Self-Reacting Friction Stir Welding for Aluminum Complex Curvature Applications

NASA Technical Reports Server (NTRS)

Brown, Randy J.; Martin, W.; Schneider, J.; Hartley, P. J.; Russell, Carolyn; Lawless, Kirby; Jones, Chip

2003-01-01

This viewgraph representation provides an overview of sucessful research conducted by Lockheed Martin and NASA to develop an advanced self-reacting friction stir technology for complex curvature aluminum alloys. The research included weld process development for 0.320 inch Al 2219, sucessful transfer from the 'lab' scale to the production scale tool and weld quality exceeding strenght goals. This process will enable development and implementation of large scale complex geometry hardware fabrication. Topics covered include: weld process development, weld process transfer, and intermediate hardware fabrication.

TomoMiner and TomoMinerCloud: A software platform for large-scale subtomogram structural analysis

PubMed Central

Frazier, Zachary; Xu, Min; Alber, Frank

2017-01-01

SUMMARY Cryo-electron tomography (cryoET) captures the 3D electron density distribution of macromolecular complexes in close to native state. With the rapid advance of cryoET acquisition technologies, it is possible to generate large numbers (>100,000) of subtomograms, each containing a macromolecular complex. Often, these subtomograms represent a heterogeneous sample due to variations in structure and composition of a complex in situ form or because particles are a mixture of different complexes. In this case subtomograms must be classified. However, classification of large numbers of subtomograms is a time-intensive task and often a limiting bottleneck. This paper introduces an open source software platform, TomoMiner, for large-scale subtomogram classification, template matching, subtomogram averaging, and alignment. Its scalable and robust parallel processing allows efficient classification of tens to hundreds of thousands of subtomograms. Additionally, TomoMiner provides a pre-configured TomoMinerCloud computing service permitting users without sufficient computing resources instant access to TomoMiners high-performance features. PMID:28552576

About the bears and the bees: Adaptive responses to asymmetric warfare

NASA Astrophysics Data System (ADS)

Ryan, Alex

Conventional military forces are organised to generate large scale effects against similarly structured adversaries. Asymmetric warfare is a 'game' between a conventional military force and a weaker adversary that is unable to match the scale of effects of the conventional force. In asymmetric warfare, an insurgents' strategy can be understood using a multi-scale perspective: by generating and exploiting fine scale complexity, insurgents prevent the conventional force from acting at the scale they are designed for. This paper presents a complex systems approach to the problem of asymmetric warfare, which shows how future force structures can be designed to adapt to environmental complexity at multiple scales and achieve full spectrum dominance.

About the bears and the bees: Adaptive responses to asymmetric warfare

NASA Astrophysics Data System (ADS)

Ryan, Alex

Conventional military forces are organised to generate large scale effects against similarly structured adversaries. Asymmetric warfare is a `game' between a conventional military force and a weaker adversary that is unable to match the scale of effects of the conventional force. In asymmetric warfare, an insurgents' strategy can be understood using a multi-scale perspective: by generating and exploiting fine scale complexity, insurgents prevent the conventional force from acting at the scale they are designed for. This paper presents a complex systems approach to the problem of asymmetric warfare, which shows how future force structures can be designed to adapt to environmental complexity at multiple scales and achieve full spectrum dominance.

Energy Spectral Behaviors of Communication Networks of Open-Source Communities

PubMed Central

Yang, Jianmei; Yang, Huijie; Liao, Hao; Wang, Jiangtao; Zeng, Jinqun

2015-01-01

Large-scale online collaborative production activities in open-source communities must be accompanied by large-scale communication activities. Nowadays, the production activities of open-source communities, especially their communication activities, have been more and more concerned. Take CodePlex C # community for example, this paper constructs the complex network models of 12 periods of communication structures of the community based on real data; then discusses the basic concepts of quantum mapping of complex networks, and points out that the purpose of the mapping is to study the structures of complex networks according to the idea of quantum mechanism in studying the structures of large molecules; finally, according to this idea, analyzes and compares the fractal features of the spectra in different quantum mappings of the networks, and concludes that there are multiple self-similarity and criticality in the communication structures of the community. In addition, this paper discusses the insights and application conditions of different quantum mappings in revealing the characteristics of the structures. The proposed quantum mapping method can also be applied to the structural studies of other large-scale organizations. PMID:26047331

The statistical power to detect cross-scale interactions at macroscales

USGS Publications Warehouse

Wagner, Tyler; Fergus, C. Emi; Stow, Craig A.; Cheruvelil, Kendra S.; Soranno, Patricia A.

2016-01-01

Macroscale studies of ecological phenomena are increasingly common because stressors such as climate and land-use change operate at large spatial and temporal scales. Cross-scale interactions (CSIs), where ecological processes operating at one spatial or temporal scale interact with processes operating at another scale, have been documented in a variety of ecosystems and contribute to complex system dynamics. However, studies investigating CSIs are often dependent on compiling multiple data sets from different sources to create multithematic, multiscaled data sets, which results in structurally complex, and sometimes incomplete data sets. The statistical power to detect CSIs needs to be evaluated because of their importance and the challenge of quantifying CSIs using data sets with complex structures and missing observations. We studied this problem using a spatially hierarchical model that measures CSIs between regional agriculture and its effects on the relationship between lake nutrients and lake productivity. We used an existing large multithematic, multiscaled database, LAke multiscaled GeOSpatial, and temporal database (LAGOS), to parameterize the power analysis simulations. We found that the power to detect CSIs was more strongly related to the number of regions in the study rather than the number of lakes nested within each region. CSI power analyses will not only help ecologists design large-scale studies aimed at detecting CSIs, but will also focus attention on CSI effect sizes and the degree to which they are ecologically relevant and detectable with large data sets.

Low-Complexity Polynomial Channel Estimation in Large-Scale MIMO With Arbitrary Statistics

NASA Astrophysics Data System (ADS)

Shariati, Nafiseh; Bjornson, Emil; Bengtsson, Mats; Debbah, Merouane

2014-10-01

This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems, also known as massive MIMO, where there are hundreds of antennas at one side of the link. Motivated by the fact that computational complexity is one of the main challenges in such systems, a set of low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel (PEACH) estimators, are introduced for arbitrary channel and interference statistics. While the conventional minimum mean square error (MMSE) estimator has cubic complexity in the dimension of the covariance matrices, due to an inversion operation, our proposed estimators significantly reduce this to square complexity by approximating the inverse by a L-degree matrix polynomial. The coefficients of the polynomial are optimized to minimize the mean square error (MSE) of the estimate. We show numerically that near-optimal MSEs are achieved with low polynomial degrees. We also derive the exact computational complexity of the proposed estimators, in terms of the floating-point operations (FLOPs), by which we prove that the proposed estimators outperform the conventional estimators in large-scale MIMO systems of practical dimensions while providing a reasonable MSEs. Moreover, we show that L needs not scale with the system dimensions to maintain a certain normalized MSE. By analyzing different interference scenarios, we observe that the relative MSE loss of using the low-complexity PEACH estimators is smaller in realistic scenarios with pilot contamination. On the other hand, PEACH estimators are not well suited for noise-limited scenarios with high pilot power; therefore, we also introduce the low-complexity diagonalized estimator that performs well in this regime. Finally, we ...

Porous microwells for geometry-selective, large-scale microparticle arrays

NASA Astrophysics Data System (ADS)

Kim, Jae Jung; Bong, Ki Wan; Reátegui, Eduardo; Irimia, Daniel; Doyle, Patrick S.

2017-01-01

Large-scale microparticle arrays (LSMAs) are key for material science and bioengineering applications. However, previous approaches suffer from trade-offs between scalability, precision, specificity and versatility. Here, we present a porous microwell-based approach to create large-scale microparticle arrays with complex motifs. Microparticles are guided to and pushed into microwells by fluid flow through small open pores at the bottom of the porous well arrays. A scaling theory allows for the rational design of LSMAs to sort and array particles on the basis of their size, shape, or modulus. Sequential particle assembly allows for proximal and nested particle arrangements, as well as particle recollection and pattern transfer. We demonstrate the capabilities of the approach by means of three applications: high-throughput single-cell arrays; microenvironment fabrication for neutrophil chemotaxis; and complex, covert tags by the transfer of an upconversion nanocrystal-laden LSMA.

Task Effects on Linguistic Complexity and Accuracy: A Large-Scale Learner Corpus Analysis Employing Natural Language Processing Techniques

ERIC Educational Resources Information Center

Alexopoulou, Theodora; Michel, Marije; Murakami, Akira; Meurers, Detmar

2017-01-01

Large-scale learner corpora collected from online language learning platforms, such as the EF-Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: How does the prompt and input of a…

Crustal-Scale Fault Interaction at Rifted Margins and the Formation of Domain-Bounding Breakaway Complexes: Insights From Offshore Norway

NASA Astrophysics Data System (ADS)

Osmundsen, P. T.; Péron-Pinvidic, G.

2018-03-01

The large-magnitude faults that control crustal thinning and excision at rifted margins combine into laterally persistent structural boundaries that separate margin domains of contrasting morphology and structure. We term them breakaway complexes. At the Mid-Norwegian margin, we identify five principal breakaway complexes that separate the proximal, necking, distal, and outer margin domains. Downdip and lateral interactions between the faults that constitute breakaway complexes became fundamental to the evolution of the 3-D margin architecture. Different types of fault interaction are observed along and between these faults, but simple models for fault growth will not fully describe their evolution. These structures operate on the crustal scale, cut large thicknesses of heterogeneously layered lithosphere, and facilitate fundamental margin processes such as deformation coupling and exhumation. Variations in large-magnitude fault geometry, erosional footwall incision, and subsequent differential subsidence along the main breakaway complexes likely record the variable efficiency of these processes.

Adjoint-Based Aerodynamic Design of Complex Aerospace Configurations

NASA Technical Reports Server (NTRS)

Nielsen, Eric J.

2016-01-01

An overview of twenty years of adjoint-based aerodynamic design research at NASA Langley Research Center is presented. Adjoint-based algorithms provide a powerful tool for efficient sensitivity analysis of complex large-scale computational fluid dynamics (CFD) simulations. Unlike alternative approaches for which computational expense generally scales with the number of design parameters, adjoint techniques yield sensitivity derivatives of a simulation output with respect to all input parameters at the cost of a single additional simulation. With modern large-scale CFD applications often requiring millions of compute hours for a single analysis, the efficiency afforded by adjoint methods is critical in realizing a computationally tractable design optimization capability for such applications.

Computing the universe: how large-scale simulations illuminate galaxies and dark energy

NASA Astrophysics Data System (ADS)

O'Shea, Brian

2015-04-01

High-performance and large-scale computing is absolutely to understanding astronomical objects such as stars, galaxies, and the cosmic web. This is because these are structures that operate on physical, temporal, and energy scales that cannot be reasonably approximated in the laboratory, and whose complexity and nonlinearity often defies analytic modeling. In this talk, I show how the growth of computing platforms over time has facilitated our understanding of astrophysical and cosmological phenomena, focusing primarily on galaxies and large-scale structure in the Universe.

Investigating large-scale secondary circulations within impact crater topographies in a refractive index-matched facility

NASA Astrophysics Data System (ADS)

Blois, Gianluca; Kim, Taehoon; Bristow, Nathan; Day, Mackenzie; Kocurek, Gary; Anderson, William; Christensen, Kenneth

2017-11-01

Impact craters, common large-scale topographic features on the surface of Mars, are circular depressions delimited by a sharp ridge. A variety of crater fill morphologies exist, suggesting that complex intracrater circulations affect their evolution. Some large craters (diameter >10 km), particularly at mid latitudes on Mars, exhibit a central mound surrounded by circular moat. Foremost among these examples is Gale crater, landing site of NASA's Curiosity rover, since large-scale climatic processes early in in the history of Mars are preserved in the stratigraphic record of the inner mound. Investigating the intracrater flow produced by large scale winds aloft Mars craters is key to a number of important scientific issues including ongoing research on Mars paleo-environmental reconstruction and the planning of future missions (these results must be viewed in conjunction with the affects of radial katabatibc flows, the importance of which is already established in preceding studies). In this work we consider a number of crater shapes inspired by Gale morphology, including idealized craters. Access to the flow field within such geometrically complex topography is achieved herein using a refractive index matched approach. Instantaneous velocity maps, using both planar and volumetric PIV techniques, are presented to elucidate complex three-dimensional flow within the crater. In addition, first- and second-order statistics will be discussed in the context of wind-driven (aeolian) excavation of crater fill.

Dynamic ruptures on faults of complex geometry: insights from numerical simulations, from large-scale curvature to small-scale fractal roughness

NASA Astrophysics Data System (ADS)

Ulrich, T.; Gabriel, A. A.

2016-12-01

The geometry of faults is subject to a large degree of uncertainty. As buried structures being not directly observable, their complex shapes may only be inferred from surface traces, if available, or through geophysical methods, such as reflection seismology. As a consequence, most studies aiming at assessing the potential hazard of faults rely on idealized fault models, based on observable large-scale features. Yet, real faults are known to be wavy at all scales, their geometric features presenting similar statistical properties from the micro to the regional scale. The influence of roughness on the earthquake rupture process is currently a driving topic in the computational seismology community. From the numerical point of view, rough faults problems are challenging problems that require optimized codes able to run efficiently on high-performance computing infrastructure and simultaneously handle complex geometries. Physically, simulated ruptures hosted by rough faults appear to be much closer to source models inverted from observation in terms of complexity. Incorporating fault geometry on all scales may thus be crucial to model realistic earthquake source processes and to estimate more accurately seismic hazard. In this study, we use the software package SeisSol, based on an ADER-Discontinuous Galerkin scheme, to run our numerical simulations. SeisSol allows solving the spontaneous dynamic earthquake rupture problem and the wave propagation problem with high-order accuracy in space and time efficiently on large-scale machines. In this study, the influence of fault roughness on dynamic rupture style (e.g. onset of supershear transition, rupture front coherence, propagation of self-healing pulses, etc) at different length scales is investigated by analyzing ruptures on faults of varying roughness spectral content. In particular, we investigate the existence of a minimum roughness length scale in terms of rupture inherent length scales below which the rupture ceases to be sensible. Finally, the effect of fault geometry on ground-motions, in the near-field, is considered. Our simulations feature a classical linear slip weakening on the fault and a viscoplastic constitutive model off the fault. The benefits of using a more elaborate fast velocity-weakening friction law will also be considered.

Flagellum synchronization inhibits large-scale hydrodynamic instabilities in sperm suspensions

NASA Astrophysics Data System (ADS)

Schöller, Simon F.; Keaveny, Eric E.

2016-11-01

Sperm in suspension can exhibit large-scale collective motion and form coherent structures. Our picture of such coherent motion is largely based on reduced models that treat the swimmers as self-locomoting rigid bodies that interact via steady dipolar flow fields. Swimming sperm, however, have many more degrees of freedom due to elasticity, have a more exotic shape, and generate spatially-complex, time-dependent flow fields. While these complexities are known to lead to phenomena such as flagellum synchronization and attraction, how these effects impact the overall suspension behaviour and coherent structure formation is largely unknown. Using a computational model that captures both flagellum beating and elasticity, we simulate suspensions on the order of 103 individual swimming sperm cells whose motion is coupled through the surrounding Stokesian fluid. We find that the tendency for flagella to synchronize and sperm to aggregate inhibits the emergence of the large-scale hydrodynamic instabilities often associated with active suspensions. However, when synchronization is repressed by adding noise in the flagellum actuation mechanism, the picture changes and the structures that resemble large-scale vortices appear to re-emerge. Supported by an Imperial College PhD scholarship.

Application of large-scale, multi-resolution watershed modeling framework using the Hydrologic and Water Quality System (HAWQS)

USDA-ARS?s Scientific Manuscript database

In recent years, large-scale watershed modeling has been implemented broadly in the field of water resources planning and management. Complex hydrological, sediment, and nutrient processes can be simulated by sophisticated watershed simulation models for important issues such as water resources all...

Science Competencies That Go Unassessed

ERIC Educational Resources Information Center

Gilmer, Penny J.; Sherdan, Danielle M.; Oosterhof, Albert; Rohani, Faranak; Rouby, Aaron

2011-01-01

Present large-scale assessments require the use of item formats, such as multiple choice, that can be administered and scored efficiently. This limits competencies that can be measured by these assessments. An alternative approach to large-scale assessments is being investigated that would include the use of complex performance assessments. As…

Next Generation Analytic Tools for Large Scale Genetic Epidemiology Studies of Complex Diseases

PubMed Central

Mechanic, Leah E.; Chen, Huann-Sheng; Amos, Christopher I.; Chatterjee, Nilanjan; Cox, Nancy J.; Divi, Rao L.; Fan, Ruzong; Harris, Emily L.; Jacobs, Kevin; Kraft, Peter; Leal, Suzanne M.; McAllister, Kimberly; Moore, Jason H.; Paltoo, Dina N.; Province, Michael A.; Ramos, Erin M.; Ritchie, Marylyn D.; Roeder, Kathryn; Schaid, Daniel J.; Stephens, Matthew; Thomas, Duncan C.; Weinberg, Clarice R.; Witte, John S.; Zhang, Shunpu; Zöllner, Sebastian; Feuer, Eric J.; Gillanders, Elizabeth M.

2012-01-01

Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized. PMID:22147673

Large-scale patterns formed by solar active regions during the ascending phase of cycle 21

NASA Astrophysics Data System (ADS)

Gaizauskas, V.; Harvey, K. L.; Harvey, J. W.; Zwaan, C.

1983-02-01

Synoptic maps of photospheric magnetic fields prepared at the Kitt Peak National Observatory are used in investigating large-scale patterns in the spatial and temporal distribution of solar active regions for 27 solar rotations between 1977 and 1979. The active regions are found to be distributed in 'complexes of activity' (Bumba and Howard, 1965). With the working definition of a complex of activity based on continuity and proximity of the constituent active regions, the phenomenology of complexes is explored. It is found that complexes of activity form within one month and that they are typically maintained for 3 to 6 solar rotations by fresh injections of magnetic flux. During the active lifetime of a complex of activity, the total magnetic flux in the complex remains steady to within a factor of 2. The magnetic polarities are closely balanced, and each complex rotates about the sun at its own special, constant rate. In certain cases, the complexes form two diverging branches.

Advances in Multi-Sensor Scanning and Visualization of Complex Plants: the Utmost Case of a Reactor Building

NASA Astrophysics Data System (ADS)

Hullo, J.-F.; Thibault, G.; Boucheny, C.

2015-02-01

In a context of increased maintenance operations and workers generational renewal, a nuclear owner and operator like Electricité de France (EDF) is interested in the scaling up of tools and methods of "as-built virtual reality" for larger buildings and wider audiences. However, acquisition and sharing of as-built data on a large scale (large and complex multi-floored buildings) challenge current scientific and technical capacities. In this paper, we first present a state of the art of scanning tools and methods for industrial plants with very complex architecture. Then, we introduce the inner characteristics of the multi-sensor scanning and visualization of the interior of the most complex building of a power plant: a nuclear reactor building. We introduce several developments that made possible a first complete survey of such a large building, from acquisition, processing and fusion of multiple data sources (3D laser scans, total-station survey, RGB panoramic, 2D floor plans, 3D CAD as-built models). In addition, we present the concepts of a smart application developed for the painless exploration of the whole dataset. The goal of this application is to help professionals, unfamiliar with the manipulation of such datasets, to take into account spatial constraints induced by the building complexity while preparing maintenance operations. Finally, we discuss the main feedbacks of this large experiment, the remaining issues for the generalization of such large scale surveys and the future technical and scientific challenges in the field of industrial "virtual reality".

Large-Scale Optimization for Bayesian Inference in Complex Systems

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

Willcox, Karen; Marzouk, Youssef

2013-11-12

The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. The MIT--Sandia component of themore » SAGUARO Project addressed the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas--Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to-observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as ``reduce then sample'' and ``sample then reduce.'' In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.« less

Final Report: Large-Scale Optimization for Bayesian Inference in Complex Systems

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

Ghattas, Omar

2013-10-15

The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimiza- tion) Project focuses on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimiza- tion and inversion methods. Our research is directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. Our efforts are integrated in the context of a challenging testbed problem that considers subsurface reacting flow and transport. The MIT component of the SAGUAROmore » Project addresses the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas-Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to- observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as "reduce then sample" and "sample then reduce." In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.« less

Wake profile measurements of fixed and oscillating flaps

NASA Technical Reports Server (NTRS)

Owen, F. K.

1984-01-01

Although the potential of laser velocimetry for the non-intrusive measurement of complex shear flows has long been recognized, there have been few applications in other small, closely controlled laboratory situations. Measurements in large scale, high speed wind tunnels are still a complex task. To support a study of periodic flows produced by an oscillating edge flap in the Ames eleven foot wind tunnel, this study was done. The potential for laser velocimeter measurements in large scale production facilities are evaluated. The results with hot wire flow field measurements are compared.

Network cosmology.

PubMed

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.

Network Cosmology

PubMed Central

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688

Using nocturnal cold air drainage flow to monitor ecosystem processes in complex terrain

Treesearch

Thomas G. Pypker; Michael H. Unsworth; Alan C. Mix; William Rugh; Troy Ocheltree; Karrin Alstad; Barbara J. Bond

2007-01-01

This paper presents initial investigations of a new approach to monitor ecosystem processes in complex terrain on large scales. Metabolic processes in mountainous ecosystems are poorly represented in current ecosystem monitoring campaigns because the methods used for monitoring metabolism at the ecosystem scale (e.g., eddy covariance) require flat study sites. Our goal...

Integrating complexity into data-driven multi-hazard supply chain network strategies

USGS Publications Warehouse

Long, Suzanna K.; Shoberg, Thomas G.; Ramachandran, Varun; Corns, Steven M.; Carlo, Hector J.

2013-01-01

Major strategies in the wake of a large-scale disaster have focused on short-term emergency response solutions. Few consider medium-to-long-term restoration strategies that reconnect urban areas to the national supply chain networks (SCN) and their supporting infrastructure. To re-establish this connectivity, the relationships within the SCN must be defined and formulated as a model of a complex adaptive system (CAS). A CAS model is a representation of a system that consists of large numbers of inter-connections, demonstrates non-linear behaviors and emergent properties, and responds to stimulus from its environment. CAS modeling is an effective method of managing complexities associated with SCN restoration after large-scale disasters. In order to populate the data space large data sets are required. Currently access to these data is hampered by proprietary restrictions. The aim of this paper is to identify the data required to build a SCN restoration model, look at the inherent problems associated with these data, and understand the complexity that arises due to integration of these data.

Large-Scale medical image analytics: Recent methodologies, applications and Future directions.

PubMed

Zhang, Shaoting; Metaxas, Dimitris

2016-10-01

Despite the ever-increasing amount and complexity of annotated medical image data, the development of large-scale medical image analysis algorithms has not kept pace with the need for methods that bridge the semantic gap between images and diagnoses. The goal of this position paper is to discuss and explore innovative and large-scale data science techniques in medical image analytics, which will benefit clinical decision-making and facilitate efficient medical data management. Particularly, we advocate that the scale of image retrieval systems should be significantly increased at which interactive systems can be effective for knowledge discovery in potentially large databases of medical images. For clinical relevance, such systems should return results in real-time, incorporate expert feedback, and be able to cope with the size, quality, and variety of the medical images and their associated metadata for a particular domain. The design, development, and testing of the such framework can significantly impact interactive mining in medical image databases that are growing rapidly in size and complexity and enable novel methods of analysis at much larger scales in an efficient, integrated fashion. Copyright © 2016. Published by Elsevier B.V.

High cell density media for Escherichia coli are generally designed for aerobic cultivations – consequences for large-scale bioprocesses and shake flask cultures

PubMed Central

Soini, Jaakko; Ukkonen, Kaisa; Neubauer, Peter

2008-01-01

Background For the cultivation of Escherichia coli in bioreactors trace element solutions are generally designed for optimal growth under aerobic conditions. They do normally not contain selenium and nickel. Molybdenum is only contained in few of them. These elements are part of the formate hydrogen lyase (FHL) complex which is induced under anaerobic conditions. As it is generally known that oxygen limitation appears in shake flask cultures and locally in large-scale bioreactors, function of the FHL complex may influence the process behaviour. Formate has been described to accumulate in large-scale cultures and may have toxic effects on E. coli. Although the anaerobic metabolism of E. coli is well studied, reference data which estimate the impact of the FHL complex on bioprocesses of E. coli with oxygen limitation have so far not been published, but are important for a better process understanding. Results Two sets of fed-batch cultures with conditions triggering oxygen limitation and formate accumulation were performed. Permanent oxygen limitation which is typical for shake flask cultures was caused in a bioreactor by reduction of the agitation rate. Transient oxygen limitation, which has been described to eventually occur in the feed-zone of large-scale bioreactors, was mimicked in a two-compartment scale-down bioreactor consisting of a stirred tank reactor and a plug flow reactor (PFR) with continuous glucose feeding into the PFR. In both models formate accumulated up to about 20 mM in the culture medium without addition of selenium, molybdenum and nickel. By addition of these trace elements the formate accumulation decreased below the level observed in well-mixed laboratory-scale cultures. Interestingly, addition of the extra trace elements caused accumulation of large amounts of lactate and reduced biomass yield in the simulator with permanent oxygen limitation, but not in the scale-down two-compartment bioreactor. Conclusion The accumulation of formate in oxygen limited cultivations of E. coli can be fully prevented by addition of the trace elements selenium, nickel and molybdenum, necessary for the function of FHL complex. For large-scale cultivations, if glucose gradients are likely, the results from the two-compartment scale-down bioreactor indicate that the addition of the extra trace elements is beneficial. No negative effects on the biomass yield or on any other bioprocess parameters could be observed in cultures with the extra trace elements if the cells were repeatedly exposed to transient oxygen limitation. PMID:18687130

Using d15N of Chironomidae to help assess lake condition and possible stressors in EPA?s National Lakes Assessment.

EPA Science Inventory

Background/Questions/Methods As interest in continental-scale ecology increases to address large-scale ecological problems, ecologists need indicators of complex processes that can be collected quickly at many sites across large areas. We are exploring the utility of stable isot...

Self-assembly of polyelectrolyte surfactant complexes using large scale MD simulation

NASA Astrophysics Data System (ADS)

Goswami, Monojoy; Sumpter, Bobby

2014-03-01

Polyelectrolytes (PE) and surfactants are known to form interesting structures with varied properties in aqueous solutions. The morphological details of the PE-surfactant complexes depend on a combination of polymer backbone, electrostatic interactions and hydrophobic interactions. We study the self-assembly of cationic PE and anionic surfactants complexes in dilute condition. The importance of such complexes of PE with oppositely charged surfactants can be found in biological systems, such as immobilization of enzymes in polyelectrolyte complexes or nonspecific association of DNA with protein. Many useful properties of PE surfactant complexes come from the highly ordered structures of surfactant self-assembly inside the PE aggregate which has applications in industry. We do large scale molecular dynamics simulation using LAMMPS to understand the structure and dynamics of PE-surfactant systems. Our investigation shows highly ordered pearl-necklace structures that have been observed experimentally in biological systems. We investigate many different properties of PE-surfactant complexation for different parameter ranges that are useful for pharmaceutical, engineering and biological applications.

Designing Cognitive Complexity in Mathematical Problem-Solving Items

ERIC Educational Resources Information Center

Daniel, Robert C.; Embretson, Susan E.

2010-01-01

Cognitive complexity level is important for measuring both aptitude and achievement in large-scale testing. Tests for standards-based assessment of mathematics, for example, often include cognitive complexity level in the test blueprint. However, little research exists on how mathematics items can be designed to vary in cognitive complexity level.…

Robust scalable stabilisability conditions for large-scale heterogeneous multi-agent systems with uncertain nonlinear interactions: towards a distributed computing architecture

NASA Astrophysics Data System (ADS)

Manfredi, Sabato

2016-06-01

Large-scale dynamic systems are becoming highly pervasive in their occurrence with applications ranging from system biology, environment monitoring, sensor networks, and power systems. They are characterised by high dimensionality, complexity, and uncertainty in the node dynamic/interactions that require more and more computational demanding methods for their analysis and control design, as well as the network size and node system/interaction complexity increase. Therefore, it is a challenging problem to find scalable computational method for distributed control design of large-scale networks. In this paper, we investigate the robust distributed stabilisation problem of large-scale nonlinear multi-agent systems (briefly MASs) composed of non-identical (heterogeneous) linear dynamical systems coupled by uncertain nonlinear time-varying interconnections. By employing Lyapunov stability theory and linear matrix inequality (LMI) technique, new conditions are given for the distributed control design of large-scale MASs that can be easily solved by the toolbox of MATLAB. The stabilisability of each node dynamic is a sufficient assumption to design a global stabilising distributed control. The proposed approach improves some of the existing LMI-based results on MAS by both overcoming their computational limits and extending the applicative scenario to large-scale nonlinear heterogeneous MASs. Additionally, the proposed LMI conditions are further reduced in terms of computational requirement in the case of weakly heterogeneous MASs, which is a common scenario in real application where the network nodes and links are affected by parameter uncertainties. One of the main advantages of the proposed approach is to allow to move from a centralised towards a distributed computing architecture so that the expensive computation workload spent to solve LMIs may be shared among processors located at the networked nodes, thus increasing the scalability of the approach than the network size. Finally, a numerical example shows the applicability of the proposed method and its advantage in terms of computational complexity when compared with the existing approaches.

A new way to protect privacy in large-scale genome-wide association studies.

PubMed

Kamm, Liina; Bogdanov, Dan; Laur, Sven; Vilo, Jaak

2013-04-01

Increased availability of various genotyping techniques has initiated a race for finding genetic markers that can be used in diagnostics and personalized medicine. Although many genetic risk factors are known, key causes of common diseases with complex heritage patterns are still unknown. Identification of such complex traits requires a targeted study over a large collection of data. Ideally, such studies bring together data from many biobanks. However, data aggregation on such a large scale raises many privacy issues. We show how to conduct such studies without violating privacy of individual donors and without leaking the data to third parties. The presented solution has provable security guarantees. Supplementary data are available at Bioinformatics online.

Large-scale structure of randomly jammed spheres

NASA Astrophysics Data System (ADS)

Ikeda, Atsushi; Berthier, Ludovic; Parisi, Giorgio

2017-05-01

We numerically analyze the density field of three-dimensional randomly jammed packings of monodisperse soft frictionless spherical particles, paying special attention to fluctuations occurring at large length scales. We study in detail the two-point static structure factor at low wave vectors in Fourier space. We also analyze the nature of the density field in real space by studying the large-distance behavior of the two-point pair correlation function, of density fluctuations in subsystems of increasing sizes, and of the direct correlation function. We show that such real space analysis can be greatly improved by introducing a coarse-grained density field to disentangle genuine large-scale correlations from purely local effects. Our results confirm that both Fourier and real space signatures of vanishing density fluctuations at large scale are absent, indicating that randomly jammed packings are not hyperuniform. In addition, we establish that the pair correlation function displays a surprisingly complex structure at large distances, which is however not compatible with the long-range negative correlation of hyperuniform systems but fully compatible with an analytic form for the structure factor. This implies that the direct correlation function is short ranged, as we also demonstrate directly. Our results reveal that density fluctuations in jammed packings do not follow the behavior expected for random hyperuniform materials, but display instead a more complex behavior.

Tail-scope: Using friends to estimate heavy tails of degree distributions in large-scale complex networks

NASA Astrophysics Data System (ADS)

Eom, Young-Ho; Jo, Hang-Hyun

2015-05-01

Many complex networks in natural and social phenomena have often been characterized by heavy-tailed degree distributions. However, due to rapidly growing size of network data and concerns on privacy issues about using these data, it becomes more difficult to analyze complete data sets. Thus, it is crucial to devise effective and efficient estimation methods for heavy tails of degree distributions in large-scale networks only using local information of a small fraction of sampled nodes. Here we propose a tail-scope method based on local observational bias of the friendship paradox. We show that the tail-scope method outperforms the uniform node sampling for estimating heavy tails of degree distributions, while the opposite tendency is observed in the range of small degrees. In order to take advantages of both sampling methods, we devise the hybrid method that successfully recovers the whole range of degree distributions. Our tail-scope method shows how structural heterogeneities of large-scale complex networks can be used to effectively reveal the network structure only with limited local information.

Evaluation of Penalized and Nonpenalized Methods for Disease Prediction with Large-Scale Genetic Data.

PubMed

Won, Sungho; Choi, Hosik; Park, Suyeon; Lee, Juyoung; Park, Changyi; Kwon, Sunghoon

2015-01-01

Owing to recent improvement of genotyping technology, large-scale genetic data can be utilized to identify disease susceptibility loci and this successful finding has substantially improved our understanding of complex diseases. However, in spite of these successes, most of the genetic effects for many complex diseases were found to be very small, which have been a big hurdle to build disease prediction model. Recently, many statistical methods based on penalized regressions have been proposed to tackle the so-called "large P and small N" problem. Penalized regressions including least absolute selection and shrinkage operator (LASSO) and ridge regression limit the space of parameters, and this constraint enables the estimation of effects for very large number of SNPs. Various extensions have been suggested, and, in this report, we compare their accuracy by applying them to several complex diseases. Our results show that penalized regressions are usually robust and provide better accuracy than the existing methods for at least diseases under consideration.

Towards large scale multi-target tracking

NASA Astrophysics Data System (ADS)

Vo, Ba-Ngu; Vo, Ba-Tuong; Reuter, Stephan; Lam, Quang; Dietmayer, Klaus

2014-06-01

Multi-target tracking is intrinsically an NP-hard problem and the complexity of multi-target tracking solutions usually do not scale gracefully with problem size. Multi-target tracking for on-line applications involving a large number of targets is extremely challenging. This article demonstrates the capability of the random finite set approach to provide large scale multi-target tracking algorithms. In particular it is shown that an approximate filter known as the labeled multi-Bernoulli filter can simultaneously track one thousand five hundred targets in clutter on a standard laptop computer.

Complexity Measures, Task Type, and Analytic Evaluations of Speaking Proficiency in a School-Based Assessment Context

ERIC Educational Resources Information Center

Gan, Zhengdong

2012-01-01

This study, which is part of a large-scale study of using objective measures to validate assessment rating scales and assessment tasks in a high-profile school-based assessment initiative in Hong Kong, examined how grammatical complexity measures relate to task type and analytic evaluations of students' speaking proficiency in a classroom-based…

Differential Relations between Facets of Complex Problem Solving and Students' Immigration Background

ERIC Educational Resources Information Center

Sonnleitner, Philipp; Brunner, Martin; Keller, Ulrich; Martin, Romain

2014-01-01

Whereas the assessment of complex problem solving (CPS) has received increasing attention in the context of international large-scale assessments, its fairness in regard to students' cultural background has gone largely unexplored. On the basis of a student sample of 9th-graders (N = 299), including a representative number of immigrant students (N…

Alternative large-scale conservation visions for northern Maine: interviews with decision leaders in Maine

Treesearch

Elizabeth Dennis Baldwin; Laura S. Kenefic; Will F. LaPage

2007-01-01

Based on confidential interviews with 21 decision leaders in Maine, Elizabeth Baldwin, Laura Kenefic, and Will LaPage examine the complexity of the conflicts over alternate visions for large-scale conservation in Maine. Exploring models that may be useful for policymakers grappling with competing values for Maine's forests, they present four alternatives: national...

How Leaders Learn to Be Successful during Large-Scale Organizational Change

ERIC Educational Resources Information Center

Rey, Donna S.

2009-01-01

The purpose of this study was to understand the strategies leaders used to learn new roles during large-scale organizational change and to understand what organizations can do to support the learning process. This was accomplished by exploring the experience of 15 school principals who learned to lead in the midst of two complex organizational…

Spatiotemporal property and predictability of large-scale human mobility

NASA Astrophysics Data System (ADS)

Zhang, Hai-Tao; Zhu, Tao; Fu, Dongfei; Xu, Bowen; Han, Xiao-Pu; Chen, Duxin

2018-04-01

Spatiotemporal characteristics of human mobility emerging from complexity on individual scale have been extensively studied due to the application potential on human behavior prediction and recommendation, and control of epidemic spreading. We collect and investigate a comprehensive data set of human activities on large geographical scales, including both websites browse and mobile towers visit. Numerical results show that the degree of activity decays as a power law, indicating that human behaviors are reminiscent of scale-free random walks known as Lévy flight. More significantly, this study suggests that human activities on large geographical scales have specific non-Markovian characteristics, such as a two-segment power-law distribution of dwelling time and a high possibility for prediction. Furthermore, a scale-free featured mobility model with two essential ingredients, i.e., preferential return and exploration, and a Gaussian distribution assumption on the exploration tendency parameter is proposed, which outperforms existing human mobility models under scenarios of large geographical scales.

Topological Properties of Some Integrated Circuits for Very Large Scale Integration Chip Designs

NASA Astrophysics Data System (ADS)

Swanson, S.; Lanzerotti, M.; Vernizzi, G.; Kujawski, J.; Weatherwax, A.

2015-03-01

This talk presents topological properties of integrated circuits for Very Large Scale Integration chip designs. These circuits can be implemented in very large scale integrated circuits, such as those in high performance microprocessors. Prior work considered basic combinational logic functions and produced a mathematical framework based on algebraic topology for integrated circuits composed of logic gates. Prior work also produced an historically-equivalent interpretation of Mr. E. F. Rent's work for today's complex circuitry in modern high performance microprocessors, where a heuristic linear relationship was observed between the number of connections and number of logic gates. This talk will examine topological properties and connectivity of more complex functionally-equivalent integrated circuits. The views expressed in this article are those of the author and do not reflect the official policy or position of the United States Air Force, Department of Defense or the U.S. Government.

Side effects of problem-solving strategies in large-scale nutrition science: towards a diversification of health.

PubMed

Penders, Bart; Vos, Rein; Horstman, Klasien

2009-11-01

Solving complex problems in large-scale research programmes requires cooperation and division of labour. Simultaneously, large-scale problem solving also gives rise to unintended side effects. Based upon 5 years of researching two large-scale nutrigenomic research programmes, we argue that problems are fragmented in order to be solved. These sub-problems are given priority for practical reasons and in the process of solving them, various changes are introduced in each sub-problem. Combined with additional diversity as a result of interdisciplinarity, this makes reassembling the original and overall goal of the research programme less likely. In the case of nutrigenomics and health, this produces a diversification of health. As a result, the public health goal of contemporary nutrition science is not reached in the large-scale research programmes we studied. Large-scale research programmes are very successful in producing scientific publications and new knowledge; however, in reaching their political goals they often are less successful.

Starch-lipid complexes: Interesting material and applications from amylose-fatty acid salt inclusion complexes

USDA-ARS?s Scientific Manuscript database

Aqueous slurries of high amylose starch can be steam jet cooked and blended with aqueous solutions of fatty acid salts to generate materials that contain inclusion complexes between amylose and the fatty acid salt. These complexes are simply prepared on large scale using commercially available steam...

The one scale that rules them all

NASA Astrophysics Data System (ADS)

Ouellette, Jennifer

2017-05-01

There are very real constraints on how large a complex organism can grow. This is the essence of all modern-day scaling laws, and the subject of Geoffrey West's provocative new book Scale: the Universal Laws of Life and Death in Organisms, Cities and Companies

Cloud Computing for Complex Performance Codes.

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

Appel, Gordon John; Hadgu, Teklu; Klein, Brandon Thorin

This report describes the use of cloud computing services for running complex public domain performance assessment problems. The work consisted of two phases: Phase 1 was to demonstrate complex codes, on several differently configured servers, could run and compute trivial small scale problems in a commercial cloud infrastructure. Phase 2 focused on proving non-trivial large scale problems could be computed in the commercial cloud environment. The cloud computing effort was successfully applied using codes of interest to the geohydrology and nuclear waste disposal modeling community.

HiQuant: Rapid Postquantification Analysis of Large-Scale MS-Generated Proteomics Data.

PubMed

Bryan, Kenneth; Jarboui, Mohamed-Ali; Raso, Cinzia; Bernal-Llinares, Manuel; McCann, Brendan; Rauch, Jens; Boldt, Karsten; Lynn, David J

2016-06-03

Recent advances in mass-spectrometry-based proteomics are now facilitating ambitious large-scale investigations of the spatial and temporal dynamics of the proteome; however, the increasing size and complexity of these data sets is overwhelming current downstream computational methods, specifically those that support the postquantification analysis pipeline. Here we present HiQuant, a novel application that enables the design and execution of a postquantification workflow, including common data-processing steps, such as assay normalization and grouping, and experimental replicate quality control and statistical analysis. HiQuant also enables the interpretation of results generated from large-scale data sets by supporting interactive heatmap analysis and also the direct export to Cytoscape and Gephi, two leading network analysis platforms. HiQuant may be run via a user-friendly graphical interface and also supports complete one-touch automation via a command-line mode. We evaluate HiQuant's performance by analyzing a large-scale, complex interactome mapping data set and demonstrate a 200-fold improvement in the execution time over current methods. We also demonstrate HiQuant's general utility by analyzing proteome-wide quantification data generated from both a large-scale public tyrosine kinase siRNA knock-down study and an in-house investigation into the temporal dynamics of the KSR1 and KSR2 interactomes. Download HiQuant, sample data sets, and supporting documentation at http://hiquant.primesdb.eu .

FEATURE 3, LARGE GUN POSITION, SHOWING MULTIPLE COMPARTMENTS, VIEW FACING ...

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

FEATURE 3, LARGE GUN POSITION, SHOWING MULTIPLE COMPARTMENTS, VIEW FACING SOUTH (with scale stick). - Naval Air Station Barbers Point, Anti-Aircraft Battery Complex-Large Gun Position, East of Coral Sea Road, northwest of Hamilton Road, Ewa, Honolulu County, HI

Using the High-Level Based Program Interface to Facilitate the Large Scale Scientific Computing

PubMed Central

Shang, Yizi; Shang, Ling; Gao, Chuanchang; Lu, Guiming; Ye, Yuntao; Jia, Dongdong

2014-01-01

This paper is to make further research on facilitating the large-scale scientific computing on the grid and the desktop grid platform. The related issues include the programming method, the overhead of the high-level program interface based middleware, and the data anticipate migration. The block based Gauss Jordan algorithm as a real example of large-scale scientific computing is used to evaluate those issues presented above. The results show that the high-level based program interface makes the complex scientific applications on large-scale scientific platform easier, though a little overhead is unavoidable. Also, the data anticipation migration mechanism can improve the efficiency of the platform which needs to process big data based scientific applications. PMID:24574931

On the large eddy simulation of turbulent flows in complex geometry

NASA Technical Reports Server (NTRS)

Ghosal, Sandip

1993-01-01

Application of the method of Large Eddy Simulation (LES) to a turbulent flow consists of three separate steps. First, a filtering operation is performed on the Navier-Stokes equations to remove the small spatial scales. The resulting equations that describe the space time evolution of the 'large eddies' contain the subgrid-scale (sgs) stress tensor that describes the effect of the unresolved small scales on the resolved scales. The second step is the replacement of the sgs stress tensor by some expression involving the large scales - this is the problem of 'subgrid-scale modeling'. The final step is the numerical simulation of the resulting 'closed' equations for the large scale fields on a grid small enough to resolve the smallest of the large eddies, but still much larger than the fine scale structures at the Kolmogorov length. In dividing a turbulent flow field into 'large' and 'small' eddies, one presumes that a cut-off length delta can be sensibly chosen such that all fluctuations on a scale larger than delta are 'large eddies' and the remainder constitute the 'small scale' fluctuations. Typically, delta would be a length scale characterizing the smallest structures of interest in the flow. In an inhomogeneous flow, the 'sensible choice' for delta may vary significantly over the flow domain. For example, in a wall bounded turbulent flow, most statistical averages of interest vary much more rapidly with position near the wall than far away from it. Further, there are dynamically important organized structures near the wall on a scale much smaller than the boundary layer thickness. Therefore, the minimum size of eddies that need to be resolved is smaller near the wall. In general, for the LES of inhomogeneous flows, the width of the filtering kernel delta must be considered to be a function of position. If a filtering operation with a nonuniform filter width is performed on the Navier-Stokes equations, one does not in general get the standard large eddy equations. The complication is caused by the fact that a filtering operation with a nonuniform filter width in general does not commute with the operation of differentiation. This is one of the issues that we have looked at in detail as it is basic to any attempt at applying LES to complex geometry flows. Our principal findings are summarized.

Starch-lipid inclusion complexes for aerogel formation

USDA-ARS?s Scientific Manuscript database

Recently we reported that aqueous slurries of starch can be excess steam jet-cooked and blended with aqueous solutions of fatty acid salts to produce inclusion complexes between amylose and the fatty acid salt. These complexes can be simply prepared on large scale using commercially available steam ...

A Life-Cycle Model of Human Social Groups Produces a U-Shaped Distribution in Group Size.

PubMed

Salali, Gul Deniz; Whitehouse, Harvey; Hochberg, Michael E

2015-01-01

One of the central puzzles in the study of sociocultural evolution is how and why transitions from small-scale human groups to large-scale, hierarchically more complex ones occurred. Here we develop a spatially explicit agent-based model as a first step towards understanding the ecological dynamics of small and large-scale human groups. By analogy with the interactions between single-celled and multicellular organisms, we build a theory of group lifecycles as an emergent property of single cell demographic and expansion behaviours. We find that once the transition from small-scale to large-scale groups occurs, a few large-scale groups continue expanding while small-scale groups gradually become scarcer, and large-scale groups become larger in size and fewer in number over time. Demographic and expansion behaviours of groups are largely influenced by the distribution and availability of resources. Our results conform to a pattern of human political change in which religions and nation states come to be represented by a few large units and many smaller ones. Future enhancements of the model should include decision-making rules and probabilities of fragmentation for large-scale societies. We suggest that the synthesis of population ecology and social evolution will generate increasingly plausible models of human group dynamics.

A Life-Cycle Model of Human Social Groups Produces a U-Shaped Distribution in Group Size

PubMed Central

Salali, Gul Deniz; Whitehouse, Harvey; Hochberg, Michael E.

2015-01-01

One of the central puzzles in the study of sociocultural evolution is how and why transitions from small-scale human groups to large-scale, hierarchically more complex ones occurred. Here we develop a spatially explicit agent-based model as a first step towards understanding the ecological dynamics of small and large-scale human groups. By analogy with the interactions between single-celled and multicellular organisms, we build a theory of group lifecycles as an emergent property of single cell demographic and expansion behaviours. We find that once the transition from small-scale to large-scale groups occurs, a few large-scale groups continue expanding while small-scale groups gradually become scarcer, and large-scale groups become larger in size and fewer in number over time. Demographic and expansion behaviours of groups are largely influenced by the distribution and availability of resources. Our results conform to a pattern of human political change in which religions and nation states come to be represented by a few large units and many smaller ones. Future enhancements of the model should include decision-making rules and probabilities of fragmentation for large-scale societies. We suggest that the synthesis of population ecology and social evolution will generate increasingly plausible models of human group dynamics. PMID:26381745

Energy Decomposition Analysis Based on Absolutely Localized Molecular Orbitals for Large-Scale Density Functional Theory Calculations in Drug Design.

PubMed

Phipps, M J S; Fox, T; Tautermann, C S; Skylaris, C-K

2016-07-12

We report the development and implementation of an energy decomposition analysis (EDA) scheme in the ONETEP linear-scaling electronic structure package. Our approach is hybrid as it combines the localized molecular orbital EDA (Su, P.; Li, H. J. Chem. Phys., 2009, 131, 014102) and the absolutely localized molecular orbital EDA (Khaliullin, R. Z.; et al. J. Phys. Chem. A, 2007, 111, 8753-8765) to partition the intermolecular interaction energy into chemically distinct components (electrostatic, exchange, correlation, Pauli repulsion, polarization, and charge transfer). Limitations shared in EDA approaches such as the issue of basis set dependence in polarization and charge transfer are discussed, and a remedy to this problem is proposed that exploits the strictly localized property of the ONETEP orbitals. Our method is validated on a range of complexes with interactions relevant to drug design. We demonstrate the capabilities for large-scale calculations with our approach on complexes of thrombin with an inhibitor comprised of up to 4975 atoms. Given the capability of ONETEP for large-scale calculations, such as on entire proteins, we expect that our EDA scheme can be applied in a large range of biomolecular problems, especially in the context of drug design.

Local and landscape associations between wintering dabbling ducks and wetland complexes in Mississippi

USGS Publications Warehouse

Pearse, Aaron T.; Kaminski, Richard M.; Reinecke, Kenneth J.; Dinsmore, Stephen J.

2012-01-01

Landscape features influence distribution of waterbirds throughout their annual cycle. A conceptual model, the wetland habitat complex, may be useful in conservation of wetland habitats for dabbling ducks (Anatini). The foundation of this conceptual model is that ducks seek complexes of wetlands containing diverse resources to meet dynamic physiological needs. We included flooded croplands, wetlands and ponds, public-land waterfowl sanctuary, and diversity of habitats as key components of wetland habitat complexes and compared their relative influence at two spatial scales (i.e., local, 0.25-km radius; landscape, 4-km) on dabbling ducks wintering in western Mississippi, USA during winters 2002–2004. Distribution of mallard (Anas platyrhynchos) groups was positively associated with flooded cropland at local and landscape scales. Models representing flooded croplands at the landscape scale best explained occurrence of other dabbling ducks. Habitat complexity measured at both scales best explained group size of other dabbling ducks. Flooded croplands likely provided food that had decreased in availability due to conversion of wetlands to agriculture. Wetland complexes at landscape scales were more attractive to wintering ducks than single or structurally simple wetlands. Conservation of wetland complexes at large spatial scales (≥5,000 ha) on public and private lands will require coordination among multiple stakeholders.

Numerical Upscaling of Solute Transport in Fractured Porous Media Based on Flow Aligned Blocks

NASA Astrophysics Data System (ADS)

Leube, P.; Nowak, W.; Sanchez-Vila, X.

2013-12-01

High-contrast or fractured-porous media (FPM) pose one of the largest unresolved challenges for simulating large hydrogeological systems. The high contrast in advective transport between fast conduits and low-permeability rock matrix, including complex mass transfer processes, leads to the typical complex characteristics of early bulk arrivals and long tailings. Adequate direct representation of FPM requires enormous numerical resolutions. For large scales, e.g. the catchment scale, and when allowing for uncertainty in the fracture network architecture or in matrix properties, computational costs quickly reach an intractable level. In such cases, multi-scale simulation techniques have become useful tools. They allow decreasing the complexity of models by aggregating and transferring their parameters to coarser scales and so drastically reduce the computational costs. However, these advantages come at a loss of detail and accuracy. In this work, we develop and test a new multi-scale or upscaled modeling approach based on block upscaling. The novelty is that individual blocks are defined by and aligned with the local flow coordinates. We choose a multi-rate mass transfer (MRMT) model to represent the remaining sub-block non-Fickian behavior within these blocks on the coarse scale. To make the scale transition simple and to save computational costs, we capture sub-block features by temporal moments (TM) of block-wise particle arrival times to be matched with the MRMT model. By predicting spatial mass distributions of injected tracers in a synthetic test scenario, our coarse-scale solution matches reasonably well with the corresponding fine-scale reference solution. For predicting higher TM-orders (such as arrival time and effective dispersion), the prediction accuracy steadily decreases. This is compensated to some extent by the MRMT model. If the MRMT model becomes too complex, it loses its effect. We also found that prediction accuracy is sensitive to the choice of the effective dispersion coefficients and on the block resolution. A key advantage of the flow-aligned blocks is that the small-scale velocity field is reproduced quite accurately on the block-scale through their flow alignment. Thus, the block-scale transverse dispersivities remain in the similar magnitude as local ones, and they do not have to represent macroscopic uncertainty. Also, the flow-aligned blocks minimize numerical dispersion when solving the large-scale transport problem.

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding

PubMed Central

Tajima, Satohiro; Yanagawa, Toru; Fujii, Naotaka; Toyoizumi, Taro

2015-01-01

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness. PMID:26584045

Roughness of stylolites: implications of 3D high resolution topography measurements.

PubMed

Schmittbuhl, J; Renard, F; Gratier, J P; Toussaint, R

2004-12-03

Stylolites are natural pressure-dissolution surfaces in sedimentary rocks. We present 3D high resolution measurements at laboratory scales of their complex roughness. The topography is shown to be described by a self-affine scaling invariance. At large scales, the Hurst exponent is zeta(1) approximately 0.5 and very different from that at small scales where zeta(2) approximately 1.2. A crossover length scale at around L(c)=1 mm is well characterized. Measurements are consistent with a Langevin equation that describes the growth of a stylolitic interface as a competition between stabilizing long range elastic interactions at large scales or local surface tension effects at small scales and a destabilizing quenched material disorder.

Retention of Habitat Complexity Minimizes Disassembly of Reef Fish Communities following Disturbance: A Large-Scale Natural Experiment

PubMed Central

Emslie, Michael J.; Cheal, Alistair J.; Johns, Kerryn A.

2014-01-01

High biodiversity ecosystems are commonly associated with complex habitats. Coral reefs are highly diverse ecosystems, but are under increasing pressure from numerous stressors, many of which reduce live coral cover and habitat complexity with concomitant effects on other organisms such as reef fishes. While previous studies have highlighted the importance of habitat complexity in structuring reef fish communities, they employed gradient or meta-analyses which lacked a controlled experimental design over broad spatial scales to explicitly separate the influence of live coral cover from overall habitat complexity. Here a natural experiment using a long term (20 year), spatially extensive (∼115,000 kms2) dataset from the Great Barrier Reef revealed the fundamental importance of overall habitat complexity for reef fishes. Reductions of both live coral cover and habitat complexity had substantial impacts on fish communities compared to relatively minor impacts after major reductions in coral cover but not habitat complexity. Where habitat complexity was substantially reduced, species abundances broadly declined and a far greater number of fish species were locally extirpated, including economically important fishes. This resulted in decreased species richness and a loss of diversity within functional groups. Our results suggest that the retention of habitat complexity following disturbances can ameliorate the impacts of coral declines on reef fishes, so preserving their capacity to perform important functional roles essential to reef resilience. These results add to a growing body of evidence about the importance of habitat complexity for reef fishes, and represent the first large-scale examination of this question on the Great Barrier Reef. PMID:25140801

Retention of habitat complexity minimizes disassembly of reef fish communities following disturbance: a large-scale natural experiment.

PubMed

Emslie, Michael J; Cheal, Alistair J; Johns, Kerryn A

2014-01-01

High biodiversity ecosystems are commonly associated with complex habitats. Coral reefs are highly diverse ecosystems, but are under increasing pressure from numerous stressors, many of which reduce live coral cover and habitat complexity with concomitant effects on other organisms such as reef fishes. While previous studies have highlighted the importance of habitat complexity in structuring reef fish communities, they employed gradient or meta-analyses which lacked a controlled experimental design over broad spatial scales to explicitly separate the influence of live coral cover from overall habitat complexity. Here a natural experiment using a long term (20 year), spatially extensive (∼ 115,000 kms(2)) dataset from the Great Barrier Reef revealed the fundamental importance of overall habitat complexity for reef fishes. Reductions of both live coral cover and habitat complexity had substantial impacts on fish communities compared to relatively minor impacts after major reductions in coral cover but not habitat complexity. Where habitat complexity was substantially reduced, species abundances broadly declined and a far greater number of fish species were locally extirpated, including economically important fishes. This resulted in decreased species richness and a loss of diversity within functional groups. Our results suggest that the retention of habitat complexity following disturbances can ameliorate the impacts of coral declines on reef fishes, so preserving their capacity to perform important functional roles essential to reef resilience. These results add to a growing body of evidence about the importance of habitat complexity for reef fishes, and represent the first large-scale examination of this question on the Great Barrier Reef.

Research directions in large scale systems and decentralized control

NASA Technical Reports Server (NTRS)

Tenney, R. R.

1980-01-01

Control theory provides a well established framework for dealing with automatic decision problems and a set of techniques for automatic decision making which exploit special structure, but it does not deal well with complexity. The potential exists for combining control theoretic and knowledge based concepts into a unified approach. The elements of control theory are diagrammed, including modern control and large scale systems.

Coronal holes, large-scale magnetic field, and activity complexes in solar cycle 23

NASA Astrophysics Data System (ADS)

Tavastsherna, K. S.; Polyakow, E. V.

2014-12-01

A correlation among coronal holes (CH), a large-scale magnetic field (LMF), and activity complexes (AC) is studied in this work for 1997-2007 with the use of a coronal hole series obtained from observations at the Kitt Peak Observatory in the HeI 10830 Å line in 1975-2003 and SOHO/EIT-195 Å in 1996-2012 (Tlatov et al., 2014), synoptic Hα charts from Kislovodsk Mountain Astonomical Station, and the catalog of AC cores (Yazev, 2012). From the imposition of CH boundaries on Hα charts, which characterize the positions of neutral lines of the radial components of a large-scale solar magnetic field, it turns out that 70% of CH are located in unipolar regions of their sign during the above period, 10% are in the region of an opposite sign, and 20% are mainly very large CH, which are often crossed by the neutral lines of several unipolar regions. Data on mutual arrangement of CH and AC cores were obtained. It was shown that only some activity comples cores have genetic relationships with CH.

Effect of thermal stability/complex terrain on wind turbine model(s): a wind tunnel study to address complex atmospheric conditions

NASA Astrophysics Data System (ADS)

Guala, M.; Hu, S. J.; Chamorro, L. P.

2011-12-01

Turbulent boundary layer measurements in both wind tunnel and in the near-neutral atmospheric surface layer revealed in the last decade the significant contribution of the large scales of motions to both turbulent kinetic energy and Reynolds stresses, for a wide range of Reynolds number. These scales are known to grow throughout the logarithmic layer and to extend several boundary layer heights in the streamwise direction. Potentially, they are a source of strong unsteadiness in the power output of wind turbines and in the aerodynamic loads of wind turbine blades. However, the large scales in realistic atmospheric conditions deserves further study, with well controlled boundary conditions. In the atmospheric wind tunnel of the St. Anthony Falls Laboratory, with a 16 m long test section and independently controlled incoming flow and floor temperatures, turbulent boundary layers in a range of stability conditions, from the stratified to the convective case, can be reproduced and monitored. Measurements of fluctuating temperature, streamwise and wall normal velocity components are simultaneously obtained by an ad hoc calibrated and customized triple-wire sensor. A wind turbine model with constant loading DC motor, constant tip speed ratio, and a rotor diameter of 0.128m is used to mimic a large full scale turbine in the atmospheric boundary layer. Measurements of the fluctuating voltage generated by the DC motor are compared with measurements of the blade's angular velocity by laser scanning, and eventually related to velocity measurements from the triple-wire sensor. This study preliminary explores the effect of weak stability and complex terrain (through a set of spanwise aligned topographic perturbations) on the large scales of the flow and on the fluctuations in the wind turbine(s) power output.

Are more complex physiological models of forest ecosystems better choices for plot and regional predictions?

Treesearch

Wenchi Jin; Hong S. He; Frank R. Thompson

2016-01-01

Process-based forest ecosystem models vary from simple physiological, complex physiological, to hybrid empirical-physiological models. Previous studies indicate that complex models provide the best prediction at plot scale with a temporal extent of less than 10 years, however, it is largely untested as to whether complex models outperform the other two types of models...

Sand waves in environmental flows: Insights gained by coupling large-eddy simulation with morphodynamics

NASA Astrophysics Data System (ADS)

Sotiropoulos, Fotis; Khosronejad, Ali

2016-02-01

Sand waves arise in subaqueous and Aeolian environments as the result of the complex interaction between turbulent flows and mobile sand beds. They occur across a wide range of spatial scales, evolve at temporal scales much slower than the integral scale of the transporting turbulent flow, dominate river morphodynamics, undermine streambank stability and infrastructure during flooding, and sculpt terrestrial and extraterrestrial landscapes. In this paper, we present the vision for our work over the last ten years, which has sought to develop computational tools capable of simulating the coupled interactions of sand waves with turbulence across the broad range of relevant scales: from small-scale ripples in laboratory flumes to mega-dunes in large rivers. We review the computational advances that have enabled us to simulate the genesis and long-term evolution of arbitrarily large and complex sand dunes in turbulent flows using large-eddy simulation and summarize numerous novel physical insights derived from our simulations. Our findings explain the role of turbulent sweeps in the near-bed region as the primary mechanism for destabilizing the sand bed, show that the seeds of the emergent structure in dune fields lie in the heterogeneity of the turbulence and bed shear stress fluctuations over the initially flatbed, and elucidate how large dunes at equilibrium give rise to energetic coherent structures and modify the spectra of turbulence. We also discuss future challenges and our vision for advancing a data-driven simulation-based engineering science approach for site-specific simulations of river flooding.

A Study of Students' Reasoning about Probabilistic Causality: Implications for Understanding Complex Systems and for Instructional Design

ERIC Educational Resources Information Center

Grotzer, Tina A.; Solis, S. Lynneth; Tutwiler, M. Shane; Cuzzolino, Megan Powell

2017-01-01

Understanding complex systems requires reasoning about causal relationships that behave or appear to behave probabilistically. Features such as distributed agency, large spatial scales, and time delays obscure co-variation relationships and complex interactions can result in non-deterministic relationships between causes and effects that are best…

Sampling from complex networks using distributed learning automata

NASA Astrophysics Data System (ADS)

Rezvanian, Alireza; Rahmati, Mohammad; Meybodi, Mohammad Reza

2014-02-01

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others.

Variations of trends of indicators describing complex systems: Change of scaling precursory to extreme events

NASA Astrophysics Data System (ADS)

Keilis-Borok, V. I.; Soloviev, A. A.

2010-09-01

Socioeconomic and natural complex systems persistently generate extreme events also known as disasters, crises, or critical transitions. Here we analyze patterns of background activity preceding extreme events in four complex systems: economic recessions, surges in homicides in a megacity, magnetic storms, and strong earthquakes. We use as a starting point the indicators describing the system's behavior and identify changes in an indicator's trend. Those changes constitute our background events (BEs). We demonstrate a premonitory pattern common to all four systems considered: relatively large magnitude BEs become more frequent before extreme event. A premonitory change of scaling has been found in various models and observations. Here we demonstrate this change in scaling of uniformly defined BEs in four real complex systems, their enormous differences notwithstanding.

What drives the formation of massive stars and clusters?

NASA Astrophysics Data System (ADS)

Ochsendorf, Bram; Meixner, Margaret; Roman-Duval, Julia; Evans, Neal J., II; Rahman, Mubdi; Zinnecker, Hans; Nayak, Omnarayani; Bally, John; Jones, Olivia C.; Indebetouw, Remy

2018-01-01

Galaxy-wide surveys allow to study star formation in unprecedented ways. In this talk, I will discuss our analysis of the Large Magellanic Cloud (LMC) and the Milky Way, and illustrate how studying both the large and small scale structure of galaxies are critical in addressing the question: what drives the formation of massive stars and clusters?I will show that ‘turbulence-regulated’ star formation models do not reproduce massive star formation properties of GMCs in the LMC and Milky Way: this suggests that theory currently does not capture the full complexity of star formation on small scales. I will also report on the discovery of a massive star forming complex in the LMC, which in many ways manifests itself as an embedded twin of 30 Doradus: this may shed light on the formation of R136 and 'Super Star Clusters' in general. Finally, I will highlight what we can expect in the next years in the field of star formation with large-scale sky surveys, ALMA, and our JWST-GTO program.

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

PubMed

Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

2017-12-20

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

Modelling turbulent boundary layer flow over fractal-like multiscale terrain using large-eddy simulations and analytical tools.

PubMed

Yang, X I A; Meneveau, C

2017-04-13

In recent years, there has been growing interest in large-eddy simulation (LES) modelling of atmospheric boundary layers interacting with arrays of wind turbines on complex terrain. However, such terrain typically contains geometric features and roughness elements reaching down to small scales that typically cannot be resolved numerically. Thus subgrid-scale models for the unresolved features of the bottom roughness are needed for LES. Such knowledge is also required to model the effects of the ground surface 'underneath' a wind farm. Here we adapt a dynamic approach to determine subgrid-scale roughness parametrizations and apply it for the case of rough surfaces composed of cuboidal elements with broad size distributions, containing many scales. We first investigate the flow response to ground roughness of a few scales. LES with the dynamic roughness model which accounts for the drag of unresolved roughness is shown to provide resolution-independent results for the mean velocity distribution. Moreover, we develop an analytical roughness model that accounts for the sheltering effects of large-scale on small-scale roughness elements. Taking into account the shading effect, constraints from fundamental conservation laws, and assumptions of geometric self-similarity, the analytical roughness model is shown to provide analytical predictions that agree well with roughness parameters determined from LES.This article is part of the themed issue 'Wind energy in complex terrains'. © 2017 The Author(s).

Turbulent pipe flow at extreme Reynolds numbers.

PubMed

Hultmark, M; Vallikivi, M; Bailey, S C C; Smits, A J

2012-03-02

Both the inherent intractability and complex beauty of turbulence reside in its large range of physical and temporal scales. This range of scales is captured by the Reynolds number, which in nature and in many engineering applications can be as large as 10(5)-10(6). Here, we report turbulence measurements over an unprecedented range of Reynolds numbers using a unique combination of a high-pressure air facility and a new nanoscale anemometry probe. The results reveal previously unknown universal scaling behavior for the turbulent velocity fluctuations, which is remarkably similar to the well-known scaling behavior of the mean velocity distribution.

Physical Modeling of Flow Over Gale Crater, Mars: Laboratory Measurements of Basin Secondary Circulations

NASA Astrophysics Data System (ADS)

Bristow, N.; Blois, G.; Kim, T.; Anderson, W.; Day, M. D.; Kocurek, G.; Christensen, K. T.

2017-12-01

Impact craters, common large-scale topographic features on the surface of Mars, are circular depressions delimited by a sharp ridge. A variety of crater fill morphologies exist, suggesting that complex intracrater circulations affect their evolution. Some large craters (diameter > 10 km), particularly at mid latitudes on Mars, exhibit a central mound surrounded by circular moat. Foremost among these examples is Gale crater, landing site of NASA's Curiosity rover, since large-scale climatic processes early in in the history of Mars are preserved in the stratigraphic record of the inner mound. Investigating the intracrater flow produced by large scale winds aloft Mars craters is key to a number of important scientific issues including ongoing research on Mars paleo-environmental reconstruction and the planning of future missions (these results must be viewed in conjunction with the affects of radial katabatibc flows, the importance of which is already established in preceding studies). In this work we consider a number of crater shapes inspired by Gale morphology, including idealized craters. Access to the flow field within such geometrically complex topography is achieved herein using a refractive index matched approach. Instantaneous velocity maps, using both planar and volumetric PIV techniques, are presented to elucidate complex three-dimensional flow within the crater. In addition, first- and second-order statistics will be discussed in the context of wind-driven (aeolian) excavation of crater fill.

How Robust Is Your Project? From Local Failures to Global Catastrophes: A Complex Networks Approach to Project Systemic Risk.

PubMed

Ellinas, Christos; Allan, Neil; Durugbo, Christopher; Johansson, Anders

2015-01-01

Current societal requirements necessitate the effective delivery of complex projects that can do more while using less. Yet, recent large-scale project failures suggest that our ability to successfully deliver them is still at its infancy. Such failures can be seen to arise through various failure mechanisms; this work focuses on one such mechanism. Specifically, it examines the likelihood of a project sustaining a large-scale catastrophe, as triggered by single task failure and delivered via a cascading process. To do so, an analytical model was developed and tested on an empirical dataset by the means of numerical simulation. This paper makes three main contributions. First, it provides a methodology to identify the tasks most capable of impacting a project. In doing so, it is noted that a significant number of tasks induce no cascades, while a handful are capable of triggering surprisingly large ones. Secondly, it illustrates that crude task characteristics cannot aid in identifying them, highlighting the complexity of the underlying process and the utility of this approach. Thirdly, it draws parallels with systems encountered within the natural sciences by noting the emergence of self-organised criticality, commonly found within natural systems. These findings strengthen the need to account for structural intricacies of a project's underlying task precedence structure as they can provide the conditions upon which large-scale catastrophes materialise.

Real-time simulation of large-scale floods

NASA Astrophysics Data System (ADS)

Liu, Q.; Qin, Y.; Li, G. D.; Liu, Z.; Cheng, D. J.; Zhao, Y. H.

2016-08-01

According to the complex real-time water situation, the real-time simulation of large-scale floods is very important for flood prevention practice. Model robustness and running efficiency are two critical factors in successful real-time flood simulation. This paper proposed a robust, two-dimensional, shallow water model based on the unstructured Godunov- type finite volume method. A robust wet/dry front method is used to enhance the numerical stability. An adaptive method is proposed to improve the running efficiency. The proposed model is used for large-scale flood simulation on real topography. Results compared to those of MIKE21 show the strong performance of the proposed model.

Breaking barriers through collaboration: the example of the Cell Migration Consortium.

PubMed

Horwitz, Alan Rick; Watson, Nikki; Parsons, J Thomas

2002-10-15

Understanding complex integrated biological processes, such as cell migration, requires interdisciplinary approaches. The Cell Migration Consortium, funded by a Large-Scale Collaborative Project Award from the National Institute of General Medical Science, develops and disseminates new technologies, data, reagents, and shared information to a wide audience. The development and operation of this Consortium may provide useful insights for those who plan similarly large-scale, interdisciplinary approaches.

Cells as advanced therapeutics: State-of-the-art, challenges, and opportunities in large scale biomanufacturing of high-quality cells for adoptive immunotherapies.

PubMed

Dwarshuis, Nate J; Parratt, Kirsten; Santiago-Miranda, Adriana; Roy, Krishnendu

2017-05-15

Therapeutic cells hold tremendous promise in treating currently incurable, chronic diseases since they perform multiple, integrated, complex functions in vivo compared to traditional small-molecule drugs or biologics. However, they also pose significant challenges as therapeutic products because (a) their complex mechanisms of actions are difficult to understand and (b) low-cost bioprocesses for large-scale, reproducible manufacturing of cells have yet to be developed. Immunotherapies using T cells and dendritic cells (DCs) have already shown great promise in treating several types of cancers, and human mesenchymal stromal cells (hMSCs) are now extensively being evaluated in clinical trials as immune-modulatory cells. Despite these exciting developments, the full potential of cell-based therapeutics cannot be realized unless new engineering technologies enable cost-effective, consistent manufacturing of high-quality therapeutic cells at large-scale. Here we review cell-based immunotherapy concepts focused on the state-of-the-art in manufacturing processes including cell sourcing, isolation, expansion, modification, quality control (QC), and culture media requirements. We also offer insights into how current technologies could be significantly improved and augmented by new technologies, and how disciplines must converge to meet the long-term needs for large-scale production of cell-based immunotherapies. Copyright © 2017 Elsevier B.V. All rights reserved.

Explaining linkages (and lack of) between riparian vegetation biodiversity and geomorphic complexity in restored streams of northern Sweden

NASA Astrophysics Data System (ADS)

Polvi, Lina; Maher Hasselquist, Eliza; Nilsson, Christer

2014-05-01

Ecological theory suggests that species richness and habitat heterogeneity are positively correlated; therefore stream restoration often relies on increasing geomorphic complexity to promote biodiversity. However, past studies have failed to demonstrate a link between post-restoration biodiversity and geomorphic complexity. These studies have usually relied on only one metric for quantifying complexity, rather than a holistic metric for complexity that represents several aspects of the channel morphology, and have based their observations in catchments with widespread land-use impacts. We use a geomorphic complexity gradient based on five geomorphic aspects (longitudinal, cross-sectional, planform, sediment texture, and instream wood) to determine whether streams with higher levels of complexity also have greater riparian vegetation biodiversity. We also compare biodiversity values with the potential complexity of reaches based on the large-scale controls of valley and channel gradient and the presence of large glacial legacy sediment (boulders). We focus on tributary channels in boreal forests of northern Sweden, where stream modification associated with log-floating from the 1850s to the 1960s created highly simplified channels. Driven by concerns for fish, restoration began in the 1970s by returning large cobbles and boulders into the main channel from the channel edge, and evolved into 'demonstration restoration,' placing very large boulders and trees into the channel, reopening side channels, and constructing fish spawning areas. We evaluate 22 reaches along tributaries of the Vindel River in northern Sweden with four restoration statuses: channelized, restored, demonstration restored, and unimpacted. Detailed morphologic, sediment, and instream wood data allow calculation of 29 metrics of geomorphic complexity, from which a complexity gradient was identified using multivariate statistics. The percent cover of riparian vegetation was identified in 0.5 x 0.5 m plots at three elevations above the low water stage (0, 40, and 80 cm) along five transects; additionally, we determined which species were present within the entire riparian zone (60 m long reach, up to 80 cm elevation). Three metrics of biodiversity were calculated on the plot level (richness, Shannon's diversity index, and evenness); only richness could be examined at the reach scale. There are significant relationships between riparian vegetation biodiversity and the overall complexity gradient at the medium elevation and, based on some metrics, at the low elevation. However, these relationships are not fully explanatory or always linear, explaining up to ~40% of the variability and often being logarithmic. We conclude that reach-scale restoration of increasing complexity in a catchment without significant land-use impacts can have positive effects on biodiversity. However, there are several limiting factors in addition to channel complexity that affect the recovery of riparian zones after restoration: the potential complexity of a reach based on large-scale controls, time since restoration—which is a disturbance in itself, buffer distance to timber harvesting, distance and connectivity to colonist sources, and upland species (e.g., spruce trees) that have managed to colonize when the riparian zone was separated from the channel.

The role of activity complexes in the distribution of solar magnetic fields.

NASA Astrophysics Data System (ADS)

García de La Rosa, J. I.; Reyes, R. C.

Using published data on the large-scale distribution of solar activity, the authors conclude that the longlived coronal holes are formed and maintained by the unbalanced magnetic flux which developes at both extremes of the complexes of activity.

Megatux

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

2012-09-25

The Megatux platform enables the emulation of large scale (multi-million node) distributed systems. In particular, it allows for the emulation of large-scale networks interconnecting a very large number of emulated computer systems. It does this by leveraging virtualization and associated technologies to allow hundreds of virtual computers to be hosted on a single moderately sized server or workstation. Virtualization technology provided by modern processors allows for multiple guest OSs to run at the same time, sharing the hardware resources. The Megatux platform can be deployed on a single PC, a small cluster of a few boxes or a large clustermore » of computers. With a modest cluster, the Megatux platform can emulate complex organizational networks. By using virtualization, we emulate the hardware, but run actual software enabling large scale without sacrificing fidelity.« less

Molecular dynamics simulations of large macromolecular complexes.

PubMed

Perilla, Juan R; Goh, Boon Chong; Cassidy, C Keith; Liu, Bo; Bernardi, Rafael C; Rudack, Till; Yu, Hang; Wu, Zhe; Schulten, Klaus

2015-04-01

Connecting dynamics to structural data from diverse experimental sources, molecular dynamics simulations permit the exploration of biological phenomena in unparalleled detail. Advances in simulations are moving the atomic resolution descriptions of biological systems into the million-to-billion atom regime, in which numerous cell functions reside. In this opinion, we review the progress, driven by large-scale molecular dynamics simulations, in the study of viruses, ribosomes, bioenergetic systems, and other diverse applications. These examples highlight the utility of molecular dynamics simulations in the critical task of relating atomic detail to the function of supramolecular complexes, a task that cannot be achieved by smaller-scale simulations or existing experimental approaches alone. Copyright © 2015 Elsevier Ltd. All rights reserved.

Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

NASA Technical Reports Server (NTRS)

Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

2001-01-01

Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

The Forest Canopy as a Temporally and Spatially Dynamic Ecosystem: Preliminary Results of Biomass Scaling and Habitat Use from a Case Study in Large Eastern White Pines (Pinus Strobus)

NASA Astrophysics Data System (ADS)

Martin, J.; Laughlin, M. M.; Olson, E.

2017-12-01

Canopy processes can be viewed at many scales and through many lenses. Fundamentally, we may wish to start by treating each canopy as a unique surface, an ecosystem unto itself. By doing so, we can may make some important observations that greatly influence our ability to scale canopies to landscape, regional and global scales. This work summarizes an ongoing endeavor to quantify various canopy level processes on individual old and large Eastern white pine trees (Pinus strobus). Our work shows that these canopies contain complex structures that vary with height and as the tree ages. This phenomenon complicates the allometric scaling of these large trees using standard methods, but detailed measurements from within the canopy provided a method to constrain scaling equations. We also quantified how these canopies change and respond to canopy disturbance, and documented disproportionate variation of growth compared to the lower stem as the trees develop. Additionally, the complex shape and surface area allow these canopies to act like ecosystems themselves; despite being relatively young and more commonplace when compared to the more notable canopies of the tropics and the Pacific Northwestern US. The white pines of these relatively simple, near boreal forests appear to house various species including many lichens. The lichen species can cover significant portions of the canopy surface area (which may be only 25 to 50 years old) and are a sizable source of potential nitrogen additions to the soils below, as well as a modulator to hydrologic cycles by holding significant amounts of precipitation. Lastly, the combined complex surface area and focused verticality offers important habitat to numerous animal species, some of which are quite surprising.

Large scale preparation of high mannose and paucimannose N-glycans from soybean proteins by oxidative release of natural glycans (ORNG).

PubMed

Zhu, Yuyang; Yan, Maomao; Lasanajak, Yi; Smith, David F; Song, Xuezheng

2018-07-15

Despite the important advances in chemical and chemoenzymatic synthesis of glycans, access to large quantities of complex natural glycans remains a major impediment to progress in Glycoscience. Here we report a large-scale preparation of N-glycans from a kilogram of commercial soy proteins using oxidative release of natural glycans (ORNG). The high mannose and paucimannose N-glycans were labeled with a fluorescent tag and purified by size exclusion and multidimensional preparative HPLC. Side products are identified and potential mechanisms for the oxidative release of natural N-glycans from glycoproteins are proposed. This study demonstrates the potential for using the ORNG approach as a complementary route to synthetic approaches for the preparation of multi-milligram quantities of biomedically relevant complex glycans. Copyright © 2018 Elsevier Ltd. All rights reserved.

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...

Genomic analysis of organismal complexity in the multicellular green alga Volvox carteri

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

Prochnik, Simon E.; Umen, James; Nedelcu, Aurora

2010-07-01

Analysis of the Volvox carteri genome reveals that this green alga's increased organismal complexity and multicellularity are associated with modifications in protein families shared with its unicellular ancestor, and not with large-scale innovations in protein coding capacity. The multicellular green alga Volvox carteri and its morphologically diverse close relatives (the volvocine algae) are uniquely suited for investigating the evolution of multicellularity and development. We sequenced the 138 Mb genome of V. carteri and compared its {approx}14,500 predicted proteins to those of its unicellular relative, Chlamydomonas reinhardtii. Despite fundamental differences in organismal complexity and life history, the two species have similarmore » protein-coding potentials, and few species-specific protein-coding gene predictions. Interestingly, volvocine algal-specific proteins are enriched in Volvox, including those associated with an expanded and highly compartmentalized extracellular matrix. Our analysis shows that increases in organismal complexity can be associated with modifications of lineage-specific proteins rather than large-scale invention of protein-coding capacity.« less

Book Review: Large-Scale Ecosystem Restoration: Five Case Studies from the United States

EPA Science Inventory

Broad-scale ecosystem restoration efforts involve a very complex set of ecological and societal components, and the success of any ecosystem restoration project rests on an integrated approach to implementation. Editors Mary Doyle and Cynthia Drew have successfully synthesized ma...

A practical overview and comparison of certain commercial forensic software tools for processing large-scale digital investigations

NASA Astrophysics Data System (ADS)

Kröger, Knut; Creutzburg, Reiner

2013-05-01

The aim of this paper is to show the usefulness of modern forensic software tools for processing large-scale digital investigations. In particular, we focus on the new version of Nuix 4.2 and compare it with AccessData FTK 4.2, X-Ways Forensics 16.9 and Guidance Encase Forensic 7 regarding its performance, functionality, usability and capability. We will show how these software tools work with large forensic images and how capable they are in examining complex and big data scenarios.

Statistical Analysis of Big Data on Pharmacogenomics

PubMed Central

Fan, Jianqing; Liu, Han

2013-01-01

This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed. PMID:23602905

A Developmental Framework for Complex Plasmodesmata Formation Revealed by Large-Scale Imaging of the Arabidopsis Leaf Epidermis[W

PubMed Central

Fitzgibbon, Jessica; Beck, Martina; Zhou, Ji; Faulkner, Christine; Robatzek, Silke; Oparka, Karl

2013-01-01

Plasmodesmata (PD) form tubular connections that function as intercellular communication channels. They are essential for transporting nutrients and for coordinating development. During cytokinesis, simple PDs are inserted into the developing cell plate, while during wall extension, more complex (branched) forms of PD are laid down. We show that complex PDs are derived from existing simple PDs in a pattern that is accelerated when leaves undergo the sink–source transition. Complex PDs are inserted initially at the three-way junctions between epidermal cells but develop most rapidly in the anisocytic complexes around stomata. For a quantitative analysis of complex PD formation, we established a high-throughput imaging platform and constructed PDQUANT, a custom algorithm that detected cell boundaries and PD numbers in different wall faces. For anticlinal walls, the number of complex PDs increased with increasing cell size, while for periclinal walls, the number of PDs decreased. Complex PD insertion was accelerated by up to threefold in response to salicylic acid treatment and challenges with mannitol. In a single 30-min run, we could derive data for up to 11k PDs from 3k epidermal cells. This facile approach opens the door to a large-scale analysis of the endogenous and exogenous factors that influence PD formation. PMID:23371949

Automation of Hessian-Based Tubularity Measure Response Function in 3D Biomedical Images.

PubMed

Dzyubak, Oleksandr P; Ritman, Erik L

2011-01-01

The blood vessels and nerve trees consist of tubular objects interconnected into a complex tree- or web-like structure that has a range of structural scale 5 μm diameter capillaries to 3 cm aorta. This large-scale range presents two major problems; one is just making the measurements, and the other is the exponential increase of component numbers with decreasing scale. With the remarkable increase in the volume imaged by, and resolution of, modern day 3D imagers, it is almost impossible to make manual tracking of the complex multiscale parameters from those large image data sets. In addition, the manual tracking is quite subjective and unreliable. We propose a solution for automation of an adaptive nonsupervised system for tracking tubular objects based on multiscale framework and use of Hessian-based object shape detector incorporating National Library of Medicine Insight Segmentation and Registration Toolkit (ITK) image processing libraries.

Combinatorial Approach for Large-scale Identification of Linked Peptides from Tandem Mass Spectrometry Spectra*

PubMed Central

Wang, Jian; Anania, Veronica G.; Knott, Jeff; Rush, John; Lill, Jennie R.; Bourne, Philip E.; Bandeira, Nuno

2014-01-01

The combination of chemical cross-linking and mass spectrometry has recently been shown to constitute a powerful tool for studying protein–protein interactions and elucidating the structure of large protein complexes. However, computational methods for interpreting the complex MS/MS spectra from linked peptides are still in their infancy, making the high-throughput application of this approach largely impractical. Because of the lack of large annotated datasets, most current approaches do not capture the specific fragmentation patterns of linked peptides and therefore are not optimal for the identification of cross-linked peptides. Here we propose a generic approach to address this problem and demonstrate it using disulfide-bridged peptide libraries to (i) efficiently generate large mass spectral reference data for linked peptides at a low cost and (ii) automatically train an algorithm that can efficiently and accurately identify linked peptides from MS/MS spectra. We show that using this approach we were able to identify thousands of MS/MS spectra from disulfide-bridged peptides through comparison with proteome-scale sequence databases and significantly improve the sensitivity of cross-linked peptide identification. This allowed us to identify 60% more direct pairwise interactions between the protein subunits in the 20S proteasome complex than existing tools on cross-linking studies of the proteasome complexes. The basic framework of this approach and the MS/MS reference dataset generated should be valuable resources for the future development of new tools for the identification of linked peptides. PMID:24493012

Scaling the Pyramid Model across Complex Systems Providing Early Care for Preschoolers: Exploring How Models for Decision Making May Enhance Implementation Science

ERIC Educational Resources Information Center

Johnson, LeAnne D.

2017-01-01

Bringing effective practices to scale across large systems requires attending to how information and belief systems come together in decisions to adopt, implement, and sustain those practices. Statewide scaling of the Pyramid Model, a framework for positive behavior intervention and support, across different types of early childhood programs…

High Fidelity Modeling of Turbulent Mixing and Chemical Kinetics Interactions in a Post-Detonation Flow Field

NASA Astrophysics Data System (ADS)

Sinha, Neeraj; Zambon, Andrea; Ott, James; Demagistris, Michael

2015-06-01

Driven by the continuing rapid advances in high-performance computing, multi-dimensional high-fidelity modeling is an increasingly reliable predictive tool capable of providing valuable physical insight into complex post-detonation reacting flow fields. Utilizing a series of test cases featuring blast waves interacting with combustible dispersed clouds in a small-scale test setup under well-controlled conditions, the predictive capabilities of a state-of-the-art code are demonstrated and validated. Leveraging physics-based, first principle models and solving large system of equations on highly-resolved grids, the combined effects of finite-rate/multi-phase chemical processes (including thermal ignition), turbulent mixing and shock interactions are captured across the spectrum of relevant time-scales and length scales. Since many scales of motion are generated in a post-detonation environment, even if the initial ambient conditions are quiescent, turbulent mixing plays a major role in the fireball afterburning as well as in dispersion, mixing, ignition and burn-out of combustible clouds in its vicinity. Validating these capabilities at the small scale is critical to establish a reliable predictive tool applicable to more complex and large-scale geometries of practical interest.

Comparison of an algebraic multigrid algorithm to two iterative solvers used for modeling ground water flow and transport

USGS Publications Warehouse

Detwiler, R.L.; Mehl, S.; Rajaram, H.; Cheung, W.W.

2002-01-01

Numerical solution of large-scale ground water flow and transport problems is often constrained by the convergence behavior of the iterative solvers used to solve the resulting systems of equations. We demonstrate the ability of an algebraic multigrid algorithm (AMG) to efficiently solve the large, sparse systems of equations that result from computational models of ground water flow and transport in large and complex domains. Unlike geometric multigrid methods, this algorithm is applicable to problems in complex flow geometries, such as those encountered in pore-scale modeling of two-phase flow and transport. We integrated AMG into MODFLOW 2000 to compare two- and three-dimensional flow simulations using AMG to simulations using PCG2, a preconditioned conjugate gradient solver that uses the modified incomplete Cholesky preconditioner and is included with MODFLOW 2000. CPU times required for convergence with AMG were up to 140 times faster than those for PCG2. The cost of this increased speed was up to a nine-fold increase in required random access memory (RAM) for the three-dimensional problems and up to a four-fold increase in required RAM for the two-dimensional problems. We also compared two-dimensional numerical simulations of steady-state transport using AMG and the generalized minimum residual method with an incomplete LU-decomposition preconditioner. For these transport simulations, AMG yielded increased speeds of up to 17 times with only a 20% increase in required RAM. The ability of AMG to solve flow and transport problems in large, complex flow systems and its ready availability make it an ideal solver for use in both field-scale and pore-scale modeling.

Digital geomorphological landslide hazard mapping of the Alpago area, Italy

NASA Astrophysics Data System (ADS)

van Westen, Cees J.; Soeters, Rob; Sijmons, Koert

Large-scale geomorphological maps of mountainous areas are traditionally made using complex symbol-based legends. They can serve as excellent "geomorphological databases", from which an experienced geomorphologist can extract a large amount of information for hazard mapping. However, these maps are not designed to be used in combination with a GIS, due to their complex cartographic structure. In this paper, two methods are presented for digital geomorphological mapping at large scales using GIS and digital cartographic software. The methods are applied to an area with a complex geomorphological setting on the Borsoia catchment, located in the Alpago region, near Belluno in the Italian Alps. The GIS database set-up is presented with an overview of the data layers that have been generated and how they are interrelated. The GIS database was also converted into a paper map, using a digital cartographic package. The resulting largescale geomorphological hazard map is attached. The resulting GIS database and cartographic product can be used to analyse the hazard type and hazard degree for each polygon, and to find the reasons for the hazard classification.

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

PubMed

Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

Active subspace: toward scalable low-rank learning.

PubMed

Liu, Guangcan; Yan, Shuicheng

2012-12-01

We address the scalability issues in low-rank matrix learning problems. Usually these problems resort to solving nuclear norm regularized optimization problems (NNROPs), which often suffer from high computational complexities if based on existing solvers, especially in large-scale settings. Based on the fact that the optimal solution matrix to an NNROP is often low rank, we revisit the classic mechanism of low-rank matrix factorization, based on which we present an active subspace algorithm for efficiently solving NNROPs by transforming large-scale NNROPs into small-scale problems. The transformation is achieved by factorizing the large solution matrix into the product of a small orthonormal matrix (active subspace) and another small matrix. Although such a transformation generally leads to nonconvex problems, we show that a suboptimal solution can be found by the augmented Lagrange alternating direction method. For the robust PCA (RPCA) (Candès, Li, Ma, & Wright, 2009 ) problem, a typical example of NNROPs, theoretical results verify the suboptimality of the solution produced by our algorithm. For the general NNROPs, we empirically show that our algorithm significantly reduces the computational complexity without loss of optimality.

Assessing Understanding of Complex Causal Networks Using an Interactive Game

ERIC Educational Resources Information Center

Ross, Joel

2013-01-01

Assessing people's understanding of the causal relationships found in large-scale complex systems may be necessary for addressing many critical social concerns, such as environmental sustainability. Existing methods for assessing systems thinking and causal understanding frequently use the technique of cognitive causal mapping. However, the…

What can one sample tell us? Stable isotopes can assess complex processes in national assessments of lakes, rivers and streams.

EPA Science Inventory

Stable isotopes can be very useful in large-scale monitoring programs because samples for isotopic analysis are easy to collect, and isotopes integrate information about complex processes such as evaporation from water isotopes and denitrification from nitrogen isotopes. Traditi...

Invasion complexity at large spatial scales is an emergent property of interactions among landscape characteristics and invader traits

USDA-ARS?s Scientific Manuscript database

Understanding the potential for invasive spread is an important consideration for novel agricultural species that may be translocated or introduced into new regions. However, estimating invasion risks remains a challenging problem, particularly in the context of real, complex landscapes. There is ...

Erosion and deposition by supercritical density flows during channel avulsion and backfilling: Field examples from coarse-grained deepwater channel-levée complexes (Sandino Forearc Basin, southern Central America)

NASA Astrophysics Data System (ADS)

Lang, Jörg; Brandes, Christian; Winsemann, Jutta

2017-03-01

Erosion and deposition by supercritical density flows can strongly impact the facies distribution and architecture of submarine fans. Field examples from coarse-grained channel-levée complexes from the Sandino Forearc Basin (southern Central America) show that cyclic-step and antidune deposits represent common sedimentary facies of these depositional systems and relate to the different stages of avulsion, bypass, levée construction and channel backfilling. During channel avulsion, large-scale scour-fill complexes (18 to 29 m deep, 18 to 25 m wide, 60 to > 120 m long) were incised by supercritical density flows. The multi-storey infill of the large-scale scour-fill complexes comprises amalgamated massive, normally coarse-tail graded or widely spaced subhorizontally stratified conglomerates and pebbly sandstones, interpreted as deposits of the hydraulic-jump zone of cyclic steps. The large-scale scour-fill complexes can be distinguished from small-scale channel fills based on the preservation of a steep upper margin and a coarse-grained infill comprising mainly amalgamated hydraulic-jump zone deposits. Channel fills include repeated successions deposited by cyclic steps with superimposed antidunes. The deposits of the hydraulic-jump zone of cyclic steps comprise regularly spaced scours (0.2 to 2.6 m deep, 0.8 to 23 m long) infilled by intraclast-rich conglomerates or pebbly sandstones, displaying normal coarse-tail grading or backsets. These deposits are laterally and vertically associated with subhorizontally stratified, low-angle cross-stratified or sinusoidally stratified sandstones and pebbly sandstones, which were deposited by antidunes on the stoss side of the cyclic steps during flow re-acceleration. The field examples indicate that so-called spaced stratified deposits may commonly represent antidune deposits with varying stratification styles controlled by the aggradation rate, grain-size distribution and amalgamation. The deposits of small-scale cyclic steps with superimposed antidunes form fining-upwards successions with decreasing antidune wavelengths, indicating waning flows. Such cyclic step-antidune successions form the characteristic basal infill of mid-fan channels, and are inferred to be related to successive supercritical high-density turbidity flows triggered by retrogressive slope failures.

Complexity, Robustness, and Network Thermodynamics in Large-Scale and Multiagent Systems: A Hybrid Control Approach

DTIC Science & Technology

2012-01-11

dynamic behavior , wherein a dissipative dynamical system can deliver only a fraction of its energy to its surroundings and can store only a fraction of the...collection of interacting subsystems. The behavior and properties of the aggregate large-scale system can then be deduced from the behaviors of the...uniqueness is established. This state space formalism of thermodynamics shows that the behavior of heat, as described by the conservation equations of

Visualization, documentation, analysis, and communication of large scale gene regulatory networks

PubMed Central

Longabaugh, William J.R.; Davidson, Eric H.; Bolouri, Hamid

2009-01-01

Summary Genetic regulatory networks (GRNs) are complex, large-scale, and spatially and temporally distributed. These characteristics impose challenging demands on computational GRN modeling tools, and there is a need for custom modeling tools. In this paper, we report on our ongoing development of BioTapestry, an open source, freely available computational tool designed specifically for GRN modeling. We also outline our future development plans, and give some examples of current applications of BioTapestry. PMID:18757046

Mapping the universe in three dimensions

PubMed Central

Haynes, Martha P.

1996-01-01

The determination of the three-dimensional layout of galaxies is critical to our understanding of the evolution of galaxies and the structures in which they lie, to our determination of the fundamental parameters of cosmology, and to our understanding of both the past and future histories of the universe at large. The mapping of the large scale structure in the universe via the determination of galaxy red shifts (Doppler shifts) is a rapidly growing industry thanks to technological developments in detectors and spectrometers at radio and optical wavelengths. First-order application of the red shift-distance relation (Hubble’s law) allows the analysis of the large-scale distribution of galaxies on scales of hundreds of megaparsecs. Locally, the large-scale structure is very complex but the overall topology is not yet clear. Comparison of the observed red shifts with ones expected on the basis of other distance estimates allows mapping of the gravitational field and the underlying total density distribution. The next decade holds great promise for our understanding of the character of large-scale structure and its origin. PMID:11607714

Mapping the universe in three dimensions.

PubMed

Haynes, M P

1996-12-10

The determination of the three-dimensional layout of galaxies is critical to our understanding of the evolution of galaxies and the structures in which they lie, to our determination of the fundamental parameters of cosmology, and to our understanding of both the past and future histories of the universe at large. The mapping of the large scale structure in the universe via the determination of galaxy red shifts (Doppler shifts) is a rapidly growing industry thanks to technological developments in detectors and spectrometers at radio and optical wavelengths. First-order application of the red shift-distance relation (Hubble's law) allows the analysis of the large-scale distribution of galaxies on scales of hundreds of megaparsecs. Locally, the large-scale structure is very complex but the overall topology is not yet clear. Comparison of the observed red shifts with ones expected on the basis of other distance estimates allows mapping of the gravitational field and the underlying total density distribution. The next decade holds great promise for our understanding of the character of large-scale structure and its origin.

Describing Ecosystem Complexity through Integrated Catchment Modeling

NASA Astrophysics Data System (ADS)

Shope, C. L.; Tenhunen, J. D.; Peiffer, S.

2011-12-01

Land use and climate change have been implicated in reduced ecosystem services (ie: high quality water yield, biodiversity, and agricultural yield. The prediction of ecosystem services expected under future land use decisions and changing climate conditions has become increasingly important. Complex policy and management decisions require the integration of physical, economic, and social data over several scales to assess effects on water resources and ecology. Field-based meteorology, hydrology, soil physics, plant production, solute and sediment transport, economic, and social behavior data were measured in a South Korean catchment. A variety of models are being used to simulate plot and field scale experiments within the catchment. Results from each of the local-scale models provide identification of sensitive, local-scale parameters which are then used as inputs into a large-scale watershed model. We used the spatially distributed SWAT model to synthesize the experimental field data throughout the catchment. The approach of our study was that the range in local-scale model parameter results can be used to define the sensitivity and uncertainty in the large-scale watershed model. Further, this example shows how research can be structured for scientific results describing complex ecosystems and landscapes where cross-disciplinary linkages benefit the end result. The field-based and modeling framework described is being used to develop scenarios to examine spatial and temporal changes in land use practices and climatic effects on water quantity, water quality, and sediment transport. Development of accurate modeling scenarios requires understanding the social relationship between individual and policy driven land management practices and the value of sustainable resources to all shareholders.

Is scale-up of community mobilisation among sex workers really possible in complex urban environments? The case of Mumbai, India.

PubMed

Kongelf, Anine; Bandewar, Sunita V S; Bharat, Shalini; Collumbien, Martine

2015-01-01

In the last decade, community mobilisation (CM) interventions targeting female sex workers (FSWs) have been scaled-up in India's national response to the HIV epidemic. This included the Bill and Melinda Gates Foundation's Avahan programme which adopted a business approach to plan and manage implementation at scale. With the focus of evaluation efforts on measuring effectiveness and health impacts there has been little analysis thus far of the interaction of the CM interventions with the sex work industry in complex urban environments. Between March and July 2012 semi-structured, in-depth interviews and focus group discussions were conducted with 63 HIV intervention implementers, to explore challenges of HIV prevention among FSWs in Mumbai. A thematic analysis identified contextual factors that impact CM implementation. Large-scale interventions are not only impacted by, but were shown to shape the dynamic social context. Registration practices and programme monitoring were experienced as stigmatising, reflected in shifting client preferences towards women not disclosing as 'sex workers'. This combined with urban redevelopment and gentrification of traditional red light areas, forcing dispersal and more 'hidden' ways of solicitation, further challenging outreach and collectivisation. Participants reported that brothel owners and 'pimps' continued to restrict access to sex workers and the heterogeneous 'community' of FSWs remains fragmented with high levels of mobility. Stakeholder engagement was poor and mobilising around HIV prevention not compelling. Interventions largely failed to respond to community needs as strong target-orientation skewed activities towards those most easily measured and reported. Large-scale interventions have been impacted by and contributed to an increasingly complex sex work environment in Mumbai, challenging outreach and mobilisation efforts. Sex workers remain a vulnerable and disempowered group needing continued support and more comprehensive services.

Regional modeling of groundwater flow and arsenic transport in the Bengal Basin: challenges of scale and complexity (Invited)

NASA Astrophysics Data System (ADS)

Michael, H. A.; Voss, C. I.

2009-12-01

Widespread arsenic poisoning is occurring in large areas of Bangladesh and West Bengal, India due to high arsenic levels in shallow groundwater, which is the primary source of irrigation and drinking water in the region. The high-arsenic groundwater exists in aquifers of the Bengal Basin, a huge sedimentary system approximately 500km x 500km wide and greater than 15km deep in places. Deeper groundwater (>150m) is nearly universally low in arsenic and a potential source of safe drinking water, but evaluation of its sustainability requires understanding of the entire, interconnected regional aquifer system. Numerical modeling of flow and arsenic transport in the basin introduces problems of scale: challenges in representing the system in enough detail to produce meaningful simulations and answer relevant questions while maintaining enough simplicity to understand controls on processes and operating within computational constraints. A regional groundwater flow and transport model of the Bengal Basin was constructed to assess the large-scale functioning of the deep groundwater flow system, the vulnerability of deep groundwater to pumping-induced migration from above, and the effect of chemical properties of sediments (sorption) on sustainability. The primary challenges include the very large spatial scale of the system, dynamic monsoonal hydrology (small temporal scale fluctuations), complex sedimentary architecture (small spatial scale heterogeneity), and a lack of reliable hydrologic and geologic data. The approach was simple. Detailed inputs were reduced to only those that affect the functioning of the deep flow system. Available data were used to estimate upscaled parameter values. Nested small-scale simulations were performed to determine the effects of the simplifications, which include treatment of the top boundary condition and transience, effects of small-scale heterogeneity, and effects of individual pumping wells. Simulation of arsenic transport at the large scale adds another element of complexity. Minimization of numerical oscillation and mass balance errors required experimentation with solvers and discretization. In the face of relatively few data in a very large-scale model, sensitivity analyses were essential. The scale of the system limits evaluation of localized behavior, but results clearly identified the primary controls on the system and effects of various pumping scenarios and sorptive properties. It was shown that limiting deep pumping to domestic supply may result in sustainable arsenic-safe water for 90% of the arsenic-affected region over a 1000 year timescale, and that sorption of arsenic onto deep, oxidized Pleistocene sediments may increase the breakthrough time in unsustainable zones by more than an order of magnitude. Thus, both hydraulic and chemical defenses indicate the potential for sustainable, managed use of deep, safe groundwater resources in the Bengal Basin.

From path models to commands during additive printing of large-scale architectural designs

NASA Astrophysics Data System (ADS)

Chepchurov, M. S.; Zhukov, E. M.; Yakovlev, E. A.; Matveykin, V. G.

2018-05-01

The article considers the problem of automation of the formation of large complex parts, products and structures, especially for unique or small-batch objects produced by a method of additive technology [1]. Results of scientific research in search for the optimal design of a robotic complex, its modes of operation (work), structure of its control helped to impose the technical requirements on the technological process for manufacturing and design installation of the robotic complex. Research on virtual models of the robotic complexes allowed defining the main directions of design improvements and the main goal (purpose) of testing of the the manufactured prototype: checking the positioning accuracy of the working part.

Large-Eddy Simulations of Atmospheric Flows Over Complex Terrain Using the Immersed-Boundary Method in the Weather Research and Forecasting Model

NASA Astrophysics Data System (ADS)

Ma, Yulong; Liu, Heping

2017-12-01

Atmospheric flow over complex terrain, particularly recirculation flows, greatly influences wind-turbine siting, forest-fire behaviour, and trace-gas and pollutant dispersion. However, there is a large uncertainty in the simulation of flow over complex topography, which is attributable to the type of turbulence model, the subgrid-scale (SGS) turbulence parametrization, terrain-following coordinates, and numerical errors in finite-difference methods. Here, we upgrade the large-eddy simulation module within the Weather Research and Forecasting model by incorporating the immersed-boundary method into the module to improve simulations of the flow and recirculation over complex terrain. Simulations over the Bolund Hill indicate improved mean absolute speed-up errors with respect to previous studies, as well an improved simulation of the recirculation zone behind the escarpment of the hill. With regard to the SGS parametrization, the Lagrangian-averaged scale-dependent Smagorinsky model performs better than the classic Smagorinsky model in reproducing both velocity and turbulent kinetic energy. A finer grid resolution also improves the strength of the recirculation in flow simulations, with a higher horizontal grid resolution improving simulations just behind the escarpment, and a higher vertical grid resolution improving results on the lee side of the hill. Our modelling approach has broad applications for the simulation of atmospheric flows over complex topography.

Epidemic dynamics and endemic states in complex networks

NASA Astrophysics Data System (ADS)

Pastor-Satorras, Romualdo; Vespignani, Alessandro

2001-06-01

We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks.

Towards a comprehensive understanding of emerging dynamics and function of pancreatic islets: A complex network approach. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

Loppini, Alessandro

2018-03-01

Complex network theory represents a comprehensive mathematical framework to investigate biological systems, ranging from sub-cellular and cellular scales up to large-scale networks describing species interactions and ecological systems. In their exhaustive and comprehensive work [1], Gosak et al. discuss several scenarios in which the network approach was able to uncover general properties and underlying mechanisms of cells organization and regulation, tissue functions and cell/tissue failure in pathology, by the study of chemical reaction networks, structural networks and functional connectivities.

Multi-thread parallel algorithm for reconstructing 3D large-scale porous structures

NASA Astrophysics Data System (ADS)

Ju, Yang; Huang, Yaohui; Zheng, Jiangtao; Qian, Xu; Xie, Heping; Zhao, Xi

2017-04-01

Geomaterials inherently contain many discontinuous, multi-scale, geometrically irregular pores, forming a complex porous structure that governs their mechanical and transport properties. The development of an efficient reconstruction method for representing porous structures can significantly contribute toward providing a better understanding of the governing effects of porous structures on the properties of porous materials. In order to improve the efficiency of reconstructing large-scale porous structures, a multi-thread parallel scheme was incorporated into the simulated annealing reconstruction method. In the method, four correlation functions, which include the two-point probability function, the linear-path functions for the pore phase and the solid phase, and the fractal system function for the solid phase, were employed for better reproduction of the complex well-connected porous structures. In addition, a random sphere packing method and a self-developed pre-conditioning method were incorporated to cast the initial reconstructed model and select independent interchanging pairs for parallel multi-thread calculation, respectively. The accuracy of the proposed algorithm was evaluated by examining the similarity between the reconstructed structure and a prototype in terms of their geometrical, topological, and mechanical properties. Comparisons of the reconstruction efficiency of porous models with various scales indicated that the parallel multi-thread scheme significantly shortened the execution time for reconstruction of a large-scale well-connected porous model compared to a sequential single-thread procedure.

Twisted versus braided magnetic flux ropes in coronal geometry. II. Comparative behaviour

NASA Astrophysics Data System (ADS)

Prior, C.; Yeates, A. R.

2016-06-01

Aims: Sigmoidal structures in the solar corona are commonly associated with magnetic flux ropes whose magnetic field lines are twisted about a mutual axis. Their dynamical evolution is well studied, with sufficient twisting leading to large-scale rotation (writhing) and vertical expansion, possibly leading to ejection. Here, we investigate the behaviour of flux ropes whose field lines have more complex entangled/braided configurations. Our hypothesis is that this internal structure will inhibit the large-scale morphological changes. Additionally, we investigate the influence of the background field within which the rope is embedded. Methods: A technique for generating tubular magnetic fields with arbitrary axial geometry and internal structure, introduced in part I of this study, provides the initial conditions for resistive-MHD simulations. The tubular fields are embedded in a linear force-free background, and we consider various internal structures for the tubular field, including both twisted and braided topologies. These embedded flux ropes are then evolved using a 3D MHD code. Results: Firstly, in a background where twisted flux ropes evolve through the expected non-linear writhing and vertical expansion, we find that flux ropes with sufficiently braided/entangled interiors show no such large-scale changes. Secondly, embedding a twisted flux rope in a background field with a sigmoidal inversion line leads to eventual reversal of the large-scale rotation. Thirdly, in some cases a braided flux rope splits due to reconnection into two twisted flux ropes of opposing chirality - a phenomenon previously observed in cylindrical configurations. Conclusions: Sufficiently complex entanglement of the magnetic field lines within a flux rope can suppress large-scale morphological changes of its axis, with magnetic energy reduced instead through reconnection and expansion. The structure of the background magnetic field can significantly affect the changing morphology of a flux rope.

Using stroboscopic flow imaging to validate large-scale computational fluid dynamics simulations

NASA Astrophysics Data System (ADS)

Laurence, Ted A.; Ly, Sonny; Fong, Erika; Shusteff, Maxim; Randles, Amanda; Gounley, John; Draeger, Erik

2017-02-01

The utility and accuracy of computational modeling often requires direct validation against experimental measurements. The work presented here is motivated by taking a combined experimental and computational approach to determine the ability of large-scale computational fluid dynamics (CFD) simulations to understand and predict the dynamics of circulating tumor cells in clinically relevant environments. We use stroboscopic light sheet fluorescence imaging to track the paths and measure the velocities of fluorescent microspheres throughout a human aorta model. Performed over complex physiologicallyrealistic 3D geometries, large data sets are acquired with microscopic resolution over macroscopic distances.

Detecting Multi-scale Structures in Chandra Images of Centaurus A

NASA Astrophysics Data System (ADS)

Karovska, M.; Fabbiano, G.; Elvis, M. S.; Evans, I. N.; Kim, D. W.; Prestwich, A. H.; Schwartz, D. A.; Murray, S. S.; Forman, W.; Jones, C.; Kraft, R. P.; Isobe, T.; Cui, W.; Schreier, E. J.

1999-12-01

Centaurus A (NGC 5128) is a giant early-type galaxy with a merger history, containing the nearest radio-bright AGN. Recent Chandra High Resolution Camera (HRC) observations of Cen A reveal X-ray multi-scale structures in this object with unprecedented detail and clarity. We show the results of an analysis of the Chandra data with smoothing and edge enhancement techniques that allow us to enhance and quantify the multi-scale structures present in the HRC images. These techniques include an adaptive smoothing algorithm (Ebeling et al 1999), and a multi-directional gradient detection algorithm (Karovska et al 1994). The Ebeling et al adaptive smoothing algorithm, which is incorporated in the CXC analysis s/w package, is a powerful tool for smoothing images containing complex structures at various spatial scales. The adaptively smoothed images of Centaurus A show simultaneously the high-angular resolution bright structures at scales as small as an arcsecond and the extended faint structures as large as several arc minutes. The large scale structures suggest complex symmetry, including a component possibly associated with the inner radio lobes (as suggested by the ROSAT HRI data, Dobereiner et al 1996), and a separate component with an orthogonal symmetry that may be associated with the galaxy as a whole. The dust lane and the x-ray ridges are very clearly visible. The adaptively smoothed images and the edge-enhanced images also suggest several filamentary features including a large filament-like structure extending as far as about 5 arcminutes to North-West.

Advances in Parallelization for Large Scale Oct-Tree Mesh Generation

NASA Technical Reports Server (NTRS)

O'Connell, Matthew; Karman, Steve L.

2015-01-01

Despite great advancements in the parallelization of numerical simulation codes over the last 20 years, it is still common to perform grid generation in serial. Generating large scale grids in serial often requires using special "grid generation" compute machines that can have more than ten times the memory of average machines. While some parallel mesh generation techniques have been proposed, generating very large meshes for LES or aeroacoustic simulations is still a challenging problem. An automated method for the parallel generation of very large scale off-body hierarchical meshes is presented here. This work enables large scale parallel generation of off-body meshes by using a novel combination of parallel grid generation techniques and a hybrid "top down" and "bottom up" oct-tree method. Meshes are generated using hardware commonly found in parallel compute clusters. The capability to generate very large meshes is demonstrated by the generation of off-body meshes surrounding complex aerospace geometries. Results are shown including a one billion cell mesh generated around a Predator Unmanned Aerial Vehicle geometry, which was generated on 64 processors in under 45 minutes.

3-D imaging of large scale buried structure by 1-D inversion of very early time electromagnetic (VETEM) data

USGS Publications Warehouse

Aydmer, A.A.; Chew, W.C.; Cui, T.J.; Wright, D.L.; Smith, D.V.; Abraham, J.D.

2001-01-01

A simple and efficient method for large scale three-dimensional (3-D) subsurface imaging of inhomogeneous background is presented. One-dimensional (1-D) multifrequency distorted Born iterative method (DBIM) is employed in the inversion. Simulation results utilizing synthetic scattering data are given. Calibration of the very early time electromagnetic (VETEM) experimental waveforms is detailed along with major problems encountered in practice and their solutions. This discussion is followed by the results of a large scale application of the method to the experimental data provided by the VETEM system of the U.S. Geological Survey. The method is shown to have a computational complexity that is promising for on-site inversion.

Mega-geomorphology: Mars vis a vis Earth

NASA Technical Reports Server (NTRS)

Sharp, R. P.

1985-01-01

The areas of chaotic terrain, the giant chasma of the Valles Marineris region, the complex linear and circular depressions of Labyrinthus Noctis on Mars all suggest the possibility of large scale collapse of parts of the martian crust within equatorial and sub equatorial latitudes. It seems generally accepted that the above features are fossil, being perhaps, more than a billion years old. It is possible that parts of Earth's crust experienced similar episodes of large scale collapse sometime early in the evolution of the planet.

Shifting Interests: Changes in the Lexical Semantics of ED-MEDIA

ERIC Educational Resources Information Center

Wild, Fridolin; Valentine, Chris; Scott, Peter

2010-01-01

Large research networks naturally form complex communities with overlapping but not identical expertise. To map the distribution of professional competence in field of "technology-enhanced learning", the lexical semantics expressed in research articles published in a representative, large-scale conference (ED-MEDIA) can be investigated and changes…

Organizational Influences on Interdisciplinary Interactions during Research and Design of Large-Scale Complex Engineered Systems

NASA Technical Reports Server (NTRS)

McGowan, Anna-Maria R.; Seifert, Colleen M.; Papalambros, Panos Y.

2012-01-01

The design of large-scale complex engineered systems (LaCES) such as an aircraft is inherently interdisciplinary. Multiple engineering disciplines, drawing from a team of hundreds to thousands of engineers and scientists, are woven together throughout the research, development, and systems engineering processes to realize one system. Though research and development (R&D) is typically focused in single disciplines, the interdependencies involved in LaCES require interdisciplinary R&D efforts. This study investigates the interdisciplinary interactions that take place during the R&D and early conceptual design phases in the design of LaCES. Our theoretical framework is informed by both engineering practices and social science research on complex organizations. This paper provides preliminary perspective on some of the organizational influences on interdisciplinary interactions based on organization theory (specifically sensemaking), data from a survey of LaCES experts, and the authors experience in the research and design. The analysis reveals couplings between the engineered system and the organization that creates it. Survey respondents noted the importance of interdisciplinary interactions and their significant benefit to the engineered system, such as innovation and problem mitigation. Substantial obstacles to interdisciplinarity are uncovered beyond engineering that include communication and organizational challenges. Addressing these challenges may ultimately foster greater efficiencies in the design and development of LaCES and improved system performance by assisting with the collective integration of interdependent knowledge bases early in the R&D effort. This research suggests that organizational and human dynamics heavily influence and even constrain the engineering effort for large-scale complex systems.

Composition of the Fusarium graminearum species complex populations in wheat cropping environments in Southern Brazil

USDA-ARS?s Scientific Manuscript database

The Fusarium graminearum species complex (FGSC) comprises several toxigenic species that cause Fusarium head blight (FHB) in wheat. In this study, high number (n=671 isolates) of pathogenic isolates (isolated from infected spikes) was obtained from a 3-year large-scale survey (2009-2011) conducted o...

Evaluating Long-Term Complex Professional Development: Using a Variation of the Cohort Control Design

ERIC Educational Resources Information Center

Sample Mcmeeking, Laura B.; Cobb, R. Brian; Basile, Carole

2010-01-01

This paper introduces a variation on the post-test only cohort control design and addresses questions concerning both the methodological credibility and the practical utility of employing this design variation in evaluations of large-scale complex professional development programmes in mathematics education. The original design and design…

Can Models Capture the Complexity of the Systems Engineering Process?

NASA Astrophysics Data System (ADS)

Boppana, Krishna; Chow, Sam; de Weck, Olivier L.; Lafon, Christian; Lekkakos, Spyridon D.; Lyneis, James; Rinaldi, Matthew; Wang, Zhiyong; Wheeler, Paul; Zborovskiy, Marat; Wojcik, Leonard A.

Many large-scale, complex systems engineering (SE) programs have been problematic; a few examples are listed below (Bar-Yam, 2003 and Cullen, 2004), and many others have been late, well over budget, or have failed: Hilton/Marriott/American Airlines system for hotel reservations and flights; 1988-1992; 125 million; "scrapped"

The Development of Complex Problem Solving in Adolescence: A Latent Growth Curve Analysis

ERIC Educational Resources Information Center

Frischkorn, Gidon T.; Greiff, Samuel; Wüstenberg, Sascha

2014-01-01

Complex problem solving (CPS) as a cross-curricular competence has recently attracted more attention in educational psychology as indicated by its implementation in international educational large-scale assessments such as the Programme for International Student Assessment. However, research on the development of CPS is scarce, and the few…

Organizational Agility and Complex Enterprise System Innovations: A Mixed Methods Study of the Effects of Enterprise Systems on Organizational Agility

ERIC Educational Resources Information Center

Kharabe, Amol T.

2012-01-01

Over the last two decades, firms have operated in "increasingly" accelerated "high-velocity" dynamic markets, which require them to become "agile." During the same time frame, firms have increasingly deployed complex enterprise systems--large-scale packaged software "innovations" that integrate and automate…

Bayesian Hierarchical Modeling for Big Data Fusion in Soil Hydrology

NASA Astrophysics Data System (ADS)

Mohanty, B.; Kathuria, D.; Katzfuss, M.

2016-12-01

Soil moisture datasets from remote sensing (RS) platforms (such as SMOS and SMAP) and reanalysis products from land surface models are typically available on a coarse spatial granularity of several square km. Ground based sensors on the other hand provide observations on a finer spatial scale (meter scale or less) but are sparsely available. Soil moisture is affected by high variability due to complex interactions between geologic, topographic, vegetation and atmospheric variables. Hydrologic processes usually occur at a scale of 1 km or less and therefore spatially ubiquitous and temporally periodic soil moisture products at this scale are required to aid local decision makers in agriculture, weather prediction and reservoir operations. Past literature has largely focused on downscaling RS soil moisture for a small extent of a field or a watershed and hence the applicability of such products has been limited. The present study employs a spatial Bayesian Hierarchical Model (BHM) to derive soil moisture products at a spatial scale of 1 km for the state of Oklahoma by fusing point scale Mesonet data and coarse scale RS data for soil moisture and its auxiliary covariates such as precipitation, topography, soil texture and vegetation. It is seen that the BHM model handles change of support problems easily while performing accurate uncertainty quantification arising from measurement errors and imperfect retrieval algorithms. The computational challenge arising due to the large number of measurements is tackled by utilizing basis function approaches and likelihood approximations. The BHM model can be considered as a complex Bayesian extension of traditional geostatistical prediction methods (such as Kriging) for large datasets in the presence of uncertainties.

Project BALLOTS: Bibliographic Automation of Large Library Operations Using a Time-Sharing System. Progress Report (3/27/69 - 6/26/69).

ERIC Educational Resources Information Center

Veaner, Allen B.

Project BALLOTS is a large-scale library automation development project of the Stanford University Libraries which has demonstrated the feasibility of conducting on-line interactive searches of complex bibliographic files, with a large number of users working simultaneously in the same or different files. This report documents the continuing…

Contribution of large scale coherence to wind turbine power: A large eddy simulation study in periodic wind farms

NASA Astrophysics Data System (ADS)

Chatterjee, Tanmoy; Peet, Yulia T.

2018-03-01

Length scales of eddies involved in the power generation of infinite wind farms are studied by analyzing the spectra of the turbulent flux of mean kinetic energy (MKE) from large eddy simulations (LES). Large-scale structures with an order of magnitude bigger than the turbine rotor diameter (D ) are shown to have substantial contribution to wind power. Varying dynamics in the intermediate scales (D -10 D ) are also observed from a parametric study involving interturbine distances and hub height of the turbines. Further insight about the eddies responsible for the power generation have been provided from the scaling analysis of two-dimensional premultiplied spectra of MKE flux. The LES code is developed in a high Reynolds number near-wall modeling framework, using an open-source spectral element code Nek5000, and the wind turbines have been modelled using a state-of-the-art actuator line model. The LES of infinite wind farms have been validated against the statistical results from the previous literature. The study is expected to improve our understanding of the complex multiscale dynamics in the domain of large wind farms and identify the length scales that contribute to the power. This information can be useful for design of wind farm layout and turbine placement that take advantage of the large-scale structures contributing to wind turbine power.

Systematic methods for defining coarse-grained maps in large biomolecules.

PubMed

Zhang, Zhiyong

2015-01-01

Large biomolecules are involved in many important biological processes. It would be difficult to use large-scale atomistic molecular dynamics (MD) simulations to study the functional motions of these systems because of the computational expense. Therefore various coarse-grained (CG) approaches have attracted rapidly growing interest, which enable simulations of large biomolecules over longer effective timescales than all-atom MD simulations. The first issue in CG modeling is to construct CG maps from atomic structures. In this chapter, we review the recent development of a novel and systematic method for constructing CG representations of arbitrarily complex biomolecules, in order to preserve large-scale and functionally relevant essential dynamics (ED) at the CG level. In this ED-CG scheme, the essential dynamics can be characterized by principal component analysis (PCA) on a structural ensemble, or elastic network model (ENM) of a single atomic structure. Validation and applications of the method cover various biological systems, such as multi-domain proteins, protein complexes, and even biomolecular machines. The results demonstrate that the ED-CG method may serve as a very useful tool for identifying functional dynamics of large biomolecules at the CG level.

Big Data Analytics with Datalog Queries on Spark.

PubMed

Shkapsky, Alexander; Yang, Mohan; Interlandi, Matteo; Chiu, Hsuan; Condie, Tyson; Zaniolo, Carlo

2016-01-01

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics.

Big Data Analytics with Datalog Queries on Spark

PubMed Central

Shkapsky, Alexander; Yang, Mohan; Interlandi, Matteo; Chiu, Hsuan; Condie, Tyson; Zaniolo, Carlo

2017-01-01

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics. PMID:28626296

Association of parameter, software, and hardware variation with large-scale behavior across 57,000 climate models

PubMed Central

Knight, Christopher G.; Knight, Sylvia H. E.; Massey, Neil; Aina, Tolu; Christensen, Carl; Frame, Dave J.; Kettleborough, Jamie A.; Martin, Andrew; Pascoe, Stephen; Sanderson, Ben; Stainforth, David A.; Allen, Myles R.

2007-01-01

In complex spatial models, as used to predict the climate response to greenhouse gas emissions, parameter variation within plausible bounds has major effects on model behavior of interest. Here, we present an unprecedentedly large ensemble of >57,000 climate model runs in which 10 parameters, initial conditions, hardware, and software used to run the model all have been varied. We relate information about the model runs to large-scale model behavior (equilibrium sensitivity of global mean temperature to a doubling of carbon dioxide). We demonstrate that effects of parameter, hardware, and software variation are detectable, complex, and interacting. However, we find most of the effects of parameter variation are caused by a small subset of parameters. Notably, the entrainment coefficient in clouds is associated with 30% of the variation seen in climate sensitivity, although both low and high values can give high climate sensitivity. We demonstrate that the effect of hardware and software is small relative to the effect of parameter variation and, over the wide range of systems tested, may be treated as equivalent to that caused by changes in initial conditions. We discuss the significance of these results in relation to the design and interpretation of climate modeling experiments and large-scale modeling more generally. PMID:17640921

EvoluCode: Evolutionary Barcodes as a Unifying Framework for Multilevel Evolutionary Data.

PubMed

Linard, Benjamin; Nguyen, Ngoc Hoan; Prosdocimi, Francisco; Poch, Olivier; Thompson, Julie D

2012-01-01

Evolutionary systems biology aims to uncover the general trends and principles governing the evolution of biological networks. An essential part of this process is the reconstruction and analysis of the evolutionary histories of these complex, dynamic networks. Unfortunately, the methodologies for representing and exploiting such complex evolutionary histories in large scale studies are currently limited. Here, we propose a new formalism, called EvoluCode (Evolutionary barCode), which allows the integration of different evolutionary parameters (eg, sequence conservation, orthology, synteny …) in a unifying format and facilitates the multilevel analysis and visualization of complex evolutionary histories at the genome scale. The advantages of the approach are demonstrated by constructing barcodes representing the evolution of the complete human proteome. Two large-scale studies are then described: (i) the mapping and visualization of the barcodes on the human chromosomes and (ii) automatic clustering of the barcodes to highlight protein subsets sharing similar evolutionary histories and their functional analysis. The methodologies developed here open the way to the efficient application of other data mining and knowledge extraction techniques in evolutionary systems biology studies. A database containing all EvoluCode data is available at: http://lbgi.igbmc.fr/barcodes.

RNA–protein binding kinetics in an automated microfluidic reactor

PubMed Central

Ridgeway, William K.; Seitaridou, Effrosyni; Phillips, Rob; Williamson, James R.

2009-01-01

Microfluidic chips can automate biochemical assays on the nanoliter scale, which is of considerable utility for RNA–protein binding reactions that would otherwise require large quantities of proteins. Unfortunately, complex reactions involving multiple reactants cannot be prepared in current microfluidic mixer designs, nor is investigation of long-time scale reactions possible. Here, a microfluidic ‘Riboreactor’ has been designed and constructed to facilitate the study of kinetics of RNA–protein complex formation over long time scales. With computer automation, the reactor can prepare binding reactions from any combination of eight reagents, and is optimized to monitor long reaction times. By integrating a two-photon microscope into the microfluidic platform, 5-nl reactions can be observed for longer than 1000 s with single-molecule sensitivity and negligible photobleaching. Using the Riboreactor, RNA–protein binding reactions with a fragment of the bacterial 30S ribosome were prepared in a fully automated fashion and binding rates were consistent with rates obtained from conventional assays. The microfluidic chip successfully combines automation, low sample consumption, ultra-sensitive fluorescence detection and a high degree of reproducibility. The chip should be able to probe complex reaction networks describing the assembly of large multicomponent RNPs such as the ribosome. PMID:19759214

Sedimentology and reservoir heterogeneity of a valley-fill deposit-A field guide to the Dakota Sandstone of the San Rafael Swell, Utah

USGS Publications Warehouse

Kirschbaum, Mark A.; Schenk, Christopher J.

2010-01-01

Valley-fill deposits form a significant class of hydrocarbon reservoirs in many basins of the world. Maximizing recovery of fluids from these reservoirs requires an understanding of the scales of fluid-flow heterogeneity present within the valley-fill system. The Upper Cretaceous Dakota Sandstone in the San Rafael Swell, Utah contains well exposed, relatively accessible outcrops that allow a unique view of the external geometry and internal complexity of a set of rocks interpreted to be deposits of an incised valley fill. These units can be traced on outcrop for tens of miles, and individual sandstone bodies are exposed in three dimensions because of modern erosion in side canyons in a semiarid setting and by exhumation of the overlying, easily erodible Mancos Shale. The Dakota consists of two major units: (1) a lower amalgamated sandstone facies dominated by large-scale cross stratification with several individual sandstone bodies ranging in thickness from 8 to 28 feet, ranging in width from 115 to 150 feet, and having lengths as much as 5,000 feet, and (2) an upper facies composed of numerous mud-encased lenticular sandstones, dominated by ripple-scale lamination, in bedsets ranging in thickness from 5 to 12 feet. The lower facies is interpreted to be fluvial, probably of mainly braided stream origin that exhibits multiple incisions amalgamated into a complex sandstone body. The upper facies has lower energy, probably anastomosed channels encased within alluvial and coastal-plain floodplain sediments. The Dakota valley-fill complex has multiple scales of heterogeneity that could affect fluid flow in similar oil and gas subsurface reservoirs. The largest scale heterogeneity is at the formation level, where the valley-fill complex is sealed within overlying and underlying units. Within the valley-fill complex, there are heterogeneities between individual sandstone bodies, and at the smallest scale, internal heterogeneities within the bodies themselves. These different scales of fluid-flow compartmentalization present a challenge to hydrocarbon exploration targeting paleovalley deposits, and producing fields containing these types of reservoirs may have significant bypassed pay, especially where well spacing is large.

Monitoring of Large Scale Volcanic Deformations in the Andes Using Geodetic Time Series (DInSAR, GPS and Microgravity)

NASA Astrophysics Data System (ADS)

Remy, D.; Froger, J. L.; Bonvalot, S.; Gabalda, G.; Albino, F.; Byrdina, S.

2010-03-01

Lastarria (25°10'S, 68°31'W, 5706 m) and Cordon del Azufre (25°18'S, 68°33'W, 5480 m) are part of a broad polygenic quaternary volcanic complex lying on the Altiplano, on the border of Chile and Argentina. This large scale volcanic area attracted particular attention in recent years because of its relatively intense unrest characterised by a peculiar style of on-going ground deformation, apparently begun in 1998. Interferometric Synthetic Aperture Radar (InSAR) data collected between 1998 and 2009 revealed two scales of inflation: a large elliptical area (50 km NNE- SSW major axis and a 40 minor axis), which has been inflating at a rate of around 3 cm.yr-1; and a short wavelength inflation (6 km wide), which is located at Lastarria volcano on the northern margin of the large elliptical area. The temporal evolution of these two distinct inflating signals suggests that they could be linked. The origin of this inflation period is still debated and various source mechanisms have been proposed to explain the observed deformation. Here, we present new observations of surface deformation in the Lastarria-Cordon del Azufre complex based on InSAR, levelling global positioning system (GPS), and microgravity data. Ascending and descending ASAR interferograms are combined to determine vertical and the EW horizontal component of displacement. Geodetic data are discussed from the standpoint of providing better constraints to understand the mechanics of the observed process at the volcanic complex.

Localisation of deformations of the midfacial complex in subjects with class III malocclusions employing thin-plate spline analysis

PubMed Central

SINGH, G. D.; McNAMARA JR, J. A.; LOZANOFF, S.

1997-01-01

This study determines deformations of the midface that contribute to a class III appearance, employing thin-plate spline analysis. A total of 135 lateral cephalographs of prepubertal children of European-American descent with either class III malocclusions or a class I molar occlusion were compared. The cephalographs were traced and checked, and 7 homologous landmarks of the midface were identified and digitised. The data sets were scaled to an equivalent size and subjected to Procrustes analysis. These statistical tests indicated significant differences (P<0.05) between the averaged class I and class III morphologies. Thin-plate spline analysis indicated that both affine and nonaffine transformations contribute towards the total spline for the averaged midfacial configuration. For nonaffine transformations, partial warp 3 had the highest magnitude, indicating the large scale deformations of the midfacial configuration. These deformations affected the palatal landmarks, and were associated with compression of the midfacial complex in the anteroposterior plane predominantly. Partial warp 4 produced some vertical compression of the posterior aspect of the midfacial complex whereas partial warps 1 and 2 indicated localised shape changes of the maxillary alveolus region. Large spatial-scale deformations therefore affect the midfacial complex in an anteroposterior axis, in combination with vertical compression and localised distortions. These deformations may represent a developmental diminution of the palatal complex anteroposteriorly that, allied with vertical shortening of midfacial height posteriorly, results in class III malocclusions with a retrusive midfacial profile. PMID:9449078

Localisation of deformations of the midfacial complex in subjects with class III malocclusions employing thin-plate spline analysis.

PubMed

Singh, G D; McNamara, J A; Lozanoff, S

1997-11-01

This study determines deformations of the midface that contribute to a class III appearance, employing thinplate spline analysis. A total of 135 lateral cephalographs of prepubertal children of European-American descent with either class III malocclusions or a class I molar occlusion were compared. The cephalographs were traced and checked, and 7 homologous landmarks of the midface were identified and digitised. The data sets were scaled to an equivalent size and subjected to Procrustes analysis. These statistical tests indicated significant differences (P < 0.05) between the averaged class I and class III morphologies. Thinplate spline analysis indicated that both affine and nonaffine transformations contribute towards the total spline for the averaged midfacial configuration. For nonaffine transformations, partial warp 3 had the highest magnitude, indicating the large scale deformations of the midfacial configuration. These deformations affected the palatal landmarks, and were associated with compression of the midfacial complex in the anteroposterior plane predominantly. Partial warp 4 produced some vertical compression of the posterior aspect of the midfacial complex whereas partial warps 1 and 2 indicated localised shape changes of the maxillary alveolus region. large spatial-scale deformations therefore affect the midfacial complex in an anteroposterior axis, in combination with vertical compression and localised distortions. These deformations may represent a developmental diminution of the palatal complex anteroposteriorly that, allied with vertical shortening of midfacial height posteriorly, results in class III malocclusions with a retrusive midfacial profile.

Numerical investigations with WRF about atmospheric features leading to heavy precipitation and flood events over the Central Andes' complex topography

NASA Astrophysics Data System (ADS)

Zamuriano, Marcelo; Brönnimann, Stefan

2017-04-01

It's known that some extremes such as heavy rainfalls, flood events, heatwaves and droughts depend largely on the atmospheric circulation and local features. Bolivia is no exception and while the large scale dynamics over the Amazon has been largely investigated, the local features driven by the Andes Cordillera and the Altiplano is still poorly documented. New insights on the regional atmospheric dynamics preceding heavy precipitation and flood events over the complex topography of the Andes-Amazon interface are added through numerical investigations of several case events: flash flood episodes over La Paz city and the extreme 2014 flood in south-western Amazon basin. Large scale atmospheric water transport is dynamically downscaled in order to take into account the complex topography forcing and local features as modulators of these events. For this purpose, a series of high resolution numerical experiments with the WRF-ARW model is conducted using various global datasets and parameterizations. While several mechanisms have been suggested to explain the dynamics of these episodes, they have not been tested yet through numerical modelling experiments. The simulations captures realistically the local water transport and the terrain influence over atmospheric circulation, even though the precipitation intensity is in general unrealistic. Nevertheless, the results show that Dynamical Downscaling over the tropical Andes' complex terrain provides useful meteorological data for a variety of studies and contributes to a better understanding of physical processes involved in the configuration of these events.

Economically viable large-scale hydrogen liquefaction

NASA Astrophysics Data System (ADS)

Cardella, U.; Decker, L.; Klein, H.

2017-02-01

The liquid hydrogen demand, particularly driven by clean energy applications, will rise in the near future. As industrial large scale liquefiers will play a major role within the hydrogen supply chain, production capacity will have to increase by a multiple of today’s typical sizes. The main goal is to reduce the total cost of ownership for these plants by increasing energy efficiency with innovative and simple process designs, optimized in capital expenditure. New concepts must ensure a manageable plant complexity and flexible operability. In the phase of process development and selection, a dimensioning of key equipment for large scale liquefiers, such as turbines and compressors as well as heat exchangers, must be performed iteratively to ensure technological feasibility and maturity. Further critical aspects related to hydrogen liquefaction, e.g. fluid properties, ortho-para hydrogen conversion, and coldbox configuration, must be analysed in detail. This paper provides an overview on the approach, challenges and preliminary results in the development of efficient as well as economically viable concepts for large-scale hydrogen liquefaction.

Development and Initial Testing of the Tiltrotor Test Rig

NASA Technical Reports Server (NTRS)

Acree, C. W., Jr.; Sheikman, A. L.

2018-01-01

The NASA Tiltrotor Test Rig (TTR) is a new, large-scale proprotor test system, developed jointly with the U.S. Army and Air Force, to develop a new, large-scale proprotor test system for the National Full-Scale Aerodynamics Complex (NFAC). The TTR is designed to test advanced proprotors up to 26 feet in diameter at speeds up to 300 knots, and even larger rotors at lower airspeeds. This combination of size and speed is unprecedented and is necessary for research into 21st-century tiltrotors and other advanced rotorcraft concepts. The TTR will provide critical data for validation of state-of-the-art design and analysis tools.

Strain localisation in the continental lithosphere, a scale-dependent process

NASA Astrophysics Data System (ADS)

Jolivet, Laurent; Burov, Evguenii

2013-04-01

Strain localisation in continents is a general question tackled by specialists of various disciplines in Earth Sciences. Field geologists working at regional scale are able to describe the succession of events leading to the formation of large strain zones that accommodate large displacement within plate boundaries. On the other end of the spectrum, laboratory experiments provide numbers that quantitatively describe the rheology of rock material at the scale of a few mm and at deformation rates up to 8-10 orders of magnitude faster than in nature. Extrapolating from the scale of the experiment to the scale of the continental lithosphere is a considerable leap across 8-10 orders of magnitude both in space and time. It is however quite obvious that different processes are at work for each scale considered. At the scale of a grain aggregate diffusion within individual grains, dislocation or grain boundary sliding, depending on temperature and fluid conditions, are of primary importance. But at the scale of a mountain belt, a major detachment or a strike-slip shear zone that have accommodated tens or hundreds of kilometres of relative displacement, other parameters will take over such as structural softening and the heterogeneity of the crust inherited from past tectonic events that have juxtaposed rock units of very different compositions and induced a strong orientation of rocks. Once the deformation is localised along major shear zones, grain size reduction, interaction between rocks and fluids and metamorphic reactions and other small-scale processes tend to further localise the strain. Because the crust is colder and more lithologically complex this heterogeneity is likely much more prominent in the crust than in the mantle and then the relative importance of "small-scale" and "large-scale" parameters will be very different in the crust and in the mantle. Thus, depending upon the relative thickness of the crust and mantle in the deforming lithosphere, the role of each mechanism will have more or less important consequences on strain localisation. This complexity sometimes leads to disregard of experimental parameters in large-scale thermo-mechanical models and to use instead ad hoc "large-scale" numbers that better fit the observed geological history. The goal of the ERC RHEOLITH project is to associate to each tectonic process the relevant rheological parameters depending upon the scale considered, in an attempt to elaborate a generalized "Preliminary Rheology Model Set for Lithosphere" (PReMSL), which will cover the entire time and spatial scale range of deformation.

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends.

PubMed

Snowden, Thomas J; van der Graaf, Piet H; Tindall, Marcus J

2017-07-01

Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed.

Integrative approaches for large-scale transcriptome-wide association studies

PubMed Central

Gusev, Alexander; Ko, Arthur; Shi, Huwenbo; Bhatia, Gaurav; Chung, Wonil; Penninx, Brenda W J H; Jansen, Rick; de Geus, Eco JC; Boomsma, Dorret I; Wright, Fred A; Sullivan, Patrick F; Nikkola, Elina; Alvarez, Marcus; Civelek, Mete; Lusis, Aldons J.; Lehtimäki, Terho; Raitoharju, Emma; Kähönen, Mika; Seppälä, Ilkka; Raitakari, Olli T.; Kuusisto, Johanna; Laakso, Markku; Price, Alkes L.; Pajukanta, Päivi; Pasaniuc, Bogdan

2016-01-01

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. Here, we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated to complex traits. We leverage expression imputation to perform a transcriptome wide association scan (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ~3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits. PMID:26854917

Similarity spectra analysis of high-performance jet aircraft noise.

PubMed

Neilsen, Tracianne B; Gee, Kent L; Wall, Alan T; James, Michael M

2013-04-01

Noise measured in the vicinity of an F-22A Raptor has been compared to similarity spectra found previously to represent mixing noise from large-scale and fine-scale turbulent structures in laboratory-scale jet plumes. Comparisons have been made for three engine conditions using ground-based sideline microphones, which covered a large angular aperture. Even though the nozzle geometry is complex and the jet is nonideally expanded, the similarity spectra do agree with large portions of the measured spectra. Toward the sideline, the fine-scale similarity spectrum is used, while the large-scale similarity spectrum provides a good fit to the area of maximum radiation. Combinations of the two similarity spectra are shown to match the data in between those regions. Surprisingly, a combination of the two is also shown to match the data at the farthest aft angle. However, at high frequencies the degree of congruity between the similarity and the measured spectra changes with engine condition and angle. At the higher engine conditions, there is a systematically shallower measured high-frequency slope, with the largest discrepancy occurring in the regions of maximum radiation.

Geospatial Optimization of Siting Large-Scale Solar Projects

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

Macknick, Jordan; Quinby, Ted; Caulfield, Emmet

2014-03-01

Recent policy and economic conditions have encouraged a renewed interest in developing large-scale solar projects in the U.S. Southwest. However, siting large-scale solar projects is complex. In addition to the quality of the solar resource, solar developers must take into consideration many environmental, social, and economic factors when evaluating a potential site. This report describes a proof-of-concept, Web-based Geographical Information Systems (GIS) tool that evaluates multiple user-defined criteria in an optimization algorithm to inform discussions and decisions regarding the locations of utility-scale solar projects. Existing siting recommendations for large-scale solar projects from governmental and non-governmental organizations are not consistent withmore » each other, are often not transparent in methods, and do not take into consideration the differing priorities of stakeholders. The siting assistance GIS tool we have developed improves upon the existing siting guidelines by being user-driven, transparent, interactive, capable of incorporating multiple criteria, and flexible. This work provides the foundation for a dynamic siting assistance tool that can greatly facilitate siting decisions among multiple stakeholders.« less

ACTIVIS: Visual Exploration of Industry-Scale Deep Neural Network Models.

PubMed

Kahng, Minsuk; Andrews, Pierre Y; Kalro, Aditya; Polo Chau, Duen Horng

2017-08-30

While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ACTIVIS, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ACTIVIS has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ACTIVIS may work with different models.

Enabling large-scale viscoelastic calculations via neural network acceleration

NASA Astrophysics Data System (ADS)

Robinson DeVries, P.; Thompson, T. B.; Meade, B. J.

2017-12-01

One of the most significant challenges involved in efforts to understand the effects of repeated earthquake cycle activity are the computational costs of large-scale viscoelastic earthquake cycle models. Deep artificial neural networks (ANNs) can be used to discover new, compact, and accurate computational representations of viscoelastic physics. Once found, these efficient ANN representations may replace computationally intensive viscoelastic codes and accelerate large-scale viscoelastic calculations by more than 50,000%. This magnitude of acceleration enables the modeling of geometrically complex faults over thousands of earthquake cycles across wider ranges of model parameters and at larger spatial and temporal scales than have been previously possible. Perhaps most interestingly from a scientific perspective, ANN representations of viscoelastic physics may lead to basic advances in the understanding of the underlying model phenomenology. We demonstrate the potential of artificial neural networks to illuminate fundamental physical insights with specific examples.

Flattening of Caribbean coral reefs: region-wide declines in architectural complexity

PubMed Central

Alvarez-Filip, Lorenzo; Dulvy, Nicholas K.; Gill, Jennifer A.; Côté, Isabelle M.; Watkinson, Andrew R.

2009-01-01

Coral reefs are rich in biodiversity, in large part because their highly complex architecture provides shelter and resources for a wide range of organisms. Recent rapid declines in hard coral cover have occurred across the Caribbean region, but the concomitant consequences for reef architecture have not been quantified on a large scale to date. We provide, to our knowledge, the first region-wide analysis of changes in reef architectural complexity, using nearly 500 surveys across 200 reefs, between 1969 and 2008. The architectural complexity of Caribbean reefs has declined nonlinearly with the near disappearance of the most complex reefs over the last 40 years. The flattening of Caribbean reefs was apparent by the early 1980s, followed by a period of stasis between 1985 and 1998 and then a resumption of the decline in complexity to the present. Rates of loss are similar on shallow (<6 m), mid-water (6–20 m) and deep (>20 m) reefs and are consistent across all five subregions. The temporal pattern of declining architecture coincides with key events in recent Caribbean ecological history: the loss of structurally complex Acropora corals, the mass mortality of the grazing urchin Diadema antillarum and the 1998 El Nino Southern Oscillation-induced worldwide coral bleaching event. The consistently low estimates of current architectural complexity suggest regional-scale degradation and homogenization of reef structure. The widespread loss of architectural complexity is likely to have serious consequences for reef biodiversity, ecosystem functioning and associated environmental services. PMID:19515663

Coupled land surface/hydrologic/atmospheric models

NASA Technical Reports Server (NTRS)

Pielke, Roger; Steyaert, Lou; Arritt, Ray; Lahtakia, Mercedes; Smith, Chris; Ziegler, Conrad; Soong, Su Tzai; Avissar, Roni; Wetzel, Peter; Sellers, Piers

1993-01-01

The topics covered include the following: prototype land cover characteristics data base for the conterminous United States; surface evapotranspiration effects on cumulus convection and implications for mesoscale models; the use of complex treatment of surface hydrology and thermodynamics within a mesoscale model and some related issues; initialization of soil-water content for regional-scale atmospheric prediction models; impact of surface properties on dryline and MCS evolution; a numerical simulation of heavy precipitation over the complex topography of California; representing mesoscale fluxes induced by landscape discontinuities in global climate models; emphasizing the role of subgrid-scale heterogeneity in surface-air interaction; and problems with modeling and measuring biosphere-atmosphere exchanges of energy, water, and carbon on large scales.

The problem of ecological scaling in spatially complex, nonequilibrium ecological systems [chapter 3

Treesearch

Samuel A. Cushman; Jeremy Littell; Kevin McGarigal

2010-01-01

In the previous chapter we reviewed the challenges posed by spatial complexity and temporal disequilibrium to efforts to understand and predict the structure and dynamics of ecological systems. The central theme was that spatial variability in the environment and population processes fundamentally alters the interactions between species and their environments, largely...

Randomization-Based Inference about Latent Variables from Complex Samples: The Case of Two-Stage Sampling

ERIC Educational Resources Information Center

Li, Tiandong

2012-01-01

In large-scale assessments, such as the National Assessment of Educational Progress (NAEP), plausible values based on Multiple Imputations (MI) have been used to estimate population characteristics for latent constructs under complex sample designs. Mislevy (1991) derived a closed-form analytic solution for a fixed-effect model in creating…

Complex Problem Solving in Educational Contexts--Something beyond "g": Concept, Assessment, Measurement Invariance, and Construct Validity

ERIC Educational Resources Information Center

Greiff, Samuel; Wustenberg, Sascha; Molnar, Gyongyver; Fischer, Andreas; Funke, Joachim; Csapo, Beno

2013-01-01

Innovative assessments of cross-curricular competencies such as complex problem solving (CPS) have currently received considerable attention in large-scale educational studies. This study investigated the nature of CPS by applying a state-of-the-art approach to assess CPS in high school. We analyzed whether two processes derived from cognitive…

Assessment of Complex Problem Solving: What We Know and What We Don't Know

ERIC Educational Resources Information Center

Herde, Christoph Nils; Wüstenberg, Sascha; Greiff, Samuel

2016-01-01

Complex Problem Solving (CPS) is seen as a cross-curricular 21st century skill that has attracted interest in large-scale-assessments. In the Programme for International Student Assessment (PISA) 2012, CPS was assessed all over the world to gain information on students' skills to acquire and apply knowledge while dealing with nontransparent…

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.

E-Learning in a Large Organization: A Study of the Critical Role of Information Sharing

ERIC Educational Resources Information Center

Netteland, Grete; Wasson, Barbara; Morch, Anders I

2007-01-01

Purpose: The purpose of this paper is to provide new insights into the implementation of large-scale learning projects; thereby better understanding the difficulties, frustrations, and obstacles encountered when implementing enterprise-wide e-learning as a tool for training and organization transformation in a complex organization.…

Triton Blushes: A Clue to Global Warming?

NASA Technical Reports Server (NTRS)

Buratti, B. J.; Hicks, M. D.; Newburn, R. L., Jr.

1998-01-01

The large Neptunian satellite Triton is a geologically active body that apparently undergoes complex seasonal changes in its 165 year journey around the sun. Because it is the vehicle for the seasonal transport of volatiles, Triton's atmosphere is expected to undergo large changes in temperature and pressure on a time scale of decades.

Visual attention mitigates information loss in small- and large-scale neural codes

PubMed Central

Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-01-01

Summary The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires processing sensory signals in a manner that protects information about relevant stimuli from degradation. Such selective processing – or selective attention – is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. PMID:25769502

Lagrangian space consistency relation for large scale structure

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

Horn, Bart; Hui, Lam; Xiao, Xiao

Consistency relations, which relate the squeezed limit of an (N+1)-point correlation function to an N-point function, are non-perturbative symmetry statements that hold even if the associated high momentum modes are deep in the nonlinear regime and astrophysically complex. Recently, Kehagias & Riotto and Peloso & Pietroni discovered a consistency relation applicable to large scale structure. We show that this can be recast into a simple physical statement in Lagrangian space: that the squeezed correlation function (suitably normalized) vanishes. This holds regardless of whether the correlation observables are at the same time or not, and regardless of whether multiple-streaming is present.more » Furthermore, the simplicity of this statement suggests that an analytic understanding of large scale structure in the nonlinear regime may be particularly promising in Lagrangian space.« less

Lagrangian space consistency relation for large scale structure

DOE PAGES

Horn, Bart; Hui, Lam; Xiao, Xiao

2015-09-29

Consistency relations, which relate the squeezed limit of an (N+1)-point correlation function to an N-point function, are non-perturbative symmetry statements that hold even if the associated high momentum modes are deep in the nonlinear regime and astrophysically complex. Recently, Kehagias & Riotto and Peloso & Pietroni discovered a consistency relation applicable to large scale structure. We show that this can be recast into a simple physical statement in Lagrangian space: that the squeezed correlation function (suitably normalized) vanishes. This holds regardless of whether the correlation observables are at the same time or not, and regardless of whether multiple-streaming is present.more » Furthermore, the simplicity of this statement suggests that an analytic understanding of large scale structure in the nonlinear regime may be particularly promising in Lagrangian space.« less

An interactive display system for large-scale 3D models

NASA Astrophysics Data System (ADS)

Liu, Zijian; Sun, Kun; Tao, Wenbing; Liu, Liman

2018-04-01

With the improvement of 3D reconstruction theory and the rapid development of computer hardware technology, the reconstructed 3D models are enlarging in scale and increasing in complexity. Models with tens of thousands of 3D points or triangular meshes are common in practical applications. Due to storage and computing power limitation, it is difficult to achieve real-time display and interaction with large scale 3D models for some common 3D display software, such as MeshLab. In this paper, we propose a display system for large-scale 3D scene models. We construct the LOD (Levels of Detail) model of the reconstructed 3D scene in advance, and then use an out-of-core view-dependent multi-resolution rendering scheme to realize the real-time display of the large-scale 3D model. With the proposed method, our display system is able to render in real time while roaming in the reconstructed scene and 3D camera poses can also be displayed. Furthermore, the memory consumption can be significantly decreased via internal and external memory exchange mechanism, so that it is possible to display a large scale reconstructed scene with over millions of 3D points or triangular meshes in a regular PC with only 4GB RAM.

Multi-level discriminative dictionary learning with application to large scale image classification.

PubMed

Shen, Li; Sun, Gang; Huang, Qingming; Wang, Shuhui; Lin, Zhouchen; Wu, Enhua

2015-10-01

The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.

Geomorphic analysis of large alluvial rivers

NASA Astrophysics Data System (ADS)

Thorne, Colin R.

2002-05-01

Geomorphic analysis of a large river presents particular challenges and requires a systematic and organised approach because of the spatial scale and system complexity involved. This paper presents a framework and blueprint for geomorphic studies of large rivers developed in the course of basic, strategic and project-related investigations of a number of large rivers. The framework demonstrates the need to begin geomorphic studies early in the pre-feasibility stage of a river project and carry them through to implementation and post-project appraisal. The blueprint breaks down the multi-layered and multi-scaled complexity of a comprehensive geomorphic study into a number of well-defined and semi-independent topics, each of which can be performed separately to produce a clearly defined, deliverable product. Geomorphology increasingly plays a central role in multi-disciplinary river research and the importance of effective quality assurance makes it essential that audit trails and quality checks are hard-wired into study design. The structured approach presented here provides output products and production trails that can be rigorously audited, ensuring that the results of a geomorphic study can stand up to the closest scrutiny.

Development of the US3D Code for Advanced Compressible and Reacting Flow Simulations

NASA Technical Reports Server (NTRS)

Candler, Graham V.; Johnson, Heath B.; Nompelis, Ioannis; Subbareddy, Pramod K.; Drayna, Travis W.; Gidzak, Vladimyr; Barnhardt, Michael D.

2015-01-01

Aerothermodynamics and hypersonic flows involve complex multi-disciplinary physics, including finite-rate gas-phase kinetics, finite-rate internal energy relaxation, gas-surface interactions with finite-rate oxidation and sublimation, transition to turbulence, large-scale unsteadiness, shock-boundary layer interactions, fluid-structure interactions, and thermal protection system ablation and thermal response. Many of the flows have a large range of length and time scales, requiring large computational grids, implicit time integration, and large solution run times. The University of Minnesota NASA US3D code was designed for the simulation of these complex, highly-coupled flows. It has many of the features of the well-established DPLR code, but uses unstructured grids and has many advanced numerical capabilities and physical models for multi-physics problems. The main capabilities of the code are described, the physical modeling approaches are discussed, the different types of numerical flux functions and time integration approaches are outlined, and the parallelization strategy is overviewed. Comparisons between US3D and the NASA DPLR code are presented, and several advanced simulations are presented to illustrate some of novel features of the code.

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

Jacques Hugo

Traditional engineering methods do not make provision for the integration of human considerations, while traditional human factors methods do not scale well to the complexity of large-scale nuclear power plant projects. Although the need for up-to-date human factors engineering processes and tools is recognised widely in industry, so far no formal guidance has been developed. This article proposes such a framework.

Scaling laws of strategic behavior and size heterogeneity in agent dynamics

NASA Astrophysics Data System (ADS)

Vaglica, Gabriella; Lillo, Fabrizio; Moro, Esteban; Mantegna, Rosario N.

2008-03-01

We consider the financial market as a model system and study empirically how agents strategically adjust the properties of large orders in order to meet their preference and minimize their impact. We quantify this strategic behavior by detecting scaling relations between the variables characterizing the trading activity of different institutions. We also observe power-law distributions in the investment time horizon, in the number of transactions needed to execute a large order, and in the traded value exchanged by large institutions, and we show that heterogeneity of agents is a key ingredient for the emergence of some aggregate properties characterizing this complex system.

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

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

Hero, Alfred O.; Rajaratnam, Bala

When can reliable inference be drawn in the ‘‘Big Data’’ context? This article presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large-scale inference. In large-scale data applications like genomics, connectomics, and eco-informatics, the data set is often variable rich but sample starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than the number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for ‘‘Big Data.’’ Sample complexity, however, hasmore » received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address this gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; and 3) the purely high-dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa-scale data dimension. We illustrate this high-dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables that are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. We demonstrate various regimes of correlation mining based on the unifying perspective of high-dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.« less

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

DOE PAGES

Hero, Alfred O.; Rajaratnam, Bala

2015-12-09

When can reliable inference be drawn in the ‘‘Big Data’’ context? This article presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large-scale inference. In large-scale data applications like genomics, connectomics, and eco-informatics, the data set is often variable rich but sample starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than the number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for ‘‘Big Data.’’ Sample complexity, however, hasmore » received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address this gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; and 3) the purely high-dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa-scale data dimension. We illustrate this high-dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables that are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. We demonstrate various regimes of correlation mining based on the unifying perspective of high-dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.« less

Scaling and self-organized criticality in proteins I

PubMed Central

Phillips, J. C.

2009-01-01

The complexity of proteins is substantially simplified by regarding them as archetypical examples of self-organized criticality (SOC). To test this idea and elaborate on it, this article applies the Moret–Zebende SOC hydrophobicity scale to the large-scale scaffold repeat protein of the HEAT superfamily, PR65/A. Hydrophobic plasticity is defined and used to identify docking platforms and hinges from repeat sequences alone. The difference between the MZ scale and conventional hydrophobicity scales reflects long-range conformational forces that are central to protein functionality. PMID:19218446

Sensemaking in a Value Based Context for Large Scale Complex Engineered Systems

NASA Astrophysics Data System (ADS)

Sikkandar Basha, Nazareen

The design and the development of Large-Scale Complex Engineered Systems (LSCES) requires the involvement of multiple teams and numerous levels of the organization and interactions with large numbers of people and interdisciplinary departments. Traditionally, requirements-driven Systems Engineering (SE) is used in the design and development of these LSCES. The requirements are used to capture the preferences of the stakeholder for the LSCES. Due to the complexity of the system, multiple levels of interactions are required to elicit the requirements of the system within the organization. Since LSCES involves people and interactions between the teams and interdisciplinary departments, it should be socio-technical in nature. The elicitation of the requirements of most large-scale system projects are subjected to creep in time and cost due to the uncertainty and ambiguity of requirements during the design and development. In an organization structure, the cost and time overrun can occur at any level and iterate back and forth thus increasing the cost and time. To avoid such creep past researches have shown that rigorous approaches such as value based designing can be used to control it. But before the rigorous approaches can be used, the decision maker should have a proper understanding of requirements creep and the state of the system when the creep occurs. Sensemaking is used to understand the state of system when the creep occurs and provide a guidance to decision maker. This research proposes the use of the Cynefin framework, sensemaking framework which can be used in the design and development of LSCES. It can aide in understanding the system and decision making to minimize the value gap due to requirements creep by eliminating ambiguity which occurs during design and development. A sample hierarchical organization is used to demonstrate the state of the system at the occurrence of requirements creep in terms of cost and time using the Cynefin framework. These trials are continued for different requirements and at different sub-system level. The results obtained show that the Cynefin framework can be used to improve the value of the system and can be used for predictive analysis. The decision makers can use these findings and use rigorous approaches and improve the design of Large Scale Complex Engineered Systems.

Applying the 15 Public Health Emergency Preparedness Capabilities to Support Large-Scale Tuberculosis Investigations in Complex Congregate Settings

PubMed Central

Toren, Katelynne Gardner; Elsenboss, Carina; Narita, Masahiro

2017-01-01

Public Health—Seattle and King County, a metropolitan health department in western Washington, experiences rates of tuberculosis (TB) that are 1.6 times higher than are state and national averages. The department’s TB Control Program uses public health emergency management tools and capabilities sustained with Centers for Disease Control and Prevention grant funding to manage large-scale complex case investigations. We have described 3 contact investigations in large congregate settings that the TB Control Program conducted in 2015 and 2016. The program managed the investigations using public health emergency management tools, with support from the Preparedness Program. The 3 investigations encompassed medical evaluation of more than 1600 people, used more than 100 workers, identified nearly 30 individuals with latent TB infection, and prevented an estimated 3 cases of active disease. These incidents exemplify how investments in public health emergency preparedness can enhance health outcomes in traditional areas of public health. PMID:28892445

Neural encoding of large-scale three-dimensional space-properties and constraints.

PubMed

Jeffery, Kate J; Wilson, Jonathan J; Casali, Giulio; Hayman, Robin M

2015-01-01

How the brain represents represent large-scale, navigable space has been the topic of intensive investigation for several decades, resulting in the discovery that neurons in a complex network of cortical and subcortical brain regions co-operatively encode distance, direction, place, movement etc. using a variety of different sensory inputs. However, such studies have mainly been conducted in simple laboratory settings in which animals explore small, two-dimensional (i.e., flat) arenas. The real world, by contrast, is complex and three dimensional with hills, valleys, tunnels, branches, and-for species that can swim or fly-large volumetric spaces. Adding an additional dimension to space adds coding challenges, a primary reason for which is that several basic geometric properties are different in three dimensions. This article will explore the consequences of these challenges for the establishment of a functional three-dimensional metric map of space, one of which is that the brains of some species might have evolved to reduce the dimensionality of the representational space and thus sidestep some of these problems.

Biologically-Inspired Concepts for Self-Management of Complexity

NASA Technical Reports Server (NTRS)

Sterritt, Roy; Hinchey, G.

2006-01-01

Inherent complexity in large-scale applications may be impossible to eliminate or even ameliorate despite a number of promising advances. In such cases, the complexity must be tolerated and managed. Such management may be beyond the abilities of humans, or require such overhead as to make management by humans unrealistic. A number of initiatives inspired by concepts in biology have arisen for self-management of complex systems. We present some ideas and techniques we have been experimenting with, inspired by lesser-known concepts in biology that show promise in protecting complex systems and represent a step towards self-management of complexity.

Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study

NASA Astrophysics Data System (ADS)

Wolf-Grosse, Tobias; Esau, Igor; Reuder, Joachim

2017-06-01

Street-level urban air pollution is a challenging concern for modern urban societies. Pollution dispersion models assume that the concentrations decrease monotonically with raising wind speed. This convenient assumption breaks down when applied to flows with local recirculations such as those found in topographically complex coastal areas. This study looks at a practically important and sufficiently common case of air pollution in a coastal valley city. Here, the observed concentrations are determined by the interaction between large-scale topographically forced and local-scale breeze-like recirculations. Analysis of a long observational dataset in Bergen, Norway, revealed that the most extreme cases of recurring wintertime air pollution episodes were accompanied by increased large-scale wind speeds above the valley. Contrary to the theoretical assumption and intuitive expectations, the maximum NO2 concentrations were not found for the lowest 10 m ERA-Interim wind speeds but in situations with wind speeds of 3 m s-1. To explain this phenomenon, we investigated empirical relationships between the large-scale forcing and the local wind and air quality parameters. We conducted 16 large-eddy simulation (LES) experiments with the Parallelised Large-Eddy Simulation Model (PALM) for atmospheric and oceanic flows. The LES accounted for the realistic relief and coastal configuration as well as for the large-scale forcing and local surface condition heterogeneity in Bergen. They revealed that emerging local breeze-like circulations strongly enhance the urban ventilation and dispersion of the air pollutants in situations with weak large-scale winds. Slightly stronger large-scale winds, however, can counteract these local recirculations, leading to enhanced surface air stagnation. Furthermore, this study looks at the concrete impact of the relative configuration of warmer water bodies in the city and the major transport corridor. We found that a relatively small local water body acted as a barrier for the horizontal transport of air pollutants from the largest street in the valley and along the valley bottom, transporting them vertically instead and hence diluting them. We found that the stable stratification accumulates the street-level pollution from the transport corridor in shallow air pockets near the surface. The polluted air pockets are transported by the local recirculations to other less polluted areas with only slow dilution. This combination of relatively long distance and complex transport paths together with weak dispersion is not sufficiently resolved in classical air pollution models. The findings have important implications for the air quality predictions over urban areas. Any prediction not resolving these, or similar local dynamic features, might not be able to correctly simulate the dispersion of pollutants in cities.

A laser-sheet flow visualization technique for the large wind tunnels of the National Full-Scale Aerodynamics Complex

NASA Technical Reports Server (NTRS)

Reinath, M. S.; Ross, J. C.

1990-01-01

A flow visualization technique for the large wind tunnels of the National Full Scale Aerodynamics Complex (NFAC) is described. The technique uses a laser sheet generated by the NFAC Long Range Laser Velocimeter (LRLV) to illuminate a smoke-like tracer in the flow. The LRLV optical system is modified slightly, and a scanned mirror is added to generate the sheet. These modifications are described, in addition to the results of an initial performance test conducted in the 80- by 120-Foot Wind Tunnel. During this test, flow visualization was performed in the wake region behind a truck as part of a vehicle drag reduction study. The problems encountered during the test are discussed, in addition to the recommended improvements needed to enhance the performance of the technique for future applications.

Building and measuring a high performance network architecture

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

Kramer, William T.C.; Toole, Timothy; Fisher, Chuck

2001-04-20

Once a year, the SC conferences present a unique opportunity to create and build one of the most complex and highest performance networks in the world. At SC2000, large-scale and complex local and wide area networking connections were demonstrated, including large-scale distributed applications running on different architectures. This project was designed to use the unique opportunity presented at SC2000 to create a testbed network environment and then use that network to demonstrate and evaluate high performance computational and communication applications. This testbed was designed to incorporate many interoperable systems and services and was designed for measurement from the very beginning.more » The end results were key insights into how to use novel, high performance networking technologies and to accumulate measurements that will give insights into the networks of the future.« less

Towards the understanding of network information processing in biology

NASA Astrophysics Data System (ADS)

Singh, Vijay

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

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining.

PubMed

Hero, Alfred O; Rajaratnam, Bala

2016-01-01

When can reliable inference be drawn in fue "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than fue number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for "Big Data". Sample complexity however has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address fuis gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where fue variable dimension is fixed and fue sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa cale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables fua t are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. we demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.

Online Low-Rank Representation Learning for Joint Multi-subspace Recovery and Clustering.

PubMed

Li, Bo; Liu, Risheng; Cao, Junjie; Zhang, Jie; Lai, Yu-Kun; Liua, Xiuping

2017-10-06

Benefiting from global rank constraints, the lowrank representation (LRR) method has been shown to be an effective solution to subspace learning. However, the global mechanism also means that the LRR model is not suitable for handling large-scale data or dynamic data. For large-scale data, the LRR method suffers from high time complexity, and for dynamic data, it has to recompute a complex rank minimization for the entire data set whenever new samples are dynamically added, making it prohibitively expensive. Existing attempts to online LRR either take a stochastic approach or build the representation purely based on a small sample set and treat new input as out-of-sample data. The former often requires multiple runs for good performance and thus takes longer time to run, and the latter formulates online LRR as an out-ofsample classification problem and is less robust to noise. In this paper, a novel online low-rank representation subspace learning method is proposed for both large-scale and dynamic data. The proposed algorithm is composed of two stages: static learning and dynamic updating. In the first stage, the subspace structure is learned from a small number of data samples. In the second stage, the intrinsic principal components of the entire data set are computed incrementally by utilizing the learned subspace structure, and the low-rank representation matrix can also be incrementally solved by an efficient online singular value decomposition (SVD) algorithm. The time complexity is reduced dramatically for large-scale data, and repeated computation is avoided for dynamic problems. We further perform theoretical analysis comparing the proposed online algorithm with the batch LRR method. Finally, experimental results on typical tasks of subspace recovery and subspace clustering show that the proposed algorithm performs comparably or better than batch methods including the batch LRR, and significantly outperforms state-of-the-art online methods.

'Fracking', Induced Seismicity and the Critical Earth

NASA Astrophysics Data System (ADS)

Leary, P.; Malin, P. E.

2012-12-01

Issues of 'fracking' and induced seismicity are reverse-analogous to the equally complex issues of well productivity in hydrocarbon, geothermal and ore reservoirs. In low hazard reservoir economics, poorly producing wells and low grade ore bodies are many while highly producing wells and high grade ores are rare but high pay. With induced seismicity factored in, however, the same distribution physics reverses the high/low pay economics: large fracture-connectivity systems are hazardous hence low pay, while high probability small fracture-connectivity systems are non-hazardous hence high pay. Put differently, an economic risk abatement tactic for well productivity and ore body pay is to encounter large-scale fracture systems, while an economic risk abatement tactic for 'fracking'-induced seismicity is to avoid large-scale fracture systems. Well productivity and ore body grade distributions arise from three empirical rules for fluid flow in crustal rock: (i) power-law scaling of grain-scale fracture density fluctuations; (ii) spatial correlation between spatial fluctuations in well-core porosity and the logarithm of well-core permeability; (iii) frequency distributions of permeability governed by a lognormality skewness parameter. The physical origin of rules (i)-(iii) is the universal existence of a critical-state-percolation grain-scale fracture-density threshold for crustal rock. Crustal fractures are effectively long-range spatially-correlated distributions of grain-scale defects permitting fluid percolation on mm to km scales. The rule is, the larger the fracture system the more intense the percolation throughput. As percolation pathways are spatially erratic and unpredictable on all scales, they are difficult to model with sparsely sampled well data. Phenomena such as well productivity, induced seismicity, and ore body fossil fracture distributions are collectively extremely difficult to predict. Risk associated with unpredictable reservoir well productivity and ore body distributions can be managed by operating in a context which affords many small failures for a few large successes. In reverse view, 'fracking' and induced seismicity could be rationally managed in a context in which many small successes can afford a few large failures. However, just as there is every incentive to acquire information leading to higher rates of productive well drilling and ore body exploration, there are equal incentives for acquiring information leading to lower rates of 'fracking'-induced seismicity. Current industry practice of using an effective medium approach to reservoir rock creates an uncritical sense that property distributions in rock are essentially uniform. Well-log data show that the reverse is true: the larger the length scale the greater the deviation from uniformity. Applying the effective medium approach to large-scale rock formations thus appears to be unnecessarily hazardous. It promotes the notion that large scale fluid pressurization acts against weakly cohesive but essentially uniform rock to produce large-scale quasi-uniform tensile discontinuities. Indiscriminate hydrofacturing appears to be vastly more problematic in reality than as pictured by the effective medium hypothesis. The spatial complexity of rock, especially at large scales, provides ample reason to find more controlled pressurization strategies for enhancing in situ flow.

Cyclicity in Upper Mississippian Bangor Limestone, Blount County, Alabama

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

Bronner, R.L.

1988-01-01

The Upper Mississippian (Chesterian) Bangor Limestone in Alabama consists of a thick, complex sequence of carbonate platform deposits. A continuous core through the Bangor on Blount Mountain in north-central Alabama provides the opportunity to analyze the unit for cyclicity and to identify controls on vertical facies sequence. Lithologies from the core represent four general environments of deposition: (1) subwave-base, open marine, (2) shoal, (3) lagoon, and (4) peritidal. Analysis of the vertical sequence of lithologies in the core indicates the presence of eight large-scale cycles dominated by subtidal deposits, but defined on the basis of peritidal caps. These large-scale cyclesmore » can be subdivided into 16 small-scale cycles that may be entirely subtidal but illustrate upward shallowing followed by rapid deepening. Large-scale cycles range from 33 to 136 ft thick, averaging 68 ft; small-scale cycles range from 5 to 80 ft thick and average 34 ft. Small-scale cycles have an average duration of approximately 125,000 years, which is compatible with Milankovitch periodicity. The large-scale cycles have an average duration of approximately 250,000 years, which may simply reflect variations in amplitude of sea level fluctuation or the influence of tectonic subsidence along the southeastern margin of the North American craton.« less

Small scale exact coherent structures at large Reynolds numbers in plane Couette flow

NASA Astrophysics Data System (ADS)

Eckhardt, Bruno; Zammert, Stefan

2018-02-01

The transition to turbulence in plane Couette flow and several other shear flows is connected with saddle node bifurcations in which fully three-dimensional, nonlinear solutions to the Navier-Stokes equation, so-called exact coherent states (ECS), appear. As the Reynolds number increases, the states undergo secondary bifurcations and their time-evolution becomes increasingly more complex. Their spatial complexity, in contrast, remains limited so that these states cannot contribute to the spatial complexity and cascade to smaller scales expected for higher Reynolds numbers. We here present families of scaling ECS that exist on ever smaller scales as the Reynolds number is increased. We focus in particular on two such families for plane Couette flow, one centered near the midplane and the other close to a wall. We discuss their scaling and localization properties and the bifurcation diagrams. All solutions are localized in the wall-normal direction. In the spanwise and downstream direction, they are either periodic or localized as well. The family of scaling ECS localized near a wall is reminiscent of attached eddies, and indicates how self-similar ECS can contribute to the formation of boundary layer profiles.

Multiscale unfolding of real networks by geometric renormalization

NASA Astrophysics Data System (ADS)

García-Pérez, Guillermo; Boguñá, Marián; Serrano, M. Ángeles

2018-06-01

Symmetries in physical theories denote invariance under some transformation, such as self-similarity under a change of scale. The renormalization group provides a powerful framework to study these symmetries, leading to a better understanding of the universal properties of phase transitions. However, the small-world property of complex networks complicates application of the renormalization group by introducing correlations between coexisting scales. Here, we provide a framework for the investigation of complex networks at different resolutions. The approach is based on geometric representations, which have been shown to sustain network navigability and to reveal the mechanisms that govern network structure and evolution. We define a geometric renormalization group for networks by embedding them into an underlying hidden metric space. We find that real scale-free networks show geometric scaling under this renormalization group transformation. We unfold the networks in a self-similar multilayer shell that distinguishes the coexisting scales and their interactions. This in turn offers a basis for exploring critical phenomena and universality in complex networks. It also affords us immediate practical applications, including high-fidelity smaller-scale replicas of large networks and a multiscale navigation protocol in hyperbolic space, which betters those on single layers.

A study of the spreading scheme for viral marketing based on a complex network model

NASA Astrophysics Data System (ADS)

Yang, Jianmei; Yao, Canzhong; Ma, Weicheng; Chen, Guanrong

2010-02-01

Buzzword-based viral marketing, known also as digital word-of-mouth marketing, is a marketing mode attached to some carriers on the Internet, which can rapidly copy marketing information at a low cost. Viral marketing actually uses a pre-existing social network where, however, the scale of the pre-existing network is believed to be so large and so random, so that its theoretical analysis is intractable and unmanageable. There are very few reports in the literature on how to design a spreading scheme for viral marketing on real social networks according to the traditional marketing theory or the relatively new network marketing theory. Complex network theory provides a new model for the study of large-scale complex systems, using the latest developments of graph theory and computing techniques. From this perspective, the present paper extends the complex network theory and modeling into the research of general viral marketing and develops a specific spreading scheme for viral marking and an approach to design the scheme based on a real complex network on the QQ instant messaging system. This approach is shown to be rather universal and can be further extended to the design of various spreading schemes for viral marketing based on different instant messaging systems.

Annual Research Briefs

NASA Technical Reports Server (NTRS)

Spinks, Debra (Compiler)

1997-01-01

This report contains the 1997 annual progress reports of the research fellows and students supported by the Center for Turbulence Research (CTR). Titles include: Invariant modeling in large-eddy simulation of turbulence; Validation of large-eddy simulation in a plain asymmetric diffuser; Progress in large-eddy simulation of trailing-edge turbulence and aeronautics; Resolution requirements in large-eddy simulations of shear flows; A general theory of discrete filtering for LES in complex geometry; On the use of discrete filters for large eddy simulation; Wall models in large eddy simulation of separated flow; Perspectives for ensemble average LES; Anisotropic grid-based formulas for subgrid-scale models; Some modeling requirements for wall models in large eddy simulation; Numerical simulation of 3D turbulent boundary layers using the V2F model; Accurate modeling of impinging jet heat transfer; Application of turbulence models to high-lift airfoils; Advances in structure-based turbulence modeling; Incorporating realistic chemistry into direct numerical simulations of turbulent non-premixed combustion; Effects of small-scale structure on turbulent mixing; Turbulent premixed combustion in the laminar flamelet and the thin reaction zone regime; Large eddy simulation of combustion instabilities in turbulent premixed burners; On the generation of vorticity at a free-surface; Active control of turbulent channel flow; A generalized framework for robust control in fluid mechanics; Combined immersed-boundary/B-spline methods for simulations of flow in complex geometries; and DNS of shock boundary-layer interaction - preliminary results for compression ramp flow.

The challenge of transferring an implementation strategy from academia to the field: a process evaluation of local quality improvement collaboratives in Dutch primary care using the normalization process theory.

PubMed

Trietsch, Jasper; van Steenkiste, Ben; Hobma, Sjoerd; Frericks, Arnoud; Grol, Richard; Metsemakers, Job; van der Weijden, Trudy

2014-12-01

A quality improvement strategy consisting of comparative feedback and peer review embedded in available local quality improvement collaboratives proved to be effective in changing the test-ordering behaviour of general practitioners. However, implementing this strategy was problematic. We aimed for large-scale implementation of an adapted strategy covering both test ordering and prescribing performance. Because we failed to achieve large-scale implementation, the aim of this study was to describe and analyse the challenges of the transferring process. In a qualitative study 19 regional health officers, pharmacists, laboratory specialists and general practitioners were interviewed within 6 months after the transfer period. The interviews were audiotaped, transcribed and independently coded by two of the authors. The codes were matched to the dimensions of the normalization process theory. The general idea of the strategy was widely supported, but generating the feedback was more complex than expected and the need for external support after transfer of the strategy remained high because participants did not assume responsibility for the work and the distribution of resources that came with it. Evidence on effectiveness, a national infrastructure for these collaboratives and a general positive attitude were not sufficient for normalization. Thinking about managing large databases, responsibility for tasks and distribution of resources should start as early as possible when planning complex quality improvement strategies. Merely exploring the barriers and facilitators experienced in a preceding trial is not sufficient. Although multifaceted implementation strategies to change professional behaviour are attractive, their inherent complexity is also a pitfall for large-scale implementation. © 2014 John Wiley & Sons, Ltd.

Syntactic Complexity Measures and Their Relation to Oral Proficiency in Japanese as a Foreign Language

ERIC Educational Resources Information Center

Iwashita, Noriko

2006-01-01

The study reported in this article is a part of a large-scale study investigating syntactic complexity in second language (L2) oral data in commonly taught foreign languages (English, German, Japanese, and Spanish; Ortega, Iwashita, Rabie, & Norris, in preparation). In this article, preliminary findings of the analysis of the Japanese data are…

The Complexity of Teacher Questions in Chemistry Classrooms: An Empirical Analysis on the Basis of Two Competence Models

ERIC Educational Resources Information Center

Nehring, Andreas; Päßler, Andreas; Tiemann, Rüdiger

2017-01-01

With regard to the moderate performance of German students in international large-scale assessments, one branch of German science education research is concerned with the construction and evaluation of competence models. Based on the theory-driven definition of competence levels, these models imply a correlation between the complexity of a…

SIGN SINGULARITY AND FLARES IN SOLAR ACTIVE REGION NOAA 11158

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

Sorriso-Valvo, L.; De Vita, G.; Kazachenko, M. D.

Solar Active Region NOAA 11158 has hosted a number of strong flares, including one X2.2 event. The complexity of current density and current helicity are studied through cancellation analysis of their sign-singular measure, which features power-law scaling. Spectral analysis is also performed, revealing the presence of two separate scaling ranges with different spectral index. The time evolution of parameters is discussed. Sudden changes of the cancellation exponents at the time of large flares and the presence of correlation with Extreme-Ultra-Violet and X-ray flux suggest that eruption of large flares can be linked to the small-scale properties of the current structures.

Should Child Protection Services Respond Differently to Maltreatment, Risk of Maltreatment, and Risk of Harm?

ERIC Educational Resources Information Center

Fallon, Barbara; Trocme, Nico; MacLaurin, Bruce

2011-01-01

Objective: To examine evidence available in large-scale North American datasets on child abuse and neglect that can assist in understanding the complexities of child protection case classifications. Methods: A review of child abuse and neglect data from large North American epidemiological studies including the Canadian Incidence Study of Reported…

Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys

ERIC Educational Resources Information Center

Si, Yajuan; Reiter, Jerome P.

2013-01-01

In many surveys, the data comprise a large number of categorical variables that suffer from item nonresponse. Standard methods for multiple imputation, like log-linear models or sequential regression imputation, can fail to capture complex dependencies and can be difficult to implement effectively in high dimensions. We present a fully Bayesian,…

The lost summer: Community experiences of large wildfires in Trinity County, California

Treesearch

Emily J. Davis; Cassandra Moseley; Pamela Jakes; Max Nielsen-Pincus

2011-01-01

As wildfires are increasing in scale and duration, and communities are increasingly located where these wildfires are occurring, we need a clearer understanding of how large wildfires affect economic and social well being. These wildfires can have complex impacts on rural public lands communities. They can threaten homes, public health, and livelihoods. Wildfires can...

The Renewed Primary School in Belgium: Analysis of the Local Innovation Policy.

ERIC Educational Resources Information Center

Vandenberghe, Roland

The Renewed Primary School project in Belgium is analyzed in this paper in terms of organizational response to a large-scale innovation, which is characterized by its multidimensionality, by the large number of participating schools, and by a complex support structure. Section 2 of the report presents an elaborated description of these…

Large space telescope engineering scale model optical design

NASA Technical Reports Server (NTRS)

Facey, T. A.

1973-01-01

The objective is to develop the detailed design and tolerance data for the LST engineering scale model optical system. This will enable MSFC to move forward to the optical element procurement phase and also to evaluate tolerances, manufacturing requirements, assembly/checkout procedures, reliability, operational complexity, stability requirements of the structure and thermal system, and the flexibility to change and grow.

The Multi-Scale Network Landscape of Collaboration.

PubMed

Bae, Arram; Park, Doheum; Ahn, Yong-Yeol; Park, Juyong

2016-01-01

Propelled by the increasing availability of large-scale high-quality data, advanced data modeling and analysis techniques are enabling many novel and significant scientific understanding of a wide range of complex social, natural, and technological systems. These developments also provide opportunities for studying cultural systems and phenomena--which can be said to refer to all products of human creativity and way of life. An important characteristic of a cultural product is that it does not exist in isolation from others, but forms an intricate web of connections on many levels. In the creation and dissemination of cultural products and artworks in particular, collaboration and communication of ideas play an essential role, which can be captured in the heterogeneous network of the creators and practitioners of art. In this paper we propose novel methods to analyze and uncover meaningful patterns from such a network using the network of western classical musicians constructed from a large-scale comprehensive Compact Disc recordings data. We characterize the complex patterns in the network landscape of collaboration between musicians across multiple scales ranging from the macroscopic to the mesoscopic and microscopic that represent the diversity of cultural styles and the individuality of the artists.

The Multi-Scale Network Landscape of Collaboration

PubMed Central

Ahn, Yong-Yeol; Park, Juyong

2016-01-01

Propelled by the increasing availability of large-scale high-quality data, advanced data modeling and analysis techniques are enabling many novel and significant scientific understanding of a wide range of complex social, natural, and technological systems. These developments also provide opportunities for studying cultural systems and phenomena—which can be said to refer to all products of human creativity and way of life. An important characteristic of a cultural product is that it does not exist in isolation from others, but forms an intricate web of connections on many levels. In the creation and dissemination of cultural products and artworks in particular, collaboration and communication of ideas play an essential role, which can be captured in the heterogeneous network of the creators and practitioners of art. In this paper we propose novel methods to analyze and uncover meaningful patterns from such a network using the network of western classical musicians constructed from a large-scale comprehensive Compact Disc recordings data. We characterize the complex patterns in the network landscape of collaboration between musicians across multiple scales ranging from the macroscopic to the mesoscopic and microscopic that represent the diversity of cultural styles and the individuality of the artists. PMID:26990088

The challenges associated with developing science-based landscape scale management plans

USGS Publications Warehouse

Szaro, Robert C.; Boyce, D.A.; Puchlerz, T.

2005-01-01

Planning activities over large landscapes poses a complex of challenges when trying to balance the implementation of a conservation strategy while still allowing for a variety of consumptive and nonconsumptive uses. We examine a case in southeast Alaska to illustrate the breadth of these challenges and an approach to developing a science-based resource plan. Not only was the planning area, the Tongass National Forest, USA, exceptionally large (approximately 17 million acres or 6.9 million ha), but it also is primarily an island archipelago environment. The water system surrounding and going through much of the forest provides access to facilitate the movement of people, animals, and plants but at the same time functions as a barrier to others. This largest temperate rainforest in the world is an exceptional example of the complexity of managing at such a scale but also illustrates the role of science in the planning process. As we enter the 21st century, the list of questions needing scientific investigation has not only changed dramatically, but the character of the questions also has changed. Questions are contentious, cover broad scales in space and time, and are highly complex and interdependent. The provision of unbiased and objective information to all stakeholders is an important step in informed decision-making.

Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware

PubMed Central

Srinivasa, Narayan; Stepp, Nigel D.; Cruz-Albrecht, Jose

2015-01-01

Neuromorphic hardware are designed by drawing inspiration from biology to overcome limitations of current computer architectures while forging the development of a new class of autonomous systems that can exhibit adaptive behaviors. Several designs in the recent past are capable of emulating large scale networks but avoid complexity in network dynamics by minimizing the number of dynamic variables that are supported and tunable in hardware. We believe that this is due to the lack of a clear understanding of how to design self-tuning complex systems. It has been widely demonstrated that criticality appears to be the default state of the brain and manifests in the form of spontaneous scale-invariant cascades of neural activity. Experiment, theory and recent models have shown that neuronal networks at criticality demonstrate optimal information transfer, learning and information processing capabilities that affect behavior. In this perspective article, we argue that understanding how large scale neuromorphic electronics can be designed to enable emergent adaptive behavior will require an understanding of how networks emulated by such hardware can self-tune local parameters to maintain criticality as a set-point. We believe that such capability will enable the design of truly scalable intelligent systems using neuromorphic hardware that embrace complexity in network dynamics rather than avoiding it. PMID:26648839

Is Scale-Up of Community Mobilisation among Sex Workers Really Possible in Complex Urban Environments? The Case of Mumbai, India

PubMed Central

Kongelf, Anine; Bandewar, Sunita V. S.; Bharat, Shalini; Collumbien, Martine

2015-01-01

Background In the last decade, community mobilisation (CM) interventions targeting female sex workers (FSWs) have been scaled-up in India’s national response to the HIV epidemic. This included the Bill and Melinda Gates Foundation’s Avahan programme which adopted a business approach to plan and manage implementation at scale. With the focus of evaluation efforts on measuring effectiveness and health impacts there has been little analysis thus far of the interaction of the CM interventions with the sex work industry in complex urban environments. Methods and Findings Between March and July 2012 semi-structured, in-depth interviews and focus group discussions were conducted with 63 HIV intervention implementers, to explore challenges of HIV prevention among FSWs in Mumbai. A thematic analysis identified contextual factors that impact CM implementation. Large-scale interventions are not only impacted by, but were shown to shape the dynamic social context. Registration practices and programme monitoring were experienced as stigmatising, reflected in shifting client preferences towards women not disclosing as ‘sex workers’. This combined with urban redevelopment and gentrification of traditional red light areas, forcing dispersal and more ‘hidden’ ways of solicitation, further challenging outreach and collectivisation. Participants reported that brothel owners and ‘pimps’ continued to restrict access to sex workers and the heterogeneous ‘community’ of FSWs remains fragmented with high levels of mobility. Stakeholder engagement was poor and mobilising around HIV prevention not compelling. Interventions largely failed to respond to community needs as strong target-orientation skewed activities towards those most easily measured and reported. Conclusion Large-scale interventions have been impacted by and contributed to an increasingly complex sex work environment in Mumbai, challenging outreach and mobilisation efforts. Sex workers remain a vulnerable and disempowered group needing continued support and more comprehensive services. PMID:25811484

A numerical projection technique for large-scale eigenvalue problems

NASA Astrophysics Data System (ADS)

Gamillscheg, Ralf; Haase, Gundolf; von der Linden, Wolfgang

2011-10-01

We present a new numerical technique to solve large-scale eigenvalue problems. It is based on the projection technique, used in strongly correlated quantum many-body systems, where first an effective approximate model of smaller complexity is constructed by projecting out high energy degrees of freedom and in turn solving the resulting model by some standard eigenvalue solver. Here we introduce a generalization of this idea, where both steps are performed numerically and which in contrast to the standard projection technique converges in principle to the exact eigenvalues. This approach is not just applicable to eigenvalue problems encountered in many-body systems but also in other areas of research that result in large-scale eigenvalue problems for matrices which have, roughly speaking, mostly a pronounced dominant diagonal part. We will present detailed studies of the approach guided by two many-body models.

An evaluation of multi-probe locality sensitive hashing for computing similarities over web-scale query logs.

PubMed

Cormode, Graham; Dasgupta, Anirban; Goyal, Amit; Lee, Chi Hoon

2018-01-01

Many modern applications of AI such as web search, mobile browsing, image processing, and natural language processing rely on finding similar items from a large database of complex objects. Due to the very large scale of data involved (e.g., users' queries from commercial search engines), computing such near or nearest neighbors is a non-trivial task, as the computational cost grows significantly with the number of items. To address this challenge, we adopt Locality Sensitive Hashing (a.k.a, LSH) methods and evaluate four variants in a distributed computing environment (specifically, Hadoop). We identify several optimizations which improve performance, suitable for deployment in very large scale settings. The experimental results demonstrate our variants of LSH achieve the robust performance with better recall compared with "vanilla" LSH, even when using the same amount of space.

Visual attention mitigates information loss in small- and large-scale neural codes.

PubMed

Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-04-01

The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires that sensory signals are processed in a manner that protects information about relevant stimuli from degradation. Such selective processing--or selective attention--is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, thereby providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. Copyright © 2015 Elsevier Ltd. All rights reserved.

Lagrangian space consistency relation for large scale structure

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

Horn, Bart; Hui, Lam; Xiao, Xiao, E-mail: bh2478@columbia.edu, E-mail: lh399@columbia.edu, E-mail: xx2146@columbia.edu

Consistency relations, which relate the squeezed limit of an (N+1)-point correlation function to an N-point function, are non-perturbative symmetry statements that hold even if the associated high momentum modes are deep in the nonlinear regime and astrophysically complex. Recently, Kehagias and Riotto and Peloso and Pietroni discovered a consistency relation applicable to large scale structure. We show that this can be recast into a simple physical statement in Lagrangian space: that the squeezed correlation function (suitably normalized) vanishes. This holds regardless of whether the correlation observables are at the same time or not, and regardless of whether multiple-streaming is present.more » The simplicity of this statement suggests that an analytic understanding of large scale structure in the nonlinear regime may be particularly promising in Lagrangian space.« less

Large-scale erosional and depositional features of the Channeled Scabland

NASA Technical Reports Server (NTRS)

Baker, V. R.

1978-01-01

The channeled scabland is a great anastomosing complex of highly overfit stand channels eroded into the basalt bedrock and overlying sediments of the Columbia Plateau. Both the erosional and depositional bed forms in these channels are described according to a simple hierarchical classification. The catastrophic flood flows produced macroforms (scale controlled by channel width) through the erosion of rock and sediment and by deposition (bars). Mesoforms (scale controlled by channel depth) are also erosional and depositional.

Waves associated to COMPLEX EVENTS observed by STEREO

NASA Astrophysics Data System (ADS)

Siu Tapia, A. L.; Blanco-Cano, X.; Kajdic, P.; Aguilar-Rodriguez, E.; Russell, C. T.; Jian, L. K.; Luhmann, J. G.

2012-12-01

Complex Events are formed by two or more large-scale solar wind structures which interact in space. Typical cases are interactions of: (i) a Magnetic Cloud/Interplanetary Coronal Mass Ejection (MC/ICME) with another MC/ICME transient; and (ii) an ICME followed by a Stream Interaction Region (SIR). Complex Events are of importance for space weather studies and studying them can enhance our understanding of collisionless plasma physics. Some of these structures can produce or enhance southward magnetic fields, a key factor in geomagnetic storm generation. Using data from the STEREO mission during the years 2006-2011, we found 17 Complex Events preceded by a shock wave. We use magnetic field and plasma data to study the micro-scale structure of the shocks, and the waves associated to these shocks and within Complex Events structures. To determine wave characteristics we perform Power Spectra and Minimum Variance Analysis. We also use PLASTIC WAP protons data to study foreshock extensions and the relationship between Complex Regions and particle acceleration to suprathermal energies.

Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments.

PubMed

Ionescu, Catalin; Papava, Dragos; Olaru, Vlad; Sminchisescu, Cristian

2014-07-01

We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m.

Developing Renewable Energy Projects Larger Than 10 MWs at Federal Facilities (Book)

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

Not Available

2013-03-01

To accomplish Federal goals for renewable energy, sustainability, and energy security, large-scale renewable energy projects must be developed and constructed on Federal sites at a significant scale with significant private investment. The U.S. Department of Energy's Federal Energy Management Program (FEMP) helps Federal agencies meet these goals and assists agency personnel navigate the complexities of developing such projects and attract the necessary private capital to complete them. This guide is intended to provide a general resource that will begin to develop the Federal employee's awareness and understanding of the project developer's operating environment and the private sector's awareness and understandingmore » of the Federal environment. Because the vast majority of the investment that is required to meet the goals for large-scale renewable energy projects will come from the private sector, this guide has been organized to match Federal processes with typical phases of commercial project development. The main purpose of this guide is to provide a project development framework to allow the Federal Government, private developers, and investors to work in a coordinated fashion on large-scale renewable energy projects. The framework includes key elements that describe a successful, financially attractive large-scale renewable energy project.« less

Information Power Grid Posters

NASA Technical Reports Server (NTRS)

Vaziri, Arsi

2003-01-01

This document is a summary of the accomplishments of the Information Power Grid (IPG). Grids are an emerging technology that provide seamless and uniform access to the geographically dispersed, computational, data storage, networking, instruments, and software resources needed for solving large-scale scientific and engineering problems. The goal of the NASA IPG is to use NASA's remotely located computing and data system resources to build distributed systems that can address problems that are too large or complex for a single site. The accomplishments outlined in this poster presentation are: access to distributed data, IPG heterogeneous computing, integration of large-scale computing node into distributed environment, remote access to high data rate instruments,and exploratory grid environment.

Coagulation-Fragmentation Model for Animal Group-Size Statistics

NASA Astrophysics Data System (ADS)

Degond, Pierre; Liu, Jian-Guo; Pego, Robert L.

2017-04-01

We study coagulation-fragmentation equations inspired by a simple model proposed in fisheries science to explain data for the size distribution of schools of pelagic fish. Although the equations lack detailed balance and admit no H-theorem, we are able to develop a rather complete description of equilibrium profiles and large-time behavior, based on recent developments in complex function theory for Bernstein and Pick functions. In the large-population continuum limit, a scaling-invariant regime is reached in which all equilibria are determined by a single scaling profile. This universal profile exhibits power-law behavior crossing over from exponent -2/3 for small size to -3/2 for large size, with an exponential cutoff.

Combinatorial depletion analysis to assemble the network architecture of the SAGA and ADA chromatin remodeling complexes.

PubMed

Lee, Kenneth K; Sardiu, Mihaela E; Swanson, Selene K; Gilmore, Joshua M; Torok, Michael; Grant, Patrick A; Florens, Laurence; Workman, Jerry L; Washburn, Michael P

2011-07-05

Despite the availability of several large-scale proteomics studies aiming to identify protein interactions on a global scale, little is known about how proteins interact and are organized within macromolecular complexes. Here, we describe a technique that consists of a combination of biochemistry approaches, quantitative proteomics and computational methods using wild-type and deletion strains to investigate the organization of proteins within macromolecular protein complexes. We applied this technique to determine the organization of two well-studied complexes, Spt-Ada-Gcn5 histone acetyltransferase (SAGA) and ADA, for which no comprehensive high-resolution structures exist. This approach revealed that SAGA/ADA is composed of five distinct functional modules, which can persist separately. Furthermore, we identified a novel subunit of the ADA complex, termed Ahc2, and characterized Sgf29 as an ADA family protein present in all Gcn5 histone acetyltransferase complexes. Finally, we propose a model for the architecture of the SAGA and ADA complexes, which predicts novel functional associations within the SAGA complex and provides mechanistic insights into phenotypical observations in SAGA mutants.

Combinatorial depletion analysis to assemble the network architecture of the SAGA and ADA chromatin remodeling complexes

PubMed Central

Lee, Kenneth K; Sardiu, Mihaela E; Swanson, Selene K; Gilmore, Joshua M; Torok, Michael; Grant, Patrick A; Florens, Laurence; Workman, Jerry L; Washburn, Michael P

2011-01-01

Despite the availability of several large-scale proteomics studies aiming to identify protein interactions on a global scale, little is known about how proteins interact and are organized within macromolecular complexes. Here, we describe a technique that consists of a combination of biochemistry approaches, quantitative proteomics and computational methods using wild-type and deletion strains to investigate the organization of proteins within macromolecular protein complexes. We applied this technique to determine the organization of two well-studied complexes, Spt–Ada–Gcn5 histone acetyltransferase (SAGA) and ADA, for which no comprehensive high-resolution structures exist. This approach revealed that SAGA/ADA is composed of five distinct functional modules, which can persist separately. Furthermore, we identified a novel subunit of the ADA complex, termed Ahc2, and characterized Sgf29 as an ADA family protein present in all Gcn5 histone acetyltransferase complexes. Finally, we propose a model for the architecture of the SAGA and ADA complexes, which predicts novel functional associations within the SAGA complex and provides mechanistic insights into phenotypical observations in SAGA mutants. PMID:21734642

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

PubMed

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

2018-01-01

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

Hydrogeologic processes of large-scale tectonomagmatic complexes in Mongolia-southern Siberia and on Mars

USGS Publications Warehouse

Komatsu, G.; Dohm, J.M.; Hare, T.M.

2004-01-01

Large-scale tectonomagmatic complexes are common on Earth and Mars. Many of these complexes are created or at least influenced by mantle processes, including a wide array of plume types ranging from superplumes to mantle plumes. Among the most prominent complexes, the Mongolian plateau on Earth and the Tharsis bulge on Mars share remarkable similarities in terms of large domal uplifted areas, great rift canyon systems, and widespread volcanism on their surfaces. Water has also played an important role in the development of the two complexes. In general, atmospheric and surface water play a bigger role in the development of the present-day Mongolian plateau than for the Tharsis bulge, as evidenced by highly developed drainages and thick accumulation of sediments in the basins of the Baikal rift system. On the Tharsis bulge, however, water appears to have remained as ground ice except during periods of elevated magmatic activity. Glacial and periglacial processes are well documented for the Mongolian plateau and are also reported for parts of the Tharsis bulge. Ice-magma interactions, which are represented by the formation of subice volcanoes in parts of the Mongolian plateau region, have been reported for the Valles Marineris region of Mars. The complexes are also characterized by cataclysmic floods, but their triggering mechanism may differ: mainly ice-dam failures for the Mongolian plateau and outburst of groundwater for the Tharsis bulge, probably by magma-ice interactions, although ice-dam failures within the Valles Marineris region cannot be ruled out as a possible contributor. Comparative studies of the Mongolian plateau and Tharsis bulge provide excellent opportunities for understanding surface manifestations of plume-driven processes on terrestrial planets and how they interact with hydro-cryospheres. ?? 2004 Geological Society of America.

A Parallel Sliding Region Algorithm to Make Agent-Based Modeling Possible for a Large-Scale Simulation: Modeling Hepatitis C Epidemics in Canada.

PubMed

Wong, William W L; Feng, Zeny Z; Thein, Hla-Hla

2016-11-01

Agent-based models (ABMs) are computer simulation models that define interactions among agents and simulate emergent behaviors that arise from the ensemble of local decisions. ABMs have been increasingly used to examine trends in infectious disease epidemiology. However, the main limitation of ABMs is the high computational cost for a large-scale simulation. To improve the computational efficiency for large-scale ABM simulations, we built a parallelizable sliding region algorithm (SRA) for ABM and compared it to a nonparallelizable ABM. We developed a complex agent network and performed two simulations to model hepatitis C epidemics based on the real demographic data from Saskatchewan, Canada. The first simulation used the SRA that processed on each postal code subregion subsequently. The second simulation processed the entire population simultaneously. It was concluded that the parallelizable SRA showed computational time saving with comparable results in a province-wide simulation. Using the same method, SRA can be generalized for performing a country-wide simulation. Thus, this parallel algorithm enables the possibility of using ABM for large-scale simulation with limited computational resources.

Plasmonic nanoparticle lithography: Fast resist-free laser technique for large-scale sub-50 nm hole array fabrication

NASA Astrophysics Data System (ADS)

Pan, Zhenying; Yu, Ye Feng; Valuckas, Vytautas; Yap, Sherry L. K.; Vienne, Guillaume G.; Kuznetsov, Arseniy I.

2018-05-01

Cheap large-scale fabrication of ordered nanostructures is important for multiple applications in photonics and biomedicine including optical filters, solar cells, plasmonic biosensors, and DNA sequencing. Existing methods are either expensive or have strict limitations on the feature size and fabrication complexity. Here, we present a laser-based technique, plasmonic nanoparticle lithography, which is capable of rapid fabrication of large-scale arrays of sub-50 nm holes on various substrates. It is based on near-field enhancement and melting induced under ordered arrays of plasmonic nanoparticles, which are brought into contact or in close proximity to a desired material and acting as optical near-field lenses. The nanoparticles are arranged in ordered patterns on a flexible substrate and can be attached and removed from the patterned sample surface. At optimized laser fluence, the nanohole patterning process does not create any observable changes to the nanoparticles and they have been applied multiple times as reusable near-field masks. This resist-free nanolithography technique provides a simple and cheap solution for large-scale nanofabrication.

Line segment extraction for large scale unorganized point clouds

NASA Astrophysics Data System (ADS)

Lin, Yangbin; Wang, Cheng; Cheng, Jun; Chen, Bili; Jia, Fukai; Chen, Zhonggui; Li, Jonathan

2015-04-01

Line segment detection in images is already a well-investigated topic, although it has received considerably less attention in 3D point clouds. Benefiting from current LiDAR devices, large-scale point clouds are becoming increasingly common. Most human-made objects have flat surfaces. Line segments that occur where pairs of planes intersect give important information regarding the geometric content of point clouds, which is especially useful for automatic building reconstruction and segmentation. This paper proposes a novel method that is capable of accurately extracting plane intersection line segments from large-scale raw scan points. The 3D line-support region, namely, a point set near a straight linear structure, is extracted simultaneously. The 3D line-support region is fitted by our Line-Segment-Half-Planes (LSHP) structure, which provides a geometric constraint for a line segment, making the line segment more reliable and accurate. We demonstrate our method on the point clouds of large-scale, complex, real-world scenes acquired by LiDAR devices. We also demonstrate the application of 3D line-support regions and their LSHP structures on urban scene abstraction.

Assessment of the Suitability of High Resolution Numerical Weather Model Outputs for Hydrological Modelling in Mountainous Cold Regions

NASA Astrophysics Data System (ADS)

Rasouli, K.; Pomeroy, J. W.; Hayashi, M.; Fang, X.; Gutmann, E. D.; Li, Y.

2017-12-01

The hydrology of mountainous cold regions has a large spatial variability that is driven both by climate variability and near-surface process variability associated with complex terrain and patterns of vegetation, soils, and hydrogeology. There is a need to downscale large-scale atmospheric circulations towards the fine scales that cold regions hydrological processes operate at to assess their spatial variability in complex terrain and quantify uncertainties by comparison to field observations. In this research, three high resolution numerical weather prediction models, namely, the Intermediate Complexity Atmosphere Research (ICAR), Weather Research and Forecasting (WRF), and Global Environmental Multiscale (GEM) models are used to represent spatial and temporal patterns of atmospheric conditions appropriate for hydrological modelling. An area covering high mountains and foothills of the Canadian Rockies was selected to assess and compare high resolution ICAR (1 km × 1 km), WRF (4 km × 4 km), and GEM (2.5 km × 2.5 km) model outputs with station-based meteorological measurements. ICAR with very low computational cost was run with different initial and boundary conditions and with finer spatial resolution, which allowed an assessment of modelling uncertainty and scaling that was difficult with WRF. Results show that ICAR, when compared with WRF and GEM, performs very well in precipitation and air temperature modelling in the Canadian Rockies, while all three models show a fair performance in simulating wind and humidity fields. Representation of local-scale atmospheric dynamics leading to realistic fields of temperature and precipitation by ICAR, WRF, and GEM makes these models suitable for high resolution cold regions hydrological predictions in complex terrain, which is a key factor in estimating water security in western Canada.

Large-scale glacitectonic deformation in response to active ice sheet retreat across Dogger Bank (southern central North Sea) during the Last Glacial Maximum

NASA Astrophysics Data System (ADS)

Phillips, Emrys; Cotterill, Carol; Johnson, Kirstin; Crombie, Kirstin; James, Leo; Carr, Simon; Ruiter, Astrid

2018-01-01

High resolution seismic data from the Dogger Bank in the central southern North Sea has revealed that the Dogger Bank Formation records a complex history of sedimentation and penecontemporaneous, large-scale, ice-marginal to proglacial glacitectonic deformation. These processes led to the development of a large thrust-block moraine complex which is buried beneath a thin sequence of Holocene sediments. This buried glacitectonic landsystem comprises a series of elongate, arcuate moraine ridges (200 m up to > 15 km across; over 40-50 km long) separated by low-lying ice marginal to proglacial sedimentary basins and/or meltwater channels, preserving the shape of the margin of this former ice sheet. The moraines are composed of highly deformed (folded and thrust) Dogger Bank Formation with the lower boundary of the deformed sequence (up to 40-50 m thick) being marked by a laterally extensive décollement. The ice-distal parts of the thrust moraine complex are interpreted as a "forward" propagating imbricate thrust stack developed in response to S/SE-directed ice-push. The more complex folding and thrusting within the more ice-proximal parts of the thrust-block moraines record the accretion of thrust slices of highly deformed sediment as the ice repeatedly reoccupied this ice marginal position. Consequently, the internal structure of the Dogger Bank thrust-moraine complexes can be directly related to ice sheet dynamics, recording the former positions of a highly dynamic, oscillating Weichselian ice sheet margin as it retreated northwards at the end of the Last Glacial Maximum.

Decoupling local mechanics from large-scale structure in modular metamaterials.

PubMed

Yang, Nan; Silverberg, Jesse L

2017-04-04

A defining feature of mechanical metamaterials is that their properties are determined by the organization of internal structure instead of the raw fabrication materials. This shift of attention to engineering internal degrees of freedom has coaxed relatively simple materials into exhibiting a wide range of remarkable mechanical properties. For practical applications to be realized, however, this nascent understanding of metamaterial design must be translated into a capacity for engineering large-scale structures with prescribed mechanical functionality. Thus, the challenge is to systematically map desired functionality of large-scale structures backward into a design scheme while using finite parameter domains. Such "inverse design" is often complicated by the deep coupling between large-scale structure and local mechanical function, which limits the available design space. Here, we introduce a design strategy for constructing 1D, 2D, and 3D mechanical metamaterials inspired by modular origami and kirigami. Our approach is to assemble a number of modules into a voxelized large-scale structure, where the module's design has a greater number of mechanical design parameters than the number of constraints imposed by bulk assembly. This inequality allows each voxel in the bulk structure to be uniquely assigned mechanical properties independent from its ability to connect and deform with its neighbors. In studying specific examples of large-scale metamaterial structures we show that a decoupling of global structure from local mechanical function allows for a variety of mechanically and topologically complex designs.

Decoupling local mechanics from large-scale structure in modular metamaterials

NASA Astrophysics Data System (ADS)

Yang, Nan; Silverberg, Jesse L.

2017-04-01

A defining feature of mechanical metamaterials is that their properties are determined by the organization of internal structure instead of the raw fabrication materials. This shift of attention to engineering internal degrees of freedom has coaxed relatively simple materials into exhibiting a wide range of remarkable mechanical properties. For practical applications to be realized, however, this nascent understanding of metamaterial design must be translated into a capacity for engineering large-scale structures with prescribed mechanical functionality. Thus, the challenge is to systematically map desired functionality of large-scale structures backward into a design scheme while using finite parameter domains. Such “inverse design” is often complicated by the deep coupling between large-scale structure and local mechanical function, which limits the available design space. Here, we introduce a design strategy for constructing 1D, 2D, and 3D mechanical metamaterials inspired by modular origami and kirigami. Our approach is to assemble a number of modules into a voxelized large-scale structure, where the module’s design has a greater number of mechanical design parameters than the number of constraints imposed by bulk assembly. This inequality allows each voxel in the bulk structure to be uniquely assigned mechanical properties independent from its ability to connect and deform with its neighbors. In studying specific examples of large-scale metamaterial structures we show that a decoupling of global structure from local mechanical function allows for a variety of mechanically and topologically complex designs.

Commissioning MMS

NASA Technical Reports Server (NTRS)

Wood, Paul; Gramling, Cheryl; Stone, John; Smith, Patrick; Reiter, Jenifer

2016-01-01

This paper discusses commissioning of NASAs Magnetospheric MultiScale (MMS) Mission. The mission includes four identical spacecraft with a large, complex set of instrumentation. The planning for and execution of commissioning for this mission is described. The paper concludes by discussing lessons learned.

Identifying large scale structures at 1 AU using fluctuations and wavelets

NASA Astrophysics Data System (ADS)

Niembro, T.; Lara, A.

2016-12-01

The solar wind (SW) is inhomogeneous and it is dominated for two types of flows: one quasi-stationary and one related to large scale transients (such as coronal mass ejections and co-rotating interaction regions). The SW inhomogeneities can be study as fluctuations characterized by a wide range of length and time scales. We are interested in the study of the characteristic fluctuations caused by large scale transient events. To do so, we define the vector space F with the normalized moving monthly/annual deviations as the orthogonal basis. Then, we compute the norm in this space of the solar wind parameters (velocity, magnetic field, density and temperature) fluctuations using WIND data from August 1992 to August 2015. This norm gives important information about the presence of a large structure disturbance in the solar wind and by applying a wavelet transform to this norm, we are able to determine, without subjectivity, the duration of the compression regions of these large transient structures and, even more, to identify if the structure corresponds to a single or complex (or merged) event. With this method we have automatically detected most of the events identified and published by other authors.

Spatio-temporal precipitation climatology over complex terrain using a censored additive regression model.

PubMed

Stauffer, Reto; Mayr, Georg J; Messner, Jakob W; Umlauf, Nikolaus; Zeileis, Achim

2017-06-15

Flexible spatio-temporal models are widely used to create reliable and accurate estimates for precipitation climatologies. Most models are based on square root transformed monthly or annual means, where a normal distribution seems to be appropriate. This assumption becomes invalid on a daily time scale as the observations involve large fractions of zero observations and are limited to non-negative values. We develop a novel spatio-temporal model to estimate the full climatological distribution of precipitation on a daily time scale over complex terrain using a left-censored normal distribution. The results demonstrate that the new method is able to account for the non-normal distribution and the large fraction of zero observations. The new climatology provides the full climatological distribution on a very high spatial and temporal resolution, and is competitive with, or even outperforms existing methods, even for arbitrary locations.

Voltage collapse in complex power grids

PubMed Central

Simpson-Porco, John W.; Dörfler, Florian; Bullo, Francesco

2016-01-01

A large-scale power grid's ability to transfer energy from producers to consumers is constrained by both the network structure and the nonlinear physics of power flow. Violations of these constraints have been observed to result in voltage collapse blackouts, where nodal voltages slowly decline before precipitously falling. However, methods to test for voltage collapse are dominantly simulation-based, offering little theoretical insight into how grid structure influences stability margins. For a simplified power flow model, here we derive a closed-form condition under which a power network is safe from voltage collapse. The condition combines the complex structure of the network with the reactive power demands of loads to produce a node-by-node measure of grid stress, a prediction of the largest nodal voltage deviation, and an estimate of the distance to collapse. We extensively test our predictions on large-scale systems, highlighting how our condition can be leveraged to increase grid stability margins. PMID:26887284

Toward a theoretical framework for trustworthy cyber sensing

NASA Astrophysics Data System (ADS)

Xu, Shouhuai

2010-04-01

Cyberspace is an indispensable part of the economy and society, but has been "polluted" with many compromised computers that can be abused to launch further attacks against the others. Since it is likely that there always are compromised computers, it is important to be aware of the (dynamic) cyber security-related situation, which is however challenging because cyberspace is an extremely large-scale complex system. Our project aims to investigate a theoretical framework for trustworthy cyber sensing. With the perspective of treating cyberspace as a large-scale complex system, the core question we aim to address is: What would be a competent theoretical (mathematical and algorithmic) framework for designing, analyzing, deploying, managing, and adapting cyber sensor systems so as to provide trustworthy information or input to the higher layer of cyber situation-awareness management, even in the presence of sophisticated malicious attacks against the cyber sensor systems?

Parallel Clustering Algorithm for Large-Scale Biological Data Sets

PubMed Central

Wang, Minchao; Zhang, Wu; Ding, Wang; Dai, Dongbo; Zhang, Huiran; Xie, Hao; Chen, Luonan; Guo, Yike; Xie, Jiang

2014-01-01

Backgrounds Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. Methods Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. Result A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies. PMID:24705246

Measures of Agreement Between Many Raters for Ordinal Classifications

PubMed Central

Nelson, Kerrie P.; Edwards, Don

2015-01-01

Screening and diagnostic procedures often require a physician's subjective interpretation of a patient's test result using an ordered categorical scale to define the patient's disease severity. Due to wide variability observed between physicians’ ratings, many large-scale studies have been conducted to quantify agreement between multiple experts’ ordinal classifications in common diagnostic procedures such as mammography. However, very few statistical approaches are available to assess agreement in these large-scale settings. Existing summary measures of agreement rely on extensions of Cohen's kappa [1 - 5]. These are prone to prevalence and marginal distribution issues, become increasingly complex for more than three experts or are not easily implemented. Here we propose a model-based approach to assess agreement in large-scale studies based upon a framework of ordinal generalized linear mixed models. A summary measure of agreement is proposed for multiple experts assessing the same sample of patients’ test results according to an ordered categorical scale. This measure avoids some of the key flaws associated with Cohen's kappa and its extensions. Simulation studies are conducted to demonstrate the validity of the approach with comparison to commonly used agreement measures. The proposed methods are easily implemented using the software package R and are applied to two large-scale cancer agreement studies. PMID:26095449

Large area sub-micron chemical imaging of magnesium in sea urchin teeth.

PubMed

Masic, Admir; Weaver, James C

2015-03-01

The heterogeneous and site-specific incorporation of inorganic ions can profoundly influence the local mechanical properties of damage tolerant biological composites. Using the sea urchin tooth as a research model, we describe a multi-technique approach to spatially map the distribution of magnesium in this complex multiphase system. Through the combined use of 16-bit backscattered scanning electron microscopy, multi-channel energy dispersive spectroscopy elemental mapping, and diffraction-limited confocal Raman spectroscopy, we demonstrate a new set of high throughput, multi-spectral, high resolution methods for the large scale characterization of mineralized biological materials. In addition, instrument hardware and data collection protocols can be modified such that several of these measurements can be performed on irregularly shaped samples with complex surface geometries and without the need for extensive sample preparation. Using these approaches, in conjunction with whole animal micro-computed tomography studies, we have been able to spatially resolve micron and sub-micron structural features across macroscopic length scales on entire urchin tooth cross-sections and correlate these complex morphological features with local variability in elemental composition. Copyright © 2015 Elsevier Inc. All rights reserved.

Impact of environment on dynamics of exciton complexes in a WS2 monolayer

NASA Astrophysics Data System (ADS)

Jakubczyk, Tomasz; Nogajewski, Karol; Molas, Maciej R.; Bartos, Miroslav; Langbein, Wolfgang; Potemski, Marek; Kasprzak, Jacek

2018-07-01

Scientific curiosity to uncover original optical properties and functionalities of atomically thin semiconductors, stemming from unusual Coulomb interactions in the two-dimensional geometry and multi-valley band structure, drives the research on monolayers of transition metal dichalcogenides (TMDs). While recent works ascertained the exotic energetic schemes of exciton complexes in TMDs, we here infer their unusual coherent dynamics occurring on subpicosecond time scale. The dynamics is largely affected by the disorder landscape on the submicron scale, thus can be uncovered using four-wave mixing in the frequency domain, which enables microscopic investigations and imaging. Focusing on a WS2 monolayer, we observe that exciton coherence is lost primarily due to interaction with phonons and relaxation processes towards optically dark excitonic states. Notably, when temperature is low and disorder weak, excitons large coherence volume results in enhanced oscillator strength, allowing to reach the regime of radiatively limited dephasing. Additionally, we observe long valley coherence for the negatively charged exciton complex. We therefore elucidate the crucial role of exciton environment in the TMDs on its dynamics and show that revealed mechanisms are ubiquitous within this family.

COMPUTATIONAL METHODOLOGIES for REAL-SPACE STRUCTURAL REFINEMENT of LARGE MACROMOLECULAR COMPLEXES

PubMed Central

Goh, Boon Chong; Hadden, Jodi A.; Bernardi, Rafael C.; Singharoy, Abhishek; McGreevy, Ryan; Rudack, Till; Cassidy, C. Keith; Schulten, Klaus

2017-01-01

The rise of the computer as a powerful tool for model building and refinement has revolutionized the field of structure determination for large biomolecular systems. Despite the wide availability of robust experimental methods capable of resolving structural details across a range of spatiotemporal resolutions, computational hybrid methods have the unique ability to integrate the diverse data from multimodal techniques such as X-ray crystallography and electron microscopy into consistent, fully atomistic structures. Here, commonly employed strategies for computational real-space structural refinement are reviewed, and their specific applications are illustrated for several large macromolecular complexes: ribosome, virus capsids, chemosensory array, and photosynthetic chromatophore. The increasingly important role of computational methods in large-scale structural refinement, along with current and future challenges, is discussed. PMID:27145875

Protein docking by the interface structure similarity: how much structure is needed?

PubMed

Sinha, Rohita; Kundrotas, Petras J; Vakser, Ilya A

2012-01-01

The increasing availability of co-crystallized protein-protein complexes provides an opportunity to use template-based modeling for protein-protein docking. Structure alignment techniques are useful in detection of remote target-template similarities. The size of the structure involved in the alignment is important for the success in modeling. This paper describes a systematic large-scale study to find the optimal definition/size of the interfaces for the structure alignment-based docking applications. The results showed that structural areas corresponding to the cutoff values <12 Å across the interface inadequately represent structural details of the interfaces. With the increase of the cutoff beyond 12 Å, the success rate for the benchmark set of 99 protein complexes, did not increase significantly for higher accuracy models, and decreased for lower-accuracy models. The 12 Å cutoff was optimal in our interface alignment-based docking, and a likely best choice for the large-scale (e.g., on the scale of the entire genome) applications to protein interaction networks. The results provide guidelines for the docking approaches, including high-throughput applications to modeled structures.

Simulation of electron energy loss spectra of nanomaterials with linear-scaling density functional theory

DOE PAGES

Tait, E. W.; Ratcliff, L. E.; Payne, M. C.; ...

2016-04-20

Experimental techniques for electron energy loss spectroscopy (EELS) combine high energy resolution with high spatial resolution. They are therefore powerful tools for investigating the local electronic structure of complex systems such as nanostructures, interfaces and even individual defects. Interpretation of experimental electron energy loss spectra is often challenging and can require theoretical modelling of candidate structures, which themselves may be large and complex, beyond the capabilities of traditional cubic-scaling density functional theory. In this work, we present functionality to compute electron energy loss spectra within the onetep linear-scaling density functional theory code. We first demonstrate that simulated spectra agree withmore » those computed using conventional plane wave pseudopotential methods to a high degree of precision. The ability of onetep to tackle large problems is then exploited to investigate convergence of spectra with respect to supercell size. As a result, we apply the novel functionality to a study of the electron energy loss spectra of defects on the (1 0 1) surface of an anatase slab and determine concentrations of defects which might be experimentally detectable.« less

Kernel methods for large-scale genomic data analysis

PubMed Central

Xing, Eric P.; Schaid, Daniel J.

2015-01-01

Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today’s explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion. PMID:25053743

VLSI (Very Large Scale Integrated Circuits) Device Reliability Models.

DTIC Science & Technology

1984-12-01

CIRCUIT COMPLEXITY FAILURE RATES FOR... A- 40 MOS SSI/MSI DEVICES IN FAILURE PER 106 HOURS TABLE 5.1.2.5-19: C1 AND C2 CIRCUIT COMPLEXITY FAILURE RATES FOR...A- 40 MOS SSI/MSI DEVICES IN FAILURE PER 106 HOURS TABLE 5.1.2.5-19: Cl AND C2 CIRCUIT COMPLEXITY FAILURE RATES FOR... A-41 LINEAR DEVICES IN...19 National Semiconductor 20 Nitron 21 Raytheon 22 Sprague 23 Synertek 24 Teledyne Crystalonics 25 TRW Semiconductor 26 Zilog The following companies

Evolution of the Busbar Structure in Large-Scale Aluminum Reduction Cells

NASA Astrophysics Data System (ADS)

Zhang, Hongliang; Liang, Jinding; Li, Jie; Sun, Kena; Xiao, Jin

2017-02-01

Studies of magnetic field and magneto-hydro-dynamics are regarded as the foundation for the development of large-scale aluminum reduction cells, while due to the direct relationship between the busbar configuration and magnetic compensation, the actual key content is the configuration of the busbar. As the line current has been increased from 160 kA to 600 kA, the configuration of the busbar was becoming more complex. To summarize and explore the evolution of busbar configuration in aluminum reduction cells, this paper has reviewed various representative large-scale pre-baked aluminum reduction cell busbar structures, such as end-to-end potlines, side-by-side potlines and external compensation current. The advantages and disadvantages in the magnetic distribution or technical specifications have also been introduced separately, especially for the configurations of the mainstream 400-kA potlines. In the end, the development trends of the bus structure configuration were prospected, based on the recent successful applications of super-scale cell busbar structures in China (500-600 kA).

A 100,000 Scale Factor Radar Range.

PubMed

Blanche, Pierre-Alexandre; Neifeld, Mark; Peyghambarian, Nasser

2017-12-19

The radar cross section of an object is an important electromagnetic property that is often measured in anechoic chambers. However, for very large and complex structures such as ships or sea and land clutters, this common approach is not practical. The use of computer simulations is also not viable since it would take many years of computational time to model and predict the radar characteristics of such large objects. We have now devised a new scaling technique to overcome these difficulties, and make accurate measurements of the radar cross section of large items. In this article we demonstrate that by reducing the scale of the model by a factor 100,000, and using near infrared wavelength, the radar cross section can be determined in a tabletop setup. The accuracy of the method is compared to simulations, and an example of measurement is provided on a 1 mm highly detailed model of a ship. The advantages of this scaling approach is its versatility, and the possibility to perform fast, convenient, and inexpensive measurements.

Environment and host as large-scale controls of ectomycorrhizal fungi.

PubMed

van der Linde, Sietse; Suz, Laura M; Orme, C David L; Cox, Filipa; Andreae, Henning; Asi, Endla; Atkinson, Bonnie; Benham, Sue; Carroll, Christopher; Cools, Nathalie; De Vos, Bruno; Dietrich, Hans-Peter; Eichhorn, Johannes; Gehrmann, Joachim; Grebenc, Tine; Gweon, Hyun S; Hansen, Karin; Jacob, Frank; Kristöfel, Ferdinand; Lech, Paweł; Manninger, Miklós; Martin, Jan; Meesenburg, Henning; Merilä, Päivi; Nicolas, Manuel; Pavlenda, Pavel; Rautio, Pasi; Schaub, Marcus; Schröck, Hans-Werner; Seidling, Walter; Šrámek, Vít; Thimonier, Anne; Thomsen, Iben Margrete; Titeux, Hugues; Vanguelova, Elena; Verstraeten, Arne; Vesterdal, Lars; Waldner, Peter; Wijk, Sture; Zhang, Yuxin; Žlindra, Daniel; Bidartondo, Martin I

2018-06-06

Explaining the large-scale diversity of soil organisms that drive biogeochemical processes-and their responses to environmental change-is critical. However, identifying consistent drivers of belowground diversity and abundance for some soil organisms at large spatial scales remains problematic. Here we investigate a major guild, the ectomycorrhizal fungi, across European forests at a spatial scale and resolution that is-to our knowledge-unprecedented, to explore key biotic and abiotic predictors of ectomycorrhizal diversity and to identify dominant responses and thresholds for change across complex environmental gradients. We show the effect of 38 host, environment, climate and geographical variables on ectomycorrhizal diversity, and define thresholds of community change for key variables. We quantify host specificity and reveal plasticity in functional traits involved in soil foraging across gradients. We conclude that environmental and host factors explain most of the variation in ectomycorrhizal diversity, that the environmental thresholds used as major ecosystem assessment tools need adjustment and that the importance of belowground specificity and plasticity has previously been underappreciated.

Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation.

PubMed

Pesaran, Bijan; Vinck, Martin; Einevoll, Gaute T; Sirota, Anton; Fries, Pascal; Siegel, Markus; Truccolo, Wilson; Schroeder, Charles E; Srinivasan, Ramesh

2018-06-25

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (electroencephalograms, magnetoencephalograms, electrocorticograms and local field potentials) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide recommendations for interpreting the data using forward and inverse models. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.

Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.

PubMed

de Groot, Reinoud; Lüthi, Joel; Lindsay, Helen; Holtackers, René; Pelkmans, Lucas

2018-01-23

High-content imaging using automated microscopy and computer vision allows multivariate profiling of single-cell phenotypes. Here, we present methods for the application of the CISPR-Cas9 system in large-scale, image-based, gene perturbation experiments. We show that CRISPR-Cas9-mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image-based phenotyping. We developed a pipeline to construct a large-scale arrayed library of 2,281 sequence-verified CRISPR-Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine-learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in-depth characterization of gene perturbation effects. This approach enables genome-scale image-based multivariate gene perturbation profiling using CRISPR-Cas9. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

Enabling Large-Scale Biomedical Analysis in the Cloud

PubMed Central

Lin, Ying-Chih; Yu, Chin-Sheng; Lin, Yen-Jen

2013-01-01

Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable. PMID:24288665

REVIEWS OF TOPICAL PROBLEMS: Large-scale star formation in galaxies

NASA Astrophysics Data System (ADS)

Efremov, Yurii N.; Chernin, Artur D.

2003-01-01

A brief review is given of the history of modern ideas on the ongoing star formation process in the gaseous disks of galaxies. Recent studies demonstrate the key role of the interplay between the gas self-gravitation and its turbulent motions. The large scale supersonic gas flows create structures of enhanced density which then give rise to the gravitational condensation of gas into stars and star clusters. Formation of star clusters, associations and complexes is considered, as well as the possibility of isolated star formation. Special emphasis is placed on star formation under the action of ram pressure.

bigSCale: an analytical framework for big-scale single-cell data.

PubMed

Iacono, Giovanni; Mereu, Elisabetta; Guillaumet-Adkins, Amy; Corominas, Roser; Cuscó, Ivon; Rodríguez-Esteban, Gustavo; Gut, Marta; Pérez-Jurado, Luis Alberto; Gut, Ivo; Heyn, Holger

2018-06-01

Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin ( Reln )-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets. © 2018 Iacono et al.; Published by Cold Spring Harbor Laboratory Press.

Collaboratively Architecting a Scalable and Adaptable Petascale Infrastructure to Support Transdisciplinary Scientific Research for the Australian Earth and Environmental Sciences

NASA Astrophysics Data System (ADS)

Wyborn, L. A.; Evans, B. J. K.; Pugh, T.; Lescinsky, D. T.; Foster, C.; Uhlherr, A.

2014-12-01

The National Computational Infrastructure (NCI) at the Australian National University (ANU) is a partnership between CSIRO, ANU, Bureau of Meteorology (BoM) and Geoscience Australia. Recent investments in a 1.2 PFlop Supercomputer (Raijin), ~ 20 PB data storage using Lustre filesystems and a 3000 core high performance cloud have created a hybrid platform for higher performance computing and data-intensive science to enable large scale earth and climate systems modelling and analysis. There are > 3000 users actively logging in and > 600 projects on the NCI system. Efficiently scaling and adapting data and software systems to petascale infrastructures requires the collaborative development of an architecture that is designed, programmed and operated to enable users to interactively invoke different forms of in-situ computation over complex and large scale data collections. NCI makes available major and long tail data collections from both the government and research sectors based on six themes: 1) weather, climate and earth system science model simulations, 2) marine and earth observations, 3) geosciences, 4) terrestrial ecosystems, 5) water and hydrology and 6) astronomy, bio and social. Collectively they span the lithosphere, crust, biosphere, hydrosphere, troposphere, and stratosphere. Collections are the operational form for data management and access. Similar data types from individual custodians are managed cohesively. Use of international standards for discovery and interoperability allow complex interactions within and between the collections. This design facilitates a transdisciplinary approach to research and enables a shift from small scale, 'stove-piped' science efforts to large scale, collaborative systems science. This new and complex infrastructure requires a move to shared, globally trusted software frameworks that can be maintained and updated. Workflow engines become essential and need to integrate provenance, versioning, traceability, repeatability and publication. There are also human resource challenges as highly skilled HPC/HPD specialists, specialist programmers, and data scientists are required whose skills can support scaling to the new paradigm of effective and efficient data-intensive earth science analytics on petascale, and soon to be exascale systems.

On the context-dependent scaling of consumer feeding rates.

PubMed

Barrios-O'Neill, Daniel; Kelly, Ruth; Dick, Jaimie T A; Ricciardi, Anthony; MacIsaac, Hugh J; Emmerson, Mark C

2016-06-01

The stability of consumer-resource systems can depend on the form of feeding interactions (i.e. functional responses). Size-based models predict interactions - and thus stability - based on consumer-resource size ratios. However, little is known about how interaction contexts (e.g. simple or complex habitats) might alter scaling relationships. Addressing this, we experimentally measured interactions between a large size range of aquatic predators (4-6400 mg over 1347 feeding trials) and an invasive prey that transitions among habitats: from the water column (3D interactions) to simple and complex benthic substrates (2D interactions). Simple and complex substrates mediated successive reductions in capture rates - particularly around the unimodal optimum - and promoted prey population stability in model simulations. Many real consumer-resource systems transition between 2D and 3D interactions, and along complexity gradients. Thus, Context-Dependent Scaling (CDS) of feeding interactions could represent an unrecognised aspect of food webs, and quantifying the extent of CDS might enhance predictive ecology. © The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

Persistent model order reduction for complex dynamical systems using smooth orthogonal decomposition

NASA Astrophysics Data System (ADS)

Ilbeigi, Shahab; Chelidze, David

2017-11-01

Full-scale complex dynamic models are not effective for parametric studies due to the inherent constraints on available computational power and storage resources. A persistent reduced order model (ROM) that is robust, stable, and provides high-fidelity simulations for a relatively wide range of parameters and operating conditions can provide a solution to this problem. The fidelity of a new framework for persistent model order reduction of large and complex dynamical systems is investigated. The framework is validated using several numerical examples including a large linear system and two complex nonlinear systems with material and geometrical nonlinearities. While the framework is used for identifying the robust subspaces obtained from both proper and smooth orthogonal decompositions (POD and SOD, respectively), the results show that SOD outperforms POD in terms of stability, accuracy, and robustness.

Epidemic outbreaks in complex heterogeneous networks

NASA Astrophysics Data System (ADS)

Moreno, Y.; Pastor-Satorras, R.; Vespignani, A.

2002-04-01

We present a detailed analytical and numerical study for the spreading of infections with acquired immunity in complex population networks. We show that the large connectivity fluctuations usually found in these networks strengthen considerably the incidence of epidemic outbreaks. Scale-free networks, which are characterized by diverging connectivity fluctuations in the limit of a very large number of nodes, exhibit the lack of an epidemic threshold and always show a finite fraction of infected individuals. This particular weakness, observed also in models without immunity, defines a new epidemiological framework characterized by a highly heterogeneous response of the system to the introduction of infected individuals with different connectivity. The understanding of epidemics in complex networks might deliver new insights in the spread of information and diseases in biological and technological networks that often appear to be characterized by complex heterogeneous architectures.

Patterns and Variation in Benthic Biodiversity in a Large Marine Ecosystem.

PubMed

Piacenza, Susan E; Barner, Allison K; Benkwitt, Cassandra E; Boersma, Kate S; Cerny-Chipman, Elizabeth B; Ingeman, Kurt E; Kindinger, Tye L; Lee, Jonathan D; Lindsley, Amy J; Reimer, Jessica N; Rowe, Jennifer C; Shen, Chenchen; Thompson, Kevin A; Thurman, Lindsey L; Heppell, Selina S

2015-01-01

While there is a persistent inverse relationship between latitude and species diversity across many taxa and ecosystems, deviations from this norm offer an opportunity to understand the conditions that contribute to large-scale diversity patterns. Marine systems, in particular, provide such an opportunity, as marine diversity does not always follow a strict latitudinal gradient, perhaps because several hypothesized drivers of the latitudinal diversity gradient are uncorrelated in marine systems. We used a large scale public monitoring dataset collected over an eight year period to examine benthic marine faunal biodiversity patterns for the continental shelf (55-183 m depth) and slope habitats (184-1280 m depth) off the US West Coast (47°20'N-32°40'N). We specifically asked whether marine biodiversity followed a strict latitudinal gradient, and if these latitudinal patterns varied across depth, in different benthic substrates, and over ecological time scales. Further, we subdivided our study area into three smaller regions to test whether coast-wide patterns of biodiversity held at regional scales, where local oceanographic processes tend to influence community structure and function. Overall, we found complex patterns of biodiversity on both the coast-wide and regional scales that differed by taxonomic group. Importantly, marine biodiversity was not always highest at low latitudes. We found that latitude, depth, substrate, and year were all important descriptors of fish and invertebrate diversity. Invertebrate richness and taxonomic diversity were highest at high latitudes and in deeper waters. Fish richness also increased with latitude, but exhibited a hump-shaped relationship with depth, increasing with depth up to the continental shelf break, ~200 m depth, and then decreasing in deeper waters. We found relationships between fish taxonomic and functional diversity and latitude, depth, substrate, and time at the regional scale, but not at the coast-wide scale, suggesting that coast-wide patterns can obscure important correlates at smaller scales. Our study provides insight into complex diversity patterns of the deep water soft substrate benthic ecosystems off the US West Coast.

Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra; Rahmede, Christoph

2015-09-01

In quantum gravity, several approaches have been proposed until now for the quantum description of discrete geometries. These theoretical frameworks include loop quantum gravity, causal dynamical triangulations, causal sets, quantum graphity, and energetic spin networks. Most of these approaches describe discrete spaces as homogeneous network manifolds. Here we define Complex Quantum Network Manifolds (CQNM) describing the evolution of quantum network states, and constructed from growing simplicial complexes of dimension . We show that in d = 2 CQNM are homogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomogeneities of degrees like most complex networks. From the self-organized evolution of CQNM quantum statistics emerge spontaneously. Here we define the generalized degrees associated with the -faces of the -dimensional CQNMs, and we show that the statistics of these generalized degrees can either follow Fermi-Dirac, Boltzmann or Bose-Einstein distributions depending on the dimension of the -faces.

Habitat complexity influences fine scale hydrological processes and the incidence of stormwater runoff in managed urban ecosystems.

PubMed

Ossola, Alessandro; Hahs, Amy Kristin; Livesley, Stephen John

2015-08-15

Urban ecosystems have traditionally been considered to be pervious features of our cities. Their hydrological properties have largely been investigated at the landscape scale and in comparison with other urban land use types. However, hydrological properties can vary at smaller scales depending upon changes in soil, surface litter and vegetation components. Management practices can directly and indirectly affect each of these components and the overall habitat complexity, ultimately affecting hydrological processes. This study aims to investigate the influence that habitat components and habitat complexity have upon key hydrological processes and the implications for urban habitat management. Using a network of urban parks and remnant nature reserves in Melbourne, Australia, replicate plots representing three types of habitat complexity were established: low-complexity parks, high-complexity parks, and high-complexity remnants. Saturated soil hydraulic conductivity in low-complexity parks was an order of magnitude lower than that measured in the more complex habitat types, due to fewer soil macropores. Conversely, soil water holding capacity in low-complexity parks was significantly higher compared to the two more complex habitat types. Low-complexity parks would generate runoff during modest precipitation events, whereas high-complexity parks and remnants would be able to absorb the vast majority of rainfall events without generating runoff. Litter layers on the soil surface would absorb most of precipitation events in high-complexity parks and high-complexity remnants. To minimize the incidence of stormwater runoff from urban ecosystems, land managers could incrementally increase the complexity of habitat patches, by increasing canopy density and volume, preserving surface litter and maintaining soil macropore structure. Copyright © 2015 Elsevier Ltd. All rights reserved.

Advanced computer architecture for large-scale real-time applications.

DOT National Transportation Integrated Search

1973-04-01

Air traffic control automation is identified as a crucial problem which provides a complex, real-time computer application environment. A novel computer architecture in the form of a pipeline associative processor is conceived to achieve greater perf...

Enzymatic regeneration of adenosine triphosphate cofactor

NASA Technical Reports Server (NTRS)

Marshall, D. L.

1974-01-01

Regenerating adenosine triphosphate (ATP) from adenosine diphosphate (ADP) by enzymatic process which utilizes carbamyl phosphate as phosphoryl donor is technique used to regenerate expensive cofactors. Process allows complex enzymatic reactions to be considered as candidates for large-scale continuous processes.

Diazo compounds in continuous-flow technology.

PubMed

Müller, Simon T R; Wirth, Thomas

2015-01-01

Diazo compounds are very versatile reagents in organic chemistry and meet the challenge of selective assembly of structurally complex molecules. Their leaving group is dinitrogen; therefore, they are very clean and atom-efficient reagents. However, diazo compounds are potentially explosive and extremely difficult to handle on an industrial scale. In this review, it is discussed how continuous flow technology can help to make these powerful reagents accessible on large scale. Microstructured devices can improve heat transfer greatly and help with the handling of dangerous reagents safely. The in situ formation and subsequent consumption of diazo compounds are discussed along with advances in handling diazomethane and ethyl diazoacetate. The potential large-scale applications of a given methodology is emphasized. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

PubMed Central

Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

2016-01-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

Biogas utilization

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

Moser, M.A.

1996-01-01

Options for successfully using biogas depend on project scale. Almost all biogas from anaerobic digesters must first go through a gas handling system that pressurizes, meters, and filters the biogas. Additional treatment, including hydrogen sulfide-mercaptan scrubbing, gas drying, and carbon dioxide removal may be necessary for specialized uses, but these are complex and expensive processes. Thus, they can be justified only for large-scale projects that require high-quality biogas. Small-scale projects (less than 65 cfm) generally use biogas (as produced) as a boiler fuel or for fueling internal combustion engine-generators to produce electricity. If engines or boilers are selected properly, theremore » should be no need to remove hydrogen sulfide. Small-scale combustion turbines, steam turbines, and fuel cells are not used because of their technical complexity and high capital cost. Biogas cleanup to pipeline or transportation fuel specifications is very costly, and energy economics preclude this level of treatment.« less

Active Brownian rods

NASA Astrophysics Data System (ADS)

Peruani, Fernando

2016-11-01

Bacteria, chemically-driven rods, and motility assays are examples of active (i.e. self-propelled) Brownian rods (ABR). The physics of ABR, despite their ubiquity in experimental systems, remains still poorly understood. Here, we review the large-scale properties of collections of ABR moving in a dissipative medium. We address the problem by presenting three different models, of decreasing complexity, which we refer to as model I, II, and III, respectively. Comparing model I, II, and III, we disentangle the role of activity and interactions. In particular, we learn that in two dimensions by ignoring steric or volume exclusion effects, large-scale nematic order seems to be possible, while steric interactions prevent the formation of orientational order at large scales. The macroscopic behavior of ABR results from the interplay between active stresses and local alignment. ABR exhibit, depending on where we locate ourselves in parameter space, a zoology of macroscopic patterns that ranges from polar and nematic bands to dynamic aggregates.

An evaluation of multi-probe locality sensitive hashing for computing similarities over web-scale query logs

PubMed Central

2018-01-01

Many modern applications of AI such as web search, mobile browsing, image processing, and natural language processing rely on finding similar items from a large database of complex objects. Due to the very large scale of data involved (e.g., users’ queries from commercial search engines), computing such near or nearest neighbors is a non-trivial task, as the computational cost grows significantly with the number of items. To address this challenge, we adopt Locality Sensitive Hashing (a.k.a, LSH) methods and evaluate four variants in a distributed computing environment (specifically, Hadoop). We identify several optimizations which improve performance, suitable for deployment in very large scale settings. The experimental results demonstrate our variants of LSH achieve the robust performance with better recall compared with “vanilla” LSH, even when using the same amount of space. PMID:29346410

Recent "Ground Testing" Experiences in the National Full-Scale Aerodynamics Complex

NASA Technical Reports Server (NTRS)

Zell, Peter; Stich, Phil; Sverdrup, Jacobs; George, M. W. (Technical Monitor)

2002-01-01

The large test sections of the National Full-scale Aerodynamics Complex (NFAC) wind tunnels provide ideal controlled wind environments to test ground-based objects and vehicles. Though this facility was designed and provisioned primarily for aeronautical testing requirements, several experiments have been designed to utilize existing model mount structures to support "non-flying" systems. This presentation will discuss some of the ground-based testing capabilities of the facility and provide examples of groundbased tests conducted in the facility to date. It will also address some future work envisioned and solicit input from the SATA membership on ways to improve the service that NASA makes available to customers.

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.

CORALINA: a universal method for the generation of gRNA libraries for CRISPR-based screening.

PubMed

Köferle, Anna; Worf, Karolina; Breunig, Christopher; Baumann, Valentin; Herrero, Javier; Wiesbeck, Maximilian; Hutter, Lukas H; Götz, Magdalena; Fuchs, Christiane; Beck, Stephan; Stricker, Stefan H

2016-11-14

The bacterial CRISPR system is fast becoming the most popular genetic and epigenetic engineering tool due to its universal applicability and adaptability. The desire to deploy CRISPR-based methods in a large variety of species and contexts has created an urgent need for the development of easy, time- and cost-effective methods enabling large-scale screening approaches. Here we describe CORALINA (comprehensive gRNA library generation through controlled nuclease activity), a method for the generation of comprehensive gRNA libraries for CRISPR-based screens. CORALINA gRNA libraries can be derived from any source of DNA without the need of complex oligonucleotide synthesis. We show the utility of CORALINA for human and mouse genomic DNA, its reproducibility in covering the most relevant genomic features including regulatory, coding and non-coding sequences and confirm the functionality of CORALINA generated gRNAs. The simplicity and cost-effectiveness make CORALINA suitable for any experimental system. The unprecedented sequence complexities obtainable with CORALINA libraries are a necessary pre-requisite for less biased large scale genomic and epigenomic screens.

The PREP pipeline: standardized preprocessing for large-scale EEG analysis.

PubMed

Bigdely-Shamlo, Nima; Mullen, Tim; Kothe, Christian; Su, Kyung-Min; Robbins, Kay A

2015-01-01

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.

Stable isotope probing to study functional components of complex microbial ecosystems.

PubMed

Mazard, Sophie; Schäfer, Hendrik

2014-01-01

This protocol presents a method of dissecting the DNA or RNA of key organisms involved in a specific biochemical process within a complex ecosystem. Stable isotope probing (SIP) allows the labelling and separation of nucleic acids from community members that are involved in important biochemical transformations, yet are often not the most numerically abundant members of a community. This pure culture-independent technique circumvents limitations of traditional microbial isolation techniques or data mining from large-scale whole-community metagenomic studies to tease out the identities and genomic repertoires of microorganisms participating in biological nutrient cycles. SIP experiments can be applied to virtually any ecosystem and biochemical pathway under investigation provided a suitable stable isotope substrate is available. This versatile methodology allows a wide range of analyses to be performed, from fatty-acid analyses, community structure and ecology studies, and targeted metagenomics involving nucleic acid sequencing. SIP experiments provide an effective alternative to large-scale whole-community metagenomic studies by specifically targeting the organisms or biochemical transformations of interest, thereby reducing the sequencing effort and time-consuming bioinformatics analyses of large datasets.

Energy scaling and reduction in controlling complex networks

PubMed Central

Chen, Yu-Zhong; Wang, Le-Zhi; Wang, Wen-Xu; Lai, Ying-Cheng

2016-01-01

Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks. PMID:27152220

An immersed boundary method for direct and large eddy simulation of stratified flows in complex geometry

NASA Astrophysics Data System (ADS)

Rapaka, Narsimha R.; Sarkar, Sutanu

2016-10-01

A sharp-interface Immersed Boundary Method (IBM) is developed to simulate density-stratified turbulent flows in complex geometry using a Cartesian grid. The basic numerical scheme corresponds to a central second-order finite difference method, third-order Runge-Kutta integration in time for the advective terms and an alternating direction implicit (ADI) scheme for the viscous and diffusive terms. The solver developed here allows for both direct numerical simulation (DNS) and large eddy simulation (LES) approaches. Methods to enhance the mass conservation and numerical stability of the solver to simulate high Reynolds number flows are discussed. Convergence with second-order accuracy is demonstrated in flow past a cylinder. The solver is validated against past laboratory and numerical results in flow past a sphere, and in channel flow with and without stratification. Since topographically generated internal waves are believed to result in a substantial fraction of turbulent mixing in the ocean, we are motivated to examine oscillating tidal flow over a triangular obstacle to assess the ability of this computational model to represent nonlinear internal waves and turbulence. Results in laboratory-scale (order of few meters) simulations show that the wave energy flux, mean flow properties and turbulent kinetic energy agree well with our previous results obtained using a body-fitted grid (BFG). The deviation of IBM results from BFG results is found to increase with increasing nonlinearity in the wave field that is associated with either increasing steepness of the topography relative to the internal wave propagation angle or with the amplitude of the oscillatory forcing. LES is performed on a large scale ridge, of the order of few kilometers in length, that has the same geometrical shape and same non-dimensional values for the governing flow and environmental parameters as the laboratory-scale topography, but significantly larger Reynolds number. A non-linear drag law is utilized in the large-scale application to parameterize turbulent losses due to bottom friction at high Reynolds number. The large scale problem exhibits qualitatively similar behavior to the laboratory scale problem with some differences: slightly larger intensification of the boundary flow and somewhat higher non-dimensional values for the energy fluxed away by the internal wave field. The phasing of wave breaking and turbulence exhibits little difference between small-scale and large-scale obstacles as long as the important non-dimensional parameters are kept the same. We conclude that IBM is a viable approach to the simulation of internal waves and turbulence in high Reynolds number stratified flows over topography.

IP-Based Video Modem Extender Requirements

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

Pierson, L G; Boorman, T M; Howe, R E

2003-12-16

Visualization is one of the keys to understanding large complex data sets such as those generated by the large computing resources purchased and developed by the Advanced Simulation and Computing program (aka ASCI). In order to be convenient to researchers, visualization data must be distributed to offices and large complex visualization theaters. Currently, local distribution of the visual data is accomplished by distance limited modems and RGB switches that simply do not scale to hundreds of users across the local, metropolitan, and WAN distances without incurring large costs in fiber plant installation and maintenance. Wide Area application over the DOEmore » Complex is infeasible using these limited distance RGB extenders. On the other hand, Internet Protocols (IP) over Ethernet is a scalable well-proven technology that can distribute large volumes of data over these distances. Visual data has been distributed at lower resolutions over IP in industrial applications. This document describes requirements of the ASCI program in visual signal distribution for the purpose of identifying industrial partners willing to develop products to meet ASCI's needs.« less

Synchronization and Causality Across Time-scales: Complex Dynamics and Extremes in El Niño/Southern Oscillation

NASA Astrophysics Data System (ADS)

Jajcay, N.; Kravtsov, S.; Tsonis, A.; Palus, M.

2017-12-01

A better understanding of dynamics in complex systems, such as the Earth's climate is one of the key challenges for contemporary science and society. A large amount of experimental data requires new mathematical and computational approaches. Natural complex systems vary on many temporal and spatial scales, often exhibiting recurring patterns and quasi-oscillatory phenomena. The statistical inference of causal interactions and synchronization between dynamical phenomena evolving on different temporal scales is of vital importance for better understanding of underlying mechanisms and a key for modeling and prediction of such systems. This study introduces and applies information theory diagnostics to phase and amplitude time series of different wavelet components of the observed data that characterizes El Niño. A suite of significant interactions between processes operating on different time scales was detected, and intermittent synchronization among different time scales has been associated with the extreme El Niño events. The mechanisms of these nonlinear interactions were further studied in conceptual low-order and state-of-the-art dynamical, as well as statistical climate models. Observed and simulated interactions exhibit substantial discrepancies, whose understanding may be the key to an improved prediction. Moreover, the statistical framework which we apply here is suitable for direct usage of inferring cross-scale interactions in nonlinear time series from complex systems such as the terrestrial magnetosphere, solar-terrestrial interactions, seismic activity or even human brain dynamics.

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

PubMed Central

Kaltenbacher, Barbara; Hasenauer, Jan

2017-01-01

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351

Systems metabolic engineering of microorganisms to achieve large-scale production of flavonoid scaffolds.

PubMed

Wu, Junjun; Du, Guocheng; Zhou, Jingwen; Chen, Jian

2014-10-20

Flavonoids possess pharmaceutical potential due to their health-promoting activities. The complex structures of these products make extraction from plants difficult, and chemical synthesis is limited because of the use of many toxic solvents. Microbial production offers an alternate way to produce these compounds on an industrial scale in a more economical and environment-friendly manner. However, at present microbial production has been achieved only on a laboratory scale and improvements and scale-up of these processes remain challenging. Naringenin and pinocembrin, which are flavonoid scaffolds and precursors for most of the flavonoids, are the model molecules that are key to solving the current issues restricting industrial production of these chemicals. The emergence of systems metabolic engineering, which combines systems biology with synthetic biology and evolutionary engineering at the systems level, offers new perspectives on strain and process optimization. In this review, current challenges in large-scale fermentation processes involving flavonoid scaffolds and the strategies and tools of systems metabolic engineering used to overcome these challenges are summarized. This will offer insights into overcoming the limitations and challenges of large-scale microbial production of these important pharmaceutical compounds. Copyright © 2014 Elsevier B.V. All rights reserved.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

PubMed Central

Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav

2015-01-01

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa. PMID:26327290

Modeling Costal Zone Responses to Sea-Level Rise Using MoCCS: A Model of Complex Coastal System

NASA Astrophysics Data System (ADS)

Dai, H.; Niedoroda, A. W.; Ye, M.; Saha, B.; Donoghue, J. F.; Kish, S.

2011-12-01

Large-scale coastal systems consisting of several morphological components (e.g. beach, surf zone, dune, inlet, shoreface, and estuary) can be expected to exhibit complex and interacting responses to changes in the rate of sea level rise and storm climate. We have developed a numerical model of complex coastal systems (MoCCS), derived from earlier morphdynamic models, to represent the large-scale time-averaged physical processes that shape each component and govern the component interactions. These control the ongoing evolution of the barrier islands, beach and dune erosion, shoal formation and sand withdrawal at tidal inlets, depth changes in the bay, and changes in storm flooding. The model has been used to study the response of an idealized coastal system with physical characteristics and storm climatology similar to Santa Rosa Island on the Florida Panhandle coast. Five SLR scenarios have been used, covering the range of recently published projections for the next century. Each scenario has been input with a constant and then a time-varying storm climate. The results indicate that substantial increases in the rate of beach erosion are largely due to increased sand transfer to inlet shoals with increased rates of sea level rise. The barrier island undergoes cycles of dune destruction and regrowth, leading to sand deposition. This largely maintains island freeboard but is progressively less effective in offsetting bayside inundation and marsh habitat loss at accelerated sea level rise rates.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.

PubMed

Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav

2015-01-01

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close "neighborhood" of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

Reaction dynamics analysis of a reconstituted Escherichia coli protein translation system by computational modeling

PubMed Central

Matsuura, Tomoaki; Tanimura, Naoki; Hosoda, Kazufumi; Yomo, Tetsuya; Shimizu, Yoshihiro

2017-01-01

To elucidate the dynamic features of a biologically relevant large-scale reaction network, we constructed a computational model of minimal protein synthesis consisting of 241 components and 968 reactions that synthesize the Met-Gly-Gly (MGG) peptide based on an Escherichia coli-based reconstituted in vitro protein synthesis system. We performed a simulation using parameters collected primarily from the literature and found that the rate of MGG peptide synthesis becomes nearly constant in minutes, thus achieving a steady state similar to experimental observations. In addition, concentration changes to 70% of the components, including intermediates, reached a plateau in a few minutes. However, the concentration change of each component exhibits several temporal plateaus, or a quasi-stationary state (QSS), before reaching the final plateau. To understand these complex dynamics, we focused on whether the components reached a QSS, mapped the arrangement of components in a QSS in the entire reaction network structure, and investigated time-dependent changes. We found that components in a QSS form clusters that grow over time but not in a linear fashion, and that this process involves the collapse and regrowth of clusters before the formation of a final large single cluster. These observations might commonly occur in other large-scale biological reaction networks. This developed analysis might be useful for understanding large-scale biological reactions by visualizing complex dynamics, thereby extracting the characteristics of the reaction network, including phase transitions. PMID:28167777

Tests of dynamic Lagrangian eddy viscosity models in Large Eddy Simulations of flow over three-dimensional bluff bodies

NASA Astrophysics Data System (ADS)

Tseng, Yu-Heng; Meneveau, Charles; Parlange, Marc B.

2004-11-01

Large Eddy Simulations (LES) of atmospheric boundary-layer air movement in urban environments are especially challenging due to complex ground topography. Typically in such applications, fairly coarse grids must be used where the subgrid-scale (SGS) model is expected to play a crucial role. A LES code using pseudo-spectral discretization in horizontal planes and second-order differencing in the vertical is implemented in conjunction with the immersed boundary method to incorporate complex ground topography, with the classic equilibrium log-law boundary condition in the new-wall region, and with several versions of the eddy-viscosity model: (1) the constant-coefficient Smagorinsky model, (2) the dynamic, scale-invariant Lagrangian model, and (3) the dynamic, scale-dependent Lagrangian model. Other planar-averaged type dynamic models are not suitable because spatial averaging is not possible without directions of statistical homogeneity. These SGS models are tested in LES of flow around a square cylinder and of flow over surface-mounted cubes. Effects on the mean flow are documented and found not to be major. Dynamic Lagrangian models give a physically more realistic SGS viscosity field, and in general, the scale-dependent Lagrangian model produces larger Smagorinsky coefficient than the scale-invariant one, leading to reduced distributions of resolved rms velocities especially in the boundary layers near the bluff bodies.

Modal interactions between a large-wavelength inclined interface and small-wavelength multimode perturbations in a Richtmyer-Meshkov instability

NASA Astrophysics Data System (ADS)

McFarland, Jacob A.; Reilly, David; Black, Wolfgang; Greenough, Jeffrey A.; Ranjan, Devesh

2015-07-01

The interaction of a small-wavelength multimodal perturbation with a large-wavelength inclined interface perturbation is investigated for the reshocked Richtmyer-Meshkov instability using three-dimensional simulations. The ares code, developed at Lawrence Livermore National Laboratory, was used for these simulations and a detailed comparison of simulation results and experiments performed at the Georgia Tech Shock Tube facility is presented first for code validation. Simulation results are presented for four cases that vary in large-wavelength perturbation amplitude and the presence of secondary small-wavelength multimode perturbations. Previously developed measures of mixing and turbulence quantities are presented that highlight the large variation in perturbation length scales created by the inclined interface and the multimode complex perturbation. Measures are developed for entrainment, and turbulence anisotropy that help to identify the effects of and competition between each perturbations type. It is shown through multiple measures that before reshock the flow processes a distinct memory of the initial conditions that is present in both large-scale-driven entrainment measures and small-scale-driven mixing measures. After reshock the flow develops to a turbulentlike state that retains a memory of high-amplitude but not low-amplitude large-wavelength perturbations. It is also shown that the high-amplitude large-wavelength perturbation is capable of producing small-scale mixing and turbulent features similar to the small-wavelength multimode perturbations.

Factors influencing efficient structure of fuel and energy complex

NASA Astrophysics Data System (ADS)

Sidorova, N. G.; Novikova, S. A.

2017-10-01

The development of the Russian fuel-energy complex is a priority for the national economic policy, and the Far East is a link between Russia and the Asia-Pacific region. Large-scale engineering of numerous resources of the Far East will force industrial development, increase living standard and strengthen Russia’s position in the global energy market. So, revealing the factors which influence rational structure of the fuel-energy complex is very urgent nowadays. With the use of depth analysis of development tendencies of the complex and its problems the authors show ways of its efficiency improvement.

An innovative large scale integration of silicon nanowire-based field effect transistors

NASA Astrophysics Data System (ADS)

Legallais, M.; Nguyen, T. T. T.; Mouis, M.; Salem, B.; Robin, E.; Chenevier, P.; Ternon, C.

2018-05-01

Since the early 2000s, silicon nanowire field effect transistors are emerging as ultrasensitive biosensors while offering label-free, portable and rapid detection. Nevertheless, their large scale production remains an ongoing challenge due to time consuming, complex and costly technology. In order to bypass these issues, we report here on the first integration of silicon nanowire networks, called nanonet, into long channel field effect transistors using standard microelectronic process. A special attention is paid to the silicidation of the contacts which involved a large number of SiNWs. The electrical characteristics of these FETs constituted by randomly oriented silicon nanowires are also studied. Compatible integration on the back-end of CMOS readout and promising electrical performances open new opportunities for sensing applications.

Particle physics and polyedra proximity calculation for hazard simulations in large-scale industrial plants

NASA Astrophysics Data System (ADS)

Plebe, Alice; Grasso, Giorgio

2016-12-01

This paper describes a system developed for the simulation of flames inside an open-source 3D computer graphic software, Blender, with the aim of analyzing in virtual reality scenarios of hazards in large-scale industrial plants. The advantages of Blender are of rendering at high resolution the very complex structure of large industrial plants, and of embedding a physical engine based on smoothed particle hydrodynamics. This particle system is used to evolve a simulated fire. The interaction of this fire with the components of the plant is computed using polyhedron separation distance, adopting a Voronoi-based strategy that optimizes the number of feature distance computations. Results on a real oil and gas refining industry are presented.

Complexity as a Factor of Quality and Cost in Large Scale Software Development.

DTIC Science & Technology

1979-12-01

allocating testing resources." [69 69I V. THE ROLE OF COMPLEXITY IN RESOURCE ESTIMATION AND ALLOCATION A. GENERAL It can be argued that blame for the...and allocation of testing resource by - identifying independent substructures and - identifying heavily used logic paths. 2. Setting a Design Threshold... RESOURCE ESTIMATION -------- 70 1. New Dynamic Field ------------------------- 70 2. Quality and Testing ----------------------- 71 3. Programming Units of

Potential utilization of the NASA/George C. Marshall Space Flight Center in earthquake engineering research

NASA Technical Reports Server (NTRS)

Scholl, R. E. (Editor)

1979-01-01

Earthquake engineering research capabilities of the National Aeronautics and Space Administration (NASA) facilities at George C. Marshall Space Flight Center (MSFC), Alabama, were evaluated. The results indicate that the NASA/MSFC facilities and supporting capabilities offer unique opportunities for conducting earthquake engineering research. Specific features that are particularly attractive for large scale static and dynamic testing of natural and man-made structures include the following: large physical dimensions of buildings and test bays; high loading capacity; wide range and large number of test equipment and instrumentation devices; multichannel data acquisition and processing systems; technical expertise for conducting large-scale static and dynamic testing; sophisticated techniques for systems dynamics analysis, simulation, and control; and capability for managing large-size and technologically complex programs. Potential uses of the facilities for near and long term test programs to supplement current earthquake research activities are suggested.

A new large-scale manufacturing platform for complex biopharmaceuticals.

PubMed

Vogel, Jens H; Nguyen, Huong; Giovannini, Roberto; Ignowski, Jolene; Garger, Steve; Salgotra, Anil; Tom, Jennifer

2012-12-01

Complex biopharmaceuticals, such as recombinant blood coagulation factors, are addressing critical medical needs and represent a growing multibillion-dollar market. For commercial manufacturing of such, sometimes inherently unstable, molecules it is important to minimize product residence time in non-ideal milieu in order to obtain acceptable yields and consistently high product quality. Continuous perfusion cell culture allows minimization of residence time in the bioreactor, but also brings unique challenges in product recovery, which requires innovative solutions. In order to maximize yield, process efficiency, facility and equipment utilization, we have developed, scaled-up and successfully implemented a new integrated manufacturing platform in commercial scale. This platform consists of a (semi-)continuous cell separation process based on a disposable flow path and integrated with the upstream perfusion operation, followed by membrane chromatography on large-scale adsorber capsules in rapid cycling mode. Implementation of the platform at commercial scale for a new product candidate led to a yield improvement of 40% compared to the conventional process technology, while product quality has been shown to be more consistently high. Over 1,000,000 L of cell culture harvest have been processed with 100% success rate to date, demonstrating the robustness of the new platform process in GMP manufacturing. While membrane chromatography is well established for polishing in flow-through mode, this is its first commercial-scale application for bind/elute chromatography in the biopharmaceutical industry and demonstrates its potential in particular for manufacturing of potent, low-dose biopharmaceuticals. Copyright © 2012 Wiley Periodicals, Inc.

Path changing methods applied to the 4-D guidance of STOL aircraft.

DOT National Transportation Integrated Search

1971-11-01

Prior to the advent of large-scale commercial STOL service, some challenging navigation and guidance problems must be solved. Proposed terminal area operations may require that these aircraft be capable of accurately flying complex flight paths, and ...

Commissioning MMS: Challenges and Lessons Learned

NASA Technical Reports Server (NTRS)

Wood, Paul; Gramling, Cheryl; Reiter, Jennifer; Smith, Patrick; Stone, John

2016-01-01

This paper discusses commissioning of NASA's Magnetospheric MultiScale (MMS) Mission. The mission includes four identical spacecraft with a large, complex set of instrumentation. The planning for and execution of commissioning for this mission is described. The paper concludes by discussing lessons learned.

Framework for Smart Electronic Health Record-Linked Predictive Models to Optimize Care for Complex Digestive Diseases

DTIC Science & Technology

2014-07-01

mucos"x1; N Acquired Abnormality 4.7350 93696 76 0.85771...4. Roden DM, Pulley JM, Basford MA, et al. Development of a large- scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol...large healthcare system which incorporated clinical information from a 20-hospital setting (both aca- demic and community hospitals) of University of

Self-affinity in the dengue fever time series

NASA Astrophysics Data System (ADS)

Azevedo, S. M.; Saba, H.; Miranda, J. G. V.; Filho, A. S. Nascimento; Moret, M. A.

2016-06-01

Dengue is a complex public health problem that is common in tropical and subtropical regions. This disease has risen substantially in the last three decades, and the physical symptoms depict the self-affine behavior of the occurrences of reported dengue cases in Bahia, Brazil. This study uses detrended fluctuation analysis (DFA) to verify the scale behavior in a time series of dengue cases and to evaluate the long-range correlations that are characterized by the power law α exponent for different cities in Bahia, Brazil. The scaling exponent (α) presents different long-range correlations, i.e. uncorrelated, anti-persistent, persistent and diffusive behaviors. The long-range correlations highlight the complex behavior of the time series of this disease. The findings show that there are two distinct types of scale behavior. In the first behavior, the time series presents a persistent α exponent for a one-month period. For large periods, the time series signal approaches subdiffusive behavior. The hypothesis of the long-range correlations in the time series of the occurrences of reported dengue cases was validated. The observed self-affinity is useful as a forecasting tool for future periods through extrapolation of the α exponent behavior. This complex system has a higher predictability in a relatively short time (approximately one month), and it suggests a new tool in epidemiological control strategies. However, predictions for large periods using DFA are hidden by the subdiffusive behavior.

Modeling the Effects of Light and Sucrose on In Vitro Propagated Plants: A Multiscale System Analysis Using Artificial Intelligence Technology

PubMed Central

Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P.

2014-01-01

Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m−2 s−1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. PMID:24465829

Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.

PubMed

Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P

2014-01-01

Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1). Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.

Large/Complex Antenna Performance Validation for Spaceborne Radar/Radiometeric Instruments

NASA Technical Reports Server (NTRS)

Focardi, Paolo; Harrell, Jefferson; Vacchione, Joseph

2013-01-01

Over the past decade, Earth observing missions which employ spaceborne combined radar & radiometric instruments have been developed and implemented. These instruments include the use of large and complex deployable antennas whose radiation characteristics need to be accurately determined over 4 pisteradians. Given the size and complexity of these antennas, the performance of the flight units cannot be readily measured. In addition, the radiation performance is impacted by the presence of the instrument's service platform which cannot easily be included in any measurement campaign. In order to meet the system performance knowledge requirements, a two pronged approach has been employed. The first is to use modeling tools to characterize the system and the second is to build a scale model of the system and use RF measurements to validate the results of the modeling tools. This paper demonstrates the resulting level of agreement between scale model and numerical modeling for two recent missions: (1) the earlier Aquarius instrument currently in Earth orbit and (2) the upcoming Soil Moisture Active Passive (SMAP) mission. The results from two modeling approaches, Ansoft's High Frequency Structure Simulator (HFSS) and TICRA's General RF Applications Software Package (GRASP), were compared with measurements of approximately 1/10th scale models of the Aquarius and SMAP systems. Generally good agreement was found between the three methods but each approach had its shortcomings as will be detailed in this paper.

Progressive Mid-latitude Afforestation: Local and Remote Climate Impacts in the Framework of Two Coupled Earth System Models

NASA Astrophysics Data System (ADS)

Lague, Marysa

Vegetation influences the atmosphere in complex and non-linear ways, such that large-scale changes in vegetation cover can drive changes in climate on both local and global scales. Large-scale land surface changes have been shown to introduce excess energy to one hemisphere, causing a shift in atmospheric circulation on a global scale. However, past work has not quantified how the climate response scales with the area of vegetation. Here, we systematically evaluate the response of climate to linearly increasing the area of forest cover over the northern mid-latitudes. We show that the magnitude of afforestation of the northern mid-latitudes determines the climate response in a non-linear fashion, and identify a threshold in vegetation-induced cloud feedbacks - a concept not previously addressed by large-scale vegetation manipulation experiments. Small increases in tree cover drive compensating cloud feedbacks, while latent heat fluxes reach a threshold after sufficiently large increases in tree cover, causing the troposphere to warm and dry, subsequently reducing cloud cover. Increased absorption of solar radiation at the surface is driven by both surface albedo changes and cloud feedbacks. We identify how vegetation-induced changes in cloud cover further feedback on changes in the global energy balance. We also show how atmospheric cross-equatorial energy transport changes as the area of afforestation is incrementally increased (a relationship which has not previously been demonstrated). This work demonstrates that while some climate effects (such as energy transport) of large scale mid-latitude afforestation scale roughly linearly across a wide range of afforestation areas, others (such as the local partitioning of the surface energy budget) are non-linear, and sensitive to the particular magnitude of mid-latitude forcing. Our results highlight the importance of considering both local and remote climate responses to large-scale vegetation change, and explore the scaling relationship between changes in vegetation cover and the resulting climate impacts.

Hydropower and sustainability: resilience and vulnerability in China's powersheds.

PubMed

McNally, Amy; Magee, Darrin; Wolf, Aaron T

2009-07-01

Large dams represent a whole complex of social, economic and ecological processes, perhaps more than any other large infrastructure project. Today, countries with rapidly developing economies are constructing new dams to provide energy and flood control to growing populations in riparian and distant urban communities. If the system is lacking institutional capacity to absorb these physical and institutional changes there is potential for conflict, thereby threatening human security. In this paper, we propose analyzing sustainability (political, socioeconomic, and ecological) in terms of resilience versus vulnerability, framed within the spatial abstraction of a powershed. The powershed framework facilitates multi-scalar and transboundary analysis while remaining focused on the questions of resilience and vulnerability relating to hydropower dams. Focusing on examples from China, this paper describes the complex nature of dams using the sustainability and powershed frameworks. We then analyze the roles of institutions in China to understand the relationships between power, human security and the socio-ecological system. To inform the study of conflicts over dams China is a particularly useful case study because we can examine what happens at the international, national and local scales. The powershed perspective allows us to examine resilience and vulnerability across political boundaries from a dynamic, process-defined analytical scale while remaining focused on a host of questions relating to hydro-development that invoke drivers and impacts on national and sub-national scales. The ability to disaggregate the affects of hydropower dam construction from political boundaries allows for a deeper analysis of resilience and vulnerability. From our analysis we find that reforms in China's hydropower sector since 1996 have been motivated by the need to create stability at the national scale rather than resilient solutions to China's growing demand for energy and water resource control at the local and international scales. Some measures that improved economic development through the market economy and a combination of dam construction and institutional reform may indeed improve hydro-political resilience at a single scale. However, if China does address large-scale hydropower construction's potential to create multi-scale geopolitical tensions, they may be vulnerable to conflict - though not necessarily violent - in domestic and international political arenas. We conclude with a look toward a resilient basin institution for the Nu/Salween River, the site of a proposed large-scale hydropower development effort in China and Myanmar.

Turbulence and entrainment length scales in large wind farms.

PubMed

Andersen, Søren J; Sørensen, Jens N; Mikkelsen, Robert F

2017-04-13

A number of large wind farms are modelled using large eddy simulations to elucidate the entrainment process. A reference simulation without turbines and three farm simulations with different degrees of imposed atmospheric turbulence are presented. The entrainment process is assessed using proper orthogonal decomposition, which is employed to detect the largest and most energetic coherent turbulent structures. The dominant length scales responsible for the entrainment process are shown to grow further into the wind farm, but to be limited in extent by the streamwise turbine spacing, which could be taken into account when developing farm layouts. The self-organized motion or large coherent structures also yield high correlations between the power productions of consecutive turbines, which can be exploited through dynamic farm control.This article is part of the themed issue 'Wind energy in complex terrains'. © 2017 The Author(s).

Turbulence and entrainment length scales in large wind farms

PubMed Central

2017-01-01

A number of large wind farms are modelled using large eddy simulations to elucidate the entrainment process. A reference simulation without turbines and three farm simulations with different degrees of imposed atmospheric turbulence are presented. The entrainment process is assessed using proper orthogonal decomposition, which is employed to detect the largest and most energetic coherent turbulent structures. The dominant length scales responsible for the entrainment process are shown to grow further into the wind farm, but to be limited in extent by the streamwise turbine spacing, which could be taken into account when developing farm layouts. The self-organized motion or large coherent structures also yield high correlations between the power productions of consecutive turbines, which can be exploited through dynamic farm control. This article is part of the themed issue ‘Wind energy in complex terrains’. PMID:28265028

Using complexity theory to develop a student-directed interprofessional learning activity for 1220 healthcare students.

PubMed

Jorm, Christine; Nisbet, Gillian; Roberts, Chris; Gordon, Christopher; Gentilcore, Stacey; Chen, Timothy F

2016-08-08

More and better interprofessional practice is predicated to be necessary to deliver good care to the patients of the future. However, universities struggle to create authentic learning activities that enable students to experience the dynamic interprofessional interactions common in healthcare and that can accommodate large interprofessional student cohorts. We investigated a large-scale mandatory interprofessional learning (IPL) activity for health professional students designed to promote social learning. A mixed methods research approach determined feasibility, acceptability and the extent to which student IPL outcomes were met. We developed an IPL activity founded in complexity theory to prepare students for future practice by engaging them in a self-directed (self-organised) learning activity with a diverse team, whose assessable products would be emergent creations. Complicated but authentic clinical cases (n = 12) were developed to challenge student teams (n = 5 or 6). Assessment consisted of a written management plan (academically marked) and a five-minute video (peer marked) designed to assess creative collaboration as well as provide evidence of integrated collective knowledge; the cohesive patient-centred management plan. All students (including the disciplines of diagnostic radiology, exercise physiology, medicine, nursing, occupational therapy, pharmacy, physiotherapy and speech pathology), completed all tasks successfully. Of the 26 % of students who completed the evaluation survey, 70 % agreed or strongly agreed that the IPL activity was worthwhile, and 87 % agreed or strongly agreed that their case study was relevant. Thematic analysis found overarching themes of engagement and collaboration-in-action suggesting that the IPL activity enabled students to achieve the intended learning objectives. Students recognised the contribution of others and described negotiation, collaboration and creation of new collective knowledge after working together on the complicated patient case studies. The novel video assessment was challenging to many students and contextual issues limited engagement for some disciplines. We demonstrated the feasibility and acceptability of a large scale IPL activity where design of cases, format and assessment tasks was founded in complexity theory. This theoretically based design enabled students to achieve complex IPL outcomes relevant to future practice. Future research could establish the psychometric properties of assessments of student performance in large-scale IPL events.

Planning and executing complex large-scale exercises.

PubMed

McCormick, Lisa C; Hites, Lisle; Wakelee, Jessica F; Rucks, Andrew C; Ginter, Peter M

2014-01-01

Increasingly, public health departments are designing and engaging in complex operations-based full-scale exercises to test multiple public health preparedness response functions. The Department of Homeland Security's Homeland Security Exercise and Evaluation Program (HSEEP) supplies benchmark guidelines that provide a framework for both the design and the evaluation of drills and exercises; however, the HSEEP framework does not seem to have been designed to manage the development and evaluation of multiple, operations-based, parallel exercises combined into 1 complex large-scale event. Lessons learned from the planning of the Mississippi State Department of Health Emergency Support Function--8 involvement in National Level Exercise 2011 were used to develop an expanded exercise planning model that is HSEEP compliant but accounts for increased exercise complexity and is more functional for public health. The Expanded HSEEP (E-HSEEP) model was developed through changes in the HSEEP exercise planning process in areas of Exercise Plan, Controller/Evaluator Handbook, Evaluation Plan, and After Action Report and Improvement Plan development. The E-HSEEP model was tested and refined during the planning and evaluation of Mississippi's State-level Emergency Support Function-8 exercises in 2012 and 2013. As a result of using the E-HSEEP model, Mississippi State Department of Health was able to capture strengths, lessons learned, and areas for improvement, and identify microlevel issues that may have been missed using the traditional HSEEP framework. The South Central Preparedness and Emergency Response Learning Center is working to create an Excel-based E-HSEEP tool that will allow practice partners to build a database to track corrective actions and conduct many different types of analyses and comparisons.

Molecular Gas toward the Gemini OB1 Molecular Cloud Complex. II. CO Outflow Candidates with Possible WISE Associations

NASA Astrophysics Data System (ADS)

Li, Yingjie; Li, Fa-Cheng; Xu, Ye; Wang, Chen; Du, Xin-Yu; Yang, Wenjin; Yang, Ji

2018-03-01

We present a large-scale survey of CO outflows in the Gem OB1 molecular cloud complex and its surroundings, using the Purple Mountain Observatory Delingha 13.7 m telescope. A total of 198 outflow candidates were identified over a large area (∼58.5 square degrees), of which 193 are newly detected. Approximately 68% (134/198) are associated with the Gem OB1 molecular cloud complex, including clouds GGMC 1, GGMC 2, BFS 52, GGMC 3, and GGMC 4. Other regions studied are: the Local arm (Local Lynds, West Front), Swallow, Horn, and Remote cloud. Outflow candidates in GGMC 1, BFS 52, and Swallow are mainly located at ring-like or filamentary structures. To avoid excessive uncertainty in distant regions (≳3.8 kpc), we only estimated the physical parameters for clouds in the Gem OB1 molecular cloud complex and in the Local arm. In those clouds, the total kinetic energy and the energy injection rate of the identified outflow candidates are ≲1% and ≲3% of the turbulent energy and the turbulent dissipation rate of each cloud, indicating that the identified outflow candidates cannot provide enough energy to balance turbulence of their host cloud at the scale of the entire cloud (several to dozens of parsecs). The gravitational binding energy of each cloud is ≳135 times the total kinetic energy of the identified outflow candidates within the corresponding cloud, indicating that the identified outflow candidates cannot cause major disruptions to the integrity of their host cloud at the scale of the entire cloud.

A Lagrangian subgrid-scale model with dynamic estimation of Lagrangian time scale for large eddy simulation of complex flows

NASA Astrophysics Data System (ADS)

Verma, Aman; Mahesh, Krishnan

2012-08-01

The dynamic Lagrangian averaging approach for the dynamic Smagorinsky model for large eddy simulation is extended to an unstructured grid framework and applied to complex flows. The Lagrangian time scale is dynamically computed from the solution and does not need any adjustable parameter. The time scale used in the standard Lagrangian model contains an adjustable parameter θ. The dynamic time scale is computed based on a "surrogate-correlation" of the Germano-identity error (GIE). Also, a simple material derivative relation is used to approximate GIE at different events along a pathline instead of Lagrangian tracking or multi-linear interpolation. Previously, the time scale for homogeneous flows was computed by averaging along directions of homogeneity. The present work proposes modifications for inhomogeneous flows. This development allows the Lagrangian averaged dynamic model to be applied to inhomogeneous flows without any adjustable parameter. The proposed model is applied to LES of turbulent channel flow on unstructured zonal grids at various Reynolds numbers. Improvement is observed when compared to other averaging procedures for the dynamic Smagorinsky model, especially at coarse resolutions. The model is also applied to flow over a cylinder at two Reynolds numbers and good agreement with previous computations and experiments is obtained. Noticeable improvement is obtained using the proposed model over the standard Lagrangian model. The improvement is attributed to a physically consistent Lagrangian time scale. The model also shows good performance when applied to flow past a marine propeller in an off-design condition; it regularizes the eddy viscosity and adjusts locally to the dominant flow features.

Walking the Filament of Feasibility: Global Optimization of Highly-Constrained, Multi-Modal Interplanetary Trajectories Using a Novel Stochastic Search Technique

NASA Technical Reports Server (NTRS)

Englander, Arnold C.; Englander, Jacob A.

2017-01-01

Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.

An Illustrative Guide to the Minerva Framework

NASA Astrophysics Data System (ADS)

Flom, Erik; Leonard, Patrick; Hoeffel, Udo; Kwak, Sehyun; Pavone, Andrea; Svensson, Jakob; Krychowiak, Maciej; Wendelstein 7-X Team Collaboration

2017-10-01

Modern phsyics experiments require tracking and modelling data and their associated uncertainties on a large scale, as well as the combined implementation of multiple independent data streams for sophisticated modelling and analysis. The Minerva Framework offers a centralized, user-friendly method of large-scale physics modelling and scientific inference. Currently used by teams at multiple large-scale fusion experiments including the Joint European Torus (JET) and Wendelstein 7-X (W7-X), the Minerva framework provides a forward-model friendly architecture for developing and implementing models for large-scale experiments. One aspect of the framework involves so-called data sources, which are nodes in the graphical model. These nodes are supplied with engineering and physics parameters. When end-user level code calls a node, it is checked network-wide against its dependent nodes for changes since its last implementation and returns version-specific data. Here, a filterscope data node is used as an illustrative example of the Minerva Framework's data management structure and its further application to Bayesian modelling of complex systems. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under Grant Agreement No. 633053.

Large-Scale Dynamics of the Magnetospheric Boundary: Comparisons between Global MHD Simulation Results and ISTP Observations

NASA Technical Reports Server (NTRS)

Berchem, J.; Raeder, J.; Ashour-Abdalla, M.; Frank, L. A.; Paterson, W. R.; Ackerson, K. L.; Kokubun, S.; Yamamoto, T.; Lepping, R. P.

1998-01-01

Understanding the large-scale dynamics of the magnetospheric boundary is an important step towards achieving the ISTP mission's broad objective of assessing the global transport of plasma and energy through the geospace environment. Our approach is based on three-dimensional global magnetohydrodynamic (MHD) simulations of the solar wind-magnetosphere- ionosphere system, and consists of using interplanetary magnetic field (IMF) and plasma parameters measured by solar wind monitors upstream of the bow shock as input to the simulations for predicting the large-scale dynamics of the magnetospheric boundary. The validity of these predictions is tested by comparing local data streams with time series measured by downstream spacecraft crossing the magnetospheric boundary. In this paper, we review results from several case studies which confirm that our MHD model reproduces very well the large-scale motion of the magnetospheric boundary. The first case illustrates the complexity of the magnetic field topology that can occur at the dayside magnetospheric boundary for periods of northward IMF with strong Bx and By components. The second comparison reviewed combines dynamic and topological aspects in an investigation of the evolution of the distant tail at 200 R(sub E) from the Earth.

A large-scale forest fragmentation experiment: the Stability of Altered Forest Ecosystems Project.

PubMed

Ewers, Robert M; Didham, Raphael K; Fahrig, Lenore; Ferraz, Gonçalo; Hector, Andy; Holt, Robert D; Kapos, Valerie; Reynolds, Glen; Sinun, Waidi; Snaddon, Jake L; Turner, Edgar C

2011-11-27

Opportunities to conduct large-scale field experiments are rare, but provide a unique opportunity to reveal the complex processes that operate within natural ecosystems. Here, we review the design of existing, large-scale forest fragmentation experiments. Based on this review, we develop a design for the Stability of Altered Forest Ecosystems (SAFE) Project, a new forest fragmentation experiment to be located in the lowland tropical forests of Borneo (Sabah, Malaysia). The SAFE Project represents an advance on existing experiments in that it: (i) allows discrimination of the effects of landscape-level forest cover from patch-level processes; (ii) is designed to facilitate the unification of a wide range of data types on ecological patterns and processes that operate over a wide range of spatial scales; (iii) has greater replication than existing experiments; (iv) incorporates an experimental manipulation of riparian corridors; and (v) embeds the experimentally fragmented landscape within a wider gradient of land-use intensity than do existing projects. The SAFE Project represents an opportunity for ecologists across disciplines to participate in a large initiative designed to generate a broad understanding of the ecological impacts of tropical forest modification.

A large-scale forest fragmentation experiment: the Stability of Altered Forest Ecosystems Project

PubMed Central

Ewers, Robert M.; Didham, Raphael K.; Fahrig, Lenore; Ferraz, Gonçalo; Hector, Andy; Holt, Robert D.; Kapos, Valerie; Reynolds, Glen; Sinun, Waidi; Snaddon, Jake L.; Turner, Edgar C.

2011-01-01

Opportunities to conduct large-scale field experiments are rare, but provide a unique opportunity to reveal the complex processes that operate within natural ecosystems. Here, we review the design of existing, large-scale forest fragmentation experiments. Based on this review, we develop a design for the Stability of Altered Forest Ecosystems (SAFE) Project, a new forest fragmentation experiment to be located in the lowland tropical forests of Borneo (Sabah, Malaysia). The SAFE Project represents an advance on existing experiments in that it: (i) allows discrimination of the effects of landscape-level forest cover from patch-level processes; (ii) is designed to facilitate the unification of a wide range of data types on ecological patterns and processes that operate over a wide range of spatial scales; (iii) has greater replication than existing experiments; (iv) incorporates an experimental manipulation of riparian corridors; and (v) embeds the experimentally fragmented landscape within a wider gradient of land-use intensity than do existing projects. The SAFE Project represents an opportunity for ecologists across disciplines to participate in a large initiative designed to generate a broad understanding of the ecological impacts of tropical forest modification. PMID:22006969

Highly Efficient Large-Scale Lentiviral Vector Concentration by Tandem Tangential Flow Filtration

PubMed Central

Cooper, Aaron R.; Patel, Sanjeet; Senadheera, Shantha; Plath, Kathrin; Kohn, Donald B.; Hollis, Roger P.

2014-01-01

Large-scale lentiviral vector (LV) concentration can be inefficient and time consuming, often involving multiple rounds of filtration and centrifugation. This report describes a simpler method using two tangential flow filtration (TFF) steps to concentrate liter-scale volumes of LV supernatant, achieving in excess of 2000-fold concentration in less than 3 hours with very high recovery (>97%). Large volumes of LV supernatant can be produced easily through the use of multi-layer flasks, each having 1720 cm2 surface area and producing ~560 mL of supernatant per flask. Combining the use of such flasks and TFF greatly simplifies large-scale production of LV. As a demonstration, the method is used to produce a very high titer LV (>1010 TU/mL) and transduce primary human CD34+ hematopoietic stem/progenitor cells at high final vector concentrations with no overt toxicity. A complex LV (STEMCCA) for induced pluripotent stem cell generation is also concentrated from low initial titer and used to transduce and reprogram primary human fibroblasts with no overt toxicity. Additionally, a generalized and simple multiplexed real- time PCR assay is described for lentiviral vector titer and copy number determination. PMID:21784103

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

PubMed Central

Hero, Alfred O.; Rajaratnam, Bala

2015-01-01

When can reliable inference be drawn in fue “Big Data” context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than fue number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for “Big Data”. Sample complexity however has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address fuis gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where fue variable dimension is fixed and fue sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa cale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables fua t are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. we demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks. PMID:27087700

Applying Adaptive Variables in Computerised Adaptive Testing

ERIC Educational Resources Information Center

Triantafillou, Evangelos; Georgiadou, Elissavet; Economides, Anastasios A.

2007-01-01

Current research in computerised adaptive testing (CAT) focuses on applications, in small and large scale, that address self assessment, training, employment, teacher professional development for schools, industry, military, assessment of non-cognitive skills, etc. Dynamic item generation tools and automated scoring of complex, constructed…

Modeling Watershed Mercury Response to Atmospheric Loadings: Response Time and Challenges

EPA Science Inventory

The relationship between sources of mercury to watersheds and its fate in surface waters is invariably complex. Large scale monitoring studies, such as the METAALICUS project, have advanced current understanding of the links between atmospheric deposition of mercury and accumulat...

Managing large-scale workflow execution from resource provisioning to provenance tracking: The CyberShake example

USGS Publications Warehouse

Deelman, E.; Callaghan, S.; Field, E.; Francoeur, H.; Graves, R.; Gupta, N.; Gupta, V.; Jordan, T.H.; Kesselman, C.; Maechling, P.; Mehringer, J.; Mehta, G.; Okaya, D.; Vahi, K.; Zhao, L.

2006-01-01

This paper discusses the process of building an environment where large-scale, complex, scientific analysis can be scheduled onto a heterogeneous collection of computational and storage resources. The example application is the Southern California Earthquake Center (SCEC) CyberShake project, an analysis designed to compute probabilistic seismic hazard curves for sites in the Los Angeles area. We explain which software tools were used to build to the system, describe their functionality and interactions. We show the results of running the CyberShake analysis that included over 250,000 jobs using resources available through SCEC and the TeraGrid. ?? 2006 IEEE.

An adaptive response surface method for crashworthiness optimization

NASA Astrophysics Data System (ADS)

Shi, Lei; Yang, Ren-Jye; Zhu, Ping

2013-11-01

Response surface-based design optimization has been commonly used for optimizing large-scale design problems in the automotive industry. However, most response surface models are built by a limited number of design points without considering data uncertainty. In addition, the selection of a response surface in the literature is often arbitrary. This article uses a Bayesian metric to systematically select the best available response surface among several candidates in a library while considering data uncertainty. An adaptive, efficient response surface strategy, which minimizes the number of computationally intensive simulations, was developed for design optimization of large-scale complex problems. This methodology was demonstrated by a crashworthiness optimization example.

Distributed intrusion detection system based on grid security model

NASA Astrophysics Data System (ADS)

Su, Jie; Liu, Yahui

2008-03-01

Grid computing has developed rapidly with the development of network technology and it can solve the problem of large-scale complex computing by sharing large-scale computing resource. In grid environment, we can realize a distributed and load balance intrusion detection system. This paper first discusses the security mechanism in grid computing and the function of PKI/CA in the grid security system, then gives the application of grid computing character in the distributed intrusion detection system (IDS) based on Artificial Immune System. Finally, it gives a distributed intrusion detection system based on grid security system that can reduce the processing delay and assure the detection rates.

Web-based Visual Analytics for Extreme Scale Climate Science

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

Steed, Chad A; Evans, Katherine J; Harney, John F

In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, ineffi- cient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via newmore » visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.« less

Molecular Precision at Micrometer Length Scales: Hierarchical Assembly of DNA-Protein Nanostructures.

PubMed

Schiffels, Daniel; Szalai, Veronika A; Liddle, J Alexander

2017-07-25

Robust self-assembly across length scales is a ubiquitous feature of biological systems but remains challenging for synthetic structures. Taking a cue from biology-where disparate molecules work together to produce large, functional assemblies-we demonstrate how to engineer microscale structures with nanoscale features: Our self-assembly approach begins by using DNA polymerase to controllably create double-stranded DNA (dsDNA) sections on a single-stranded template. The single-stranded DNA (ssDNA) sections are then folded into a mechanically flexible skeleton by the origami method. This process simultaneously shapes the structure at the nanoscale and directs the large-scale geometry. The DNA skeleton guides the assembly of RecA protein filaments, which provides rigidity at the micrometer scale. We use our modular design strategy to assemble tetrahedral, rectangular, and linear shapes of defined dimensions. This method enables the robust construction of complex assemblies, greatly extending the range of DNA-based self-assembly methods.

Best geoscience approach to complex systems in environment

NASA Astrophysics Data System (ADS)

Mezemate, Yacine; Tchiguirinskaia, Ioulia; Schertzer, Daniel

2017-04-01

The environment is a social issue that continues to grow in importance. Its complexity, both cross-disciplinary and multi-scale, has given rise to a large number of scientific and technological locks, that complex systems approaches can solve. Significant challenges must met to achieve the understanding of the environmental complexes systems. There study should proceed in some steps in which the use of data and models is crucial: - Exploration, observation and basic data acquisition - Identification of correlations, patterns, and mechanisms - Modelling - Model validation, implementation and prediction - Construction of a theory Since the e-learning becomes a powerful tool for knowledge and best practice shearing, we use it to teach the environmental complexities and systems. In this presentation we promote the e-learning course dedicated for a large public (undergraduates, graduates, PhD students and young scientists) which gather and puts in coherence different pedagogical materials of complex systems and environmental studies. This course describes a complex processes using numerous illustrations, examples and tests that make it "easy to enjoy" learning process. For the seek of simplicity, the course is divided in different modules and at the end of each module a set of exercises and program codes are proposed for a best practice. The graphical user interface (GUI) which is constructed using an open source Opale Scenari offers a simple navigation through the different module. The course treats the complex systems that can be found in environment and their observables, we particularly highlight the extreme variability of these observables over a wide range of scales. Using the multifractal formalism through different applications (turbulence, precipitation, hydrology) we demonstrate how such extreme variability of the geophysical/biological fields should be used solving everyday (geo-)environmental chalenges.

Large-Scale Analysis of Auditory Segregation Behavior Crowdsourced via a Smartphone App.

PubMed

Teki, Sundeep; Kumar, Sukhbinder; Griffiths, Timothy D

2016-01-01

The human auditory system is adept at detecting sound sources of interest from a complex mixture of several other simultaneous sounds. The ability to selectively attend to the speech of one speaker whilst ignoring other speakers and background noise is of vital biological significance-the capacity to make sense of complex 'auditory scenes' is significantly impaired in aging populations as well as those with hearing loss. We investigated this problem by designing a synthetic signal, termed the 'stochastic figure-ground' stimulus that captures essential aspects of complex sounds in the natural environment. Previously, we showed that under controlled laboratory conditions, young listeners sampled from the university subject pool (n = 10) performed very well in detecting targets embedded in the stochastic figure-ground signal. Here, we presented a modified version of this cocktail party paradigm as a 'game' featured in a smartphone app (The Great Brain Experiment) and obtained data from a large population with diverse demographical patterns (n = 5148). Despite differences in paradigms and experimental settings, the observed target-detection performance by users of the app was robust and consistent with our previous results from the psychophysical study. Our results highlight the potential use of smartphone apps in capturing robust large-scale auditory behavioral data from normal healthy volunteers, which can also be extended to study auditory deficits in clinical populations with hearing impairments and central auditory disorders.

Microfluidic large-scale integration: the evolution of design rules for biological automation.

PubMed

Melin, Jessica; Quake, Stephen R

2007-01-01

Microfluidic large-scale integration (mLSI) refers to the development of microfluidic chips with thousands of integrated micromechanical valves and control components. This technology is utilized in many areas of biology and chemistry and is a candidate to replace today's conventional automation paradigm, which consists of fluid-handling robots. We review the basic development of mLSI and then discuss design principles of mLSI to assess the capabilities and limitations of the current state of the art and to facilitate the application of mLSI to areas of biology. Many design and practical issues, including economies of scale, parallelization strategies, multiplexing, and multistep biochemical processing, are discussed. Several microfluidic components used as building blocks to create effective, complex, and highly integrated microfluidic networks are also highlighted.

Initial conditions and modeling for simulations of shock driven turbulent material mixing

DOE PAGES

Grinstein, Fernando F.

2016-11-17

Here, we focus on the simulation of shock-driven material mixing driven by flow instabilities and initial conditions (IC). Beyond complex multi-scale resolution issues of shocks and variable density turbulence, me must address the equally difficult problem of predicting flow transition promoted by energy deposited at the material interfacial layer during the shock interface interactions. Transition involves unsteady large-scale coherent-structure dynamics capturable by a large eddy simulation (LES) strategy, but not by an unsteady Reynolds-Averaged Navier–Stokes (URANS) approach based on developed equilibrium turbulence assumptions and single-point-closure modeling. On the engineering end of computations, such URANS with reduced 1D/2D dimensionality and coarsermore » grids, tend to be preferred for faster turnaround in full-scale configurations.« less

Mesoscale Dynamical Regimes in the Midlatitudes

NASA Astrophysics Data System (ADS)

Craig, G. C.; Selz, T.

2018-01-01

The atmospheric mesoscales are characterized by a complex variety of meteorological phenomena that defy simple classification. Here a full space-time spectral analysis is carried out, based on a 7 day convection-permitting simulation of springtime midlatitude weather on a large domain. The kinetic energy is largest at synoptic scales, and on the mesoscale it is largely confined to an "advective band" where space and time scales are related by a constant of proportionality which corresponds to a velocity scale of about 10 m s-1. Computing the relative magnitude of different terms in the governing equations allows the identification of five dynamical regimes. These are tentatively identified as quasi-geostrophic flow, propagating gravity waves, stationary gravity waves related to orography, acoustic modes, and a weak temperature gradient regime, where vertical motions are forced by diabatic heating.

[Research progress on hydrological scaling].

PubMed

Liu, Jianmei; Pei, Tiefan

2003-12-01

With the development of hydrology and the extending effect of mankind on environment, scale issue has become a great challenge to many hydrologists due to the stochasticism and complexity of hydrological phenomena and natural catchments. More and more concern has been given to the scaling issues to gain a large-scale (or small-scale) hydrological characteristic from a certain known catchments, but hasn't been solved successfully. The first part of this paper introduced some concepts about hydrological scale, scale issue and scaling. The key problem is the spatial heterogeneity of catchments and the temporal and spatial variability of hydrological fluxes. Three approaches to scale were put forward in the third part, which were distributed modeling, fractal theory and statistical self similarity analyses. Existing problems and future research directions were proposed in the last part.

Study designs for identification of rare disease variants in complex diseases: the utility of family-based designs.

PubMed

Ionita-Laza, Iuliana; Ottman, Ruth

2011-11-01

The recent progress in sequencing technologies makes possible large-scale medical sequencing efforts to assess the importance of rare variants in complex diseases. The results of such efforts depend heavily on the use of efficient study designs and analytical methods. We introduce here a unified framework for association testing of rare variants in family-based designs or designs based on unselected affected individuals. This framework allows us to quantify the enrichment in rare disease variants in families containing multiple affected individuals and to investigate the optimal design of studies aiming to identify rare disease variants in complex traits. We show that for many complex diseases with small values for the overall sibling recurrence risk ratio, such as Alzheimer's disease and most cancers, sequencing affected individuals with a positive family history of the disease can be extremely advantageous for identifying rare disease variants. In contrast, for complex diseases with large values of the sibling recurrence risk ratio, sequencing unselected affected individuals may be preferable.

Numerical Technology for Large-Scale Computational Electromagnetics

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

Sharpe, R; Champagne, N; White, D

The key bottleneck of implicit computational electromagnetics tools for large complex geometries is the solution of the resulting linear system of equations. The goal of this effort was to research and develop critical numerical technology that alleviates this bottleneck for large-scale computational electromagnetics (CEM). The mathematical operators and numerical formulations used in this arena of CEM yield linear equations that are complex valued, unstructured, and indefinite. Also, simultaneously applying multiple mathematical modeling formulations to different portions of a complex problem (hybrid formulations) results in a mixed structure linear system, further increasing the computational difficulty. Typically, these hybrid linear systems aremore » solved using a direct solution method, which was acceptable for Cray-class machines but does not scale adequately for ASCI-class machines. Additionally, LLNL's previously existing linear solvers were not well suited for the linear systems that are created by hybrid implicit CEM codes. Hence, a new approach was required to make effective use of ASCI-class computing platforms and to enable the next generation design capabilities. Multiple approaches were investigated, including the latest sparse-direct methods developed by our ASCI collaborators. In addition, approaches that combine domain decomposition (or matrix partitioning) with general-purpose iterative methods and special purpose pre-conditioners were investigated. Special-purpose pre-conditioners that take advantage of the structure of the matrix were adapted and developed based on intimate knowledge of the matrix properties. Finally, new operator formulations were developed that radically improve the conditioning of the resulting linear systems thus greatly reducing solution time. The goal was to enable the solution of CEM problems that are 10 to 100 times larger than our previous capability.« less

Evaluation of the scale dependent dynamic SGS model in the open source code caffa3d.MBRi in wall-bounded flows

NASA Astrophysics Data System (ADS)

Draper, Martin; Usera, Gabriel

2015-04-01

The Scale Dependent Dynamic Model (SDDM) has been widely validated in large-eddy simulations using pseudo-spectral codes [1][2][3]. The scale dependency, particularly the potential law, has been proved also in a priori studies [4][5]. To the authors' knowledge there have been only few attempts to use the SDDM in finite difference (FD) and finite volume (FV) codes [6][7], finding some improvements with the dynamic procedures (scale independent or scale dependent approach), but not showing the behavior of the scale-dependence parameter when using the SDDM. The aim of the present paper is to evaluate the SDDM in the open source code caffa3d.MBRi, an updated version of the code presented in [8]. caffa3d.MBRi is a FV code, second-order accurate, parallelized with MPI, in which the domain is divided in unstructured blocks of structured grids. To accomplish this, 2 cases are considered: flow between flat plates and flow over a rough surface with the presence of a model wind turbine, taking for this case the experimental data presented in [9]. In both cases the standard Smagorinsky Model (SM), the Scale Independent Dynamic Model (SIDM) and the SDDM are tested. As presented in [6][7] slight improvements are obtained with the SDDM. Nevertheless, the behavior of the scale-dependence parameter supports the generalization of the dynamic procedure proposed in the SDDM, particularly taking into account that no explicit filter is used (the implicit filter is unknown). [1] F. Porté-Agel, C. Meneveau, M.B. Parlange. "A scale-dependent dynamic model for large-eddy simulation: application to a neutral atmospheric boundary layer". Journal of Fluid Mechanics, 2000, 415, 261-284. [2] E. Bou-Zeid, C. Meneveau, M. Parlante. "A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows". Physics of Fluids, 2005, 17, 025105 (18p). [3] R. Stoll, F. Porté-Agel. "Dynamic subgrid-scale models for momentum and scalar fluxes in large-eddy simulations of neutrally stratified atmospheric boundary layers over heterogeneous terrain". Water Resources Research, 2006, 42, WO1409 (18 p). [4] J. Keissl, M. Parlange, C. Meneveau. "Field experimental study of dynamic Smagorinsky models in the atmospheric surface layer". Journal of the Atmospheric Science, 2004, 61, 2296-2307. [5] E. Bou-Zeid, N. Vercauteren, M.B. Parlange, C. Meneveau. "Scale dependence of subgrid-scale model coefficients: An a priori study". Physics of Fluids, 2008, 20, 115106. [6] G. Kirkil, J. Mirocha, E. Bou-Zeid, F.K. Chow, B. Kosovic, "Implementation and evaluation of dynamic subfilter - scale stress models for large - eddy simulation using WRF". Monthly Weather Review, 2012, 140, 266-284. [7] S. Radhakrishnan, U. Piomelli. "Large-eddy simulation of oscillating boundary layers: model comparison and validation". Journal of Geophysical Research, 2008, 113, C02022. [8] G. Usera, A. Vernet, J.A. Ferré. "A parallel block-structured finite volume method for flows in complex geometry with sliding interfaces". Flow, Turbulence and Combustion, 2008, 81, 471-495. [9] Y-T. Wu, F. Porté-Agel. "Large-eddy simulation of wind-turbine wakes: evaluation of turbine parametrisations". BoundaryLayerMeteorology, 2011, 138, 345-366.

Complexity Characteristics of Currency Networks

NASA Astrophysics Data System (ADS)

Gorski, A. Z.; Drozdz, S.; Kwapien, J.; Oswiecimka, P.

2006-11-01

A large set of daily FOREX time series is analyzed. The corresponding correlation matrices (CM) are constructed for USD, EUR and PLN used as the base currencies. The triangle rule is interpreted as constraints reducing the number of independent returns. The CM spectrum is computed and compared with the cases of shuffled currencies and a fictitious random currency taken as a base currency. The Minimal Spanning Tree (MST) graphs are calculated and the clustering effects for strong currencies are found. It is shown that for MSTs the node rank has power like, scale free behavior. Finally, the scaling exponents are evaluated and found in the range analogous to those identified recently for various complex networks.

Evolving from bioinformatics in-the-small to bioinformatics in-the-large.

PubMed

Parker, D Stott; Gorlick, Michael M; Lee, Christopher J

2003-01-01

We argue the significance of a fundamental shift in bioinformatics, from in-the-small to in-the-large. Adopting a large-scale perspective is a way to manage the problems endemic to the world of the small-constellations of incompatible tools for which the effort required to assemble an integrated system exceeds the perceived benefit of the integration. Where bioinformatics in-the-small is about data and tools, bioinformatics in-the-large is about metadata and dependencies. Dependencies represent the complexities of large-scale integration, including the requirements and assumptions governing the composition of tools. The popular make utility is a very effective system for defining and maintaining simple dependencies, and it offers a number of insights about the essence of bioinformatics in-the-large. Keeping an in-the-large perspective has been very useful to us in large bioinformatics projects. We give two fairly different examples, and extract lessons from them showing how it has helped. These examples both suggest the benefit of explicitly defining and managing knowledge flows and knowledge maps (which represent metadata regarding types, flows, and dependencies), and also suggest approaches for developing bioinformatics database systems. Generally, we argue that large-scale engineering principles can be successfully adapted from disciplines such as software engineering and data management, and that having an in-the-large perspective will be a key advantage in the next phase of bioinformatics development.

Interactive exploration of coastal restoration modeling in virtual environments

NASA Astrophysics Data System (ADS)

Gerndt, Andreas; Miller, Robert; Su, Simon; Meselhe, Ehab; Cruz-Neira, Carolina

2009-02-01

Over the last decades, Louisiana has lost a substantial part of its coastal region to the Gulf of Mexico. The goal of the project depicted in this paper is to investigate the complex ecological and geophysical system not only to find solutions to reverse this development but also to protect the southern landscape of Louisiana for disastrous impacts of natural hazards like hurricanes. This paper sets a focus on the interactive data handling of the Chenier Plain which is only one scenario of the overall project. The challenge addressed is the interactive exploration of large-scale time-depending 2D simulation results and of terrain data with a high resolution that is available for this region. Besides data preparation, efficient visualization approaches optimized for the usage in virtual environments are presented. These are embedded in a complex framework for scientific visualization of time-dependent large-scale datasets. To provide a straightforward interface for rapid application development, a software layer called VRFlowVis has been developed. Several architectural aspects to encapsulate complex virtual reality aspects like multi-pipe vs. cluster-based rendering are discussed. Moreover, the distributed post-processing architecture is investigated to prove its efficiency for the geophysical domain. Runtime measurements conclude this paper.

Intelligent systems engineering methodology

NASA Technical Reports Server (NTRS)

Fouse, Scott

1990-01-01

An added challenge for the designers of large scale systems such as Space Station Freedom is the appropriate incorporation of intelligent system technology (artificial intelligence, expert systems, knowledge-based systems, etc.) into their requirements and design. This presentation will describe a view of systems engineering which successfully addresses several aspects of this complex problem: design of large scale systems, design with requirements that are so complex they only completely unfold during the development of a baseline system and even then continue to evolve throughout the system's life cycle, design that involves the incorporation of new technologies, and design and development that takes place with many players in a distributed manner yet can be easily integrated to meet a single view of the requirements. The first generation of this methodology was developed and evolved jointly by ISX and the Lockheed Aeronautical Systems Company over the past five years on the Defense Advanced Research Projects Agency/Air Force Pilot's Associate Program, one of the largest, most complex, and most successful intelligent systems constructed to date. As the methodology has evolved it has also been applied successfully to a number of other projects. Some of the lessons learned from this experience may be applicable to Freedom.

A methodology towards virtualisation-based high performance simulation platform supporting multidisciplinary design of complex products

NASA Astrophysics Data System (ADS)

Ren, Lei; Zhang, Lin; Tao, Fei; (Luke) Zhang, Xiaolong; Luo, Yongliang; Zhang, Yabin

2012-08-01

Multidisciplinary design of complex products leads to an increasing demand for high performance simulation (HPS) platforms. One great challenge is how to achieve high efficient utilisation of large-scale simulation resources in distributed and heterogeneous environments. This article reports a virtualisation-based methodology to realise a HPS platform. This research is driven by the issues concerning large-scale simulation resources deployment and complex simulation environment construction, efficient and transparent utilisation of fine-grained simulation resources and high reliable simulation with fault tolerance. A framework of virtualisation-based simulation platform (VSIM) is first proposed. Then the article investigates and discusses key approaches in VSIM, including simulation resources modelling, a method to automatically deploying simulation resources for dynamic construction of system environment, and a live migration mechanism in case of faults in run-time simulation. Furthermore, the proposed methodology is applied to a multidisciplinary design system for aircraft virtual prototyping and some experiments are conducted. The experimental results show that the proposed methodology can (1) significantly improve the utilisation of fine-grained simulation resources, (2) result in a great reduction in deployment time and an increased flexibility for simulation environment construction and (3)achieve fault tolerant simulation.

A Large-Scale Design Integration Approach Developed in Conjunction with the Ares Launch Vehicle Program

NASA Technical Reports Server (NTRS)

Redmon, John W.; Shirley, Michael C.; Kinard, Paul S.

2012-01-01

This paper presents a method for performing large-scale design integration, taking a classical 2D drawing envelope and interface approach and applying it to modern three dimensional computer aided design (3D CAD) systems. Today, the paradigm often used when performing design integration with 3D models involves a digital mockup of an overall vehicle, in the form of a massive, fully detailed, CAD assembly; therefore, adding unnecessary burden and overhead to design and product data management processes. While fully detailed data may yield a broad depth of design detail, pertinent integration features are often obscured under the excessive amounts of information, making them difficult to discern. In contrast, the envelope and interface method results in a reduction in both the amount and complexity of information necessary for design integration while yielding significant savings in time and effort when applied to today's complex design integration projects. This approach, combining classical and modern methods, proved advantageous during the complex design integration activities of the Ares I vehicle. Downstream processes, benefiting from this approach by reducing development and design cycle time, include: Creation of analysis models for the Aerodynamic discipline; Vehicle to ground interface development; Documentation development for the vehicle assembly.

Extracting Communities from Complex Networks by the k-Dense Method

NASA Astrophysics Data System (ADS)

Saito, Kazumi; Yamada, Takeshi; Kazama, Kazuhiro

To understand the structural and functional properties of large-scale complex networks, it is crucial to efficiently extract a set of cohesive subnetworks as communities. There have been proposed several such community extraction methods in the literature, including the classical k-core decomposition method and, more recently, the k-clique based community extraction method. The k-core method, although computationally efficient, is often not powerful enough for uncovering a detailed community structure and it produces only coarse-grained and loosely connected communities. The k-clique method, on the other hand, can extract fine-grained and tightly connected communities but requires a substantial amount of computational load for large-scale complex networks. In this paper, we present a new notion of a subnetwork called k-dense, and propose an efficient algorithm for extracting k-dense communities. We applied our method to the three different types of networks assembled from real data, namely, from blog trackbacks, word associations and Wikipedia references, and demonstrated that the k-dense method could extract communities almost as efficiently as the k-core method, while the qualities of the extracted communities are comparable to those obtained by the k-clique method.

Overview of Accelerator Applications in Energy

NASA Astrophysics Data System (ADS)

Garnett, Robert W.; Sheffield, Richard L.

An overview of the application of accelerators and accelerator technology in energy is presented. Applications span a broad range of cost, size, and complexity and include large-scale systems requiring high-power or high-energy accelerators to drive subcritical reactors for energy production or waste transmutation, as well as small-scale industrial systems used to improve oil and gas exploration and production. The enabling accelerator technologies will also be reviewed and future directions discussed.

Conducting Automated Test Assembly Using the Premium Solver Platform Version 7.0 with Microsoft Excel and the Large-Scale LP/QP Solver Engine Add-In

ERIC Educational Resources Information Center

Cor, Ken; Alves, Cecilia; Gierl, Mark J.

2008-01-01

This review describes and evaluates a software add-in created by Frontline Systems, Inc., that can be used with Microsoft Excel 2007 to solve large, complex test assembly problems. The combination of Microsoft Excel 2007 with the Frontline Systems Premium Solver Platform is significant because Microsoft Excel is the most commonly used spreadsheet…

Population-wide mortality in multiple forest types in western North America: onset, extent, and severity of impacts as indicators of climatic influence

Treesearch

J. D. Shaw; J. N. Long; M. T. Thompson; R. J. DeRose

2010-01-01

A complex of drought, insects, and disease is causing widespread mortality in multiple forest types across western North America. These forest types range from dry Pinus-Juniperus woodlands to moist, montane Picea-Abies forests. Although large-scale mortality events are known from the past and considered part of natural cycles, recent events have largely been...

Revealing the Hidden Relationship by Sparse Modules in Complex Networks with a Large-Scale Analysis

PubMed Central

Jiao, Qing-Ju; Huang, Yan; Liu, Wei; Wang, Xiao-Fan; Chen, Xiao-Shuang; Shen, Hong-Bin

2013-01-01

One of the remarkable features of networks is module that can provide useful insights into not only network organizations but also functional behaviors between their components. Comprehensive efforts have been devoted to investigating cohesive modules in the past decade. However, it is still not clear whether there are important structural characteristics of the nodes that do not belong to any cohesive module. In order to answer this question, we performed a large-scale analysis on 25 complex networks with different types and scales using our recently developed BTS (bintree seeking) algorithm, which is able to detect both cohesive and sparse modules in the network. Our results reveal that the sparse modules composed by the cohesively isolated nodes widely co-exist with the cohesive modules. Detailed analysis shows that both types of modules provide better characterization for the division of a network into functional units than merely cohesive modules, because the sparse modules possibly re-organize the nodes in the so-called cohesive modules, which lack obvious modular significance, into meaningful groups. Compared with cohesive modules, the sizes of sparse ones are generally smaller. Sparse modules are also found to have preferences in social and biological networks than others. PMID:23762457

Multi-view L2-SVM and its multi-view core vector machine.

PubMed

Huang, Chengquan; Chung, Fu-lai; Wang, Shitong

2016-03-01

In this paper, a novel L2-SVM based classifier Multi-view L2-SVM is proposed to address multi-view classification tasks. The proposed Multi-view L2-SVM classifier does not have any bias in its objective function and hence has the flexibility like μ-SVC in the sense that the number of the yielded support vectors can be controlled by a pre-specified parameter. The proposed Multi-view L2-SVM classifier can make full use of the coherence and the difference of different views through imposing the consensus among multiple views to improve the overall classification performance. Besides, based on the generalized core vector machine GCVM, the proposed Multi-view L2-SVM classifier is extended into its GCVM version MvCVM which can realize its fast training on large scale multi-view datasets, with its asymptotic linear time complexity with the sample size and its space complexity independent of the sample size. Our experimental results demonstrated the effectiveness of the proposed Multi-view L2-SVM classifier for small scale multi-view datasets and the proposed MvCVM classifier for large scale multi-view datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Observations of Seafloor Roughness in a Tidally Modulated Inlet

NASA Astrophysics Data System (ADS)

Lippmann, T. C.; Hunt, J.

2014-12-01

The vertical structure of shallow water flows are influenced by the presence of a bottom boundary layer, which spans the water column for long period waves or mean flows. The nature of the boundary is determined in part by the roughness elements that make up the seafloor, and includes sometimes complex undulations associated with regular and irregular shaped bedforms whose scales range several orders of magnitude from orbital wave ripples (10-1 m) to mega-ripples (100 m) and even larger features (101-103) such as sand waves, bars, and dunes. Modeling efforts often parameterize the effects of roughness elements on flow fields, depending on the complexity of the boundary layer formulations. The problem is exacerbated by the transient nature of bedforms and their large spatial extent and variability. This is particularly important in high flow areas with large sediment transport, such as tidally dominated sandy inlets like New River Inlet, NC. Quantification of small scale seafloor variability over large spatial areas requires the use of mobile platforms that can measure with fine scale (order cm) accuracy in wide swaths. The problem is difficult in shallow water where waves and currents are large, and water clarity is often limited. In this work, we present results from bathymetric surveys obtained with the Coastal Bathymetry Survey System, a personal watercraft equipped with a Imagenex multibeam acoustic echosounder and Applanix POS-MV 320 GPS-aided inertial measurement unit. This system is able to measure shallow water seafloor bathymetry and backscatter intensity with very fine scale (10-1 m) resolution and over relatively large scales (103 m) in the presence of high waves and currents. Wavenumber spectra show that the noise floor of the resolved multibeam bathymetry is on the order of 2.5 - 5 cm in amplitude, depending on water depths ranging 2 - 6 m, and about 30 cm in wavelength. Seafloor roughness elements are estimated from wavenumber spectra across the inlet from bathymetric maps of the seafloor obtained with 10-25 cm horizontal resolution. Implications of the effects of the bottom variability on the vertical structure of the currents will be discussed. This work was supported by ONR and NOAA.

Driving terrestrial ecosystem models from space

NASA Technical Reports Server (NTRS)

Waring, R. H.

1993-01-01

Regional air pollution, land-use conversion, and projected climate change all affect ecosystem processes at large scales. Changes in vegetation cover and growth dynamics can impact the functioning of ecosystems, carbon fluxes, and climate. As a result, there is a need to assess and monitor vegetation structure and function comprehensively at regional to global scales. To provide a test of our present understanding of how ecosystems operate at large scales we can compare model predictions of CO2, O2, and methane exchange with the atmosphere against regional measurements of interannual variation in the atmospheric concentration of these gases. Recent advances in remote sensing of the Earth's surface are beginning to provide methods for estimating important ecosystem variables at large scales. Ecologists attempting to generalize across landscapes have made extensive use of models and remote sensing technology. The success of such ventures is dependent on merging insights and expertise from two distinct fields. Ecologists must provide the understanding of how well models emulate important biological variables and their interactions; experts in remote sensing must provide the biophysical interpretation of complex optical reflectance and radar backscatter data.

Ordering Unstructured Meshes for Sparse Matrix Computations on Leading Parallel Systems

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Li, Xiaoye; Heber, Gerd; Biswas, Rupak

2000-01-01

The ability of computers to solve hitherto intractable problems and simulate complex processes using mathematical models makes them an indispensable part of modern science and engineering. Computer simulations of large-scale realistic applications usually require solving a set of non-linear partial differential equations (PDES) over a finite region. For example, one thrust area in the DOE Grand Challenge projects is to design future accelerators such as the SpaHation Neutron Source (SNS). Our colleagues at SLAC need to model complex RFQ cavities with large aspect ratios. Unstructured grids are currently used to resolve the small features in a large computational domain; dynamic mesh adaptation will be added in the future for additional efficiency. The PDEs for electromagnetics are discretized by the FEM method, which leads to a generalized eigenvalue problem Kx = AMx, where K and M are the stiffness and mass matrices, and are very sparse. In a typical cavity model, the number of degrees of freedom is about one million. For such large eigenproblems, direct solution techniques quickly reach the memory limits. Instead, the most widely-used methods are Krylov subspace methods, such as Lanczos or Jacobi-Davidson. In all the Krylov-based algorithms, sparse matrix-vector multiplication (SPMV) must be performed repeatedly. Therefore, the efficiency of SPMV usually determines the eigensolver speed. SPMV is also one of the most heavily used kernels in large-scale numerical simulations.

Strategies for Large Scale Implementation of a Multiscale, Multiprocess Integrated Hydrologic Model

NASA Astrophysics Data System (ADS)

Kumar, M.; Duffy, C.

2006-05-01

Distributed models simulate hydrologic state variables in space and time while taking into account the heterogeneities in terrain, surface, subsurface properties and meteorological forcings. Computational cost and complexity associated with these model increases with its tendency to accurately simulate the large number of interacting physical processes at fine spatio-temporal resolution in a large basin. A hydrologic model run on a coarse spatial discretization of the watershed with limited number of physical processes needs lesser computational load. But this negatively affects the accuracy of model results and restricts physical realization of the problem. So it is imperative to have an integrated modeling strategy (a) which can be universally applied at various scales in order to study the tradeoffs between computational complexity (determined by spatio- temporal resolution), accuracy and predictive uncertainty in relation to various approximations of physical processes (b) which can be applied at adaptively different spatial scales in the same domain by taking into account the local heterogeneity of topography and hydrogeologic variables c) which is flexible enough to incorporate different number and approximation of process equations depending on model purpose and computational constraint. An efficient implementation of this strategy becomes all the more important for Great Salt Lake river basin which is relatively large (~89000 sq. km) and complex in terms of hydrologic and geomorphic conditions. Also the types and the time scales of hydrologic processes which are dominant in different parts of basin are different. Part of snow melt runoff generated in the Uinta Mountains infiltrates and contributes as base flow to the Great Salt Lake over a time scale of decades to centuries. The adaptive strategy helps capture the steep topographic and climatic gradient along the Wasatch front. Here we present the aforesaid modeling strategy along with an associated hydrologic modeling framework which facilitates a seamless, computationally efficient and accurate integration of the process model with the data model. The flexibility of this framework leads to implementation of multiscale, multiresolution, adaptive refinement/de-refinement and nested modeling simulations with least computational burden. However, performing these simulations and related calibration of these models over a large basin at higher spatio- temporal resolutions is computationally intensive and requires use of increasing computing power. With the advent of parallel processing architectures, high computing performance can be achieved by parallelization of existing serial integrated-hydrologic-model code. This translates to running the same model simulation on a network of large number of processors thereby reducing the time needed to obtain solution. The paper also discusses the implementation of the integrated model on parallel processors. Also will be discussed the mapping of the problem on multi-processor environment, method to incorporate coupling between hydrologic processes using interprocessor communication models, model data structure and parallel numerical algorithms to obtain high performance.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

PubMed

Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

2016-12-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Multi-color electron microscopy by element-guided identification of cells, organelles and molecules.

PubMed

Scotuzzi, Marijke; Kuipers, Jeroen; Wensveen, Dasha I; de Boer, Pascal; Hagen, Kees C W; Hoogenboom, Jacob P; Giepmans, Ben N G

2017-04-07

Cellular complexity is unraveled at nanometer resolution using electron microscopy (EM), but interpretation of macromolecular functionality is hampered by the difficulty in interpreting grey-scale images and the unidentified molecular content. We perform large-scale EM on mammalian tissue complemented with energy-dispersive X-ray analysis (EDX) to allow EM-data analysis based on elemental composition. Endogenous elements, labels (gold and cadmium-based nanoparticles) as well as stains are analyzed at ultrastructural resolution. This provides a wide palette of colors to paint the traditional grey-scale EM images for composition-based interpretation. Our proof-of-principle application of EM-EDX reveals that endocrine and exocrine vesicles exist in single cells in Islets of Langerhans. This highlights how elemental mapping reveals unbiased biomedical relevant information. Broad application of EM-EDX will further allow experimental analysis on large-scale tissue using endogenous elements, multiple stains, and multiple markers and thus brings nanometer-scale 'color-EM' as a promising tool to unravel molecular (de)regulation in biomedicine.

Multi-color electron microscopy by element-guided identification of cells, organelles and molecules

PubMed Central

Scotuzzi, Marijke; Kuipers, Jeroen; Wensveen, Dasha I.; de Boer, Pascal; Hagen, Kees (C.) W.; Hoogenboom, Jacob P.; Giepmans, Ben N. G.

2017-01-01

Cellular complexity is unraveled at nanometer resolution using electron microscopy (EM), but interpretation of macromolecular functionality is hampered by the difficulty in interpreting grey-scale images and the unidentified molecular content. We perform large-scale EM on mammalian tissue complemented with energy-dispersive X-ray analysis (EDX) to allow EM-data analysis based on elemental composition. Endogenous elements, labels (gold and cadmium-based nanoparticles) as well as stains are analyzed at ultrastructural resolution. This provides a wide palette of colors to paint the traditional grey-scale EM images for composition-based interpretation. Our proof-of-principle application of EM-EDX reveals that endocrine and exocrine vesicles exist in single cells in Islets of Langerhans. This highlights how elemental mapping reveals unbiased biomedical relevant information. Broad application of EM-EDX will further allow experimental analysis on large-scale tissue using endogenous elements, multiple stains, and multiple markers and thus brings nanometer-scale ‘color-EM’ as a promising tool to unravel molecular (de)regulation in biomedicine. PMID:28387351

Ga(+) Basicity and Affinity Scales Based on High-Level Ab Initio Calculations.

PubMed

Brea, Oriana; Mó, Otilia; Yáñez, Manuel

2015-10-26

The structure, relative stability and bonding of complexes formed by the interaction between Ga(+) and a large set of compounds, including hydrocarbons, aromatic systems, and oxygen-, nitrogen-, fluorine and sulfur-containing Lewis bases have been investigated through the use of the high-level composite ab initio Gaussian-4 theory. This allowed us to establish rather accurate Ga(+) cation affinity (GaCA) and Ga(+) cation basicity (GaCB) scales. The bonding analysis of the complexes under scrutiny shows that, even though one of the main ingredients of the Ga(+) -base interaction is electrostatic, it exhibits a non-negligible covalent character triggered by the presence of the low-lying empty 4p orbital of Ga(+) , which favors a charge donation from occupied orbitals of the base to the metal ion. This partial covalent character, also observed in AlCA scales, is behind the dissimilarities observed when GaCA are compared with Li(+) cation affinities, where these covalent contributions are practically nonexistent. Quite unexpectedly, there are some dissimilarities between several Ga(+) -complexes and the corresponding Al(+) -analogues, mainly affecting the relative stability of π-complexes involving aromatic compounds. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Complex Genetics of Behavior: BXDs in the Automated Home-Cage.

PubMed

Loos, Maarten; Verhage, Matthijs; Spijker, Sabine; Smit, August B

2017-01-01

This chapter describes a use case for the genetic dissection and automated analysis of complex behavioral traits using the genetically diverse panel of BXD mouse recombinant inbred strains. Strains of the BXD resource differ widely in terms of gene and protein expression in the brain, as well as in their behavioral repertoire. A large mouse resource opens the possibility for gene finding studies underlying distinct behavioral phenotypes, however, such a resource poses a challenge in behavioral phenotyping. To address the specifics of large-scale screening we describe how to investigate: (1) how to assess mouse behavior systematically in addressing a large genetic cohort, (2) how to dissect automation-derived longitudinal mouse behavior into quantitative parameters, and (3) how to map these quantitative traits to the genome, deriving loci underlying aspects of behavior.

Platinum clusters with precise numbers of atoms for preparative-scale catalysis.

PubMed

Imaoka, Takane; Akanuma, Yuki; Haruta, Naoki; Tsuchiya, Shogo; Ishihara, Kentaro; Okayasu, Takeshi; Chun, Wang-Jae; Takahashi, Masaki; Yamamoto, Kimihisa

2017-09-25

Subnanometer noble metal clusters have enormous potential, mainly for catalytic applications. Because a difference of only one atom may cause significant changes in their reactivity, a preparation method with atomic-level precision is essential. Although such a precision with enough scalability has been achieved by gas-phase synthesis, large-scale preparation is still at the frontier, hampering practical applications. We now show the atom-precise and fully scalable synthesis of platinum clusters on a milligram scale from tiara-like platinum complexes with various ring numbers (n = 5-13). Low-temperature calcination of the complexes on a carbon support under hydrogen stream affords monodispersed platinum clusters, whose atomicity is equivalent to that of the precursor complex. One of the clusters (Pt 10 ) exhibits high catalytic activity in the hydrogenation of styrene compared to that of the other clusters. This method opens an avenue for the application of these clusters to preparative-scale catalysis.The catalytic activity of a noble metal nanocluster is tied to its atomicity. Here, the authors report an atom-precise, fully scalable synthesis of platinum clusters from molecular ring precursors, and show that a variation of only one atom can dramatically change a cluster's reactivity.

Simulations of Sea Level Rise Effects on Complex Coastal Systems

NASA Astrophysics Data System (ADS)

Niedoroda, A. W.; Ye, M.; Saha, B.; Donoghue, J. F.; Reed, C. W.

2009-12-01

It is now established that complex coastal systems with elements such as beaches, inlets, bays, and rivers adjust their morphologies according to time-varying balances in between the processes that control the exchange of sediment. Accelerated sea level rise introduces a major perturbation into the sediment-sharing systems. A modeling framework based on a new SL-PR model which is an advanced version of the aggregate-scale CST Model and the event-scale CMS-2D and CMS-Wave combination have been used to simulate the recent evolution of a portion of the Florida panhandle coast. This combination of models provides a method to evaluate coefficients in the aggregate-scale model that were previously treated as fitted parameters. That is, by carrying out simulations of a complex coastal system with runs of the event-scale model representing more than a year it is now possible to directly relate the coefficients in the large-scale SL-PR model to measureable physical parameters in the current and wave fields. This cross-scale modeling procedure has been used to simulate the shoreline evolution at the Santa Rosa Island, a long barrier which houses significant military infrastructure at the north Gulf Coast. The model has been used to simulate 137 years of measured shoreline change and to extend these to predictions of future rates of shoreline migration.

Development of a Catalytic Combustor for Aircraft Gas Turbine Engines.

DTIC Science & Technology

1976-09-22

80 VI. DESIGN OF 7.6 CI CIANETE COMBUSTORS . . . . . . . . . . . 86 1. Design and Fabrication of CombusLors for Large Scale T est in...obtained for this program included round holes of different diameters, squares, rectangles, triangles, and other more complex hollow configurations

Harnessing quantitative genetics and genomics for understanding and improving complex traits in crops

USDA-ARS?s Scientific Manuscript database

Classical quantitative genetics aids crop improvement by providing the means to estimate heritability, genetic correlations, and predicted responses to various selection schemes. Genomics has the potential to aid quantitative genetics and applied crop improvement programs via large-scale, high-thro...

Complex dewetting scenarios of ultrathin silicon films for large-scale nanoarchitectures

PubMed Central

Naffouti, Meher; Backofen, Rainer; Salvalaglio, Marco; Bottein, Thomas; Lodari, Mario; Voigt, Axel; David, Thomas; Benkouider, Abdelmalek; Fraj, Ibtissem; Favre, Luc; Ronda, Antoine; Berbezier, Isabelle; Grosso, David; Abbarchi, Marco; Bollani, Monica

2017-01-01

Dewetting is a ubiquitous phenomenon in nature; many different thin films of organic and inorganic substances (such as liquids, polymers, metals, and semiconductors) share this shape instability driven by surface tension and mass transport. Via templated solid-state dewetting, we frame complex nanoarchitectures of monocrystalline silicon on insulator with unprecedented precision and reproducibility over large scales. Phase-field simulations reveal the dominant role of surface diffusion as a driving force for dewetting and provide a predictive tool to further engineer this hybrid top-down/bottom-up self-assembly method. Our results demonstrate that patches of thin monocrystalline films of metals and semiconductors share the same dewetting dynamics. We also prove the potential of our method by fabricating nanotransfer molding of metal oxide xerogels on silicon and glass substrates. This method allows the novel possibility of transferring these Si-based patterns on different materials, which do not usually undergo dewetting, offering great potential also for microfluidic or sensing applications. PMID:29296680

Complex dewetting scenarios of ultrathin silicon films for large-scale nanoarchitectures.

PubMed

Naffouti, Meher; Backofen, Rainer; Salvalaglio, Marco; Bottein, Thomas; Lodari, Mario; Voigt, Axel; David, Thomas; Benkouider, Abdelmalek; Fraj, Ibtissem; Favre, Luc; Ronda, Antoine; Berbezier, Isabelle; Grosso, David; Abbarchi, Marco; Bollani, Monica

2017-11-01

Dewetting is a ubiquitous phenomenon in nature; many different thin films of organic and inorganic substances (such as liquids, polymers, metals, and semiconductors) share this shape instability driven by surface tension and mass transport. Via templated solid-state dewetting, we frame complex nanoarchitectures of monocrystalline silicon on insulator with unprecedented precision and reproducibility over large scales. Phase-field simulations reveal the dominant role of surface diffusion as a driving force for dewetting and provide a predictive tool to further engineer this hybrid top-down/bottom-up self-assembly method. Our results demonstrate that patches of thin monocrystalline films of metals and semiconductors share the same dewetting dynamics. We also prove the potential of our method by fabricating nanotransfer molding of metal oxide xerogels on silicon and glass substrates. This method allows the novel possibility of transferring these Si-based patterns on different materials, which do not usually undergo dewetting, offering great potential also for microfluidic or sensing applications.

Collaborative mining and interpretation of large-scale data for biomedical research insights.

PubMed

Tsiliki, Georgia; Karacapilidis, Nikos; Christodoulou, Spyros; Tzagarakis, Manolis

2014-01-01

Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence.

Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights

PubMed Central

Tsiliki, Georgia; Karacapilidis, Nikos; Christodoulou, Spyros; Tzagarakis, Manolis

2014-01-01

Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence. PMID:25268270

The Segmented Aperture Interferometric Nulling Testbed (SAINT) I: Overview and Air-side System Description

NASA Technical Reports Server (NTRS)

Hicks, Brian A.; Lyon, Richard G.; Petrone, Peter, III; Bolcar, Matthew R.; Bolognese, Jeff; Clampin, Mark; Dogoda, Peter; Dworzanski, Daniel; Helmbrecht, Michael A.; Koca, Corina;

Evolution of tag-mediated altruistic behavior in one-shot encounters on large-scale complex networks

NASA Astrophysics Data System (ADS)

Hadzibeganovic, Tarik; Lima, F. Welington S.; Stauffer, Dietrich

2012-11-01

An agent-based evolutionary model of tag-mediated altruism is studied on large-scale complex networks addressing multiplayer one-shot Prisoner’s Dilemma-like games with four competing strategies. Contrary to standard theoretical predictions, but in line with recent empirical findings, we observed that altruistic acts in non-repeated interactions can emerge as a natural consequence of recognition of heritable phenotypic traits such as visual tags, which enable the discrimination between potentially beneficial and unproductive encounters. Moreover, we identified topological regimes in which cooperation always prevails at short times, but where unconditional cooperators are favored over conditional tag-based helpers, even though the latter initially have a slight reproductive advantage. After very long times, we found that all four strategies appeared about equally often, meaning that only one quarter of agents refused cooperation egoistically. However, our study suggests that intra-tag generosity can quickly evolve to dominate over other strategies in spatially structured environments that are otherwise detrimental to cooperative behavior.

Speeding up GW Calculations to Meet the Challenge of Large Scale Quasiparticle Predictions.

PubMed

Gao, Weiwei; Xia, Weiyi; Gao, Xiang; Zhang, Peihong

2016-11-11

Although the GW approximation is recognized as one of the most accurate theories for predicting materials excited states properties, scaling up conventional GW calculations for large systems remains a major challenge. We present a powerful and simple-to-implement method that can drastically accelerate fully converged GW calculations for large systems, enabling fast and accurate quasiparticle calculations for complex materials systems. We demonstrate the performance of this new method by presenting the results for ZnO and MgO supercells. A speed-up factor of nearly two orders of magnitude is achieved for a system containing 256 atoms (1024 valence electrons) with a negligibly small numerical error of ±0.03 eV. Finally, we discuss the application of our method to the GW calculations for 2D materials.

Multiple scales and phases in discrete chains with application to folded proteins

NASA Astrophysics Data System (ADS)

Sinelnikova, A.; Niemi, A. J.; Nilsson, Johan; Ulybyshev, M.

2018-05-01

Chiral heteropolymers such as large globular proteins can simultaneously support multiple length scales. The interplay between the different scales brings about conformational diversity, determines the phase properties of the polymer chain, and governs the structure of the energy landscape. Most importantly, multiple scales produce complex dynamics that enable proteins to sustain live matter. However, at the moment there is incomplete understanding of how to identify and distinguish the various scales that determine the structure and dynamics of a complex protein. Here we address this impending problem. We develop a methodology with the potential to systematically identify different length scales, in the general case of a linear polymer chain. For this we introduce and analyze the properties of an order parameter that can both reveal the presence of different length scales and can also probe the phase structure. We first develop our concepts in the case of chiral homopolymers. We introduce a variant of Kadanoff's block-spin transformation to coarse grain piecewise linear chains, such as the C α backbone of a protein. We derive analytically, and then verify numerically, a number of properties that the order parameter can display, in the case of a chiral polymer chain. In particular, we propose that in the case of a chiral heteropolymer the order parameter can reveal traits of several different phases, contingent on the length scale at which it is scrutinized. We confirm that this is the case with crystallographic protein structures in the Protein Data Bank. Thus our results suggest relations between the scales, the phases, and the complexity of folding pathways.

An intermediate level of abstraction for computational systems chemistry.

PubMed

Andersen, Jakob L; Flamm, Christoph; Merkle, Daniel; Stadler, Peter F

2017-12-28

Computational techniques are required for narrowing down the vast space of possibilities to plausible prebiotic scenarios, because precise information on the molecular composition, the dominant reaction chemistry and the conditions for that era are scarce. The exploration of large chemical reaction networks is a central aspect in this endeavour. While quantum chemical methods can accurately predict the structures and reactivities of small molecules, they are not efficient enough to cope with large-scale reaction systems. The formalization of chemical reactions as graph grammars provides a generative system, well grounded in category theory, at the right level of abstraction for the analysis of large and complex reaction networks. An extension of the basic formalism into the realm of integer hyperflows allows for the identification of complex reaction patterns, such as autocatalysis, in large reaction networks using optimization techniques.This article is part of the themed issue 'Reconceptualizing the origins of life'. © 2017 The Author(s).

Openwebglobe 2: Visualization of Complex 3D-GEODATA in the (mobile) Webbrowser

NASA Astrophysics Data System (ADS)

Christen, M.

2016-06-01

Providing worldwide high resolution data for virtual globes consists of compute and storage intense tasks for processing data. Furthermore, rendering complex 3D-Geodata, such as 3D-City models with an extremely high polygon count and a vast amount of textures at interactive framerates is still a very challenging task, especially on mobile devices. This paper presents an approach for processing, caching and serving massive geospatial data in a cloud-based environment for large scale, out-of-core, highly scalable 3D scene rendering on a web based virtual globe. Cloud computing is used for processing large amounts of geospatial data and also for providing 2D and 3D map data to a large amount of (mobile) web clients. In this paper the approach for processing, rendering and caching very large datasets in the currently developed virtual globe "OpenWebGlobe 2" is shown, which displays 3D-Geodata on nearly every device.

MacroBac: New Technologies for Robust and Efficient Large-Scale Production of Recombinant Multiprotein Complexes.

PubMed

Gradia, Scott D; Ishida, Justin P; Tsai, Miaw-Sheue; Jeans, Chris; Tainer, John A; Fuss, Jill O

2017-01-01

Recombinant expression of large, multiprotein complexes is essential and often rate limiting for determining structural, biophysical, and biochemical properties of DNA repair, replication, transcription, and other key cellular processes. Baculovirus-infected insect cell expression systems are especially well suited for producing large, human proteins recombinantly, and multigene baculovirus systems have facilitated studies of multiprotein complexes. In this chapter, we describe a multigene baculovirus system called MacroBac that uses a Biobricks-type assembly method based on restriction and ligation (Series 11) or ligation-independent cloning (Series 438). MacroBac cloning and assembly is efficient and equally well suited for either single subcloning reactions or high-throughput cloning using 96-well plates and liquid handling robotics. MacroBac vectors are polypromoter with each gene flanked by a strong polyhedrin promoter and an SV40 poly(A) termination signal that minimize gene order expression level effects seen in many polycistronic assemblies. Large assemblies are robustly achievable, and we have successfully assembled as many as 10 genes into a single MacroBac vector. Importantly, we have observed significant increases in expression levels and quality of large, multiprotein complexes using a single, multigene, polypromoter virus rather than coinfection with multiple, single-gene viruses. Given the importance of characterizing functional complexes, we believe that MacroBac provides a critical enabling technology that may change the way that structural, biophysical, and biochemical research is done. © 2017 Elsevier Inc. All rights reserved.

Nesting large-eddy simulations within mesoscale simulations for wind energy applications

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

Lundquist, J K; Mirocha, J D; Chow, F K

2008-09-08

With increasing demand for more accurate atmospheric simulations for wind turbine micrositing, for operational wind power forecasting, and for more reliable turbine design, simulations of atmospheric flow with resolution of tens of meters or higher are required. These time-dependent large-eddy simulations (LES), which resolve individual atmospheric eddies on length scales smaller than turbine blades and account for complex terrain, are possible with a range of commercial and open-source software, including the Weather Research and Forecasting (WRF) model. In addition to 'local' sources of turbulence within an LES domain, changing weather conditions outside the domain can also affect flow, suggesting thatmore » a mesoscale model provide boundary conditions to the large-eddy simulations. Nesting a large-eddy simulation within a mesoscale model requires nuanced representations of turbulence. Our group has improved the Weather and Research Forecasting model's (WRF) LES capability by implementing the Nonlinear Backscatter and Anisotropy (NBA) subfilter stress model following Kosovic (1997) and an explicit filtering and reconstruction technique to compute the Resolvable Subfilter-Scale (RSFS) stresses (following Chow et al, 2005). We have also implemented an immersed boundary method (IBM) in WRF to accommodate complex terrain. These new models improve WRF's LES capabilities over complex terrain and in stable atmospheric conditions. We demonstrate approaches to nesting LES within a mesoscale simulation for farms of wind turbines in hilly regions. Results are sensitive to the nesting method, indicating that care must be taken to provide appropriate boundary conditions, and to allow adequate spin-up of turbulence in the LES domain.« less

Panoptes: web-based exploration of large scale genome variation data.

PubMed

Vauterin, Paul; Jeffery, Ben; Miles, Alistair; Amato, Roberto; Hart, Lee; Wright, Ian; Kwiatkowski, Dominic

2017-10-15

The size and complexity of modern large-scale genome variation studies demand novel approaches for exploring and sharing the data. In order to unlock the potential of these data for a broad audience of scientists with various areas of expertise, a unified exploration framework is required that is accessible, coherent and user-friendly. Panoptes is an open-source software framework for collaborative visual exploration of large-scale genome variation data and associated metadata in a web browser. It relies on technology choices that allow it to operate in near real-time on very large datasets. It can be used to browse rich, hybrid content in a coherent way, and offers interactive visual analytics approaches to assist the exploration. We illustrate its application using genome variation data of Anopheles gambiae, Plasmodium falciparum and Plasmodium vivax. Freely available at https://github.com/cggh/panoptes, under the GNU Affero General Public License. paul.vauterin@gmail.com. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Effectively-truncated large-scale shell-model calculations and nuclei around 100Sn

NASA Astrophysics Data System (ADS)

Gargano, A.; Coraggio, L.; Itaco, N.

2017-09-01

This paper presents a short overview of a procedure we have recently introduced, dubbed the double-step truncation method, which is aimed to reduce the computational complexity of large-scale shell-model calculations. Within this procedure, one starts with a realistic shell-model Hamiltonian defined in a large model space, and then, by analyzing the effective single particle energies of this Hamiltonian as a function of the number of valence protons and/or neutrons, reduced model spaces are identified containing only the single-particle orbitals relevant to the description of the spectroscopic properties of a certain class of nuclei. As a final step, new effective shell-model Hamiltonians defined within the reduced model spaces are derived by way of a unitary transformation of the original large-scale Hamiltonian. A detailed account of this transformation is given and the merit of the double-step truncation method is illustrated by discussing few selected results for 96Mo, described as four protons and four neutrons outside 88Sr. Some new preliminary results for light odd-tin isotopes from A = 101 to 107 are also reported.

Spatial Heterogeneity, Scale, Data Character and Sustainable Transport in the Big Data Era

NASA Astrophysics Data System (ADS)

Jiang, Bin

2018-04-01

In light of the emergence of big data, I have advocated and argued for a paradigm shift from Tobler's law to scaling law, from Euclidean geometry to fractal geometry, from Gaussian statistics to Paretian statistics, and - more importantly - from Descartes' mechanistic thinking to Alexander's organic thinking. Fractal geometry falls under the third definition of fractal - that is, a set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times (Jiang and Yin 2014) - rather than under the second definition of fractal, which requires a power law between scales and details (Mandelbrot 1982). The new fractal geometry is more towards living geometry that "follows the rules, constraints, and contingent conditions that are, inevitably, encountered in the real world" (Alexander et al. 2012, p. 395), not only for understanding complexity, but also for creating complex or living structure (Alexander 2002-2005). This editorial attempts to clarify why the paradigm shift is essential and to elaborate on several concepts, including spatial heterogeneity (scaling law), scale (or the fourth meaning of scale), data character (in contrast to data quality), and sustainable transport in the big data era.

Model Uncertainty Quantification Methods For Data Assimilation In Partially Observed Multi-Scale Systems

NASA Astrophysics Data System (ADS)

Pathiraja, S. D.; van Leeuwen, P. J.

2017-12-01

Model Uncertainty Quantification remains one of the central challenges of effective Data Assimilation (DA) in complex partially observed non-linear systems. Stochastic parameterization methods have been proposed in recent years as a means of capturing the uncertainty associated with unresolved sub-grid scale processes. Such approaches generally require some knowledge of the true sub-grid scale process or rely on full observations of the larger scale resolved process. We present a methodology for estimating the statistics of sub-grid scale processes using only partial observations of the resolved process. It finds model error realisations over a training period by minimizing their conditional variance, constrained by available observations. Special is that these realisations are binned conditioned on the previous model state during the minimization process, allowing for the recovery of complex error structures. The efficacy of the approach is demonstrated through numerical experiments on the multi-scale Lorenz 96' model. We consider different parameterizations of the model with both small and large time scale separations between slow and fast variables. Results are compared to two existing methods for accounting for model uncertainty in DA and shown to provide improved analyses and forecasts.

War, space, and the evolution of Old World complex societies.

PubMed

Turchin, Peter; Currie, Thomas E; Turner, Edward A L; Gavrilets, Sergey

2013-10-08

How did human societies evolve from small groups, integrated by face-to-face cooperation, to huge anonymous societies of today, typically organized as states? Why is there so much variation in the ability of different human populations to construct viable states? Existing theories are usually formulated as verbal models and, as a result, do not yield sharply defined, quantitative predictions that could be unambiguously tested with data. Here we develop a cultural evolutionary model that predicts where and when the largest-scale complex societies arose in human history. The central premise of the model, which we test, is that costly institutions that enabled large human groups to function without splitting up evolved as a result of intense competition between societies-primarily warfare. Warfare intensity, in turn, depended on the spread of historically attested military technologies (e.g., chariots and cavalry) and on geographic factors (e.g., rugged landscape). The model was simulated within a realistic landscape of the Afroeurasian landmass and its predictions were tested against a large dataset documenting the spatiotemporal distribution of historical large-scale societies in Afroeurasia between 1,500 BCE and 1,500 CE. The model-predicted pattern of spread of large-scale societies was very similar to the observed one. Overall, the model explained 65% of variance in the data. An alternative model, omitting the effect of diffusing military technologies, explained only 16% of variance. Our results support theories that emphasize the role of institutions in state-building and suggest a possible explanation why a long history of statehood is positively correlated with political stability, institutional quality, and income per capita.

War, space, and the evolution of Old World complex societies

PubMed Central

Turchin, Peter; Currie, Thomas E.; Turner, Edward A. L.; Gavrilets, Sergey

2013-01-01

How did human societies evolve from small groups, integrated by face-to-face cooperation, to huge anonymous societies of today, typically organized as states? Why is there so much variation in the ability of different human populations to construct viable states? Existing theories are usually formulated as verbal models and, as a result, do not yield sharply defined, quantitative predictions that could be unambiguously tested with data. Here we develop a cultural evolutionary model that predicts where and when the largest-scale complex societies arose in human history. The central premise of the model, which we test, is that costly institutions that enabled large human groups to function without splitting up evolved as a result of intense competition between societies—primarily warfare. Warfare intensity, in turn, depended on the spread of historically attested military technologies (e.g., chariots and cavalry) and on geographic factors (e.g., rugged landscape). The model was simulated within a realistic landscape of the Afroeurasian landmass and its predictions were tested against a large dataset documenting the spatiotemporal distribution of historical large-scale societies in Afroeurasia between 1,500 BCE and 1,500 CE. The model-predicted pattern of spread of large-scale societies was very similar to the observed one. Overall, the model explained 65% of variance in the data. An alternative model, omitting the effect of diffusing military technologies, explained only 16% of variance. Our results support theories that emphasize the role of institutions in state-building and suggest a possible explanation why a long history of statehood is positively correlated with political stability, institutional quality, and income per capita. PMID:24062433

High-throughput analysis of peptide binding modules

PubMed Central

Liu, Bernard A.; Engelmann, Brett; Nash, Piers D.

2014-01-01

Modular protein interaction domains that recognize linear peptide motifs are found in hundreds of proteins within the human genome. Some protein interaction domains such as SH2, 14-3-3, Chromo and Bromo domains serve to recognize post-translational modification of amino acids (such as phosphorylation, acetylation, methylation etc.) and translate these into discrete cellular responses. Other modules such as SH3 and PDZ domains recognize linear peptide epitopes and serve to organize protein complexes based on localization and regions of elevated concentration. In both cases, the ability to nucleate specific signaling complexes is in large part dependent on the selectivity of a given protein module for its cognate peptide ligand. High throughput analysis of peptide-binding domains by peptide or protein arrays, phage display, mass spectrometry or other HTP techniques provides new insight into the potential protein-protein interactions prescribed by individual or even whole families of modules. Systems level analyses have also promoted a deeper understanding of the underlying principles that govern selective protein-protein interactions and how selectivity evolves. Lastly, there is a growing appreciation for the limitations and potential pitfalls of high-throughput analysis of protein-peptide interactomes. This review will examine some of the common approaches utilized for large-scale studies of protein interaction domains and suggest a set of standards for the analysis and validation of datasets from large-scale studies of peptide-binding modules. We will also highlight how data from large-scale studies of modular interaction domain families can provide insight into systems level properties such as the linguistics of selective interactions. PMID:22610655

Large-eddy simulation of a boundary layer with concave streamwise curvature

NASA Technical Reports Server (NTRS)

Lund, Thomas S.

1994-01-01

Turbulence modeling continues to be one of the most difficult problems in fluid mechanics. Existing prediction methods are well developed for certain classes of simple equilibrium flows, but are still not entirely satisfactory for a large category of complex non-equilibrium flows found in engineering practice. Direct and large-eddy simulation (LES) approaches have long been believed to have great potential for the accurate prediction of difficult turbulent flows, but the associated computational cost has been prohibitive for practical problems. This remains true for direct simulation but is no longer clear for large-eddy simulation. Advances in computer hardware, numerical methods, and subgrid-scale modeling have made it possible to conduct LES for flows or practical interest at Reynolds numbers in the range of laboratory experiments. The objective of this work is to apply ES and the dynamic subgrid-scale model to the flow of a boundary layer over a concave surface.

Metamorphic P-T conditions across the Chugach Metamorphic Complex (Alaska)—A record of focussed exhumation during transpression

NASA Astrophysics Data System (ADS)

Bruand, Emilie; Gasser, Deta; Stüwe, Kurt

2014-03-01

The Chugach Metamorphic Complex (CMC) is a large high-grade metamorphic complex that developed in the Eocene within the Chugach accretionary complex along the margin of Alaska where subduction is still ongoing. The CMC has a conspicuous asymmetric structure with a migmatitic zone flanked in the north and west by amphibolite facies schists and in the south by a metabasite belt. To the north and south, major, crustal-scale fault zones juxtapose the Chugach terrane against much lower-grade and less-deformed sequences belonging to different terranes. Curiously these crustal-scale structures are known to have largely strike slip motion posing the question as to the nature of the exhumation of the high-grade complex between them. However, P-T conditions which would allow an estimation of the amount of exhumation were lacking for large parts of the complex. This paper presents petrographic descriptions, biotite-garnet thermometry, RSCM thermometry, average P-T calculations and pseudosection modelling from three major across-strike transects covering the complex from west to south-east. Our results reveal that, both temperature and pressure vary substantially across the complex. More specifically, peak metamorphic conditions evolve from 4-7 kbar and ~ 550-650 °C in the northern schist zone to 5-11 kbar and ~ 650-750 °C in the migmatite zone in the south of the complex. The higher pressure estimates in the south of the complex indicate that focussed exhumation must have occurred in this area and was probably initiated by the subduction of a high topographic relief (intra-oceanic arc or ridge subduction) and the accretion of the metabasite belt in the south. Exhumation of the CMC occurred in an overall transpressive strain regime, with strike-slip deformation concentrated along the northern Border Range fault zone and thrusting and exhumation focussed within the southern migmatite zone and splay faults of the Contact fault zone. The T/P ratios in the southern migmatite zone indicate that the thermal perturbation of the migmatites is less than previously inferred. These new results, associated with the structural data and the accretion of a metabasite belt in the south of the complex, seem incompatible with the existing ridge-subduction models.

A survey on routing protocols for large-scale wireless sensor networks.

PubMed

Li, Changle; Zhang, Hanxiao; Hao, Binbin; Li, Jiandong

2011-01-01

With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. "Large-scale" means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner and other metrics. Finally some open issues in routing protocol design in large-scale wireless sensor networks and conclusions are proposed.

Resolving the Circumstellar Environment of the Galactic B[e] Supergiant Star MWC 137 from Large to Small Scales

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

Kraus, Michaela; Nickeler, Dieter H.; Liimets, Tiina

The Galactic object MWC 137 has been suggested to belong to the group of B[e] supergiants. However, with its large-scale optical bipolar ring nebula and high-velocity jet and knots, it is a rather atypical representative of this class. We performed multiwavelength observations spreading from the optical to the radio regimes. Based on optical imaging and long-slit spectroscopic data, we found that the northern parts of the large-scale nebula are predominantly blueshifted, while the southern regions appear mostly redshifted. We developed a geometrical model consisting of two double cones. Although various observational features can be approximated with such a scenario, themore » observed velocity pattern is more complex. Using near-infrared integral-field unit spectroscopy, we studied the hot molecular gas in the vicinity of the star. The emission from the hot CO gas arises in a small-scale disk revolving around the star on Keplerian orbits. Although the disk itself cannot be spatially resolved, its emission is reflected by the dust arranged in arc-like structures and the clumps surrounding MWC 137 on small scales. In the radio regime, we mapped the cold molecular gas in the outskirts of the optical nebula. We found that large amounts of cool molecular gas and warm dust embrace the optical nebula in the east, south, and west. No cold gas or dust was detected in the north and northwestern regions. Despite the new insights into the nebula kinematics gained from our studies, the real formation scenario of the large-scale nebula remains an open issue.« less

Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free

PubMed Central

Bianconi, Ginestra; Rahmede, Christoph

2015-01-01

In quantum gravity, several approaches have been proposed until now for the quantum description of discrete geometries. These theoretical frameworks include loop quantum gravity, causal dynamical triangulations, causal sets, quantum graphity, and energetic spin networks. Most of these approaches describe discrete spaces as homogeneous network manifolds. Here we define Complex Quantum Network Manifolds (CQNM) describing the evolution of quantum network states, and constructed from growing simplicial complexes of dimension . We show that in d = 2 CQNM are homogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomogeneities of degrees like most complex networks. From the self-organized evolution of CQNM quantum statistics emerge spontaneously. Here we define the generalized degrees associated with the -faces of the -dimensional CQNMs, and we show that the statistics of these generalized degrees can either follow Fermi-Dirac, Boltzmann or Bose-Einstein distributions depending on the dimension of the -faces. PMID:26356079

Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free.

PubMed

Bianconi, Ginestra; Rahmede, Christoph

2015-09-10

In quantum gravity, several approaches have been proposed until now for the quantum description of discrete geometries. These theoretical frameworks include loop quantum gravity, causal dynamical triangulations, causal sets, quantum graphity, and energetic spin networks. Most of these approaches describe discrete spaces as homogeneous network manifolds. Here we define Complex Quantum Network Manifolds (CQNM) describing the evolution of quantum network states, and constructed from growing simplicial complexes of dimension d. We show that in d = 2 CQNM are homogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomogeneities of degrees like most complex networks. From the self-organized evolution of CQNM quantum statistics emerge spontaneously. Here we define the generalized degrees associated with the δ-faces of the d-dimensional CQNMs, and we show that the statistics of these generalized degrees can either follow Fermi-Dirac, Boltzmann or Bose-Einstein distributions depending on the dimension of the δ-faces.

Ecological hierarchies and self-organisation - Pattern analysis, modelling and process integration across scales

USGS Publications Warehouse

Reuter, H.; Jopp, F.; Blanco-Moreno, J. M.; Damgaard, C.; Matsinos, Y.; DeAngelis, D.L.

2010-01-01

A continuing discussion in applied and theoretical ecology focuses on the relationship of different organisational levels and on how ecological systems interact across scales. We address principal approaches to cope with complex across-level issues in ecology by applying elements of hierarchy theory and the theory of complex adaptive systems. A top-down approach, often characterised by the use of statistical techniques, can be applied to analyse large-scale dynamics and identify constraints exerted on lower levels. Current developments are illustrated with examples from the analysis of within-community spatial patterns and large-scale vegetation patterns. A bottom-up approach allows one to elucidate how interactions of individuals shape dynamics at higher levels in a self-organisation process; e.g., population development and community composition. This may be facilitated by various modelling tools, which provide the distinction between focal levels and resulting properties. For instance, resilience in grassland communities has been analysed with a cellular automaton approach, and the driving forces in rodent population oscillations have been identified with an agent-based model. Both modelling tools illustrate the principles of analysing higher level processes by representing the interactions of basic components.The focus of most ecological investigations on either top-down or bottom-up approaches may not be appropriate, if strong cross-scale relationships predominate. Here, we propose an 'across-scale-approach', closely interweaving the inherent potentials of both approaches. This combination of analytical and synthesising approaches will enable ecologists to establish a more coherent access to cross-level interactions in ecological systems. ?? 2010 Gesellschaft f??r ??kologie.

The PREP pipeline: standardized preprocessing for large-scale EEG analysis

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Kothe, Christian; Su, Kyung-Min; Robbins, Kay A.

2015-01-01

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode. PMID:26150785

Mass-transport deposits and reservoir quality of Upper Cretaceous Chalk within the German Central Graben, North Sea

NASA Astrophysics Data System (ADS)

Arfai, Jashar; Lutz, Rüdiger; Franke, Dieter; Gaedicke, Christoph; Kley, Jonas

2016-04-01

The architecture of intra-chalk deposits in the `Entenschnabel' area of the German North Sea is studied based on 3D seismic data. Adapted from seismic reflection characteristics, four types of mass-transport deposits (MTDs) are distinguished, i.e. slumps, slides, channels and frontal splay deposits. The development of these systems can be linked to inversion tectonics and halotectonic movements of Zechstein salt. Tectonic uplift is interpreted to have caused repeated tilting of the sea floor. This triggered large-scale slump deposition during Turonian-Santonian times. Slump deposits are characterised by chaotic reflection patterns interpreted to result from significant stratal distortion. The south-eastern study area is characterised by a large-scale frontal splay complex. This comprises a network of shallow channel systems arranged in a distributive pattern. Several slide complexes are observed near the Top Chalk in Maastrichtian and Danian sediments. These slides are commonly associated with large incisions into the sediments below. Best reservoir properties with high producible porosities are found in the reworked chalk strata, e.g. Danish North Sea, therefore MTDs detected in the study area are regarded as potential hydrocarbon reservoirs and considered as exploration targets.

Size and structure of Chlorella zofingiensis /FeCl 3 flocs in a shear flow: Algae Floc Structure

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

Wyatt, Nicholas B.; O'Hern, Timothy J.; Shelden, Bion

Flocculation is a promising method to overcome the economic hurdle to separation of algae from its growth medium in large scale operations. But, understanding of the floc structure and the effects of shear on the floc structure are crucial to the large scale implementation of this technique. The floc structure is important because it determines, in large part, the density and settling behavior of the algae. Freshwater algae floc size distributions and fractal dimensions are presented as a function of applied shear rate in a Couette cell using ferric chloride as a flocculant. Comparisons are made with measurements made formore » a polystyrene microparticle model system taken here as well as reported literature results. The algae floc size distributions are found to be self-preserving with respect to shear rate, consistent with literature data for polystyrene. Moreover, three fractal dimensions are calculated which quantitatively characterize the complexity of the floc structure. Low shear rates result in large, relatively dense packed flocs which elongate and fracture as the shear rate is increased. Our results presented here provide crucial information for economically implementing flocculation as a large scale algae harvesting strategy.« less

A Spectral Method for Spatial Downscaling

PubMed Central

Reich, Brian J.; Chang, Howard H.; Foley, Kristen M.

2014-01-01

Summary Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this article, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales. PMID:24965037

Modeling relief demands in an emergency supply chain system under large-scale disasters based on a queuing network.

PubMed

He, Xinhua; Hu, Wenfa

2014-01-01

This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites) located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model.

Implementation of a multi-threaded framework for large-scale scientific applications

DOE PAGES

Sexton-Kennedy, E.; Gartung, Patrick; Jones, C. D.; ...

2015-05-22

The CMS experiment has recently completed the development of a multi-threaded capable application framework. In this paper, we will discuss the design, implementation and application of this framework to production applications in CMS. For the 2015 LHC run, this functionality is particularly critical for both our online and offline production applications, which depend on faster turn-around times and a reduced memory footprint relative to before. These applications are complex codes, each including a large number of physics-driven algorithms. While the framework is capable of running a mix of thread-safe and 'legacy' modules, algorithms running in our production applications need tomore » be thread-safe for optimal use of this multi-threaded framework at a large scale. Towards this end, we discuss the types of changes, which were necessary for our algorithms to achieve good performance of our multithreaded applications in a full-scale application. Lastly performance numbers for what has been achieved for the 2015 run are presented.« less

Limitations and tradeoffs in synchronization of large-scale networks with uncertain links

PubMed Central

Diwadkar, Amit; Vaidya, Umesh

2016-01-01

The synchronization of nonlinear systems connected over large-scale networks has gained popularity in a variety of applications, such as power grids, sensor networks, and biology. Stochastic uncertainty in the interconnections is a ubiquitous phenomenon observed in these physical and biological networks. We provide a size-independent network sufficient condition for the synchronization of scalar nonlinear systems with stochastic linear interactions over large-scale networks. This sufficient condition, expressed in terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the variance and location of the stochastic uncertainty, allows us to define a synchronization margin. We provide an analytical characterization of important trade-offs between the internal nonlinear dynamics, network topology, and uncertainty in synchronization. For nearest neighbour networks, the existence of an optimal number of neighbours with a maximum synchronization margin is demonstrated. An analytical formula for the optimal gain that produces the maximum synchronization margin allows us to compare the synchronization properties of various complex network topologies. PMID:27067994

Modeling Relief Demands in an Emergency Supply Chain System under Large-Scale Disasters Based on a Queuing Network

PubMed Central

He, Xinhua

2014-01-01

This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites) located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model. PMID:24688367

"Tactic": Traffic Aware Cloud for Tiered Infrastructure Consolidation

ERIC Educational Resources Information Center

Sangpetch, Akkarit

2013-01-01

Large-scale enterprise applications are deployed as distributed applications. These applications consist of many inter-connected components with heterogeneous roles and complex dependencies. Each component typically consumes 5-15% of the server capacity. Deploying each component as a separate virtual machine (VM) allows us to consolidate the…

MANAGEMENT OF DIFFUSE POLLUTION IN AGRICULTURAL WATERSHEDS: LESSONS FROM THE MINNESOTA RIVER BASIN. (R825290)

EPA Science Inventory

Abstract

The Minnesota River (Minnesota, USA) receives large non-point source pollutant loads. Complex interactions between agricultural, state agency, environmental groups, and issues of scale make watershed management difficult. Subdividing the basin's 12 major water...

Analysis of genetic diversity using SNP markers in oat

USDA-ARS?s Scientific Manuscript database

A large-scale single nucleotide polymorphism (SNP) discovery was carried out in cultivated oat using Roche 454 sequencing methods. DNA sequences were generated from cDNAs originating from a panel of 20 diverse oat cultivars, and from Diversity Array Technology (DArT) genomic complexity reductions fr...

Autonomous Energy Grids | Grid Modernization | NREL

Science.gov Websites

control themselves using advanced machine learning and simulation to create resilient, reliable, and affordable optimized energy systems. Current frameworks to monitor, control, and optimize large-scale energy of optimization theory, control theory, big data analytics, and complex system theory and modeling to

Economizing Education: Assessment Algorithms and Calculative Agencies

ERIC Educational Resources Information Center

O'Keeffe, Cormac

2017-01-01

International Large Scale Assessments have been producing data about educational attainment for over 60 years. More recently however, these assessments as tests have become digitally and computationally complex and increasingly rely on the calculative work performed by algorithms. In this article I first consider the coordination of relations…

New Roles for Occupational Instructors.

ERIC Educational Resources Information Center

Campbell, Dale F.

Changes in the future role of occupational instructors which will be brought about by advances in educational technology are illustrated by the description of the Advanced Instructional System (AIS), a complex approach to occupational training which permits large-scale application of individualized instruction through the use of computer-assisted…

Structural Equation Modeling of School Violence Data: Methodological Considerations

ERIC Educational Resources Information Center

Mayer, Matthew J.

2004-01-01

Methodological challenges associated with structural equation modeling (SEM) and structured means modeling (SMM) in research on school violence and related topics in the social and behavioral sciences are examined. Problems associated with multiyear implementations of large-scale surveys are discussed. Complex sample designs, part of any…

Evolution of scaling emergence in large-scale spatial epidemic spreading.

PubMed

Wang, Lin; Li, Xiang; Zhang, Yi-Qing; Zhang, Yan; Zhang, Kan

2011-01-01

Zipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which has still hardly been clarified. In this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence. The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease.

Solar Wind Turbulent Cascade from MHD to Sub-ion Scales: Large-size 3D Hybrid Particle-in-cell Simulations

NASA Astrophysics Data System (ADS)

Franci, Luca; Landi, Simone; Verdini, Andrea; Matteini, Lorenzo; Hellinger, Petr

2018-01-01

Properties of the turbulent cascade from fluid to kinetic scales in collisionless plasmas are investigated by means of large-size 3D hybrid (fluid electrons, kinetic protons) particle-in-cell simulations. Initially isotropic Alfvénic fluctuations rapidly develop a strongly anisotropic turbulent cascade, mainly in the direction perpendicular to the ambient magnetic field. The omnidirectional magnetic field spectrum shows a double power-law behavior over almost two decades in wavenumber, with a Kolmogorov-like index at large scales, a spectral break around ion scales, and a steepening at sub-ion scales. Power laws are also observed in the spectra of the ion bulk velocity, density, and electric field, at both magnetohydrodynamic (MHD) and kinetic scales. Despite the complex structure, the omnidirectional spectra of all fields at ion and sub-ion scales are in remarkable quantitative agreement with those of a 2D simulation with similar physical parameters. This provides a partial, a posteriori validation of the 2D approximation at kinetic scales. Conversely, at MHD scales, the spectra of the density and of the velocity (and, consequently, of the electric field) exhibit differences between the 2D and 3D cases. Although they can be partly ascribed to the lower spatial resolution, the main reason is likely the larger importance of compressible effects in the full 3D geometry. Our findings are also in remarkable quantitative agreement with solar wind observations.

Patterns and Variation in Benthic Biodiversity in a Large Marine Ecosystem

PubMed Central

Lee, Jonathan D.

2015-01-01

While there is a persistent inverse relationship between latitude and species diversity across many taxa and ecosystems, deviations from this norm offer an opportunity to understand the conditions that contribute to large-scale diversity patterns. Marine systems, in particular, provide such an opportunity, as marine diversity does not always follow a strict latitudinal gradient, perhaps because several hypothesized drivers of the latitudinal diversity gradient are uncorrelated in marine systems. We used a large scale public monitoring dataset collected over an eight year period to examine benthic marine faunal biodiversity patterns for the continental shelf (55–183 m depth) and slope habitats (184–1280 m depth) off the US West Coast (47°20′N—32°40′N). We specifically asked whether marine biodiversity followed a strict latitudinal gradient, and if these latitudinal patterns varied across depth, in different benthic substrates, and over ecological time scales. Further, we subdivided our study area into three smaller regions to test whether coast-wide patterns of biodiversity held at regional scales, where local oceanographic processes tend to influence community structure and function. Overall, we found complex patterns of biodiversity on both the coast-wide and regional scales that differed by taxonomic group. Importantly, marine biodiversity was not always highest at low latitudes. We found that latitude, depth, substrate, and year were all important descriptors of fish and invertebrate diversity. Invertebrate richness and taxonomic diversity were highest at high latitudes and in deeper waters. Fish richness also increased with latitude, but exhibited a hump-shaped relationship with depth, increasing with depth up to the continental shelf break, ~200 m depth, and then decreasing in deeper waters. We found relationships between fish taxonomic and functional diversity and latitude, depth, substrate, and time at the regional scale, but not at the coast-wide scale, suggesting that coast-wide patterns can obscure important correlates at smaller scales. Our study provides insight into complex diversity patterns of the deep water soft substrate benthic ecosystems off the US West Coast. PMID:26308521

Collective evolution of submicron hillocks during the early stages of anisotropic alkaline wet chemical etching of Si(1 0 0) surfaces

NASA Astrophysics Data System (ADS)

Sana, P.; Vázquez, Luis; Cuerno, Rodolfo; Sarkar, Subhendu

2017-11-01

We address experimentally the large-scale dynamics of Si(1 0 0) surfaces during the initial stages of anisotropic wet (KOH) chemical etching, which are characterized through atomic force microscopy. These systems are known to lead to the formation of characteristic pyramids, or hillocks, of typical sizes in the nanometric/micrometer scales, thus with the potential for a large number of applications that can benefit from the nanotexturing of Si surfaces. The present pattern formation process is very strongly disordered in space. We assess the space correlations in such a type of rough surface and elucidate the existence of a complex and rich morphological evolution, featuring at least three different regimes in just 10 min of etching. Such a complex time behavior cannot be consistently explained within a single formalism for dynamic scaling. The pyramidal structure reveals itself as the basic morphological motif of the surface throughout the dynamics. A detailed analysis of the surface slope distribution with etching time reveals that the texturing process induced by the KOH etching is rather gradual and progressive, which accounts for the dynamic complexity. The various stages of the morphological evolution can be accurately reproduced by computer-generated surfaces composed by uncorrelated pyramidal structures. To reach such an agreement, the key parameters are the average pyramid size, which increases with etching time, its distribution and the surface coverage by the pyramidal structures.

An instrument design and sample strategy for measuring soil respiration in the coastal temperate rain forest

NASA Astrophysics Data System (ADS)

Nay, S. M.; D'Amore, D. V.

2009-12-01

The coastal temperate rainforest (CTR) along the northwest coast of North America is a large and complex mosaic of forests and wetlands located on an undulating terrain ranging from sea level to thousands of meters in elevation. This biome stores a dynamic portion of the total carbon stock of North America. The fate of the terrestrial carbon stock is of concern due to the potential for mobilization and export of this store to both the atmosphere as carbon respiration flux and ocean as dissolved organic and inorganic carbon flux. Soil respiration is the largest export vector in the system and must be accurately measured to gain any comprehensive understanding of how carbon moves though this system. Suitable monitoring tools capable of measuring carbon fluxes at small spatial scales are essential for our understanding of carbon dynamics at larger spatial scales within this complex assemblage of ecosystems. We have adapted instrumentation and developed a sampling strategy for optimizing replication of soil respiration measurements to quantify differences among spatially complex landscape units of the CTR. We start with the design of the instrument to ease the technological, ergonomic and financial barriers that technicians encounter in monitoring the efflux of CO2 from the soil. Our sampling strategy optimizes the physical efforts of the field work and manages for the high variation of flux measurements encountered in this difficult environment of rough terrain, dense vegetation and wet climate. Our soil respirometer incorporates an infra-red gas analyzer (LiCor Inc. LI-820) and an 8300 cm3 soil respiration chamber; the device is durable, lightweight, easy to operate and can be built for under $5000 per unit. The modest unit price allows for a multiple unit fleet to be deployed and operated in an intensive field monitoring campaign. We use a large 346 cm2 collar to accommodate as much micro spatial variation as feasible and to facilitate repeated measures for tracking temporal trends. Our collar design minimizes root interference yet provides a highly stable platform for coupling with the respirometer. Meso-scale variability characterized by large down woody debris, wind throw pits and mounds and surface roots is negotiated with by a hexagonal array of seven collars at two meter spacing (sample pod). Landscape scale variability is managed through stratification and replication amongst ecosystem types arrayed across a hydrologic gradient from bogs to forested wetlands to upland forests. Our strategy has allowed us to gather data sets consisting of approximately 1800 total observations with approximately 600 measurements per replication per year. Mean coefficients of variation (CV) at the collar (micro-scale) were approximately 0.67. The pod level mean CV was reduced to approximately 0.29 at the pod (meso-scale). The CV at the vegetation strata were 0.43, 0.18 and 0.21 for bog, forested wetland and upland forest respectively. With temperature and hydrological data we are able to measure and model carbon dynamics in this large and complex environment. The analysis of variability at the three spatial scales has confirmed that our approach is capturing and constraining the variability.

In-chip direct laser writing of a centimeter-scale acoustic micromixer

NASA Astrophysics Data System (ADS)

van't Oever, Jorick; Spannenburg, Niels; Offerhaus, Herman; van den Ende, Dirk; Herek, Jennifer; Mugele, Frieder

2015-04-01

A centimeter-scale micromixer was fabricated by two-photon polymerization inside a closed microchannel using direct laser writing. The structure consists of a repeating pattern of 20 μm×20 μm×155 μm acrylate pillars and extends over 1.2 cm. Using external ultrasonic actuation, the micropillars locally induce streaming with flow speeds of 30 μm s-1. The fabrication method allows for large flexibility and more complex designs.

Graph-based linear scaling electronic structure theory.

PubMed

Niklasson, Anders M N; Mniszewski, Susan M; Negre, Christian F A; Cawkwell, Marc J; Swart, Pieter J; Mohd-Yusof, Jamal; Germann, Timothy C; Wall, Michael E; Bock, Nicolas; Rubensson, Emanuel H; Djidjev, Hristo

2016-06-21

We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including challenging systems such as biomolecules. The methodology combines well-controlled accuracy, low computational cost, and natural low-communication parallelism. This combination addresses substantial shortcomings of linear scaling electronic structure theory, in particular with respect to quantum-based molecular dynamics simulations.

Graph-based linear scaling electronic structure theory

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

Niklasson, Anders M. N., E-mail: amn@lanl.gov; Negre, Christian F. A.; Cawkwell, Marc J.

2016-06-21

We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including challenging systems such as biomolecules. The methodology combines well-controlled accuracy, low computational cost, and natural low-communication parallelism. This combination addresses substantial shortcomings of linear scaling electronic structure theory, in particular with respect to quantum-based molecular dynamics simulations.

Evaluation of integration methods for hybrid simulation of complex structural systems through collapse

NASA Astrophysics Data System (ADS)

Del Carpio R., Maikol; Hashemi, M. Javad; Mosqueda, Gilberto

2017-10-01

This study examines the performance of integration methods for hybrid simulation of large and complex structural systems in the context of structural collapse due to seismic excitations. The target application is not necessarily for real-time testing, but rather for models that involve large-scale physical sub-structures and highly nonlinear numerical models. Four case studies are presented and discussed. In the first case study, the accuracy of integration schemes including two widely used methods, namely, modified version of the implicit Newmark with fixed-number of iteration (iterative) and the operator-splitting (non-iterative) is examined through pure numerical simulations. The second case study presents the results of 10 hybrid simulations repeated with the two aforementioned integration methods considering various time steps and fixed-number of iterations for the iterative integration method. The physical sub-structure in these tests consists of a single-degree-of-freedom (SDOF) cantilever column with replaceable steel coupons that provides repeatable highlynonlinear behavior including fracture-type strength and stiffness degradations. In case study three, the implicit Newmark with fixed-number of iterations is applied for hybrid simulations of a 1:2 scale steel moment frame that includes a relatively complex nonlinear numerical substructure. Lastly, a more complex numerical substructure is considered by constructing a nonlinear computational model of a moment frame coupled to a hybrid model of a 1:2 scale steel gravity frame. The last two case studies are conducted on the same porotype structure and the selection of time steps and fixed number of iterations are closely examined in pre-test simulations. The generated unbalance forces is used as an index to track the equilibrium error and predict the accuracy and stability of the simulations.

Demonstration of reduced-order urban scale building energy models

DOE PAGES

Heidarinejad, Mohammad; Mattise, Nicholas; Dahlhausen, Matthew; ...

2017-09-08

The aim of this study is to demonstrate a developed framework to rapidly create urban scale reduced-order building energy models using a systematic summary of the simplifications required for the representation of building exterior and thermal zones. These urban scale reduced-order models rely on the contribution of influential variables to the internal, external, and system thermal loads. OpenStudio Application Programming Interface (API) serves as a tool to automate the process of model creation and demonstrate the developed framework. The results of this study show that the accuracy of the developed reduced-order building energy models varies only up to 10% withmore » the selection of different thermal zones. In addition, to assess complexity of the developed reduced-order building energy models, this study develops a novel framework to quantify complexity of the building energy models. Consequently, this study empowers the building energy modelers to quantify their building energy model systematically in order to report the model complexity alongside the building energy model accuracy. An exhaustive analysis on four university campuses suggests that the urban neighborhood buildings lend themselves to simplified typical shapes. Specifically, building energy modelers can utilize the developed typical shapes to represent more than 80% of the U.S. buildings documented in the CBECS database. One main benefits of this developed framework is the opportunity for different models including airflow and solar radiation models to share the same exterior representation, allowing a unifying exchange data. Altogether, the results of this study have implications for a large-scale modeling of buildings in support of urban energy consumption analyses or assessment of a large number of alternative solutions in support of retrofit decision-making in the building industry.« less

Demonstration of reduced-order urban scale building energy models

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

Heidarinejad, Mohammad; Mattise, Nicholas; Dahlhausen, Matthew

The aim of this study is to demonstrate a developed framework to rapidly create urban scale reduced-order building energy models using a systematic summary of the simplifications required for the representation of building exterior and thermal zones. These urban scale reduced-order models rely on the contribution of influential variables to the internal, external, and system thermal loads. OpenStudio Application Programming Interface (API) serves as a tool to automate the process of model creation and demonstrate the developed framework. The results of this study show that the accuracy of the developed reduced-order building energy models varies only up to 10% withmore » the selection of different thermal zones. In addition, to assess complexity of the developed reduced-order building energy models, this study develops a novel framework to quantify complexity of the building energy models. Consequently, this study empowers the building energy modelers to quantify their building energy model systematically in order to report the model complexity alongside the building energy model accuracy. An exhaustive analysis on four university campuses suggests that the urban neighborhood buildings lend themselves to simplified typical shapes. Specifically, building energy modelers can utilize the developed typical shapes to represent more than 80% of the U.S. buildings documented in the CBECS database. One main benefits of this developed framework is the opportunity for different models including airflow and solar radiation models to share the same exterior representation, allowing a unifying exchange data. Altogether, the results of this study have implications for a large-scale modeling of buildings in support of urban energy consumption analyses or assessment of a large number of alternative solutions in support of retrofit decision-making in the building industry.« less

LETTER TO THE EDITOR: Iteratively-coupled propagating exterior complex scaling method for electron hydrogen collisions

NASA Astrophysics Data System (ADS)

Bartlett, Philip L.; Stelbovics, Andris T.; Bray, Igor

2004-02-01

A newly-derived iterative coupling procedure for the propagating exterior complex scaling (PECS) method is used to efficiently calculate the electron-impact wavefunctions for atomic hydrogen. An overview of this method is given along with methods for extracting scattering cross sections. Differential scattering cross sections at 30 eV are presented for the electron-impact excitation to the n = 1, 2, 3 and 4 final states, for both PECS and convergent close coupling (CCC), which are in excellent agreement with each other and with experiment. PECS results are presented at 27.2 eV and 30 eV for symmetric and asymmetric energy-sharing triple differential cross sections, which are in excellent agreement with CCC and exterior complex scaling calculations, and with experimental data. At these intermediate energies, the efficiency of the PECS method with iterative coupling has allowed highly accurate partial-wave solutions of the full Schrödinger equation, for L les 50 and a large number of coupled angular momentum states, to be obtained with minimal computing resources.

Habitat Complexity Metrics to Guide Restoration of Large Rivers

NASA Astrophysics Data System (ADS)

Jacobson, R. B.; McElroy, B. J.; Elliott, C.; DeLonay, A.

2011-12-01

Restoration strategies on large, channelized rivers typically strive to recover lost habitat complexity, based on the assumption complexity and biophysical capacity are directly related. Although definition of links between complexity and biotic responses can be tenuous, complexity metrics have appeal because of their potential utility in quantifying habitat quality, defining reference conditions and design criteria, and measuring restoration progress. Hydroacoustic instruments provide many ways to measure complexity on large rivers, yet substantive questions remain about variables and scale of complexity that are meaningful to biota, and how complexity can be measured and monitored cost effectively. We explore these issues on the Missouri River, using the example of channel re-engineering projects that are intended to aid in recovery of the pallid sturgeon, an endangered benthic fish. We are refining understanding of what habitat complexity means for adult fish by combining hydroacoustic habitat assessments with acoustic telemetry to map locations during reproductive migrations and spawning. These data indicate that migrating sturgeon select points with relatively low velocity but adjacent to areas of high velocity (that is, with high velocity gradients); the integration of points defines pathways which minimize energy expenditures during upstream migrations of 10's to 100's of km. Complexity metrics that efficiently quantify migration potential at the reach scale are therefore directly relevant to channel restoration strategies. We are also exploring complexity as it relates to larval sturgeon dispersal. Larvae may drift for as many as 17 days (100's of km at mean velocities) before using up their yolk sac, after which they "settle" into habitats where they initiate feeding. An assumption underlying channel re-engineering is that additional channel complexity, specifically increased shallow, slow water, is necessary for early feeding and refugia. Development of complexity metrics is complicated by the fact that characteristics of channel morphology may increase complexity scores without necessarily increasing biophysical capacity for target species. For example, a cross section that samples depths and velocities across the thalweg (navigation channel) and into lentic habitat may score high on most measures of hydraulic or geomorphic complexity, but does not necessarily provide habitats beneficial to native species. Complexity measures need to be bounded by best estimates of native species requirements. In the absence of specific information, creation of habitat complexity for the sake of complexity may lead to unintended consequences, for example, lentic habitats that increase a complexity score but support invasive species. An additional practical constraint on complexity measures is the need to develop metrics that are can be deployed cost-effectively in an operational monitoring program. Design of a monitoring program requires informed choices of measurement variables, definition of reference sites, and design of sampling effort to capture spatial and temporal variability.

A Comprehensive Analysis of Multiscale Field-Aligned Currents: Characteristics, Controlling Parameters, and Relationships

NASA Astrophysics Data System (ADS)

McGranaghan, Ryan M.; Mannucci, Anthony J.; Forsyth, Colin

2017-12-01

We explore the characteristics, controlling parameters, and relationships of multiscale field-aligned currents (FACs) using a rigorous, comprehensive, and cross-platform analysis. Our unique approach combines FAC data from the Swarm satellites and the Advanced Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) to create a database of small-scale (˜10-150 km, <1° latitudinal width), mesoscale (˜150-250 km, 1-2° latitudinal width), and large-scale (>250 km) FACs. We examine these data for the repeatable behavior of FACs across scales (i.e., the characteristics), the dependence on the interplanetary magnetic field orientation, and the degree to which each scale "departs" from nominal large-scale specification. We retrieve new information by utilizing magnetic latitude and local time dependence, correlation analyses, and quantification of the departure of smaller from larger scales. We find that (1) FACs characteristics and dependence on controlling parameters do not map between scales in a straight forward manner, (2) relationships between FAC scales exhibit local time dependence, and (3) the dayside high-latitude region is characterized by remarkably distinct FAC behavior when analyzed at different scales, and the locations of distinction correspond to "anomalous" ionosphere-thermosphere behavior. Comparing with nominal large-scale FACs, we find that differences are characterized by a horseshoe shape, maximizing across dayside local times, and that difference magnitudes increase when smaller-scale observed FACs are considered. We suggest that both new physics and increased resolution of models are required to address the multiscale complexities. We include a summary table of our findings to provide a quick reference for differences between multiscale FACs.

The cosmic ray muon tomography facility based on large scale MRPC detectors

NASA Astrophysics Data System (ADS)

Wang, Xuewu; Zeng, Ming; Zeng, Zhi; Wang, Yi; Zhao, Ziran; Yue, Xiaoguang; Luo, Zhifei; Yi, Hengguan; Yu, Baihui; Cheng, Jianping

2015-06-01

Cosmic ray muon tomography is a novel technology to detect high-Z material. A prototype of TUMUTY with 73.6 cm×73.6 cm large scale position sensitive MRPC detectors has been developed and is introduced in this paper. Three test kits have been tested and image is reconstructed using MAP algorithm. The reconstruction results show that the prototype is working well and the objects with complex structure and small size (20 mm) can be imaged on it, while the high-Z material is distinguishable from the low-Z one. This prototype provides a good platform for our further studies of the physical characteristics and the performances of cosmic ray muon tomography.

Automated microscopy for high-content RNAi screening

PubMed Central

2010-01-01

Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs. PMID:20176920

Scaling-up NLP Pipelines to Process Large Corpora of Clinical Notes.

PubMed

Divita, G; Carter, M; Redd, A; Zeng, Q; Gupta, K; Trautner, B; Samore, M; Gundlapalli, A

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". This paper describes the scale-up efforts at the VA Salt Lake City Health Care System to address processing large corpora of clinical notes through a natural language processing (NLP) pipeline. The use case described is a current project focused on detecting the presence of an indwelling urinary catheter in hospitalized patients and subsequent catheter-associated urinary tract infections. An NLP algorithm using v3NLP was developed to detect the presence of an indwelling urinary catheter in hospitalized patients. The algorithm was tested on a small corpus of notes on patients for whom the presence or absence of a catheter was already known (reference standard). In planning for a scale-up, we estimated that the original algorithm would have taken 2.4 days to run on a larger corpus of notes for this project (550,000 notes), and 27 days for a corpus of 6 million records representative of a national sample of notes. We approached scaling-up NLP pipelines through three techniques: pipeline replication via multi-threading, intra-annotator threading for tasks that can be further decomposed, and remote annotator services which enable annotator scale-out. The scale-up resulted in reducing the average time to process a record from 206 milliseconds to 17 milliseconds or a 12- fold increase in performance when applied to a corpus of 550,000 notes. Purposely simplistic in nature, these scale-up efforts are the straight forward evolution from small scale NLP processing to larger scale extraction without incurring associated complexities that are inherited by the use of the underlying UIMA framework. These efforts represent generalizable and widely applicable techniques that will aid other computationally complex NLP pipelines that are of need to be scaled out for processing and analyzing big data.

Approximate kernel competitive learning.

PubMed

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Robust regression for large-scale neuroimaging studies.

PubMed

Fritsch, Virgile; Da Mota, Benoit; Loth, Eva; Varoquaux, Gaël; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Brühl, Rüdiger; Butzek, Brigitte; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Lemaitre, Hervé; Mann, Karl; Nees, Frauke; Paus, Tomas; Schad, Daniel J; Schümann, Gunter; Frouin, Vincent; Poline, Jean-Baptiste; Thirion, Bertrand

2015-05-01

Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain-behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies. Copyright © 2015 Elsevier Inc. All rights reserved.

Detecting communities in large networks

NASA Astrophysics Data System (ADS)

Capocci, A.; Servedio, V. D. P.; Caldarelli, G.; Colaiori, F.

2005-07-01

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.

Foundational perspectives on causality in large-scale brain networks

NASA Astrophysics Data System (ADS)

Mannino, Michael; Bressler, Steven L.

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.

Foundational perspectives on causality in large-scale brain networks.

PubMed

Mannino, Michael; Bressler, Steven L

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain. Copyright © 2015 Elsevier B.V. All rights reserved.

Multiresolution persistent homology for excessively large biomolecular datasets

NASA Astrophysics Data System (ADS)

Xia, Kelin; Zhao, Zhixiong; Wei, Guo-Wei

2015-10-01

Although persistent homology has emerged as a promising tool for the topological simplification of complex data, it is computationally intractable for large datasets. We introduce multiresolution persistent homology to handle excessively large datasets. We match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. We utilize flexibility-rigidity index to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution of the rigidity density, we are able to focus the topological lens on the scale of interest. The proposed multiresolution topological analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA molecules. In particular, the topological persistence of a virus capsid with 273 780 atoms is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks, and graphs.

A large-scale initiative to disseminate an evidence-based drug abuse prevention program in Italy: Lessons learned for practitioners and researchers.

PubMed

Velasco, Veronica; Griffin, Kenneth W; Antichi, Mariella; Celata, Corrado

2015-10-01

Across developed countries, experimentation with alcohol, tobacco, and other drugs often begins in the early adolescent years. Several evidence-based programs have been developed to prevent adolescent substance use. Many of the most rigorously tested and empirically supported prevention programs were initially developed and tested in the United States. Increasingly, these interventions are being adopted for use in Europe and throughout the world. This paper reports on a large-scale comprehensive initiative designed to select, adapt, implement, and sustain an evidence-based drug abuse prevention program in Italy. As part of a large-scale regionally funded collaboration in the Lombardy region of Italy, we report on processes through which a team of stakeholders selected, translated and culturally adapted, planned, implemented and evaluated the Life Skills Training (LST) school-based drug abuse prevention program, an evidence-based intervention developed in the United States. We discuss several challenges and lessons learned and implications for prevention practitioners and researchers attempting to undertake similar international dissemination projects. We review several published conceptual models designed to promote the replication and widespread dissemination of effective programs, and discuss their strengths and limitations in the context of planning and implementing a complex, large-scale real-world dissemination effort. Copyright © 2015 Elsevier Ltd. All rights reserved.

Observations of a nearby filament of galaxy clusters with the Sardinia Radio Telescope

NASA Astrophysics Data System (ADS)

Vacca, Valentina; Murgia, M.; Loi, F. Govoni F.; Vazza, F.; Finoguenov, A.; Carretti, E.; Feretti, L.; Giovannini, G.; Concu, R.; Melis, A.; Gheller, C.; Paladino, R.; Poppi, S.; Valente, G.; Bernardi, G.; Boschin, W.; Brienza, M.; Clarke, T. E.; Colafrancesco, S.; Enßlin, T.; Ferrari, C.; de Gasperin, F.; Gastaldello, F.; Girardi, M.; Gregorini, L.; Johnston-Hollitt, M.; Junklewitz, H.; Orrù, E.; Parma, P.; Perley, R.; Taylor, G. B.

2018-05-01

We report the detection of diffuse radio emission which might be connected to a large-scale filament of the cosmic web covering a 8° × 8° area in the sky, likely associated with a z≈0.1 over-density traced by nine massive galaxy clusters. In this work, we present radio observations of this region taken with the Sardinia Radio Telescope. Two of the clusters in the field host a powerful radio halo sustained by violent ongoing mergers and provide direct proof of intra-cluster magnetic fields. In order to investigate the presence of large-scale diffuse radio synchrotron emission in and beyond the galaxy clusters in this complex system, we combined the data taken at 1.4 GHz with the Sardinia Radio Telescope with higher resolution data taken with the NRAO VLA Sky Survey. We found 28 candidate new sources with a size larger and X-ray emission fainter than known diffuse large-scale synchrotron cluster sources for a given radio power. This new population is potentially the tip of the iceberg of a class of diffuse large-scale synchrotron sources associated with the filaments of the cosmic web. In addition, we found in the field a candidate new giant radio galaxy.

A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information.

PubMed

Wei, Wei; Gao, Bin; Liu, Tie-Yan; Wang, Taifeng; Li, Guohui; Li, Hang

2016-04-01

Graph-based ranking has been extensively studied and frequently applied in many applications, such as webpage ranking. It aims at mining potentially valuable information from the raw graph-structured data. Recently, with the proliferation of rich heterogeneous information (e.g., node/edge features and prior knowledge) available in many real-world graphs, how to effectively and efficiently leverage all information to improve the ranking performance becomes a new challenging problem. Previous methods only utilize part of such information and attempt to rank graph nodes according to link-based methods, of which the ranking performances are severely affected by several well-known issues, e.g., over-fitting or high computational complexity, especially when the scale of graph is very large. In this paper, we address the large-scale graph-based ranking problem and focus on how to effectively exploit rich heterogeneous information of the graph to improve the ranking performance. Specifically, we propose an innovative and effective semi-supervised PageRank (SSP) approach to parameterize the derived information within a unified semi-supervised learning framework (SSLF-GR), then simultaneously optimize the parameters and the ranking scores of graph nodes. Experiments on the real-world large-scale graphs demonstrate that our method significantly outperforms the algorithms that consider such graph information only partially.

Allometric scaling enhances stability in complex food webs.

PubMed

Brose, Ulrich; Williams, Richard J; Martinez, Neo D

2006-11-01

Classic local stability theory predicts that complex ecological networks are unstable and are unlikely to persist despite empiricists' abundant documentation of such complexity in nature. This contradiction has puzzled biologists for decades. While some have explored how stability may be achieved in small modules of a few interacting species, rigorous demonstrations of how large complex and ecologically realistic networks dynamically persist remain scarce and inadequately understood. Here, we help fill this void by combining structural models of complex food webs with nonlinear bioenergetic models of population dynamics parameterized by biological rates that are allometrically scaled to populations' average body masses. Increasing predator-prey body mass ratios increase population persistence up to a saturation level that is reached by invertebrate and ectotherm vertebrate predators when being 10 or 100 times larger than their prey respectively. These values are corroborated by empirical predator-prey body mass ratios from a global data base. Moreover, negative effects of diversity (i.e. species richness) on stability (i.e. population persistence) become neutral or positive relationships at these empirical ratios. These results demonstrate that the predator-prey body mass ratios found in nature may be key to enabling persistence of populations in complex food webs and stabilizing the diversity of natural ecosystems.

Interpretation of Recent Temperature Trends in California

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

Duffy, P B; Bonfils, C; Lobell, D

2007-09-21

Regional-scale climate change and associated societal impacts result from large-scale (e.g. well-mixed greenhouse gases) and more local (e.g. land-use change) 'forcing' (perturbing) agents. It is essential to understand these forcings and climate responses to them, in order to predict future climate and societal impacts. California is a fine example of the complex effects of multiple climate forcings. The State's natural climate is diverse, highly variable, and strongly influenced by ENSO. Humans are perturbing this complex system through urbanization, irrigation, and emission of multiple types of aerosols and greenhouse gases. Despite better-than-average observational coverage, we are only beginning to understand themore » manifestations of these forcings in California's temperature record.« less

Large-scale removal of invasive honeysuckle decreases mosquito and avian host abundance

USDA-ARS?s Scientific Manuscript database

Invasive species rank second only to habitat destruction as a threat to native biodiversity. One consequence of biological invasions is altered risk of exposure to infectious diseases in human and animal populations. The distribution and prevalence of mosquito-borne diseases depend on the complex in...

Coordinating the Commons: Diversity & Dynamics in Open Collaborations

ERIC Educational Resources Information Center

Morgan, Jonathan T.

2013-01-01

The success of Wikipedia demonstrates that open collaboration can be an effective model for organizing geographically-distributed volunteers to perform complex, sustained work at a massive scale. However, Wikipedia's history also demonstrates some of the challenges that large, long-term open collaborations face: the core community of Wikipedia…

Nuclear Pasta: Topology and Defects

NASA Astrophysics Data System (ADS)

da Silva Schneider, Andre; Horowitz, Charles; Berry, Don; Caplan, Matt; Briggs, Christian

2015-04-01

A layer of complex non-uniform phases of matter known as nuclear pasta is expected to exist at the base of the crust of neutron stars. Using large scale molecular dynamics we study the topology of some pasta shapes, the formation of defects and how these may affect properties of neutron star crusts.

Scale-invariance underlying the logistic equation and its social applications

NASA Astrophysics Data System (ADS)

Hernando, A.; Plastino, A.

2013-01-01

On the basis of dynamical principles we i) advance a derivation of the Logistic Equation (LE), widely employed (among multiple applications) in the simulation of population growth, and ii) demonstrate that scale-invariance and a mean-value constraint are sufficient and necessary conditions for obtaining it. We also generalize the LE to multi-component systems and show that the above dynamical mechanisms underlie a large number of scale-free processes. Examples are presented regarding city-populations, diffusion in complex networks, and popularity of technological products, all of them obeying the multi-component logistic equation in an either stochastic or deterministic way.

A 3D model of polarized dust emission in the Milky Way

NASA Astrophysics Data System (ADS)

Martínez-Solaeche, Ginés; Karakci, Ata; Delabrouille, Jacques

2018-05-01

We present a three-dimensional model of polarized galactic dust emission that takes into account the variation of the dust density, spectral index and temperature along the line of sight, and contains randomly generated small-scale polarization fluctuations. The model is constrained to match observed dust emission on large scales, and match on smaller scales extrapolations of observed intensity and polarization power spectra. This model can be used to investigate the impact of plausible complexity of the polarized dust foreground emission on the analysis and interpretation of future cosmic microwave background polarization observations.

Large scale affinity calculations of cyclodextrin host-guest complexes: Understanding the role of reorganization in the molecular recognition process

PubMed Central

Wickstrom, Lauren; He, Peng; Gallicchio, Emilio; Levy, Ronald M.

2013-01-01

Host-guest inclusion complexes are useful models for understanding the structural and energetic aspects of molecular recognition. Due to their small size relative to much larger protein-ligand complexes, converged results can be obtained rapidly for these systems thus offering the opportunity to more reliably study fundamental aspects of the thermodynamics of binding. In this work, we have performed a large scale binding affinity survey of 57 β-cyclodextrin (CD) host guest systems using the binding energy distribution analysis method (BEDAM) with implicit solvation (OPLS-AA/AGBNP2). Converged estimates of the standard binding free energies are obtained for these systems by employing techniques such as parallel Hamitionian replica exchange molecular dynamics, conformational reservoirs and multistate free energy estimators. Good agreement with experimental measurements is obtained in terms of both numerical accuracy and affinity rankings. Overall, average effective binding energies reproduce affinity rank ordering better than the calculated binding affinities, even though calculated binding free energies, which account for effects such as conformational strain and entropy loss upon binding, provide lower root mean square errors when compared to measurements. Interestingly, we find that binding free energies are superior rank order predictors for a large subset containing the most flexible guests. The results indicate that, while challenging, accurate modeling of reorganization effects can lead to ligand design models of superior predictive power for rank ordering relative to models based only on ligand-receptor interaction energies. PMID:25147485

Java Performance for Scientific Applications on LLNL Computer Systems

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

Kapfer, C; Wissink, A

2002-05-10

Languages in use for high performance computing at the laboratory--Fortran (f77 and f90), C, and C++--have many years of development behind them and are generally considered the fastest available. However, Fortran and C do not readily extend to object-oriented programming models, limiting their capability for very complex simulation software. C++ facilitates object-oriented programming but is a very complex and error-prone language. Java offers a number of capabilities that these other languages do not. For instance it implements cleaner (i.e., easier to use and less prone to errors) object-oriented models than C++. It also offers networking and security as part ofmore » the language standard, and cross-platform executables that make it architecture neutral, to name a few. These features have made Java very popular for industrial computing applications. The aim of this paper is to explain the trade-offs in using Java for large-scale scientific applications at LLNL. Despite its advantages, the computational science community has been reluctant to write large-scale computationally intensive applications in Java due to concerns over its poor performance. However, considerable progress has been made over the last several years. The Java Grande Forum [1] has been promoting the use of Java for large-scale computing. Members have introduced efficient array libraries, developed fast just-in-time (JIT) compilers, and built links to existing packages used in high performance parallel computing.« less

An Overview of Nitrogen Cycling in a Semiarid Savanna: Some Implications for Management and Conservation in a Large African Park

NASA Astrophysics Data System (ADS)

Coetsee, Corli; Jacobs, Shayne; Govender, Navashni

2012-02-01

Nitrogen (N) is a major control on primary productivity and hence on the productivity and diversity of secondary producers and consumers. As such, ecosystem structure and function cannot be understood without a comprehensive understanding of N cycling and dynamics. This overview describes the factors that govern N distribution and dynamics and the consequences that variable N dynamics have for structure, function and thresholds of potential concern (TPCs) for management of a semiarid southern African savanna. We focus on the Kruger National Park (KNP), a relatively intact savanna, noted for its wide array of animal and plant species and a prized tourist destination. KNP's large size ensures integrity of most ecosystem processes and much can be learned about drivers of ecosystem structure and function using this park as a baseline. Our overview shows that large scale variability in substrates exists, but do not necessarily have predictable consequences for N cycling. The impact of major drivers such as fire is complex; at a landscape scale little differences in stocks and cycling were found, though at a smaller scale changes in woody cover can lead to concomitant changes in total N. Contrasting impacts of browsers and grazers on N turnover has been recorded. Due to the complexity of this ecosystem, we conclude that it will be complicated to draw up TPCs for most transformations and pools involved with the N cycle. However, we highlight in which cases the development of TPCs will be possible.

A case report of evaluating a large-scale health systems improvement project in an uncontrolled setting: a quality improvement initiative in KwaZulu-Natal, South Africa.

PubMed

Mate, Kedar S; Ngidi, Wilbroda Hlolisile; Reddy, Jennifer; Mphatswe, Wendy; Rollins, Nigel; Barker, Pierre

2013-11-01

New approaches are needed to evaluate quality improvement (QI) within large-scale public health efforts. This case report details challenges to large-scale QI evaluation, and proposes solutions relying on adaptive study design. We used two sequential evaluative methods to study a QI effort to improve delivery of HIV preventive care in public health facilities in three districts in KwaZulu-Natal, South Africa, over a 3-year period. We initially used a cluster randomised controlled trial (RCT) design. During the RCT study period, tensions arose between intervention implementation and evaluation design due to loss of integrity of the randomisation unit over time, pressure to implement changes across the randomisation unit boundaries, and use of administrative rather than functional structures for the randomisation. In response to this loss of design integrity, we switched to a more flexible intervention design and a mixed-methods quasiexperimental evaluation relying on both a qualitative analysis and an interrupted time series quantitative analysis. Cluster RCT designs may not be optimal for evaluating complex interventions to improve implementation in uncontrolled 'real world' settings. More flexible, context-sensitive evaluation designs offer a better balance of the need to adjust the intervention during the evaluation to meet implementation challenges while providing the data required to evaluate effectiveness. Our case study involved HIV care in a resource-limited setting, but these issues likely apply to complex improvement interventions in other settings.

Methods of information geometry in computational system biology (consistency between chemical and biological evolution).

PubMed

Astakhov, Vadim

2009-01-01

Interest in simulation of large-scale metabolic networks, species development, and genesis of various diseases requires new simulation techniques to accommodate the high complexity of realistic biological networks. Information geometry and topological formalisms are proposed to analyze information processes. We analyze the complexity of large-scale biological networks as well as transition of the system functionality due to modification in the system architecture, system environment, and system components. The dynamic core model is developed. The term dynamic core is used to define a set of causally related network functions. Delocalization of dynamic core model provides a mathematical formalism to analyze migration of specific functions in biosystems which undergo structure transition induced by the environment. The term delocalization is used to describe these processes of migration. We constructed a holographic model with self-poetic dynamic cores which preserves functional properties under those transitions. Topological constraints such as Ricci flow and Pfaff dimension were found for statistical manifolds which represent biological networks. These constraints can provide insight on processes of degeneration and recovery which take place in large-scale networks. We would like to suggest that therapies which are able to effectively implement estimated constraints, will successfully adjust biological systems and recover altered functionality. Also, we mathematically formulate the hypothesis that there is a direct consistency between biological and chemical evolution. Any set of causal relations within a biological network has its dual reimplementation in the chemistry of the system environment.

Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study.

PubMed

Kaushal, Mayank; Oni-Orisan, Akinwunmi; Chen, Gang; Li, Wenjun; Leschke, Jack; Ward, Doug; Kalinosky, Benjamin; Budde, Matthew; Schmit, Brian; Li, Shi-Jiang; Muqeet, Vaishnavi; Kurpad, Shekar

2017-09-01

Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.

Speeding up GW Calculations to Meet the Challenge of Large Scale Quasiparticle Predictions

PubMed Central

Gao, Weiwei; Xia, Weiyi; Gao, Xiang; Zhang, Peihong

2016-01-01

Although the GW approximation is recognized as one of the most accurate theories for predicting materials excited states properties, scaling up conventional GW calculations for large systems remains a major challenge. We present a powerful and simple-to-implement method that can drastically accelerate fully converged GW calculations for large systems, enabling fast and accurate quasiparticle calculations for complex materials systems. We demonstrate the performance of this new method by presenting the results for ZnO and MgO supercells. A speed-up factor of nearly two orders of magnitude is achieved for a system containing 256 atoms (1024 valence electrons) with a negligibly small numerical error of ±0.03 eV. Finally, we discuss the application of our method to the GW calculations for 2D materials. PMID:27833140

Complex and biofluids: From Maxwell to nowadays

NASA Astrophysics Data System (ADS)

Misbah, Chaouqi

2009-11-01

Complex fluids are the rule in biology and in many industrial applications. Typical examples are blood, cartilage, and polymer solutions. Unlike water (as well as domestic oils, soft clear drinks, and so on), the law(s) describing the behavior of complex fluids are not yet fully established. The complexity arises from strong coupling between microscopic scales (like the motion of a red blood cell in the case of blood, or a polymer molecule for a polymer solution) and the global scale of the flow (say at the scale of a blood artery, or a channel in laboratory experiments). In this issue entitled Complex and Biofluids a large panel of experimental and theoretical problems of complex fluids is exposed. The topics range from dilute polymer solutions, food products, to biology (blood flow, cell and tissue mechanics). One of the earliest model put forward as an attempt to describe a complex fluid was suggested a long time ago by James Clerk Maxwell (in 1867). Other famous scientists, like Einstein (in 1906), and Taylor (in 1932) have made important contributions to the field, but the topic of complex fluids still continues to pose a formidable challenge to science. This field has known during the past decade an unbelievable upsurge of interest in many branches of science (physics, mechanics, chemistry, biology, medical science, mathematics, and so on). Understanding complex fluids is viewed as one of the biggest challenge of the present century. This synthesis will provide a simple introduction to the topic, summarize the main contribution of this issue, and list major open questions in this field. To cite this article: C. Misbah, C. R. Physique 10 (2009).

Large Eddy Simulation of High Reynolds Number Complex Flows

NASA Astrophysics Data System (ADS)

Verma, Aman

Marine configurations are subject to a variety of complex hydrodynamic phenomena affecting the overall performance of the vessel. The turbulent flow affects the hydrodynamic drag, propulsor performance and structural integrity, control-surface effectiveness, and acoustic signature of the marine vessel. Due to advances in massively parallel computers and numerical techniques, an unsteady numerical simulation methodology such as Large Eddy Simulation (LES) is well suited to study such complex turbulent flows whose Reynolds numbers (Re) are typically on the order of 10. 6. LES also promises increasedaccuracy over RANS based methods in predicting unsteady phenomena such as cavitation and noise production. This dissertation develops the capability to enable LES of high Re flows in complex geometries (e.g. a marine vessel) on unstructured grids and provide physical insight into the turbulent flow. LES is performed to investigate the geometry induced separated flow past a marine propeller attached to a hull, in an off-design condition called crashback. LES shows good quantitative agreement with experiments and provides a physical mechanism to explain the increase in side-force on the propeller blades below an advance ratio of J=-0.7. Fundamental developments in the dynamic subgrid-scale model for LES are pursued to improve the LES predictions, especially for complex flows on unstructured grids. A dynamic procedure is proposed to estimate a Lagrangian time scale based on a surrogate correlation without any adjustable parameter. The proposed model is applied to turbulent channel, cylinder and marine propeller flows and predicts improved results over other model variants due to a physically consistent Lagrangian time scale. A wall model is proposed for application to LES of high Reynolds number wall-bounded flows. The wall model is formulated as the minimization of a generalized constraint in the dynamic model for LES and applied to LES of turbulent channel flow at various Reynolds numbers up to Reτ=10000 and coarse grid resolutions to obtain significant improvement.

Enabling Controlling Complex Networks with Local Topological Information.

PubMed

Li, Guoqi; Deng, Lei; Xiao, Gaoxi; Tang, Pei; Wen, Changyun; Hu, Wuhua; Pei, Jing; Shi, Luping; Stanley, H Eugene

2018-03-15

Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulfilling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired final state in finite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefined state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efficiently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.

The up-scaling of ecosystem functions in a heterogeneous world

NASA Astrophysics Data System (ADS)

Lohrer, Andrew M.; Thrush, Simon F.; Hewitt, Judi E.; Kraan, Casper

2015-05-01

Earth is in the midst of a biodiversity crisis that is impacting the functioning of ecosystems and the delivery of valued goods and services. However, the implications of large scale species losses are often inferred from small scale ecosystem functioning experiments with little knowledge of how the dominant drivers of functioning shift across scales. Here, by integrating observational and manipulative experimental field data, we reveal scale-dependent influences on primary productivity in shallow marine habitats, thus demonstrating the scalability of complex ecological relationships contributing to coastal marine ecosystem functioning. Positive effects of key consumers (burrowing urchins, Echinocardium cordatum) on seafloor net primary productivity (NPP) elucidated by short-term, single-site experiments persisted across multiple sites and years. Additional experimentation illustrated how these effects amplified over time, resulting in greater primary producer biomass sediment chlorophyll a content (Chla) in the longer term, depending on climatic context and habitat factors affecting the strengths of mutually reinforcing feedbacks. The remarkable coherence of results from small and large scales is evidence of real-world ecosystem function scalability and ecological self-organisation. This discovery provides greater insights into the range of responses to broad-scale anthropogenic stressors in naturally heterogeneous environmental settings.

The up-scaling of ecosystem functions in a heterogeneous world

PubMed Central

Lohrer, Andrew M.; Thrush, Simon F.; Hewitt, Judi E.; Kraan, Casper

2015-01-01

Earth is in the midst of a biodiversity crisis that is impacting the functioning of ecosystems and the delivery of valued goods and services. However, the implications of large scale species losses are often inferred from small scale ecosystem functioning experiments with little knowledge of how the dominant drivers of functioning shift across scales. Here, by integrating observational and manipulative experimental field data, we reveal scale-dependent influences on primary productivity in shallow marine habitats, thus demonstrating the scalability of complex ecological relationships contributing to coastal marine ecosystem functioning. Positive effects of key consumers (burrowing urchins, Echinocardium cordatum) on seafloor net primary productivity (NPP) elucidated by short-term, single-site experiments persisted across multiple sites and years. Additional experimentation illustrated how these effects amplified over time, resulting in greater primary producer biomass sediment chlorophyll a content (Chla) in the longer term, depending on climatic context and habitat factors affecting the strengths of mutually reinforcing feedbacks. The remarkable coherence of results from small and large scales is evidence of real-world ecosystem function scalability and ecological self-organisation. This discovery provides greater insights into the range of responses to broad-scale anthropogenic stressors in naturally heterogeneous environmental settings. PMID:25993477

Multi-scale Fracture Patterns Associated with a Complex Anticline Structure: Insights from Field Outcrop Analogues of the Jebel Hafit Pericline, Al Ain-UAE

NASA Astrophysics Data System (ADS)

Kokkalas, S.; Jones, R. R.; Long, J. J.; Zampos, M.; Wilkinson, M. W.; Gilment, S.

2017-12-01

The formation of folds and their associated fracture patterns plays an important role in controlling the migration and concentration of fluids within the upper crust. Prediction of fracture patterns from various fold shapes and kinematics still remains poorly understood in terms of spatial and temporal distribution of fracture sets. Thus, a more detailed field-based multi scale approach is required to better constrain 3D models of fold-fracture relationships, which are critical for reservoir characterization studies. In order to generate reservoir-scale fracture models representative fracture properties across a wider range of scales are needed. For this reason we applied modern geospatial technologies, including terrestrial LiDAR, photogrammetry and satellite images in the asymmetric, east verging, four-way closure Jebel Hafit anticline, in the eastern part of the United Arab Emirates. The excellent surface outcrops allowed the rapid acquisition of extensive areas of fracture data from both limbs and fold hinge area of the anticline, even from large areas of steep exposure that are practically inaccessible on foot. The digital outcrops provide longer 1D transects, and 2D or 3D surface datasets and give more robust data, particularly for fracture heights, lengths, spacing, clustering, termination and connectivity. The fracture patterns across the folded structure are more complex than those predicted from conceptual models and geomechanical fracture modeling. Mechanical layering, pre-existing structures and sedimentation during fold growth seem to exert a critical influence in the development of fracture systems within Jebel Hafit anticline and directly affect fracture orientations, spacing/intensity, segmentation and connectivity. Seismic and borehole data provide additional constraints on the sub-surface fold geometry and existence of large-scale thrusting in the core of the anticline. The complexity of the relationship between fold geometry and fracture intensity is presented and the implications for prediction of fracture networks in naturally fractured reservoirs are discussed.

Ontogenetic functional diversity: size structure of a keystone predator drives functioning of a complex ecosystem.

PubMed

Rudolf, Volker H W; Rasmussen, Nick L

2013-05-01

A central challenge in community ecology is to understand the connection between biodiversity and the functioning of ecosystems. While traditional approaches have largely focused on species-level diversity, increasing evidence indicates that there exists substantial ecological diversity among individuals within species. By far, the largest source of this intraspecific diversity stems from variation among individuals in ontogenetic stage and size. Although such ontogenetic shifts are ubiquitous in natural communities, whether and how they scale up to influence the structure and functioning of complex ecosystems is largely unknown. Here we take an experimental approach to examine the consequences of ontogenetic niche shifts for the structure of communities and ecosystem processes. In particular we experimentally manipulated the stage structure in a keystone predator, larvae of the dragonfly Anax junius, in complex experimental pond communities to test whether changes in the population stage or size structure of a keystone species scale up to alter community structure and ecosystem processes, and how functional differences scale with relative differences in size among stages. We found that the functional role of A. junius was stage-specific. Altering what stages were present in a pond led to concurrent changes in community structure, primary producer biomass (periphyton and phytoplankton), and ultimately altered ecosystem processes (respiration and net primary productivity), indicating a strong, but stage-specific, trophic cascade. Interestingly, the stage-specific effects did not simply scale with size or biomass of the predator, but instead indicated clear ontogenetic niche shifts in ecological interactions. Thus, functional differences among stages within a keystone species scaled up to alter the functioning of entire ecosystems. Therefore, our results indicate that the classical approach of assuming an average functional role of a species can be misleading because functional roles are dynamic and will change with shifts in the stage structure of the species. In general this emphasizes the importance of accounting for functional diversity below the species level to predict how natural and anthropogenic changes alter the functioning of natural ecosystems.

Snow stratigraphic heterogeneity within ground-based passive microwave radiometer footprints: Implications for emission modeling

NASA Astrophysics Data System (ADS)

Rutter, Nick; Sandells, Mel; Derksen, Chris; Toose, Peter; Royer, Alain; Montpetit, Benoit; Langlois, Alex; Lemmetyinen, Juha; Pulliainen, Jouni

2014-03-01

Two-dimensional measurements of snowpack properties (stratigraphic layering, density, grain size, and temperature) were used as inputs to the multilayer Helsinki University of Technology (HUT) microwave emission model at a centimeter-scale horizontal resolution, across a 4.5 m transect of ground-based passive microwave radiometer footprints near Churchill, Manitoba, Canada. Snowpack stratigraphy was complex (between six and eight layers) with only three layers extending continuously throughout the length of the transect. Distributions of one-dimensional simulations, accurately representing complex stratigraphic layering, were evaluated using measured brightness temperatures. Large biases (36 to 68 K) between simulated and measured brightness temperatures were minimized (-0.5 to 0.6 K), within measurement accuracy, through application of grain scaling factors (2.6 to 5.3) at different combinations of frequencies, polarizations, and model extinction coefficients. Grain scaling factors compensated for uncertainty relating optical specific surface area to HUT effective grain size inputs and quantified relative differences in scattering and absorption properties of various extinction coefficients. The HUT model required accurate representation of ice lenses, particularly at horizontal polarization, and large grain scaling factors highlighted the need to consider microstructure beyond the size of individual grains. As variability of extinction coefficients was strongly influenced by the proportion of large (hoar) grains in a vertical profile, it is important to consider simulations from distributions of one-dimensional profiles rather than single profiles, especially in sub-Arctic snowpacks where stratigraphic variability can be high. Model sensitivity experiments suggested that the level of error in field measurements and the new methodological framework used to apply them in a snow emission model were satisfactory. Layer amalgamation showed that a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%.

Acetylcholine molecular arrays enable quantum information processing

NASA Astrophysics Data System (ADS)

Tamulis, Arvydas; Majauskaite, Kristina; Talaikis, Martynas; Zborowski, Krzysztof; Kairys, Visvaldas

2017-09-01

We have found self-assembly of four neurotransmitter acetylcholine (ACh) molecular complexes in a water molecules environment by using geometry optimization with DFT B97d method. These complexes organizes to regular arrays of ACh molecules possessing electronic spins, i.e. quantum information bits. These spin arrays could potentially be controlled by the application of a non-uniform external magnetic field. The proper sequence of resonant electromagnetic pulses would then drive all the spin groups into the 3-spin entangled state and proceed large scale quantum information bits.

Active Control of Combustion Instability in a Ramjet Using Large-Eddy Simulations

DTIC Science & Technology

1992-09-01

model is also used to determine the turbulent subgrid fluxes appearing in the momentum equations. Thus, the subgrid stresses in the momentum transport...flows and in flows with complex geometries. To include the effect of walls, an additional correction has been used to ensure that the subgrid stress ...subgrid stress Ty varies as y+3 near the wall. A major issue for LES of complex flows is whether the primary assumption that the subgrid scales are

Testing the suitability of geologic frameworks for extrapolating hydraulic properties across regional scales

DOE PAGES

Mirus, Benjamin B.; Halford, Keith J.; Sweetkind, Donald; ...

2016-02-18

The suitability of geologic frameworks for extrapolating hydraulic conductivity (K) to length scales commensurate with hydraulic data is difficult to assess. A novel method is presented for evaluating assumed relations between K and geologic interpretations for regional-scale groundwater modeling. The approach relies on simultaneous interpretation of multiple aquifer tests using alternative geologic frameworks of variable complexity, where each framework is incorporated as prior information that assumes homogeneous K within each model unit. This approach is tested at Pahute Mesa within the Nevada National Security Site (USA), where observed drawdowns from eight aquifer tests in complex, highly faulted volcanic rocks providemore » the necessary hydraulic constraints. The investigated volume encompasses 40 mi3 (167 km3) where drawdowns traversed major fault structures and were detected more than 2 mi (3.2 km) from pumping wells. Complexity of the five frameworks assessed ranges from an undifferentiated mass of rock with a single unit to 14 distinct geologic units. Results show that only four geologic units can be justified as hydraulically unique for this location. The approach qualitatively evaluates the consistency of hydraulic property estimates within extents of investigation and effects of geologic frameworks on extrapolation. Distributions of transmissivity are similar within the investigated extents irrespective of the geologic framework. In contrast, the extrapolation of hydraulic properties beyond the volume investigated with interfering aquifer tests is strongly affected by the complexity of a given framework. As a result, testing at Pahute Mesa illustrates how this method can be employed to determine the appropriate level of geologic complexity for large-scale groundwater modeling.« less

Testing the suitability of geologic frameworks for extrapolating hydraulic properties across regional scales

USGS Publications Warehouse

Mirus, Benjamin B.; Halford, Keith J.; Sweetkind, Donald; Fenelon, Joseph M.

2016-01-01

The suitability of geologic frameworks for extrapolating hydraulic conductivity (K) to length scales commensurate with hydraulic data is difficult to assess. A novel method is presented for evaluating assumed relations between K and geologic interpretations for regional-scale groundwater modeling. The approach relies on simultaneous interpretation of multiple aquifer tests using alternative geologic frameworks of variable complexity, where each framework is incorporated as prior information that assumes homogeneous K within each model unit. This approach is tested at Pahute Mesa within the Nevada National Security Site (USA), where observed drawdowns from eight aquifer tests in complex, highly faulted volcanic rocks provide the necessary hydraulic constraints. The investigated volume encompasses 40 mi3 (167 km3) where drawdowns traversed major fault structures and were detected more than 2 mi (3.2 km) from pumping wells. Complexity of the five frameworks assessed ranges from an undifferentiated mass of rock with a single unit to 14 distinct geologic units. Results show that only four geologic units can be justified as hydraulically unique for this location. The approach qualitatively evaluates the consistency of hydraulic property estimates within extents of investigation and effects of geologic frameworks on extrapolation. Distributions of transmissivity are similar within the investigated extents irrespective of the geologic framework. In contrast, the extrapolation of hydraulic properties beyond the volume investigated with interfering aquifer tests is strongly affected by the complexity of a given framework. Testing at Pahute Mesa illustrates how this method can be employed to determine the appropriate level of geologic complexity for large-scale groundwater modeling.

Testing the suitability of geologic frameworks for extrapolating hydraulic properties across regional scales

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

Mirus, Benjamin B.; Halford, Keith J.; Sweetkind, Donald

The suitability of geologic frameworks for extrapolating hydraulic conductivity (K) to length scales commensurate with hydraulic data is difficult to assess. A novel method is presented for evaluating assumed relations between K and geologic interpretations for regional-scale groundwater modeling. The approach relies on simultaneous interpretation of multiple aquifer tests using alternative geologic frameworks of variable complexity, where each framework is incorporated as prior information that assumes homogeneous K within each model unit. This approach is tested at Pahute Mesa within the Nevada National Security Site (USA), where observed drawdowns from eight aquifer tests in complex, highly faulted volcanic rocks providemore » the necessary hydraulic constraints. The investigated volume encompasses 40 mi3 (167 km3) where drawdowns traversed major fault structures and were detected more than 2 mi (3.2 km) from pumping wells. Complexity of the five frameworks assessed ranges from an undifferentiated mass of rock with a single unit to 14 distinct geologic units. Results show that only four geologic units can be justified as hydraulically unique for this location. The approach qualitatively evaluates the consistency of hydraulic property estimates within extents of investigation and effects of geologic frameworks on extrapolation. Distributions of transmissivity are similar within the investigated extents irrespective of the geologic framework. In contrast, the extrapolation of hydraulic properties beyond the volume investigated with interfering aquifer tests is strongly affected by the complexity of a given framework. As a result, testing at Pahute Mesa illustrates how this method can be employed to determine the appropriate level of geologic complexity for large-scale groundwater modeling.« less

ComplexContact: a web server for inter-protein contact prediction using deep learning.

PubMed

Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo

2018-05-22

ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

Time scales of supercooled water and implications for reversible polyamorphism

NASA Astrophysics Data System (ADS)

Limmer, David T.; Chandler, David

2015-09-01

Deeply supercooled water exhibits complex dynamics with large density fluctuations, ice coarsening and characteristic time scales extending from picoseconds to milliseconds. Here, we discuss implications of these time scales as they pertain to two-phase coexistence and to molecular simulations of supercooled water. Specifically, we argue that it is possible to discount liquid-liquid criticality because the time scales imply that correlation lengths for such behaviour would be bounded by no more than a few nanometres. Similarly, it is possible to discount two-liquid coexistence because the time scales imply a bounded interfacial free energy that cannot grow in proportion to a macroscopic surface area. From time scales alone, therefore, we see that coexisting domains of differing density in supercooled water can be no more than nanoscale transient fluctuations.

DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2014-09-30

repeating pulse-like signals were investigated. Software prototypes were developed and integrated into distinct streams of reseach ; projects...to study complex sound archives spanning large spatial and temporal scales. A new post processing method for detection and classifcation was also...false positive rates. HK-ANN was successfully tested for a large minke whale dataset, but could easily be used on other signal types. Various

Advanced Kalman Filter for Real-Time Responsiveness in Complex Systems

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

Welch, Gregory Francis; Zhang, Jinghe

2014-06-10

Complex engineering systems pose fundamental challenges in real-time operations and control because they are highly dynamic systems consisting of a large number of elements with severe nonlinearities and discontinuities. Today’s tools for real-time complex system operations are mostly based on steady state models, unable to capture the dynamic nature and too slow to prevent system failures. We developed advanced Kalman filtering techniques and the formulation of dynamic state estimation using Kalman filtering techniques to capture complex system dynamics in aiding real-time operations and control. In this work, we looked at complex system issues including severe nonlinearity of system equations, discontinuitiesmore » caused by system controls and network switches, sparse measurements in space and time, and real-time requirements of power grid operations. We sought to bridge the disciplinary boundaries between Computer Science and Power Systems Engineering, by introducing methods that leverage both existing and new techniques. While our methods were developed in the context of electrical power systems, they should generalize to other large-scale scientific and engineering applications.« less

Time to "go large" on biofilm research: advantages of an omics approach.

PubMed

Azevedo, Nuno F; Lopes, Susana P; Keevil, Charles W; Pereira, Maria O; Vieira, Maria J

2009-04-01

In nature, the biofilm mode of life is of great importance in the cell cycle for many microorganisms. Perhaps because of biofilm complexity and variability, the characterization of a given microbial system, in terms of biofilm formation potential, structure and associated physiological activity, in a large-scale, standardized and systematic manner has been hindered by the absence of high-throughput methods. This outlook is now starting to change as new methods involving the utilization of microtiter-plates and automated spectrophotometry and microscopy systems are being developed to perform large-scale testing of microbial biofilms. Here, we evaluate if the time is ripe to start an integrated omics approach, i.e., the generation and interrogation of large datasets, to biofilms--"biofomics". This omics approach would bring much needed insight into how biofilm formation ability is affected by a number of environmental, physiological and mutational factors and how these factors interplay between themselves in a standardized manner. This could then lead to the creation of a database where biofilm signatures are identified and interrogated. Nevertheless, and before embarking on such an enterprise, the selection of a versatile, robust, high-throughput biofilm growing device and of appropriate methods for biofilm analysis will have to be performed. Whether such device and analytical methods are already available, particularly for complex heterotrophic biofilms is, however, very debatable.

A Survey on Routing Protocols for Large-Scale Wireless Sensor Networks

PubMed Central

Li, Changle; Zhang, Hanxiao; Hao, Binbin; Li, Jiandong

2011-01-01

With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. “Large-scale” means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner and other metrics. Finally some open issues in routing protocol design in large-scale wireless sensor networks and conclusions are proposed. PMID:22163808

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

PubMed Central

Jensen, Tue V.; Pinson, Pierre

2017-01-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation. PMID:29182600

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system.

PubMed

Jensen, Tue V; Pinson, Pierre

2017-11-28

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

Adjoint Sensitivity Analysis for Scale-Resolving Turbulent Flow Solvers

NASA Astrophysics Data System (ADS)

Blonigan, Patrick; Garai, Anirban; Diosady, Laslo; Murman, Scott

2017-11-01

Adjoint-based sensitivity analysis methods are powerful design tools for engineers who use computational fluid dynamics. In recent years, these engineers have started to use scale-resolving simulations like large-eddy simulations (LES) and direct numerical simulations (DNS), which resolve more scales in complex flows with unsteady separation and jets than the widely-used Reynolds-averaged Navier-Stokes (RANS) methods. However, the conventional adjoint method computes large, unusable sensitivities for scale-resolving simulations, which unlike RANS simulations exhibit the chaotic dynamics inherent in turbulent flows. Sensitivity analysis based on least-squares shadowing (LSS) avoids the issues encountered by conventional adjoint methods, but has a high computational cost even for relatively small simulations. The following talk discusses a more computationally efficient formulation of LSS, ``non-intrusive'' LSS, and its application to turbulent flows simulated with a discontinuous-Galkerin spectral-element-method LES/DNS solver. Results are presented for the minimal flow unit, a turbulent channel flow with a limited streamwise and spanwise domain.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

NASA Astrophysics Data System (ADS)

Jensen, Tue V.; Pinson, Pierre

2017-11-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

Local dynamics and spatiotemporal chaos. The Kuramoto- Sivashinsky equation: A case study

NASA Astrophysics Data System (ADS)

Wittenberg, Ralf Werner

The nature of spatiotemporal chaos in extended continuous systems is not yet well-understood. In this thesis, a model partial differential equation, the Kuramoto- Sivashinsky (KS) equation ut+uxxxx+uxx+uux =0 on a large one-dimensional periodic domain, is studied analytically, numerically, and through modeling to obtain a more detailed understanding of the observed spatiotemporally complex dynamics. In particular, with the aid of a wavelet decomposition, the relevant dynamical interactions are shown to be localized in space and scale. Motivated by these results, and by the idea that the attractor on a large domain may be understood via attractors on smaller domains, a spatially localized low- dimensional model for a minimal chaotic box is proposed. A (de)stabilized extension of the KS equation has recently attracted increased interest; for this situation, dissipativity and analyticity areproven, and an explicit shock-like solution is constructed which sheds light on the difficulties in obtaining optimal bounds for the KS equation. For the usual KS equation, the spatiotemporally chaotic state is carefully characterized in real, Fourier and wavelet space. The wavelet decomposition provides good scale separation which isolates the three characteristic regions of the dynamics: large scales of slow Gaussian fluctuations, active scales containing localized interactions of coherent structures, and small scales. Space localization is shown through a comparison of various correlation lengths and a numerical experiment in which different modes are uncoupled to estimate a dynamic interaction length. A detailed picture of the contributions of different scales to the spatiotemporally complex dynamics is obtained via a Galerkin projection of the KS equation onto the wavelet basis, and an extensive series of numerical experiments in which different combinations of wavelet levels are eliminated or forced. These results, and a formalism to derive an effective equation for periodized subsystems externally forced from a larger system, motivate various models for spatially localized forced systems. There is convincing evidence that short periodized systems, internally forced at the largest scales, form a minimal model for the observed extensively chaotic dynamics in larger domains.

Developing Renewable Energy Projects Larger Than 10 MWs at Federal Facilities

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

None

2013-03-01

To accomplish Federal goals for renewable energy, sustainability, and energy security, large-scale renewable energy projects must be developed and constructed on Federal sites at a significant scale with significant private investment. For the purposes of this Guide, large-scale Federal renewable energy projects are defined as renewable energy facilities larger than 10 megawatts (MW) that are sited on Federal property and lands and typically financed and owned by third parties.1 The U.S. Department of Energy’s Federal Energy Management Program (FEMP) helps Federal agencies meet these goals and assists agency personnel navigate the complexities of developing such projects and attract the necessarymore » private capital to complete them. This Guide is intended to provide a general resource that will begin to develop the Federal employee’s awareness and understanding of the project developer’s operating environment and the private sector’s awareness and understanding of the Federal environment. Because the vast majority of the investment that is required to meet the goals for large-scale renewable energy projects will come from the private sector, this Guide has been organized to match Federal processes with typical phases of commercial project development. FEMP collaborated with the National Renewable Energy Laboratory (NREL) and professional project developers on this Guide to ensure that Federal projects have key elements recognizable to private sector developers and investors. The main purpose of this Guide is to provide a project development framework to allow the Federal Government, private developers, and investors to work in a coordinated fashion on large-scale renewable energy projects. The framework includes key elements that describe a successful, financially attractive large-scale renewable energy project. This framework begins the translation between the Federal and private sector operating environments. When viewing the overall« less

Turbulent and Laminar Flow in Karst Conduits Under Unsteady Flow Conditions: Interpretation of Pumping Tests by Discrete Conduit-Continuum Modeling

NASA Astrophysics Data System (ADS)

Giese, M.; Reimann, T.; Bailly-Comte, V.; Maréchal, J.-C.; Sauter, M.; Geyer, T.

2018-03-01

Due to the duality in terms of (1) the groundwater flow field and (2) the discharge conditions, flow patterns of karst aquifer systems are complex. Estimated aquifer parameters may differ by several orders of magnitude from local (borehole) to regional (catchment) scale because of the large contrast in hydraulic parameters between matrix and conduit, their heterogeneity and anisotropy. One approach to deal with the scale effect problem in the estimation of hydraulic parameters of karst aquifers is the application of large-scale experiments such as long-term high-abstraction conduit pumping tests, stimulating measurable groundwater drawdown in both, the karst conduit system as well as the fractured matrix. The numerical discrete conduit-continuum modeling approach MODFLOW-2005 Conduit Flow Process Mode 1 (CFPM1) is employed to simulate laminar and nonlaminar conduit flow, induced by large-scale experiments, in combination with Darcian matrix flow. Effects of large-scale experiments were simulated for idealized settings. Subsequently, diagnostic plots and analyses of different fluxes are applied to interpret differences in the simulated conduit drawdown and general flow patterns. The main focus is set on the question to which extent different conduit flow regimes will affect the drawdown in conduit and matrix depending on the hydraulic properties of the conduit system, i.e., conduit diameter and relative roughness. In this context, CFPM1 is applied to investigate the importance of considering turbulent conditions for the simulation of karst conduit flow. This work quantifies the relative error that results from assuming laminar conduit flow for the interpretation of a synthetic large-scale pumping test in karst.

Detecting vortices in superconductors: Extracting one-dimensional topological singularities from a discretized complex scalar field

DOE PAGES

Phillips, Carolyn L.; Peterka, Tom; Karpeyev, Dmitry; ...

2015-02-20

In type II superconductors, the dynamics of superconducting vortices determine their transport properties. In the Ginzburg-Landau theory, vortices correspond to topological defects in the complex order parameter. Extracting their precise positions and motion from discretized numerical simulation data is an important, but challenging, task. In the past, vortices have mostly been detected by analyzing the magnitude of the complex scalar field representing the order parameter and visualized by corresponding contour plots and isosurfaces. However, these methods, primarily used for small-scale simulations, blur the fine details of the vortices, scale poorly to large-scale simulations, and do not easily enable isolating andmore » tracking individual vortices. In this paper, we present a method for exactly finding the vortex core lines from a complex order parameter field. With this method, vortices can be easily described at a resolution even finer than the mesh itself. The precise determination of the vortex cores allows the interplay of the vortices inside a model superconductor to be visualized in higher resolution than has previously been possible. Finally, by representing the field as the set of vortices, this method also massively reduces the data footprint of the simulations and provides the data structures for further analysis and feature tracking.« less

Complex Plasmas under free fall conditions aboard the International Space Station

NASA Astrophysics Data System (ADS)

Konopka, Uwe; Thomas, Edward, Jr.; Funk, Dylan; Doyle, Brandon; Williams, Jeremiah; Knapek, Christina; Thomas, Hubertus

2017-10-01

Complex Plasmas are dynamically dominated by massive, highly negatively charged, micron-sized particles. They are usually strongly coupled and as a result can show fluid-like behavior or undergo phase transitions to form crystalline structures. The dynamical time scale of these systems is easily accessible in experiments because of the relatively high mass/inertia of the particles. However, the high mass also leads to sedimentation effects and as a result prevents the conduction of large scale, fully three dimensional experiments that are necessary to utilize complex plasmas as model systems in the transition to continuous media. To reduce sedimentation influences it becomes necessary to perform experiments in a free-fall (``microgravity'') environment, such as the ISS based experiment facility ``Plasma-Kristall-4'' (``PK-4''). In our paper we will present our recently started research activities to investigate the basic properties of complex plasmas by utilizing the PK-4 experiment facility aboard the ISS. We further give an overview of developments towards the next generation experiment facility ``Ekoplasma'' (formerly named ``PlasmaLab'') and discuss potential additional small-scale space-based experiment scenarios. This work was supported by the JPL/NASA (JPL-RSA 1571699), the US Dept. of Energy (DE-SC0016330) and the NSF (PHY-1613087).

Structural Plasticity and Conformational Transitions of HIV Envelope Glycoprotein gp120

PubMed Central

Korkut, Anil; Hendrickson, Wayne A.

2012-01-01

HIV envelope glycoproteins undergo large-scale conformational changes as they interact with cellular receptors to cause the fusion of viral and cellular membranes that permits viral entry to infect targeted cells. Conformational dynamics in HIV gp120 are also important in masking conserved receptor epitopes from being detected for effective neutralization by the human immune system. Crystal structures of HIV gp120 and its complexes with receptors and antibody fragments provide high-resolution pictures of selected conformational states accessible to gp120. Here we describe systematic computational analyses of HIV gp120 plasticity in such complexes with CD4 binding fragments, CD4 mimetic proteins, and various antibody fragments. We used three computational approaches: an isotropic elastic network analysis of conformational plasticity, a full atomic normal mode analysis, and simulation of conformational transitions with our coarse-grained virtual atom molecular mechanics (VAMM) potential function. We observe collective sub-domain motions about hinge points that coordinate those motions, correlated local fluctuations at the interfacial cavity formed when gp120 binds to CD4, and concerted changes in structural elements that form at the CD4 interface during large-scale conformational transitions to the CD4-bound state from the deformed states of gp120 in certain antibody complexes. PMID:23300605

A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics.

PubMed

Lu, Qiongshi; Li, Boyang; Ou, Derek; Erlendsdottir, Margret; Powles, Ryan L; Jiang, Tony; Hu, Yiming; Chang, David; Jin, Chentian; Dai, Wei; He, Qidu; Liu, Zefeng; Mukherjee, Shubhabrata; Crane, Paul K; Zhao, Hongyu

2017-12-07

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (N total ≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD's correlation with cognitive traits and hints at an autoimmune component for ALS. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Exploring network operations for data and information networks

NASA Astrophysics Data System (ADS)

Yao, Bing; Su, Jing; Ma, Fei; Wang, Xiaomin; Zhao, Xiyang; Yao, Ming

2017-01-01

Barabási and Albert, in 1999, formulated scale-free models based on some real networks: World-Wide Web, Internet, metabolic and protein networks, language or sexual networks. Scale-free networks not only appear around us, but also have high qualities in the world. As known, high quality information networks can transfer feasibly and efficiently data, clearly, their topological structures are very important for data safety. We build up network operations for constructing large scale of dynamic networks from smaller scale of network models having good property and high quality. We focus on the simplest operators to formulate complex operations, and are interesting on the closeness of operations to desired network properties.

Model Updating of Complex Structures Using the Combination of Component Mode Synthesis and Kriging Predictor

PubMed Central

Li, Yan; Wang, Dejun; Zhang, Shaoyi

2014-01-01

Updating the structural model of complex structures is time-consuming due to the large size of the finite element model (FEM). Using conventional methods for these cases is computationally expensive or even impossible. A two-level method, which combined the Kriging predictor and the component mode synthesis (CMS) technique, was proposed to ensure the successful implementing of FEM updating of large-scale structures. In the first level, the CMS was applied to build a reasonable condensed FEM of complex structures. In the second level, the Kriging predictor that was deemed as a surrogate FEM in structural dynamics was generated based on the condensed FEM. Some key issues of the application of the metamodel (surrogate FEM) to FEM updating were also discussed. Finally, the effectiveness of the proposed method was demonstrated by updating the FEM of a real arch bridge with the measured modal parameters. PMID:24634612

Global Simulations of Dynamo and Magnetorotational Instability in Madison Plasma Experiments and Astrophysical Disks

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

Ebrahimi, Fatima

2014-07-31

Large-scale magnetic fields have been observed in widely different types of astrophysical objects. These magnetic fields are believed to be caused by the so-called dynamo effect. Could a large-scale magnetic field grow out of turbulence (i.e. the alpha dynamo effect)? How could the topological properties and the complexity of magnetic field as a global quantity, the so called magnetic helicity, be important in the dynamo effect? In addition to understanding the dynamo mechanism in astrophysical accretion disks, anomalous angular momentum transport has also been a longstanding problem in accretion disks and laboratory plasmas. To investigate both dynamo and momentum transport,more » we have performed both numerical modeling of laboratory experiments that are intended to simulate nature and modeling of configurations with direct relevance to astrophysical disks. Our simulations use fluid approximations (Magnetohydrodynamics - MHD model), where plasma is treated as a single fluid, or two fluids, in the presence of electromagnetic forces. Our major physics objective is to study the possibility of magnetic field generation (so called MRI small-scale and large-scale dynamos) and its role in Magneto-rotational Instability (MRI) saturation through nonlinear simulations in both MHD and Hall regimes.« less

Discrete Event Modeling and Massively Parallel Execution of Epidemic Outbreak Phenomena

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

Perumalla, Kalyan S; Seal, Sudip K

2011-01-01

In complex phenomena such as epidemiological outbreaks, the intensity of inherent feedback effects and the significant role of transients in the dynamics make simulation the only effective method for proactive, reactive or post-facto analysis. The spatial scale, runtime speed, and behavioral detail needed in detailed simulations of epidemic outbreaks make it necessary to use large-scale parallel processing. Here, an optimistic parallel execution of a new discrete event formulation of a reaction-diffusion simulation model of epidemic propagation is presented to facilitate in dramatically increasing the fidelity and speed by which epidemiological simulations can be performed. Rollback support needed during optimistic parallelmore » execution is achieved by combining reverse computation with a small amount of incremental state saving. Parallel speedup of over 5,500 and other runtime performance metrics of the system are observed with weak-scaling execution on a small (8,192-core) Blue Gene / P system, while scalability with a weak-scaling speedup of over 10,000 is demonstrated on 65,536 cores of a large Cray XT5 system. Scenarios representing large population sizes exceeding several hundreds of millions of individuals in the largest cases are successfully exercised to verify model scalability.« less

An interactive web application for the dissemination of human systems immunology data.

PubMed

Speake, Cate; Presnell, Scott; Domico, Kelly; Zeitner, Brad; Bjork, Anna; Anderson, David; Mason, Michael J; Whalen, Elizabeth; Vargas, Olivia; Popov, Dimitry; Rinchai, Darawan; Jourde-Chiche, Noemie; Chiche, Laurent; Quinn, Charlie; Chaussabel, Damien

2015-06-19

Systems immunology approaches have proven invaluable in translational research settings. The current rate at which large-scale datasets are generated presents unique challenges and opportunities. Mining aggregates of these datasets could accelerate the pace of discovery, but new solutions are needed to integrate the heterogeneous data types with the contextual information that is necessary for interpretation. In addition, enabling tools and technologies facilitating investigators' interaction with large-scale datasets must be developed in order to promote insight and foster knowledge discovery. State of the art application programming was employed to develop an interactive web application for browsing and visualizing large and complex datasets. A collection of human immune transcriptome datasets were loaded alongside contextual information about the samples. We provide a resource enabling interactive query and navigation of transcriptome datasets relevant to human immunology research. Detailed information about studies and samples are displayed dynamically; if desired the associated data can be downloaded. Custom interactive visualizations of the data can be shared via email or social media. This application can be used to browse context-rich systems-scale data within and across systems immunology studies. This resource is publicly available online at [Gene Expression Browser Landing Page ( https://gxb.benaroyaresearch.org/dm3/landing.gsp )]. The source code is also available openly [Gene Expression Browser Source Code ( https://github.com/BenaroyaResearch/gxbrowser )]. We have developed a data browsing and visualization application capable of navigating increasingly large and complex datasets generated in the context of immunological studies. This intuitive tool ensures that, whether taken individually or as a whole, such datasets generated at great effort and expense remain interpretable and a ready source of insight for years to come.

Study of multi-functional precision optical measuring system for large scale equipment

NASA Astrophysics Data System (ADS)

Jiang, Wei; Lao, Dabao; Zhou, Weihu; Zhang, Wenying; Jiang, Xingjian; Wang, Yongxi

2017-10-01

The effective application of high performance measurement technology can greatly improve the large-scale equipment manufacturing ability. Therefore, the geometric parameters measurement, such as size, attitude and position, requires the measurement system with high precision, multi-function, portability and other characteristics. However, the existing measuring instruments, such as laser tracker, total station, photogrammetry system, mostly has single function, station moving and other shortcomings. Laser tracker needs to work with cooperative target, but it can hardly meet the requirement of measurement in extreme environment. Total station is mainly used for outdoor surveying and mapping, it is hard to achieve the demand of accuracy in industrial measurement. Photogrammetry system can achieve a wide range of multi-point measurement, but the measuring range is limited and need to repeatedly move station. The paper presents a non-contact opto-electronic measuring instrument, not only it can work by scanning the measurement path but also measuring the cooperative target by tracking measurement. The system is based on some key technologies, such as absolute distance measurement, two-dimensional angle measurement, automatically target recognition and accurate aiming, precision control, assembly of complex mechanical system and multi-functional 3D visualization software. Among them, the absolute distance measurement module ensures measurement with high accuracy, and the twodimensional angle measuring module provides precision angle measurement. The system is suitable for the case of noncontact measurement of large-scale equipment, it can ensure the quality and performance of large-scale equipment throughout the process of manufacturing and improve the manufacturing ability of large-scale and high-end equipment.

Hierarchical Modeling and Robust Synthesis for the Preliminary Design of Large Scale Complex Systems

NASA Technical Reports Server (NTRS)

Koch, Patrick N.

1997-01-01

Large-scale complex systems are characterized by multiple interacting subsystems and the analysis of multiple disciplines. The design and development of such systems inevitably requires the resolution of multiple conflicting objectives. The size of complex systems, however, prohibits the development of comprehensive system models, and thus these systems must be partitioned into their constituent parts. Because simultaneous solution of individual subsystem models is often not manageable iteration is inevitable and often excessive. In this dissertation these issues are addressed through the development of a method for hierarchical robust preliminary design exploration to facilitate concurrent system and subsystem design exploration, for the concurrent generation of robust system and subsystem specifications for the preliminary design of multi-level, multi-objective, large-scale complex systems. This method is developed through the integration and expansion of current design techniques: Hierarchical partitioning and modeling techniques for partitioning large-scale complex systems into more tractable parts, and allowing integration of subproblems for system synthesis; Statistical experimentation and approximation techniques for increasing both the efficiency and the comprehensiveness of preliminary design exploration; and Noise modeling techniques for implementing robust preliminary design when approximate models are employed. Hierarchical partitioning and modeling techniques including intermediate responses, linking variables, and compatibility constraints are incorporated within a hierarchical compromise decision support problem formulation for synthesizing subproblem solutions for a partitioned system. Experimentation and approximation techniques are employed for concurrent investigations and modeling of partitioned subproblems. A modified composite experiment is introduced for fitting better predictive models across the ranges of the factors, and an approach for constructing partitioned response surfaces is developed to reduce the computational expense of experimentation for fitting models in a large number of factors. Noise modeling techniques are compared and recommendations are offered for the implementation of robust design when approximate models are sought. These techniques, approaches, and recommendations are incorporated within the method developed for hierarchical robust preliminary design exploration. This method as well as the associated approaches are illustrated through their application to the preliminary design of a commercial turbofan turbine propulsion system. The case study is developed in collaboration with Allison Engine Company, Rolls Royce Aerospace, and is based on the Allison AE3007 existing engine designed for midsize commercial, regional business jets. For this case study, the turbofan system-level problem is partitioned into engine cycle design and configuration design and a compressor modules integrated for more detailed subsystem-level design exploration, improving system evaluation. The fan and low pressure turbine subsystems are also modeled, but in less detail. Given the defined partitioning, these subproblems are investigated independently and concurrently, and response surface models are constructed to approximate the responses of each. These response models are then incorporated within a commercial turbofan hierarchical compromise decision support problem formulation. Five design scenarios are investigated, and robust solutions are identified. The method and solutions identified are verified by comparison with the AE3007 engine. The solutions obtained are similar to the AE3007 cycle and configuration, but are better with respect to many of the requirements.

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.

Carbon nanotube circuit integration up to sub-20 nm channel lengths.

PubMed

Shulaker, Max Marcel; Van Rethy, Jelle; Wu, Tony F; Liyanage, Luckshitha Suriyasena; Wei, Hai; Li, Zuanyi; Pop, Eric; Gielen, Georges; Wong, H-S Philip; Mitra, Subhasish

2014-04-22

Carbon nanotube (CNT) field-effect transistors (CNFETs) are a promising emerging technology projected to achieve over an order of magnitude improvement in energy-delay product, a metric of performance and energy efficiency, compared to silicon-based circuits. However, due to substantial imperfections inherent with CNTs, the promise of CNFETs has yet to be fully realized. Techniques to overcome these imperfections have yielded promising results, but thus far only at large technology nodes (1 μm device size). Here we demonstrate the first very large scale integration (VLSI)-compatible approach to realizing CNFET digital circuits at highly scaled technology nodes, with devices ranging from 90 nm to sub-20 nm channel lengths. We demonstrate inverters functioning at 1 MHz and a fully integrated CNFET infrared light sensor and interface circuit at 32 nm channel length. This demonstrates the feasibility of realizing more complex CNFET circuits at highly scaled technology nodes.

The alignment of molecular cloud magnetic fields with the spiral arms in M33.

PubMed

Li, Hua-bai; Henning, Thomas

2011-11-16

The formation of molecular clouds, which serve as stellar nurseries in galaxies, is poorly understood. A class of cloud formation models suggests that a large-scale galactic magnetic field is irrelevant at the scale of individual clouds, because the turbulence and rotation of a cloud may randomize the orientation of its magnetic field. Alternatively, galactic fields could be strong enough to impose their direction upon individual clouds, thereby regulating cloud accumulation and fragmentation, and affecting the rate and efficiency of star formation. Our location in the disk of the Galaxy makes an assessment of the situation difficult. Here we report observations of the magnetic field orientation of six giant molecular cloud complexes in the nearby, almost face-on, galaxy M33. The fields are aligned with the spiral arms, suggesting that the large-scale field in M33 anchors the clouds. ©2011 Macmillan Publishers Limited. All rights reserved

Implicit and explicit subgrid-scale modeling in discontinuous Galerkin methods for large-eddy simulation

NASA Astrophysics Data System (ADS)

Fernandez, Pablo; Nguyen, Ngoc-Cuong; Peraire, Jaime

2017-11-01

Over the past few years, high-order discontinuous Galerkin (DG) methods for Large-Eddy Simulation (LES) have emerged as a promising approach to solve complex turbulent flows. Despite the significant research investment, the relation between the discretization scheme, the Riemann flux, the subgrid-scale (SGS) model and the accuracy of the resulting LES solver remains unclear. In this talk, we investigate the role of the Riemann solver and the SGS model in the ability to predict a variety of flow regimes, including transition to turbulence, wall-free turbulence, wall-bounded turbulence, and turbulence decay. The Taylor-Green vortex problem and the turbulent channel flow at various Reynolds numbers are considered. Numerical results show that DG methods implicitly introduce numerical dissipation in under-resolved turbulence simulations and, even in the high Reynolds number limit, this implicit dissipation provides a more accurate representation of the actual subgrid-scale dissipation than that by explicit models.

Users matter : multi-agent systems model of high performance computing cluster users.

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

North, M. J.; Hood, C. S.; Decision and Information Sciences

2005-01-01

High performance computing clusters have been a critical resource for computational science for over a decade and have more recently become integral to large-scale industrial analysis. Despite their well-specified components, the aggregate behavior of clusters is poorly understood. The difficulties arise from complicated interactions between cluster components during operation. These interactions have been studied by many researchers, some of whom have identified the need for holistic multi-scale modeling that simultaneously includes network level, operating system level, process level, and user level behaviors. Each of these levels presents its own modeling challenges, but the user level is the most complex duemore » to the adaptability of human beings. In this vein, there are several major user modeling goals, namely descriptive modeling, predictive modeling and automated weakness discovery. This study shows how multi-agent techniques were used to simulate a large-scale computing cluster at each of these levels.« less

Evolution of Scaling Emergence in Large-Scale Spatial Epidemic Spreading

PubMed Central

Wang, Lin; Li, Xiang; Zhang, Yi-Qing; Zhang, Yan; Zhang, Kan

2011-01-01

Background Zipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which has still hardly been clarified. Methodology/Principal Findings In this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence. Conclusions/Significance The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease. PMID:21747932

Epidemic extinction paths in complex networks

NASA Astrophysics Data System (ADS)

Hindes, Jason; Schwartz, Ira B.

2017-05-01

We study the extinction of long-lived epidemics on finite complex networks induced by intrinsic noise. Applying analytical techniques to the stochastic susceptible-infected-susceptible model, we predict the distribution of large fluctuations, the most probable or optimal path through a network that leads to a disease-free state from an endemic state, and the average extinction time in general configurations. Our predictions agree with Monte Carlo simulations on several networks, including synthetic weighted and degree-distributed networks with degree correlations, and an empirical high school contact network. In addition, our approach quantifies characteristic scaling patterns for the optimal path and distribution of large fluctuations, both near and away from the epidemic threshold, in networks with heterogeneous eigenvector centrality and degree distributions.

Epidemic extinction paths in complex networks.

PubMed

Hindes, Jason; Schwartz, Ira B

2017-05-01

We study the extinction of long-lived epidemics on finite complex networks induced by intrinsic noise. Applying analytical techniques to the stochastic susceptible-infected-susceptible model, we predict the distribution of large fluctuations, the most probable or optimal path through a network that leads to a disease-free state from an endemic state, and the average extinction time in general configurations. Our predictions agree with Monte Carlo simulations on several networks, including synthetic weighted and degree-distributed networks with degree correlations, and an empirical high school contact network. In addition, our approach quantifies characteristic scaling patterns for the optimal path and distribution of large fluctuations, both near and away from the epidemic threshold, in networks with heterogeneous eigenvector centrality and degree distributions.

Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

PubMed

Rey-Villamizar, Nicolas; Somasundar, Vinay; Megjhani, Murad; Xu, Yan; Lu, Yanbin; Padmanabhan, Raghav; Trett, Kristen; Shain, William; Roysam, Badri

2014-01-01

In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

Emperical Laws in Economics Uncovered Using Methods in Statistical Mechanics

NASA Astrophysics Data System (ADS)

Stanley, H. Eugene

2001-06-01

In recent years, statistical physicists and computational physicists have determined that physical systems which consist of a large number of interacting particles obey universal "scaling laws" that serve to demonstrate an intrinsic self-similarity operating in such systems. Further, the parameters appearing in these scaling laws appear to be largely independent of the microscopic details. Since economic systems also consist of a large number of interacting units, it is plausible that scaling theory can be usefully applied to economics. To test this possibility using realistic data sets, a number of scientists have begun analyzing economic data using methods of statistical physics [1]. We have found evidence for scaling (and data collapse), as well as universality, in various quantities, and these recent results will be reviewed in this talk--starting with the most recent study [2]. We also propose models that may lead to some insight into these phenomena. These results will be discussed, as well as the overall rationale for why one might expect scaling principles to hold for complex economic systems. This work on which this talk is based is supported by BP, and was carried out in collaboration with L. A. N. Amaral S. V. Buldyrev, D. Canning, P. Cizeau, X. Gabaix, P. Gopikrishnan, S. Havlin, Y. Lee, Y. Liu, R. N. Mantegna, K. Matia, M. Meyer, C.-K. Peng, V. Plerou, M. A. Salinger, and M. H. R. Stanley. [1.] See, e.g., R. N. Mantegna and H. E. Stanley, Introduction to Econophysics: Correlations & Complexity in Finance (Cambridge University Press, Cambridge, 1999). [2.] P. Gopikrishnan, B. Rosenow, V. Plerou, and H. E. Stanley, "Identifying Business Sectors from Stock Price Fluctuations," e-print cond-mat/0011145; V. Plerou, P. Gopikrishnan, L. A. N. Amaral, X. Gabaix, and H. E. Stanley, "Diffusion and Economic Fluctuations," Phys. Rev. E (Rapid Communications) 62, 3023-3026 (2000); P. Gopikrishnan, V. Plerou, X. Gabaix, and H. E. Stanley, "Statistical Properties of Share Volume Traded in Financial Markets," Phys. Rev. E (Rapid Communications) 62, 4493-4496 (2000).

Maestro: an orchestration framework for large-scale WSN simulations.

PubMed

Riliskis, Laurynas; Osipov, Evgeny

2014-03-18

Contemporary wireless sensor networks (WSNs) have evolved into large and complex systems and are one of the main technologies used in cyber-physical systems and the Internet of Things. Extensive research on WSNs has led to the development of diverse solutions at all levels of software architecture, including protocol stacks for communications. This multitude of solutions is due to the limited computational power and restrictions on energy consumption that must be accounted for when designing typical WSN systems. It is therefore challenging to develop, test and validate even small WSN applications, and this process can easily consume significant resources. Simulations are inexpensive tools for testing, verifying and generally experimenting with new technologies in a repeatable fashion. Consequently, as the size of the systems to be tested increases, so does the need for large-scale simulations. This article describes a tool called Maestro for the automation of large-scale simulation and investigates the feasibility of using cloud computing facilities for such task. Using tools that are built into Maestro, we demonstrate a feasible approach for benchmarking cloud infrastructure in order to identify cloud Virtual Machine (VM)instances that provide an optimal balance of performance and cost for a given simulation.

Fault Tolerant Frequent Pattern Mining

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

Shohdy, Sameh; Vishnu, Abhinav; Agrawal, Gagan

FP-Growth algorithm is a Frequent Pattern Mining (FPM) algorithm that has been extensively used to study correlations and patterns in large scale datasets. While several researchers have designed distributed memory FP-Growth algorithms, it is pivotal to consider fault tolerant FP-Growth, which can address the increasing fault rates in large scale systems. In this work, we propose a novel parallel, algorithm-level fault-tolerant FP-Growth algorithm. We leverage algorithmic properties and MPI advanced features to guarantee an O(1) space complexity, achieved by using the dataset memory space itself for checkpointing. We also propose a recovery algorithm that can use in-memory and disk-based checkpointing,more » though in many cases the recovery can be completed without any disk access, and incurring no memory overhead for checkpointing. We evaluate our FT algorithm on a large scale InfiniBand cluster with several large datasets using up to 2K cores. Our evaluation demonstrates excellent efficiency for checkpointing and recovery in comparison to the disk-based approach. We have also observed 20x average speed-up in comparison to Spark, establishing that a well designed algorithm can easily outperform a solution based on a general fault-tolerant programming model.« less

Maestro: An Orchestration Framework for Large-Scale WSN Simulations

PubMed Central

Riliskis, Laurynas; Osipov, Evgeny

2014-01-01

Contemporary wireless sensor networks (WSNs) have evolved into large and complex systems and are one of the main technologies used in cyber-physical systems and the Internet of Things. Extensive research on WSNs has led to the development of diverse solutions at all levels of software architecture, including protocol stacks for communications. This multitude of solutions is due to the limited computational power and restrictions on energy consumption that must be accounted for when designing typical WSN systems. It is therefore challenging to develop, test and validate even small WSN applications, and this process can easily consume significant resources. Simulations are inexpensive tools for testing, verifying and generally experimenting with new technologies in a repeatable fashion. Consequently, as the size of the systems to be tested increases, so does the need for large-scale simulations. This article describes a tool called Maestro for the automation of large-scale simulation and investigates the feasibility of using cloud computing facilities for such task. Using tools that are built into Maestro, we demonstrate a feasible approach for benchmarking cloud infrastructure in order to identify cloud Virtual Machine (VM)instances that provide an optimal balance of performance and cost for a given simulation. PMID:24647123

Scale-space measures for graph topology link protein network architecture to function.

PubMed

Hulsman, Marc; Dimitrakopoulos, Christos; de Ridder, Jeroen

2014-06-15

The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions-genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture. Matlab(TM) code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA © The Author 2014. Published by Oxford University Press.

Considering Complex Objectives and Scarce Resources in Information Systems' Analysis.

ERIC Educational Resources Information Center

Crowther, Warren

The low efficacy of many of the library and large-scale information systems that have been implemented in the developing countries has been disappointing, and their appropriateness is often questioned in the governmental and educational institutions of more industrialized countries beset by budget-crunching and a very dynamic transformation of…