System (ADS)

Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

2016-08-01

This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.

A retrospective study of phonetic inventory complexity in acquisition of Spanish: Implications for phonological universals

PubMed Central

Cataño, Lorena; Barlow, Jessica A.; Moyna, María Irene

2015-01-01

This study evaluates 39 different phonetic inventories of 16 Spanish-speaking children (ages 0;11 to 5;1) in terms of hierarchical complexity. Phonetic featural differences are considered in order to evaluate the proposed implicational hierarchy of Dinnsen et al.’s phonetic inventory typology for English. The children’s phonetic inventories are examined independently and in relation to one another. Five hierarchical complexity levels are proposed, similar to those of English and other languages, although with some language-specific differences. These findings have implications for theoretical assumptions about the universality of phonetic inventory development, and for remediation of Spanish-speaking children with phonological impairments. PMID:19504400

Hierarchy in directed random networks.

PubMed

Mones, Enys

2013-02-01

In recent years, the theory and application of complex networks have been quickly developing in a markable way due to the increasing amount of data from real systems and the fruitful application of powerful methods used in statistical physics. Many important characteristics of social or biological systems can be described by the study of their underlying structure of interactions. Hierarchy is one of these features that can be formulated in the language of networks. In this paper we present some (qualitative) analytic results on the hierarchical properties of random network models with zero correlations and also investigate, mainly numerically, the effects of different types of correlations. The behavior of the hierarchy is different in the absence and the presence of giant components. We show that the hierarchical structure can be drastically different if there are one-point correlations in the network. We also show numerical results suggesting that the hierarchy does not change monotonically with the correlations and there is an optimal level of nonzero correlations maximizing the level of hierarchy.

A Tensor-Train accelerated solver for integral equations in complex geometries

NASA Astrophysics Data System (ADS)

Corona, Eduardo; Rahimian, Abtin; Zorin, Denis

2017-04-01

We present a framework using the Quantized Tensor Train (QTT) decomposition to accurately and efficiently solve volume and boundary integral equations in three dimensions. We describe how the QTT decomposition can be used as a hierarchical compression and inversion scheme for matrices arising from the discretization of integral equations. For a broad range of problems, computational and storage costs of the inversion scheme are extremely modest O (log ⁡ N) and once the inverse is computed, it can be applied in O (Nlog ⁡ N) . We analyze the QTT ranks for hierarchically low rank matrices and discuss its relationship to commonly used hierarchical compression techniques such as FMM and HSS. We prove that the QTT ranks are bounded for translation-invariant systems and argue that this behavior extends to non-translation invariant volume and boundary integrals. For volume integrals, the QTT decomposition provides an efficient direct solver requiring significantly less memory compared to other fast direct solvers. We present results demonstrating the remarkable performance of the QTT-based solver when applied to both translation and non-translation invariant volume integrals in 3D. For boundary integral equations, we demonstrate that using a QTT decomposition to construct preconditioners for a Krylov subspace method leads to an efficient and robust solver with a small memory footprint. We test the QTT preconditioners in the iterative solution of an exterior elliptic boundary value problem (Laplace) formulated as a boundary integral equation in complex, multiply connected geometries.

Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models

PubMed Central

Azzopardi, George; Petkov, Nicolai

2014-01-01

The remarkable abilities of the primate visual system have inspired the construction of computational models of some visual neurons. We propose a trainable hierarchical object recognition model, which we call S-COSFIRE (S stands for Shape and COSFIRE stands for Combination Of Shifted FIlter REsponses) and use it to localize and recognize objects of interests embedded in complex scenes. It is inspired by the visual processing in the ventral stream (V1/V2 → V4 → TEO). Recognition and localization of objects embedded in complex scenes is important for many computer vision applications. Most existing methods require prior segmentation of the objects from the background which on its turn requires recognition. An S-COSFIRE filter is automatically configured to be selective for an arrangement of contour-based features that belong to a prototype shape specified by an example. The configuration comprises selecting relevant vertex detectors and determining certain blur and shift parameters. The response is computed as the weighted geometric mean of the blurred and shifted responses of the selected vertex detectors. S-COSFIRE filters share similar properties with some neurons in inferotemporal cortex, which provided inspiration for this work. We demonstrate the effectiveness of S-COSFIRE filters in two applications: letter and keyword spotting in handwritten manuscripts and object spotting in complex scenes for the computer vision system of a domestic robot. S-COSFIRE filters are effective to recognize and localize (deformable) objects in images of complex scenes without requiring prior segmentation. They are versatile trainable shape detectors, conceptually simple and easy to implement. The presented hierarchical shape representation contributes to a better understanding of the brain and to more robust computer vision algorithms. PMID:25126068

Model-based hierarchical reinforcement learning and human action control

PubMed Central

Botvinick, Matthew; Weinstein, Ari

2014-01-01

Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour. PMID:25267822

Concurrent planning and execution for a walking robot

NASA Astrophysics Data System (ADS)

Simmons, Reid

1990-07-01

The Planetary Rover project is developing the Ambler, a novel legged robot, and an autonomous software system for walking the Ambler over rough terrain. As part of the project, we have developed a system that integrates perception, planning, and real-time control to navigate a single leg of the robot through complex obstacle courses. The system is integrated using the Task Control Architecture (TCA), a general-purpose set of utilities for building and controlling distributed mobile robot systems. The walking system, as originally implemented, utilized a sequential sense-plan-act control cycle. This report describes efforts to improve the performance of the system by concurrently planning and executing steps. Concurrency was achieved by modifying the existing sequential system to utilize TCA features such as resource management, monitors, temporal constraints, and hierarchical task trees. Performance was increased in excess of 30 percent with only a relatively modest effort to convert and test the system. The results lend support to the utility of using TCA to develop complex mobile robot systems.

The semiotics of control and modeling relations in complex systems.

PubMed

Joslyn, C

2001-01-01

We provide a conceptual analysis of ideas and principles from the systems theory discourse which underlie Pattee's semantic or semiotic closure, which is itself foundational for a school of theoretical biology derived from systems theory and cybernetics, and is now being related to biological semiotics and explicated in the relational biological school of Rashevsky and Rosen. Atomic control systems and models are described as the canonical forms of semiotic organization, sharing measurement relations, but differing topologically in that control systems are circularly and models linearly related to their environments. Computation in control systems is introduced, motivating hierarchical decomposition, hybrid modeling and control systems, and anticipatory or model-based control. The semiotic relations in complex control systems are described in terms of relational constraints, and rules and laws are distinguished as contingent and necessary functional entailments, respectively. Finally, selection as a meta-level of constraint is introduced as the necessary condition for semantic relations in control systems and models.

Plant Phenotyping through the Eyes of Complex Systems: Theoretical Considerations

NASA Astrophysics Data System (ADS)

Kim, J.

2017-12-01

Plant phenotyping is an emerging transdisciplinary research which necessitates not only the communication and collaboration of scientists from different disciplines but also the paradigm shift to a holistic approach. Complex system is defined as a system having a large number of interacting parts (or particles, agents), whose interactions give rise to non-trivial properties like self-organization and emergence. Plant ecosystems are complex systems which are continually morphing dynamical systems, i.e. self-organizing hierarchical open systems. Such systems are composed of many subunits/subsystems with nonlinear interactions and feedback. The throughput such as the flow of energy, matter and information is the key control parameter in complex systems. Information theoretic approaches can be used to understand and identify such interactions, structures and dynamics through reductions in uncertainty (i.e. entropy). The theoretical considerations based on network and thermodynamic thinking and exemplary analyses (e.g. dynamic process network, spectral entropy) of the throughput time series will be presented. These can be used as a framework to develop more discipline-specific fundamental approaches to provide tools for the transferability of traits between measurement scales in plant phenotyping. Acknowledgment: This work was funded by the Weather Information Service Engine Program of the Korea Meteorological Administration under Grant KMIPA-2012-0001.

Hierarchical temporal structure in music, speech and animal vocalizations: jazz is like a conversation, humpbacks sing like hermit thrushes.

PubMed

Kello, Christopher T; Bella, Simone Dalla; Médé, Butovens; Balasubramaniam, Ramesh

2017-10-01

Humans talk, sing and play music. Some species of birds and whales sing long and complex songs. All these behaviours and sounds exhibit hierarchical structure-syllables and notes are positioned within words and musical phrases, words and motives in sentences and musical phrases, and so on. We developed a new method to measure and compare hierarchical temporal structures in speech, song and music. The method identifies temporal events as peaks in the sound amplitude envelope, and quantifies event clustering across a range of timescales using Allan factor (AF) variance. AF variances were analysed and compared for over 200 different recordings from more than 16 different categories of signals, including recordings of speech in different contexts and languages, musical compositions and performances from different genres. Non-human vocalizations from two bird species and two types of marine mammals were also analysed for comparison. The resulting patterns of AF variance across timescales were distinct to each of four natural categories of complex sound: speech, popular music, classical music and complex animal vocalizations. Comparisons within and across categories indicated that nested clustering in longer timescales was more prominent when prosodic variation was greater, and when sounds came from interactions among individuals, including interactions between speakers, musicians, and even killer whales. Nested clustering also was more prominent for music compared with speech, and reflected beat structure for popular music and self-similarity across timescales for classical music. In summary, hierarchical temporal structures reflect the behavioural and social processes underlying complex vocalizations and musical performances. © 2017 The Author(s).

Automated plant, production management system

NASA Astrophysics Data System (ADS)

Aksenova, V. I.; Belov, V. I.

1984-12-01

The development of a complex of tasks for the operational management of production (OUP) within the framework of an automated system for production management (ASUP) shows that it is impossible to have effective computations without reliable initial information. The influence of many factors involving the production and economic activity of the entire enterprise upon the plan and course of production are considered. It is suggested that an adequate model should be available which covers all levels of the hierarchical system: workplace, section (bridgade), shop, enterprise, and the model should be incorporated into the technological sequence of performance and there should be provisions for an adequate man machine system.

Hierarchical Model for the Analysis of Scattering Data of Complex Materials

DOE PAGES

Oyedele, Akinola; Mcnutt, Nicholas W.; Rios, Orlando; ...

2016-05-16

Interpreting the results of scattering data for complex materials with a hierarchical structure in which at least one phase is amorphous presents a significant challenge. Often the interpretation relies on the use of large-scale molecular dynamics (MD) simulations, in which a structure is hypothesized and from which a radial distribution function (RDF) can be extracted and directly compared against an experimental RDF. This computationally intensive approach presents a bottleneck in the efficient characterization of the atomic structure of new materials. Here, we propose and demonstrate an approach for a hierarchical decomposition of the RDF in which MD simulations are replacedmore » by a combination of tractable models and theory at the atomic scale and the mesoscale, which when combined yield the RDF. We apply the procedure to a carbon composite, in which graphitic nanocrystallites are distributed in an amorphous domain. We compare the model with the RDF from both MD simulation and neutron scattering data. Ultimately, this procedure is applicable for understanding the fundamental processing-structure-property relationships in complex magnetic materials.« less

A hierarchical approach to reliability modeling of fault-tolerant systems. M.S. Thesis

NASA Technical Reports Server (NTRS)

Gossman, W. E.

1986-01-01

A methodology for performing fault tolerant system reliability analysis is presented. The method decomposes a system into its subsystems, evaluates vent rates derived from the subsystem's conditional state probability vector and incorporates those results into a hierarchical Markov model of the system. This is done in a manner that addresses failure sequence dependence associated with the system's redundancy management strategy. The method is derived for application to a specific system definition. Results are presented that compare the hierarchical model's unreliability prediction to that of a more complicated tandard Markov model of the system. The results for the example given indicate that the hierarchical method predicts system unreliability to a desirable level of accuracy while achieving significant computational savings relative to component level Markov model of the system.

The Origin of Hierarchical Structure Formation in Highly Grafted Symmetric Supramolecular Double-Comb Diblock Copolymers.

PubMed

Hofman, Anton H; Reza, Mehedi; Ruokolainen, Janne; Ten Brinke, Gerrit; Loos, Katja

2017-09-01

Involving supramolecular chemistry in self-assembling block copolymer systems enables design of complex macromolecular architectures that, in turn, could lead to complex phase behavior. It is an elegant route, as complicated and sensitive synthesis techniques can be avoided. Highly grafted double-comb diblock copolymers based on symmetric double hydrogen bond accepting poly(4-vinylpyridine)-block-poly(N-acryloylpiperidine) diblock copolymers and donating 3-nonadecylphenol amphiphiles are realized and studied systematically by changing the molecular weight of the copolymer. Double perpendicular lamellae-in-lamellae are formed in all complexes, independent of the copolymer molecular weight. Temperature-resolved measurements demonstrate that the supramolecular nature and ability to crystallize are responsible for the formation of such multiblock-like structures. Because of these driving forces and severe plasticization of the complexes in the liquid crystalline state, this supramolecular approach can be useful for steering self-assembly of both low- and high-molecular-weight block copolymer systems. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

In-plane crashworthiness of bio-inspired hierarchical honeycombs

DOE PAGES

Yin, Hanfeng; Huang, Xiaofei; Scarpa, Fabrizio; ...

2018-03-13

Biological tissues like bone, wood, and sponge possess hierarchical cellular topologies, which are lightweight and feature an excellent energy absorption capability. Here we present a system of bio-inspired hierarchical honeycomb structures based on hexagonal, Kagome, and triangular tessellations. The hierarchical designs and a reference regular honeycomb configuration are subjected to simulated in-plane impact using the nonlinear finite element code LS-DYNA. The numerical simulation results show that the triangular hierarchical honeycomb provides the best performance compared to the other two hierarchical honeycombs, and features more than twice the energy absorbed by the regular honeycomb under similar loading conditions. We also proposemore » a parametric study correlating the microstructure parameters (hierarchical length ratio r and the number of sub cells N) to the energy absorption capacity of these hierarchical honeycombs. The triangular hierarchical honeycomb with N = 2 and r = 1/8 shows the highest energy absorption capacity among all the investigated cases, and this configuration could be employed as a benchmark for the design of future safety protective systems.« less

In-plane crashworthiness of bio-inspired hierarchical honeycombs

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

Yin, Hanfeng; Huang, Xiaofei; Scarpa, Fabrizio

Biological tissues like bone, wood, and sponge possess hierarchical cellular topologies, which are lightweight and feature an excellent energy absorption capability. Here we present a system of bio-inspired hierarchical honeycomb structures based on hexagonal, Kagome, and triangular tessellations. The hierarchical designs and a reference regular honeycomb configuration are subjected to simulated in-plane impact using the nonlinear finite element code LS-DYNA. The numerical simulation results show that the triangular hierarchical honeycomb provides the best performance compared to the other two hierarchical honeycombs, and features more than twice the energy absorbed by the regular honeycomb under similar loading conditions. We also proposemore » a parametric study correlating the microstructure parameters (hierarchical length ratio r and the number of sub cells N) to the energy absorption capacity of these hierarchical honeycombs. The triangular hierarchical honeycomb with N = 2 and r = 1/8 shows the highest energy absorption capacity among all the investigated cases, and this configuration could be employed as a benchmark for the design of future safety protective systems.« less

Understanding the biological underpinnings of ecohydrological processes

NASA Astrophysics Data System (ADS)

Huxman, T. E.; Scott, R. L.; Barron-Gafford, G. A.; Hamerlynck, E. P.; Jenerette, D.; Tissue, D. T.; Breshears, D. D.; Saleska, S. R.

2012-12-01

Climate change presents a challenge for predicting ecosystem response, as multiple factors drive both the physical and life processes happening on the land surface and their interactions result in a complex, evolving coupled system. For example, changes in surface temperature and precipitation influence near-surface hydrology through impacts on system energy balance, affecting a range of physical processes. These changes in the salient features of the environment affect biological processes and elicit responses along the hierarchy of life (biochemistry to community composition). Many of these structural or process changes can alter patterns of soil water-use and influence land surface characteristics that affect local climate. Of the many features that affect our ability to predict the future dynamics of ecosystems, it is this hierarchical response of life that creates substantial complexity. Advances in the ability to predict or understand aspects of demography help describe thresholds in coupled ecohydrological system. Disentangling the physical and biological features that underlie land surface dynamics following disturbance are allowing a better understanding of the partitioning of water in the time-course of recovery. Better predicting the timing of phenology and key seasonal events allow for a more accurate description of the full functional response of the land surface to climate. In addition, explicitly considering the hierarchical structural features of life are helping to describe complex time-dependent behavior in ecosystems. However, despite this progress, we have yet to build an ability to fully account for the generalization of the main features of living systems into models that can describe ecohydrological processes, especially acclimation, assembly and adaptation. This is unfortunate, given that many key ecosystem services are functions of these coupled co-evolutionary processes. To date, both the lack of controlled measurements and experimentation has precluded determination of sufficient theoretical development. Understanding the land-surface response and feedback to climate change requires a mechanistic understanding of the coupling of ecological and hydrological processes and an expansion of theory from the life sciences to appropriately contribute to the broader Earth system science goal.

Mathematical Modeling Of Life-Support Systems

NASA Technical Reports Server (NTRS)

Seshan, Panchalam K.; Ganapathi, Balasubramanian; Jan, Darrell L.; Ferrall, Joseph F.; Rohatgi, Naresh K.

1994-01-01

Generic hierarchical model of life-support system developed to facilitate comparisons of options in design of system. Model represents combinations of interdependent subsystems supporting microbes, plants, fish, and land animals (including humans). Generic model enables rapid configuration of variety of specific life support component models for tradeoff studies culminating in single system design. Enables rapid evaluation of effects of substituting alternate technologies and even entire groups of technologies and subsystems. Used to synthesize and analyze life-support systems ranging from relatively simple, nonregenerative units like aquariums to complex closed-loop systems aboard submarines or spacecraft. Model, called Generic Modular Flow Schematic (GMFS), coded in such chemical-process-simulation languages as Aspen Plus and expressed as three-dimensional spreadsheet.

Applying macromolecular crowding to 3D bioprinting: fabrication of 3D hierarchical porous collagen-based hydrogel constructs.

PubMed

Ng, Wei Long; Goh, Min Hao; Yeong, Wai Yee; Naing, May Win

2018-02-27

Native tissues and/or organs possess complex hierarchical porous structures that confer highly-specific cellular functions. Despite advances in fabrication processes, it is still very challenging to emulate the hierarchical porous collagen architecture found in most native tissues. Hence, the ability to recreate such hierarchical porous structures would result in biomimetic tissue-engineered constructs. Here, a single-step drop-on-demand (DOD) bioprinting strategy is proposed to fabricate hierarchical porous collagen-based hydrogels. Printable macromolecule-based bio-inks (polyvinylpyrrolidone, PVP) have been developed and printed in a DOD manner to manipulate the porosity within the multi-layered collagen-based hydrogels by altering the collagen fibrillogenesis process. The experimental results have indicated that hierarchical porous collagen structures could be achieved by controlling the number of macromolecule-based bio-ink droplets printed on each printed collagen layer. This facile single-step bioprinting process could be useful for the structural design of collagen-based hydrogels for various tissue engineering applications.

SBML Level 3 package: Hierarchical Model Composition, Version 1 Release 3

PubMed Central

Smith, Lucian P.; Hucka, Michael; Hoops, Stefan; Finney, Andrew; Ginkel, Martin; Myers, Chris J.; Moraru, Ion; Liebermeister, Wolfram

2017-01-01

Summary Constructing a model in a hierarchical fashion is a natural approach to managing model complexity, and offers additional opportunities such as the potential to re-use model components. The SBML Level 3 Version 1 Core specification does not directly provide a mechanism for defining hierarchical models, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The SBML Hierarchical Model Composition package for SBML Level 3 adds the necessary features to SBML to support hierarchical modeling. The package enables a modeler to include submodels within an enclosing SBML model, delete unneeded or redundant elements of that submodel, replace elements of that submodel with element of the containing model, and replace elements of the containing model with elements of the submodel. In addition, the package defines an optional “port” construct, allowing a model to be defined with suggested interfaces between hierarchical components; modelers can chose to use these interfaces, but they are not required to do so and can still interact directly with model elements if they so chose. Finally, the SBML Hierarchical Model Composition package is defined in such a way that a hierarchical model can be “flattened” to an equivalent, non-hierarchical version that uses only plain SBML constructs, thus enabling software tools that do not yet support hierarchy to nevertheless work with SBML hierarchical models. PMID:26528566

Tailored hierarchical micelle architectures using living crystallization-driven self-assembly in two dimensions

NASA Astrophysics Data System (ADS)

Hudson, Zachary M.; Boott, Charlotte E.; Robinson, Matthew E.; Rupar, Paul A.; Winnik, Mitchell A.; Manners, Ian

2014-10-01

Recent advances in the self-assembly of block copolymers have enabled the precise fabrication of hierarchical nanostructures using low-cost solution-phase protocols. However, the preparation of well-defined and complex planar nanostructures in which the size is controlled in two dimensions (2D) has remained a challenge. Using a series of platelet-forming block copolymers, we have demonstrated through quantitative experiments that the living crystallization-driven self-assembly (CDSA) approach can be extended to growth in 2D. We used 2D CDSA to prepare uniform lenticular platelet micelles of controlled size and to construct precisely concentric lenticular micelles composed of spatially distinct functional regions, as well as complex structures analogous to nanoscale single- and double-headed arrows and spears. These methods represent a route to hierarchical nanostructures that can be tailored in 2D, with potential applications as diverse as liquid crystals, diagnostic technology and composite reinforcement.

Coordinated Voltage Control of Transformer Taps on account of Hierarchical Structure in Power System

NASA Astrophysics Data System (ADS)

Nakachi, Yoshiki; Kato, Satoshi; Ukai, Hiroyuki

Participation of distributed generators (DG), such as wind turbines, co-generation system etc., is natural trend from ecological point of view and will increase more and more. The outputs of these DGs mainly depend on weather condition but don't correspond to the changes of electrical load demand necessarily. On the other hand, due to the deregulation of electric power market, the power flow in power system will uncertainly vary with several power transactions. Thus, complex power flow by DGs or transactions will cause the voltage deviation. It will be difficult to sustain the voltage quality by using the conventional voltage/reactive power control in near future. In this paper, in order to avoid such a voltage deviation and to decrease the frequency of transformer tap actions, the coordinated voltage control scheme of transformer taps on account of hierarchical structure in power system is proposed. In the proposed scheme, integral of voltage deviation at each layer bus is applied to decide the timing of each transformer tap action. It is confirmed by some numerical simulations that the proposed scheme is able to respond to every conditions on voltage deviation.

Space-Time Smoothing of Complex Survey Data: Small Area Estimation for Child Mortality

PubMed Central

Mercer, Laina D; Wakefield, Jon; Pantazis, Athena; Lutambi, Angelina M; Masanja, Honorati; Clark, Samuel

2016-01-01

Many people living in low and middle-income countries are not covered by civil registration and vital statistics systems. Consequently, a wide variety of other types of data including many household sample surveys are used to estimate health and population indicators. In this paper we combine data from sample surveys and demographic surveillance systems to produce small area estimates of child mortality through time. Small area estimates are necessary to understand geographical heterogeneity in health indicators when full-coverage vital statistics are not available. For this endeavor spatio-temporal smoothing is beneficial to alleviate problems of data sparsity. The use of conventional hierarchical models requires careful thought since the survey weights may need to be considered to alleviate bias due to non-random sampling and non-response. The application that motivated this work is estimation of child mortality rates in five-year time intervals in regions of Tanzania. Data come from Demographic and Health Surveys conducted over the period 1991–2010 and two demographic surveillance system sites. We derive a variance estimator of under five years child mortality that accounts for the complex survey weighting. For our application, the hierarchical models we consider include random effects for area, time and survey and we compare models using a variety of measures including the conditional predictive ordinate (CPO). The method we propose is implemented via the fast and accurate integrated nested Laplace approximation (INLA). PMID:27468328

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

Moore, Jason H; Amos, Ryan; Kiralis, Jeff; Andrews, Peter C

2015-01-01

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects. PMID:25395175

Efficiency of the energy transfer in the FMO complex using hierarchical equations on Graphics Processing Units

NASA Astrophysics Data System (ADS)

Kramer, Tobias; Kreisbeck, Christoph; Rodriguez, Mirta; Hein, Birgit

2011-03-01

We study the efficiency of the energy transfer in the Fenna-Matthews-Olson complex solving the non-Markovian hierarchical equations (HE) proposed by Ishizaki and Fleming in 2009, which include properly the reorganization process. We compare it to the Markovian approach and find that the Markovian dynamics overestimates the thermalization rate, yielding higher efficiencies than the HE. Using the high-performance of graphics processing units (GPU) we cover a large range of reorganization energies and temperatures and find that initial quantum beatings are important for the energy distribution, but of limited influence to the efficiency. Our efficient GPU implementation of the HE allows us to calculate nonlinear spectra of the FMO complex. References see www.quantumdynamics.de

Application of a hierarchical habitat unit classification system: stream habitat and salmonid distribution in Ward Creek, southeast Alaska.

Treesearch

M.D. Bryant; B.E. Wright; B.J. Davies

1992-01-01

A hierarchical classification system separating stream habitat into habitat units defined by stream morphology and hydrology was used in a pre-enhancement stream survey. The system separates habitat units into macrounits, mesounits, and micro- units and includes a separate evaluation of instream cover that also uses the hierarchical scheme. This paper presents an...

Accurate crop classification using hierarchical genetic fuzzy rule-based systems

NASA Astrophysics Data System (ADS)

Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

2014-10-01

This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

Graph-theoretic quantum system modelling for neuronal microtubules as hierarchical clustered quantum Hopfield networks

NASA Astrophysics Data System (ADS)

Srivastava, D. P.; Sahni, V.; Satsangi, P. S.

2014-08-01

Graph-theoretic quantum system modelling (GTQSM) is facilitated by considering the fundamental unit of quantum computation and information, viz. a quantum bit or qubit as a basic building block. Unit directional vectors "ket 0" and "ket 1" constitute two distinct fundamental quantum across variable orthonormal basis vectors, for the Hilbert space, specifying the direction of propagation of information, or computation data, while complementary fundamental quantum through, or flow rate, variables specify probability parameters, or amplitudes, as surrogates for scalar quantum information measure (von Neumann entropy). This paper applies GTQSM in continuum of protein heterodimer tubulin molecules of self-assembling polymers, viz. microtubules in the brain as a holistic system of interacting components representing hierarchical clustered quantum Hopfield network, hQHN, of networks. The quantum input/output ports of the constituent elemental interaction components, or processes, of tunnelling interactions and Coulombic bidirectional interactions are in cascade and parallel interconnections with each other, while the classical output ports of all elemental components are interconnected in parallel to accumulate micro-energy functions generated in the system as Hamiltonian, or Lyapunov, energy function. The paper presents an insight, otherwise difficult to gain, for the complex system of systems represented by clustered quantum Hopfield network, hQHN, through the application of GTQSM construct.

Collective helicity switching of a DNA-coat assembly

NASA Astrophysics Data System (ADS)

Kim, Yongju; Li, Huichang; He, Ying; Chen, Xi; Ma, Xiaoteng; Lee, Myongsoo

2017-07-01

Hierarchical assemblies of biomolecular subunits can carry out versatile tasks at the cellular level with remarkable spatial and temporal precision. As an example, the collective motion and mutual cooperation between complex protein machines mediate essential functions for life, such as replication, synthesis, degradation, repair and transport. Nucleic acid molecules are far less dynamic than proteins and need to bind to specific proteins to form hierarchical structures. The simplest example of these nucleic acid-based structures is provided by a rod-shaped tobacco mosaic virus, which consists of genetic material surrounded by coat proteins. Inspired by the complexity and hierarchical assembly of viruses, a great deal of effort has been devoted to design similarly constructed artificial viruses. However, such a wrapping approach makes nucleic acid dynamics insensitive to environmental changes. This limitation generally restricts, for example, the amplification of the conformational dynamics between the right-handed B form to the left-handed Z form of double-stranded deoxyribonucleic acid (DNA). Here we report a virus-like hierarchical assembly in which the native DNA and a synthetic coat undergo repeated collective helicity switching triggered by pH change under physiological conditions. We also show that this collective helicity inversion occurs during translocation of the DNA-coat assembly into intracellular compartments. Translating DNA conformational dynamics into a higher level of hierarchical dynamics may provide an approach to create DNA-based nanomachines.

Structural Group-based Auditing of Missing Hierarchical Relationships in UMLS

PubMed Central

Chen, Yan; Gu, Huanying(Helen); Perl, Yehoshua; Geller, James

2009-01-01

The Metathesaurus of the UMLS was created by integrating various source terminologies. The inter-concept relationships were either integrated into the UMLS from the source terminologies or specially generated. Due to the extensive size and inherent complexity of the Metathesaurus, the accidental omission of some hierarchical relationships was inevitable. We present a recursive procedure which allows a human expert, with the support of an algorithm, to locate missing hierarchical relationships. The procedure starts with a group of concepts with exactly the same (correct) semantic type assignments. It then partitions the concepts, based on child-of hierarchical relationships, into smaller, singly rooted, hierarchically connected subgroups. The auditor only needs to focus on the subgroups with very few concepts and their concepts with semantic type reassignments. The procedure was evaluated by comparing it with a comprehensive manual audit and it exhibits a perfect error recall. PMID:18824248

Predictability of extremes in non-linear hierarchically organized systems

NASA Astrophysics Data System (ADS)

Kossobokov, V. G.; Soloviev, A.

2011-12-01

Understanding the complexity of non-linear dynamics of hierarchically organized systems progresses to new approaches in assessing hazard and risk of the extreme catastrophic events. In particular, a series of interrelated step-by-step studies of seismic process along with its non-stationary though self-organized behaviors, has led already to reproducible intermediate-term middle-range earthquake forecast/prediction technique that has passed control in forward real-time applications during the last two decades. The observed seismic dynamics prior to and after many mega, great, major, and strong earthquakes demonstrate common features of predictability and diverse behavior in course durable phase transitions in complex hierarchical non-linear system of blocks-and-faults of the Earth lithosphere. The confirmed fractal nature of earthquakes and their distribution in space and time implies that many traditional estimations of seismic hazard (from term-less to short-term ones) are usually based on erroneous assumptions of easy tractable analytical models, which leads to widespread practice of their deceptive application. The consequences of underestimation of seismic hazard propagate non-linearly into inflicted underestimation of risk and, eventually, into unexpected societal losses due to earthquakes and associated phenomena (i.e., collapse of buildings, landslides, tsunamis, liquefaction, etc.). The studies aimed at forecast/prediction of extreme events (interpreted as critical transitions) in geophysical and socio-economical systems include: (i) large earthquakes in geophysical systems of the lithosphere blocks-and-faults, (ii) starts and ends of economic recessions, (iii) episodes of a sharp increase in the unemployment rate, (iv) surge of the homicides in socio-economic systems. These studies are based on a heuristic search of phenomena preceding critical transitions and application of methodologies of pattern recognition of infrequent events. Any study of rare phenomena of highly complex origin, by their nature, implies using problem oriented methods, which design breaks the limits of classical statistical or econometric applications. The unambiguously designed forecast/prediction algorithms of the "yes or no" variety, analyze the observable quantitative integrals and indicators available to a given date, then provides unambiguous answer to the question whether a critical transition should be expected or not in the next time interval. Since the predictability of an originating non-linear dynamical system is limited in principle, the probabilistic component of forecast/prediction algorithms is represented by the empirical probabilities of alarms, on one side, and failures-to-predict, on the other, estimated on control sets achieved in the retro- and prospective experiments. Predicting in advance is the only decisive test of forecast/predictions and the relevant on-going experiments are conducted in the case seismic extremes, recessions, and increases of unemployment rate. The results achieved in real-time testing keep being encouraging and confirm predictability of the extremes.

A linguistic geometry for 3D strategic planning

NASA Technical Reports Server (NTRS)

Stilman, Boris

1995-01-01

This paper is a new step in the development and application of the Linguistic Geometry. This formal theory is intended to discover the inner properties of human expert heuristics, which have been successful in a certain class of complex control systems, and apply them to different systems. In this paper we investigate heuristics extracted in the form of hierarchical networks of planning paths of autonomous agents. Employing Linguistic Geometry tools the dynamic hierarchy of networks is represented as a hierarchy of formal attribute languages. The main ideas of this methodology are shown in this paper on the new pilot example of the solution of the extremely complex 3D optimization problem of strategic planning for the space combat of autonomous vehicles. This example demonstrates deep and highly selective search in comparison with conventional search algorithms.

Scale of association: hierarchical linear models and the measurement of ecological systems

Treesearch

Sean M. McMahon; Jeffrey M. Diez

2007-01-01

A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance-covariance parameters in hierarchically structured...

Hierarchical ferroelectric and ferrotoroidic polarizations coexistent in nano-metamaterials

PubMed Central

Shimada, Takahiro; Lich, Le Van; Nagano, Koyo; Wang, Jie; Kitamura, Takayuki

2015-01-01

Tailoring materials to obtain unique, or significantly enhanced material properties through rationally designed structures rather than chemical constituents is principle of metamaterial concept, which leads to the realization of remarkable optical and mechanical properties. Inspired by the recent progress in electromagnetic and mechanical metamaterials, here we introduce the concept of ferroelectric nano-metamaterials, and demonstrate through an experiment in silico with hierarchical nanostructures of ferroelectrics using sophisticated real-space phase-field techniques. This new concept enables variety of unusual and complex yet controllable domain patterns to be achieved, where the coexistence between hierarchical ferroelectric and ferrotoroidic polarizations establishes a new benchmark for exploration of complexity in spontaneous polarization ordering. The concept opens a novel route to effectively tailor domain configurations through the control of internal structure, facilitating access to stabilization and control of complex domain patterns that provide high potential for novel functionalities. A key design parameter to achieve such complex patterns is explored based on the parity of junctions that connect constituent nanostructures. We further highlight the variety of additional functionalities that are potentially obtained from ferroelectric nano-metamaterials, and provide promising perspectives for novel multifunctional devices. This study proposes an entirely new discipline of ferroelectric nano-metamaterials, further driving advances in metamaterials research. PMID:26424484

Reaction-diffusion controlled growth of complex structures

NASA Astrophysics Data System (ADS)

Noorduin, Willem; Mahadevan, L.; Aizenberg, Joanna

2013-03-01

Understanding how the emergence of complex forms and shapes in biominerals came about is both of fundamental and practical interest. Although biomineralization processes and organization strategies to give higher order architectures have been studied extensively, synthetic approaches to mimic these self-assembled structures are highly complex and have been difficult to emulate, let alone replicate. The emergence of solution patterns has been found in reaction-diffusion systems such as Turing patterns and the BZ reaction. Intrigued by this spontaneous formation of complexity we explored if similar processes can lead to patterns in the solid state. We here identify a reaction-diffusion system in which the shape of the solidified products is a direct readout of the environmental conditions. Based on insights in the underlying mechanism, we developed a toolbox of engineering strategies to deterministically sculpt patterns and shapes, and combine different morphologies to create a landscape of hierarchical multi scale-complex tectonic architectures with unprecedented levels of complexity. These findings may hold profound implications for understanding, mimicking and ultimately expanding upon nature's morphogenesis strategies, allowing the synthesis of advanced highly complex microscale materials and devices. WLN acknowledges the Netherlands Organization for Scientific Research for financial support

Systemic Darwinism

PubMed Central

Winther, Rasmus Grønfeldt

2008-01-01

Darwin's 19th century evolutionary theory of descent with modification through natural selection opened up a multidimensional and integrative conceptual space for biology. We explore three dimensions of this space: explanatory pattern, levels of selection, and degree of difference among units of the same type. Each dimension is defined by a respective pair of poles: law and narrative explanation, organismic and hierarchical selection, and variational and essentialist thinking. As a consequence of conceptual debates in the 20th century biological sciences, the poles of each pair came to be seen as mutually exclusive opposites. A significant amount of 21st century research focuses on systems (e.g., genomic, cellular, organismic, and ecological/global). Systemic Darwinism is emerging in this context. It follows a “compositional paradigm” according to which complex systems and their hierarchical networks of parts are the focus of biological investigation. Through the investigation of systems, Systemic Darwinism promises to reintegrate each dimension of Darwin's original logical space. Moreover, this ideally and potentially unified theory of biological ontology coordinates and integrates a plurality of mathematical biological theories (e.g., self-organization/structure, cladistics/history, and evolutionary genetics/function). Integrative Systemic Darwinism requires communal articulation from a plurality of perspectives. Although it is more general than these, it draws on previous advances in Systems Theory, Systems Biology, and Hierarchy Theory. Systemic Darwinism would greatly further bioengineering research and would provide a significantly deeper and more critical understanding of biological reality. PMID:18697926

Systemic darwinism.

PubMed

Winther, Rasmus Grønfeldt

2008-08-19

Darwin's 19th century evolutionary theory of descent with modification through natural selection opened up a multidimensional and integrative conceptual space for biology. We explore three dimensions of this space: explanatory pattern, levels of selection, and degree of difference among units of the same type. Each dimension is defined by a respective pair of poles: law and narrative explanation, organismic and hierarchical selection, and variational and essentialist thinking. As a consequence of conceptual debates in the 20th century biological sciences, the poles of each pair came to be seen as mutually exclusive opposites. A significant amount of 21st century research focuses on systems (e.g., genomic, cellular, organismic, and ecological/global). Systemic Darwinism is emerging in this context. It follows a "compositional paradigm" according to which complex systems and their hierarchical networks of parts are the focus of biological investigation. Through the investigation of systems, Systemic Darwinism promises to reintegrate each dimension of Darwin's original logical space. Moreover, this ideally and potentially unified theory of biological ontology coordinates and integrates a plurality of mathematical biological theories (e.g., self-organization/structure, cladistics/history, and evolutionary genetics/function). Integrative Systemic Darwinism requires communal articulation from a plurality of perspectives. Although it is more general than these, it draws on previous advances in Systems Theory, Systems Biology, and Hierarchy Theory. Systemic Darwinism would greatly further bioengineering research and would provide a significantly deeper and more critical understanding of biological reality.

Metrics required for Power System Resilient Operations and Protection

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

Eshghi, K.; Johnson, B. K.; Rieger, C. G.

Today’s complex grid involves many interdependent systems. Various layers of hierarchical control and communication systems are coordinated, both spatially and temporally to achieve gird reliability. As new communication network based control system technologies are being deployed, the interconnected nature of these systems is becoming more complex. Deployment of smart grid concepts promises effective integration of renewable resources, especially if combined with energy storage. However, without a philosophical focus on resilience, a smart grid will potentially lead to higher magnitude and/or duration of disruptive events. The effectiveness of a resilient infrastructure depends upon its ability to anticipate, absorb, adapt to, and/ormore » rapidly recover from a potentially catastrophic event. Future system operations can be enhanced with a resilient philosophy through architecting the complexity with state awareness metrics that recognize changing system conditions and provide for an agile and adaptive response. The starting point for metrics lies in first understanding the attributes of performance that will be qualified. In this paper, we will overview those attributes and describe how they will be characterized by designing a distributed agent that can be applied to the power grid.« less

An integration architecture for the automation of a continuous production complex.

PubMed

Chacón, Edgar; Besembel, Isabel; Narciso, Flor; Montilva, Jonás; Colina, Eliezer

2002-01-01

The development of integrated automation systems for continuous production plants is a very complicated process. A variety of factors must be taken into account, such as their different components (e.g., production units control systems, planning systems, financial systems, etc.), the interaction among them, and their different behavior (continuous or discrete). Moreover, the difficulty of this process is increased by the fact that each component can be viewed in a different way depending on the kind of decisions to be made, and its specific behavior. Modeling continuous production complexes as a composition of components, where, in turn, each component may also be a composite, appears to be the simplest and safest way to develop integrated automation systems. In order to provide the most versatile way to develop this kind of system, this work proposes a new approach for designing and building them, where process behavior, operation conditions and equipment conditions are integrated into a hierarchical automation architecture.

Hierarchical and Complex System Entropy Clustering Analysis Based Validation for Traditional Chinese Medicine Syndrome Patterns of Chronic Atrophic Gastritis.

PubMed

Zhang, Yin; Liu, Yue; Li, Yannan; Zhao, Xia; Zhuo, Lin; Zhou, Ajian; Zhang, Li; Su, Zeqi; Chen, Cen; Du, Shiyu; Liu, Daming; Ding, Xia

2018-03-22

Chronic atrophic gastritis (CAG) is the precancerous stage of gastric carcinoma. Traditional Chinese Medicine (TCM) has been widely used in treating CAG. This study aimed to reveal core pathogenesis of CAG by validating the TCM syndrome patterns and provide evidence for optimization of treatment strategies. This is a cross-sectional study conducted in 4 hospitals in China. Hierarchical clustering analysis (HCA) and complex system entropy clustering analysis (CSECA) were performed, respectively, to achieve syndrome pattern validation. Based on HCA, 15 common factors were assigned to 6 syndrome patterns: liver depression and spleen deficiency and blood stasis in the stomach collateral, internal harassment of phlegm-heat and blood stasis in the stomach collateral, phlegm-turbidity internal obstruction, spleen yang deficiency, internal harassment of phlegm-heat and spleen deficiency, and spleen qi deficiency. By CSECA, 22 common factors were assigned to 7 syndrome patterns: qi deficiency, qi stagnation, blood stasis, phlegm turbidity, heat, yang deficiency, and yin deficiency. Combination of qi deficiency, qi stagnation, blood stasis, phlegm turbidity, heat, yang deficiency, and yin deficiency may play a crucial role in CAG pathogenesis. In accord with this, treatment strategies by TCM herbal prescriptions should be targeted to regulating qi, activating blood, resolving turbidity, clearing heat, removing toxin, nourishing yin, and warming yang. Further explorations are needed to verify and expand the current conclusions.

Estimation of polarization distribution on gold nanorods system from hierarchical features of optical near-field

NASA Astrophysics Data System (ADS)

Uchiyama, Kazuharu; Nishikawa, Naoki; Nakagomi, Ryo; Kobayashi, Kiyoshi; Hori, Hirokazu

2018-02-01

To design optoelectronic functionalities in nanometer scale based on interactions of electronic system with optical near-fields, it is essential to evaluate the relationship between optical near-fields and their sources. Several theoretical studies have been performed, so far, to analyze such complex relationship to design the interaction fields of several specific scales. In this study, we have performed detailed and high-precision measurements of optical near-field structures woven by a large number of independent polarizations generated in the gold nanorods array under laser light irradiation at the resonant frequency. We have accumulated the multi-layered data of optical near-field imaging at different heights above the planar surface with the resolution of several nm by a STM-assisted scanning near-field optical microscope. Based on these data, we have performed an inverse calculation to estimate the position, direction, and strength of the local polarization buried under the flat surface of the sample. As a result of the inverse operation, we have confirmed that the complexities in the nanometer scale optical near-fields could be reconstructed by combinations of induced polarization in each gold nanorod. We have demonstrated the hierarchical properties of optical near-fields based on spatial frequency expansion and superposition of dipole fields to provide insightful information for applications such for secure multi-layered information storage.

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring

Treesearch

Carlos Carroll; Devin S. Johnson; Jeffrey R. Dunk; William J. Zielinski

2010-01-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data’s spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and...

Hierarchical nanostructures for functional materials.

PubMed

Qin, Zhao; Buehler, Markus J

2018-07-13

Naturally occurring biomaterials often have amazing functions, such as mechanical, thermal, electromagnetic, biological, optical and acoustic. These superior performances are often due to their hierarchical organizations of natural materials, starting from the nanoscopic scale and extending all the way to the macroscopic level. This topical issue features articles dedicated to understanding, designing and characterizing complex de novo hierarchical materials for a variety of applications. This research area is quickly evolving, and we hope that future work will drive the rational designs of innovative functional materials and generate deep impacts to broad engineering fields that address major societal challenges and needs.

Hierarchical nanostructures for functional materials

NASA Astrophysics Data System (ADS)

Qin, Zhao; Buehler, Markus J.

2018-07-01

Naturally occurring biomaterials often have amazing functions, such as mechanical, thermal, electromagnetic, biological, optical and acoustic. These superior performances are often due to their hierarchical organizations of natural materials, starting from the nanoscopic scale and extending all the way to the macroscopic level. This topical issue features articles dedicated to understanding, designing and characterizing complex de novo hierarchical materials for a variety of applications. This research area is quickly evolving, and we hope that future work will drive the rational designs of innovative functional materials and generate deep impacts to broad engineering fields that address major societal challenges and needs.

Assessing Complexity in Learning Outcomes--A Comparison between the SOLO Taxonomy and the Model of Hierarchical Complexity

ERIC Educational Resources Information Center

Stålne, Kristian; Kjellström, Sofia; Utriainen, Jukka

2016-01-01

An important aspect of higher education is to educate students who can manage complex relationships and solve complex problems. Teachers need to be able to evaluate course content with regard to complexity, as well as evaluate students' ability to assimilate complex content and express it in the form of a learning outcome. One model for evaluating…

Retrieval Capabilities of Hierarchical Networks: From Dyson to Hopfield

NASA Astrophysics Data System (ADS)

Agliari, Elena; Barra, Adriano; Galluzzi, Andrea; Guerra, Francesco; Tantari, Daniele; Tavani, Flavia

2015-01-01

We consider statistical-mechanics models for spin systems built on hierarchical structures, which provide a simple example of non-mean-field framework. We show that the coupling decay with spin distance can give rise to peculiar features and phase diagrams much richer than their mean-field counterpart. In particular, we consider the Dyson model, mimicking ferromagnetism in lattices, and we prove the existence of a number of metastabilities, beyond the ordered state, which become stable in the thermodynamic limit. Such a feature is retained when the hierarchical structure is coupled with the Hebb rule for learning, hence mimicking the modular architecture of neurons, and gives rise to an associative network able to perform single pattern retrieval as well as multiple-pattern retrieval, depending crucially on the external stimuli and on the rate of interaction decay with distance; however, those emergent multitasking features reduce the network capacity with respect to the mean-field counterpart. The analysis is accomplished through statistical mechanics, Markov chain theory, signal-to-noise ratio technique, and numerical simulations in full consistency. Our results shed light on the biological complexity shown by real networks, and suggest future directions for understanding more realistic models.

Glassy nature of hierarchical organizations.

PubMed

Zamani, Maryam; Vicsek, Tamas

2017-05-03

The question of why and how animal and human groups form temporarily stable hierarchical organizations has long been a great challenge from the point of quantitative interpretations. The prevailing observation/consensus is that a hierarchical social or technological structure is optimal considering a variety of aspects. Here we introduce a simple quantitative interpretation of this situation using a statistical mechanics-type approach. We look for the optimum of the efficiency function [Formula: see text] with J ij denoting the nature of the interaction between the units i and j and a i standing for the ability of member i to contribute to the efficiency of the system. Notably, this expression for E eff has a similar structure to that of the energy as defined for spin-glasses. Unconventionally, we assume that J ij -s can have the values 0 (no interaction), +1 and -1; furthermore, a direction is associated with each edge. The essential and novel feature of our approach is that instead of optimizing the state of the nodes of a pre-defined network, we search for extrema for given a i -s in the complex efficiency landscape by finding locally optimal network topologies for a given number of edges of the subgraphs considered.

Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

NASA Astrophysics Data System (ADS)

Alshehhi, Rasha; Marpu, Prashanth Reddy

2017-04-01

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical kriging

NASA Astrophysics Data System (ADS)

Hu, Jiexiang; Zhou, Qi; Jiang, Ping; Shao, Xinyu; Xie, Tingli

2018-01-01

Variable-fidelity (VF) modelling methods have been widely used in complex engineering system design to mitigate the computational burden. Building a VF model generally includes two parts: design of experiments and metamodel construction. In this article, an adaptive sampling method based on improved hierarchical kriging (ASM-IHK) is proposed to refine the improved VF model. First, an improved hierarchical kriging model is developed as the metamodel, in which the low-fidelity model is varied through a polynomial response surface function to capture the characteristics of a high-fidelity model. Secondly, to reduce local approximation errors, an active learning strategy based on a sequential sampling method is introduced to make full use of the already required information on the current sampling points and to guide the sampling process of the high-fidelity model. Finally, two numerical examples and the modelling of the aerodynamic coefficient for an aircraft are provided to demonstrate the approximation capability of the proposed approach, as well as three other metamodelling methods and two sequential sampling methods. The results show that ASM-IHK provides a more accurate metamodel at the same simulation cost, which is very important in metamodel-based engineering design problems.

An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

NASA Astrophysics Data System (ADS)

Zhang, Guannan; Lu, Dan; Ye, Ming; Gunzburger, Max; Webster, Clayton

2013-10-01

Bayesian analysis has become vital to uncertainty quantification in groundwater modeling, but its application has been hindered by the computational cost associated with numerous model executions required by exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, a new approach is developed to improve the computational efficiency of Bayesian inference by constructing a surrogate of the PPDF, using an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, this paper utilizes a compactly supported higher-order hierarchical basis to construct the surrogate system, resulting in a significant reduction in the number of required model executions. In addition, using the hierarchical surplus as an error indicator allows locally adaptive refinement of sparse grids in the parameter space, which further improves computational efficiency. To efficiently build the surrogate system for the PPDF with multiple significant modes, optimization techniques are used to identify the modes, for which high-probability regions are defined and components of the aSG-hSC approximation are constructed. After the surrogate is determined, the PPDF can be evaluated by sampling the surrogate system directly without model execution, resulting in improved efficiency of the surrogate-based MCMC compared with conventional MCMC. The developed method is evaluated using two synthetic groundwater reactive transport models. The first example involves coupled linear reactions and demonstrates the accuracy of our high-order hierarchical basis approach in approximating high-dimensional posteriori distribution. The second example is highly nonlinear because of the reactions of uranium surface complexation, and demonstrates how the iterative aSG-hSC method is able to capture multimodal and non-Gaussian features of PPDF caused by model nonlinearity. Both experiments show that aSG-hSC is an effective and efficient tool for Bayesian inference.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

PubMed

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

Using AI and Semantic Web Technologies to attack Process Complexity in Open Systems

NASA Astrophysics Data System (ADS)

Thompson, Simon; Giles, Nick; Li, Yang; Gharib, Hamid; Nguyen, Thuc Duong

Recently many vendors and groups have advocated using BPEL and WS-BPEL as a workflow language to encapsulate business logic. While encapsulating workflow and process logic in one place is a sensible architectural decision the implementation of complex workflows suffers from the same problems that made managing and maintaining hierarchical procedural programs difficult. BPEL lacks constructs for logical modularity such as the requirements construct from the STL [12] or the ability to adapt constructs like pure abstract classes for the same purpose. We describe a system that uses semantic web and agent concepts to implement an abstraction layer for BPEL based on the notion of Goals and service typing. AI planning was used to enable process engineers to create and validate systems that used services and goals as first class concepts and compiled processes at run time for execution.

Dynamical states, possibilities and propagation of stress signal

PubMed Central

Malik, Md. Zubbair; Ali, Shahnawaz; Singh, Soibam Shyamchand; Ishrat, Romana; Singh, R. K. Brojen

2017-01-01

The stress driven dynamics of Notch-Wnt-p53 cross-talk is subjected to a few possible dynamical states governed by simple fractal rules, and allowed to decide its own fate by choosing one of these states which are contributed from long range correlation with varied fluctuations due to active molecular interaction. The topological properties of the networks corresponding to these dynamical states have hierarchical features with assortive structure. The stress signal driven by nutlin and modulated by mediator GSK3 acts as anti-apoptotic signal in this system, whereas, the stress signal driven by Axin and modulated by GSK3 behaves as anti-apoptotic for a certain range of Axin and GSK3 interaction, and beyond which the signal acts as favor-apoptotic signal. However, this stress system prefers to stay in an active dynamical state whose counterpart complex network is closest to hierarchical topology with exhibited roles of few interacting hubs. During the propagation of stress signal, the system allows the propagator pathway to inherit all possible properties of the state to the receiver pathway/pathways with slight modifications, indicating efficient information processing and democratic sharing of responsibilities in the system via cross-talk. The increase in the number of cross-talk pathways in the system favors to establish self-organization. PMID:28106087

Dynamical states, possibilities and propagation of stress signal.

PubMed

Malik, Md Zubbair; Ali, Shahnawaz; Singh, Soibam Shyamchand; Ishrat, Romana; Singh, R K Brojen

2017-01-20

The stress driven dynamics of Notch-Wnt-p53 cross-talk is subjected to a few possible dynamical states governed by simple fractal rules, and allowed to decide its own fate by choosing one of these states which are contributed from long range correlation with varied fluctuations due to active molecular interaction. The topological properties of the networks corresponding to these dynamical states have hierarchical features with assortive structure. The stress signal driven by nutlin and modulated by mediator GSK3 acts as anti-apoptotic signal in this system, whereas, the stress signal driven by Axin and modulated by GSK3 behaves as anti-apoptotic for a certain range of Axin and GSK3 interaction, and beyond which the signal acts as favor-apoptotic signal. However, this stress system prefers to stay in an active dynamical state whose counterpart complex network is closest to hierarchical topology with exhibited roles of few interacting hubs. During the propagation of stress signal, the system allows the propagator pathway to inherit all possible properties of the state to the receiver pathway/pathways with slight modifications, indicating efficient information processing and democratic sharing of responsibilities in the system via cross-talk. The increase in the number of cross-talk pathways in the system favors to establish self-organization.

Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models

PubMed Central

Chen, Yang; Shen, Kuang

2017-01-01

To maintain proper cellular functions, over 50% of proteins encoded in the genome need to be transported to cellular membranes. The molecular mechanism behind such a process, often referred to as protein targeting, is not well understood. Single-molecule experiments are designed to unveil the detailed mechanisms and reveal the functions of different molecular machineries involved in the process. The experimental data consist of hundreds of stochastic time traces from the fluorescence recordings of the experimental system. We introduce a Bayesian hierarchical model on top of hidden Markov models (HMMs) to analyze these data and use the statistical results to answer the biological questions. In addition to resolving the biological puzzles and delineating the regulating roles of different molecular complexes, our statistical results enable us to propose a more detailed mechanism for the late stages of the protein targeting process. PMID:28943680

MX Hierarchical Networking System.

DTIC Science & Technology

1982-02-01

criteria considered in this report are defined as follows: 1. Single Network Limitation. Will the scheme allow the maximum size network anticipated to be...AD-AI16 758 CONSTRUCTION ENGINEERING RESEARCH LAB (ARMY) CHAMPAIGN IL F/G 5/2 MX HIERARCHICAL NETWORKING SYSTEM.(U) FEB 82 M O-CONNOR, L M SOLISH, L...laboratory__ _ _ _ _ _ _ _ _ _ _ _ MX HIERARCHICAL NETWORKING SYSTEM by Michael O’Connor L. Michael Golish Lee Boyer IsI Approved for public

Hierarchical structural health monitoring system combining a fiber optic spinal cord network and distributed nerve cell devices

NASA Astrophysics Data System (ADS)

Minakuchi, Shu; Tsukamoto, Haruka; Takeda, Nobuo

2009-03-01

This study proposes novel hierarchical sensing concept for detecting damages in composite structures. In the hierarchical system, numerous three-dimensionally structured sensor devices are distributed throughout the whole structural area and connected with the optical fiber network through transducing mechanisms. The distributed "sensory nerve cell" devices detect the damage, and the fiber optic "spinal cord" network gathers damage signals and transmits the information to a measuring instrument. This study began by discussing the basic concept of the hierarchical sensing system thorough comparison with existing fiber optic based systems and nerve systems in the animal kingdom. Then, in order to validate the proposed sensing concept, impact damage detection system for the composite structure was proposed. The sensor devices were developed based on Comparative Vacuum Monitoring (CVM) system and the Brillouin based distributed strain sensing was utilized to gather the damage signals from the distributed devices. Finally a verification test was conducted using prototype devices. Occurrence of barely visible impact damage was successfully detected and it was clearly indicated that the hierarchical system has better repairability, higher robustness, and wider monitorable area compared to existing systems utilizing embedded optical fiber sensors.

Holonic Rationale and Bio-inspiration on Design of Complex Emergent and Evolvable Systems

NASA Astrophysics Data System (ADS)

Leitao, Paulo

Traditional centralized and rigid control structures are becoming inflexible to face the requirements of reconfigurability, responsiveness and robustness, imposed by customer demands in the current global economy. The Holonic Manufacturing Systems (HMS) paradigm, which was pointed out as a suitable solution to face these requirements, translates the concepts inherited from social organizations and biology to the manufacturing world. It offers an alternative way of designing adaptive systems where the traditional centralized control is replaced by decentralization over distributed and autonomous entities organized in hierarchical structures formed by intermediate stable forms. In spite of its enormous potential, methods regarding the self-adaptation and self-organization of complex systems are still missing. This paper discusses how the insights from biology in connection with new fields of computer science can be useful to enhance the holonic design aiming to achieve more self-adaptive and evolvable systems. Special attention is devoted to the discussion of emergent behavior and self-organization concepts, and the way they can be combined with the holonic rationale.

An intelligent decomposition approach for efficient design of non-hierarchic systems

NASA Technical Reports Server (NTRS)

Bloebaum, Christina L.

1992-01-01

The design process associated with large engineering systems requires an initial decomposition of the complex systems into subsystem modules which are coupled through transference of output data. The implementation of such a decomposition approach assumes the ability exists to determine what subsystems and interactions exist and what order of execution will be imposed during the analysis process. Unfortunately, this is quite often an extremely complex task which may be beyond human ability to efficiently achieve. Further, in optimizing such a coupled system, it is essential to be able to determine which interactions figure prominently enough to significantly affect the accuracy of the optimal solution. The ability to determine 'weak' versus 'strong' coupling strengths would aid the designer in deciding which couplings could be permanently removed from consideration or which could be temporarily suspended so as to achieve computational savings with minimal loss in solution accuracy. An approach that uses normalized sensitivities to quantify coupling strengths is presented. The approach is applied to a coupled system composed of analysis equations for verification purposes.

Panarchy use in environmental science for risk and resilience planning

USGS Publications Warehouse

Angeler, David G.; Allen, Craig R.; Garmestani, Ahjond S.; Gunderson, Lance H.; Linkov, Igor

2016-01-01

Environmental sciences have an important role in informing sustainable management of built environments by providing insights about the drivers and potentially negative impacts of global environmental change. Here, we discuss panarchy theory, a multi-scale hierarchical concept that accounts for the dynamism of complex socio-ecological systems, especially for those systems with strong cross-scale feedbacks. The idea of panarchy underlies much of system resilience, focusing on how systems respond to known and unknown threats. Panarchy theory can provide a framework for qualitative and quantitative research and application in the environmental sciences, which can in turn inform the ongoing efforts in socio-technical resilience thinking and adaptive and transformative approaches to management.

Aging, memory, and nonhierarchical energy landscape of spin jam

NASA Astrophysics Data System (ADS)

Samarakoon, Anjana; Sato, Taku J.; Chen, Tianran; Chern, Gai-Wei; Yang, Junjie; Klich, Israel; Sinclair, Ryan; Zhou, Haidong; Lee, Seung-Hun

2016-10-01

The notion of complex energy landscape underpins the intriguing dynamical behaviors in many complex systems ranging from polymers, to brain activity, to social networks and glass transitions. The spin glass state found in dilute magnetic alloys has been an exceptionally convenient laboratory frame for studying complex dynamics resulting from a hierarchical energy landscape with rugged funnels. Here, we show, by a bulk susceptibility and Monte Carlo simulation study, that densely populated frustrated magnets in a spin jam state exhibit much weaker memory effects than spin glasses, and the characteristic properties can be reproduced by a nonhierarchical landscape with a wide and nearly flat but rough bottom. Our results illustrate that the memory effects can be used to probe different slow dynamics of glassy materials, hence opening a window to explore their distinct energy landscapes.

Coarse-grained molecular dynamics simulations for giant protein-DNA complexes

NASA Astrophysics Data System (ADS)

Takada, Shoji

Biomolecules are highly hierarchic and intrinsically flexible. Thus, computational modeling calls for multi-scale methodologies. We have been developing a coarse-grained biomolecular model where on-average 10-20 atoms are grouped into one coarse-grained (CG) particle. Interactions among CG particles are tuned based on atomistic interactions and the fluctuation matching algorithm. CG molecular dynamics methods enable us to simulate much longer time scale motions of much larger molecular systems than fully atomistic models. After broad sampling of structures with CG models, we can easily reconstruct atomistic models, from which one can continue conventional molecular dynamics simulations if desired. Here, we describe our CG modeling methodology for protein-DNA complexes, together with various biological applications, such as the DNA duplication initiation complex, model chromatins, and transcription factor dynamics on chromatin-like environment.

A Feasible One-Step Synthesis of Hierarchical Zeolite Beta with Uniform Nanocrystals via CTAB

PubMed Central

Zhang, Weimin; Hu, Sufang; Qin, Bo; Li, Ruifeng

2018-01-01

A hierarchical zeolite Beta has been prepared by a feasible one-pot and one-step method, which is suitable for application in industrial production. The synthesis is a simple hydrothermal process with low-cost raw materials, without adding alcohol or adding seeds, and without aging, recrystallization, and other complex steps. The hierarchical zeolite Beta is a uniform nanocrystal (20–50 nm) aggregation with high external surface area (300 m2/g) and mesoporous volume (0.50 cm3/g), with the mesoporous structure composed of intercrystal and intracrystal pores. As an acid catalyst in benzylation of naphthalene with benzyl chloride, the hierarchical zeolite Beta has shown high activity in the bulky molecule reaction due to its introduction of mesostructure. PMID:29695044

Autonomous intelligent military robots: Army ants, killer bees, and cybernetic soldiers

NASA Astrophysics Data System (ADS)

Finkelstein, Robert

The rationale for developing autonomous intelligent robots in the military is to render conventional warfare systems ineffective and indefensible. The Desert Storm operation demonstrated the effectiveness of such systems as unmanned air and ground vehicles and indicated the future possibilities of robotic technology. Robotic military vehicles would have the advantages of expendability, low cost, lower complexity compared to manned systems, survivability, maneuverability, and a capability to share in instantaneous communication and distributed processing of combat information. Basic characteristics of intelligent systems and hierarchical control systems with sensor inputs are described. Genetic algorithms are seen as a means of achieving appropriate levels of intelligence in a robotic system. Potential impacts of robotic technology in the military are outlined.

Hierarchical Ensemble Methods for Protein Function Prediction

PubMed Central

2014-01-01

Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954

Masking effects of speech and music: does the masker's hierarchical structure matter?

PubMed

Shi, Lu-Feng; Law, Yvonne

2010-04-01

Speech and music are time-varying signals organized by parallel hierarchical rules. Through a series of four experiments, this study compared the masking effects of single-talker speech and instrumental music on speech perception while manipulating the complexity of hierarchical and temporal structures of the maskers. Listeners' word recognition was found to be similar between hierarchically intact and disrupted speech or classical music maskers (Experiment 1). When sentences served as the signal, significantly greater masking effects were observed with disrupted than intact speech or classical music maskers (Experiment 2), although not with jazz or serial music maskers, which differed from the classical music masker in their hierarchical structures (Experiment 3). Removing the classical music masker's temporal dynamics or partially restoring it affected listeners' sentence recognition; yet, differences in performance between intact and disrupted maskers remained robust (Experiment 4). Hence, the effect of structural expectancy was largely present across maskers when comparing them before and after their hierarchical structure was purposefully disrupted. This effect seemed to lend support to the auditory stream segregation theory.

Robust Real-Time Music Transcription with a Compositional Hierarchical Model.

PubMed

Pesek, Matevž; Leonardis, Aleš; Marolt, Matija

2017-01-01

The paper presents a new compositional hierarchical model for robust music transcription. Its main features are unsupervised learning of a hierarchical representation of input data, transparency, which enables insights into the learned representation, as well as robustness and speed which make it suitable for real-world and real-time use. The model consists of multiple layers, each composed of a number of parts. The hierarchical nature of the model corresponds well to hierarchical structures in music. The parts in lower layers correspond to low-level concepts (e.g. tone partials), while the parts in higher layers combine lower-level representations into more complex concepts (tones, chords). The layers are learned in an unsupervised manner from music signals. Parts in each layer are compositions of parts from previous layers based on statistical co-occurrences as the driving force of the learning process. In the paper, we present the model's structure and compare it to other hierarchical approaches in the field of music information retrieval. We evaluate the model's performance for the multiple fundamental frequency estimation. Finally, we elaborate on extensions of the model towards other music information retrieval tasks.

Construction of Matryoshka-type structures from supercharged protein nanocages.

PubMed

Beck, Tobias; Tetter, Stephan; Künzle, Matthias; Hilvert, Donald

2015-01-12

Designing nanoscaled hierarchical structures with increasing levels of complexity is challenging. Here we show that electrostatic interactions between two complementarily supercharged protein nanocages can be effectively utilized to create nested Matryoshka-type structures. Cage-within-cage complexes containing spatially ordered iron oxide nanoparticles spontaneously self-assemble upon mixing positively supercharged ferritin compartments with AaLS-13, a larger shell-forming protein with a negatively supercharged lumen. Exploiting engineered Coulombic interactions and protein dynamics in this way opens up new avenues for creating hierarchically organized supramolecular assemblies for application as delivery vehicles, reaction chambers, and artificial organelles. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hierarchically clustered adaptive quantization CMAC and its learning convergence.

PubMed

Teddy, S D; Lai, E M K; Quek, C

2007-11-01

The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: (1) a constant output resolution associated with the entire input space and (2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages. Index Terms-Cerebellar model articulation controller (CMAC), hierarchical clustering, hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC), learning convergence, nonuniform quantization.

Testing for Divergent Transmission Histories among Cultural Characters: A Study Using Bayesian Phylogenetic Methods and Iranian Tribal Textile Data

PubMed Central

Matthews, Luke J.; Tehrani, Jamie J.; Jordan, Fiona M.; Collard, Mark; Nunn, Charles L.

2011-01-01

Background Archaeologists and anthropologists have long recognized that different cultural complexes may have distinct descent histories, but they have lacked analytical techniques capable of easily identifying such incongruence. Here, we show how Bayesian phylogenetic analysis can be used to identify incongruent cultural histories. We employ the approach to investigate Iranian tribal textile traditions. Methods We used Bayes factor comparisons in a phylogenetic framework to test two models of cultural evolution: the hierarchically integrated system hypothesis and the multiple coherent units hypothesis. In the hierarchically integrated system hypothesis, a core tradition of characters evolves through descent with modification and characters peripheral to the core are exchanged among contemporaneous populations. In the multiple coherent units hypothesis, a core tradition does not exist. Rather, there are several cultural units consisting of sets of characters that have different histories of descent. Results For the Iranian textiles, the Bayesian phylogenetic analyses supported the multiple coherent units hypothesis over the hierarchically integrated system hypothesis. Our analyses suggest that pile-weave designs represent a distinct cultural unit that has a different phylogenetic history compared to other textile characters. Conclusions The results from the Iranian textiles are consistent with the available ethnographic evidence, which suggests that the commercial rug market has influenced pile-rug designs but not the techniques or designs incorporated in the other textiles produced by the tribes. We anticipate that Bayesian phylogenetic tests for inferring cultural units will be of great value for researchers interested in studying the evolution of cultural traits including language, behavior, and material culture. PMID:21559083

Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical Bayesian modeling

Treesearch

Wei Wu; James Clark; James Vose

2010-01-01

Hierarchical Bayesian (HB) modeling allows for multiple sources of uncertainty by factoring complex relationships into conditional distributions that can be used to draw inference and make predictions. We applied an HB model to estimate the parameters and state variables of a parsimonious hydrological model – GR4J – by coherently assimilating the uncertainties from the...

Tensegrity I. Cell structure and hierarchical systems biology

NASA Technical Reports Server (NTRS)

Ingber, Donald E.

2003-01-01

In 1993, a Commentary in this journal described how a simple mechanical model of cell structure based on tensegrity architecture can help to explain how cell shape, movement and cytoskeletal mechanics are controlled, as well as how cells sense and respond to mechanical forces (J. Cell Sci. 104, 613-627). The cellular tensegrity model can now be revisited and placed in context of new advances in our understanding of cell structure, biological networks and mechanoregulation that have been made over the past decade. Recent work provides strong evidence to support the use of tensegrity by cells, and mathematical formulations of the model predict many aspects of cell behavior. In addition, development of the tensegrity theory and its translation into mathematical terms are beginning to allow us to define the relationship between mechanics and biochemistry at the molecular level and to attack the larger problem of biological complexity. Part I of this two-part article covers the evidence for cellular tensegrity at the molecular level and describes how this building system may provide a structural basis for the hierarchical organization of living systems--from molecule to organism. Part II, which focuses on how these structural networks influence information processing networks, appears in the next issue.

[Application prospect of human-artificial intelligence system in future manned space flight].

PubMed

Wei, Jin-he

2003-01-01

To make the manned space flight more efficient and safer, a concept of human-artificial (AI) system is proposed in the present paper. The task of future manned space flight and the technique requirement with respect to the human-AI system development were analyzed. The main points are as follows: 1)Astronaut and AI are complementary to each other functionally; 2) Both symbol AI and connectionist AI should be included in the human-AI system, but expert system and Soar-like system are used mainly inside the cabin, the COG-like robots are mainly assigned for EVA either in LEO flight or on the surface of Moon or Mars; 3) The human-AI system is hierarchical in nature with astronaut at the top level; 4) The complex interfaces between astronaut and AI are the key points for running the system reliably and efficiently. As the importance of human-AI system in future manned space flight and the complexity of related technology, it is suggested that the R/D should be planned as early as possible.

Performance assessment in complex individual and team tasks

NASA Technical Reports Server (NTRS)

Eddy, Douglas R.

1992-01-01

Described here is an eclectic, performance based approach to assessing cognitive performance from multiple perspectives. The experience gained from assessing the effects of antihistamines and scenario difficulty on C (exp 2) decision making performance in Airborne Warning and Control Systems (AWACS) weapons director (WD) teams can serve as a model for realistic simulations in space operations. Emphasis is placed on the flexibility of measurement, hierarchical organization of measurement levels, data collection from multiple perspectives, and the difficulty of managing large amounts of data.

Nanostructured core-shell electrode materials for electrochemical capacitors

NASA Astrophysics Data System (ADS)

Jiang, Long-bo; Yuan, Xing-zhong; Liang, Jie; Zhang, Jin; Wang, Hou; Zeng, Guang-ming

2016-11-01

Core-shell nanostructure represents a unique system for applications in electrochemical energy storage devices. Owing to the unique characteristics featuring high power delivery and long-term cycling stability, electrochemical capacitors (ECs) have emerged as one of the most attractive electrochemical storage systems since they can complement or even replace batteries in the energy storage field, especially when high power delivery or uptake is needed. This review aims to summarize recent progress on core-shell nanostructures for advanced supercapacitor applications in view of their hierarchical architecture which not only create the desired hierarchical porous channels, but also possess higher electrical conductivity and better structural mechanical stability. The core-shell nanostructures include carbon/carbon, carbon/metal oxide, carbon/conducting polymer, metal oxide/metal oxide, metal oxide/conducting polymer, conducting polymer/conducting polymer, and even more complex ternary core-shell nanoparticles. The preparation strategies, electrochemical performances, and structural stabilities of core-shell materials for ECs are summarized. The relationship between core-shell nanostructure and electrochemical performance is discussed in detail. In addition, the challenges and new trends in core-shell nanomaterials development have also been proposed.

Hierarchical organization as a diagnostic approach to volcano mechanics: Validation on Piton de la Fournaise

NASA Astrophysics Data System (ADS)

Grasso, J. R.; Bachèlery, P.

Self-organized systems are often used to describe natural phenomena where power laws and scale invariant geometry are observed. The Piton de la Fournaise volcano shows power-law behavior in many aspects. These include the temporal distribution of eruptions, the frequency-size distributions of induced earthquakes, dikes, fissures, lava flows and interflow periods, all evidence of self-similarity over a finite scale range. We show that the bounds to scale-invariance can be used to derive geomechanical constraints on both the volcano structure and the volcano mechanics. We ascertain that the present magma bodies are multi-lens reservoirs in a quasi-eruptive condition, i.e. a marginally critical state. The scaling organization of dynamic fluid-induced observables on the volcano, such as fluid induced earthquakes, dikes and surface fissures, appears to be controlled by underlying static hierarchical structure (geology) similar to that proposed for fluid circulations in human physiology. The emergence of saturation lengths for the scalable volcanic observable argues for the finite scalability of complex naturally self-organized critical systems, including volcano dynamics.

Application of Smart Infrastructure Systems approach to precision medicine.

PubMed

Govindaraju, Diddahally R; Annaswamy, Anuradha M

2015-12-01

All biological variation is hierarchically organized dynamic network system of genomic components, organelles, cells, tissues, organs, individuals, families, populations and metapopulations. Individuals are axial in this hierarchy, as they represent antecedent, attendant and anticipated aspects of health, disease, evolution and medical care. Humans show individual specific genetic and clinical features such as complexity, cooperation, resilience, robustness, vulnerability, self-organization, latent and emergent behavior during their development, growth and senescence. Accurate collection, measurement, organization and analyses of individual specific data, embedded at all stratified levels of biological, demographic and cultural diversity - the big data - is necessary to make informed decisions on health, disease and longevity; which is a central theme of precision medicine initiative (PMI). This initiative also calls for the development of novel analytical approaches to handle complex multidimensional data. Here we suggest the application of Smart Infrastructure Systems (SIS) approach to accomplish some of the goals set forth by the PMI on the premise that biological systems and the SIS share many common features. The latter has been successfully employed in managing complex networks of non-linear adaptive controls, commonly encountered in smart engineering systems. We highlight their concordance and discuss the utility of the SIS approach in precision medicine programs.

The VMC Survey. XXII. Hierarchical Star Formation in the 30 Doradus-N158-N159-N160 Star-forming Complex

NASA Astrophysics Data System (ADS)

Sun, Ning-Chen; de Grijs, Richard; Subramanian, Smitha; Cioni, Maria-Rosa L.; Rubele, Stefano; Bekki, Kenji; Ivanov, Valentin D.; Piatti, Andrés E.; Ripepi, Vincenzo

2017-02-01

We study the hierarchical stellar structures in a ˜1.5 deg2 area covering the 30 Doradus-N158-N159-N160 star-forming complex with the VISTA Survey of Magellanic Clouds. Based on the young upper main-sequence stars, we find that the surface densities cover a wide range of values, from log({{Σ }}\\cdot pc2) ≲ -2.0 to log({{Σ }}\\cdot pc2) ≳ 0.0. Their distributions are highly non-uniform, showing groups that frequently have subgroups inside. The sizes of the stellar groups do not exhibit characteristic values, and range continuously from several parsecs to more than 100 pc the cumulative size distribution can be well described by a single power law, with the power-law index indicating a projected fractal dimension D2 = 1.6 ± 0.3. We suggest that the phenomena revealed here support a scenario of hierarchical star formation. Comparisons with other star-forming regions and galaxies are also discussed.

An Adaptive Complex Network Model for Brain Functional Networks

PubMed Central

Gomez Portillo, Ignacio J.; Gleiser, Pablo M.

2009-01-01

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution. PMID:19738902

Modularity and the spread of perturbations in complex dynamical systems

NASA Astrophysics Data System (ADS)

Kolchinsky, Artemy; Gates, Alexander J.; Rocha, Luis M.

2015-12-01

We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize "perturbation modularity," defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the "Markov stability" method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., "relevance networks" or "functional networks"). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.

Modularity and the spread of perturbations in complex dynamical systems.

PubMed

Kolchinsky, Artemy; Gates, Alexander J; Rocha, Luis M

2015-12-01

We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize "perturbation modularity," defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the "Markov stability" method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., "relevance networks" or "functional networks"). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.

Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site

NASA Astrophysics Data System (ADS)

Gavish, Yoni; O'Connell, Jerome; Marsh, Charles J.; Tarantino, Cristina; Blonda, Palma; Tomaselli, Valeria; Kunin, William E.

2018-02-01

The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre-defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2-3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into "black-box" based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps.

3D printed hierarchical honeycombs with shape integrity under large compressive deformations

DOE PAGES

Chen, Yanyu; Li, Tiantian; Jia, Zian; ...

2017-10-12

Here, we describe the in-plane compressive performance of a new type of hierarchical cellular structure created by replacing cell walls in regular honeycombs with triangular lattice configurations. The fabrication of this relatively complex material architecture with size features spanning from micrometer to centimeter is facilitated by the availability of commercial 3D printers. We apply to these hierarchical honeycombs a thermal treatment that facilitates the shape preservation and structural integrity of the structures under large compressive loading. The proposed hierarchical honeycombs exhibit a progressive failure mode, along with improved stiffness and energy absorption under uniaxial compression. High energy dissipation and shapemore » integrity at large imposed strains (up to 60%) have also been observed in these hierarchical honeycombs under cyclic loading. Experimental and numerical studies suggest that these anomalous mechanical behaviors are attributed to the introduction of a structural hierarchy, intrinsically controlled by the cell wall slenderness of the triangular lattice and by the shape memory effect induced by the thermal and mechanical compressive treatment.« less

Recognizing Chinese characters in digital ink from non-native language writers using hierarchical models

NASA Astrophysics Data System (ADS)

Bai, Hao; Zhang, Xi-wen

2017-06-01

While Chinese is learned as a second language, its characters are taught step by step from their strokes to components, radicals to components, and their complex relations. Chinese Characters in digital ink from non-native language writers are deformed seriously, thus the global recognition approaches are poorer. So a progressive approach from bottom to top is presented based on hierarchical models. Hierarchical information includes strokes and hierarchical components. Each Chinese character is modeled as a hierarchical tree. Strokes in one Chinese characters in digital ink are classified with Hidden Markov Models and concatenated to the stroke symbol sequence. And then the structure of components in one ink character is extracted. According to the extraction result and the stroke symbol sequence, candidate characters are traversed and scored. Finally, the recognition candidate results are listed by descending. The method of this paper is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.

3D printed hierarchical honeycombs with shape integrity under large compressive deformations

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

Chen, Yanyu; Li, Tiantian; Jia, Zian

Here, we describe the in-plane compressive performance of a new type of hierarchical cellular structure created by replacing cell walls in regular honeycombs with triangular lattice configurations. The fabrication of this relatively complex material architecture with size features spanning from micrometer to centimeter is facilitated by the availability of commercial 3D printers. We apply to these hierarchical honeycombs a thermal treatment that facilitates the shape preservation and structural integrity of the structures under large compressive loading. The proposed hierarchical honeycombs exhibit a progressive failure mode, along with improved stiffness and energy absorption under uniaxial compression. High energy dissipation and shapemore » integrity at large imposed strains (up to 60%) have also been observed in these hierarchical honeycombs under cyclic loading. Experimental and numerical studies suggest that these anomalous mechanical behaviors are attributed to the introduction of a structural hierarchy, intrinsically controlled by the cell wall slenderness of the triangular lattice and by the shape memory effect induced by the thermal and mechanical compressive treatment.« less

Unpacking the Complexity of Linear Equations from a Cognitive Load Theory Perspective

ERIC Educational Resources Information Center

Ngu, Bing Hiong; Phan, Huy P.

2016-01-01

The degree of element interactivity determines the complexity and therefore the intrinsic cognitive load of linear equations. The unpacking of linear equations at the level of operational and relational lines allows the classification of linear equations in a hierarchical level of complexity. Mapping similar operational and relational lines across…

Control Centrality and Hierarchical Structure in Complex Networks

PubMed Central

Liu, Yang-Yu; Slotine, Jean-Jacques; Barabási, Albert-László

2012-01-01

We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks. PMID:23028542

Topology of foreign exchange markets using hierarchical structure methods

NASA Astrophysics Data System (ADS)

Naylor, Michael J.; Rose, Lawrence C.; Moyle, Brendan J.

2007-08-01

This paper uses two physics derived hierarchical techniques, a minimal spanning tree and an ultrametric hierarchical tree, to extract a topological influence map for major currencies from the ultrametric distance matrix for 1995-2001. We find that these two techniques generate a defined and robust scale free network with meaningful taxonomy. The topology is shown to be robust with respect to method, to time horizon and is stable during market crises. This topology, appropriately used, gives a useful guide to determining the underlying economic or regional causal relationships for individual currencies and to understanding the dynamics of exchange rate price determination as part of a complex network.

Multidisciplinary optimization for engineering systems - Achievements and potential

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1989-01-01

The currently common sequential design process for engineering systems is likely to lead to suboptimal designs. Recently developed decomposition methods offer an alternative for coming closer to optimum by breaking the large task of system optimization into smaller, concurrently executed and, yet, coupled tasks, identified with engineering disciplines or subsystems. The hierarchic and non-hierarchic decompositions are discussed and illustrated by examples. An organization of a design process centered on the non-hierarchic decomposition is proposed.

Multidisciplinary optimization for engineering systems: Achievements and potential

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1989-01-01

The currently common sequential design process for engineering systems is likely to lead to suboptimal designs. Recently developed decomposition methods offer an alternative for coming closer to optimum by breaking the large task of system optimization into smaller, concurrently executed and, yet, coupled tasks, identified with engineering disciplines or subsystems. The hierarchic and non-hierarchic decompositions are discussed and illustrated by examples. An organization of a design process centered on the non-hierarchic decomposition is proposed.

Rhythmic complexity and predictive coding: a novel approach to modeling rhythm and meter perception in music

PubMed Central

Vuust, Peter; Witek, Maria A. G.

2014-01-01

Musical rhythm, consisting of apparently abstract intervals of accented temporal events, has a remarkable capacity to move our minds and bodies. How does the cognitive system enable our experiences of rhythmically complex music? In this paper, we describe some common forms of rhythmic complexity in music and propose the theory of predictive coding (PC) as a framework for understanding how rhythm and rhythmic complexity are processed in the brain. We also consider why we feel so compelled by rhythmic tension in music. First, we consider theories of rhythm and meter perception, which provide hierarchical and computational approaches to modeling. Second, we present the theory of PC, which posits a hierarchical organization of brain responses reflecting fundamental, survival-related mechanisms associated with predicting future events. According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Third, we develop a PC model of musical rhythm, in which rhythm perception is conceptualized as an interaction between what is heard (“rhythm”) and the brain’s anticipatory structuring of music (“meter”). Finally, we review empirical studies of the neural and behavioral effects of syncopation, polyrhythm and groove, and propose how these studies can be seen as special cases of the PC theory. We argue that musical rhythm exploits the brain’s general principles of prediction and propose that pleasure and desire for sensorimotor synchronization from musical rhythm may be a result of such mechanisms. PMID:25324813

Sensorimotor Integration by Corticospinal System

PubMed Central

Moreno-López, Yunuen; Olivares-Moreno, Rafael; Cordero-Erausquin, Matilde; Rojas-Piloni, Gerardo

2016-01-01

The corticospinal (CS) tract is a complex system which targets several areas of the spinal cord. In particular, the CS descending projection plays a major role in motor command, which results from direct and indirect control of spinal cord pre-motor interneurons as well as motoneurons. But in addition, this system is also involved in a selective and complex modulation of sensory feedback. Despite recent evidence confirms that CS projections drive distinct segmental neural circuits that are part of the sensory and pre-motor pathways, little is known about the spinal networks engaged by the corticospinal tract (CST), the organization of CS projections, the intracortical microcircuitry, and the synaptic interactions in the sensorimotor cortex (SMC) that may encode different cortical outputs to the spinal cord. Here is stressed the importance of integrated approaches for the study of sensorimotor function of CS system, in order to understand the functional compartmentalization and hierarchical organization of layer 5 output neurons, who are key elements for motor control and hence, of behavior. PMID:27013985

Sensorimotor Integration by Corticospinal System.

PubMed

Moreno-López, Yunuen; Olivares-Moreno, Rafael; Cordero-Erausquin, Matilde; Rojas-Piloni, Gerardo

2016-01-01

The corticospinal (CS) tract is a complex system which targets several areas of the spinal cord. In particular, the CS descending projection plays a major role in motor command, which results from direct and indirect control of spinal cord pre-motor interneurons as well as motoneurons. But in addition, this system is also involved in a selective and complex modulation of sensory feedback. Despite recent evidence confirms that CS projections drive distinct segmental neural circuits that are part of the sensory and pre-motor pathways, little is known about the spinal networks engaged by the corticospinal tract (CST), the organization of CS projections, the intracortical microcircuitry, and the synaptic interactions in the sensorimotor cortex (SMC) that may encode different cortical outputs to the spinal cord. Here is stressed the importance of integrated approaches for the study of sensorimotor function of CS system, in order to understand the functional compartmentalization and hierarchical organization of layer 5 output neurons, who are key elements for motor control and hence, of behavior.

Energy-efficient hierarchical processing in the network of wireless intelligent sensors (WISE)

NASA Astrophysics Data System (ADS)

Raskovic, Dejan

Sensor network nodes have benefited from technological advances in the field of wireless communication, processing, and power sources. However, the processing power of microcontrollers is often not sufficient to perform sophisticated processing, while the power requirements of digital signal processing boards or handheld computers are usually too demanding for prolonged system use. We are matching the intrinsic hierarchical nature of many digital signal-processing applications with the natural hierarchy in distributed wireless networks, and building the hierarchical system of wireless intelligent sensors. Our goal is to build a system that will exploit the hierarchical organization to optimize the power consumption and extend battery life for the given time and memory constraints, while providing real-time processing of sensor signals. In addition, we are designing our system to be able to adapt to the current state of the environment, by dynamically changing the algorithm through procedure replacement. This dissertation presents the analysis of hierarchical environment and methods for energy profiling used to evaluate different system design strategies, and to optimize time-effective and energy-efficient processing.

Processing prosodic structure by adults with language-based learning disability.

PubMed

Bahl, Megha; Plante, Elena; Gerken, LouAnn

2009-01-01

Two experiments investigated the ability of adults with a history of language-based learning disability (hLLD) and their normal language (NL) peers to learn prosodic patterns of a novel language. Participants were exposed to stimuli from an artificial language and tested on items that required generalization of the stress patterns and the hierarchical principles of stress assignment that could be inferred from the input. In Study 1, the NL group successfully generalized the patterns of stress heard during familiarization, but failed to show generalization of the hierarchical principles. The hLLD group performed at chance for both types of generalization items. In Study 2, the intensity of stress elements was increased. The performance of the NL group improved whereas the hLLD groups' performance decreased on both types of generalization items. The results indicate that NL adults are able to successfully abstract the complex hierarchical rules of stress if the prosodic cues are made sufficiently salient, but this same task is difficult for adults with hLLD. The reader will be able to understand: (1) the difference in the ability of hLLD and NL adults to process stress assignment in an implicit learning context and (2) that typical adults can abstract complex hierarchical rules of stress assignment when provided with strong cues.

Globular cluster formation with multiple stellar populations from hierarchical star cluster complexes

NASA Astrophysics Data System (ADS)

Bekki, Kenji

2017-05-01

Most old globular clusters (GCs) in the Galaxy are observed to have internal chemical abundance spreads in light elements. We discuss a new GC formation scenario based on hierarchical star formation within fractal molecular clouds. In the new scenario, a cluster of bound and unbound star clusters ('star cluster complex', SCC) that have a power-law cluster mass function with a slope (β) of 2 is first formed from a massive gas clump developed in a dwarf galaxy. Such cluster complexes and β = 2 are observed and expected from hierarchical star formation. The most massive star cluster ('main cluster'), which is the progenitor of a GC, can accrete gas ejected from asymptotic giant branch (AGB) stars initially in the cluster and other low-mass clusters before the clusters are tidally stripped or destroyed to become field stars in the dwarf. The SCC is initially embedded in a giant gas hole created by numerous supernovae of the SCC so that cold gas outside the hole can be accreted on to the main cluster later. New stars formed from the accreted gas have chemical abundances that are different from those of the original SCC. Using hydrodynamical simulations of GC formation based on this scenario, we show that the main cluster with the initial mass as large as [2-5] × 105 M⊙ can accrete more than 105 M⊙ gas from AGB stars of the SCC. We suggest that merging of hierarchical SSCs can play key roles in stellar halo formation around GCs and self-enrichment processes in the early phase of GC formation.

Multi-agent based control of large-scale complex systems employing distributed dynamic inference engine

NASA Astrophysics Data System (ADS)

Zhang, Daili

Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications are made for critical agents and are organized into logical rings. This architecture maintains clear guidelines for complexity decomposition and also increases the robustness of the whole system. Multiple Sectioned Dynamic Bayesian Networks (MSDBNs) as a distributed dynamic probabilistic inference engine, can be embedded into the control architecture to handle uncertainties of general large-scale complex systems. MSDBNs decomposes a large knowledge-based system into many agents. Each agent holds its partial perspective of a large problem domain by representing its knowledge as a Dynamic Bayesian Network (DBN). Each agent accesses local evidence from its corresponding local sensors and communicates with other agents through finite message passing. If the distributed agents can be organized into a tree structure, satisfying the running intersection property and d-sep set requirements, globally consistent inferences are achievable in a distributed way. By using different frequencies for local DBN agent belief updating and global system belief updating, it balances the communication cost with the global consistency of inferences. In this dissertation, a fully factorized Boyen-Koller (BK) approximation algorithm is used for local DBN agent belief updating, and the static Junction Forest Linkage Tree (JFLT) algorithm is used for global system belief updating. MSDBNs assume a static structure and a stable communication network for the whole system. However, for a real system, sub-Bayesian networks as nodes could be lost, and the communication network could be shut down due to partial damage in the system. Therefore, on-line and automatic MSDBNs structure formation is necessary for making robust state estimations and increasing survivability of the whole system. A Distributed Spanning Tree Optimization (DSTO) algorithm, a Distributed D-Sep Set Satisfaction (DDSSS) algorithm, and a Distributed Running Intersection Satisfaction (DRIS) algorithm are proposed in this dissertation. Combining these three distributed algorithms and a Distributed Belief Propagation (DBP) algorithm in MSDBNs makes state estimations robust to partial damage in the whole system. Combining the distributed control architecture design and the distributed inference engine design leads to a process of control system design for a general large-scale complex system. As applications of the proposed methodology, the control system design of a simplified ship chilled water system and a notional ship chilled water system have been demonstrated step by step. Simulation results not only show that the proposed methodology gives a clear guideline for control system design for general large-scale complex systems with dynamic and uncertain environment, but also indicate that the combination of MSDBNs and HyMABC can provide excellent performance for controlling general large-scale complex systems.

Integrated Hydro-geomorphological Monitoring System of the Upper Bussento river basin (Cilento and Vallo Diano Geopark, S-Italy)

NASA Astrophysics Data System (ADS)

Guida, D.; Cuomo, A.; Longobardi, A.; Villani, P.; Guida, M.; Guadagnuolo, D.; Cestari, A.; Siervo, V.; Benevento, G.; Sorvino, S.; Doto, R.; Verrone, M.; De Vita, A.; Aloia, A.; Positano, P.

2012-04-01

The Mediterranean river ecosystem functionings are supported by river-aquifer interactions. The assessment of their ecological services requires interdisciplinary scientific approaches, integrate monitoring systems and inter-institutional planning and management. This poster illustrates the Hydro-geomorphological Monitoring System build-up in the Upper Bussento river basin by the University of Salerno, in agreement with the local Basin Autorities and in extension to the other river basins located in the Cilento and Vallo Diano National Park (southern Italy), recently accepted in the European Geopark Network. The Monitoring System is based on a hierarchical Hydro-geomorphological Model (HGM), improved in a multiscale, nested and object-oriented Hydro-geomorphological Informative System (HGIS, Figure 1). Hydro-objects are topologically linked and functionally bounded by Hydro-elements at various levels of homogeneity (Table 1). Spatial Hydro-geomorpho-system, HG-complex and HG-unit support respectively areal Hydro-objects, as basin, sector and catchment and linear Hydro-objects, as river, segment, reach and section. Runoff initiation points, springs, disappearing points, junctions, gaining and water losing points complete the Hydro-systems. An automatic procedure use the Pfafstetter coding to hierarchically divide a terrain into arbitrarily small hydro-geomorphological units (basin, interfluve, headwater and no-contribution areas, each with a unique label with hierarchical topological properties. To obtain a hierarchy of hydro-geomorphological units, the method is then applied recursively on each basin and interbasin, and labels of the subdivided regions are appended to the existing label of the original region. The monitoring stations are ranked consequently in main, secondary, temporary and random and located progressively at the points or sections representative for the hydro-geomorphological responses by validation control and modeling calibration. The datasets are organized into a relational geodatabase supporting tracer testings, space-time analysis and hydrological modeling. At the moment, three main station for hourly streamflow measurements are located at the terminal sections of the main basin and the two main sub-basin; secondary stations for weekly discharge measurements are located along the Upper Bussento river segment, upstream and downstream of each river reach or tributary catchments or karst spring inflow. Temporary stations are located in the representative sections of the catchments to detect stream flow losses into alluvial beds or experimental parcels in the bare karst and forested sandstone headwaters. Streamflow measurements are combined with geochemical survey and water sampling for Radon activity concentration measurements. Results of measurement campains in Radon space-time distribution within the basin are given in other contribution of same EGU session. Monitoring results confirm the hourly, daily, weekly and monthly hydrological data and validate outcomes of semi-distributed hydrological models based on previously time series, allowing both academic consultants and institutional subject to extend the Integrated Hydro-geomorphological Monitoring System to the surrounding drainage areas of the Cilento and Vallo di Diano Geopark. Keywords: River-aquifer interaction, Upper Bussento river basin, monitoring system, hydro-geomorphology, semi-distributed hydrological model. Table 1: Comparative, hierarchical Hydro-morpho-climate entities Hierarchy levelArea (Km2) Scale Orography Entity Climate Entity Morfological Entity Areal Drainage Entity Linear Drainage Entity VIII 106 1:15E6 Orogen Macroscale α Morphological Region Hydrological Region VII 105 1:10E6 Chain Sistem Macroscale β Morphological Province Hydrological Province VI 104 1:5E5 Chain Mesoscale α Morphological Sistem Basin River V 103 1:2,5E5Chain Segment Mesoscale β Morphological Sub-systemSub-Basin Torrent IV 100 1:1,0E5Orographic Group Mesoscale γ Morphological Complex Basin Sector Mid Order Channel/ Segment III 10 1: 5E4 Orographic System Microscale αMorphological Unit Watershed Low Order Channel/ Reach II 1 1:2,5E3Orographic ComplexMicroscale βMorphological ComponentCatchment Transient Channel/ Pool I 10-2 1:5E3 Orographic Unit Microscale γMorphological Element Hollow Zero Order Channel PIC

A Hierarchical Learning Control Framework for an Aerial Manipulation System

NASA Astrophysics Data System (ADS)

Ma, Le; Chi, yanxun; Li, Jiapeng; Li, Zhongsheng; Ding, Yalei; Liu, Lixing

2017-07-01

A hierarchical learning control framework for an aerial manipulation system is proposed. Firstly, the mechanical design of aerial manipulation system is introduced and analyzed, and the kinematics and the dynamics based on Newton-Euler equation are modeled. Secondly, the framework of hierarchical learning for this system is presented, in which flight platform and manipulator are controlled by different controller respectively. The RBF (Radial Basis Function) neural networks are employed to estimate parameters and control. The Simulation and experiment demonstrate that the methods proposed effective and advanced.

Hybrid modeling and empirical analysis of automobile supply chain network

NASA Astrophysics Data System (ADS)

Sun, Jun-yan; Tang, Jian-ming; Fu, Wei-ping; Wu, Bing-ying

2017-05-01

Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer-driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.

Biological hierarchies and the nature of extinction.

PubMed

Congreve, Curtis R; Falk, Amanda R; Lamsdell, James C

2018-05-01

Hierarchy theory recognises that ecological and evolutionary units occur in a nested and interconnected hierarchical system, with cascading effects occurring between hierarchical levels. Different biological disciplines have routinely come into conflict over the primacy of different forcing mechanisms behind evolutionary and ecological change. These disconnects arise partly from differences in perspective (with some researchers favouring ecological forcing mechanisms while others favour developmental/historical mechanisms), as well as differences in the temporal framework in which workers operate. In particular, long-term palaeontological data often show that large-scale (macro) patterns of evolution are predominantly dictated by shifts in the abiotic environment, while short-term (micro) modern biological studies stress the importance of biotic interactions. We propose that thinking about ecological and evolutionary interactions in a hierarchical framework is a fruitful way to resolve these conflicts. Hierarchy theory suggests that changes occurring at lower hierarchical levels can have unexpected, complex effects at higher scales due to emergent interactions between simple systems. In this way, patterns occurring on short- and long-term time scales are equally valid, as changes that are driven from lower levels will manifest in different forms at higher levels. We propose that the dual hierarchy framework fits well with our current understanding of evolutionary and ecological theory. Furthermore, we describe how this framework can be used to understand major extinction events better. Multi-generational attritional loss of reproductive fitness (MALF) has recently been proposed as the primary mechanism behind extinction events, whereby extinction is explainable solely through processes that result in extirpation of populations through a shutdown of reproduction. While not necessarily explicit, the push to explain extinction through solely population-level dynamics could be used to suggest that environmentally mediated patterns of extinction or slowed speciation across geological time are largely artefacts of poor preservation or a coarse temporal scale. We demonstrate how MALF fits into a hierarchical framework, showing that MALF can be a primary forcing mechanism at lower scales that still results in differential survivorship patterns at the species and clade level which vary depending upon the initial environmental forcing mechanism. Thus, even if MALF is the primary mechanism of extinction across all mass extinction events, the primary environmental cause of these events will still affect the system and result in differential responses. Therefore, patterns at both temporal scales are relevant. © 2017 Cambridge Philosophical Society.

Efficient Parallel Formulations of Hierarchical Methods and Their Applications

NASA Astrophysics Data System (ADS)

Grama, Ananth Y.

1996-01-01

Hierarchical methods such as the Fast Multipole Method (FMM) and Barnes-Hut (BH) are used for rapid evaluation of potential (gravitational, electrostatic) fields in particle systems. They are also used for solving integral equations using boundary element methods. The linear systems arising from these methods are dense and are solved iteratively. Hierarchical methods reduce the complexity of the core matrix-vector product from O(n^2) to O(n log n) and the memory requirement from O(n^2) to O(n). We have developed highly scalable parallel formulations of a hybrid FMM/BH method that are capable of handling arbitrarily irregular distributions. We apply these formulations to astrophysical simulations of Plummer and Gaussian galaxies. We have used our parallel formulations to solve the integral form of the Laplace equation. We show that our parallel hierarchical mat-vecs yield high efficiency and overall performance even on relatively small problems. A problem containing approximately 200K nodes takes under a second to compute on 256 processors and yet yields over 85% efficiency. The efficiency and raw performance is expected to increase for bigger problems. For the 200K node problem, our code delivers about 5 GFLOPS of performance on a 256 processor T3D. This is impressive considering the fact that the problem has floating point divides and roots, and very little locality resulting in poor cache performance. A dense matrix-vector product of the same dimensions would require about 0.5 TeraBytes of memory and about 770 TeraFLOPS of computing speed. Clearly, if the loss in accuracy resulting from the use of hierarchical methods is acceptable, our code yields significant savings in time and memory. We also study the convergence of a GMRES solver built around this mat-vec. We accelerate the convergence of the solver using three preconditioning techniques: diagonal scaling, block-diagonal preconditioning, and inner-outer preconditioning. We study the performance and parallel efficiency of these preconditioned solvers. Using this solver, we solve dense linear systems with hundreds of thousands of unknowns. Solving a 105K unknown problem takes about 10 minutes on a 64 processor T3D. Until very recently, boundary element problems of this magnitude could not even be generated, let alone solved.

Materiomics: biological protein materials, from nano to macro.

PubMed

Cranford, Steven; Buehler, Markus J

2010-11-12

Materiomics is an emerging field of science that provides a basis for multiscale material system characterization, inspired in part by natural, for example, protein-based materials. Here we outline the scope and explain the motivation of the field of materiomics, as well as demonstrate the benefits of a materiomic approach in the understanding of biological and natural materials as well as in the design of de novo materials. We discuss recent studies that exemplify the impact of materiomics - discovering Nature's complexity through a materials science approach that merges concepts of material and structure throughout all scales and incorporates feedback loops that facilitate sensing and resulting structural changes at multiple scales. The development and application of materiomics is illustrated for the specific case of protein-based materials, which constitute the building blocks of a variety of biological systems such as tendon, bone, skin, spider silk, cells, and tissue, as well as natural composite material systems (a combination of protein-based and inorganic constituents) such as nacre and mollusk shells, and other natural multiscale systems such as cellulose-based plant and wood materials. An important trait of these materials is that they display distinctive hierarchical structures across multiple scales, where molecular details are exhibited in macroscale mechanical responses. Protein materials are intriguing examples of materials that balance multiple tasks, representing some of the most sustainable material solutions that integrate structure and function despite severe limitations in the quality and quantity of material building blocks. However, up until now, our attempts to analyze and replicate Nature's materials have been hindered by our lack of fundamental understanding of these materials' intricate hierarchical structures, scale-bridging mechanisms, and complex material components that bestow protein-based materials their unique properties. Recent advances in analytical tools and experimental methods allow a holistic view of such a hierarchical biological material system. The integration of these approaches and amalgamation of material properties at all scale levels to develop a complete description of a material system falls within the emerging field of materiomics. Materiomics is the result of the convergence of engineering and materials science with experimental and computational biology in the context of natural and synthetic materials. Through materiomics, fundamental advances in our understanding of structure-property-process relations of biological systems contribute to the mechanistic understanding of certain diseases and facilitate the development of novel biological, biologically inspired, and completely synthetic materials for applications in medicine (biomaterials), nanotechnology, and engineering.

Materiomics: biological protein materials, from nano to macro

PubMed Central

Cranford, Steven; Buehler, Markus J

2010-01-01

Materiomics is an emerging field of science that provides a basis for multiscale material system characterization, inspired in part by natural, for example, protein-based materials. Here we outline the scope and explain the motivation of the field of materiomics, as well as demonstrate the benefits of a materiomic approach in the understanding of biological and natural materials as well as in the design of de novo materials. We discuss recent studies that exemplify the impact of materiomics – discovering Nature’s complexity through a materials science approach that merges concepts of material and structure throughout all scales and incorporates feedback loops that facilitate sensing and resulting structural changes at multiple scales. The development and application of materiomics is illustrated for the specific case of protein-based materials, which constitute the building blocks of a variety of biological systems such as tendon, bone, skin, spider silk, cells, and tissue, as well as natural composite material systems (a combination of protein-based and inorganic constituents) such as nacre and mollusk shells, and other natural multiscale systems such as cellulose-based plant and wood materials. An important trait of these materials is that they display distinctive hierarchical structures across multiple scales, where molecular details are exhibited in macroscale mechanical responses. Protein materials are intriguing examples of materials that balance multiple tasks, representing some of the most sustainable material solutions that integrate structure and function despite severe limitations in the quality and quantity of material building blocks. However, up until now, our attempts to analyze and replicate Nature’s materials have been hindered by our lack of fundamental understanding of these materials’ intricate hierarchical structures, scale-bridging mechanisms, and complex material components that bestow protein-based materials their unique properties. Recent advances in analytical tools and experimental methods allow a holistic view of such a hierarchical biological material system. The integration of these approaches and amalgamation of material properties at all scale levels to develop a complete description of a material system falls within the emerging field of materiomics. Materiomics is the result of the convergence of engineering and materials science with experimental and computational biology in the context of natural and synthetic materials. Through materiomics, fundamental advances in our understanding of structure–property–process relations of biological systems contribute to the mechanistic understanding of certain diseases and facilitate the development of novel biological, biologically inspired, and completely synthetic materials for applications in medicine (biomaterials), nanotechnology, and engineering. PMID:24198478

The Design Manager's Aid for Intelligent Decomposition (DeMAID)

NASA Technical Reports Server (NTRS)

Rogers, James L.

1994-01-01

Before the design of new complex systems such as large space platforms can begin, the possible interactions among subsystems and their parts must be determined. Once this is completed, the proposed system can be decomposed to identify its hierarchical structure. The design manager's aid for intelligent decomposition (DeMAID) is a knowledge based system for ordering the sequence of modules and identifying a possible multilevel structure for design. Although DeMAID requires an investment of time to generate and refine the list of modules for input, it could save considerable money and time in the total design process, particularly in new design problems where the ordering of the modules has not been defined.

Providing data science support for systems pharmacology and its implications to drug discovery.

PubMed

Hart, Thomas; Xie, Lei

2016-01-01

The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology. This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction. Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.

A Literature Review and Compilation of Nuclear Waste Management System Attributes for Use in Multi-Objective System Evaluations.

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

Kalinina, Elena Arkadievna; Samsa, Michael

The purpose of this work was to compile a comprehensive initial set of potential nuclear waste management system attributes. This initial set of attributes is intended to serve as a starting point for additional consideration by system analysts and planners to facilitate the development of a waste management system multi-objective evaluation framework based on the principles and methodology of multi-attribute utility analysis. The compilation is primarily based on a review of reports issued by the Canadian Nuclear Waste Management Organization (NWMO) and the Blue Ribbon Commission on America's Nuclear Future (BRC), but also an extensive review of the available literaturemore » for similar and past efforts as well. Numerous system attributes found in different sources were combined into a single objectives-oriented hierarchical structure. This study provides a discussion of the data sources and the descriptions of the hierarchical structure. A particular focus of this study was on collecting and compiling inputs from past studies that involved the participation of various external stakeholders. However, while the important role of stakeholder input in a country's waste management decision process is recognized in the referenced sources, there are only a limited number of in-depth studies of the stakeholders' differing perspectives. Compiling a comprehensive hierarchical listing of attributes is a complex task since stakeholders have multiple and often conflicting interests. The BRC worked for two years (January 2010 to January 2012) to "ensure it has heard from as many points of view as possible." The Canadian NWMO study took four years and ample resources, involving national and regional stakeholders' dialogs, internet-based dialogs, information and discussion sessions, open houses, workshops, round tables, public attitude research, website, and topic reports. The current compilation effort benefited from the distillation of these many varied inputs conducted by the previous studies.« less

Self-assembly of hierarchically ordered structures in DNA nanotube systems

NASA Astrophysics Data System (ADS)

Glaser, Martin; Schnauß, Jörg; Tschirner, Teresa; Schmidt, B. U. Sebastian; Moebius-Winkler, Maximilian; Käs, Josef A.; Smith, David M.

2016-05-01

The self-assembly of molecular and macromolecular building blocks into organized patterns is a complex process found in diverse systems over a wide range of size and time scales. The formation of star- or aster-like configurations, for example, is a common characteristic in solutions of polymers or other molecules containing multi-scaled, hierarchical assembly processes. This is a recurring phenomenon in numerous pattern-forming systems ranging from cellular constructs to solutions of ferromagnetic colloids or synthetic plastics. To date, however, it has not been possible to systematically parameterize structural properties of the constituent components in order to study their influence on assembled states. Here, we circumvent this limitation by using DNA nanotubes with programmable mechanical properties as our basic building blocks. A small set of DNA oligonucleotides can be chosen to hybridize into micron-length DNA nanotubes with a well-defined circumference and stiffness. The self-assembly of these nanotubes to hierarchically ordered structures is driven by depletion forces caused by the presence of polyethylene glycol. This trait allowed us to investigate self-assembly effects while maintaining a complete decoupling of density, self-association or bundling strength, and stiffness of the nanotubes. Our findings show diverse ranges of emerging structures including heterogeneous networks, aster-like structures, and densely bundled needle-like structures, which compare to configurations found in many other systems. These show a strong dependence not only on concentration and bundling strength, but also on the underlying mechanical properties of the nanotubes. Similar network architectures to those caused by depletion forces in the low-density regime are obtained when an alternative hybridization-based bundling mechanism is employed to induce self-assembly in an isotropic network of pre-formed DNA nanotubes. This emphasizes the universal effect inevitable attractive forces in crowded environments have on systems of self-assembling soft matter, which should be considered for macromolecular structures applied in crowded systems such as cells.

Local complexity predicts global synchronization of hierarchically networked oscillators

NASA Astrophysics Data System (ADS)

Xu, Jin; Park, Dong-Ho; Jo, Junghyo

2017-07-01

We study the global synchronization of hierarchically-organized Stuart-Landau oscillators, where each subsystem consists of three oscillators with activity-dependent couplings. We considered all possible coupling signs between the three oscillators, and found that they can generate different numbers of phase attractors depending on the network motif. Here, the subsystems are coupled through mean activities of total oscillators. Under weak inter-subsystem couplings, we demonstrate that the synchronization between subsystems is highly correlated with the number of attractors in uncoupled subsystems. Among the network motifs, perfect anti-symmetric ones are unique to generate both single and multiple attractors depending on the activities of oscillators. The flexible local complexity can make global synchronization controllable.

Measuring the hierarchy of feedforward networks

NASA Astrophysics Data System (ADS)

Corominas-Murtra, Bernat; Rodríguez-Caso, Carlos; Goñi, Joaquín; Solé, Ricard

2011-03-01

In this paper we explore the concept of hierarchy as a quantifiable descriptor of ordered structures, departing from the definition of three conditions to be satisfied for a hierarchical structure: order, predictability, and pyramidal structure. According to these principles, we define a hierarchical index taking concepts from graph and information theory. This estimator allows to quantify the hierarchical character of any system susceptible to be abstracted in a feedforward causal graph, i.e., a directed acyclic graph defined in a single connected structure. Our hierarchical index is a balance between this predictability and pyramidal condition by the definition of two entropies: one attending the onward flow and the other for the backward reversion. We show how this index allows to identify hierarchical, antihierarchical, and nonhierarchical structures. Our formalism reveals that departing from the defined conditions for a hierarchical structure, feedforward trees and the inverted tree graphs emerge as the only causal structures of maximal hierarchical and antihierarchical systems respectively. Conversely, null values of the hierarchical index are attributed to a number of different configuration networks; from linear chains, due to their lack of pyramid structure, to full-connected feedforward graphs where the diversity of onward pathways is canceled by the uncertainty (lack of predictability) when going backward. Some illustrative examples are provided for the distinction among these three types of hierarchical causal graphs.

Aging, memory, and nonhierarchical energy landscape of spin jam

PubMed Central

Samarakoon, Anjana; Sato, Taku J.; Chen, Tianran; Chern, Gai-Wei; Yang, Junjie; Klich, Israel; Sinclair, Ryan; Zhou, Haidong; Lee, Seung-Hun

2016-01-01

The notion of complex energy landscape underpins the intriguing dynamical behaviors in many complex systems ranging from polymers, to brain activity, to social networks and glass transitions. The spin glass state found in dilute magnetic alloys has been an exceptionally convenient laboratory frame for studying complex dynamics resulting from a hierarchical energy landscape with rugged funnels. Here, we show, by a bulk susceptibility and Monte Carlo simulation study, that densely populated frustrated magnets in a spin jam state exhibit much weaker memory effects than spin glasses, and the characteristic properties can be reproduced by a nonhierarchical landscape with a wide and nearly flat but rough bottom. Our results illustrate that the memory effects can be used to probe different slow dynamics of glassy materials, hence opening a window to explore their distinct energy landscapes. PMID:27698141

Application of hierarchical Bayesian unmixing models in river sediment source apportionment

NASA Astrophysics Data System (ADS)

Blake, Will; Smith, Hugh; Navas, Ana; Bodé, Samuel; Goddard, Rupert; Zou Kuzyk, Zou; Lennard, Amy; Lobb, David; Owens, Phil; Palazon, Leticia; Petticrew, Ellen; Gaspar, Leticia; Stock, Brian; Boeckx, Pacsal; Semmens, Brice

2016-04-01

Fingerprinting and unmixing concepts are used widely across environmental disciplines for forensic evaluation of pollutant sources. In aquatic and marine systems, this includes tracking the source of organic and inorganic pollutants in water and linking problem sediment to soil erosion and land use sources. It is, however, the particular complexity of ecological systems that has driven creation of the most sophisticated mixing models, primarily to (i) evaluate diet composition in complex ecological food webs, (ii) inform population structure and (iii) explore animal movement. In the context of the new hierarchical Bayesian unmixing model, MIXSIAR, developed to characterise intra-population niche variation in ecological systems, we evaluate the linkage between ecological 'prey' and 'consumer' concepts and river basin sediment 'source' and sediment 'mixtures' to exemplify the value of ecological modelling tools to river basin science. Recent studies have outlined advantages presented by Bayesian unmixing approaches in handling complex source and mixture datasets while dealing appropriately with uncertainty in parameter probability distributions. MixSIAR is unique in that it allows individual fixed and random effects associated with mixture hierarchy, i.e. factors that might exert an influence on model outcome for mixture groups, to be explored within the source-receptor framework. This offers new and powerful ways of interpreting river basin apportionment data. In this contribution, key components of the model are evaluated in the context of common experimental designs for sediment fingerprinting studies namely simple, nested and distributed catchment sampling programmes. Illustrative examples using geochemical and compound specific stable isotope datasets are presented and used to discuss best practice with specific attention to (1) the tracer selection process, (2) incorporation of fixed effects relating to sample timeframe and sediment type in the modelling process, (3) deriving and using informative priors in sediment fingerprinting context and (4) transparency of the process and replication of model results by other users.

Variational Integrators for Interconnected Lagrange-Dirac Systems

NASA Astrophysics Data System (ADS)

Parks, Helen; Leok, Melvin

2017-10-01

Interconnected systems are an important class of mathematical models, as they allow for the construction of complex, hierarchical, multiphysics, and multiscale models by the interconnection of simpler subsystems. Lagrange-Dirac mechanical systems provide a broad category of mathematical models that are closed under interconnection, and in this paper, we develop a framework for the interconnection of discrete Lagrange-Dirac mechanical systems, with a view toward constructing geometric structure-preserving discretizations of interconnected systems. This work builds on previous work on the interconnection of continuous Lagrange-Dirac systems (Jacobs and Yoshimura in J Geom Mech 6(1):67-98, 2014) and discrete Dirac variational integrators (Leok and Ohsawa in Found Comput Math 11(5), 529-562, 2011). We test our results by simulating some of the continuous examples given in Jacobs and Yoshimura (2014).

Modeling a hierarchical structure of factors influencing exploitation policy for water distribution systems using ISM approach

NASA Astrophysics Data System (ADS)

Jasiulewicz-Kaczmarek, Małgorzata; Wyczółkowski, Ryszard; Gładysiak, Violetta

2017-12-01

Water distribution systems are one of the basic elements of contemporary technical infrastructure of urban and rural areas. It is a complex engineering system composed of transmission networks and auxiliary equipment (e.g. controllers, checkouts etc.), scattered territorially over a large area. From the water distribution system operation point of view, its basic features are: functional variability, resulting from the need to adjust the system to temporary fluctuations in demand for water and territorial dispersion. The main research questions are: What external factors should be taken into account when developing an effective water distribution policy? Does the size and nature of the water distribution system significantly affect the exploitation policy implemented? These questions have shaped the objectives of research and the method of research implementation.

Application of a hierarchical structure stochastic learning automation

NASA Technical Reports Server (NTRS)

Neville, R. G.; Chrystall, M. S.; Mars, P.

1979-01-01

A hierarchical structure automaton was developed using a two state stochastic learning automato (SLA) in a time shared model. Application of the hierarchical SLA to systems with multidimensional, multimodal performance criteria is described. Results of experiments performed with the hierarchical SLA using a performance index with a superimposed noise component of ? or - delta distributed uniformly over the surface are discussed.

Nonsomatotopic organization of the higher motor centers in octopus.

PubMed

Zullo, Letizia; Sumbre, German; Agnisola, Claudio; Flash, Tamar; Hochner, Binyamin

2009-10-13

Hyperredundant limbs with a virtually unlimited number of degrees of freedom (DOFs) pose a challenge for both biological and computational systems of motor control. In the flexible arms of the octopus, simplification strategies have evolved to reduce the number of controlled DOFs. Motor control in the octopus nervous system is hierarchically organized. A relatively small central brain integrates a huge amount of visual and tactile information from the large optic lobes and the peripheral nervous system of the arms and issues commands to lower motor centers controlling the elaborated neuromuscular system of the arms. This unique organization raises new questions on the organization of the octopus brain and whether and how it represents the rich movement repertoire. We developed a method of brain microstimulation in freely behaving animals and stimulated the higher motor centers-the basal lobes-thus inducing discrete and complex sets of movements. As stimulation strength increased, complex movements were recruited from basic components shared by different types of movement. We found no stimulation site where movements of a single arm or body part could be elicited. Discrete and complex components have no central topographical organization but are distributed over wide regions.

OPTICAL INFORMATION PROCESSING: Synthesis of an object recognition system based on the profile of the envelope of a laser pulse in pulsed lidars

NASA Astrophysics Data System (ADS)

Buryi, E. V.

1998-05-01

The main problems in the synthesis of an object recognition system, based on the principles of operation of neuron networks, are considered. Advantages are demonstrated of a hierarchical structure of the recognition algorithm. The use of reading of the amplitude spectrum of signals as information tags is justified and a method is developed for determination of the dimensionality of the tag space. Methods are suggested for ensuring the stability of object recognition in the optical range. It is concluded that it should be possible to recognise perspectives of complex objects.

Astrobo: Towards a new observatory control system for the Garching Observatory 0.6m

NASA Astrophysics Data System (ADS)

Schweyer, T.; Jarmatz, P.; Burwitz, V.

2016-12-01

The recently installed Campus Observatory Garching (COG) 0.6m telescope features a wide array of instruments, including a wide-field imager and a variety of spectrographs. To support all these different instruments and improve time usage, it was decided to develop a new control system from scratch, that will be able to safely observe autonomously as well as manually (for student lab courses). It is built using an hierarchical microservice architecture, which allows well-specified communication between its components regardless of the programming language used. This modular design allows for fast prototyping of components as well as easy implementation of complex instrumentation control software.

A general framework for multicharacter segmentation and its application in recognizing multilingual Asian documents

NASA Astrophysics Data System (ADS)

Wen, Di; Ding, Xiaoqing

2003-12-01

In this paper we propose a general framework for character segmentation in complex multilingual documents, which is an endeavor to combine the traditionally separated segmentation and recognition processes into a cooperative system. The framework contains three basic steps: Dissection, Local Optimization and Global Optimization, which are designed to fuse various properties of the segmentation hypotheses hierarchically into a composite evaluation to decide the final recognition results. Experimental results show that this framework is general enough to be applied in variety of documents. A sample system based on this framework to recognize Chinese, Japanese and Korean documents and experimental performance is reported finally.

On Decision-Making Among Multiple Rule-Bases in Fuzzy Control Systems

NASA Technical Reports Server (NTRS)

Tunstel, Edward; Jamshidi, Mo

1997-01-01

Intelligent control of complex multi-variable systems can be a challenge for single fuzzy rule-based controllers. This class of problems cam often be managed with less difficulty by distributing intelligent decision-making amongst a collection of rule-bases. Such an approach requires that a mechanism be chosen to ensure goal-oriented interaction between the multiple rule-bases. In this paper, a hierarchical rule-based approach is described. Decision-making mechanisms based on generalized concepts from single-rule-based fuzzy control are described. Finally, the effects of different aggregation operators on multi-rule-base decision-making are examined in a navigation control problem for mobile robots.

A Cognitive Complexity Metric Applied to Cognitive Development

ERIC Educational Resources Information Center

Andrews, Glenda; Halford, Graeme S.

2002-01-01

Two experiments tested predictions from a theory in which processing load depends on relational complexity (RC), the number of variables related in a single decision. Tasks from six domains (transitivity, hierarchical classification, class inclusion, cardinality, relative-clause sentence comprehension, and hypothesis testing) were administered to…

Hierarchical structure of biological systems

PubMed Central

Alcocer-Cuarón, Carlos; Rivera, Ana L; Castaño, Victor M

2014-01-01

A general theory of biological systems, based on few fundamental propositions, allows a generalization of both Wierner and Berthalanffy approaches to theoretical biology. Here, a biological system is defined as a set of self-organized, differentiated elements that interact pair-wise through various networks and media, isolated from other sets by boundaries. Their relation to other systems can be described as a closed loop in a steady-state, which leads to a hierarchical structure and functioning of the biological system. Our thermodynamical approach of hierarchical character can be applied to biological systems of varying sizes through some general principles, based on the exchange of energy information and/or mass from and within the systems. PMID:24145961

Hierarchical structure of biological systems: a bioengineering approach.

PubMed

Alcocer-Cuarón, Carlos; Rivera, Ana L; Castaño, Victor M

2014-01-01

A general theory of biological systems, based on few fundamental propositions, allows a generalization of both Wierner and Berthalanffy approaches to theoretical biology. Here, a biological system is defined as a set of self-organized, differentiated elements that interact pair-wise through various networks and media, isolated from other sets by boundaries. Their relation to other systems can be described as a closed loop in a steady-state, which leads to a hierarchical structure and functioning of the biological system. Our thermodynamical approach of hierarchical character can be applied to biological systems of varying sizes through some general principles, based on the exchange of energy information and/or mass from and within the systems.

Scale free effects in world currency exchange network

NASA Astrophysics Data System (ADS)

Górski, A. Z.; Drożdż, S.; Kwapień, J.

2008-11-01

A large collection of daily time series for 60 world currencies' exchange rates is considered. The correlation matrices are calculated and the corresponding Minimal Spanning Tree (MST) graphs are constructed for each of those currencies used as reference for the remaining ones. It is shown that multiplicity of the MST graphs' nodes to a good approximation develops a power like, scale free distribution with the scaling exponent similar as for several other complex systems studied so far. Furthermore, quantitative arguments in favor of the hierarchical organization of the world currency exchange network are provided by relating the structure of the above MST graphs and their scaling exponents to those that are derived from an exactly solvable hierarchical network model. A special status of the USD during the period considered can be attributed to some departures of the MST features, when this currency (or some other tied to it) is used as reference, from characteristics typical to such a hierarchical clustering of nodes towards those that correspond to the random graphs. Even though in general the basic structure of the MST is robust with respect to changing the reference currency some trace of a systematic transition from somewhat dispersed - like the USD case - towards more compact MST topology can be observed when correlations increase.

Circular instead of hierarchical: methodological principles for the evaluation of complex interventions

PubMed Central

Walach, Harald; Falkenberg, Torkel; Fønnebø, Vinjar; Lewith, George; Jonas, Wayne B

2006-01-01

Background The reasoning behind evaluating medical interventions is that a hierarchy of methods exists which successively produce improved and therefore more rigorous evidence based medicine upon which to make clinical decisions. At the foundation of this hierarchy are case studies, retrospective and prospective case series, followed by cohort studies with historical and concomitant non-randomized controls. Open-label randomized controlled studies (RCTs), and finally blinded, placebo-controlled RCTs, which offer most internal validity are considered the most reliable evidence. Rigorous RCTs remove bias. Evidence from RCTs forms the basis of meta-analyses and systematic reviews. This hierarchy, founded on a pharmacological model of therapy, is generalized to other interventions which may be complex and non-pharmacological (healing, acupuncture and surgery). Discussion The hierarchical model is valid for limited questions of efficacy, for instance for regulatory purposes and newly devised products and pharmacological preparations. It is inadequate for the evaluation of complex interventions such as physiotherapy, surgery and complementary and alternative medicine (CAM). This has to do with the essential tension between internal validity (rigor and the removal of bias) and external validity (generalizability). Summary Instead of an Evidence Hierarchy, we propose a Circular Model. This would imply a multiplicity of methods, using different designs, counterbalancing their individual strengths and weaknesses to arrive at pragmatic but equally rigorous evidence which would provide significant assistance in clinical and health systems innovation. Such evidence would better inform national health care technology assessment agencies and promote evidence based health reform. PMID:16796762

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

NASA Astrophysics Data System (ADS)

Dai, Heng; Chen, Xingyuan; Ye, Ming; Song, Xuehang; Zachara, John M.

2017-05-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.

An adaptable XML based approach for scientific data management and integration

NASA Astrophysics Data System (ADS)

Wang, Fusheng; Thiel, Florian; Furrer, Daniel; Vergara-Niedermayr, Cristobal; Qin, Chen; Hackenberg, Georg; Bourgue, Pierre-Emmanuel; Kaltschmidt, David; Wang, Mo

2008-03-01

Increased complexity of scientific research poses new challenges to scientific data management. Meanwhile, scientific collaboration is becoming increasing important, which relies on integrating and sharing data from distributed institutions. We develop SciPort, a Web-based platform on supporting scientific data management and integration based on a central server based distributed architecture, where researchers can easily collect, publish, and share their complex scientific data across multi-institutions. SciPort provides an XML based general approach to model complex scientific data by representing them as XML documents. The documents capture not only hierarchical structured data, but also images and raw data through references. In addition, SciPort provides an XML based hierarchical organization of the overall data space to make it convenient for quick browsing. To provide generalization, schemas and hierarchies are customizable with XML-based definitions, thus it is possible to quickly adapt the system to different applications. While each institution can manage documents on a Local SciPort Server independently, selected documents can be published to a Central Server to form a global view of shared data across all sites. By storing documents in a native XML database, SciPort provides high schema extensibility and supports comprehensive queries through XQuery. By providing a unified and effective means for data modeling, data access and customization with XML, SciPort provides a flexible and powerful platform for sharing scientific data for scientific research communities, and has been successfully used in both biomedical research and clinical trials.

A Geostatistics-Informed Hierarchical Sensitivity Analysis Method for Complex Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2017-12-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multi-layer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed input variables.

An Adaptable XML Based Approach for Scientific Data Management and Integration.

PubMed

Wang, Fusheng; Thiel, Florian; Furrer, Daniel; Vergara-Niedermayr, Cristobal; Qin, Chen; Hackenberg, Georg; Bourgue, Pierre-Emmanuel; Kaltschmidt, David; Wang, Mo

2008-02-20

Increased complexity of scientific research poses new challenges to scientific data management. Meanwhile, scientific collaboration is becoming increasing important, which relies on integrating and sharing data from distributed institutions. We develop SciPort, a Web-based platform on supporting scientific data management and integration based on a central server based distributed architecture, where researchers can easily collect, publish, and share their complex scientific data across multi-institutions. SciPort provides an XML based general approach to model complex scientific data by representing them as XML documents. The documents capture not only hierarchical structured data, but also images and raw data through references. In addition, SciPort provides an XML based hierarchical organization of the overall data space to make it convenient for quick browsing. To provide generalization, schemas and hierarchies are customizable with XML-based definitions, thus it is possible to quickly adapt the system to different applications. While each institution can manage documents on a Local SciPort Server independently, selected documents can be published to a Central Server to form a global view of shared data across all sites. By storing documents in a native XML database, SciPort provides high schema extensibility and supports comprehensive queries through XQuery. By providing a unified and effective means for data modeling, data access and customization with XML, SciPort provides a flexible and powerful platform for sharing scientific data for scientific research communities, and has been successfully used in both biomedical research and clinical trials.

Dynamic partial reconfiguration of logic controllers implemented in FPGAs

NASA Astrophysics Data System (ADS)

Bazydło, Grzegorz; Wiśniewski, Remigiusz

2016-09-01

Technological progress in recent years benefits in digital circuits containing millions of logic gates with the capability for reprogramming and reconfiguring. On the one hand it provides the unprecedented computational power, but on the other hand the modelled systems are becoming increasingly complex, hierarchical and concurrent. Therefore, abstract modelling supported by the Computer Aided Design tools becomes a very important task. Even the higher consumption of the basic electronic components seems to be acceptable because chip manufacturing costs tend to fall over the time. The paper presents a modelling approach for logic controllers with the use of Unified Modelling Language (UML). Thanks to the Model Driven Development approach, starting with a UML state machine model, through the construction of an intermediate Hierarchical Concurrent Finite State Machine model, a collection of Verilog files is created. The system description generated in hardware description language can be synthesized and implemented in reconfigurable devices, such as FPGAs. Modular specification of the prototyped controller permits for further dynamic partial reconfiguration of the prototyped system. The idea bases on the exchanging of the functionality of the already implemented controller without stopping of the FPGA device. It means, that a part (for example a single module) of the logic controller is replaced by other version (called context), while the rest of the system is still running. The method is illustrated by a practical example by an exemplary Home Area Network system.

High speed clinical data retrieval system with event time sequence feature: with 10 years of clinical data of Hamamatsu University Hospital CPOE.

PubMed

Kimura, M; Tani, S; Watanabe, H; Naito, Y; Sakusabe, T; Watanabe, H; Nakaya, J; Sasaki, F; Numano, T; Furuta, T; Furuta, T

2008-01-01

This paper illustrates a high speed clinical data retrieving system, from 10 years of data of operating hospital information system for the purposes of research, evidence creation, patient safety, etc., even incorporating time sequence of causal relations. Total of 73,709,298 records of 10 years at Hamamatsu University Hospital (as of June 2008) are sent from HIS to retrieval system in HL7 v2.5 format. Hierarchical variable length database is used to install them. A search for "listing patients who were prescribed Pravastatin (Mevalotin and generic drugs, any titer)" took 1.92 seconds. "Pravastatin (any) prescribed and recorded AST >150 within two weeks" took 112.22 seconds. Searching conditions can be set to be more complex, connected by Boolean operator and/or. This system called D*D is in operation at Hamamatsu University Hospital since August 2002. It is used for 48,518 times (monthly average of 703 searches). Neither searching, nor background export of data from HIS caused delay of routine operating CPOE. Search database outside of routine operating CPOE, with daily export of order data in HL7 v2.5 format, is proved to provide excellent search environment without causing trouble. Hierarchical representation gives high-speed search response, especially with time sequence of events.

Developing an Integration Infrastructure for Distributed Engine Control Technologies

NASA Technical Reports Server (NTRS)

Culley, Dennis; Zinnecker, Alicia; Aretskin-Hariton, Eliot; Kratz, Jonathan

2014-01-01

Turbine engine control technology is poised to make the first revolutionary leap forward since the advent of full authority digital engine control in the mid-1980s. This change aims squarely at overcoming the physical constraints that have historically limited control system hardware on aero-engines to a federated architecture. Distributed control architecture allows complex analog interfaces existing between system elements and the control unit to be replaced by standardized digital interfaces. Embedded processing, enabled by high temperature electronics, provides for digitization of signals at the source and network communications resulting in a modular system at the hardware level. While this scheme simplifies the physical integration of the system, its complexity appears in other ways. In fact, integration now becomes a shared responsibility among suppliers and system integrators. While these are the most obvious changes, there are additional concerns about performance, reliability, and failure modes due to distributed architecture that warrant detailed study. This paper describes the development of a new facility intended to address the many challenges of the underlying technologies of distributed control. The facility is capable of performing both simulation and hardware studies ranging from component to system level complexity. Its modular and hierarchical structure allows the user to focus their interaction on specific areas of interest.

Slow feature analysis: unsupervised learning of invariances.

PubMed

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

Merging K-means with hierarchical clustering for identifying general-shaped groups.

PubMed

Peterson, Anna D; Ghosh, Arka P; Maitra, Ranjan

2018-01-01

Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and K -means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while K -means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using K -means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.

Hierarchical and non-hierarchical mineralisation of collagen

PubMed Central

Liu, Yan; Kim, Young-Kyung; Dai, Lin; Li, Nan; Khan, Sara; Pashley, David H.; Tay, Franklin R.

2010-01-01

Biomineralisation of collagen involves functional motifs incorporated in extracellular matrix protein molecules to accomplish the objectives of stabilising amorphous calcium phosphate into nanoprecursors and directing the nucleation and growth of apatite within collagen fibrils. Here we report the use of small inorganic polyphosphate molecules to template hierarchical intrafibrillar apatite assembly in reconstituted collagen in the presence of polyacrylic acid to sequester calcium and phosphate into transient amorphous nanophases. The use of polyphosphate without a sequestration analogue resulted only in randomly-oriented extrafibrillar precipitations along the fibrillar surface. Conversely, the use of polyacrylic acid without a templating analogue resulted only in non-hierarchical intrafibrillar mineralisation with continuous apatite strands instead of discrete crystallites. The ability of using simple non-protein molecules to recapitulate different levels of structural hierarchy in mineralised collagen signifies the ultimate simplicity in Nature’s biomineralisation design principles and challenges the need for using more complex recombinant matrix proteins in bioengineering applications. PMID:21040969

Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, Robert M.

2008-01-01

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.

3D reconstruction from non-uniform point clouds via local hierarchical clustering

NASA Astrophysics Data System (ADS)

Yang, Jiaqi; Li, Ruibo; Xiao, Yang; Cao, Zhiguo

2017-07-01

Raw scanned 3D point clouds are usually irregularly distributed due to the essential shortcomings of laser sensors, which therefore poses a great challenge for high-quality 3D surface reconstruction. This paper tackles this problem by proposing a local hierarchical clustering (LHC) method to improve the consistency of point distribution. Specifically, LHC consists of two steps: 1) adaptive octree-based decomposition of 3D space, and 2) hierarchical clustering. The former aims at reducing the computational complexity and the latter transforms the non-uniform point set into uniform one. Experimental results on real-world scanned point clouds validate the effectiveness of our method from both qualitative and quantitative aspects.

Fuzzy logic control and optimization system

DOEpatents

Lou, Xinsheng [West Hartford, CT

2012-04-17

A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

A phenomenographic study of the ability to address complex socio-technical systems via variation theory

NASA Astrophysics Data System (ADS)

Mendoza Garcia, John A.

Sometimes engineers fail when addressing the inherent complexity of socio-technical systems because they lack the ability to address the complexity of socio-technical systems. Teaching undergraduate engineering students how to address complex socio-technical systems, has been an educational endeavor at different levels ranging from kindergarten to post-graduate education. The literature presents different pedagogical strategies and content to reach this goal. However, there are no existing empirically-based assessments guided by a learning theory. This may be because at the same time explanations of how the skill is developed are scarce. My study bridges this gap, and I propose a developmental path for the ability to address the complex socio-technical systems via Variation Theory, and according to the conceptual framework provided by Variation Theory, my research question was "What are the various ways in which engineers address complex socio-technical systems?" I chose the research approach of phenomenography to answer my research question. I also chose to use a blended approach, Marton's approach for finding the dimensions of variation, and the developmental approach (Australian) for finding a hierarchical relationship between the dimensions. Accordingly, I recruited 25 participants with different levels of experience with addressing complex socio-technical systems and asked them all to address the same two tasks: A design of a system for a county, and a case study in a manufacturing firm. My outcome space is a nona-dimensional (nine) developmental path for the ability to address the complexity in socio-technical systems, and I propose 9 different ways of experiencing the complexity of a socio-technical system. The findings of this study suggest that the critical aspects that are needed to address the complexity of socio-technical systems are: being aware of the use of models, the ecosystem around, start recognizing different boundaries, being aware of time as a factor, recognizing the part-whole relationships, make effort in tailoring a solution that responds to stakeholders' needs, find the right problem, giving voice to others, and finally be aware of the need to iterate.

Merging OLTP and OLAP - Back to the Future

NASA Astrophysics Data System (ADS)

Lehner, Wolfgang

When the terms "Data Warehousing" and "Online Analytical Processing" were coined in the 1990s by Kimball, Codd, and others, there was an obvious need for separating data and workload for operational transactional-style processing and decision-making implying complex analytical queries over large and historic data sets. Large data warehouse infrastructures have been set up to cope with the special requirements of analytical query answering for multiple reasons: For example, analytical thinking heavily relies on predefined navigation paths to guide the user through the data set and to provide different views on different aggregation levels.Multi-dimensional queries exploiting hierarchically structured dimensions lead to complex star queries at a relational backend, which could hardly be handled by classical relational systems.

A tool for filtering information in complex systems

PubMed Central

Tumminello, M.; Aste, T.; Di Matteo, T.; Mantegna, R. N.

2005-01-01

We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph. We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant relationships with the market structure and properties. PMID:16027373

A tool for filtering information in complex systems.

PubMed

Tumminello, M; Aste, T; Di Matteo, T; Mantegna, R N

2005-07-26

We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph. We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant relationships with the market structure and properties.

Reducing the complexity of the software design process with object-oriented design

NASA Technical Reports Server (NTRS)

Schuler, M. P.

1991-01-01

Designing software is a complex process. How object-oriented design (OOD), coupled with formalized documentation and tailored object diagraming techniques, can reduce the complexity of the software design process is described and illustrated. The described OOD methodology uses a hierarchical decomposition approach in which parent objects are decomposed into layers of lower level child objects. A method of tracking the assignment of requirements to design components is also included. Increases in the reusability, portability, and maintainability of the resulting products are also discussed. This method was built on a combination of existing technology, teaching experience, consulting experience, and feedback from design method users. The discussed concepts are applicable to hierarchal OOD processes in general. Emphasis is placed on improving the design process by documenting the details of the procedures involved and incorporating improvements into those procedures as they are developed.

A search for tight hierarchical triple systems amongst the eclipsing binaries in the CoRoT fields

NASA Astrophysics Data System (ADS)

Hajdu, T.; Borkovits, T.; Forgács-Dajka, E.; Sztakovics, J.; Marschalkó, G.; Benkő, J. M.; Klagyivik, P.; Sallai, M. J.

2017-10-01

We report a comprehensive search for hierarchical triple stellar system candidates amongst eclipsing binaries (EBs) observed by the CoRoT spacecraft. We calculate and check eclipse timing variation (ETV) diagrams for almost 1500 EBs in an automated manner. We identify five relatively short period Algol systems for which our combined light-curve and complex ETV analyses (including both the light-travel time effect and short-term dynamical third-body perturbations) resulted in consistent third-body solutions. The computed periods of the outer bodies are between 82 and 272 d (with an alternative solution of 831 d for one of the targets). We find that the inner and outer orbits are near coplanar in all but one case. The dynamical masses of the outer subsystems determined from the ETV analyses are consistent with both the results of our light-curve analyses and the spectroscopic information available in the literature. One of our candidate systems exhibits outer eclipsing events as well, the locations of which are in good agreement with the ETV solution. We also report another certain triply eclipsing triple system that, however, is lacking a reliable ETV solution due to the very short time range of the data, and four new blended systems (composite light curves of two EBs each), where we cannot decide whether the components are gravitationally bounded or not. Amongst these blended systems, we identify the longest period and highest eccentricity EB in the entire CoRoT sample.

Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

PubMed

Fesharaki, Nooshin Jafari; Pourghassem, Hossein

2013-07-01

Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

Principles of Protein Recognition and Properties of Protein-protein Interfaces

NASA Astrophysics Data System (ADS)

Keskin, Ozlem; Gursoy, Attila; Nussinov, Ruth

In this chapter we address two aspects - the static physical interactions which allow the information transfer for the function to be performed; and the dynamic, i.e. how the information is transmitted between the binding sites in the single protein molecule and in the network. We describe the single protein molecules and their complexes; and the analogy between protein folding and protein binding. Eventually, to fully understand the interactome and how it performs the essential cellular functions, we have to put all together - and hierarchically progress through the system.

Star Clusters in the Magellanic Clouds

NASA Astrophysics Data System (ADS)

Gallagher, J. S., III

2014-09-01

The Magellanic Clouds (MC) are prime locations for studies of star clusters covering a full range in age and mass. This contribution briefly reviews selected properties of Magellanic star clusters, by focusing first on young systems that show evidence for hierarchical star formation. The structures and chemical abundance patterns of older intermediate age star clusters in the Small Magellanic Cloud (SMC) are a second topic. These suggest a complex history has affected the chemical enrichment in the SMC and that low tidal stresses in the SMC foster star cluster survival.

On the Analysis of Output Information of S-tree Method

NASA Astrophysics Data System (ADS)

Bekaryan, Karen M.; Melkonyan, Anahit A.

2007-08-01

On of the most popular and effective method of analysis of hierarchical structure of N-body gravitating systems is method of S-tree diagrams. Apart from many interesting peculiarities, the method, unfortunately, is not free from some disadvantages, among which most important is an extremely complexity of analysis of output information. To solve this problem a number of methods are suggested. From our point of view, most effective approach is an application of all these methods simultaneousely. This allows to obtaine more complete and objective «picture» concerning a final distribution.

Three-Dimensional ZnO Hierarchical Nanostructures: Solution Phase Synthesis and Applications

PubMed Central

Wang, Xiaoliang; Ahmad, Mashkoor

2017-01-01

Zinc oxide (ZnO) nanostructures have been studied extensively in the past 20 years due to their novel electronic, photonic, mechanical and electrochemical properties. Recently, more attention has been paid to assemble nanoscale building blocks into three-dimensional (3D) complex hierarchical structures, which not only inherit the excellent properties of the single building blocks but also provide potential applications in the bottom-up fabrication of functional devices. This review article focuses on 3D ZnO hierarchical nanostructures, and summarizes major advances in the solution phase synthesis, applications in environment, and electrical/electrochemical devices. We present the principles and growth mechanisms of ZnO nanostructures via different solution methods, with an emphasis on rational control of the morphology and assembly. We then discuss the applications of 3D ZnO hierarchical nanostructures in photocatalysis, field emission, electrochemical sensor, and lithium ion batteries. Throughout the discussion, the relationship between the device performance and the microstructures of 3D ZnO hierarchical nanostructures will be highlighted. This review concludes with a personal perspective on the current challenges and future research. PMID:29137195

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

Chimeras in leaky integrate-and-fire neural networks: effects of reflecting connectivities

NASA Astrophysics Data System (ADS)

Tsigkri-DeSmedt, Nefeli Dimitra; Hizanidis, Johanne; Schöll, Eckehard; Hövel, Philipp; Provata, Astero

2017-07-01

The effects of attracting-nonlocal and reflecting connectivity are investigated in coupled Leaky Integrate-and-Fire (LIF) elements, which model the exchange of electrical signals between neurons. Earlier investigations have demonstrated that repulsive-nonlocal and hierarchical network connectivity can induce complex synchronization patterns and chimera states in systems of coupled oscillators. In the LIF system we show that if the elements are nonlocally linked with positive diffusive coupling on a ring network, the system splits into a number of alternating domains. Half of these domains contain elements whose potential stays near the threshold and they are interrupted by active domains where the elements perform regular LIF oscillations. The active domains travel along the ring with constant velocity, depending on the system parameters. When we introduce reflecting coupling in LIF networks unexpected complex spatio-temporal structures arise. For relatively extensive ranges of parameter values, the system splits into two coexisting domains: one where all elements stay near the threshold and one where incoherent states develop, characterized by multi-leveled mean phase velocity profiles.

Enriched Environment Increases PCNA and PARP1 Levels in Octopus vulgaris Central Nervous System: First Evidence of Adult Neurogenesis in Lophotrochozoa.

PubMed

Bertapelle, Carla; Polese, Gianluca; Di Cosmo, Anna

2017-06-01

Organisms showing a complex and centralized nervous system, such as teleosts, amphibians, reptiles, birds and mammals, and among invertebrates, crustaceans and insects, can adjust their behavior according to the environmental challenges. Proliferation, differentiation, migration, and axonal and dendritic development of newborn neurons take place in brain areas where structural plasticity, involved in learning, memory, and sensory stimuli integration, occurs. Octopus vulgaris has a complex and centralized nervous system, located between the eyes, with a hierarchical organization. It is considered the most "intelligent" invertebrate for its advanced cognitive capabilities, as learning and memory, and its sophisticated behaviors. The experimental data obtained by immunohistochemistry and western blot assay using proliferating cell nuclear antigen and poli (ADP-ribose) polymerase 1 as marker of cell proliferation and synaptogenesis, respectively, reviled cell proliferation in areas of brain involved in learning, memory, and sensory stimuli integration. Furthermore, we showed how enriched environmental conditions affect adult neurogenesis. © 2017 Wiley Periodicals, Inc.

Complex Hollow Nanostructures: Synthesis and Energy-Related Applications.

PubMed

Yu, Le; Hu, Han; Wu, Hao Bin; Lou, Xiong Wen David

2017-04-01

Hollow nanostructures offer promising potential for advanced energy storage and conversion applications. In the past decade, considerable research efforts have been devoted to the design and synthesis of hollow nanostructures with high complexity by manipulating their geometric morphology, chemical composition, and building block and interior architecture to boost their electrochemical performance, fulfilling the increasing global demand for renewable and sustainable energy sources. In this Review, we present a comprehensive overview of the synthesis and energy-related applications of complex hollow nanostructures. After a brief classification, the design and synthesis of complex hollow nanostructures are described in detail, which include hierarchical hollow spheres, hierarchical tubular structures, hollow polyhedra, and multi-shelled hollow structures, as well as their hybrids with nanocarbon materials. Thereafter, we discuss their niche applications as electrode materials for lithium-ion batteries and hybrid supercapacitors, sulfur hosts for lithium-sulfur batteries, and electrocatalysts for oxygen- and hydrogen-involving energy conversion reactions. The potential superiorities of complex hollow nanostructures for these applications are particularly highlighted. Finally, we conclude this Review with urgent challenges and further research directions of complex hollow nanostructures for energy-related applications. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Equity inthe use of dental services provided by the Brazilian Unified Health System (SUS) among the elderly: a population-based study.

PubMed

Oliveira, Renata Francine Rodrigues de; Souza, João Gabriel Silva; Haikal, Desireé Sant'Ana; Ferreira, Efigênia Ferreira E; Martins, Andréa Maria Eleutério de Barros Lima

2016-11-01

The scope of this study is to establish the profile of elderly users of dental services provided by the Brazilian Unified Health System(SUS) and associated factors from the standpoint of equity. It involves an analytical cross-sectional study with hierarchical modeling conducted on the basis of a complex probabilistic sample of groups of the elderly (65-74 years of age) living in a densely populated Brazilian city. Independent variables were included relating to: socio-demographic characteristics, access to information on health, behaviors/health-care system and health outcomes. Descriptive, bivariate and multiple hierarchical analysis was performed. Of the 480 elderly persons included, 138 (31.2%) used dental services from the SUS. Use of these services was greater as per capita income and level of schooling decreased. It was lower among those who had not conducted exams of their own mouths (oral self-examinations) and higher among those individuals who used dental services for non-routine procedures. In addition, people whose relationship had been affected by oral health issues and a negative perception of their appearance used the SUS more frequently. The conclusion drawn is that the use of dental services of the SUS was most prevalent among the elderly living in precarious conditions.

Predicting the performance uncertainty of a 1-MW pilot-scale carbon capture system after hierarchical laboratory-scale calibration and validation

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

Xu, Zhijie; Lai, Canhai; Marcy, Peter William

2017-05-01

A challenging problem in designing pilot-scale carbon capture systems is to predict, with uncertainty, the adsorber performance and capture efficiency under various operating conditions where no direct experimental data exist. Motivated by this challenge, we previously proposed a hierarchical framework in which relevant parameters of physical models were sequentially calibrated from different laboratory-scale carbon capture unit (C2U) experiments. Specifically, three models of increasing complexity were identified based on the fundamental physical and chemical processes of the sorbent-based carbon capture technology. Results from the corresponding laboratory experiments were used to statistically calibrate the physical model parameters while quantifying some of theirmore » inherent uncertainty. The parameter distributions obtained from laboratory-scale C2U calibration runs are used in this study to facilitate prediction at a larger scale where no corresponding experimental results are available. In this paper, we first describe the multiphase reactive flow model for a sorbent-based 1-MW carbon capture system then analyze results from an ensemble of simulations with the upscaled model. The simulation results are used to quantify uncertainty regarding the design’s predicted efficiency in carbon capture. In particular, we determine the minimum gas flow rate necessary to achieve 90% capture efficiency with 95% confidence.« less

Hierarchical Management Information Systems: A Decentralized Approach for University Administration

ERIC Educational Resources Information Center

Wager, J. James

1977-01-01

A Hierarchical Management Information System (HMIS) provides decision-making as well as operational information to all groups of the institution in a timely and predictable manner. Its operational aspects and benefits are discussed. (Author/LBH)

Collective influence in evolutionary social dilemmas

NASA Astrophysics Data System (ADS)

Szolnoki, Attila; Perc, Matjaž

2016-03-01

When evolutionary games are contested in structured populations, the degree of each player in the network plays an important role. If they exist, hubs often determine the fate of the population in remarkable ways. Recent research based on optimal percolation in random networks has shown, however, that the degree is neither the sole nor the best predictor of influence in complex networks. Low-degree nodes may also be optimal influencers if they are hierarchically linked to hubs. Taking this into account leads to the formalism of collective influence in complex networks, which as we show here, has far-reaching implications for the favorable resolution of social dilemmas. In particular, there exists an optimal hierarchical depth for the determination of collective influence that we use to describe the potency of players for passing their strategies, which depends on the strength of the social dilemma. Interestingly, the degree, which corresponds to the baseline depth zero, is optimal only when the temptation to defect is small. Our research reveals that evolutionary success stories are related to spreading processes which are rooted in favorable hierarchical structures that extend beyond local neighborhoods.

Discriminative Hierarchical K-Means Tree for Large-Scale Image Classification.

PubMed

Chen, Shizhi; Yang, Xiaodong; Tian, Yingli

2015-09-01

A key challenge in large-scale image classification is how to achieve efficiency in terms of both computation and memory without compromising classification accuracy. The learning-based classifiers achieve the state-of-the-art accuracies, but have been criticized for the computational complexity that grows linearly with the number of classes. The nonparametric nearest neighbor (NN)-based classifiers naturally handle large numbers of categories, but incur prohibitively expensive computation and memory costs. In this brief, we present a novel classification scheme, i.e., discriminative hierarchical K-means tree (D-HKTree), which combines the advantages of both learning-based and NN-based classifiers. The complexity of the D-HKTree only grows sublinearly with the number of categories, which is much better than the recent hierarchical support vector machines-based methods. The memory requirement is the order of magnitude less than the recent Naïve Bayesian NN-based approaches. The proposed D-HKTree classification scheme is evaluated on several challenging benchmark databases and achieves the state-of-the-art accuracies, while with significantly lower computation cost and memory requirement.

Uncovering three-dimensional gradients in fibrillar orientation in an impact-resistant biological armour.

PubMed

Zhang, Y; Paris, O; Terrill, N J; Gupta, H S

2016-05-23

The complex hierarchical structure in biological and synthetic fibrous nanocomposites entails considerable difficulties in the interpretation of the crystallographic texture from diffraction data. Here, we present a novel reconstruction method to obtain the 3D distribution of fibres in such systems. An analytical expression is derived for the diffraction intensity from fibres, explaining the azimuthal intensity distribution in terms of the angles of the three dimensional fibre orientation distributions. The telson of stomatopod (mantis shrimp) serves as an example of natural biological armour whose high impact resistance property is believed to arise from the hierarchical organization of alpha chitin nanofibrils into fibres and twisted plywood (Bouligand) structures at the sub-micron and micron scale. Synchrotron microfocus scanning X-ray diffraction data on stomatopod telson were used as a test case to map the 3D fibre orientation across the entire tissue section. The method is applicable to a range of biological and biomimetic structures with graded 3D fibre texture at the sub-micron and micron length scales.

Hierarchical matrices implemented into the boundary integral approaches for gravity field modelling

NASA Astrophysics Data System (ADS)

Čunderlík, Róbert; Vipiana, Francesca

2017-04-01

Boundary integral approaches applied for gravity field modelling have been recently developed to solve the geodetic boundary value problems numerically, or to process satellite observations, e.g. from the GOCE satellite mission. In order to obtain numerical solutions of "cm-level" accuracy, such approaches require very refined level of the disretization or resolution. This leads to enormous memory requirements that need to be reduced. An implementation of the Hierarchical Matrices (H-matrices) can significantly reduce a numerical complexity of these approaches. A main idea of the H-matrices is based on an approximation of the entire system matrix that is split into a family of submatrices. Large submatrices are stored in factorized representation, while small submatrices are stored in standard representation. This allows reducing memory requirements significantly while improving the efficiency. The poster presents our preliminary results of implementations of the H-matrices into the existing boundary integral approaches based on the boundary element method or the method of fundamental solution.

Uncovering three-dimensional gradients in fibrillar orientation in an impact-resistant biological armour

NASA Astrophysics Data System (ADS)

Zhang, Y.; Paris, O.; Terrill, N. J.; Gupta, H. S.

2016-05-01

The complex hierarchical structure in biological and synthetic fibrous nanocomposites entails considerable difficulties in the interpretation of the crystallographic texture from diffraction data. Here, we present a novel reconstruction method to obtain the 3D distribution of fibres in such systems. An analytical expression is derived for the diffraction intensity from fibres, explaining the azimuthal intensity distribution in terms of the angles of the three dimensional fibre orientation distributions. The telson of stomatopod (mantis shrimp) serves as an example of natural biological armour whose high impact resistance property is believed to arise from the hierarchical organization of alpha chitin nanofibrils into fibres and twisted plywood (Bouligand) structures at the sub-micron and micron scale. Synchrotron microfocus scanning X-ray diffraction data on stomatopod telson were used as a test case to map the 3D fibre orientation across the entire tissue section. The method is applicable to a range of biological and biomimetic structures with graded 3D fibre texture at the sub-micron and micron length scales.

3D hierarchical interface-enriched finite element method: Implementation and applications

NASA Astrophysics Data System (ADS)

Soghrati, Soheil; Ahmadian, Hossein

2015-10-01

A hierarchical interface-enriched finite element method (HIFEM) is proposed for the mesh-independent treatment of 3D problems with intricate morphologies. The HIFEM implements a recursive algorithm for creating enrichment functions that capture gradient discontinuities in nonconforming finite elements cut by arbitrary number and configuration of materials interfaces. The method enables the mesh-independent simulation of multiphase problems with materials interfaces that are in close proximity or contact while providing a straightforward general approach for evaluating the enrichments. In this manuscript, we present a detailed discussion on the implementation issues and required computational geometry considerations associated with the HIFEM approximation of thermal and mechanical responses of 3D problems. A convergence study is provided to investigate the accuracy and convergence rate of the HIFEM and compare them with standard FEM benchmark solutions. We will also demonstrate the application of this mesh-independent method for simulating the thermal and mechanical responses of two composite materials systems with complex microstructures.

Uncovering three-dimensional gradients in fibrillar orientation in an impact-resistant biological armour

PubMed Central

Zhang, Y.; Paris, O.; Terrill, N. J.; Gupta, H. S.

2016-01-01

The complex hierarchical structure in biological and synthetic fibrous nanocomposites entails considerable difficulties in the interpretation of the crystallographic texture from diffraction data. Here, we present a novel reconstruction method to obtain the 3D distribution of fibres in such systems. An analytical expression is derived for the diffraction intensity from fibres, explaining the azimuthal intensity distribution in terms of the angles of the three dimensional fibre orientation distributions. The telson of stomatopod (mantis shrimp) serves as an example of natural biological armour whose high impact resistance property is believed to arise from the hierarchical organization of alpha chitin nanofibrils into fibres and twisted plywood (Bouligand) structures at the sub-micron and micron scale. Synchrotron microfocus scanning X-ray diffraction data on stomatopod telson were used as a test case to map the 3D fibre orientation across the entire tissue section. The method is applicable to a range of biological and biomimetic structures with graded 3D fibre texture at the sub-micron and micron length scales. PMID:27211574

Assembly of porous hierarchical copolymers/resin proppants: New approaches to smart proppant immobilization via molecular anchors.

PubMed

Alexander, Shirin; Dunnill, Charles W; Barron, Andrew R

2016-03-15

The assembly of temperature/pH sensitive complex microparticle structures through chemisorption and physisorption provides a responsive system that offers application as routes to immobilization of proppants in-situ. Thermogravimetric analysis (TGA) and scanning electron microscopy (SEM) along with energy dispersive X-ray analysis (EDX) have been used to characterize a series of bi-functionalized monolayers and/or multilayers grown on alumina microparticles and investigate the reactive nature of both temperature sensitive cross-linker (epoxy resin) with the layers and pH-responsive bridging layer (polyetheramine). The bifunctional acids, behaving as molecular anchors, allow for a controlled reaction with a cross-linker (resin or polymer) with the formation of networks, which is either irreversible or reversible based on the nature of the cross-linker. The networks results in formation of porous hierarchical particles that offer a potential route to the creation of immobile proppant pack. Copyright © 2015 Elsevier Inc. All rights reserved.

Self-assembly synthesis of precious-metal-free 3D ZnO nano/micro spheres with excellent photocatalytic hydrogen production from solar water splitting

NASA Astrophysics Data System (ADS)

Guo, Si-yao; Zhao, Tie-jun; Jin, Zu-quan; Wan, Xiao-mei; Wang, Peng-gang; Shang, Jun; Han, Song

2015-10-01

A simple and straightforward solution growth routine is developed to prepare microporous 3D nano/micro ZnO microsphere with a large BET surface area of 288 m2 g-1 at room temperature. The formation mechanism of the hierarchical 3D nano/micro ZnO microsphere and its corresponding hydrogen evolution performance has been deeply discussed. In particular, this novel hierarchical 3D ZnO microspheres performs undiminished hydrogen evolution for at least 24 h under simulated solar light illumination, even under the condition of no precious metal as cocatalyst. Since the complex production process of photocatalysts and high cost of precious metal cocatalyst remains a major constraint that hinders the application of solar water splitting, this 3D nano/micro ZnO microspheres could be expected to be applicable in the precious-metal-free solar water splitting system due to its merits of low cost, simple procedure and high catalytic activity.

HIERARCHICAL STRUCTURE FORMATION AND MODES OF STAR FORMATION IN HICKSON COMPACT GROUP 31

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

Gallagher, S. C.; Durrell, P. R.; Elmegreen, D. M.

2010-02-15

The handful of low-mass, late-type galaxies that comprise Hickson Compact Group 31 (HCG 31) is in the midst of complex, ongoing gravitational interactions, evocative of the process of hierarchical structure formation at higher redshifts. With sensitive, multicolor Hubble Space Telescope imaging, we characterize the large population of < 10 Myr old star clusters (SCs) that suffuse the system. From the colors and luminosities of the young SCs, we find that the galaxies in HCG 31 follow the same universal scaling relations as actively star-forming galaxies in the local universe despite the unusual compact group environment. Furthermore, the specific frequency ofmore » the globular cluster system is consistent with the low end of galaxies of comparable masses locally. This, combined with the large mass of neutral hydrogen and tight constraints on the amount of intragroup light, indicate that the group is undergoing its first epoch of interaction-induced star formation. In both the main galaxies and the tidal-dwarf candidate, F, stellar complexes, which are sensitive to the magnitude of disk turbulence, have both sizes and masses more characteristic of z = 1-2 galaxies. After subtracting the light from compact sources, we find no evidence for an underlying old stellar population in F-it appears to be a truly new structure. The low-velocity dispersion of the system components, available reservoir of H I, and current star formation rate of {approx}10 M {sub sun} yr{sup -1} indicate that HCG 31 is likely to both exhaust its cold gas supply and merge within {approx}1 Gyr. We conclude that the end product will be an isolated, X-ray-faint, low-mass elliptical.« less

LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.

PubMed

Cannon, Robert C; Gleeson, Padraig; Crook, Sharon; Ganapathy, Gautham; Marin, Boris; Piasini, Eugenio; Silver, R Angus

2014-01-01

Computational models are increasingly important for studying complex neurophysiological systems. As scientific tools, it is essential that such models can be reproduced and critically evaluated by a range of scientists. However, published models are currently implemented using a diverse set of modeling approaches, simulation tools, and computer languages making them inaccessible and difficult to reproduce. Models also typically contain concepts that are tightly linked to domain-specific simulators, or depend on knowledge that is described exclusively in text-based documentation. To address these issues we have developed a compact, hierarchical, XML-based language called LEMS (Low Entropy Model Specification), that can define the structure and dynamics of a wide range of biological models in a fully machine readable format. We describe how LEMS underpins the latest version of NeuroML and show that this framework can define models of ion channels, synapses, neurons and networks. Unit handling, often a source of error when reusing models, is built into the core of the language by specifying physical quantities in models in terms of the base dimensions. We show how LEMS, together with the open source Java and Python based libraries we have developed, facilitates the generation of scripts for multiple neuronal simulators and provides a route for simulator free code generation. We establish that LEMS can be used to define models from systems biology and map them to neuroscience-domain specific simulators, enabling models to be shared between these traditionally separate disciplines. LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties.

LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2

PubMed Central

Cannon, Robert C.; Gleeson, Padraig; Crook, Sharon; Ganapathy, Gautham; Marin, Boris; Piasini, Eugenio; Silver, R. Angus

2014-01-01

Computational models are increasingly important for studying complex neurophysiological systems. As scientific tools, it is essential that such models can be reproduced and critically evaluated by a range of scientists. However, published models are currently implemented using a diverse set of modeling approaches, simulation tools, and computer languages making them inaccessible and difficult to reproduce. Models also typically contain concepts that are tightly linked to domain-specific simulators, or depend on knowledge that is described exclusively in text-based documentation. To address these issues we have developed a compact, hierarchical, XML-based language called LEMS (Low Entropy Model Specification), that can define the structure and dynamics of a wide range of biological models in a fully machine readable format. We describe how LEMS underpins the latest version of NeuroML and show that this framework can define models of ion channels, synapses, neurons and networks. Unit handling, often a source of error when reusing models, is built into the core of the language by specifying physical quantities in models in terms of the base dimensions. We show how LEMS, together with the open source Java and Python based libraries we have developed, facilitates the generation of scripts for multiple neuronal simulators and provides a route for simulator free code generation. We establish that LEMS can be used to define models from systems biology and map them to neuroscience-domain specific simulators, enabling models to be shared between these traditionally separate disciplines. LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties. PMID:25309419

Causal Relation Analysis Tool of the Case Study in the Engineer Ethics Education

NASA Astrophysics Data System (ADS)

Suzuki, Yoshio; Morita, Keisuke; Yasui, Mitsukuni; Tanada, Ichirou; Fujiki, Hiroyuki; Aoyagi, Manabu

In engineering ethics education, the virtual experiencing of dilemmas is essential. Learning through the case study method is a particularly effective means. Many case studies are, however, difficult to deal with because they often include many complex causal relationships and social factors. It would thus be convenient if there were a tool that could analyze the factors of a case example and organize them into a hierarchical structure to get a better understanding of the whole picture. The tool that was developed applies a cause-and-effect matrix and simple graph theory. It analyzes the causal relationship between facts in a hierarchical structure and organizes complex phenomena. The effectiveness of this tool is shown by presenting an actual example.

Competitive cluster growth in complex networks.

PubMed

Moreira, André A; Paula, Demétrius R; Costa Filho, Raimundo N; Andrade, José S

2006-06-01

In this work we propose an idealized model for competitive cluster growth in complex networks. Each cluster can be thought of as a fraction of a community that shares some common opinion. Our results show that the cluster size distribution depends on the particular choice for the topology of the network of contacts among the agents. As an application, we show that the cluster size distributions obtained when the growth process is performed on hierarchical networks, e.g., the Apollonian network, have a scaling form similar to what has been observed for the distribution of a number of votes in an electoral process. We suggest that this similarity may be due to the fact that social networks involved in the electoral process may also possess an underlining hierarchical structure.

Neuropsychological study of FASD in a sample of American Indian children: processing simple versus complex information.

PubMed

Aragón, Alfredo S; Kalberg, Wendy O; Buckley, David; Barela-Scott, Lindsey M; Tabachnick, Barbara G; May, Philip A

2008-12-01

Although a large body of literature exists on cognitive functioning in alcohol-exposed children, it is unclear if there is a signature neuropsychological profile in children with Fetal Alcohol Spectrum Disorders (FASD). This study assesses cognitive functioning in children with FASD from several American Indian reservations in the Northern Plains States, and it applies a hierarchical model of simple versus complex information processing to further examine cognitive function. We hypothesized that complex tests would discriminate between children with FASD and culturally similar controls, while children with FASD would perform similar to controls on relatively simple tests. Our sample includes 32 control children and 24 children with a form of FASD [fetal alcohol syndrome (FAS) = 10, partial fetal alcohol syndrome (PFAS) = 14]. The test battery measures general cognitive ability, verbal fluency, executive functioning, memory, and fine-motor skills. Many of the neuropsychological tests produced results consistent with a hierarchical model of simple versus complex processing. The complexity of the tests was determined "a priori" based on the number of cognitive processes involved in them. Multidimensional scaling was used to statistically analyze the accuracy of classifying the neurocognitive tests into a simple versus complex dichotomy. Hierarchical logistic regression models were then used to define the contribution made by complex versus simple tests in predicting the significant differences between children with FASD and controls. Complex test items discriminated better than simple test items. The tests that conformed well to the model were the Verbal Fluency, Progressive Planning Test (PPT), the Lhermitte memory tasks, and the Grooved Pegboard Test (GPT). The FASD-grouped children, when compared with controls, demonstrated impaired performance on letter fluency, while their performance was similar on category fluency. On the more complex PPT trials (problems 5 to 8), as well as the Lhermitte logical tasks, the FASD group performed the worst. The differential performance between children with FASD and controls was evident across various neuropsychological measures. The children with FASD performed significantly more poorly on the complex tasks than did the controls. The identification of a neurobehavioral profile in children with prenatal alcohol exposure will help clinicians identify and diagnose children with FASD.

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

Chen, Zhe; Cao, Minhua, E-mail: caomh@bit.edu.cn; Key Laboratory of Cluster Science, Ministry of Education of China, Department of Chemistry, Beijing Institute of Technology, Beijing 100081

Research highlights: {yields} Novel Bi{sub 2}S{sub 3} hierarchical nanostructures self-assembled by nanorods are successfully synthesized in mild benzyl alcohol system under hydrothermal conditions. {yields} The hierarchical nanostructures exhibit a flower-like shape. {yields} PVP plays an important role for the formation of the hierarchical nanostructures. {yields} Bi{sub 2}S{sub 3} film prepared from the flower-like hierarchical nanostructures exhibits good hydrophobic properties. -- Abstract: Novel Bi{sub 2}S{sub 3} hierarchical nanostructures self-assembled by nanorods are successfully synthesized in mild benzyl alcohol system under hydrothermal conditions. The hierarchical nanostructures exhibit a flower-like shape. X-ray diffraction (XRD), X-ray photoelectron spectra (XPS), scanning electron microscopy (SEM), transmissionmore » electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), and selected area electron diffraction (SAED) were used to characterize the as-synthesized samples. Meanwhile, the effect of various experimental parameters including the concentration of reagents and reaction time on final product has been investigated. In our experiment, PVP plays an important role for the formation of the hierarchical nanostructures and the possible mechanism was proposed. In addition, Bi{sub 2}S{sub 3} film prepared from the flower-like hierarchical nanostructures exhibits good hydrophobic properties, which may bring nontrivial functionalities and may have some promising applications in the future.« less

Potential of Laboratory Execution Systems (LESs) to Simplify the Application of Business Process Management Systems (BPMSs) in Laboratory Automation.

PubMed

Neubert, Sebastian; Göde, Bernd; Gu, Xiangyu; Stoll, Norbert; Thurow, Kerstin

2017-04-01

Modern business process management (BPM) is increasingly interesting for laboratory automation. End-to-end workflow automation and improved top-level systems integration for information technology (IT) and automation systems are especially prominent objectives. With the ISO Standard Business Process Model and Notation (BPMN) 2.X, a system-independent and interdisciplinary accepted graphical process control notation is provided, allowing process analysis, while also being executable. The transfer of BPM solutions to structured laboratory automation places novel demands, for example, concerning the real-time-critical process and systems integration. The article discusses the potential of laboratory execution systems (LESs) for an easier implementation of the business process management system (BPMS) in hierarchical laboratory automation. In particular, complex application scenarios, including long process chains based on, for example, several distributed automation islands and mobile laboratory robots for a material transport, are difficult to handle in BPMSs. The presented approach deals with the displacement of workflow control tasks into life science specialized LESs, the reduction of numerous different interfaces between BPMSs and subsystems, and the simplification of complex process modelings. Thus, the integration effort for complex laboratory workflows can be significantly reduced for strictly structured automation solutions. An example application, consisting of a mixture of manual and automated subprocesses, is demonstrated by the presented BPMS-LES approach.

The hidden hyperbolic geometry of international trade: World Trade Atlas 1870-2013.

PubMed

García-Pérez, Guillermo; Boguñá, Marián; Allard, Antoine; Serrano, M Ángeles

2016-09-16

Here, we present the World Trade Atlas 1870-2013, a collection of annual world trade maps in which distance combines economic size and the different dimensions that affect international trade beyond mere geography. Trade distances, based on a gravity model predicting the existence of significant trade channels, are such that the closer countries are in trade space, the greater their chance of becoming connected. The atlas provides us with information regarding the long-term evolution of the international trade system and demonstrates that, in terms of trade, the world is not flat but hyperbolic, as a reflection of its complex architecture. The departure from flatness has been increasing since World War I, meaning that differences in trade distances are growing and trade networks are becoming more hierarchical. Smaller-scale economies are moving away from other countries except for the largest economies; meanwhile those large economies are increasing their chances of becoming connected worldwide. At the same time, Preferential Trade Agreements do not fit in perfectly with natural communities within the trade space and have not necessarily reduced internal trade barriers. We discuss an interpretation in terms of globalization, hierarchization, and localization; three simultaneous forces that shape the international trade system.

The hidden hyperbolic geometry of international trade: World Trade Atlas 1870-2013

NASA Astrophysics Data System (ADS)

García-Pérez, Guillermo; Boguñá, Marián; Allard, Antoine; Serrano, M. Ángeles

2016-09-01

Here, we present the World Trade Atlas 1870-2013, a collection of annual world trade maps in which distance combines economic size and the different dimensions that affect international trade beyond mere geography. Trade distances, based on a gravity model predicting the existence of significant trade channels, are such that the closer countries are in trade space, the greater their chance of becoming connected. The atlas provides us with information regarding the long-term evolution of the international trade system and demonstrates that, in terms of trade, the world is not flat but hyperbolic, as a reflection of its complex architecture. The departure from flatness has been increasing since World War I, meaning that differences in trade distances are growing and trade networks are becoming more hierarchical. Smaller-scale economies are moving away from other countries except for the largest economies; meanwhile those large economies are increasing their chances of becoming connected worldwide. At the same time, Preferential Trade Agreements do not fit in perfectly with natural communities within the trade space and have not necessarily reduced internal trade barriers. We discuss an interpretation in terms of globalization, hierarchization, and localization; three simultaneous forces that shape the international trade system.

Hierarchical processing in music, language, and action: Lashley revisited.

PubMed

Fitch, W Tecumseh; Martins, Mauricio D

2014-05-01

Sixty years ago, Karl Lashley suggested that complex action sequences, from simple motor acts to language and music, are a fundamental but neglected aspect of neural function. Lashley demonstrated the inadequacy of then-standard models of associative chaining, positing a more flexible and generalized "syntax of action" necessary to encompass key aspects of language and music. He suggested that hierarchy in language and music builds upon a more basic sequential action system, and provided several concrete hypotheses about the nature of this system. Here, we review a diverse set of modern data concerning musical, linguistic, and other action processing, finding them largely consistent with an updated neuroanatomical version of Lashley's hypotheses. In particular, the lateral premotor cortex, including Broca's area, plays important roles in hierarchical processing in language, music, and at least some action sequences. Although the precise computational function of the lateral prefrontal regions in action syntax remains debated, Lashley's notion-that this cortical region implements a working-memory buffer or stack scannable by posterior and subcortical brain regions-is consistent with considerable experimental data. © 2014 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals Inc. on behalf of The New York Academy of Sciences.

Re-examining the paradox of structure: a child health network perspective.

PubMed

McPherson, Charmaine M; Popp, Janice K; Lindstrom, Ronald R

2006-01-01

In their lead paper, Huerta, Casebeer and VanderPlaat argue that there are several key forces driving the development of health services delivery (HSD) networks, and propose a series of paradoxes and propositions to initiate this timely and essential dialogue. Ultimately, they submit that networks are likely to remain within the healthcare system to build system capacity and drive integration. Given this, they challenge us to further the dialogue and investigate these networks. While this peer commentary shares many of the lead author's perspectives, the generic nature of the discussion does not bring us to the relative complexities revealed in some HSD network practices. A Canadian child health network lens is used to re-examine the lead paper's conceptualization of network typologies and the proposed paradox of structure. We combine network practice and academic expertise to highlight the structural, governance and leadership tensions between traditional hierarchical public service organizations and the non-hierarchical nature of inter-organizational networks. Child health network leaders and members must examine and work with the challenges associated with importing traditional organizational cultures into an inter-organizationally networked context, while simultaneously maintaining these dual (or duelling) cultures.

Vehicle Integrated Prognostic Reasoner (VIPR) Metric Report

NASA Technical Reports Server (NTRS)

Cornhill, Dennis; Bharadwaj, Raj; Mylaraswamy, Dinkar

2013-01-01

This document outlines a set of metrics for evaluating the diagnostic and prognostic schemes developed for the Vehicle Integrated Prognostic Reasoner (VIPR), a system-level reasoner that encompasses the multiple levels of large, complex systems such as those for aircraft and spacecraft. VIPR health managers are organized hierarchically and operate together to derive diagnostic and prognostic inferences from symptoms and conditions reported by a set of diagnostic and prognostic monitors. For layered reasoners such as VIPR, the overall performance cannot be evaluated by metrics solely directed toward timely detection and accuracy of estimation of the faults in individual components. Among other factors, overall vehicle reasoner performance is governed by the effectiveness of the communication schemes between monitors and reasoners in the architecture, and the ability to propagate and fuse relevant information to make accurate, consistent, and timely predictions at different levels of the reasoner hierarchy. We outline an extended set of diagnostic and prognostics metrics that can be broadly categorized as evaluation measures for diagnostic coverage, prognostic coverage, accuracy of inferences, latency in making inferences, computational cost, and sensitivity to different fault and degradation conditions. We report metrics from Monte Carlo experiments using two variations of an aircraft reference model that supported both flat and hierarchical reasoning.

Oak Ridge Computerized Hierarchical Information System (ORCHIS) status report, July 1973

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

Brooks, A.A.

1974-01-01

This report summarizes the concepts, software, and contents of the Oak Ridge Computerized Hierarchical Information System. This data analysis and text processing system was developed as an integrated, comprehensive information processing capability to meet the needs of an on-going multidisciplinary research and development organization. (auth)

Hierarchical Discrete Event Supervisory Control of Aircraft Propulsion Systems

NASA Technical Reports Server (NTRS)

Yasar, Murat; Tolani, Devendra; Ray, Asok; Shah, Neerav; Litt, Jonathan S.

2004-01-01

This paper presents a hierarchical application of Discrete Event Supervisory (DES) control theory for intelligent decision and control of a twin-engine aircraft propulsion system. A dual layer hierarchical DES controller is designed to supervise and coordinate the operation of two engines of the propulsion system. The two engines are individually controlled to achieve enhanced performance and reliability, necessary for fulfilling the mission objectives. Each engine is operated under a continuously varying control system that maintains the specified performance and a local discrete-event supervisor for condition monitoring and life extending control. A global upper level DES controller is designed for load balancing and overall health management of the propulsion system.

Synthesis of Porous Carbon Monoliths Using Hard Templates.

PubMed

Klepel, Olaf; Danneberg, Nina; Dräger, Matti; Erlitz, Marcel; Taubert, Michael

2016-03-21

The preparation of porous carbon monoliths with a defined shape via template-assisted routes is reported. Monoliths made from porous concrete and zeolite were each used as the template. The porous concrete-derived carbon monoliths exhibited high gravimetric specific surface areas up to 2000 m²·g -1 . The pore system comprised macro-, meso-, and micropores. These pores were hierarchically arranged. The pore system was created by the complex interplay of the actions of both the template and the activating agent as well. On the other hand, zeolite-made template shapes allowed for the preparation of microporous carbon monoliths with a high volumetric specific surface area. This feature could be beneficial if carbon monoliths must be integrated into technical systems under space-limited conditions.

Synthesis of Porous Carbon Monoliths Using Hard Templates

PubMed Central

Klepel, Olaf; Danneberg, Nina; Dräger, Matti; Erlitz, Marcel; Taubert, Michael

2016-01-01

The preparation of porous carbon monoliths with a defined shape via template-assisted routes is reported. Monoliths made from porous concrete and zeolite were each used as the template. The porous concrete-derived carbon monoliths exhibited high gravimetric specific surface areas up to 2000 m2·g−1. The pore system comprised macro-, meso-, and micropores. These pores were hierarchically arranged. The pore system was created by the complex interplay of the actions of both the template and the activating agent as well. On the other hand, zeolite-made template shapes allowed for the preparation of microporous carbon monoliths with a high volumetric specific surface area. This feature could be beneficial if carbon monoliths must be integrated into technical systems under space-limited conditions. PMID:28773338

Evolution and development of brain networks: from Caenorhabditis elegans to Homo sapiens.

PubMed

Kaiser, Marcus; Varier, Sreedevi

2011-01-01

Neural networks show a progressive increase in complexity during the time course of evolution. From diffuse nerve nets in Cnidaria to modular, hierarchical systems in macaque and humans, there is a gradual shift from simple processes involving a limited amount of tasks and modalities to complex functional and behavioral processing integrating different kinds of information from highly specialized tissue. However, studies in a range of species suggest that fundamental similarities, in spatial and topological features as well as in developmental mechanisms for network formation, are retained across evolution. 'Small-world' topology and highly connected regions (hubs) are prevalent across the evolutionary scale, ensuring efficient processing and resilience to internal (e.g. lesions) and external (e.g. environment) changes. Furthermore, in most species, even the establishment of hubs, long-range connections linking distant components, and a modular organization, relies on similar mechanisms. In conclusion, evolutionary divergence leads to greater complexity while following essential developmental constraints.

SharP

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

Venkata, Manjunath Gorentla; Aderholdt, William F

The pre-exascale systems are expected to have a significant amount of hierarchical and heterogeneous on-node memory, and this trend of system architecture in extreme-scale systems is expected to continue into the exascale era. along with hierarchical-heterogeneous memory, the system typically has a high-performing network ad a compute accelerator. This system architecture is not only effective for running traditional High Performance Computing (HPC) applications (Big-Compute), but also for running data-intensive HPC applications and Big-Data applications. As a consequence, there is a growing desire to have a single system serve the needs of both Big-Compute and Big-Data applications. Though the system architecturemore » supports the convergence of the Big-Compute and Big-Data, the programming models and software layer have yet to evolve to support either hierarchical-heterogeneous memory systems or the convergence. A programming abstraction to address this problem. The programming abstraction is implemented as a software library and runs on pre-exascale and exascale systems supporting current and emerging system architecture. Using distributed data-structures as a central concept, it provides (1) a simple, usable, and portable abstraction for hierarchical-heterogeneous memory and (2) a unified programming abstraction for Big-Compute and Big-Data applications.« less

The VMC Survey. XXVII. Young Stellar Structures in the LMC’s Bar Star-forming Complex

NASA Astrophysics Data System (ADS)

Sun, Ning-Chen; de Grijs, Richard; Subramanian, Smitha; Bekki, Kenji; Bell, Cameron P. M.; Cioni, Maria-Rosa L.; Ivanov, Valentin D.; Marconi, Marcella; Oliveira, Joana M.; Piatti, Andrés E.; Ripepi, Vincenzo; Rubele, Stefano; Tatton, Ben L.; van Loon, Jacco Th.

2017-11-01

Star formation is a hierarchical process, forming young stellar structures of star clusters, associations, and complexes over a wide range of scales. The star-forming complex in the bar region of the Large Magellanic Cloud is investigated with upper main-sequence stars observed by the VISTA Survey of the Magellanic Clouds. The upper main-sequence stars exhibit highly nonuniform distributions. Young stellar structures inside the complex are identified from the stellar density map as density enhancements of different significance levels. We find that these structures are hierarchically organized such that larger, lower-density structures contain one or several smaller, higher-density ones. They follow power-law size and mass distributions, as well as a lognormal surface density distribution. All these results support a scenario of hierarchical star formation regulated by turbulence. The temporal evolution of young stellar structures is explored by using subsamples of upper main-sequence stars with different magnitude and age ranges. While the youngest subsample, with a median age of log(τ/yr) = 7.2, contains the most substructure, progressively older ones are less and less substructured. The oldest subsample, with a median age of log(τ/yr) = 8.0, is almost indistinguishable from a uniform distribution on spatial scales of 30-300 pc, suggesting that the young stellar structures are completely dispersed on a timescale of ˜100 Myr. These results are consistent with the characteristics of the 30 Doradus complex and the entire Large Magellanic Cloud, suggesting no significant environmental effects. We further point out that the fractal dimension may be method dependent for stellar samples with significant age spreads.

Statistical label fusion with hierarchical performance models

PubMed Central

Asman, Andrew J.; Dagley, Alexander S.; Landman, Bennett A.

2014-01-01

Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally – fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance. Building on the seminal work in statistical fusion, we reformulate the traditional rater performance model from a multi-tiered hierarchical perspective. This new approach provides a natural framework for leveraging known anatomical relationships and accurately modeling the types of errors that raters (or atlases) make within a hierarchically consistent formulation. Herein, we describe several contributions. First, we derive a theoretical advancement to the statistical fusion framework that enables the simultaneous estimation of multiple (hierarchical) performance models within the statistical fusion context. Second, we demonstrate that the proposed hierarchical formulation is highly amenable to the state-of-the-art advancements that have been made to the statistical fusion framework. Lastly, in an empirical whole-brain segmentation task we demonstrate substantial qualitative and significant quantitative improvement in overall segmentation accuracy. PMID:24817809

Method and system for rendering and interacting with an adaptable computing environment

DOEpatents

Osbourn, Gordon Cecil [Albuquerque, NM; Bouchard, Ann Marie [Albuquerque, NM

2012-06-12

An adaptable computing environment is implemented with software entities termed "s-machines", which self-assemble into hierarchical data structures capable of rendering and interacting with the computing environment. A hierarchical data structure includes a first hierarchical s-machine bound to a second hierarchical s-machine. The first hierarchical s-machine is associated with a first layer of a rendering region on a display screen and the second hierarchical s-machine is associated with a second layer of the rendering region overlaying at least a portion of the first layer. A screen element s-machine is linked to the first hierarchical s-machine. The screen element s-machine manages data associated with a screen element rendered to the display screen within the rendering region at the first layer.

A hierarchical framework of aquatic ecological units in North America (Nearctic Zone).

Treesearch

James R. Maxwell; Clayton J. Edwards; Mark E. Jensen; Steven J. Paustian; Harry Parrott; Donley M. Hill

1995-01-01

Proposes a framework for classifying and mapping aquatic systems at various scales using ecologically significant physical and biological criteria. Classification and mapping concepts follow tenets of hierarchical theory, pattern recognition, and driving variables. Criteria are provided for the hierarchical classification and mapping of aquatic ecological units of...

Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets

NASA Astrophysics Data System (ADS)

Wyche, K. P.; Monks, P. S.; Smallbone, K. L.; Hamilton, J. F.; Alfarra, M. R.; Rickard, A. R.; McFiggans, G. B.; Jenkin, M. E.; Bloss, W. J.; Ryan, A. C.; Hewitt, C. N.; MacKenzie, A. R.

2015-07-01

Highly non-linear dynamical systems, such as those found in atmospheric chemistry, necessitate hierarchical approaches to both experiment and modelling in order to ultimately identify and achieve fundamental process-understanding in the full open system. Atmospheric simulation chambers comprise an intermediate in complexity, between a classical laboratory experiment and the full, ambient system. As such, they can generate large volumes of difficult-to-interpret data. Here we describe and implement a chemometric dimension reduction methodology for the deconvolution and interpretation of complex gas- and particle-phase composition spectra. The methodology comprises principal component analysis (PCA), hierarchical cluster analysis (HCA) and positive least-squares discriminant analysis (PLS-DA). These methods are, for the first time, applied to simultaneous gas- and particle-phase composition data obtained from a comprehensive series of environmental simulation chamber experiments focused on biogenic volatile organic compound (BVOC) photooxidation and associated secondary organic aerosol (SOA) formation. We primarily investigated the biogenic SOA precursors isoprene, α-pinene, limonene, myrcene, linalool and β-caryophyllene. The chemometric analysis is used to classify the oxidation systems and resultant SOA according to the controlling chemistry and the products formed. Results show that "model" biogenic oxidative systems can be successfully separated and classified according to their oxidation products. Furthermore, a holistic view of results obtained across both the gas- and particle-phases shows the different SOA formation chemistry, initiating in the gas-phase, proceeding to govern the differences between the various BVOC SOA compositions. The results obtained are used to describe the particle composition in the context of the oxidised gas-phase matrix. An extension of the technique, which incorporates into the statistical models data from anthropogenic (i.e. toluene) oxidation and "more realistic" plant mesocosm systems, demonstrates that such an ensemble of chemometric mapping has the potential to be used for the classification of more complex spectra of unknown origin. More specifically, the addition of mesocosm data from fig and birch tree experiments shows that isoprene and monoterpene emitting sources, respectively, can be mapped onto the statistical model structure and their positional vectors can provide insight into their biological sources and controlling oxidative chemistry. The potential to extend the methodology to the analysis of ambient air is discussed using results obtained from a zero-dimensional box model incorporating mechanistic data obtained from the Master Chemical Mechanism (MCMv3.2). Such an extension to analysing ambient air would prove a powerful asset in assisting with the identification of SOA sources and the elucidation of the underlying chemical mechanisms involved.

Decision Support System for Determining Scholarship Selection using an Analytical Hierarchy Process

NASA Astrophysics Data System (ADS)

Puspitasari, T. D.; Sari, E. O.; Destarianto, P.; Riskiawan, H. Y.

2018-01-01

Decision Support System is a computer program application that analyzes data and presents it so that users can make decision more easily. Determining Scholarship Selection study case in Senior High School in east Java wasn’t easy. It needed application to solve the problem, to improve the accuracy of targets for prospective beneficiaries of poor students and to speed up the screening process. This research will build system uses the method of Analytical Hierarchy Process (AHP) is a method that solves a complex and unstructured problem into its group, organizes the groups into a hierarchical order, inputs numerical values instead of human perception in comparing relative and ultimately with a synthesis determined elements that have the highest priority. The accuracy system for this research is 90%.

Silk-regulated hierarchical hollow magnetite/carbon nanocomposite spheroids for lithium-ion battery anodes.

PubMed

Sheng, Weiqin; Zhu, Guobin; Kaplan, David L; Cao, Chuanbao; Zhu, Hesun; Lu, Qiang

2015-03-20

Hierarchical olive-like structured carbon-Fe3O4 nanocomposite particles composed of a hollow interior and a carbon coated surface are prepared by a facile, silk protein-assisted hydrothermal method. Silk nanofibers as templates and carbon precursors first regulate the formation of hollow Fe2O3 microspheres and then they are converted into carbon by a reduction process into Fe3O4. This process significantly simplifies the fabrication and carbon coating processes to form complex hollow structures. When tested as anode materials for lithium-ion batteries, these hollow carbon-coated particles exhibit high capacity (900 mAh g(-1)), excellent cycle stability (180 cycles) and rate performance due to their unique hierarchical hollow structure and carbon coating.

Discrimination of complex mixtures by a colorimetric sensor array: coffee aromas.

PubMed

Suslick, Benjamin A; Feng, Liang; Suslick, Kenneth S

2010-03-01

The analysis of complex mixtures presents a difficult challenge even for modern analytical techniques, and the ability to discriminate among closely similar such mixtures often remains problematic. Coffee provides a readily available archetype of such highly multicomponent systems. The use of a low-cost, sensitive colorimetric sensor array for the detection and identification of coffee aromas is reported. The color changes of the sensor array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). PCA revealed that the sensor array has exceptionally high dimensionality with 18 dimensions required to define 90% of the total variance. In quintuplicate runs of 10 commercial coffees and controls, no confusions or errors in classification by HCA were observed in 55 trials. In addition, the effects of temperature and time in the roasting of green coffee beans were readily observed and distinguishable with a resolution better than 10 degrees C and 5 min, respectively. Colorimetric sensor arrays demonstrate excellent potential for complex systems analysis in real-world applications and provide a novel method for discrimination among closely similar complex mixtures.

Discrimination of Complex Mixtures by a Colorimetric Sensor Array: Coffee Aromas

PubMed Central

Suslick, Benjamin A.; Feng, Liang; Suslick, Kenneth S.

2010-01-01

The analysis of complex mixtures presents a difficult challenge even for modern analytical techniques, and the ability to discriminate among closely similar such mixtures often remains problematic. Coffee provides a readily available archetype of such highly multicomponent systems. The use of a low-cost, sensitive colorimetric sensor array for the detection and identification of coffee aromas is reported. The color changes of the sensor array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). PCA revealed that the sensor array has exceptionally high dimensionality with 18 dimensions required to define 90% of the total variance. In quintuplicate runs of 10 commercial coffees and controls, no confusions or errors in classification by HCA were observed in 55 trials. In addition, the effects of temperature and time in the roasting of green coffee beans were readily observed and distinguishable with a resolution better than 10 °C and 5 min, respectively. Colorimetric sensor arrays demonstrate excellent potential for complex systems analysis in real-world applications and provide a novel method for discrimination among closely similar complex mixtures. PMID:20143838

Templated bilayer self-assembly of fully conjugated π-expanded macrocyclic oligothiophenes complexed with fullerenes

PubMed Central

Cojal González, José D.; Iyoda, Masahiko; Rabe, Jürgen P.

2017-01-01

Fully conjugated macrocyclic oligothiophenes exhibit a combination of highly attractive structural, optical and electronic properties, and multifunctional molecular thin film architectures thereof are envisioned. However, control over the self-assembly of such systems becomes increasingly challenging, the more complex the target structures are. Here we show a robust self-assembly based on hierarchical non-covalent interactions. A self-assembled monolayer of hydrogen-bonded trimesic acid at the interface between an organic solution and graphite provides host-sites for the epitaxial ordering of Saturn-like complexes of fullerenes with oligothiophene macrocycles in mono- and bilayers. STM tomography verifies the formation of the templated layers. Molecular dynamics simulations corroborate the conformational stability and assign the adsorption sites of the adlayers. Scanning tunnelling spectroscopy determines their rectification characteristics. Current–voltage characteristics reveal the modification of the rectifying properties of the macrocycles by the formation of donor–acceptor complexes in a densely packed all-self-assembled supramolecular nanostructure. PMID:28281557

Templated bilayer self-assembly of fully conjugated π-expanded macrocyclic oligothiophenes complexed with fullerenes

NASA Astrophysics Data System (ADS)

Cojal González, José D.; Iyoda, Masahiko; Rabe, Jürgen P.

2017-03-01

Fully conjugated macrocyclic oligothiophenes exhibit a combination of highly attractive structural, optical and electronic properties, and multifunctional molecular thin film architectures thereof are envisioned. However, control over the self-assembly of such systems becomes increasingly challenging, the more complex the target structures are. Here we show a robust self-assembly based on hierarchical non-covalent interactions. A self-assembled monolayer of hydrogen-bonded trimesic acid at the interface between an organic solution and graphite provides host-sites for the epitaxial ordering of Saturn-like complexes of fullerenes with oligothiophene macrocycles in mono- and bilayers. STM tomography verifies the formation of the templated layers. Molecular dynamics simulations corroborate the conformational stability and assign the adsorption sites of the adlayers. Scanning tunnelling spectroscopy determines their rectification characteristics. Current-voltage characteristics reveal the modification of the rectifying properties of the macrocycles by the formation of donor-acceptor complexes in a densely packed all-self-assembled supramolecular nanostructure.

New insight in magnetic saturation behavior of nickel hierarchical structures

NASA Astrophysics Data System (ADS)

Ma, Ji; Zhang, Jianxing; Liu, Chunting; Chen, Kezheng

2017-09-01

It is unanimously accepted that non-ferromagnetic inclusions in a ferromagnetic system will lower down total saturation magnetization in unit of emu/g. In this study, ;lattice strain; was found to be another key factor to have critical impact on magnetic saturation behavior of the system. The lattice strain determined assembling patterns of primary nanoparticles in hierarchical structures and was intimately related with the formation process of these architectures. Therefore, flower-necklace-like and cauliflower-like nickel hierarchical structures were used as prototype systems to evidence the relationship between assembling patterns of primary nanoparticles and magnetic saturation behaviors of these architectures. It was found that the influence of lattice strain on saturation magnetization outperformed that of non-ferromagnetic inclusions in these hierarchical structures. This will enable new insights into fundamental understanding of related magnetic effects.

Perspectives on object manipulation and action grammar for percussive actions in primates

PubMed Central

Hayashi, Misato

2015-01-01

The skill of object manipulation is a common feature of primates including humans, although there are species-typical patterns of manipulation. Object manipulation can be used as a comparative scale of cognitive development, focusing on its complexity. Nut cracking in chimpanzees has the highest hierarchical complexity of tool use reported in non-human primates. An analysis of the patterns of object manipulation in naive chimpanzees after nut-cracking demonstrations revealed the cause of difficulties in learning nut-cracking behaviour. Various types of behaviours exhibited within a nut-cracking context can be examined in terms of the application of problem-solving strategies, focusing on their basis in causal understanding or insightful intentionality. Captive chimpanzees also exhibit complex forms of combinatory manipulation, which is the precursor of tool use. A new notation system of object manipulation was invented to assess grammatical rules in manipulative actions. The notation system of action grammar enabled direct comparisons to be made between primates including humans in a variety of object-manipulation tasks, including percussive-tool use. PMID:26483528

Perspectives on object manipulation and action grammar for percussive actions in primates.

PubMed

Hayashi, Misato

2015-11-19

The skill of object manipulation is a common feature of primates including humans, although there are species-typical patterns of manipulation. Object manipulation can be used as a comparative scale of cognitive development, focusing on its complexity. Nut cracking in chimpanzees has the highest hierarchical complexity of tool use reported in non-human primates. An analysis of the patterns of object manipulation in naive chimpanzees after nut-cracking demonstrations revealed the cause of difficulties in learning nut-cracking behaviour. Various types of behaviours exhibited within a nut-cracking context can be examined in terms of the application of problem-solving strategies, focusing on their basis in causal understanding or insightful intentionality. Captive chimpanzees also exhibit complex forms of combinatory manipulation, which is the precursor of tool use. A new notation system of object manipulation was invented to assess grammatical rules in manipulative actions. The notation system of action grammar enabled direct comparisons to be made between primates including humans in a variety of object-manipulation tasks, including percussive-tool use. © 2015 The Author(s).

Dissecting children's observational learning of complex actions through selective video displays.

PubMed

Flynn, Emma; Whiten, Andrew

2013-10-01

Children can learn how to use complex objects by watching others, yet the relative importance of different elements they may observe, such as the interactions of the individual parts of the apparatus, a model's movements, and desirable outcomes, remains unclear. In total, 140 3-year-olds and 140 5-year-olds participated in a study where they observed a video showing tools being used to extract a reward item from a complex puzzle box. Conditions varied according to the elements that could be seen in the video: (a) the whole display, including the model's hands, the tools, and the box; (b) the tools and the box but not the model's hands; (c) the model's hands and the tools but not the box; (d) only the end state with the box opened; and (e) no demonstration. Children's later attempts at the task were coded to establish whether they imitated the hierarchically organized sequence of the model's actions, the action details, and/or the outcome. Children's successful retrieval of the reward from the box and the replication of hierarchical sequence information were reduced in all but the whole display condition. Only once children had attempted the task and witnessed a second demonstration did the display focused on the tools and box prove to be better for hierarchical sequence information than the display focused on the tools and hands only. Copyright © 2013 Elsevier Inc. All rights reserved.

Straightforward and precise approach to replicate complex hierarchical structures from plant surfaces onto soft matter polymer

PubMed Central

Speck, Thomas; Bohn, Holger F.

2018-01-01

The surfaces of plant leaves are rarely smooth and often possess a species-specific micro- and/or nano-structuring. These structures usually influence the surface functionality of the leaves such as wettability, optical properties, friction and adhesion in insect–plant interactions. This work presents a simple, convenient, inexpensive and precise two-step micro-replication technique to transfer surface microstructures of plant leaves onto highly transparent soft polymer material. Leaves of three different plants with variable size (0.5–100 µm), shape and complexity (hierarchical levels) of their surface microstructures were selected as model bio-templates. A thermoset epoxy resin was used at ambient conditions to produce negative moulds directly from fresh plant leaves. An alkaline chemical treatment was established to remove the entirety of the leaf material from the cured negative epoxy mould when necessary, i.e. for highly complex hierarchical structures. Obtained moulds were filled up afterwards with low viscosity silicone elastomer (PDMS) to obtain positive surface replicas. Comparative scanning electron microscopy investigations (original plant leaves and replicated polymeric surfaces) reveal the high precision and versatility of this replication technique. This technique has promising future application for the development of bioinspired functional surfaces. Additionally, the fabricated polymer replicas provide a model to systematically investigate the structural key points of surface functionalities. PMID:29765666

[Conceptual approach to formation of a modern system of medical provision].

PubMed

Belevitin, A B; Miroshnichenko, Iu V; Bunin, S A; Goriachev, A B; Krasavin, K D

2009-09-01

Within the frame of forming of a new face of medical service of the Armed Forces, were determined the principle approaches to optimization of the process of development of the system of medical supply. It was proposed to use the following principles: principle of hierarchic structuring, principle of purposeful orientation, principle of vertical task sharing, principle of horizontal task sharing, principle of complex simulation, principle of permanent perfection. The main direction of optimization of structure and composition of system of medical supply of the Armed Forces are: forming of modern institutes of medical supply--centers of support by technique and facilities on the base of central, regional storehouses, and attachment of several functions of organs of military government to them; creation of medical supply office on the base military hospitals, being basing treatment-prophylaxis institutes, in adjusted territorial zones of responsibility for the purpose of realization of complex of tasks of supplying the units and institutes, attached to them on medical support, by medical equipment. Building of medical support system is realized on three levels: Center - Military region (NAVY region) - territorial zone of responsibility.

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

Yusufoglu, Yusuf

Nature offers many exciting ideas and inspiration for the development of new materials and processes. The toughness of spider silk, the strength and lightweight of bone, and the adhesion abilities of the gecko's feet are some of the many examples of highperformance natural materials, which have attracted the interest of scientist to duplicate their properties in man-made materials. Materials found in nature combine many inspiring properties such as miniaturization, sophistication, hierarchical organization, hybridization, and adaptability. In all biological systems, whether very basic or highly complex, nature provides a multiplicity of materials, architectures, systems and functions. Generally, the architectural configurations andmore » material characteristics are the important features that have been duplicated from nature for building synthetic structural composites.« less

Evolution of Geometric Sensitivity Derivatives from Computer Aided Design Models

NASA Technical Reports Server (NTRS)

Jones, William T.; Lazzara, David; Haimes, Robert

2010-01-01

The generation of design parameter sensitivity derivatives is required for gradient-based optimization. Such sensitivity derivatives are elusive at best when working with geometry defined within the solid modeling context of Computer-Aided Design (CAD) systems. Solid modeling CAD systems are often proprietary and always complex, thereby necessitating ad hoc procedures to infer parameter sensitivity. A new perspective is presented that makes direct use of the hierarchical associativity of CAD features to trace their evolution and thereby track design parameter sensitivity. In contrast to ad hoc methods, this method provides a more concise procedure following the model design intent and determining the sensitivity of CAD geometry directly to its respective defining parameters.

A bioinspired flexible organic artificial afferent nerve

NASA Astrophysics Data System (ADS)

Kim, Yeongin; Chortos, Alex; Xu, Wentao; Liu, Yuxin; Oh, Jin Young; Son, Donghee; Kang, Jiheong; Foudeh, Amir M.; Zhu, Chenxin; Lee, Yeongjun; Niu, Simiao; Liu, Jia; Pfattner, Raphael; Bao, Zhenan; Lee, Tae-Woo

2018-06-01

The distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. We used flexible organic electronics to mimic the functions of a sensory nerve. Our artificial afferent nerve collects pressure information (1 to 80 kilopascals) from clusters of pressure sensors, converts the pressure information into action potentials (0 to 100 hertz) by using ring oscillators, and integrates the action potentials from multiple ring oscillators with a synaptic transistor. Biomimetic hierarchical structures can detect movement of an object, combine simultaneous pressure inputs, and distinguish braille characters. Furthermore, we connected our artificial afferent nerve to motor nerves to construct a hybrid bioelectronic reflex arc to actuate muscles. Our system has potential applications in neurorobotics and neuroprosthetics.

Model-free learning on robot kinematic chains using a nested multi-agent topology

NASA Astrophysics Data System (ADS)

Karigiannis, John N.; Tzafestas, Costas S.

2016-11-01

This paper proposes a model-free learning scheme for the developmental acquisition of robot kinematic control and dexterous manipulation skills. The approach is based on a nested-hierarchical multi-agent architecture that intuitively encapsulates the topology of robot kinematic chains, where the activity of each independent degree-of-freedom (DOF) is finally mapped onto a distinct agent. Each one of those agents progressively evolves a local kinematic control strategy in a game-theoretic sense, that is, based on a partial (local) view of the whole system topology, which is incrementally updated through a recursive communication process according to the nested-hierarchical topology. Learning is thus approached not through demonstration and training but through an autonomous self-exploration process. A fuzzy reinforcement learning scheme is employed within each agent to enable efficient exploration in a continuous state-action domain. This paper constitutes in fact a proof of concept, demonstrating that global dexterous manipulation skills can indeed evolve through such a distributed iterative learning of local agent sensorimotor mappings. The main motivation behind the development of such an incremental multi-agent topology is to enhance system modularity, to facilitate extensibility to more complex problem domains and to improve robustness with respect to structural variations including unpredictable internal failures. These attributes of the proposed system are assessed in this paper through numerical experiments in different robot manipulation task scenarios, involving both single and multi-robot kinematic chains. The generalisation capacity of the learning scheme is experimentally assessed and robustness properties of the multi-agent system are also evaluated with respect to unpredictable variations in the kinematic topology. Furthermore, these numerical experiments demonstrate the scalability properties of the proposed nested-hierarchical architecture, where new agents can be recursively added in the hierarchy to encapsulate individual active DOFs. The results presented in this paper demonstrate the feasibility of such a distributed multi-agent control framework, showing that the solutions which emerge are plausible and near-optimal. Numerical efficiency and computational cost issues are also discussed.

Hierarchical structure in sharply divided phase space for the piecewise linear map

NASA Astrophysics Data System (ADS)

Akaishi, Akira; Aoki, Kazuki; Shudo, Akira

2017-05-01

We have studied a two-dimensional piecewise linear map to examine how the hierarchical structure of stable regions affects the slow dynamics in Hamiltonian systems. In the phase space there are infinitely many stable regions, each of which is polygonal-shaped, and the rest is occupied by chaotic orbits. By using symbolic representation of stable regions, a procedure to compute the edges of the polygons is presented. The stable regions are hierarchically distributed in phase space and the edges of the stable regions show the marginal instability. The cumulative distribution of the recurrence time obeys a power law as ˜t-2 , the same as the one for the system with phase space, which is composed of a single stable region and chaotic components. By studying the symbol sequence of recurrence trajectories, we show that the hierarchical structure of stable regions has no significant effect on the power-law exponent and that only the marginal instability on the boundary of stable regions is responsible for determining the exponent. We also discuss the relevance of the hierarchical structure to those in more generic chaotic systems.

Hierarchical Discrete Event Supervisory Control of Aircraft Propulsion Systems

DTIC Science & Technology

2004-11-01

Systems Murat Yasar, Devendra Tolani, and Asok Ray The Pennsylvania State University, University Park, Pennsylvania Neerav Shah Glenn Research Center...Hierarchical Discrete Event Supervisory Control of Aircraft Propulsion Systems Murat Yasar, Devendra Tolani, and Asok Ray The Pennsylvania State University...Systems Murat Yasar, Devendra Tolani, and Asok Ray The Pennsylvania State University University Park, Pennsylvania 16802 Neerav Shah National

Hierarchical Modeling of Sequential Behavioral Data: Examining Complex Association Patterns in Mediation Models

ERIC Educational Resources Information Center

Dagne, Getachew A.; Brown, C. Hendricks; Howe, George W.

2007-01-01

This article presents new methods for modeling the strength of association between multiple behaviors in a behavioral sequence, particularly those involving substantively important interaction patterns. Modeling and identifying such interaction patterns becomes more complex when behaviors are assigned to more than two categories, as is the case…

Optimal Rating Procedures and Methodology for NAEP Open- Ended Items. Working Paper Series.

ERIC Educational Resources Information Center

Patz, Richard J.; Wilson, Mark; Hoskens, Machteld

The National Assessment of Educational Progress (NAEP) collects data in the form of repeated, discrete measures (test items) with hierarchical structure for both measures and subjects, that is complex by any standard. This complexity has been managed through a "divide and conquer" approach of isolating and evaluating sources of…

Troubleshooting Complex Equipment in the Military Services: Research and Prospects. Technical Report No. 92.

ERIC Educational Resources Information Center

Bond, Nicholas A.; Towne, Douglas M.

Psychological approaches to the troubleshooting of complex military equipments are designed to improve the selection, motivation, and training of technicians. Methods for enhancing the understanding of the physical relations in equipment, the hierarchical analysis and practice of sub-skills, and the general logic of searching behavior are aspects…

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

NASA Astrophysics Data System (ADS)

Zamora-López, Gorka; Chen, Yuhan; Deco, Gustavo; Kringelbach, Morten L.; Zhou, Changsong

2016-12-01

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks.

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

PubMed Central

Zamora-López, Gorka; Chen, Yuhan; Deco, Gustavo; Kringelbach, Morten L.; Zhou, Changsong

2016-01-01

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks. PMID:27917958

Transitions between superstatistical regimes: Validity, breakdown and applications

NASA Astrophysics Data System (ADS)

Jizba, Petr; Korbel, Jan; Lavička, Hynek; Prokš, Martin; Svoboda, Václav; Beck, Christian

2018-03-01

Superstatistics is a widely employed tool of non-equilibrium statistical physics which plays an important rôle in analysis of hierarchical complex dynamical systems. Yet, its "canonical" formulation in terms of a single nuisance parameter is often too restrictive when applied to complex empirical data. Here we show that a multi-scale generalization of the superstatistics paradigm is more versatile, allowing to address such pertinent issues as transmutation of statistics or inter-scale stochastic behavior. To put some flesh on the bare bones, we provide a numerical evidence for a transition between two superstatistics regimes, by analyzing high-frequency (minute-tick) data for share-price returns of seven selected companies. Salient issues, such as breakdown of superstatistics in fractional diffusion processes or connection with Brownian subordination are also briefly discussed.

Heterogeneous fractionation profiles of meta-analytic coactivation networks.

PubMed

Laird, Angela R; Riedel, Michael C; Okoe, Mershack; Jianu, Radu; Ray, Kimberly L; Eickhoff, Simon B; Smith, Stephen M; Fox, Peter T; Sutherland, Matthew T

2017-04-01

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication. Copyright © 2017 Elsevier Inc. All rights reserved.

Heterogeneous fractionation profiles of meta-analytic coactivation networks

PubMed Central

Laird, Angela R.; Riedel, Michael C.; Okoe, Mershack; Jianu, Radu; Ray, Kimberly L.; Eickhoff, Simon B.; Smith, Stephen M.; Fox, Peter T.; Sutherland, Matthew T.

2017-01-01

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d = 20 to 300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how “parent” functional brain systems decompose into constituent “child” sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication. PMID:28222386

Hierarchically Self-Assembled Supramolecular Host-Guest Delivery System for Drug Resistant Cancer Therapy.

PubMed

Cheng, Hongwei; Fan, Xiaoshan; Wang, Xiaoyuan; Ye, Enyi; Loh, Xian Jun; Li, Zibiao; Wu, Yun-Long

2018-06-11

In this report, a new star-like copolymer β-CD- g-(PNIPAAm- b-POEGA) x , consisting of a β-CD core, grafted with temperature-responsive poly( N-isopropylacrylamide) (PNIPAAm) and biocompatible poly(oligo(ethylene glycol) acrylate) (POEGA) in a block copolymer of the arms, was used to deliver chemotherapeutics to drug resistant cancer cells and tumors. The first step of the self-assembly process involves the encapsulation of chemotherapeutics through host-guest inclusion complexation between the β-cyclodextrin cavity and the anticancer drug. Next, the chain interaction of the PNIPAAm segment at elevated temperature drives the drug-loaded β-CD- g-(PNIPAAm- b-POEGA) x /PTX inclusion complex to hierarchically self-assemble into nanosized supramolecular assemblies at 37 °C, whereas the presence of poly(ethylene glycol) (PEG) chains in the distal end of the star-like copolymer arms impart enhanced stability to the self-assembled structure. More interestingly, this supramolecular host-guest nanocomplex promoted the enhanced cellular uptake of chemotherapeutics in MDR-1 up-regulated drug resistant cancer cells and exhibited high therapeutic efficacy for suppressing drug resistant tumor growth in an in vivo mouse model, due to the increased stability, improvement in aqueous solubility, enhanced cellular uptake, and partial membrane pump impairment by taking the advantage of PEGylation and supramolecular complex between this star-like copolymer and chemotherapeutics. This work signifies that temperature-sensitive PEGylated supramolecular nanocarriers with good biocompatibility are effective in combating MDR-1 mediated drug resistance in both in vitro and in vivo models, which is of significant importance for the advanced drug delivery platform designed to combat drug resistant cancer.

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling: GEOSTATISTICAL SENSITIVITY ANALYSIS

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

Dai, Heng; Chen, Xingyuan; Ye, Ming

Sensitivity analysis is an important tool for quantifying uncertainty in the outputs of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a hierarchical sensitivity analysis method that (1) constructs an uncertainty hierarchy by analyzing the input uncertainty sources, and (2) accounts for the spatial correlation among parameters at each level ofmore » the hierarchy using geostatistical tools. The contribution of uncertainty source at each hierarchy level is measured by sensitivity indices calculated using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport in model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally as driven by the dynamic interaction between groundwater and river water at the site. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed parameters.« less

Robust hierarchical state-space models reveal diel variation in travel rates of migrating leatherback turtles.

PubMed

Jonsen, Ian D; Myers, Ransom A; James, Michael C

2006-09-01

1. Biological and statistical complexity are features common to most ecological data that hinder our ability to extract meaningful patterns using conventional tools. Recent work on implementing modern statistical methods for analysis of such ecological data has focused primarily on population dynamics but other types of data, such as animal movement pathways obtained from satellite telemetry, can also benefit from the application of modern statistical tools. 2. We develop a robust hierarchical state-space approach for analysis of multiple satellite telemetry pathways obtained via the Argos system. State-space models are time-series methods that allow unobserved states and biological parameters to be estimated from data observed with error. We show that the approach can reveal important patterns in complex, noisy data where conventional methods cannot. 3. Using the largest Atlantic satellite telemetry data set for critically endangered leatherback turtles, we show that the diel pattern in travel rates of these turtles changes over different phases of their migratory cycle. While foraging in northern waters the turtles show similar travel rates during day and night, but on their southward migration to tropical waters travel rates are markedly faster during the day. These patterns are generally consistent with diving data, and may be related to changes in foraging behaviour. Interestingly, individuals that migrate southward to breed generally show higher daytime travel rates than individuals that migrate southward in a non-breeding year. 4. Our approach is extremely flexible and can be applied to many ecological analyses that use complex, sequential data.

Programming Hierarchical Self-Assembly of Patchy Particles into Colloidal Crystals via Colloidal Molecules.

PubMed

Morphew, Daniel; Shaw, James; Avins, Christopher; Chakrabarti, Dwaipayan

2018-03-27

Colloidal self-assembly is a promising bottom-up route to a wide variety of three-dimensional structures, from clusters to crystals. Programming hierarchical self-assembly of colloidal building blocks, which can give rise to structures ordered at multiple levels to rival biological complexity, poses a multiscale design problem. Here we explore a generic design principle that exploits a hierarchy of interaction strengths and employ this design principle in computer simulations to demonstrate the hierarchical self-assembly of triblock patchy colloidal particles into two distinct colloidal crystals. We obtain cubic diamond and body-centered cubic crystals via distinct clusters of uniform size and shape, namely, tetrahedra and octahedra, respectively. Such a conceptual design framework has the potential to reliably encode hierarchical self-assembly of colloidal particles into a high level of sophistication. Moreover, the design framework underpins a bottom-up route to cubic diamond colloidal crystals, which have remained elusive despite being much sought after for their attractive photonic applications.

Development of an intelligent diagnostic system for reusable rocket engine control

NASA Technical Reports Server (NTRS)

Anex, R. P.; Russell, J. R.; Guo, T.-H.

1991-01-01

A description of an intelligent diagnostic system for the Space Shuttle Main Engines (SSME) is presented. This system is suitable for incorporation in an intelligent controller which implements accommodating closed-loop control to extend engine life and maximize available performance. The diagnostic system architecture is a modular, hierarchical, blackboard system which is particularly well suited for real-time implementation of a system which must be repeatedly updated and extended. The diagnostic problem is formulated as a hierarchical classification problem in which the failure hypotheses are represented in terms of predefined data patterns. The diagnostic expert system incorporates techniques for priority-based diagnostics, the combination of analytical and heuristic knowledge for diagnosis, integration of different AI systems, and the implementation of hierarchical distributed systems. A prototype reusable rocket engine diagnostic system (ReREDS) has been implemented. The prototype user interface and diagnostic performance using SSME test data are described.

Spectral analysis for automated exploration and sample acquisition

NASA Technical Reports Server (NTRS)

Eberlein, Susan; Yates, Gigi

1992-01-01

Future space exploration missions will rely heavily on the use of complex instrument data for determining the geologic, chemical, and elemental character of planetary surfaces. One important instrument is the imaging spectrometer, which collects complete images in multiple discrete wavelengths in the visible and infrared regions of the spectrum. Extensive computational effort is required to extract information from such high-dimensional data. A hierarchical classification scheme allows multispectral data to be analyzed for purposes of mineral classification while limiting the overall computational requirements. The hierarchical classifier exploits the tunability of a new type of imaging spectrometer which is based on an acousto-optic tunable filter. This spectrometer collects a complete image in each wavelength passband without spatial scanning. It may be programmed to scan through a range of wavelengths or to collect only specific bands for data analysis. Spectral classification activities employ artificial neural networks, trained to recognize a number of mineral classes. Analysis of the trained networks has proven useful in determining which subsets of spectral bands should be employed at each step of the hierarchical classifier. The network classifiers are capable of recognizing all mineral types which were included in the training set. In addition, the major components of many mineral mixtures can also be recognized. This capability may prove useful for a system designed to evaluate data in a strange environment where details of the mineral composition are not known in advance.

Feature integration and object representations along the dorsal stream visual hierarchy

PubMed Central

Perry, Carolyn Jeane; Fallah, Mazyar

2014-01-01

The visual system is split into two processing streams: a ventral stream that receives color and form information and a dorsal stream that receives motion information. Each stream processes that information hierarchically, with each stage building upon the previous. In the ventral stream this leads to the formation of object representations that ultimately allow for object recognition regardless of changes in the surrounding environment. In the dorsal stream, this hierarchical processing has classically been thought to lead to the computation of complex motion in three dimensions. However, there is evidence to suggest that there is integration of both dorsal and ventral stream information into motion computation processes, giving rise to intermediate object representations, which facilitate object selection and decision making mechanisms in the dorsal stream. First we review the hierarchical processing of motion along the dorsal stream and the building up of object representations along the ventral stream. Then we discuss recent work on the integration of ventral and dorsal stream features that lead to intermediate object representations in the dorsal stream. Finally we propose a framework describing how and at what stage different features are integrated into dorsal visual stream object representations. Determining the integration of features along the dorsal stream is necessary to understand not only how the dorsal stream builds up an object representation but also which computations are performed on object representations instead of local features. PMID:25140147

An Efficient, Hierarchical Viewpoint Planning Strategy for Terrestrial Laser Scanner Networks

NASA Astrophysics Data System (ADS)

Jia, F.; Lichti, D. D.

2018-05-01

Terrestrial laser scanner (TLS) techniques have been widely adopted in a variety of applications. However, unlike in geodesy or photogrammetry, insufficient attention has been paid to the optimal TLS network design. It is valuable to develop a complete design system that can automatically provide an optimal plan, especially for high-accuracy, large-volume scanning networks. To achieve this goal, one should look at the "optimality" of the solution as well as the computational complexity in reaching it. In this paper, a hierarchical TLS viewpoint planning strategy is developed to solve the optimal scanner placement problems. If one targeted object to be scanned is simplified as discretized wall segments, any possible viewpoint can be evaluated by a score table representing its visible segments under certain scanning geometry constraints. Thus, the design goal is to find a minimum number of viewpoints that achieves complete coverage of all wall segments. The efficiency is improved by densifying viewpoints hierarchically, instead of a "brute force" search within the entire workspace. The experiment environments in this paper were simulated from two buildings located on University of Calgary campus. Compared with the "brute force" strategy in terms of the quality of the solutions and the runtime, it is shown that the proposed strategy can provide a scanning network with a compatible quality but with more than a 70 % time saving.

General and craniofacial development are complex adaptive processes influenced by diversity.

PubMed

Brook, A H; O'Donnell, M Brook; Hone, A; Hart, E; Hughes, T E; Smith, R N; Townsend, G C

2014-06-01

Complex systems are present in such diverse areas as social systems, economies, ecosystems and biology and, therefore, are highly relevant to dental research, education and practice. A Complex Adaptive System in biological development is a dynamic process in which, from interacting components at a lower level, higher level phenomena and structures emerge. Diversity makes substantial contributions to the performance of complex adaptive systems. It enhances the robustness of the process, allowing multiple responses to external stimuli as well as internal changes. From diversity comes variation in outcome and the possibility of major change; outliers in the distribution enhance the tipping points. The development of the dentition is a valuable, accessible model with extensive and reliable databases for investigating the role of complex adaptive systems in craniofacial and general development. The general characteristics of such systems are seen during tooth development: self-organization; bottom-up emergence; multitasking; self-adaptation; variation; tipping points; critical phases; and robustness. Dental findings are compatible with the Random Network Model, the Threshold Model and also with the Scale Free Network Model which has a Power Law distribution. In addition, dental development shows the characteristics of Modularity and Clustering to form Hierarchical Networks. The interactions between the genes (nodes) demonstrate Small World phenomena, Subgraph Motifs and Gene Regulatory Networks. Genetic mechanisms are involved in the creation and evolution of variation during development. The genetic factors interact with epigenetic and environmental factors at the molecular level and form complex networks within the cells. From these interactions emerge the higher level tissues, tooth germs and mineralized teeth. Approaching development in this way allows investigation of why there can be variations in phenotypes from identical genotypes; the phenotype is the outcome of perturbations in the cellular systems and networks, as well as of the genotype. Understanding and applying complexity theory will bring about substantial advances not only in dental research and education but also in the organization and delivery of oral health care. © 2014 Australian Dental Association.

Using a data base management system for modelling SSME test history data

NASA Technical Reports Server (NTRS)

Abernethy, K.

1985-01-01

The usefulness of a data base management system (DBMS) for modelling historical test data for the complete series of static test firings for the Space Shuttle Main Engine (SSME) was assessed. From an analysis of user data base query requirements, it became clear that a relational DMBS which included a relationally complete query language would permit a model satisfying the query requirements. Representative models and sample queries are discussed. A list of environment-particular evaluation criteria for the desired DBMS was constructed; these criteria include requirements in the areas of user-interface complexity, program independence, flexibility, modifiability, and output capability. The evaluation process included the construction of several prototype data bases for user assessement. The systems studied, representing the three major DBMS conceptual models, were: MIRADS, a hierarchical system; DMS-1100, a CODASYL-based network system; ORACLE, a relational system; and DATATRIEVE, a relational-type system.

On Developing a Taxonomy for Multidisciplinary Design Optimization: A Decision-Based Perspective

NASA Technical Reports Server (NTRS)

Lewis, Kemper; Mistree, Farrokh

1995-01-01

In this paper, we approach MDO from a Decision-Based Design (DBD) perspective and explore classification schemes for designing complex systems and processes. Specifically, we focus on decisions, which are only a small portion of the Decision Support Problem (DSP) Technique, our implementation of DBD. We map coupled nonhierarchical and hierarchical representations from the DSP Technique into the Balling-Sobieski (B-S) framework (Balling and Sobieszczanski-Sobieski, 1994), and integrate domain-independent linguistic terms to complete our taxonomy. Application of DSPs to the design of complex, multidisciplinary systems include passenger aircraft, ships, damage tolerant structural and mechanical systems, and thermal energy systems. In this paper we show that Balling-Sobieski framework is consistent with that of the Decision Support Problem Technique through the use of linguistic entities to describe the same type of formulations. We show that the underlying linguistics of the solution approaches are the same and can be coalesced into a homogeneous framework with which to base the research, application, and technology MDO upon. We introduce, in the Balling-Sobieski framework, examples of multidisciplinary design, namely, aircraft, damage tolerant structural and mechanical systems, and thermal energy systems.

Complementary and alternative medicine whole systems research: beyond identification of inadequacies of the RCT.

PubMed

Verhoef, Marja J; Lewith, George; Ritenbaugh, Cheryl; Boon, Heather; Fleishman, Susan; Leis, Anne

2005-09-01

Complementary and alternative medicine (CAM) often consists of whole systems of care (such as naturopathic medicine or traditional Chinese medicine (TCM)) that combine a wide range of modalities to provide individualised treatment. The complexity of these interventions and their potential synergistic effect requires innovative evaluative approaches. Model validity, which encompasses the need for research to adequately address the unique healing theory and therapeutic context of the intervention, is central to whole systems research (WSR). Classical randomised controlled trials (RCTs) are limited in their ability to address this need. Therefore, we propose a mixed methods approach that includes a range of relevant and holistic outcome measures. As the individual components of most whole systems are inseparable, complementary and synergistic, WSR must not focus only on the "active" ingredients of a system. An emerging WSR framework must be non-hierarchical, cyclical, flexible and adaptive, as knowledge creation is continuous, evolutionary and necessitates a continuous interplay between research methods and "phases" of knowledge. Finally, WSR must hold qualitative and quantitative research methods in equal esteem to realize their unique research contribution. Whole systems are complex and therefore no one method can adequately capture the meaning, process and outcomes of these interventions.

Multi-Scale and Object-Oriented Analysis for Mountain Terrain Segmentation and Geomorphological Assessment

NASA Astrophysics Data System (ADS)

Marston, B. K.; Bishop, M. P.; Shroder, J. F.

2009-12-01

Digital terrain analysis of mountain topography is widely utilized for mapping landforms, assessing the role of surface processes in landscape evolution, and estimating the spatial variation of erosion. Numerous geomorphometry techniques exist to characterize terrain surface parameters, although their utility to characterize the spatial hierarchical structure of the topography and permit an assessment of the erosion/tectonic impact on the landscape is very limited due to scale and data integration issues. To address this problem, we apply scale-dependent geomorphometric and object-oriented analyses to characterize the hierarchical spatial structure of mountain topography. Specifically, we utilized a high resolution digital elevation model to characterize complex topography in the Shimshal Valley in the Western Himalaya of Pakistan. To accomplish this, we generate terrain objects (geomorphological features and landform) including valley floors and walls, drainage basins, drainage network, ridge network, slope facets, and elemental forms based upon curvature. Object-oriented analysis was used to characterize object properties accounting for object size, shape, and morphometry. The spatial overlay and integration of terrain objects at various scales defines the nature of the hierarchical organization. Our results indicate that variations in the spatial complexity of the terrain hierarchical organization is related to the spatio-temporal influence of surface processes and landscape evolution dynamics. Terrain segmentation and the integration of multi-scale terrain information permits further assessment of process domains and erosion, tectonic impact potential, and natural hazard potential. We demonstrate this with landform mapping and geomorphological assessment examples.

A review of surrogate models and their application to groundwater modeling

NASA Astrophysics Data System (ADS)

Asher, M. J.; Croke, B. F. W.; Jakeman, A. J.; Peeters, L. J. M.

2015-08-01

The spatially and temporally variable parameters and inputs to complex groundwater models typically result in long runtimes which hinder comprehensive calibration, sensitivity, and uncertainty analysis. Surrogate modeling aims to provide a simpler, and hence faster, model which emulates the specified output of a more complex model in function of its inputs and parameters. In this review paper, we summarize surrogate modeling techniques in three categories: data-driven, projection, and hierarchical-based approaches. Data-driven surrogates approximate a groundwater model through an empirical model that captures the input-output mapping of the original model. Projection-based models reduce the dimensionality of the parameter space by projecting the governing equations onto a basis of orthonormal vectors. In hierarchical or multifidelity methods the surrogate is created by simplifying the representation of the physical system, such as by ignoring certain processes, or reducing the numerical resolution. In discussing the application to groundwater modeling of these methods, we note several imbalances in the existing literature: a large body of work on data-driven approaches seemingly ignores major drawbacks to the methods; only a fraction of the literature focuses on creating surrogates to reproduce outputs of fully distributed groundwater models, despite these being ubiquitous in practice; and a number of the more advanced surrogate modeling methods are yet to be fully applied in a groundwater modeling context.

Distributed Decision Making Environment.

DTIC Science & Technology

1982-12-01

Findeisen , F. N. Bailey, M. Brdys, K. Malinowski, P. Tatjewoki and A. Wozniak, Control and Coordination in Hierarchical Systems, New York, NY: Wiley...1977. [99] W. Findeisen et al., "On-line hierarchical control for steady-state systems," IEEE Trans. Automat. Conts., vol. AC-23, no. 2, pp. 189-209

Fast and Accurate Circuit Design Automation through Hierarchical Model Switching.

PubMed

Huynh, Linh; Tagkopoulos, Ilias

2015-08-21

In computer-aided biological design, the trifecta of characterized part libraries, accurate models and optimal design parameters is crucial for producing reliable designs. As the number of parts and model complexity increase, however, it becomes exponentially more difficult for any optimization method to search the solution space, hence creating a trade-off that hampers efficient design. To address this issue, we present a hierarchical computer-aided design architecture that uses a two-step approach for biological design. First, a simple model of low computational complexity is used to predict circuit behavior and assess candidate circuit branches through branch-and-bound methods. Then, a complex, nonlinear circuit model is used for a fine-grained search of the reduced solution space, thus achieving more accurate results. Evaluation with a benchmark of 11 circuits and a library of 102 experimental designs with known characterization parameters demonstrates a speed-up of 3 orders of magnitude when compared to other design methods that provide optimality guarantees.

Electroactive nanoparticle directed assembly of functionalized graphene nanosheets into hierarchical structures with hybrid compositions for flexible supercapacitors

NASA Astrophysics Data System (ADS)

Choi, Bong Gill; Huh, Yun Suk; Hong, Won Hi; Erickson, David; Park, Ho Seok

2013-04-01

Hierarchical structures of hybrid materials with the controlled compositions have been shown to offer a breakthrough for energy storage and conversion. Here, we report the integrative assembly of chemically modified graphene (CMG) building blocks into hierarchical complex structures with the hybrid composition for high performance flexible pseudocapacitors. The formation mechanism of hierarchical CMG/Nafion/RuO2 (CMGNR) microspheres, which is triggered by the cooperative interplay during the in situ synthesis of RuO2 nanoparticles (NPs), was extensively investigated. In particular, the hierarchical CMGNR microspheres consisting of the aggregates of CMG/Nafion (CMGN) nanosheets and RuO2 NPs provided large surface area and facile ion accessibility to storage sites, while the interconnected nanosheets offered continuous electron pathways and mechanical integrity. The synergistic effect of CMGNR hybrids on the supercapacitor (SC) performance was derived from the hybrid composition of pseudocapacitive RuO2 NPs with the conductive CMGNs as well as from structural features. Consequently, the CMGNR-SCs showed a specific capacitance as high as 160 F g-1, three-fold higher than that of conventional graphene SCs, and a capacitance retention of >95% of the maximum value even after severe bending and 1000 charge-discharge tests due to the structural and compositional features.Hierarchical structures of hybrid materials with the controlled compositions have been shown to offer a breakthrough for energy storage and conversion. Here, we report the integrative assembly of chemically modified graphene (CMG) building blocks into hierarchical complex structures with the hybrid composition for high performance flexible pseudocapacitors. The formation mechanism of hierarchical CMG/Nafion/RuO2 (CMGNR) microspheres, which is triggered by the cooperative interplay during the in situ synthesis of RuO2 nanoparticles (NPs), was extensively investigated. In particular, the hierarchical CMGNR microspheres consisting of the aggregates of CMG/Nafion (CMGN) nanosheets and RuO2 NPs provided large surface area and facile ion accessibility to storage sites, while the interconnected nanosheets offered continuous electron pathways and mechanical integrity. The synergistic effect of CMGNR hybrids on the supercapacitor (SC) performance was derived from the hybrid composition of pseudocapacitive RuO2 NPs with the conductive CMGNs as well as from structural features. Consequently, the CMGNR-SCs showed a specific capacitance as high as 160 F g-1, three-fold higher than that of conventional graphene SCs, and a capacitance retention of >95% of the maximum value even after severe bending and 1000 charge-discharge tests due to the structural and compositional features. Electronic supplementary information (ESI) available: Electrodeposition procedure, TEM, SEM, and AFM images, XPS, FT-IR, and XRD spectra, mechanical strain-stress curve, textural and conductive properties, and impedance spectroscopy. See DOI: 10.1039/c3nr33674c

Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey

USGS Publications Warehouse

Link, William; Sauer, John R.

2016-01-01

The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

PubMed

Zhou, Feng; De la Torre, Fernando; Hodgins, Jessica K

2013-03-01

Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

Hierarchical Bayesian Markov switching models with application to predicting spawning success of shovelnose sturgeon

USGS Publications Warehouse

Holan, S.H.; Davis, G.M.; Wildhaber, M.L.; DeLonay, A.J.; Papoulias, D.M.

2009-01-01

The timing of spawning in fish is tightly linked to environmental factors; however, these factors are not very well understood for many species. Specifically, little information is available to guide recruitment efforts for endangered species such as the sturgeon. Therefore, we propose a Bayesian hierarchical model for predicting the success of spawning of the shovelnose sturgeon which uses both biological and behavioural (longitudinal) data. In particular, we use data that were produced from a tracking study that was conducted in the Lower Missouri River. The data that were produced from this study consist of biological variables associated with readiness to spawn along with longitudinal behavioural data collected by using telemetry and archival data storage tags. These high frequency data are complex both biologically and in the underlying behavioural process. To accommodate such complexity we developed a hierarchical linear regression model that uses an eigenvalue predictor, derived from the transition probability matrix of a two-state Markov switching model with generalized auto-regressive conditional heteroscedastic dynamics. Finally, to minimize the computational burden that is associated with estimation of this model, a parallel computing approach is proposed. ?? Journal compilation 2009 Royal Statistical Society.

Facile synthesis of hierarchical dendritic PtPd nanogarlands supported on reduced graphene oxide with enhanced electrocatalytic properties

NASA Astrophysics Data System (ADS)

Li, Shan-Shan; Zheng, Jie-Ning; Ma, Xiaohong; Hu, Yuan-Yuan; Wang, Ai-Jun; Chen, Jian-Rong; Feng, Jiu-Ju

2014-05-01

A simple and facile method is developed for one-pot preparation of hierarchical dendritic PtPd nanogarlands supported on reduced graphene oxide (PtPd/RGO) at room temperature, without using any seed, organic solvent, or complex apparatus. It is found that octylphenoxypolyethoxyethanol (NP-40) as a soft template and its amount are critical to the formation of PtPd garlands. The as-prepared nanocomposites are further applied to methanol and ethanol oxidation with significantly enhanced electrocatalytic activity and better stability in alkaline media.A simple and facile method is developed for one-pot preparation of hierarchical dendritic PtPd nanogarlands supported on reduced graphene oxide (PtPd/RGO) at room temperature, without using any seed, organic solvent, or complex apparatus. It is found that octylphenoxypolyethoxyethanol (NP-40) as a soft template and its amount are critical to the formation of PtPd garlands. The as-prepared nanocomposites are further applied to methanol and ethanol oxidation with significantly enhanced electrocatalytic activity and better stability in alkaline media. Electronic supplementary information (ESI) available: Experimental section, Fig. S1-S12 and Tables S1 and S2. See DOI: 10.1039/c3nr06808k

Hierarchical classification in high dimensional numerous class cases

NASA Technical Reports Server (NTRS)

Kim, Byungyong; Landgrebe, D. A.

1990-01-01

As progress in new sensor technology continues, increasingly high resolution imaging sensors are being developed. These sensors give more detailed and complex data for each picture element and greatly increase the dimensionality of data over past systems. Three methods for designing a decision tree classifier are discussed: a top down approach, a bottom up approach, and a hybrid approach. Three feature extraction techniques are implemented. Canonical and extended canonical techniques are mainly dependent upon the mean difference between two classes. An autocorrelation technique is dependent upon the correlation differences. The mathematical relationship between sample size, dimensionality, and risk value is derived.

Thermotropic nanostructured gels with complex hierarchical structure and two gelling components for water shut-off and enhance of oil recovery

NASA Astrophysics Data System (ADS)

Altunina, L. K.; Kuvshinov, I. V.; Kuvshinov, V. A.; Kozlov, V. V.; Stasyeva, L. A.

2017-12-01

This work presents the results of laboratory and field tests of thermotropic composition MEGA with two simultaneously acting gelling components, polymer and inorganic. The composition is intended for improving oil recovery and water shut-off at oilfields developed by thermal flooding, and cyclic-steam stimulated oil production wells. The composition forms an in-situ "gel-in-gel" system with improved structural-mechanical properties, using reservoir or carrier fluid heat for gelling. The gel blocks water breakthrough into producing wells and redistribute fluid flows, thus increasing the oil recovery factor.

Spatial-area selective retrieval of multiple object-place associations in a hierarchical cognitive map formed by theta phase coding.

PubMed

Sato, Naoyuki; Yamaguchi, Yoko

2009-06-01

The human cognitive map is known to be hierarchically organized consisting of a set of perceptually clustered landmarks. Patient studies have demonstrated that these cognitive maps are maintained by the hippocampus, while the neural dynamics are still poorly understood. The authors have shown that the neural dynamic "theta phase precession" observed in the rodent hippocampus may be capable of forming hierarchical cognitive maps in humans. In the model, a visual input sequence consisting of object and scene features in the central and peripheral visual fields, respectively, results in the formation of a hierarchical cognitive map for object-place associations. Surprisingly, it is possible for such a complex memory structure to be formed in a few seconds. In this paper, we evaluate the memory retrieval of object-place associations in the hierarchical network formed by theta phase precession. The results show that multiple object-place associations can be retrieved with the initial cue of a scene input. Importantly, according to the wide-to-narrow unidirectional connections among scene units, the spatial area for object-place retrieval can be controlled by the spatial area of the initial cue input. These results indicate that the hierarchical cognitive maps have computational advantages on a spatial-area selective retrieval of multiple object-place associations. Theta phase precession dynamics is suggested as a fundamental neural mechanism of the human cognitive map.

Real-time UNIX in HEP data acquisition

NASA Astrophysics Data System (ADS)

Buono, S.; Gaponenko, I.; Jones, R.; Mapelli, L.; Mornacchi, G.; Prigent, D.; Sanchez-Corral, E.; Skiadelli, M.; Toppers, A.; Duval, P. Y.; Ferrato, D.; Le Van Suu, A.; Qian, Z.; Rondot, C.; Ambrosini, G.; Fumagalli, G.; Aguer, M.; Huet, M.

1994-12-01

Today's experimentation in high energy physics is characterized by an increasing need for sensitivity to rare phenomena and complex physics signatures, which require the use of huge and sophisticated detectors and consequently a high performance readout and data acquisition. Multi-level triggering, hierarchical data collection and an always increasing amount of processing power, distributed throughout the data acquisition layers, will impose a number of features on the software environment, especially the need for a high level of standardization. Real-time UNIX seems, today, the best solution for the platform independence, operating system interface standards and real-time features necessary for data acquisition in HEP experiments. We present the results of the evaluation, in a realistic application environment, of a Real-Time UNIX operating system: the EP/LX real-time UNIX system.

A systems-based approach for integrated design of materials, products and design process chains

NASA Astrophysics Data System (ADS)

Panchal, Jitesh H.; Choi, Hae-Jin; Allen, Janet K.; McDowell, David L.; Mistree, Farrokh

2007-12-01

The concurrent design of materials and products provides designers with flexibility to achieve design objectives that were not previously accessible. However, the improved flexibility comes at a cost of increased complexity of the design process chains and the materials simulation models used for executing the design chains. Efforts to reduce the complexity generally result in increased uncertainty. We contend that a systems based approach is essential for managing both the complexity and the uncertainty in design process chains and simulation models in concurrent material and product design. Our approach is based on simplifying the design process chains systematically such that the resulting uncertainty does not significantly affect the overall system performance. Similarly, instead of striving for accurate models for multiscale systems (that are inherently complex), we rely on making design decisions that are robust to uncertainties in the models. Accordingly, we pursue hierarchical modeling in the context of design of multiscale systems. In this paper our focus is on design process chains. We present a systems based approach, premised on the assumption that complex systems can be designed efficiently by managing the complexity of design process chains. The approach relies on (a) the use of reusable interaction patterns to model design process chains, and (b) consideration of design process decisions using value-of-information based metrics. The approach is illustrated using a Multifunctional Energetic Structural Material (MESM) design example. Energetic materials store considerable energy which can be released through shock-induced detonation; conventionally, they are not engineered for strength properties. The design objectives for the MESM in this paper include both sufficient strength and energy release characteristics. The design is carried out by using models at different length and time scales that simulate different aspects of the system. Finally, by applying the method to the MESM design problem, we show that the integrated design of materials and products can be carried out more efficiently by explicitly accounting for design process decisions with the hierarchy of models.

Reflection of processes of non-equilibrium two-phase filtration in oil-saturated hierarchical medium in data of active wave geophysical monitoring

NASA Astrophysics Data System (ADS)

Hachay, Olga; Khachay, Andrey; Khachay, Oleg

2016-04-01

The processes of oil extraction from deposit are linked with the movement of multi-phase multi-component media, which are characterized by non-equilibrium and non-linear rheological features. The real behavior of layered systems is defined by the complexity of the rheology of moving fluids and the morphology structure of the porous medium, and also by the great variety of interactions between the fluid and the porous medium [Hasanov and Bulgakova, 2003]. It is necessary to take into account these features in order to informatively describe the filtration processes due to the non-linearity, non-equilibrium and heterogeneity that are features of real systems. In this way, new synergetic events can be revealed (namely, a loss of stability when oscillations occur, and the formation of ordered structures). This allows us to suggest new methods for the control and management of complicated natural systems that are constructed on account of these phenomena. Thus the layered system, from which it is necessary to extract the oil, is a complicated dynamical hierarchical system. A comparison is provided of non-equilibrium effects of the influence of independent hydrodynamic and electromagnetic induction on an oil layer and the medium which it surrounds. It is known that by drainage and steeping the hysteresis effect on curves of the relative phase permeability in dependence on the porous medium's water saturation in some cycles of influence (drainage-steep-drainage) is observed. Using the earlier developed 3D method of induction electromagnetic frequency geometric monitoring, we showed the possibility of defining the physical and structural features of a hierarchical oil layer structure and estimating the water saturation from crack inclusions. This effect allows managing the process of drainage and steeping the oil out of the layer by water displacement. An algorithm was constructed for 2D modeling of sound diffraction on a porous fluid-saturated intrusion of a hierarchical structure located in layer number J of an N-layered elastic medium. The algorithm developed for modeling, and the method of mapping and monitoring of heterogenic highly complicated two-phase medium can be used for managing viscous oil extraction in mining conditions and light oil in sub-horizontal boreholes. The demand for effective economic parameters and fuller extraction of oil and gas from deposits dictates the necessity of developing new geotechnology based on the fundamental achievements in the area of geophysics and geomechanics

Design and Implementation of a Metadata-rich File System

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

Ames, S; Gokhale, M B; Maltzahn, C

2010-01-19

Despite continual improvements in the performance and reliability of large scale file systems, the management of user-defined file system metadata has changed little in the past decade. The mismatch between the size and complexity of large scale data stores and their ability to organize and query their metadata has led to a de facto standard in which raw data is stored in traditional file systems, while related, application-specific metadata is stored in relational databases. This separation of data and semantic metadata requires considerable effort to maintain consistency and can result in complex, slow, and inflexible system operation. To address thesemore » problems, we have developed the Quasar File System (QFS), a metadata-rich file system in which files, user-defined attributes, and file relationships are all first class objects. In contrast to hierarchical file systems and relational databases, QFS defines a graph data model composed of files and their relationships. QFS incorporates Quasar, an XPATH-extended query language for searching the file system. Results from our QFS prototype show the effectiveness of this approach. Compared to the de facto standard, the QFS prototype shows superior ingest performance and comparable query performance on user metadata-intensive operations and superior performance on normal file metadata operations.« less

Evaluating a Bilingual Text-Mining System with a Taxonomy of Key Words and Hierarchical Visualization for Understanding Learner-Generated Text

ERIC Educational Resources Information Center

Kong, Siu Cheung; Li, Ping; Song, Yanjie

2018-01-01

This study evaluated a bilingual text-mining system, which incorporated a bilingual taxonomy of key words and provided hierarchical visualization, for understanding learner-generated text in the learning management systems through automatic identification and counting of matching key words. A class of 27 in-service teachers studied a course…

Network representations of immune system complexity

PubMed Central

Subramanian, Naeha; Torabi-Parizi, Parizad; Gottschalk, Rachel A.; Germain, Ronald N.; Dutta, Bhaskar

2015-01-01

The mammalian immune system is a dynamic multi-scale system composed of a hierarchically organized set of molecular, cellular and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein-protein interactions underlying intracellular signaling pathways and single cell responses to increasingly complex networks of in vivo cellular interaction, positioning and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather non-linear behaviors arising from dynamic, feedback-regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multi-scale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular and organism-level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. PMID:25625853

Integrated therapy safety management system

PubMed Central

Podtschaske, Beatrice; Fuchs, Daniela; Friesdorf, Wolfgang

2013-01-01

Aims The aim is to demonstrate the benefit of the medico-ergonomic approach for the redesign of clinical work systems. Based on the six layer model, a concept for an ‘integrated therapy safety management’ is drafted. This concept could serve as a basis to improve resilience. Methods The concept is developed through a concept-based approach. The state of the art of safety and complexity research in human factors and ergonomics forms the basis. The findings are synthesized to a concept for ‘integrated therapy safety management’. The concept is applied by way of example for the ‘medication process’ to demonstrate its practical implementation. Results The ‘integrated therapy safety management’ is drafted in accordance with the six layer model. This model supports a detailed description of specific work tasks, the corresponding responsibilities and related workflows at different layers by using the concept of ‘bridge managers’. ‘Bridge managers’ anticipate potential errors and monitor the controlled system continuously. If disruptions or disturbances occur, they respond with corrective actions which ensure that no harm results and they initiate preventive measures for future procedures. The concept demonstrates that in a complex work system, the human factor is the key element and final authority to cope with the residual complexity. The expertise of the ‘bridge managers’ and the recursive hierarchical structure results in highly adaptive clinical work systems and increases their resilience. Conclusions The medico-ergonomic approach is a highly promising way of coping with two complexities. It offers a systematic framework for comprehensive analyses of clinical work systems and promotes interdisciplinary collaboration. PMID:24007448

Control, responses and modularity of cellular regulatory networks: a control analysis perspective.

PubMed

Bruggeman, F J; Snoep, J L; Westerhoff, H V

2008-11-01

Cells adapt to changes in environmental conditions through the concerted action of signalling, gene expression and metabolic subsystems. The authors will discuss a theoretical framework addressing such integrated systems. This 'hierarchical analysis' was first developed as an extension to a metabolic control analysis. It builds on the phenomenon that often the communication between signalling, gene expression and metabolic subsystems is almost exclusively via regulatory interactions and not via mass flow interactions. This allows for the treatment of the said subsystems as 'levels' in a hierarchical view of the organisation of the molecular reaction network of cells. Such a hierarchical approach has as a major advantage that levels can be analysed conceptually in isolation of each other (from a local intra-level perspective) and at a later stage integrated via their interactions (from a global inter-level perspective). Hereby, it allows for a modular approach with variable scope. A number of different approaches have been developed for the analysis of hierarchical systems, for example hierarchical control analysis and modular response analysis. The authors, here, review these methods and illustrate the strength of these types of analyses using a core model of a system with gene expression, metabolic and signal transduction levels.

Dynamic anticipatory processing of hierarchical sequential events: a common role for Broca's area and ventral premotor cortex across domains?

PubMed

Fiebach, Christian J; Schubotz, Ricarda I

2006-05-01

This paper proposes a domain-general model for the functional contribution of ventral premotor cortex (PMv) and adjacent Broca's area to perceptual, cognitive, and motor processing. We propose to understand this frontal region as a highly flexible sequence processor, with the PMv mapping sequential events onto stored structural templates and Broca's Area involved in more complex, hierarchical or hypersequential processing. This proposal is supported by reference to previous functional neuroimaging studies investigating abstract sequence processing and syntactic processing.

Velocity and Hierarchical Spread of Epidemic Outbreaks in Scale-Free Networks

NASA Astrophysics Data System (ADS)

Barthélemy, Marc; Barrat, Alain; Pastor-Satorras, Romualdo; Vespignani, Alessandro

2004-04-01

We study the effect of the connectivity pattern of complex networks on the propagation dynamics of epidemics. The growth time scale of outbreaks is inversely proportional to the network degree fluctuations, signaling that epidemics spread almost instantaneously in networks with scale-free degree distributions. This feature is associated with an epidemic propagation that follows a precise hierarchical dynamics. Once the highly connected hubs are reached, the infection pervades the network in a progressive cascade across smaller degree classes. The present results are relevant for the development of adaptive containment strategies.

Culture Modulates the Brain Response to Harmonic Violations: An EEG Study on Hierarchical Syntactic Structure in Music.

PubMed

Akrami, Haleh; Moghimi, Sahar

2017-01-01

We investigated the role of culture in processing hierarchical syntactic structures in music. We examined whether violation of non-local dependencies manifest in event related potentials (ERP) for Western and Iranian excerpts by recording EEG while participants passively listened to sequences of modified/original excerpts. We also investigated oscillatory and synchronization properties of brain responses during processing of hierarchical structures. For the Western excerpt, subjective ratings of conclusiveness were marginally significant and the difference in the ERP components fell short of significance. However, ERP and behavioral results showed that while listening to culturally familiar music, subjects comprehended whether or not the hierarchical syntactic structure was fulfilled. Irregularities in the hierarchical structures of the Iranian excerpt elicited an early negativity in the central regions bilaterally, followed by two later negativities from 450-700 to 750-950 ms. The latter manifested throughout the scalp. Moreover, violations of hierarchical structure in the Iranian excerpt were associated with (i) an early decrease in the long range alpha phase synchronization, (ii) an early increase in the oscillatory activity in the beta band over the central areas, and (iii) a late decrease in the theta band phase synchrony between left anterior and right posterior regions. Results suggest that rhythmic structures and melodic fragments, representative of Iranian music, created a familiar context in which recognition of complex non-local syntactic structures was feasible for Iranian listeners. Processing of neural responses to the Iranian excerpt indicated neural mechanisms for processing of hierarchical syntactic structures in music at different levels of cortical integration.

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

PubMed Central

Chen, Heng; Chen, Xinying

2018-01-01

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

Object-Oriented Bayesian Networks (OOBN) for Aviation Accident Modeling and Technology Portfolio Impact Assessment

NASA Technical Reports Server (NTRS)

Shih, Ann T.; Ancel, Ersin; Jones, Sharon M.

2012-01-01

The concern for reducing aviation safety risk is rising as the National Airspace System in the United States transforms to the Next Generation Air Transportation System (NextGen). The NASA Aviation Safety Program is committed to developing an effective aviation safety technology portfolio to meet the challenges of this transformation and to mitigate relevant safety risks. The paper focuses on the reasoning of selecting Object-Oriented Bayesian Networks (OOBN) as the technique and commercial software for the accident modeling and portfolio assessment. To illustrate the benefits of OOBN in a large and complex aviation accident model, the in-flight Loss-of-Control Accident Framework (LOCAF) constructed as an influence diagram is presented. An OOBN approach not only simplifies construction and maintenance of complex causal networks for the modelers, but also offers a well-organized hierarchical network that is easier for decision makers to exploit the model examining the effectiveness of risk mitigation strategies through technology insertions.

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

PubMed

Chen, Heng; Chen, Xinying; Liu, Haitao

2018-01-01

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

Adding Hierarchical Objects to Relational Database General-Purpose XML-Based Information Managements

NASA Technical Reports Server (NTRS)

Lin, Shu-Chun; Knight, Chris; La, Tracy; Maluf, David; Bell, David; Tran, Khai Peter; Gawdiak, Yuri

2006-01-01

NETMARK is a flexible, high-throughput software system for managing, storing, and rapid searching of unstructured and semi-structured documents. NETMARK transforms such documents from their original highly complex, constantly changing, heterogeneous data formats into well-structured, common data formats in using Hypertext Markup Language (HTML) and/or Extensible Markup Language (XML). The software implements an object-relational database system that combines the best practices of the relational model utilizing Structured Query Language (SQL) with those of the object-oriented, semantic database model for creating complex data. In particular, NETMARK takes advantage of the Oracle 8i object-relational database model using physical-address data types for very efficient keyword searches of records across both context and content. NETMARK also supports multiple international standards such as WEBDAV for drag-and-drop file management and SOAP for integrated information management using Web services. The document-organization and -searching capabilities afforded by NETMARK are likely to make this software attractive for use in disciplines as diverse as science, auditing, and law enforcement.

Exploring the neural bases of goal-directed motor behavior using fully resolved simulations

NASA Astrophysics Data System (ADS)

Patel, Namu; Patankar, Neelesh A.

2016-11-01

Undulatory swimming is an ideal problem for understanding the neural architecture for motor control and movement; a vertebrate's robust morphology and adaptive locomotive gait allows the swimmer to navigate complex environments. Simple mathematical models for neurally activated muscle contractions have been incorporated into a swimmer immersed in fluid. Muscle contractions produce bending moments which determine the swimming kinematics. The neurobiology of goal-directed locomotion is explored using fast, efficient, and fully resolved constraint-based immersed boundary simulations. Hierarchical control systems tune the strength, frequency, and duty cycle for neural activation waves to produce multifarious swimming gaits or synergies. Simulation results are used to investigate why the basal ganglia and other control systems may command a particular neural pattern to accomplish a task. Using simple neural models, the effect of proprioceptive feedback on refining the body motion is demonstrated. Lastly, the ability for a learned swimmer to successfully navigate a complex environment is tested. This work is supported by NSF CBET 1066575 and NSF CMMI 0941674.

Segmentation in cohesive systems constrained by elastic environments

NASA Astrophysics Data System (ADS)

Novak, I.; Truskinovsky, L.

2017-04-01

The complexity of fracture-induced segmentation in elastically constrained cohesive (fragile) systems originates from the presence of competing interactions. The role of discreteness in such phenomena is of interest in a variety of fields, from hierarchical self-assembly to developmental morphogenesis. In this paper, we study the analytically solvable example of segmentation in a breakable mass-spring chain elastically linked to a deformable lattice structure. We explicitly construct the complete set of local minima of the energy in this prototypical problem and identify among them the states corresponding to the global energy minima. We show that, even in the continuum limit, the dependence of the segmentation topology on the stretching/pre-stress parameter in this problem takes the form of a devil's type staircase. The peculiar nature of this staircase, characterized by locking in rational microstructures, is of particular importance for biological applications, where its structure may serve as an explanation of the robustness of stress-driven segmentation. This article is part of the themed issue 'Patterning through instabilities in complex media: theory and applications.'

A Qualitative Model of Human Interaction with Complex Dynamic Systems

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1987-01-01

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

A qualitative model of human interaction with complex dynamic systems

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1987-01-01

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

Rewiring cells: synthetic biology as a tool to interrogate the organizational principles of living systems.

PubMed

Bashor, Caleb J; Horwitz, Andrew A; Peisajovich, Sergio G; Lim, Wendell A

2010-01-01

The living cell is an incredibly complex entity, and the goal of predictively and quantitatively understanding its function is one of the next great challenges in biology. Much of what we know about the cell concerns its constituent parts, but to a great extent we have yet to decode how these parts are organized to yield complex physiological function. Classically, we have learned about the organization of cellular networks by disrupting them through genetic or chemical means. The emerging discipline of synthetic biology offers an additional, powerful approach to study systems. By rearranging the parts that comprise existing networks, we can gain valuable insight into the hierarchical logic of the networks and identify the modular building blocks that evolution uses to generate innovative function. In addition, by building minimal toy networks, one can systematically explore the relationship between network structure and function. Here, we outline recent work that uses synthetic biology approaches to investigate the organization and function of cellular networks, and describe a vision for a synthetic biology toolkit that could be used to interrogate the design principles of diverse systems.

A new multi-scale method to reveal hierarchical modular structures in biological networks.

PubMed

Jiao, Qing-Ju; Huang, Yan; Shen, Hong-Bin

2016-11-15

Biological networks are effective tools for studying molecular interactions. Modular structure, in which genes or proteins may tend to be associated with functional modules or protein complexes, is a remarkable feature of biological networks. Mining modular structure from biological networks enables us to focus on a set of potentially important nodes, which provides a reliable guide to future biological experiments. The first fundamental challenge in mining modular structure from biological networks is that the quality of the observed network data is usually low owing to noise and incompleteness in the obtained networks. The second problem that poses a challenge to existing approaches to the mining of modular structure is that the organization of both functional modules and protein complexes in networks is far more complicated than was ever thought. For instance, the sizes of different modules vary considerably from each other and they often form multi-scale hierarchical structures. To solve these problems, we propose a new multi-scale protocol for mining modular structure (named ISIMB) driven by a node similarity metric, which works in an iteratively converged space to reduce the effects of the low data quality of the observed network data. The multi-scale node similarity metric couples both the local and the global topology of the network with a resolution regulator. By varying this resolution regulator to give different weightings to the local and global terms in the metric, the ISIMB method is able to fit the shape of modules and to detect them on different scales. Experiments on protein-protein interaction and genetic interaction networks show that our method can not only mine functional modules and protein complexes successfully, but can also predict functional modules from specific to general and reveal the hierarchical organization of protein complexes.

NASA thesaurus. Volume 1: Hierarchical listing

NASA Technical Reports Server (NTRS)

1985-01-01

There are 16,835 postable terms and 3,765 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary.

NASA Thesaurus. Volume 1: Hierarchical listing

NASA Technical Reports Server (NTRS)

1982-01-01

There are 16,713 postable terms and 3,716 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary.

Physical approach to complex systems

NASA Astrophysics Data System (ADS)

Kwapień, Jarosław; Drożdż, Stanisław

2012-06-01

Typically, complex systems are natural or social systems which consist of a large number of nonlinearly interacting elements. These systems are open, they interchange information or mass with environment and constantly modify their internal structure and patterns of activity in the process of self-organization. As a result, they are flexible and easily adapt to variable external conditions. However, the most striking property of such systems is the existence of emergent phenomena which cannot be simply derived or predicted solely from the knowledge of the systems’ structure and the interactions among their individual elements. This property points to the holistic approaches which require giving parallel descriptions of the same system on different levels of its organization. There is strong evidence-consolidated also in the present review-that different, even apparently disparate complex systems can have astonishingly similar characteristics both in their structure and in their behaviour. One can thus expect the existence of some common, universal laws that govern their properties. Physics methodology proves helpful in addressing many of the related issues. In this review, we advocate some of the computational methods which in our opinion are especially fruitful in extracting information on selected-but at the same time most representative-complex systems like human brain, financial markets and natural language, from the time series representing the observables associated with these systems. The properties we focus on comprise the collective effects and their coexistence with noise, long-range interactions, the interplay between determinism and flexibility in evolution, scale invariance, criticality, multifractality and hierarchical structure. The methods described either originate from “hard” physics-like the random matrix theory-and then were transmitted to other fields of science via the field of complex systems research, or they originated elsewhere but turned out to be very useful also in physics - like, for example, fractal geometry. Further methods discussed borrow from the formalism of complex networks, from the theory of critical phenomena and from nonextensive statistical mechanics. Each of these methods is helpful in analyses of specific aspects of complexity and all of them are mutually complementary.

The value of prior knowledge in machine learning of complex network systems.

PubMed

Ferranti, Dana; Krane, David; Craft, David

2017-11-15

Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available. The simulations are based on Boolean networks-directed graphs with 0/1 node states and logical node update rules-which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics and as such provide a useful framework for developing machine-learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine-learning algorithms to predict network steady-state node values ('phenotypes') and perturbation responses ('drug effects'). Links to codes and datasets here: https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd. dcraft@broadinstitute.org. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

The hidden hyperbolic geometry of international trade: World Trade Atlas 1870–2013

PubMed Central

García-Pérez, Guillermo; Boguñá, Marián; Allard, Antoine; Serrano, M. Ángeles

2016-01-01

Here, we present the World Trade Atlas 1870–2013, a collection of annual world trade maps in which distance combines economic size and the different dimensions that affect international trade beyond mere geography. Trade distances, based on a gravity model predicting the existence of significant trade channels, are such that the closer countries are in trade space, the greater their chance of becoming connected. The atlas provides us with information regarding the long-term evolution of the international trade system and demonstrates that, in terms of trade, the world is not flat but hyperbolic, as a reflection of its complex architecture. The departure from flatness has been increasing since World War I, meaning that differences in trade distances are growing and trade networks are becoming more hierarchical. Smaller-scale economies are moving away from other countries except for the largest economies; meanwhile those large economies are increasing their chances of becoming connected worldwide. At the same time, Preferential Trade Agreements do not fit in perfectly with natural communities within the trade space and have not necessarily reduced internal trade barriers. We discuss an interpretation in terms of globalization, hierarchization, and localization; three simultaneous forces that shape the international trade system. PMID:27633649

Stretchable conducting materials with multi-scale hierarchical structures for biomedical applications

NASA Astrophysics Data System (ADS)

Kim, Hyun; Shim, Bong Sup

2014-08-01

Electrogenetic tissues in human body such as central and peripheral nerve systems, muscular and cardiomuscular systems are soft and stretchable materials. However, most of the artificial materials, interfacing with those conductive tissues, such as neural electrodes and cardiac pacemakers, have stiff mechanical properties. The rather contradictory properties between natural and artificial materials usually cause critical incompatibility problems in implanting bodymachine interfaces for wide ranges of biomedical devices. Thus, we developed a stretchable and electrically conductive material with complex hierarchical structures; multi-scale microstructures and nanostructural electrical pathways. For biomedical purposes, an implantable polycaprolactone (PCL) membrane was coated by molecularly controlled layer-bylayer (LBL) assembly of single-walled carbon nanotubes (SWNTs) or poly(3,4-ethylenedioxythiophene) (PEDOT). The soft PCL membrane with asymmetric micro- and nano-pores provides elastic properties, while conductive SWNT or PEDOT coating preserves stable electrical conductivity even in a fully stretched state. This electrical conductivity enhanced ionic cell transmission and cell-to-cell interactions as well as electrical cellular stimulation on the membrane. Our novel stretchable conducting materials will overcome long-lasting challenges for bioelectronic applications by significantly reducing mechanical property gaps between tissues and artificial materials and by providing 3D interconnected electro-active pathways which can be available even at a fully stretched state.

Hierarchical Nanostructures Self-Assembled from a Mixture System Containing Rod-Coil Block Copolymers and Rigid Homopolymers

PubMed Central

Li, Yongliang; Jiang, Tao; Lin, Shaoliang; Lin, Jiaping; Cai, Chunhua; Zhu, Xingyu

2015-01-01

Self-assembly behavior of a mixture system containing rod-coil block copolymers and rigid homopolymers was investigated by using Brownian dynamics simulations. The morphologies of formed hierarchical self-assemblies were found to be dependent on the Lennard-Jones (LJ) interaction εRR between rod blocks, lengths of rod and coil blocks in copolymer, and mixture ratio of block copolymers to homopolymers. As the εRR value decreases, the self-assembled structures of mixtures are transformed from an abacus-like structure to a helical structure, to a plain fiber, and finally are broken into unimers. The order parameter of rod blocks was calculated to confirm the structure transition. Through varying the length of rod and coil blocks, the regions of thermodynamic stability of abacus, helix, plain fiber, and unimers were mapped. Moreover, it was discovered that two levels of rod block ordering exist in the helices. The block copolymers are helically wrapped on the homopolymer bundles to form helical string, while the rod blocks are twistingly packed inside the string. In addition, the simulation results are in good agreement with experimental observations. The present work reveals the mechanism behind the formation of helical (experimentally super-helical) structures and may provide useful information for design and preparation of the complex structures. PMID:25965726

Human life support during interplanetary travel and domicile. II - Generic Modular Flow Schematic modeling

NASA Technical Reports Server (NTRS)

Farral, Joseph F.; Seshan, P. K.; Rohatgi, Naresh K.

1991-01-01

This paper describes the Generic Modular Flow Schematic (GMFS) architecture capable of encompassing all functional elements of a physical/chemical life support system (LSS). The GMFS can be implemented to synthesize, model, analyze, and quantitatively compare many configurations of LSSs, from a simple, completely open-loop to a very complex closed-loop. The GMFS model is coded in ASPEN, a state-of-the-art chemical process simulation program, to accurately compute the material, heat, and power flow quantities for every stream in each of the subsystem functional elements (SFEs) in the chosen configuration of a life support system. The GMFS approach integrates the various SFEs and subsystems in a hierarchical and modular fashion facilitating rapid substitutions and reconfiguration of a life support system. The comprehensive ASPEN material and energy balance output is transferred to a systems and technology assessment spreadsheet for rigorous system analysis and trade studies.

Music acquisition: effects of enculturation and formal training on development.

PubMed

Hannon, Erin E; Trainor, Laurel J

2007-11-01

Musical structure is complex, consisting of a small set of elements that combine to form hierarchical levels of pitch and temporal structure according to grammatical rules. As with language, different systems use different elements and rules for combination. Drawing on recent findings, we propose that music acquisition begins with basic features, such as peripheral frequency-coding mechanisms and multisensory timing connections, and proceeds through enculturation, whereby everyday exposure to a particular music system creates, in a systematic order of acquisition, culture-specific brain structures and representations. Finally, we propose that formal musical training invokes domain-specific processes that affect salience of musical input and the amount of cortical tissue devoted to its processing, as well as domain-general processes of attention and executive functioning.

Three-dimensional distribution of polymorphs and magnesium in a calcified underwater attachment system by diffraction tomography

PubMed Central

Leemreize, Hanna; Almer, Jonathan D.; Stock, Stuart R.; Birkedal, Henrik

2013-01-01

Biological materials display complicated three-dimensional hierarchical structures. Determining these structures is essential in understanding the link between material design and properties. Herein, we show how diffraction tomography can be used to determine the relative placement of the calcium carbonate polymorphs calcite and aragonite in the highly mineralized holdfast system of the bivalve Anomia simplex. In addition to high fidelity and non-destructive mapping of polymorphs, we use detailed analysis of X-ray diffraction peak positions in reconstructed powder diffraction data to determine the local degree of Mg substitution in the calcite phase. These data show how diffraction tomography can provide detailed multi-length scale information on complex materials in general and of biomineralized tissues in particular. PMID:23804437