WorldWideScience

Sample records for supported substructure prediction

  1. Analysis of substructural variation in families of enzymatic proteins with applications to protein function prediction

    Directory of Open Access Journals (Sweden)

    Fofanov Viacheslav Y

    2010-05-01

    Full Text Available Abstract Background Structural variations caused by a wide range of physico-chemical and biological sources directly influence the function of a protein. For enzymatic proteins, the structure and chemistry of the catalytic binding site residues can be loosely defined as a substructure of the protein. Comparative analysis of drug-receptor substructures across and within species has been used for lead evaluation. Substructure-level similarity between the binding sites of functionally similar proteins has also been used to identify instances of convergent evolution among proteins. In functionally homologous protein families, shared chemistry and geometry at catalytic sites provide a common, local point of comparison among proteins that may differ significantly at the sequence, fold, or domain topology levels. Results This paper describes two key results that can be used separately or in combination for protein function analysis. The Family-wise Analysis of SubStructural Templates (FASST method uses all-against-all substructure comparison to determine Substructural Clusters (SCs. SCs characterize the binding site substructural variation within a protein family. In this paper we focus on examples of automatically determined SCs that can be linked to phylogenetic distance between family members, segregation by conformation, and organization by homology among convergent protein lineages. The Motif Ensemble Statistical Hypothesis (MESH framework constructs a representative motif for each protein cluster among the SCs determined by FASST to build motif ensembles that are shown through a series of function prediction experiments to improve the function prediction power of existing motifs. Conclusions FASST contributes a critical feedback and assessment step to existing binding site substructure identification methods and can be used for the thorough investigation of structure-function relationships. The application of MESH allows for an automated

  2. In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.

    Science.gov (United States)

    Cheng, Feixiong; Shen, Jie; Yu, Yue; Li, Weihua; Liu, Guixia; Lee, Philip W; Tang, Yun

    2011-03-01

    There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

  3. Free Vibration Analysis of a Spinning Flexible DISK-SPINDLE System Supported by Ball Bearing and Flexible Shaft Using the Finite Element Method and Substructure Synthesis

    Science.gov (United States)

    JANG, G. H.; LEE, S. H.; JUNG, M. S.

    2002-03-01

    Free vibration of a spinning flexible disk-spindle system supported by ball bearing and flexible shaft is analyzed by using Hamilton's principle, FEM and substructure synthesis. The spinning disk is described by using the Kirchhoff plate theory and von Karman non-linear strain. The rotating spindle and stationary shaft are modelled by Rayleigh beam and Euler beam respectively. Using Hamilton's principle and including the rigid body translation and tilting motion, partial differential equations of motion of the spinning flexible disk and spindle are derived consistently to satisfy the geometric compatibility in the internal boundary between substructures. FEM is used to discretize the derived governing equations, and substructure synthesis is introduced to assemble each component of the disk-spindle-bearing-shaft system. The developed method is applied to the spindle system of a computer hard disk drive with three disks, and modal testing is performed to verify the simulation results. The simulation result agrees very well with the experimental one. This research investigates critical design parameters in an HDD spindle system, i.e., the non-linearity of a spinning disk and the flexibility and boundary condition of a stationary shaft, to predict the free vibration characteristics accurately. The proposed method may be effectively applied to predict the vibration characteristics of a spinning flexible disk-spindle system supported by ball bearing and flexible shaft in the various forms of computer storage device, i.e., FDD, CD, HDD and DVD.

  4. Predictive modeling of interfacial damage in substructured steels: application to martensitic microstructures

    International Nuclear Information System (INIS)

    Maresca, F; Kouznetsova, V G; Geers, M G D

    2016-01-01

    Metallic composite phases, like martensite present in conventional steels and new generation high strength steels exhibit microscale, locally lamellar microstructures characterized by alternating layers of phases or crystallographic variants. The layers can be sub-micron down to a few nanometers thick, and they are often characterized by high contrasts in plastic properties. As a consequence, fracture in these lamellar microstructures generally occurs along the layer interfaces or within one of the layers, typically parallel to the interface. This paper presents a computational framework that addresses the lamellar nature of these microstructures, by homogenizing the plastic deformation at the mesoscale by using the microscale response of the laminates. Failure is accounted for by introducing a family of damaging planes that are parallel to the layer interface. Mode I, mode II and mixed-mode opening are incorporated. The planes along which failure occurs are captured using a smeared damage approach. Coupling of damage with isotropic or anisotropic plasticity models, like crystal plasticity, is straightforward. The damaging planes and directions do not need to correspond to crystalline slip planes, and normal opening is also included. Focus is given on rate-dependent formulations of plasticity and damage, i.e. converged results can be obtained without further regularization techniques. The validation of the model using experimental observations in martensite-austenite lamellar microstructures in steels reveals that the model correctly predicts the main features of the onset of failure, e.g. the necking point, the failure initiation region and the failure mode. Finally, based on the qualitative results obtained, some material design guidelines are provided for martensitic and multi-phase steels. (paper)

  5. Type Classes for Lightweight Substructural Types

    Directory of Open Access Journals (Sweden)

    Edward Gan

    2015-02-01

    Full Text Available Linear and substructural types are powerful tools, but adding them to standard functional programming languages often means introducing extra annotations and typing machinery. We propose a lightweight substructural type system design that recasts the structural rules of weakening and contraction as type classes; we demonstrate this design in a prototype language, Clamp. Clamp supports polymorphic substructural types as well as an expressive system of mutable references. At the same time, it adds little additional overhead to a standard Damas-Hindley-Milner type system enriched with type classes. We have established type safety for the core model and implemented a type checker with type inference in Haskell.

  6. An online substructure identification method for local structural health monitoring

    International Nuclear Information System (INIS)

    Hou, Jilin; Ou, Jinping; Jankowski, Łukasz

    2013-01-01

    This paper proposes a substructure isolation method, which uses time series of measured local response for online monitoring of substructures. The proposed monitoring process consists of two key steps: construction of the isolated substructure, and its identification. The isolated substructure is an independent virtual structure, which is numerically isolated from the global structure by placing virtual supports on the interface. First, the isolated substructure is constructed by a specific linear combination of time series of its measured local responses. Then, the isolated substructure is identified using its local natural frequencies extracted from the combined responses. The substructure is assumed to be linear; the outside part of the global structure can have any characteristics. The method has no requirements on the initial state of the structure, and so the process can be carried out repetitively for online monitoring. Online isolation and monitoring is illustrated in a numerical example with a frame model, and then verified in a cantilever beam experiment. (paper)

  7. Jet substructure in ATLAS

    CERN Document Server

    Miller, David W

    2011-01-01

    Measurements are presented of the jet invariant mass and substructure in proton-proton collisions at $\\sqrt{s} = 7$ TeV with the ATLAS detector using an integrated luminosity of 37 pb$^{-1}$. These results exercise the tools for distinguishing the signatures of new boosted massive particles in the hadronic final state. Two "fat" jet algorithms are used, along with the filtering jet grooming technique that was pioneered in ATLAS. New jet substructure observables are compared for the first time to data at the LHC. Finally, a sample of candidate boosted top quark events collected in the 2010 data is analyzed in detail for the jet substructure properties of hadronic "top-jets" in the final state. These measurements demonstrate not only our excellent understanding of QCD in a new energy regime but open the path to using complex jet substructure observables in the search for new physics.

  8. Jet Substructure Without Trees

    Energy Technology Data Exchange (ETDEWEB)

    Jankowiak, Martin; Larkoski, Andrew J.; /SLAC /Stanford U., ITP

    2011-08-19

    We present an alternative approach to identifying and characterizing jet substructure. An angular correlation function is introduced that can be used to extract angular and mass scales within a jet without reference to a clustering algorithm. This procedure gives rise to a number of useful jet observables. As an application, we construct a top quark tagging algorithm that is competitive with existing methods. In preparation for the LHC, the past several years have seen extensive work on various aspects of collider searches. With the excellent resolution of the ATLAS and CMS detectors as a catalyst, one area that has undergone significant development is jet substructure physics. The use of jet substructure techniques, which probe the fine-grained details of how energy is distributed in jets, has two broad goals. First, measuring more than just the bulk properties of jets allows for additional probes of QCD. For example, jet substructure measurements can be compared against precision perturbative QCD calculations or used to tune Monte Carlo event generators. Second, jet substructure allows for additional handles in event discrimination. These handles could play an important role at the LHC in discriminating between signal and background events in a wide variety of particle searches. For example, Monte Carlo studies indicate that jet substructure techniques allow for efficient reconstruction of boosted heavy objects such as the W{sup {+-}} and Z{sup 0} gauge bosons, the top quark, and the Higgs boson.

  9. The wave-based substructuring approach for the efficient description of interface dynamics in substructuring

    Science.gov (United States)

    Donders, S.; Pluymers, B.; Ragnarsson, P.; Hadjit, R.; Desmet, W.

    2010-04-01

    In the vehicle design process, design decisions are more and more based on virtual prototypes. Due to competitive and regulatory pressure, vehicle manufacturers are forced to improve product quality, to reduce time-to-market and to launch an increasing number of design variants on the global market. To speed up the design iteration process, substructuring and component mode synthesis (CMS) methods are commonly used, involving the analysis of substructure models and the synthesis of the substructure analysis results. Substructuring and CMS enable efficient decentralized collaboration across departments and allow to benefit from the availability of parallel computing environments. However, traditional CMS methods become prohibitively inefficient when substructures are coupled along large interfaces, i.e. with a large number of degrees of freedom (DOFs) at the interface between substructures. The reason is that the analysis of substructures involves the calculation of a number of enrichment vectors, one for each interface degree of freedom (DOF). Since large interfaces are common in vehicles (e.g. the continuous line connections to connect the body with the windshield, roof or floor), this interface bottleneck poses a clear limitation in the vehicle noise, vibration and harshness (NVH) design process. Therefore there is a need to describe the interface dynamics more efficiently. This paper presents a wave-based substructuring (WBS) approach, which allows reducing the interface representation between substructures in an assembly by expressing the interface DOFs in terms of a limited set of basis functions ("waves"). As the number of basis functions can be much lower than the number of interface DOFs, this greatly facilitates the substructure analysis procedure and results in faster design predictions. The waves are calculated once from a full nominal assembly analysis, but these nominal waves can be re-used for the assembly of modified components. The WBS approach thus

  10. Substructure in clusters of galaxies

    International Nuclear Information System (INIS)

    Fitchett, M.J.

    1988-01-01

    Optical observations suggesting the existence of substructure in clusters of galaxies are examined. Models of cluster formation and methods used to detect substructure in clusters are reviewed. Consideration is given to classification schemes based on a departure of bright cluster galaxies from a spherically symmetric distribution, evidence for statistically significant substructure, and various types of substructure, including velocity, spatial, and spatial-velocity substructure. The substructure observed in the galaxy distribution in clusters is discussed, focusing on observations from general cluster samples, the Virgo cluster, the Hydra cluster, Centaurus, the Coma cluster, and the Cancer cluster. 88 refs

  11. A Search for Starless Core Substructure in Ophiuchus

    Science.gov (United States)

    Kirk, Helen

    2017-06-01

    Density substructure is expected in evolved starless cores: a single peak to form a protostar, or multiple peaks from fragmentation. Searches for this substructure have had mixed success. In an ALMA survey of Ophiuchus, we find two starless cores with signs of substructure, consistent with simulation predictions. A similar survey in Chameleon (Dunham et al. 2016) had no detections, despite expecting at least two. Our results suggest that Chamleon may lack a more evolved starless cores. Future ALMA observations will better trace the influence of environment on core substructure formation.

  12. Urban structures and substructures

    Directory of Open Access Journals (Sweden)

    Mierzejewska Lidia

    2017-06-01

    Full Text Available In urban geography, a traditional but always important research problem has been the spatial-functional structure of towns and changes that occur in this field. Two approaches can be distinguished here: the sociological and the geographical. The former follows in the steps of the so-called Chicago school, i.e. Park, Burgess and Hoyt, and the other of Ullman and Harris. It seems, however, that those two approaches do not exhaust the range of spatial-structural studies which may be conducted in modern towns since there are areas within them endowed with specific properties that can be called their substructures. This paper will present the general characteristics of such substructures and identify factors responsible for their appearance and development. It will also propose an empirical research pattern. The term ‘substructures’ is taken to denote relatively autonomous, highly uniform wholes standing out in the spatial-functional structure of a town, distinguished on the basis of spatial relations generated by people. While structural elements of towns in the approach of the Chicago school or that of Harris and Ullman can be identified with structural regions, urban substructures show a similarity to functional regions in their organisation, structure and operation. Thus, towns with identified substructures have a polycentric spatial- functional structure, favourable in terms of both the level of service of their inhabitants and their sustainable development.

  13. Sub-structure

    CSIR Research Space (South Africa)

    Van Wyk, Llewellyn V

    2010-04-01

    Full Text Available in Conventional Sub-structure Element Concrete Volume (m 3 ) kgCO2/m 3 (see footnote) Total CO2 (kg) Foundations 1 3.69 209 2 771 Foundation walls 3 1.79 174 4 311 Concrete slab 5 4.09 250 6 1022 Total 9.57 2104 Raft foundations...

  14. A note on the substructural hierarchy

    Czech Academy of Sciences Publication Activity Database

    Jeřábek, Emil

    2016-01-01

    Roč. 62, 1-2 (2016), s. 102-110 ISSN 0942-5616 EU Projects: European Commission(XE) 339691 - FEALORA Institutional support: RVO:67985840 Keywords : substructural hierarchy * full Lambek calculus * extension variables Subject RIV: BA - General Mathematics Impact factor: 0.250, year: 2016 http://dx.doi.org/10.1002/malq.201500066

  15. Evaluation of a timber column bent substructure after more than 60 years in-service

    Science.gov (United States)

    James P. Wacker; Xiping Wang; Douglas R. Rammer; William J. Nelson

    2011-01-01

    This paper describes both the field evaluation and laboratory testing of two timber-column-bent bridge substructures. These substructures served as intermediate pier supports for the East Deer Park Drive Bridge located in Gaithersburg, Maryland. A field evaluation of the bridge substructure was conducted in September 2008. Nondestructive testing was performed with a...

  16. Prediction of the vibroacoustic behavior of a submerged shell with non-axisymmetric internal substructures by a condensed transfer function method

    Science.gov (United States)

    Meyer, V.; Maxit, L.; Guyader, J.-L.; Leissing, T.

    2016-01-01

    The vibroacoustic behavior of axisymmetric stiffened shells immersed in water has been intensively studied in the past. On the contrary, little attention has been paid to the modeling of these shells coupled to non-axisymmetric internal frames. Indeed, breaking the axisymmetry couples the circumferential orders of the Fourier series and considerably increases the computational costs. In order to tackle this issue, we propose a sub-structuring approach called the Condensed Transfer Function (CTF) method that will allow assembling a model of axisymmetric stiffened shell with models of non-axisymmetric internal frames. The CTF method is developed in the general case of mechanical subsystems coupled along curves. A set of orthonormal functions called condensation functions, which depend on the curvilinear abscissa along the coupling line, is considered. This set is then used as a basis for approximating and decomposing the displacements and the applied forces at the line junctions. Thanks to the definition and calculation of condensed transfer functions for each uncoupled subsystem and by using the superposition principle for passive linear systems, the behavior of the coupled subsystems can be deduced. A plane plate is considered as a test case to study the convergence of the method with respect to the type and the number of condensation functions taken into account. The CTF method is then applied to couple a submerged non-periodically stiffened shell described using the Circumferential Admittance Approach (CAA) with internal substructures described by Finite Element Method (FEM). The influence of non-axisymmetric internal substructures can finally be studied and it is shown that it tends to increase the radiation efficiency of the shell and can modify the vibrational and acoustic energy distribution.

  17. Searches for new physics using jet grooming and substructure

    CERN Document Server

    Burr, Jonathan Thomas Peter; The ATLAS collaboration

    2017-01-01

    Models predicting the production and decay of supersymmetric (SUSY) particles often have promising search channels involving decays through heavy intermediate states such as top quarks and heavy bosons. However, unlike in most exotics scenarios these heavy states are only moderately boosted which can make traditional substructure techniques less useful and motivates the development of alternative techniques. The results of several SUSY analyses using substructure techniques are presented.

  18. Floating substructure flexibility of large-volume 10MW offshore wind turbine platforms in dynamic calculations

    International Nuclear Information System (INIS)

    Borg, Michael; Hansen, Anders Melchior; Bredmose, Henrik

    2016-01-01

    Designing floating substructures for the next generation of 10MW and larger wind turbines has introduced new challenges in capturing relevant physical effects in dynamic simulation tools. In achieving technically and economically optimal floating substructures, structural flexibility may increase to the extent that it becomes relevant to include in addition to the standard rigid body substructure modes which are typically described through linear radiation-diffraction theory. This paper describes a method for the inclusion of substructural flexibility in aero-hydro-servo-elastic dynamic simulations for large-volume substructures, including wave-structure interactions, to form the basis of deriving sectional loads and stresses within the substructure. The method is applied to a case study to illustrate the implementation and relevance. It is found that the flexible mode is significantly excited in an extreme event, indicating an increase in predicted substructure internal loads. (paper)

  19. Jet substructure with analytical methods

    Energy Technology Data Exchange (ETDEWEB)

    Dasgupta, Mrinal [University of Manchester, Consortium for Fundamental Physics, School of Physics and Astronomy, Manchester (United Kingdom); Fregoso, Alessandro; Powling, Alexander [University of Manchester, School of Physics and Astronomy, Manchester (United Kingdom); Marzani, Simone [Durham University, Institute for Particle Physics Phenomenology, Durham (United Kingdom)

    2013-11-15

    We consider the mass distribution of QCD jets after the application of jet-substructure methods, specifically the mass-drop tagger, pruning, trimming and their variants. In contrast to most current studies employing Monte Carlo methods, we carry out analytical calculations at the next-to-leading order level, which are sufficient to extract the dominant logarithmic behaviour for each technique, and compare our findings to exact fixed-order results. Our results should ultimately lead to a better understanding of these jet-substructure methods which in turn will influence the development of future substructure tools for LHC phenomenology. (orig.)

  20. Vector boson tagged jets and jet substructure

    Directory of Open Access Journals (Sweden)

    Vitev Ivan

    2018-01-01

    Full Text Available In these proceedings, we report on recent results related to vector boson-tagged jet production in heavy ion collisions and the related modification of jet substructure, such as jet shapes and jet momentum sharing distributions. Z0-tagging and γ-tagging of jets provides new opportunities to study parton shower formation and propagation in the quark-gluon plasma and has been argued to provide tight constrains on the energy loss of reconstructed jets. We present theoretical predictions for isolated photon-tagged and electroweak boson-tagged jet production in Pb+Pb collisions at √sNN = 5.02 TeV at the LHC, addressing the modification of their transverse momentum and transverse momentum imbalance distributions. Comparison to recent ATLAS and CMS experimental measurements is performed that can shed light on the medium-induced radiative corrections and energy dissipation due to collisional processes of predominantly quark-initiated jets. The modification of parton splitting functions in the QGP further implies that the substructure of jets in heavy ion collisions may differ significantly from the corresponding substructure in proton-proton collisions. Two such observables and the implication of tagging on their evaluation is also discussed.

  1. Composite Octet Searches with Jet Substructure

    Energy Technology Data Exchange (ETDEWEB)

    Bai, Yang; /SLAC; Shelton, Jessie; /Yale U.

    2012-02-14

    Many new physics models with strongly interacting sectors predict a mass hierarchy between the lightest vector meson and the lightest pseudoscalar mesons. We examine the power of jet substructure tools to extend the 7 TeV LHC sensitivity to these new states for the case of QCD octet mesons, considering both two gluon and two b-jet decay modes for the pseudoscalar mesons. We develop both a simple dijet search using only the jet mass and a more sophisticated jet substructure analysis, both of which can discover the composite octets in a dijet-like signature. The reach depends on the mass hierarchy between the vector and pseudoscalar mesons. We find that for the pseudoscalar-to-vector meson mass ratio below approximately 0.2 the simple jet mass analysis provides the best discovery limit; for a ratio between 0.2 and the QCD-like value of 0.3, the sophisticated jet substructure analysis has the best discovery potential; for a ratio above approximately 0.3, the standard four-jet analysis is more suitable.

  2. Galaxy Clusters: Substructure and Mass Systematics

    Science.gov (United States)

    Zhang, Yu-Ying

    2010-07-01

    We calibrate the X-ray measured hydrostatic equilibrium (H.E.) mass and assess the origin of the H.E. mass systematics using 2-D spectrally measured X-ray properties. We obtained that the average X-ray mass derived from H.E. using XMM-Newton data is lower compared to the weak lensing mass from Subaru data for relaxed clusters in a sample of 12 clusters at z~0.2. This is comparable to the expectation of numerical simulations because of the non-thermal pressure support due to turbulence and bulk motions. The gas mass to weak lensing mass ratio shows no dependence on the cluster morphology, which indicates that the gas mass may be a good mass proxy regardless of the cluster dynamical state. To understand the origin of the systematics of the H.E. mass, we investigated 4 nearby clusters, for which the substructure is quantified by the radial fluctuations in the spectrally measured 2-D maps by a cumulative/differential scatter profile relative to the mean profile within/at a given radius. The amplitude of and the discontinuity in the scatter complements 2-D substructure diagnostics, e.g. indicating the most disturbed radial range. There is a tantalizing link between the substructure identified using the scatter of the entropy and pressure fluctuations and the deviation of the H.E. mass relative to the expected mass based on the representative scaling relation, e.g., M-Mgas, particularly at r500-the radius within which the over-density, Δ, is 500 with respect to the critical density. This indicates that at larger radii, the systematic error of the H.E. mass may well be caused by substructure.

  3. DNBR Prediction Using a Support Vector Regression

    International Nuclear Information System (INIS)

    Yang, Heon Young; Na, Man Gyun

    2008-01-01

    PWRs (Pressurized Water Reactors) generally operate in the nucleate boiling state. However, the conversion of nucleate boiling into film boiling with conspicuously reduced heat transfer induces a boiling crisis that may cause the fuel clad melting in the long run. This type of boiling crisis is called Departure from Nucleate Boiling (DNB) phenomena. Because the prediction of minimum DNBR in a reactor core is very important to prevent the boiling crisis such as clad melting, a lot of research has been conducted to predict DNBR values. The object of this research is to predict minimum DNBR applying support vector regression (SVR) by using the measured signals of a reactor coolant system (RCS). The SVR has extensively and successfully been applied to nonlinear function approximation like the proposed problem for estimating DNBR values that will be a function of various input variables such as reactor power, reactor pressure, core mass flowrate, control rod positions and so on. The minimum DNBR in a reactor core is predicted using these various operating condition data as the inputs to the SVR. The minimum DBNR values predicted by the SVR confirm its correctness compared with COLSS values

  4. Natural draft cooling tower with shell disconnected from the substructure

    International Nuclear Information System (INIS)

    Diver, Marius

    1982-01-01

    The aim of this paper is the analysis of results of a research done by Electricite de France, concerning a new type of cooling tower. The traditional structure (i.e. a hyperbolic shell supported by X shaped or diagonal columns) is replaced by two independent structures: the shell, becoming a self-contained structure, the lower rim being stiffened by an annular beam; the substructure, resting on the soil. This new type of cooling tower has an improved thermal performance due to the increase of the area of air entrance. Bearing pads are provided between the lower ring beam of the shell and the substructure. Any differential settlement can be coped with by jacking. The water distribution structure can be laid out so as to benefit from advantages offered by the presence of the stiff ring and columns of the substructure [fr

  5. A Comparison of Reduced Order Modeling Techniques Used in Dynamic Substructuring [PowerPoint

    Energy Technology Data Exchange (ETDEWEB)

    Roettgen, Dan [Wisc; Seeger, Benjamin [Stuttgart; Tai, Wei Che [Washington; Baek, Seunghun [Michigan; Dossogne, Tilan [Liege; Allen, Matthew S [Wisc; Kuether, Robert J.; Brake, Matthew Robert; Mayes, Randall L.

    2016-01-01

    Experimental dynamic substructuring is a means whereby a mathematical model for a substructure can be obtained experimentally and then coupled to a model for the rest of the assembly to predict the response. Recently, various methods have been proposed that use a transmission simulator to overcome sensitivity to measurement errors and to exercise the interface between the substructures; including the Craig-Bampton, Dual Craig-Bampton, and Craig-Mayes methods. This work compares the advantages and disadvantages of these reduced order modeling strategies for two dynamic substructuring problems. The methods are first used on an analytical beam model to validate the methodologies. Then they are used to obtain an experimental model for structure consisting of a cylinder with several components inside connected to the outside case by foam with uncertain properties. This represents an exceedingly difficult structure to model and so experimental substructuring could be an attractive way to obtain a model of the system.

  6. Substructure of Highly Boosted Massive Jets

    Energy Technology Data Exchange (ETDEWEB)

    Alon, Raz [Weizmann Inst. of Science, Rehovot (Israel)

    2012-10-01

    Modern particle accelerators enable researchers to study new high energy frontiers which have never been explored before. This realm opens possibilities to further examine known fields such as Quantum Chromodynamics. In addition, it allows searching for new physics and setting new limits on the existence of such. This study examined the substructure of highly boosted massive jets measured by the CDF II detector. Events from 1.96 TeV proton-antiproton collisions at the Fermilab Tevatron Collider were collected out of a total integrated luminosity of 5.95 fb$^{-1}$. They were selected to have at least one jet with transverse momentum above 400 GeV/c. The jet mass, angularity, and planar flow were measured and compared with predictions of perturbative Quantum Chromodynamics, and were found to be consistent with the theory. A search for boosted top quarks was conducted and resulted in an upper limit on the production cross section of such top quarks.

  7. Chemical substructure analysis in toxicology

    Energy Technology Data Exchange (ETDEWEB)

    Beauchamp, R.O. Jr. [Center for Information on Toxicology and Environment, Raleigh, NC (United States)

    1990-12-31

    A preliminary examination of chemical-substructure analysis (CSA) demonstrates the effective use of the Chemical Abstracts compound connectivity file in conjunction with the bibliographic file for relating chemical structures to biological activity. The importance of considering the role of metabolic intermediates under a variety of conditions is illustrated, suggesting structures that should be examined that may exhibit potential activity. This CSA technique, which utilizes existing large files accessible with online personal computers, is recommended for use as another tool in examining chemicals in drugs. 2 refs., 4 figs.

  8. Evolutionarily conserved substrate substructures for automated annotation of enzyme superfamilies.

    Directory of Open Access Journals (Sweden)

    Ranyee A Chiang

    2008-08-01

    Full Text Available The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized

  9. Evolutionarily conserved substrate substructures for automated annotation of enzyme superfamilies.

    Science.gov (United States)

    Chiang, Ranyee A; Sali, Andrej; Babbitt, Patricia C

    2008-08-01

    The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized

  10. Jet substructure measurements at ATLAS and CMS

    CERN Document Server

    Dattagupta, Aparajita; The ATLAS collaboration

    2017-01-01

    A review is given of recent Run II measurements of jet substructure at CMS and ATLAS, as well of the most relevant measurements from Run I. Quark and gluon discrimination, jet mass and other substructure observable are discussed together with prospects for future measurements with new insight from theory.

  11. Opportune maintenance and predictive maintenance decision support

    OpenAIRE

    Thomas , Edouard; Levrat , Eric; Iung , Benoît; Cocheteux , Pierre

    2009-01-01

    International audience; Conventional maintenance strategies on a single component are being phased out in favour of more predictive maintenance actions. These new kinds of actions are performed in order to control the global performances of the whole industrial system. They are anticipative in nature, which allows a maintenance expert to consider non-already-planned maintenance actions. Two questions naturally emerge: when to perform a predictive maintenance action; how a maintenance expert c...

  12. The nonlinear response of the complex structural system in nuclear reactors using dynamic substructure method

    International Nuclear Information System (INIS)

    Zheng, Z.C.; Xie, G.; Du, Q.H.

    1987-01-01

    Because of the existence of nonlinear characteristics in practical engineering structures, such as large steam turbine-foundation system and offshore platform, it is necessary to predict nonlinear dynamic responses for these very large and complex structural systems subjected extreme load. Due to the limited storage and high executing cost of computers, there are still some difficulties in the analysis for such systems although the traditional finite element methods provide basic available methods to the problems. The dynamic substructure methods, which were developed as a branch of general structural dynamics in the past more than 20 years and have been widely used from aircraft, space vehicles to other mechanical and civil engineering structures, present a powerful method to the analysis of very large structural systems. The key to success is due to the considerable reduction in the number of degrees of freedom while not changing the physical essence of the problems investigated. The dynamic substructure method has been extended to nonlinear system and applicated to the analysis of nonlinear dynamic response of an offshore platform by Z.C. Zheng, et al. (1983, 1985a, b, c). In this paper, the method is presented to analyze dynamic responses of the systems contained intrinsic nonlinearities and with nonlinear attachments and nonlinear supports of nuclear structural systems. The efficiency of the method becomes more clear for nonlinear dynamic problems due to the adoption of iterating processes. For simplicity, the analysis procedure is demonstrated briefly. The generalized substructure method of nonlinear systems is similar to linear systems, only the nonlinear terms are treated as pseudo-forces. Interface coordinates are classified into two categories, the connecting interface coordinates which connect with each other directly in the global system and the linking interface coordinates which link to each other through attachments. (orig./GL)

  13. Predictive analytics can support the ACO model.

    Science.gov (United States)

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  14. Fault trend prediction of device based on support vector regression

    International Nuclear Information System (INIS)

    Song Meicun; Cai Qi

    2011-01-01

    The research condition of fault trend prediction and the basic theory of support vector regression (SVR) were introduced. SVR was applied to the fault trend prediction of roller bearing, and compared with other methods (BP neural network, gray model, and gray-AR model). The results show that BP network tends to overlearn and gets into local minimum so that the predictive result is unstable. It also shows that the predictive result of SVR is stabilization, and SVR is superior to BP neural network, gray model and gray-AR model in predictive precision. SVR is a kind of effective method of fault trend prediction. (authors)

  15. Child Support Payment: A Structural Model of Predictive Variables.

    Science.gov (United States)

    Wright, David W.; Price, Sharon J.

    A major area of concern in divorced families is compliance with child support payments. Aspects of the former spouse relationship that are predictive of compliance with court-ordered payment of child support were investigated in a sample of 58 divorced persons all of whom either paid or received child support. Structured interviews and…

  16. Prediction of Machine Tool Condition Using Support Vector Machine

    International Nuclear Information System (INIS)

    Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

    2011-01-01

    Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

  17. Small but mighty: Dark matter substructures

    Science.gov (United States)

    Cyr-Racine, Francis-Yan; Keeton, Charles; Moustakas, Leonidas

    2018-01-01

    The fundamental properties of dark matter, such as its mass, self-interaction, and coupling to other particles, can have a major impact on the evolution of cosmological density fluctuations on small length scales. Strong gravitational lenses have long been recognized as powerful tools to study the dark matter distribution on these small subgalactic scales. In this talk, we discuss how gravitationally lensed quasars and extended lensed arcs could be used to probe non minimal dark matter models. We comment on the possibilities enabled by precise astrometry, deep imaging, and time delays to extract information about mass substructures inside lens galaxies. To this end, we introduce a new lensing statistics that allows for a robust diagnostic of the presence of perturbations caused by substructures. We determine which properties of mass substructures are most readily constrained by lensing data and forecast the constraining power of current and future observations.

  18. Slope Deformation Prediction Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei JIA

    2013-07-01

    Full Text Available This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM. In the prediction process,explore how to reconstruct the phase space. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.

  19. Predicting catalyst-support interactions between metal nanoparticles and amorphous silica supports

    Science.gov (United States)

    Ewing, Christopher S.; Veser, Götz; McCarthy, Joseph J.; Lambrecht, Daniel S.; Johnson, J. Karl

    2016-10-01

    Metal-support interactions significantly affect the stability and activity of supported catalytic nanoparticles (NPs), yet there is no simple and reliable method for estimating NP-support interactions, especially for amorphous supports. We present an approach for rapid prediction of catalyst-support interactions between Pt NPs and amorphous silica supports for NPs of various sizes and shapes. We use density functional theory calculations of 13 atom Pt clusters on model amorphous silica supports to determine linear correlations relating catalyst properties to NP-support interactions. We show that these correlations can be combined with fast discrete element method simulations to predict adhesion energy and NP net charge for NPs of larger sizes and different shapes. Furthermore, we demonstrate that this approach can be successfully transferred to Pd, Au, Ni, and Fe NPs. This approach can be used to quickly screen stability and net charge transfer and leads to a better fundamental understanding of catalyst-support interactions.

  20. Galactic densities, substructure and the initial power spectrum

    International Nuclear Information System (INIS)

    Bullock, J.S.; Zentner, A.R.

    2003-01-01

    Although the currently favored cold dark matter plus cosmological constant model for structure formation assumes an n = 1 scale-invariant initial power spectrum, most inflation models produce at least mild deviations from n = 1. Because the lever arm from the CMB normalization to galaxy scales is long, even a small 'tilt' can have important implications for galactic observations. Here we calculate the COBS-normalized power spectra for several well-motivated models of inflation and compute implications for the substructure content and central densities of galaxy halos. Using an analytic model, normalized against N-body simulations, we show that while halos in the standard (n = 1) model are overdense by a factor of ∼ 6 compared to observations, several of our example inflation+LCDM models predict halo densities well within the range of observations, which prefer models with n ∼ 0.85. We go on to use a semi-analytic model (also normalized against N-body simulations) to follow the merger histories of galaxy-sized halos and track the orbital decay, disruption, and evolution of the merging substructure. Models with n ∼ 0.85 predict a factor of ∼ 3 fewer subhalos at a fixed circular velocity than the standard n 1 case. Although this level of reduction does not resolve the 'dwarf satellite problem', it does imply that the level of feedback required to match the observed number of dwarfs is sensitive to the initial power spectrum. Finally, the fraction of galaxy-halo mass that is bound up in substructure is consistent with limits imposed by multiply imaged quasars for all models considered: f sat > 0.01 even for an effective tilt of n ∼ 0.8. We conclude that, at their current level, lensing constraints of this kind do not provide an interesting probe of the primordial power spectrum

  1. Decision Support for Flood Event Prediction and Monitoring

    DEFF Research Database (Denmark)

    Mioc, Darka; Anton, François; Liang, Gengsheng

    2007-01-01

    In this paper the development of Web GIS based decision support system for flood events is presented. To improve flood prediction we developed the decision support system for flood prediction and monitoring that integrates hydrological modelling and CARIS GIS. We present the methodology for data...... integration, floodplain delineation, and online map interfaces. Our Web-based GIS model can dynamically display observed and predicted flood extents for decision makers and the general public. The users can access Web-based GIS that models current flood events and displays satellite imagery and digital...... elevation model integrated with flood plain area. The system can show how the flooding prediction based on the output from hydrological modeling for the next 48 hours along the lower Saint John River Valley....

  2. Fracture behaviour of zirconia FPDs substructures.

    Science.gov (United States)

    Kou, W; Sjögren, G

    2010-04-01

    The purpose of this study was to evaluate the occurrence of superficial flaws after machining and to identify fracture initiation and propagation in three-unit heat-treated machined fixed partial dentures (FPDs) substructures made of hot isostatic pressed (HIPed) yttria-stabilized tetragonal zirconia polycrystal (Y-TZP) after loaded to fracture. Four three-unit HIPed Y-TZP-based FPDs substructures were examined. To evaluate the occurrence of superficial flaws after machining, the surfaces were studied utilizing a fluorescent penetrant method. After static loading to fracture, characteristic fracture features on both mating halves of the fractured specimens were studied using a stereomicroscope and a scanning electron microscope. Grinding grooves were clearly visible on the surfaces of the machined FPDs substructures, but no other flaws could be seen with the fluorescent penetrant method. After loading to fracture, the characteristic fracture features of arrest lines, compression curl, fracture mirror, fracture origin, hackle and twist hackle were detected. These findings indicated that the decisive fracture was initiated at the gingival embrasure of the pontic in association with a grinding groove. Thus, in three-unit heat-treated machined HIPed Y-TZP FPDs substructures, with the shape studied in this study, the gingival embrasure of the pontic seems to be a weak area providing a location for tensile stresses when they are occlusally loaded. In this area, fracture initiation may be located to a grinding groove.

  3. THE UNORTHODOX ORBITS OF SUBSTRUCTURE HALOS

    NARCIS (Netherlands)

    Ludlow, Aaron D.; Navarro, Julio F.; Springel, Volker; Jenkins, Adrian; Frenk, Carlos S.; Helmi, Amina

    2009-01-01

    We use a suite of cosmological N-body simulations to study the properties of substructure halos (subhalos) in galaxy-sized cold dark matter halos. We extend prior work on the subject by considering the whole population of subhalos physically associated with the main system. These are defined as

  4. Prediction of Banking Systemic Risk Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Shouwei Li

    2013-01-01

    Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

  5. Toward Predicting Social Support Needs in Online Health Social Networks.

    Science.gov (United States)

    Choi, Min-Je; Kim, Sung-Hee; Lee, Sukwon; Kwon, Bum Chul; Yi, Ji Soo; Choo, Jaegul; Huh, Jina

    2017-08-02

    While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey

  6. Substructure identification for shear structures: cross-power spectral density method

    International Nuclear Information System (INIS)

    Zhang, Dongyu; Johnson, Erik A

    2012-01-01

    In this paper, a substructure identification method for shear structures is proposed. A shear structure is divided into many small substructures; utilizing the dynamic equilibrium of a one-floor substructure, an inductive identification problem is formulated, using the cross-power spectral densities between structural floor accelerations and a reference response, to estimate the parameters of that one story. Repeating this procedure, all story parameters of the shear structure are identified from top to bottom recursively. An identification error analysis is performed for the proposed substructure method, revealing how uncertain factors (e.g. measurement noise) in the identification process affect the identification accuracy. According to the error analysis, a smart reference selection rule is designed to choose the optimal reference response that further enhances the identification accuracy. Moreover, based on the identification error analysis, explicit formulae are developed to calculate the variances of the parameter identification errors. A ten-story shear structure is used to illustrate the effectiveness of the proposed substructure method. The simulation results show that the method, combined with the reference selection rule, can very accurately identify structural parameters despite large measurement noise. Furthermore, the proposed formulae provide good predictions for the variances of the parameter identification errors, which are vital for providing accurate warnings of structural damage. (paper)

  7. Substructure evolution of Zircaloy-4 during creep and implications for the Modified Jogged-Screw model

    Energy Technology Data Exchange (ETDEWEB)

    Morrow, B.M., E-mail: morrow@lanl.gov [The Ohio State University, 2041 College Rd., 477 Watts Hall, Columbus, OH 43210 (United States); Los Alamos National Laboratory, P.O. Box 1663, MS G755, Los Alamos, NM 87545 (United States); Kozar, R.W.; Anderson, K.R. [Bettis Laboratory, Bechtel Marine Propulsion Corp., West Mifflin, PA 15122 (United States); Mills, M.J., E-mail: millsmj@mse.osu.edu [The Ohio State University, 2041 College Rd., 477 Watts Hall, Columbus, OH 43210 (United States)

    2016-05-17

    Several specimens of Zircaloy-4 were creep tested at a single stress-temperature condition, and interrupted at different accumulated strain levels. Substructural observations were performed using bright field scanning transmission electron microscopy (BF STEM). The dislocation substructure was characterized to ascertain how creep strain evolution impacts the Modified Jogged-Screw (MJS) model, which has previously been utilized to predict steady-state strain rates in Zircaloy-4. Special attention was paid to the evolution of individual model parameters with increasing strain. Results of model parameter measurements are reported and discussed, along with possible extensions to the MJS model.

  8. Indonesian Stock Prediction using Support Vector Machine (SVM

    Directory of Open Access Journals (Sweden)

    Santoso Murtiyanto

    2018-01-01

    Full Text Available This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data and the remainder (20 % was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

  9. Infinite ensemble of support vector machines for prediction of ...

    African Journals Online (AJOL)

    Many researchers have demonstrated the use of artificial neural networks (ANNs) to predict musculoskeletal disorders risk associated with occupational exposures. In order to improve the accuracy of LBDs risk classification, this paper proposes to use the support vector machines (SVMs), a machine learning algorithm used ...

  10. The gamma-ray-flux PDF from galactic halo substructure

    International Nuclear Information System (INIS)

    Lee, Samuel K.; Ando, Shin'ichiro; Kamionkowski, Marc

    2009-01-01

    One of the targets of the recently launched Fermi Gamma-ray Space Telescope is a diffuse gamma-ray background from dark-matter annihilation or decay in the Galactic halo. N-body simulations and theoretical arguments suggest that the dark matter in the Galactic halo may be clumped into substructure, rather than smoothly distributed. Here we propose the gamma-ray-flux probability distribution function (PDF) as a probe of substructure in the Galactic halo. We calculate this PDF for a phenomenological model of halo substructure and determine the regions of the substructure parameter space in which the PDF may be distinguished from the PDF for a smooth distribution of dark matter. In principle, the PDF allows a statistical detection of substructure, even if individual halos cannot be detected. It may also allow detection of substructure on the smallest microhalo mass scales, ∼ M ⊕ , for weakly-interacting massive particles (WIMPs). Furthermore, it may also provide a method to measure the substructure mass function. However, an analysis that assumes a typical halo substructure model and a conservative estimate of the diffuse background suggests that the substructure PDF may not be detectable in the lifespan of Fermi in the specific case that the WIMP is a neutralino. Nevertheless, for a large range of substructure, WIMP annihilation, and diffuse background models, PDF analysis may provide a clear signature of substructure

  11. Smart variations: Functional substructures for part compatibility

    KAUST Repository

    Zheng, Youyi

    2013-05-01

    As collections of 3D models continue to grow, reusing model parts allows generation of novel model variations. Naïvely swapping parts across models, however, leads to implausible results, especially when mixing parts across different model families. Hence, the user has to manually ensure that the final model remains functionally valid. We claim that certain symmetric functional arrangements (sFarr-s), which are special arrangements among symmetrically related substructures, bear close relation to object functions. Hence, we propose a purely geometric approach based on such substructures to match, replace, and position triplets of parts to create non-trivial, yet functionally plausible, model variations. We demonstrate that starting even from a small set of models such a simple geometric approach can produce a diverse set of non-trivial and plausible model variations. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.

  12. Towards an understanding of jet substructure

    CERN Document Server

    Dasgupta, Mrinal; Marzani, Simone; Salam, Gavin P

    2013-01-01

    We present first analytic, resummed calculations of the rates at which widespread jet substructure tools tag QCD jets. As well as considering trimming, pruning and the mass-drop tagger, we introduce modified tools with improved analytical and phenomenological behaviours. Most taggers have double logarithmic resummed structures. The modified mass-drop tagger is special in that it involves only single logarithms, and is free from a complex class of terms known as non-global logarithms. The modification of pruning brings an improved ability to discriminate between the different colour structures that characterise signal and background. As we outline in an extensive phenomenological discussion, these results provide valuable insight into the performance of existing tools and help lay robust foundations for future substructure studies.

  13. Tagging partially reconstructed objects with jet substructure

    Energy Technology Data Exchange (ETDEWEB)

    Freytsis, Marat, E-mail: freytsis@uoregon.edu [Department of Physics, Harvard University, Cambridge, MA, 02138 (United States); Volansky, Tomer [Raymond and Beverly Sackler School of Physics and Astronomy, Tel-Aviv University, Tel-Aviv 69978 (Israel); Walsh, Jonathan R. [Ernest Orlando Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720 (United States); Berkeley Center for Theoretical Physics, University of California, Berkeley, CA 94720 (United States)

    2017-06-10

    We present a new tagger which aims at identifying partially reconstructed objects, in which only some of the constituents are collected in a single jet. As an example, we focus on top decays in which either part of the hadronically decaying W or the b jet is soft or falls outside of the top jet cone. We construct an observable to identify remnant substructure from the decay and employ aggressive jet grooming to reject QCD backgrounds. The tagger is complementary to existing ones and works well in the intermediate boost regime where jet substructure techniques usually fail. It is anticipated that a similar tagger can be used to identify non-QCD hadronic jets, such as those expected from hidden valleys.

  14. Tagging partially reconstructed objects with jet substructure

    International Nuclear Information System (INIS)

    Freytsis, Marat; Volansky, Tomer; Walsh, Jonathan R.

    2017-01-01

    We present a new tagger which aims at identifying partially reconstructed objects, in which only some of the constituents are collected in a single jet. As an example, we focus on top decays in which either part of the hadronically decaying W or the b jet is soft or falls outside of the top jet cone. We construct an observable to identify remnant substructure from the decay and employ aggressive jet grooming to reject QCD backgrounds. The tagger is complementary to existing ones and works well in the intermediate boost regime where jet substructure techniques usually fail. It is anticipated that a similar tagger can be used to identify non-QCD hadronic jets, such as those expected from hidden valleys.

  15. Tagging partially reconstructed objects with jet substructure

    Science.gov (United States)

    Freytsis, Marat; Volansky, Tomer; Walsh, Jonathan R.

    2017-06-01

    We present a new tagger which aims at identifying partially reconstructed objects, in which only some of the constituents are collected in a single jet. As an example, we focus on top decays in which either part of the hadronically decaying W or the b jet is soft or falls outside of the top jet cone. We construct an observable to identify remnant substructure from the decay and employ aggressive jet grooming to reject QCD backgrounds. The tagger is complementary to existing ones and works well in the intermediate boost regime where jet substructure techniques usually fail. It is anticipated that a similar tagger can be used to identify non-QCD hadronic jets, such as those expected from hidden valleys.

  16. Substructure boosts to dark matter annihilation from Sommerfeld enhancement

    International Nuclear Information System (INIS)

    Bovy, Jo

    2009-01-01

    The recently introduced Sommerfeld enhancement of the dark matter annihilation cross section has important implications for the detection of dark matter annihilation in subhalos in the Galactic halo. In addition to the boost to the dark matter annihilation cross section from the high densities of these subhalos with respect to the main halo, an additional boost caused by the Sommerfeld enhancement results from the fact that they are kinematically colder than the Galactic halo. If we further believe the generic prediction of the cold dark matter paradigm that in each subhalo there is an abundance of substructure which is approximately self-similar to that of the Galactic halo, then I show that additional boosts coming from the density enhancements of these small substructures and their small velocity dispersions enhance the dark matter annihilation cross section even further. I find that very large boost factors (10 5 to 10 9 ) are obtained in a large class of models. The implications of these boost factors for the detection of dark matter annihilation from dwarf spheroidal galaxies in the Galactic halo are such that, generically, they outshine the background gamma-ray flux and are detectable by the Fermi Gamma-ray Space Telescope.

  17. MAPPING THE GALACTIC HALO. VIII. QUANTIFYING SUBSTRUCTURE

    International Nuclear Information System (INIS)

    Starkenburg, Else; Helmi, Amina; Van Woerden, Hugo; Morrison, Heather L.; Harding, Paul; Frey, Lucy; Oravetz, Dan; Mateo, Mario; Dohm-Palmer, R. C.; Olszewski, Edward W.; Sivarani, Thirupathi; Norris, John E.; Freeman, Kenneth C.; Shectman, Stephen A.

    2009-01-01

    We have measured the amount of kinematic substructure in the Galactic halo using the final data set from the Spaghetti project, a pencil-beam high-latitude sky survey. Our sample contains 101 photometrically selected and spectroscopically confirmed giants with accurate distance, radial velocity, and metallicity information. We have developed a new clustering estimator: the '4distance' measure, which when applied to our data set leads to the identification of one group and seven pairs of clumped stars. The group, with six members, can confidently be matched to tidal debris of the Sagittarius dwarf galaxy. Two pairs match the properties of known Virgo structures. Using models of the disruption of Sagittarius in Galactic potentials with different degrees of dark halo flattening, we show that this favors a spherical or prolate halo shape, as demonstrated by Newberg et al. using the Sloan Digital Sky Survey data. One additional pair can be linked to older Sagittarius debris. We find that 20% of the stars in the Spaghetti data set are in substructures. From comparison with random data sets, we derive a very conservative lower limit of 10% to the amount of substructure in the halo. However, comparison to numerical simulations shows that our results are also consistent with a halo entirely built up from disrupted satellites, provided that the dominating features are relatively broad due to early merging or relatively heavy progenitor satellites.

  18. Efficient heuristics for maximum common substructure search.

    Science.gov (United States)

    Englert, Péter; Kovács, Péter

    2015-05-26

    Maximum common substructure search is a computationally hard optimization problem with diverse applications in the field of cheminformatics, including similarity search, lead optimization, molecule alignment, and clustering. Most of these applications have strict constraints on running time, so heuristic methods are often preferred. However, the development of an algorithm that is both fast enough and accurate enough for most practical purposes is still a challenge. Moreover, in some applications, the quality of a common substructure depends not only on its size but also on various topological features of the one-to-one atom correspondence it defines. Two state-of-the-art heuristic algorithms for finding maximum common substructures have been implemented at ChemAxon Ltd., and effective heuristics have been developed to improve both their efficiency and the relevance of the atom mappings they provide. The implementations have been thoroughly evaluated and compared with existing solutions (KCOMBU and Indigo). The heuristics have been found to greatly improve the performance and applicability of the algorithms. The purpose of this paper is to introduce the applied methods and present the experimental results.

  19. Identifying a new particle with jet substructures

    CERN Document Server

    Lim, Sung Hak; Kim, Doojin; Kim, Minho; Kong, Kyoungchul; Park, Myeonghun

    2017-01-01

    We investigate a potential of measuring properties of a heavy resonance X, exploiting jet substructure techniques. Motivated by heavy higgs boson searches, we focus on the decays of X into a pair of (massive) electroweak gauge bosons. More specifically, we consider a hadronic Z boson, which makes it possible to determine properties of X at an earlier stage. For $m_X$ of O(1) TeV, two quarks from a Z boson would be captured as a "merged jet" in a significant fraction of events. The use of the merged jet enables us to consider a Z-induced jet as a reconstructed object without any combinatorial ambiguity. We apply a conventional jet substructure method to extract four-momenta of subjets from a merged jet. We find that jet substructure procedures may enhance features in some kinematic observables formed with subjets. Subjet momenta are fed into the matrix element associated with a given hypothesis on the nature of X, which is further processed to construct a matrix element method (MEM)-based observable. For both ...

  20. Identifying a new particle with jet substructures

    International Nuclear Information System (INIS)

    Han, Chengcheng; Kim, Doojin; Kim, Minho; Postech, Pohang

    2017-01-01

    Here, we investigate a potential of determining properties of a new heavy resonance of mass O(1)TeV which decays to collimated jets via heavy Standard Model intermediary states, exploiting jet substructure techniques. Employing the Z gauge boson as a concrete example for the intermediary state, we utilize a "merged jet" defined by a large jet size to capture the two quarks from its decay. The use of the merged jet bene ts the identification of a Z-induced jet as a single, reconstructed object without any combinatorial ambiguity. We also find that jet substructure procedures may enhance features in some kinematic observables formed with subjet four-momenta extracted from a merged jet. This observation motivates us to feed subjet momenta into the matrix elements associated with plausible hypotheses on the nature of the heavy resonance, which are further processed to construct a matrix element method (MEM)-based observable. For both moderately and highly boosted Z bosons, we demonstrate that the MEM in combination with jet substructure techniques can be a very powerful tool for identifying its physical properties. Finally, we discuss effects from choosing different jet sizes for merged jets and jet-grooming parameters upon the MEM analyses.

  1. Star formation and substructure in galaxy clusters

    International Nuclear Information System (INIS)

    Cohen, Seth A.; Hickox, Ryan C.; Wegner, Gary A.; Einasto, Maret; Vennik, Jaan

    2014-01-01

    We investigate the relationship between star formation (SF) and substructure in a sample of 107 nearby galaxy clusters using data from the Sloan Digital Sky Survey. Several past studies of individual galaxy clusters have suggested that cluster mergers enhance cluster SF, while others find no such relationship. The SF fraction in multi-component clusters (0.228 ± 0.007) is higher than that in single-component clusters (0.175 ± 0.016) for galaxies with M r 0.1 <−20.5. In both single- and multi-component clusters, the fraction of star-forming galaxies increases with clustercentric distance and decreases with local galaxy number density, and multi-component clusters show a higher SF fraction than single-component clusters at almost all clustercentric distances and local densities. Comparing the SF fraction in individual clusters to several statistical measures of substructure, we find weak, but in most cases significant at greater than 2σ, correlations between substructure and SF fraction. These results could indicate that cluster mergers may cause weak but significant SF enhancement in clusters, or unrelaxed clusters exhibit slightly stronger SF due to their less evolved states relative to relaxed clusters.

  2. Modeling and prediction of flotation performance using support vector regression

    Directory of Open Access Journals (Sweden)

    Despotović Vladimir

    2017-01-01

    Full Text Available Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR, is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.

  3. Measurement and prediction of voice support and room gain

    DEFF Research Database (Denmark)

    Pelegrin Garcia, David; Brunskog, Jonas; Lyberg-Åhlander, Viveka

    2012-01-01

    and good acoustical quality lies in the range between 14 and 9 dB, whereas the room gain is in the range between 0.2 and 0.5 dB. The prediction model for voice support describes the measurements in the classrooms with a coefficient of determination of 0.84 and a standard deviation of 1.2 dB....

  4. Intelligent Quality Prediction Using Weighted Least Square Support Vector Regression

    Science.gov (United States)

    Yu, Yaojun

    A novel quality prediction method with mobile time window is proposed for small-batch producing process based on weighted least squares support vector regression (LS-SVR). The design steps and learning algorithm are also addressed. In the method, weighted LS-SVR is taken as the intelligent kernel, with which the small-batch learning is solved well and the nearer sample is set a larger weight, while the farther is set the smaller weight in the history data. A typical machining process of cutting bearing outer race is carried out and the real measured data are used to contrast experiment. The experimental results demonstrate that the prediction accuracy of the weighted LS-SVR based model is only 20%-30% that of the standard LS-SVR based one in the same condition. It provides a better candidate for quality prediction of small-batch producing process.

  5. Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Yang Sun

    2018-01-01

    Full Text Available Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses. It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties. Therefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels. This study presents a modified application of a multiclass support vector machine (SVM to predict tunnel squeezing based on four parameters, that is, diameter (D, buried depth (H, support stiffness (K, and rock tunneling quality index (Q. We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model. The proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%. Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy. More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.

  6. Profiled support vector machines for antisense oligonucleotide efficacy prediction

    Directory of Open Access Journals (Sweden)

    Martín-Guerrero José D

    2004-09-01

    Full Text Available Abstract Background This paper presents the use of Support Vector Machines (SVMs for prediction and analysis of antisense oligonucleotide (AO efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1 feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE, and (2 AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions. Results In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, r; mean error, ME; and root-mean-square-error, RMSE using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278 and predicted high (>75% inhibition of gene expression and low efficacy (http://aosvm.cgb.ki.se/. Conclusions The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well.

  7. Perceived social support predicts increased conscientiousness during older adulthood.

    Science.gov (United States)

    Hill, Patrick L; Payne, Brennan R; Jackson, Joshua J; Stine-Morrow, Elizabeth A L; Roberts, Brent W

    2014-07-01

    This study examined whether perceived social support predicted adaptive personality change in older adulthood, focusing on the trait of conscientiousness. We tested this hypothesis both at the broad domain level and with respect to the specific lower order facets that comprise conscientiousness: order, self-control, industriousness, responsibility, and traditionalism. A sample of 143 older adults (aged 60-91) completed measures of conscientiousness and social support during 2 assessments 7 months apart. Social support and conscientiousness were positively correlated among older adults. Moreover, older adults who perceived greater social support at baseline were more likely to gain in conscientiousness over time. The magnitude of this effect was relatively similar across the order, self-control, and industriousness facets. Perceived social support provides multiple benefits later in life, and the current results add to this literature by showing that it also promotes conscientiousness. As conscientiousness is linked to a variety of positive outcomes later in life, including health, future research should examine whether conscientiousness change may be an important mechanism through which social support enhances resilience in older adulthood. © The Author 2013. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  8. Soil and gas and radon entry potentials for substructure surfaces

    International Nuclear Information System (INIS)

    Harrison, J.; Sextro, R.G.

    1990-01-01

    This paper reports on measurement techniques and parameters that describe the potential for areas of a building substructure to have high soil gas and radon entry rates which have been developed. Flows and pressures measured at test holes in substructure surfaces while the substructure was intentionally depressurized were used in a highly simplified electrical circuit to model the substructure/soil network. Data from four New Jersey houses indicate that the soil was a factor of two to six times more resistant to soil gas flow than substructure surfaces, concrete slab floors, including perimeter gaps, cracks, and other penetrations, were approximately five times more resistant to soil gas movement than hollow block walls, and radon entry potentials were highest for slab floors. These indices of entry potential may be useful for characterizing the relative leakiness of below-grade substructure surfaces and for determining the selection and placement of radon control systems

  9. Predicting SVOC Emissions into Air and Foods in Support of ...

    Science.gov (United States)

    The release of semi-volatile organic compounds (SVOCs) from consumer articles may be a critical human exposure pathway. In addition, the migration of SVOCs from food packaging materials into foods may also be a dominant source of exposure for some chemicals. Here we describe recent efforts to characterize emission-related parameters for these exposure pathways to support prediction of aggregate exposures for thousands of chemicals For chemicals in consumer articles, Little et al. (2012) developed a screening-level indoor exposure prediction model which, for a given SVOC, principally depends on steady-state gas-phase concentrations (y0). We have developed a model that predicts y0 for SVOCs in consumer articles, allowing exposure predictions for 274 ToxCast chemicals. Published emissions data for 31 SVOCs found in flooring materials, provided a training set where both chemical-specific physicochemical properties, article specific formulation properties, and experimental design aspects were available as modeling descriptors. A linear regression yielded R2- and p- values of approximately 0.62 and 3.9E-05, respectively. A similar model was developed based upon physicochemical properties alone, since article information is often not available for a given SVOC or product. This latter model yielded R2 - and p- values of approximately 0.47 and 1.2E-10, respectively. Many SVOCs are also used as additives (e.g. plasticizers, antioxidants, lubricants) in plastic food pac

  10. CHEMTRAN and the Interconversion of Chemical Substructure Systems

    Science.gov (United States)

    Granito, Charles E.

    1973-01-01

    The need for the interconversion of chemical substructure systems is discussed and CHEMTRAN, a new service, designed especially for creating interconversion programs, is introduced. (7 references) (Author)

  11. Universal parametrization for quark and lepton substructure

    International Nuclear Information System (INIS)

    Akama, Keiichi; Terazawa, Hidezumi.

    1994-01-01

    A universal parametrization for possible quark and lepton substructure is advocated in terms of quark and lepton form factors. It is emphasized that the lower bounds on compositeness scale, Λ c , to be determined experimentally strongly depend on their definitions in composite models. From the recent HERA data, it is estimated to be Λ c > 50 GeV, 0.4 TeV and 10 TeV, depending on the parametrizations with a single-pole form factor, a contact interaction and a logarithmic form factor, respectively. (author)

  12. Jacket Substructure Fatigue Mitigation through Active Control

    DEFF Research Database (Denmark)

    Hanis, Tomas; Natarajan, Anand

    2014-01-01

    to the fatigue design loads on the braces of the jacket. Since large wind turbines of 10MW rating have low rotor speeds (p), the modal frequencies of the sub structures approach 3p at low wind speeds, which leads to a modal coupling and resonance. Therefore an active control system is developed which provides...... sufficient structural damping and consequently a fatigue reduction at the substructure. The resulting reduction in fatigue design loads on the jacket structure based on the active control system is presented....

  13. Coal demand prediction based on a support vector machine model

    Energy Technology Data Exchange (ETDEWEB)

    Jia, Cun-liang; Wu, Hai-shan; Gong, Dun-wei [China University of Mining & Technology, Xuzhou (China). School of Information and Electronic Engineering

    2007-01-15

    A forecasting model for coal demand of China using a support vector regression was constructed. With the selected embedding dimension, the output vectors and input vectors were constructed based on the coal demand of China from 1980 to 2002. After compared with lineal kernel and Sigmoid kernel, a radial basis function(RBF) was adopted as the kernel function. By analyzing the relationship between the error margin of prediction and the model parameters, the proper parameters were chosen. The support vector machines (SVM) model with multi-input and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM predictor has higher precision and greater generalization ability. In the end, the coal demand from 2003 to 2006 is accurately forecasted. l0 refs., 2 figs., 4 tabs.

  14. Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    R. Gholami

    2012-01-01

    Full Text Available Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability.

  15. Full Vehicle Vibration and Noise Analysis Based on Substructure Power Flow

    Directory of Open Access Journals (Sweden)

    Zhien Liu

    2017-01-01

    Full Text Available Combining substructure and power flow theory, in this paper an external program is written to control MSC. Nastran solution process and the substructure frequency response are also formulated accordingly. Based on a simple vehicle model, characteristics of vibration, noise, and power flow are studied, respectively. After being compared with the result of conventional FEM (finite element method, the new method is confirmed to be feasible. When it comes to a vehicle with the problem of low-frequency noise, finite element models of substructures for vehicle body and chassis are established, respectively. In addition, substructure power flow method is also employed to examine the transfer characteristics of multidimensional vibration energy for the whole vehicle system. By virtue of the adjustment stiffness of drive shaft support and bushes at rear suspension lower arm, the vehicle interior noise is decreased by about 3 dB when the engine speed is near 1050 rpm and 1650 rpm in experiment. At the same time, this method can increase the computation efficiency by 78%, 38%, and 98% when it comes to the optimization of chassis structure, body structure, and vibration isolation components, respectively.

  16. Support vector machine incremental learning triggered by wrongly predicted samples

    Science.gov (United States)

    Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

    2018-05-01

    According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

  17. Weighted K-means support vector machine for cancer prediction.

    Science.gov (United States)

    Kim, SungHwan

    2016-01-01

    To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

  18. Social-group identity and population substructure in admixed populations in New Mexico and Latin America.

    Directory of Open Access Journals (Sweden)

    Meghan E Healy

    Full Text Available We examined the relationship between continental-level genetic ancestry and racial and ethnic identity in an admixed population in New Mexico with the goal of increasing our understanding of how racial and ethnic identity influence genetic substructure in admixed populations. Our sample consists of 98 New Mexicans who self-identified as Hispanic or Latino (NM-HL and who further categorized themselves by race and ethnic subgroup membership. The genetic data consist of 270 newly-published autosomal microsatellites from the NM-HL sample and previously published data from 57 globally distributed populations, including 13 admixed samples from Central and South America. For these data, we 1 summarized the major axes of genetic variation using principal component analyses, 2 performed tests of Hardy Weinberg equilibrium, 3 compared empirical genetic ancestry distributions to those predicted under a model of admixture that lacked substructure, 4 tested the hypotheses that individuals in each sample had 100%, 0%, and the sample-mean percentage of African, European, and Native American ancestry. We found that most NM-HL identify themselves and their parents as belonging to one of two groups, conforming to a region-specific narrative that distinguishes recent immigrants from Mexico from individuals whose families have resided in New Mexico for generations and who emphasize their Spanish heritage. The "Spanish" group had significantly lower Native American ancestry and higher European ancestry than the "Mexican" group. Positive FIS values, PCA plots, and heterogeneous ancestry distributions suggest that most Central and South America admixed samples also contain substructure, and that this substructure may be related to variation in social identity. Genetic substructure appears to be common in admixed populations in the Americas and may confound attempts to identify disease-causing genes and to understand the social causes of variation in health outcomes

  19. Supporting change processes in design: Complexity, prediction and reliability

    Energy Technology Data Exchange (ETDEWEB)

    Eckert, Claudia M. [Engineering Design Centre, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ (United Kingdom)]. E-mail: cme26@cam.ac.uk; Keller, Rene [Engineering Design Centre, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ (United Kingdom)]. E-mail: rk313@cam.ac.uk; Earl, Chris [Open University, Department of Design and Innovation, Walton Hall, Milton Keynes MK7 6AA (United Kingdom)]. E-mail: C.F.Earl@open.ac.uk; Clarkson, P. John [Engineering Design Centre, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ (United Kingdom)]. E-mail: pjc10@cam.ac.uk

    2006-12-15

    Change to existing products is fundamental to design processes. New products are often designed through change or modification to existing products. Specific parts or subsystems are changed to similar ones whilst others are directly reused. Design by modification applies particularly to safety critical products where the reuse of existing working parts and subsystems can reduce cost and risk. However change is rarely a matter of just reusing or modifying parts. Changing one part can propagate through the entire design leading to costly rework or jeopardising the integrity of the whole product. This paper characterises product change based on studies in the aerospace and automotive industry and introduces tools to aid designers in understanding the potential effects of change. Two ways of supporting designers are described: probabilistic prediction of the effects of change and visualisation of change propagation through product connectivities. Change propagation has uncertainties which are amplified by the choices designers make in practice as they implement change. Change prediction and visualisation is discussed with reference to complexity in three areas of product development: the structural backcloth of connectivities in the existing product (and its processes), the descriptions of the product used in design and the actions taken to carry out changes.

  20. On jet substructure methods for signal jets

    Energy Technology Data Exchange (ETDEWEB)

    Dasgupta, Mrinal [Consortium for Fundamental Physics, School of Physics & Astronomy, University of Manchester,Oxford Road, Manchester M13 9PL (United Kingdom); Powling, Alexander [School of Physics & Astronomy, University of Manchester,Oxford Road, Manchester M13 9PL (United Kingdom); Siodmok, Andrzej [Institute of Nuclear Physics, Polish Academy of Sciences,ul. Radzikowskiego 152, 31-342 Kraków (Poland); CERN, PH-TH,CH-1211 Geneva 23 (Switzerland)

    2015-08-17

    We carry out simple analytical calculations and Monte Carlo studies to better understand the impact of QCD radiation on some well-known jet substructure methods for jets arising from the decay of boosted Higgs bosons. Understanding differences between taggers for these signal jets assumes particular significance in situations where they perform similarly on QCD background jets. As an explicit example of this we compare the Y-splitter method to the more recently proposed Y-pruning technique. We demonstrate how the insight we gain can be used to significantly improve the performance of Y-splitter by combining it with trimming and show that this combination outperforms the other taggers studied here, at high p{sub T}. We also make analytical estimates for optimal parameter values, for a range of methods and compare to results from Monte Carlo studies.

  1. Substructure and electrical resistivity analyses of pure tungsten sheet

    International Nuclear Information System (INIS)

    Trybus, C.L.; Sellers, C.H.; Anderl, R.A.

    1991-01-01

    The substructure of pure tungsten sheet (0.025 mm thick) is examined and quantified by transmission electron microscopy (TEM). Dislocation populations and arrangements are evaluated for as-worked and various annealed conditions of the tungsten sheet. The worked (rolled) tungsten substructure was nonhomogeneous, consisting of areas of very high and low dislocation densities. These results are correlated to resistivity measurements of the tungsten sheet following thermal cycling to 1200 degrees C to determine the substructural changes as a function of temperature. The comparison between the two characterization techniques is used to examine the relationship between structural and electronic properties in tungsten. 15 refs., 6 figs., 2 tabs

  2. Substructuring by Lagrange multipliers for solids and plates

    Energy Technology Data Exchange (ETDEWEB)

    Mandel, J.; Tezaur, R. [Univ. of Colorado, Denver, CO (United States); Farhat, C. [Univ. of Colorado, Boulder, CO (United States)

    1996-12-31

    We present principles and theoreretical foundation of a substructuring method for large structural problems. The algorithm is preconditioned conjugate gradients on a subspace for the dual problem. The preconditioning is proved asymptotically optimal and the method is shown to be parallel scalable, i.e., the condition number is bounded independently of the number of substructures. For plate problems, a special modification is needed that retains continuity of the displacement solution at substructure crosspoints, resulting in an asymptically optimal method. The results are confirmed by numerical experiments.

  3. Probabilistic source term predictions for use with decision support systems

    International Nuclear Information System (INIS)

    Grindon, E.; Kinniburgh, C.G.

    2003-01-01

    Full text: Decision Support Systems for use in off-site emergency management, following an incident at a Nuclear Power Plant (NPP) within Europe, are becoming accepted as a useful and appropriate tool to aid decision makers. An area which is not so well developed is the 'upstream' prediction of the source term released into the environment. Rapid prediction of this source term is crucial to the appropriate early management of a nuclear emergency. The initial source term prediction would today be typically based on simple tabulations taking little, or no, account of plant status. It is the interface between the inward looking plant control room team and the outward looking off-site emergency management team that needs to be addressed. This is not an easy proposition as these two distinct disciplines have little common basis from which to communicate their immediate findings and concerns. Within the Euratom Fifth Framework Programme (FP5), complementary approaches are being developed to the pre-release stage; each based on software tools to help bridge this gap. Traditionally source terms (or releases into the environment) provided for use with Decision Support Systems are estimated on a deterministic basis. These approaches use a single, deterministic assumption about plant status. The associated source term represents the 'best estimate' based an available information. No information is provided an the potential for uncertainty in the source term estimate. Using probabilistic methods the outcome is typically a number of possible plant states each with an associated source term and probability. These represent both the best estimate and the spread of the likely source term. However, this is a novel approach and the usefulness of such source term prediction tools is yet to be tested on a wide scale. The benefits of probabilistic source term estimation are presented here; using, as an example, the SPRINT tool developed within the FP5 STERPS project. System for the

  4. RAG-3D: a search tool for RNA 3D substructures

    Science.gov (United States)

    Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef; Schlick, Tamar

    2015-01-01

    To address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally described in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding. PMID:26304547

  5. Indirect Inverse Substructuring Method for Multibody Product Transport System with Rigid and Flexible Coupling

    Directory of Open Access Journals (Sweden)

    Jun Wang

    2015-01-01

    Full Text Available The aim of this paper is to develop a new frequency response function- (FRF- based indirect inverse substructuring method without measuring system-level FRFs in the coupling DOFs for the analysis of the dynamic characteristics of a three-substructure coupled product transport system with rigid and flexible coupling. By enforcing the dynamic equilibrium conditions at the coupling coordinates and the displacement compatibility conditions, a closed-form analytical solution to inverse substructuring analysis of multisubstructure coupled product transport system is derived based on the relationship of easy-to-monitor component-level FRFs and the system-level FRFs at the coupling coordinates. The proposed method is validated by a lumped mass-spring-damper model, and the predicted coupling dynamic stiffness is compared with the direct computation, showing exact agreement. The method developed offers an approach to predict the unknown coupling dynamic stiffness from measured FRFs purely. The suggested method may help to obtain the main controlling factors and contributions from the various structure-borne paths for product transport system.

  6. DETECTION OF LENSING SUBSTRUCTURE USING ALMA OBSERVATIONS OF THE DUSTY GALAXY SDP.81

    Energy Technology Data Exchange (ETDEWEB)

    Hezaveh, Yashar D.; Mao, Yao-Yuan; Morningstar, Warren; Blandford, Roger D.; Levasseur, Laurence Perreault; Wechsler, Risa H. [Kavli Institute for Particle Astrophysics and Cosmology and Department of Physics, Stanford University, 452 Lomita Mall, Stanford, CA 94305-4085 (United States); Dalal, Neal; Wen, Di; Kemball, Athol; Vieira, Joaquin D. [Astronomy Department, University of Illinois at Urbana-Champaign, 1002 W. Green Street, Urbana IL 61801 (United States); Marrone, Daniel P. [Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721 (United States); Carlstrom, John E. [Kavli Institute for Cosmological Physics, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637 (United States); Fassnacht, Christopher D. [Department of Physics, University of California, One Shields Avenue, Davis, CA 95616 (United States); Holder, Gilbert P. [Department of Physics, McGill University, 3600 Rue University, Montreal, Quebec H3A 2T8 (Canada); Marshall, Philip J. [Kavli Institute for Particle Astrophysics and Cosmology and Department of Particle Physics and Astrophysics, SLAC National Accelerator Laboratory, Menlo Park, CA 94305 (United States); Murray, Norman [CITA, University of Toronto, 60 St. George St., Toronto ON M5S 3H8 (Canada)

    2016-05-20

    We study the abundance of substructure in the matter density near galaxies using ALMA Science Verification observations of the strong lensing system SDP.81. We present a method to measure the abundance of subhalos around galaxies using interferometric observations of gravitational lenses. Using simulated ALMA observations we explore the effects of various systematics, including antenna phase errors and source priors, and show how such errors may be measured or marginalized. We apply our formalism to ALMA observations of SDP.81. We find evidence for the presence of a M = 10{sup 8.96±0.12} M {sub ⊙} subhalo near one of the images, with a significance of 6.9 σ in a joint fit to data from bands 6 and 7; the effect of the subhalo is also detected in both bands individually. We also derive constraints on the abundance of dark matter (DM) subhalos down to M ∼ 2 × 10{sup 7} M {sub ⊙}, pushing down to the mass regime of the smallest detected satellites in the Local Group, where there are significant discrepancies between the observed population of luminous galaxies and predicted DM subhalos. We find hints of additional substructure, warranting further study using the full SDP.81 data set (including, for example, the spectroscopic imaging of the lensed carbon monoxide emission). We compare the results of this search to the predictions of ΛCDM halos, and find that given current uncertainties in the host halo properties of SDP.81, our measurements of substructure are consistent with theoretical expectations. Observations of larger samples of gravitational lenses with ALMA should be able to improve the constraints on the abundance of galactic substructure.

  7. DETECTION OF LENSING SUBSTRUCTURE USING ALMA OBSERVATIONS OF THE DUSTY GALAXY SDP.81

    International Nuclear Information System (INIS)

    Hezaveh, Yashar D.; Mao, Yao-Yuan; Morningstar, Warren; Blandford, Roger D.; Levasseur, Laurence Perreault; Wechsler, Risa H.; Dalal, Neal; Wen, Di; Kemball, Athol; Vieira, Joaquin D.; Marrone, Daniel P.; Carlstrom, John E.; Fassnacht, Christopher D.; Holder, Gilbert P.; Marshall, Philip J.; Murray, Norman

    2016-01-01

    We study the abundance of substructure in the matter density near galaxies using ALMA Science Verification observations of the strong lensing system SDP.81. We present a method to measure the abundance of subhalos around galaxies using interferometric observations of gravitational lenses. Using simulated ALMA observations we explore the effects of various systematics, including antenna phase errors and source priors, and show how such errors may be measured or marginalized. We apply our formalism to ALMA observations of SDP.81. We find evidence for the presence of a M = 10 8.96±0.12 M ⊙ subhalo near one of the images, with a significance of 6.9 σ in a joint fit to data from bands 6 and 7; the effect of the subhalo is also detected in both bands individually. We also derive constraints on the abundance of dark matter (DM) subhalos down to M ∼ 2 × 10 7 M ⊙ , pushing down to the mass regime of the smallest detected satellites in the Local Group, where there are significant discrepancies between the observed population of luminous galaxies and predicted DM subhalos. We find hints of additional substructure, warranting further study using the full SDP.81 data set (including, for example, the spectroscopic imaging of the lensed carbon monoxide emission). We compare the results of this search to the predictions of ΛCDM halos, and find that given current uncertainties in the host halo properties of SDP.81, our measurements of substructure are consistent with theoretical expectations. Observations of larger samples of gravitational lenses with ALMA should be able to improve the constraints on the abundance of galactic substructure.

  8. Rapid bridge construction technology : precast elements for substructures.

    Science.gov (United States)

    2011-06-01

    The goal of this research was to propose an alternate system of precast bridge substructures which can : substitute for conventional cast in place systems in Wisconsin to achieve accelerated construction. : Three types of abutment modules (hollow wal...

  9. A sub-structure method for multidimensional integral transport calculations

    International Nuclear Information System (INIS)

    Kavenoky, A.; Stankovski, Z.

    1983-03-01

    A new method has been developed for fine structure burn-up calculations of very heterogeneous large size media. It is a generalization of the well-known surface-source method, allowing coupling actual two-dimensional heterogeneous assemblies, called sub-structures. The method has been applied to a rectangular medium, divided into sub-structures, containing rectangular and/or cylindrical fuel, moderator and structure elements. The sub-structures are divided into homogeneous zones. A zone-wise flux expansion is used to formulate a direct collision probability problem within it (linear or flat flux expansion in the rectangular zones, flat flux in the others). The coupling of the sub-structures is performed by making extra assumptions on the currents entering and leaving the interfaces. The accuracies and computing times achieved are illustrated by numerical results on two benchmark problems

  10. Modeling of rail track substructure linear elastic coupling

    Science.gov (United States)

    2015-09-30

    Most analyses of rail dynamics neglect contribution of the soil, or treat it in a very simple manner such as using spring elements. This can cause accuracy issues in examining dynamics for passenger comfort, derailment, substructure analysis, or othe...

  11. Boosted Higgs boson tagging using jet substructures

    CERN Document Server

    Shvydkin, Pavel

    2016-01-01

    Searching BSM particles via the Higgs boson final state has now become common. The mass of desired BSM particle is more than 1 TeV, thereby its decay products are highly Lorentz-boosted. Hence the jets from b quark-antiquark pair - which the Higgs boson mostly decays into - are very closed to each other, and merged into one jet, that is typically reconstructed using large jet sizes (∆R = 0.8). In this work regression technique is applied to AK8 jets (which defined by anti-kT algorithm, using ΔR = 0.8). The regression makes use of boosted jets with substructure information, coupled with the pecularities of a b quark decay, like the presence of a soft lepton (SL) inside the jet. It has allowed to improve the resolution of the mass reconstruction and transverse momentum of the Higgs boson. This application results in improvement of the mass reconstruction by 3-4 percent. These result may be improved firstly by making more careful pileup rejection. Then it is possible to combine base regression train for dif...

  12. Sub-structure formation in starless cores

    Science.gov (United States)

    Toci, C.; Galli, D.; Verdini, A.; Del Zanna, L.; Landi, S.

    2018-02-01

    Motivated by recent observational searches of sub-structure in starless molecular cloud cores, we investigate the evolution of density perturbations on scales smaller than the Jeans length embedded in contracting isothermal clouds, adopting the same formalism developed for the expanding Universe and the solar wind. We find that initially small amplitude, Jeans-stable perturbations (propagating as sound waves in the absence of a magnetic field) are amplified adiabatically during the contraction, approximately conserving the wave action density, until they either become non-linear and steepen into shocks at a time tnl, or become gravitationally unstable when the Jeans length decreases below the scale of the perturbations at a time tgr. We evaluate analytically the time tnl at which the perturbations enter the non-linear stage using a Burgers' equation approach, and we verify numerically that this time marks the beginning of the phase of rapid dissipation of the kinetic energy of the perturbations. We then show that for typical values of the rms Mach number in molecular cloud cores, tnl is smaller than tgr, and therefore density perturbations likely dissipate before becoming gravitational unstable. Solenoidal modes grow at a faster rate than compressible modes, and may eventually promote fragmentation through the formation of vortical structures.

  13. THE SEGUE K GIANT SURVEY. III. QUANTIFYING GALACTIC HALO SUBSTRUCTURE

    Energy Technology Data Exchange (ETDEWEB)

    Janesh, William; Morrison, Heather L.; Ma, Zhibo; Harding, Paul [Department of Astronomy, Case Western Reserve University, Cleveland, OH 44106 (United States); Rockosi, Constance [UCO/Lick Observatory, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064 (United States); Starkenburg, Else [Department of Physics and Astronomy, University of Victoria, P.O. Box 1700, STN CSC, Victoria BC V8W 3P6 (Canada); Xue, Xiang Xiang; Rix, Hans-Walter [Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg (Germany); Beers, Timothy C. [Department of Physics and JINA Center for the Evolution of the Elements, University of Notre Dame, Notre Dame, IN 46556 (United States); Johnson, Jennifer [Department of Astronomy, Ohio State University, 140 West 18th Avenue, Columbus, OH 43210 (United States); Lee, Young Sun [Department of Astronomy and Space Science, Chungnam National University, Daejeon 34134 (Korea, Republic of); Schneider, Donald P. [Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA 16802 (United States)

    2016-01-10

    We statistically quantify the amount of substructure in the Milky Way stellar halo using a sample of 4568 halo K giant stars at Galactocentric distances ranging over 5–125 kpc. These stars have been selected photometrically and confirmed spectroscopically as K giants from the Sloan Digital Sky Survey’s Sloan Extension for Galactic Understanding and Exploration project. Using a position–velocity clustering estimator (the 4distance) and a model of a smooth stellar halo, we quantify the amount of substructure in the halo, divided by distance and metallicity. Overall, we find that the halo as a whole is highly structured. We also confirm earlier work using blue horizontal branch (BHB) stars which showed that there is an increasing amount of substructure with increasing Galactocentric radius, and additionally find that the amount of substructure in the halo increases with increasing metallicity. Comparing to resampled BHB stars, we find that K giants and BHBs have similar amounts of substructure over equivalent ranges of Galactocentric radius. Using a friends-of-friends algorithm to identify members of individual groups, we find that a large fraction (∼33%) of grouped stars are associated with Sgr, and identify stars belonging to other halo star streams: the Orphan Stream, the Cetus Polar Stream, and others, including previously unknown substructures. A large fraction of sample K giants (more than 50%) are not grouped into any substructure. We find also that the Sgr stream strongly dominates groups in the outer halo for all except the most metal-poor stars, and suggest that this is the source of the increase of substructure with Galactocentric radius and metallicity.

  14. Blooming Trees: Substructures and Surrounding Groups of Galaxy Clusters

    Science.gov (United States)

    Yu, Heng; Diaferio, Antonaldo; Serra, Ana Laura; Baldi, Marco

    2018-06-01

    We develop the Blooming Tree Algorithm, a new technique that uses spectroscopic redshift data alone to identify the substructures and the surrounding groups of galaxy clusters, along with their member galaxies. Based on the estimated binding energy of galaxy pairs, the algorithm builds a binary tree that hierarchically arranges all of the galaxies in the field of view. The algorithm searches for buds, corresponding to gravitational potential minima on the binary tree branches; for each bud, the algorithm combines the number of galaxies, their velocity dispersion, and their average pairwise distance into a parameter that discriminates between the buds that do not correspond to any substructure or group, and thus eventually die, and the buds that correspond to substructures and groups, and thus bloom into the identified structures. We test our new algorithm with a sample of 300 mock redshift surveys of clusters in different dynamical states; the clusters are extracted from a large cosmological N-body simulation of a ΛCDM model. We limit our analysis to substructures and surrounding groups identified in the simulation with mass larger than 1013 h ‑1 M ⊙. With mock redshift surveys with 200 galaxies within 6 h ‑1 Mpc from the cluster center, the technique recovers 80% of the real substructures and 60% of the surrounding groups; in 57% of the identified structures, at least 60% of the member galaxies of the substructures and groups belong to the same real structure. These results improve by roughly a factor of two the performance of the best substructure identification algorithm currently available, the σ plateau algorithm, and suggest that our Blooming Tree Algorithm can be an invaluable tool for detecting substructures of galaxy clusters and investigating their complex dynamics.

  15. Structural analysis and optimization procedure of the TFTR device substructure

    International Nuclear Information System (INIS)

    Driesen, G.

    1975-10-01

    A structural evaluation of the TFTR device substructure is performed in order to verify the feasibility of the proposed design concept as well as to establish a design optimization procedure for minimizing the material and fabrication cost of the substructure members. A preliminary evaluation of the seismic capability is also presented. The design concept on which the analysis is based is consistent with that described in the Conceptual Design Status Briefing report dated June 18, 1975

  16. Evolution of the degree of substructures in simulated galaxy clusters

    Science.gov (United States)

    De Boni, Cristiano; Böhringer, Hans; Chon, Gayoung; Dolag, Klaus

    2018-05-01

    We study the evolution of substructure in the mass distribution with mass, redshift and radius in a sample of simulated galaxy clusters. The sample, containing 1226 objects, spans the mass range M200 = 1014 - 1.74 × 1015 M⊙ h-1 in six redshift bins from z = 0 to z = 1.179. We consider three different diagnostics: 1) subhalos identified with SUBFIND; 2) overdense regions localized by dividing the cluster into octants; 3) offset between the potential minimum and the center of mass. The octant analysis is a new method that we introduce in this work. We find that none of the diagnostics indicate a correlation between the mass of the cluster and the fraction of substructures. On the other hand, all the diagnostics suggest an evolution of substructures with redshift. For SUBFIND halos, the mass fraction is constant with redshift at Rvir, but shows a mild evolution at R200 and R500. Also, the fraction of clusters with at least a subhalo more massive than one thirtieth of the total mass is less than 20%. Our new method based on the octants returns a mass fraction in substructures which has a strong evolution with redshift at all radii. The offsets also evolve strongly with redshift. We also find a strong correlation for individual clusters between the offset and the fraction of substructures identified with the octant analysis. Our work puts strong constraints on the amount of substructures we expect to find in galaxy clusters and on their evolution with redshift.

  17. Exploring Milkyway Halo Substructures with Large-Area Sky Surveys

    Energy Technology Data Exchange (ETDEWEB)

    Li, Ting [Texas A & M Univ., College Station, TX (United States)

    2016-01-01

    Over the last two decades, our understanding of the Milky Way has been improved thanks to large data sets arising from large-area digital sky surveys. The stellar halo is now known to be inhabited by a variety of spatial and kinematic stellar substructures, including stellar streams and stellar clouds, all of which are predicted by hierarchical Lambda Cold Dark Matter models of galaxy formation. In this dissertation, we first present the analysis of spectroscopic observations of individual stars from the two candidate structures discovered using an M-giant catalog from the Two Micron All-Sky Survey. The follow-up observations show that one of the candidates is a genuine structure which might be associated with the Galactic Anticenter Stellar Structure, while the other one is a false detection due to the systematic photometric errors in the survey or dust extinction in low Galactic latitudes. We then presented the discovery of an excess of main sequence turn-off stars in the direction of the constellations of Eridanus and Phoenix from the first-year data of the Dark Energy Survey (DES) – a five-year, 5,000 deg2 optical imaging survey in the Southern Hemisphere. The Eridanus-Phoenix (EriPhe) overdensity is centered around l ~ 285° and b ~ -60° and the Poisson significance of the detection is at least 9σ. The EriPhe overdensity has a cloud-like morphology and the extent is at least ~ 4 kpc by ~ 3 kpc in projection, with a heliocentric distance of about d ~ 16 kpc. The EriPhe overdensity is morphologically similar to the previously-discovered Virgo overdensity and Hercules-Aquila cloud. These three overdensities lie along a polar plane separated by ~ 120° and may share a common origin. In addition to the scientific discoveries, we also present the work to improve the photometric calibration in DES using auxiliary calibration systems, since the photometric errors can cause false detection in first the halo substructure. We present a detailed description of the two

  18. DISCOVERY OF SUBSTRUCTURE IN THE SCATTER-BROADENED IMAGE OF SGR A*

    Energy Technology Data Exchange (ETDEWEB)

    Gwinn, C. R. [Physics Department, Broida Hall, University of California, Santa Barbara, CA 93117 (United States); Kovalev, Y. Y.; Soglasnov, V. A. [Astro Space Center, Lebedev Physical Institute, Russian Academy of Sciences, Profsoyuznaya Str. 84/32, Moscow 117997 (Russian Federation); Johnson, M. D., E-mail: cgwinn@physics.ucsb.edu [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States)

    2014-10-10

    We have detected substructure within the smooth scattering disk of the celebrated Galactic center radio source Sagittarius A* (Sgr A*). We observed this structure at 1.3 cm wavelength with the Very Long Baseline Array together with the Green Bank Telescope, on baselines of up to 3000 km, long enough to completely resolve the average scattering disk. Such structure is predicted theoretically as a consequence of refraction by large-scale plasma fluctuations in the interstellar medium. Along with the much-studied θ{sub d}∝λ{sup 2} scaling of angular broadening θ{sub d} with observing wavelength λ, our observations indicate that the spectrum of interstellar turbulence is shallow with an inner scale larger than 300 km. The substructure is consistent with an intrinsic size of about 1 mas at 1.3 cm wavelength, as inferred from deconvolution of the average scattering. Further observations of the substructure can set stronger constraints on the properties of scattering material and on the intrinsic size of Sgr A*. These constraints will guide our understanding of the effects of scatter broadening and the emission physics near the black hole in images with the Event Horizon Telescope at millimeter wavelengths.

  19. Jet Substructure Measurements Sensitive to Soft QCD effects with the ATLAS Detector

    CERN Document Server

    Asquith, Lily; The ATLAS collaboration

    2017-01-01

    Calculations of jet substructure observables which are accurate beyond leading-logarithmic accuracy have recently become available. Such observables are significant not only for probing a new regime of QCD at a hadron collider, but also for improving the understanding of jet substructure properties that are used in many studies at the Large Hadron Collider. In this talk, we discuss first measurement of jet substructure quantities at a hadron collider, calculated at next-to-next-to-leading-logarithm accuracy. The soft drop mass is measured in dijet events with the ATLAS detector at 13 TeV, unfolded to particle-level and compared to Monte Carlo simulations. In addition, we present a measurement of the splitting scales in the kt jet-clustering algorithm for final states containing a Z-boson candidate at a centre-of-mass energy of 8 TeV.  The data are also corrected for detector effects and are compared to state-of-the-art Monte Carlo predictions.

  20. Factorization for groomed jet substructure beyond the next-to-leading logarithm

    International Nuclear Information System (INIS)

    Frye, Christopher; Larkoski, Andrew J.; Schwartz, Matthew D.; Yan, Kai

    2016-01-01

    Jet grooming algorithms are widely used in experimental analyses at hadron colliders to remove contaminating radiation from within jets. While the algorithms perform a great service to the experiments, their intricate algorithmic structure and multiple parameters has frustrated precision theoretic understanding. In this paper, we demonstrate that one particular groomer called soft drop actually makes precision jet substructure easier. In particular, we derive a factorization formula for a large class of soft drop jet substructure observables, including jet mass. The essential observation that allows for this factorization is that, without the soft wide-angle radiation groomed by soft drop, all singular contributions are collinear. The simplicity and universality of the collinear limit in QCD allows us to show that to all orders, the normalized differential cross section has no contributions from non-global logarithms. It is also independent of process, up to the relative fraction of quark and gluon jets. In fact, soft drop allows us to define this fraction precisely. The factorization theorem also explains why soft drop observables are less sensitive to hadronization than their ungroomed counterparts. Using the factorization theorem, we resum the soft drop jet mass to next-to-next-to-leading logarithmic accuracy. This requires calculating some clustering effects that are closely related to corresponding effects found in jet veto calculations. We match our resummed calculation to fixed order results for both e + e − → dijets and pp→Z+j events, producing the first jet substructure predictions (groomed or ungroomed) to this accuracy for the LHC.

  1. RESONANT CLUMPING AND SUBSTRUCTURE IN GALACTIC DISKS

    International Nuclear Information System (INIS)

    Molloy, Matthew; Smith, Martin C.; Shen, Juntai; Evans, N. Wyn

    2015-01-01

    We describe a method to extract resonant orbits from N-body simulations, exploiting the fact that they close in frames rotating with a constant pattern speed. Our method is applied to the N-body simulation of the Milky Way by Shen et al. This simulation hosts a massive bar, which drives strong resonances and persistent angular momentum exchange. Resonant orbits are found throughout the disk, both close to the bar and out to the very edges of the disk. Using Fourier spectrograms, we demonstrate that the bar is driving kinematic substructure even in the very outer parts of the disk. We identify two major orbit families in the outskirts of the disk, one of which makes significant contributions to the kinematic landscape, namely, the m:l = 3:−2 family, resonating with the bar. A mechanism is described that produces bimodal distributions of Galactocentric radial velocities at selected azimuths in the outer disk. It occurs as a result of the temporal coherence of particles on the 3:−2 resonant orbits, which causes them to arrive simultaneously at pericenter or apocenter. This resonant clumping, due to the in-phase motion of the particles through their epicycle, leads to both inward and outward moving groups that belong to the same orbital family and consequently produce bimodal radial velocity distributions. This is a possible explanation of the bimodal velocity distributions observed toward the Galactic anticenter by Liu et al. Another consequence is that transient overdensities appear and dissipate (in a symmetric fashion), resulting in a periodic pulsing of the disk’s surface density

  2. RESONANT CLUMPING AND SUBSTRUCTURE IN GALACTIC DISKS

    Energy Technology Data Exchange (ETDEWEB)

    Molloy, Matthew [Kavli Institute for Astronomy and Astrophysics, Peking University, Yi He Yuan Lu 5, Hai Dian Qu, Beijing 100871 (China); Smith, Martin C.; Shen, Juntai [Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030 (China); Evans, N. Wyn, E-mail: matthewmolloy@gmail.com, E-mail: msmith@shao.ac.cn, E-mail: jshen@shao.ac.cn, E-mail: nwe@ast.cam.ac.uk [Institute of Astronomy, Madingley Road, Cambridge, CB3 0HA (United Kingdom)

    2015-05-10

    We describe a method to extract resonant orbits from N-body simulations, exploiting the fact that they close in frames rotating with a constant pattern speed. Our method is applied to the N-body simulation of the Milky Way by Shen et al. This simulation hosts a massive bar, which drives strong resonances and persistent angular momentum exchange. Resonant orbits are found throughout the disk, both close to the bar and out to the very edges of the disk. Using Fourier spectrograms, we demonstrate that the bar is driving kinematic substructure even in the very outer parts of the disk. We identify two major orbit families in the outskirts of the disk, one of which makes significant contributions to the kinematic landscape, namely, the m:l = 3:−2 family, resonating with the bar. A mechanism is described that produces bimodal distributions of Galactocentric radial velocities at selected azimuths in the outer disk. It occurs as a result of the temporal coherence of particles on the 3:−2 resonant orbits, which causes them to arrive simultaneously at pericenter or apocenter. This resonant clumping, due to the in-phase motion of the particles through their epicycle, leads to both inward and outward moving groups that belong to the same orbital family and consequently produce bimodal radial velocity distributions. This is a possible explanation of the bimodal velocity distributions observed toward the Galactic anticenter by Liu et al. Another consequence is that transient overdensities appear and dissipate (in a symmetric fashion), resulting in a periodic pulsing of the disk’s surface density.

  3. Operational Precipitation prediction in Support of Real-Time Flash Flood Prediction and Reservoir Management

    Science.gov (United States)

    Georgakakos, K. P.

    2006-05-01

    The presentation will outline the implementation and performance evaluation of a number of national and international projects pertaining to operational precipitation estimation and prediction in the context of hydrologic warning systems and reservoir management support. In all cases, uncertainty measures of the estimates and predictions are an integral part of the precipitation models. Outstanding research issues whose resolution is likely to lead to improvements in the operational environment are presented. The presentation draws from the experience of the Hydrologic Research Center (http://www.hrc-lab.org) prototype implementation projects at the Panama Canal, Central America, Northern California, and South-Central US. References: Carpenter, T.M, and K.P. Georgakakos, "Discretization Scale Dependencies of the Ensemble Flow Range versus Catchment Area Relationship in Distributed Hydrologic Modeling," Journal of Hydrology, 2006, in press. Carpenter, T.M., and K.P. Georgakakos, "Impacts of Parametric and Radar Rainfall Uncertainty on the Ensemble Streamflow Simulations of a Distributed Hydrologic Model," Journal of Hydrology, 298, 202-221, 2004. Georgakakos, K.P., Graham, N.E., Carpenter, T.M., Georgakakos, A.P., and H. Yao, "Integrating Climate- Hydrology Forecasts and Multi-Objective Reservoir Management in Northern California," EOS, 86(12), 122,127, 2005. Georgakakos, K.P., and J.A. Sperfslage, "Operational Rainfall and Flow Forecasting for the Panama Canal Watershed," in The Rio Chagres: A Multidisciplinary Profile of a Tropical Watershed, R.S. Harmon, ed., Kluwer Academic Publishers, The Netherlands, Chapter 16, 323-334, 2005. Georgakakos, K. P., "Analytical results for operational flash flood guidance," Journal of Hydrology, doi:10.1016/j.jhydrol.2005.05.009, 2005.

  4. Predicting support for restricting food marketing to youth.

    Science.gov (United States)

    Goren, Amir; Harris, Jennifer L; Schwartz, Marlene B; Brownell, Kelly D

    2010-01-01

    To address the obesity crisis, public health experts recommend major reductions in the marketing of unhealthy food to youth. However, policies to restrict food marketing are not currently viewed as politically feasible. This paper examines attitudes and knowledge about food marketing and support for restricting unhealthy food marketing [corrected] among one group of constituents: parents. A survey of 807 parents found that those most likely to support food marketing restrictions were also more likely to have negative views of current food practices. [corrected] These findings suggest that increased public education about the harm caused by food marketing may increase public support for policy interventions.

  5. Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS)

    Science.gov (United States)

    OConnor, A.; Kirtman, B. P.; Harrison, S.; Gorman, J.

    2016-02-01

    Current US Navy forecasting systems cannot easily incorporate extended-range forecasts that can improve mission readiness and effectiveness; ensure safety; and reduce cost, labor, and resource requirements. If Navy operational planners had systems that incorporated these forecasts, they could plan missions using more reliable and longer-term weather and climate predictions. Further, using multi-model forecast ensembles instead of single forecasts would produce higher predictive performance. Extended-range multi-model forecast ensembles, such as those available in the North American Multi-Model Ensemble (NMME), are ideal for system integration because of their high skill predictions; however, even higher skill predictions can be produced if forecast model ensembles are combined correctly. While many methods for weighting models exist, the best method in a given environment requires expert knowledge of the models and combination methods.We present an innovative approach that uses machine learning to combine extended-range predictions from multi-model forecast ensembles and generate a probabilistic forecast for any region of the globe up to 12 months in advance. Our machine-learning approach uses 30 years of hindcast predictions to learn patterns of forecast model successes and failures. Each model is assigned a weight for each environmental condition, 100 km2 region, and day given any expected environmental information. These weights are then applied to the respective predictions for the region and time of interest to effectively stitch together a single, coherent probabilistic forecast. Our experimental results demonstrate the benefits of our approach to produce extended-range probabilistic forecasts for regions and time periods of interest that are superior, in terms of skill, to individual NMME forecast models and commonly weighted models. The probabilistic forecast leverages the strengths of three NMME forecast models to predict environmental conditions for an

  6. Infinite ensemble of support vector machines for prediction of ...

    African Journals Online (AJOL)

    user

    the support vector machines (SVMs), a machine learning algorithm used ... work designs so that specific, quantitative workplace assessments can be made ... with SVMs can be obtained by embedding the base learners (hypothesis) into a.

  7. Prediction Models and Decision Support: Chances and Challenges

    NARCIS (Netherlands)

    Kappen, T.H.

    2015-01-01

    A clinical prediction model can assist doctors in arriving at the most likely diagnosis or estimating the prognosis. By utilizing various patient- and disease-related properties, such models can yield objective estimations of the risk of a disease or the probability of a certain disease course for

  8. Virtual Beach: Decision Support Tools for Beach Pathogen Prediction

    Science.gov (United States)

    The Virtual Beach Managers Tool (VB) is decision-making software developed to help local beach managers make decisions as to when beaches should be closed due to predicted high levels of water borne pathogens. The tool is being developed under the umbrella of EPA's Advanced Monit...

  9. Discovery of New Retrograde Substructures: The Shards of ω Centauri?

    Science.gov (United States)

    Myeong, G. C.; Evans, N. W.; Belokurov, V.; Sanders, J. L.; Koposov, S. E.

    2018-06-01

    We use the SDSS-Gaia catalogue to search for substructure in the stellar halo. The sample comprises 62 133 halo stars with full phase space coordinates and extends out to heliocentric distances of ˜10 kpc. As actions are conserved under slow changes of the potential, they permit identification of groups of stars with a common accretion history. We devise a method to identify halo substructures based on their clustering in action space, using metallicity as a secondary check. This is validated against smooth models and numerical constructed stellar halos from the Aquarius simulations. We identify 21 substructures in the SDSS-Gaia catalogue, including 7 high significance, high energy and retrograde ones. We investigate whether the retrograde substructures may be material stripped off the atypical globular cluster ω Centauri. Using a simple model of the accretion of the progenitor of the ω Centauri, we tentatively argue for the possible association of up to 5 of our new substructures (labelled Rg1, Rg3, Rg4, Rg6 and Rg7) with this event. This sets a minimum mass of 5× 108M⊙ for the progenitor, so as to bring ω Centauri to its current location in action - energy space. Our proposal can be tested by high resolution spectroscopy of the candidates to look for the unusual abundance patterns possessed by ω Centauri stars.

  10. STUDENT ACADEMIC PERFORMANCE PREDICTION USING SUPPORT VECTOR MACHINE

    OpenAIRE

    S.A. Oloruntoba1 ,J.L.Akinode2

    2017-01-01

    This paper investigates the relationship between students' preadmission academic profile and final academic performance. Data Sample of students in one of the Federal Polytechnic in south West part of Nigeria was used. The preadmission academic profile used for this study is the 'O' level grades(terminal high school results).The academic performance is defined using student's Grade Point Average(GPA). This research focused on using data mining technique to develop a model for predicting stude...

  11. A Decision Support System for Predicting Students' Performance

    Science.gov (United States)

    Livieris, Ioannis E.; Mikropoulos, Tassos A.; Pintelas, Panagiotis

    2016-01-01

    Educational data mining is an emerging research field concerned with developing methods for exploring the unique types of data that come from educational context. These data allow the educational stakeholders to discover new, interesting and valuable knowledge about students. In this paper, we present a new user-friendly decision support tool for…

  12. Predicting post-translational lysine acetylation using support vector machines

    DEFF Research Database (Denmark)

    Gnad, Florian; Ren, Shubin; Choudhary, Chunaram

    2010-01-01

    spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico...

  13. How to support action prediction: Evidence from human coordination tasks

    DEFF Research Database (Denmark)

    Vesper, Cordula

    2014-01-01

    When two or more people perform actions together such as shaking hands, playing ensemble music or carrying an object together, they often naturally adjust the spatial and temporal parameters of their movements to facilitate smooth task performance. This paper reviews recent findings from experime......”) might be a useful approach also for robotic systems to assist human users, thereby reducing cognitive load and flexibly supporting the acquisition of new skills....

  14. Support Vector Machine and Application in Seizure Prediction

    KAUST Repository

    Qiu, Simeng

    2018-04-01

    Nowadays, Machine learning (ML) has been utilized in various kinds of area which across the range from engineering field to business area. In this paper, we first present several kernel machine learning methods of solving classification, regression and clustering problems. These have good performance but also have some limitations. We present examples to each method and analyze the advantages and disadvantages for solving different scenarios. Then we focus on one of the most popular classification methods, Support Vectors Machine (SVM). In addition, we introduce the basic theory, advantages and scenarios of using Support Vector Machine (SVM) deal with classification problems. We also explain a convenient approach of tacking SVM problems which are called Sequential Minimal Optimization (SMO). Moreover, one class SVM can be understood in a different way which is called Support Vector Data Description (SVDD). This is a famous non-linear model problem compared with SVM problems, SVDD can be solved by utilizing Gaussian RBF kernel function combined with SMO. At last, we compared the difference and performance of SVM-SMO implementation and SVM-SVDD implementation. About the application part, we utilized SVM method to handle seizure forecasting in canine epilepsy, after comparing the results from different methods such as random forest, extremely randomized tree, and SVM to classify preictal (pre-seizure) and interictal (interval-seizure) binary data. We draw the conclusion that SVM has the best performance.

  15. Daily Autonomy Support and Sexual Identity Disclosure Predicts Daily Mental and Physical Health Outcomes.

    Science.gov (United States)

    Legate, Nicole; Ryan, Richard M; Rogge, Ronald D

    2017-06-01

    Using a daily diary methodology, we examined how social environments support or fail to support sexual identity disclosure, and associated mental and physical health outcomes. Results showed that variability in disclosure across the diary period related to greater psychological well-being and fewer physical symptoms, suggesting potential adaptive benefits to selectively disclosing. A multilevel path model indicated that perceiving autonomy support in conversations predicted more disclosure, which in turn predicted more need satisfaction, greater well-being, and fewer physical symptoms that day. Finally, mediation analyses revealed that disclosure and need satisfaction explained why perceiving autonomy support in a conversation predicted greater well-being and fewer physical symptoms. That is, perceiving autonomy support in conversations indirectly predicted greater wellness through sexual orientation disclosure, along with feeling authentic and connected in daily interactions with others. Discussion highlights the role of supportive social contexts and everyday opportunities to disclose in affecting sexual minority mental and physical health.

  16. How to support action prediction: Evidence from human coordination tasks

    DEFF Research Database (Denmark)

    Vesper, Cordula

    2014-01-01

    When two or more people perform actions together such as shaking hands, playing ensemble music or carrying an object together, they often naturally adjust the spatial and temporal parameters of their movements to facilitate smooth task performance. This paper reviews recent findings from experime......When two or more people perform actions together such as shaking hands, playing ensemble music or carrying an object together, they often naturally adjust the spatial and temporal parameters of their movements to facilitate smooth task performance. This paper reviews recent findings from......”) might be a useful approach also for robotic systems to assist human users, thereby reducing cognitive load and flexibly supporting the acquisition of new skills....

  17. SPECTROSCOPIC OBSERVATIONS OF AN EVOLVING FLARE RIBBON SUBSTRUCTURE SUGGESTING ORIGIN IN CURRENT SHEET WAVES

    Energy Technology Data Exchange (ETDEWEB)

    Brannon, S. R.; Longcope, D. W.; Qiu, J. [Department of Physics, Montana State University, Bozeman, MT 59717 (United States)

    2015-09-01

    We present imaging and spectroscopic observations from the Interface Region Imaging Spectrograph of the evolution of the flare ribbon in the SOL2014-04-18T13:03 M-class flare event, at high spatial resolution and time cadence. These observations reveal small-scale substructure within the ribbon, which manifests as coherent quasi-periodic oscillations in both position and Doppler velocities. We consider various alternative explanations for these oscillations, including modulation of chromospheric evaporation flows. Among these, we find the best support for some form of wave localized to the coronal current sheet, such as a tearing mode or Kelvin–Helmholtz instability.

  18. A Satellite Mortality Study to Support Space Systems Lifetime Prediction

    Science.gov (United States)

    Fox, George; Salazar, Ronald; Habib-Agahi, Hamid; Dubos, Gregory

    2013-01-01

    Estimating the operational lifetime of satellites and spacecraft is a complex process. Operational lifetime can differ from mission design lifetime for a variety of reasons. Unexpected mortality can occur due to human errors in design and fabrication, to human errors in launch and operations, to random anomalies of hardware and software or even satellite function degradation or technology change, leading to unrealized economic or mission return. This study focuses on data collection of public information using, for the first time, a large, publically available dataset, and preliminary analysis of satellite lifetimes, both operational lifetime and design lifetime. The objective of this study is the illustration of the relationship of design life to actual lifetime for some representative classes of satellites and spacecraft. First, a Weibull and Exponential lifetime analysis comparison is performed on the ratio of mission operating lifetime to design life, accounting for terminated and ongoing missions. Next a Kaplan-Meier survivor function, standard practice for clinical trials analysis, is estimated from operating lifetime. Bootstrap resampling is used to provide uncertainty estimates of selected survival probabilities. This study highlights the need for more detailed databases and engineering reliability models of satellite lifetime that include satellite systems and subsystems, operations procedures and environmental characteristics to support the design of complex, multi-generation, long-lived space systems in Earth orbit.

  19. Gas expulsion in highly substructured embedded star clusters

    Science.gov (United States)

    Farias, J. P.; Fellhauer, M.; Smith, R.; Domínguez, R.; Dabringhausen, J.

    2018-06-01

    We investigate the response of initially substructured, young, embedded star clusters to instantaneous gas expulsion of their natal gas. We introduce primordial substructure to the stars and the gas by simplistically modelling the star formation process so as to obtain a variety of substructure distributed within our modelled star-forming regions. We show that, by measuring the virial ratio of the stars alone (disregarding the gas completely), we can estimate how much mass a star cluster will retain after gas expulsion to within 10 per cent accuracy, no matter how complex the background structure of the gas is, and we present a simple analytical recipe describing this behaviour. We show that the evolution of the star cluster while still embedded in the natal gas, and the behaviour of the gas before being expelled, is crucial process that affect the time-scale on which the cluster can evolve into a virialized spherical system. Embedded star clusters that have high levels of substructure are subvirial for longer times, enabling them to survive gas expulsion better than a virialized and spherical system. By using a more realistic treatment for the background gas than our previous studies, we find it very difficult to destroy the young clusters with instantaneous gas expulsion. We conclude that gas removal may not be the main culprit for the dissolution of young star clusters.

  20. QUANTIFYING KINEMATIC SUBSTRUCTURE IN THE MILKY WAY'S STELLAR HALO

    International Nuclear Information System (INIS)

    Xue Xiangxiang; Zhao Gang; Luo Ali; Rix, Hans-Walter; Bell, Eric F.; Koposov, Sergey E.; Kang, Xi; Liu, Chao; Yanny, Brian; Beers, Timothy C.; Lee, Young Sun; Bullock, James S.; Johnston, Kathryn V.; Morrison, Heather; Rockosi, Constance; Weaver, Benjamin A.

    2011-01-01

    We present and analyze the positions, distances, and radial velocities for over 4000 blue horizontal-branch (BHB) stars in the Milky Way's halo, drawn from SDSS DR8. We search for position-velocity substructure in these data, a signature of the hierarchical assembly of the stellar halo. Using a cumulative 'close pair distribution' as a statistic in the four-dimensional space of sky position, distance, and velocity, we quantify the presence of position-velocity substructure at high statistical significance among the BHB stars: pairs of BHB stars that are close in position on the sky tend to have more similar distances and radial velocities compared to a random sampling of these overall distributions. We make analogous mock observations of 11 numerical halo formation simulations, in which the stellar halo is entirely composed of disrupted satellite debris, and find a level of substructure comparable to that seen in the actually observed BHB star sample. This result quantitatively confirms the hierarchical build-up of the stellar halo through a signature in phase (position-velocity) space. In detail, the structure present in the BHB stars is somewhat less prominent than that seen in most simulated halos, quite possibly because BHB stars represent an older sub-population. BHB stars located beyond 20 kpc from the Galactic center exhibit stronger substructure than at r gc < 20 kpc.

  1. New Developments for Jet Substructure Reconstruction in CMS

    CERN Document Server

    CMS Collaboration

    2017-01-01

    We present Monte Carlo based studies showcasing several developments for jet substructure reconstruction in CMS. This include Quark/Gluon tagging algorithms using Boosted Decision Trees and Deep Neural Networks, the XCone jet clustering algorithm and the Boosted Event Shape Tagger (BEST).

  2. Face-based selection of corners in 3D substructuring

    Czech Academy of Sciences Publication Activity Database

    Šístek, Jakub; Čertíková, M.; Burda, P.; Novotný, J.

    2012-01-01

    Roč. 82, č. 10 (2012), s. 1799-1811 ISSN 0378-4754 R&D Projects: GA AV ČR IAA100760702 Institutional research plan: CEZ:AV0Z10190503 Keywords : domain decomposition * iterative substructuring * BDDC Subject RIV: BA - General Mathematics Impact factor: 0.836, year: 2012 http://www.sciencedirect.com/science/article/pii/S0378475411001820

  3. A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

    Directory of Open Access Journals (Sweden)

    Fang Su

    2013-01-01

    Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

  4. Substructure method in high-speed monorail dynamic problems

    Science.gov (United States)

    Ivanchenko, I. I.

    2008-12-01

    combined schemes modeling a strained elastic compound moving structure and a monorail elevated track. The problems of development of methods for dynamic analysis of monorails are very topical, especially because of increasing speeds of the rolling stock motion. These structures are studied in [16-18]. In the present paper, the above problem is solved by using the method for the moving load analysis and a step procedure of integration with respect to time, which were proposed in [9, 19], respectively. Further, these components are used to enlarge the possibilities of the substructure method in problems of dynamics. In the approach proposed for moving load analysis of structures, for a substructure (having the shape of a boundary element or a superelement) we choose an object moving at a constant speed (a monorail rolling stock); in this case, we use rod boundary elements of large length, which are gathered in a system modeling these objects. In particular, sets of such elements form a model of a monorail rolling stock, namely, carriage hulls, wheeled carts, elements of the wheel spring suspension, models of continuous beams of monorail ways and piers with foundations admitting emergency subsidence and unilateral links. These specialized rigid finite elements with linear and nonlinear links, included into the set of earlier proposed finite elements [14, 19], permit studying unsteady vibrations in the "monorail train-elevated track" (MTET) system taking into account various irregularities on the beam-rail, the pier emergency subsidence, and their elastic support by the basement. In this case, a high degree of the structure spatial digitization is obtained by using rods with distributed parameters in the analysis. The displacements are approximated by linear functions and trigonometric Fourier series, which, as was already noted, permits increasing the number of degrees of freedom of the system under study simultaneously preserving the order of the resolving system of

  5. Factorization for groomed jet substructure beyond the next-to-leading logarithm

    Energy Technology Data Exchange (ETDEWEB)

    Frye, Christopher; Larkoski, Andrew J.; Schwartz, Matthew D.; Yan, Kai [Center for the Fundamental Laws of Nature, Harvard University,17 Oxford Street, Cambridge, MA 02138 (United States)

    2016-07-12

    Jet grooming algorithms are widely used in experimental analyses at hadron colliders to remove contaminating radiation from within jets. While the algorithms perform a great service to the experiments, their intricate algorithmic structure and multiple parameters has frustrated precision theoretic understanding. In this paper, we demonstrate that one particular groomer called soft drop actually makes precision jet substructure easier. In particular, we derive a factorization formula for a large class of soft drop jet substructure observables, including jet mass. The essential observation that allows for this factorization is that, without the soft wide-angle radiation groomed by soft drop, all singular contributions are collinear. The simplicity and universality of the collinear limit in QCD allows us to show that to all orders, the normalized differential cross section has no contributions from non-global logarithms. It is also independent of process, up to the relative fraction of quark and gluon jets. In fact, soft drop allows us to define this fraction precisely. The factorization theorem also explains why soft drop observables are less sensitive to hadronization than their ungroomed counterparts. Using the factorization theorem, we resum the soft drop jet mass to next-to-next-to-leading logarithmic accuracy. This requires calculating some clustering effects that are closely related to corresponding effects found in jet veto calculations. We match our resummed calculation to fixed order results for both e{sup +}e{sup −}→ dijets and pp→Z+j events, producing the first jet substructure predictions (groomed or ungroomed) to this accuracy for the LHC.

  6. Software Infrastructure to Support DSAP (Dynamic Situational Awareness and Prediction) Capabilities

    National Research Council Canada - National Science Library

    McGraw, Robert

    2006-01-01

    Today's C4I systems will be required to support faster-than-real-time predictive simulation that can determine possible outcomes by re-calibrating with real-time sensor data or extracted knowledge in real-time...

  7. Research on bearing life prediction based on support vector machine and its application

    International Nuclear Information System (INIS)

    Sun Chuang; Zhang Zhousuo; He Zhengjia

    2011-01-01

    Life prediction of rolling element bearing is the urgent demand in engineering practice, and the effective life prediction technique is beneficial to predictive maintenance. Support vector machine (SVM) is a novel machine learning method based on statistical learning theory, and is of advantage in prediction. This paper develops SVM-based model for bearing life prediction. The inputs of the model are features of bearing vibration signal and the output is the bearing running time-bearing failure time ratio. The model is built base on a few failed bearing data, and it can fuse information of the predicted bearing. So it is of advantage to bearing life prediction in practice. The model is applied to life prediction of a bearing, and the result shows the proposed model is of high precision.

  8. Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models

    Science.gov (United States)

    Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin

    2018-03-01

    The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.

  9. Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

    International Nuclear Information System (INIS)

    Xu Ruirui; Bian Guoxing; Gao Chenfeng; Chen Tianlun

    2005-01-01

    The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter γ and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

  10. Dynamic Analysis of Jacket Substructure for Offshore Wind Turbine Generators under Extreme Environmental Conditions

    Directory of Open Access Journals (Sweden)

    Wen-Jeng Lai

    2016-10-01

    Full Text Available In order to develop dynamic analysis technologies regarding the design of offshore wind turbine generators (OWTGs, a special project called Offshore Code Comparison Collaboration Continuation (OC4 was conducted by IEA (International Energy Agency in 2010. A similar project named INER-OC4 has been performed by the Institute of Nuclear Energy Research (INER to develop the OWTG technologies of Taiwan. Since the jacket substructure will be applied to Taiwan OWTGs before 2020, the INER-OC4 project has been devoted to the design and analysis of jacket support structure. In this work, the preliminary result of INER-OC4 is presented. A simplified analysis procedure for jacket support structure has been proposed. Both of the NREL (National Renewable Energy Laboratory 5 MW OWTG FAST model and OC4 jacket substructure model have been built and analyzed under severe design load cases (DLCs of IEC (International Electrotechnical commission 61400-3. Simulation results of six severe DLCs are performed in this work and the results are in agreement with the requirements of API (American Petroleum Institute and NORSOK (Norwegian Petroleum Industry standards.

  11. Perceived support from a caregiver's social ties predicts subsequent care-recipient health

    Directory of Open Access Journals (Sweden)

    Dannielle E. Kelley

    2017-12-01

    Full Text Available Most social support research has examined support from an individual patient perspective and does not model the broader social context of support felt by caregivers. Understanding how social support networks may complement healthcare services is critical, considering the aging population, as social support networks may be a valuable resource to offset some of the demands placed on the healthcare system. We sought to identify how caregivers' perceived organizational and interpersonal support from their social support network influences care-recipient health.We created a dyadic dataset of care-recipient and caregivers from the first two rounds of the National Health and Aging Trends survey (2011, 2012 and the first round of the associated National Study of Caregivers survey (2011. Using structural equation modeling, we explored how caregivers' perceived social support is associated with caregiver confidence to provide care, and is associated with care-recipient health outcomes at two time points. All data were analyzed in 2016.Social engagement with members from caregivers' social support networks was positively associated with caregiver confidence, and social engagement and confidence were positively associated with care-recipient health at time 1. Social engagement positively predicted patient health at time 2 controlling for time 1. Conversely, use of organizational support negatively predicted care-recipient health at time 2.Care-recipients experience better health outcomes when caregivers are able to be more engaged with members of their social support network. Keywords: Informal caregiving, Social support, Social support network, Patient-caregiver dyads

  12. Familial social support predicts a reduced cortisol response to stress in sexual minority young adults.

    Science.gov (United States)

    Burton, C L; Bonanno, G A; Hatzenbuehler, M L

    2014-09-01

    Social support has been repeatedly associated with mental and physical health outcomes, with hypothalamic-pituitary-adrenocortical (HPA) axis activity posited as a potential mechanism. The influence of social bonds appears particularly important in the face of stigma-related stress; however, there is a dearth of research examining social support and HPA axis response among members of a stigmatized group. To address this gap in the literature, we tested in a sample of 70 lesbian, gay, and bisexual (LGB) young adults whether family support or peer support differentially predict cortisol reactivity in response to a laboratory stressor, the Trier Social Stress Test. While greater levels of family support were associated with reduced cortisol reactivity, neither peer support nor overall support satisfaction was associated with cortisol response. These findings suggest that the association between social support and neuroendocrine functioning differs according to the source of support among members of one stigmatized group. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. Predicting Resilience via Social Support and Illness Perceptions Among Patients Undergoing Hemodialysis

    Directory of Open Access Journals (Sweden)

    Reihane Hajmohammadi

    2017-07-01

    Full Text Available Background and Objectives Chronic renal disease is a threatening condition for the health, economic, and social status of the affected person and his/her family. Patients undergoing hemodialysis encounter mental and health problems; the current study aimed at predicting resilience via social support and illness perceptions among patients undergoing hemodialysis. Methods The current descriptive-correlational study had a statistical population including 308 patients undergoing hemodialysis in Kerman, Iran, in 2017. Based on the Krejcie-Morgan table, the minimum required sample size was 169. The sample was selected using a convenience sampling method. Data collection tools were the Connor-Davidson resilience scale, the medical outcome study (MOS social support survey developed by Sherbourne and Stewart, and the brief illness perception questionnaire developed by Broadbent et al. Data were analyzed using a Pearson correlation coefficient and a stepwise regression analysis via SPSS version 19. Results Results indicated that resilience was significantly and positively related to social support (r = 0.318, P < 0.05 and illness perceptions (r = 0.165, P < 0.05. Among the subscales of social support, emotional support, tangible support, and social interaction could predict resilience, and among the subscales of illness perceptions, only cognitive representation could predict resilience. Conclusions The obtained results demonstrated that resilience was significantly and positively related to social support and illness perceptions. Additionally, the subscales of social support and illness perceptions could predict resilience among the patients undergoing hemodialysis.

  14. Predicting Social Support for Grieving Persons: A Theory of Planned Behavior Perspective

    Science.gov (United States)

    Bath, Debra M.

    2009-01-01

    Research has consistently reported that social support from family, friends, and colleagues is an important factor in the bereaved person's ability to cope after the loss of a loved one. This study used a Theory of Planned Behavior framework to identify those factors that predict a person's intention to interact with, and support, a grieving…

  15. Phase Space Prediction of Chaotic Time Series with Nu-Support Vector Machine Regression

    International Nuclear Information System (INIS)

    Ye Meiying; Wang Xiaodong

    2005-01-01

    A new class of support vector machine, nu-support vector machine, is discussed which can handle both classification and regression. We focus on nu-support vector machine regression and use it for phase space prediction of chaotic time series. The effectiveness of the method is demonstrated by applying it to the Henon map. This study also compares nu-support vector machine with back propagation (BP) networks in order to better evaluate the performance of the proposed methods. The experimental results show that the nu-support vector machine regression obtains lower root mean squared error than the BP networks and provides an accurate chaotic time series prediction. These results can be attributable to the fact that nu-support vector machine implements the structural risk minimization principle and this leads to better generalization than the BP networks.

  16. Stick-slip substructure in rapid tape peeling

    KAUST Repository

    Thoroddsen, Sigurdur T.

    2010-10-15

    The peeling of adhesive tape is known to proceed with a stick-slip mechanism and produces a characteristic ripping sound. The peeling also produces light and when peeled in a vacuum, even X-rays have been observed, whose emissions are correlated with the slip events. Here we present direct imaging of the detachment zone when Scotch tape is peeled off at high speed from a solid surface, revealing a highly regular substructure, during the slip phase. The typical 4-mm-long slip region has a regular substructure of transverse 220 μm wide slip bands, which fracture sideways at speeds over 300 m/s. The fracture tip emits waves into the detached section of the tape at ∼100 m/s, which promotes the sound, so characteristic of this phenomenon.

  17. Towards an understanding of the correlations in jet substructure

    International Nuclear Information System (INIS)

    Adams, D.; Arce, A.; Asquith, L.; Backovic, M.; Barillari, T.; Menke, S.; Berta, P.; Bertolini, D.; Buckley, A.; Ferrando, J.; Pollard, C.; Butterworth, J.; Cooper, B.; Camacho Toro, R.C.; Picazio, A.; Caudron, J.; El Hedri, S.; Masetti, L.; Chien, Y.T.; Hornig, A.; Lee, C.; Cogan, J.; Nachman, B.; Nef, P.; Schwartzman, A.; Strauss, E.; Swiatlowski, M.; Curtin, D.; Debenedetti, C.; Dolen, J.; Rappoccio, S.; Eklund, M.; Embry, T.; Johns, K.; Lampl, W.; Leone, R.; Loch, P.; O'Grady, F.T.; Rutherfoord, J.; Veatch, J.; Ellis, S.D.; Ferencek, D.; Fleischmann, S.; Freytsis, M.; Lopez Mateos, D.; Schwartz, M.D.; Giulini, M.; Sosa Corral, D.E.; Han, Z.; Soper, D.; Hare, D.; Mishra, K.; Tran, N.V.; Harris, P.; Potter-Landua, B.; Potter, C.; Thomas, C.; Young, C.; Hinzmann, A.; Hoing, R.; Kogler, R.; Marchesini, I.; Usai, E.; Jankowiak, M.; Kasieczka, G.; Larkoski, A.J.; Marzani, S.; Thaler, J.; Lou, H.K.; Low, M.; Miller, D.W.; Maksimovic, P.; McCarthy, R.; Ovcharova, A.; Rojo, J.; Tseng, J.; Salam, G.P.; Schabinger, R.M.; Shuve, B.; Sinervo, P.; Spannowsky, M.; Thompson, E.; Valery, L.; Vos, M.; Waalewijn, W.; Wacker, J.

    2015-01-01

    Over the past decade, a large number of jet substructure observables have been proposed in the literature, and explored at the LHC experiments. Such observables attempt to utilize the internal structure of jets in order to distinguish those initiated by quarks, gluons, or by boosted heavy objects, such as top quarks and W bosons. This report, originating from and motivated by the BOOST2013 workshop, presents original particle-level studies that aim to improve our understanding of the relationships between jet substructure observables, their complementarity, and their dependence on the underlying jet properties, particularly the jet radius and jet transverse momentum. This is explored in the context of quark/gluon discrimination, boosted W boson tagging and boosted top quark tagging. (orig.)

  18. Observations of Cluster Substructure using Weakly Lensed Sextupole Moments

    Energy Technology Data Exchange (ETDEWEB)

    Irwin, John

    2003-08-01

    Since dark matter clusters and groups may have substructure, we have examined the sextupole content of Hubble images looking for a curvature signature in background galaxies that would arise from galaxy-galaxy lensing. We describe techniques for extracting and analyzing sextupole and higher weakly lensed moments. Indications of substructure, via spatial clumping of curved background galaxies, were observed in the image of CL0024 and then surprisingly in both Hubble deep fields. We estimate the dark cluster masses in the deep field. Alternatives to a lensing hypothesis appear improbable, but better statistics will be required to exclude them conclusively. Observation of sextupole moments would then provide a means to measure dark matter structure on smaller length scales than heretofore.

  19. Towards an understanding of the correlations in jet substructure

    Energy Technology Data Exchange (ETDEWEB)

    Adams, D. [Brookhaven National Laboratory, Upton, NY (United States); Arce, A. [Duke University, Durham, NC (United States); Asquith, L. [University of Sussex, Brighton (United Kingdom); Backovic, M. [CP3, Universite catholique du Louvain, Louvain-la-Neuve (Belgium); Barillari, T.; Menke, S. [Max-Planck-Institute fuer Physik, Munich (Germany); Berta, P. [Charles University in Prague, FMP, Prague (Czech Republic); Bertolini, D. [University of California, Berkeley, CA (United States); Buckley, A.; Ferrando, J.; Pollard, C. [University of Glasgow, G12 8QQ (United Kingdom); Butterworth, J.; Cooper, B. [University College London, WC1E 6BT (United Kingdom); Camacho Toro, R.C.; Picazio, A. [University of Geneva, Geneva 4 (Switzerland); Caudron, J.; El Hedri, S.; Masetti, L. [Universitaet Mainz (Germany); Chien, Y.T.; Hornig, A.; Lee, C. [Los Alamos National Laboratory, Los Alamos, NM (United States); Cogan, J.; Nachman, B.; Nef, P.; Schwartzman, A.; Strauss, E.; Swiatlowski, M. [SLAC National Accelerator Laboratory, Menlo Park, CA (United States); Curtin, D. [University of Maryland, College Park, MD (United States); Debenedetti, C. [University of California, Santa Cruz, CA (United States); Dolen, J.; Rappoccio, S. [University at Buffalo, Buffalo, NY (United States); Eklund, M.; Embry, T.; Johns, K.; Lampl, W.; Leone, R.; Loch, P.; O' Grady, F.T.; Rutherfoord, J.; Veatch, J. [University of Arizona, Tucson, AZ (United States); Ellis, S.D. [University of Washington, Seattle, WA (United States); Ferencek, D. [Rutgers University, Piscataway, NJ (United States); Fleischmann, S. [Bergische Universitaet Wuppertal, Wuppertal (Germany); Freytsis, M.; Lopez Mateos, D.; Schwartz, M.D. [Harvard University, Cambridge, MA (United States); Giulini, M.; Sosa Corral, D.E. [Universitaet Heidelberg, Heidelberg (Germany); Han, Z.; Soper, D. [University of Oregon, Eugene, OR (United States); Hare, D.; Mishra, K.; Tran, N.V. [Fermi National Accelerator Laboratory, Batavia, IL (United States); Harris, P.; Potter-Landua, B.; Potter, C.; Thomas, C.; Young, C. [CERN, Geneva 23 (Switzerland); Hinzmann, A. [Universitaet Zuerich, Zurich (Switzerland); Hoing, R.; Kogler, R.; Marchesini, I.; Usai, E. [Universitaet Hamburg, Hamburg (Germany); Jankowiak, M. [New York University, New York, NY (United States); Kasieczka, G. [ETH Zuerich, Zurich (Switzerland); Larkoski, A.J.; Marzani, S.; Thaler, J. [Massachusetts Institute of Technology, Cambridge, MA (United States); Lou, H.K. [Princeton University, Princeton, NJ (United States); Low, M.; Miller, D.W. [University of Chicago, Zurich, IL (United States); Maksimovic, P. [Johns Hopkins University, Baltimore, MD (United States); McCarthy, R. [YITP, Stony Brook University, Stony Brook, NY (United States); Ovcharova, A. [University of California, Berkeley National Laboratory, Berkeley, CA (United States); Rojo, J.; Tseng, J. [University of Oxford, Oxford (United Kingdom); Salam, G.P. [CERN, Geneva 23 (Switzerland); LPTHE, UPMC Univ. Paris 6 and CNRS UMR, Paris (France); Schabinger, R.M. [Universidad Autonoma de Madrid, Madrid (Spain); Shuve, B. [Perimeter Institute for Theoretical Physics, ON (Canada); Sinervo, P. [University of Toronto, Toronto, ON (Canada); Spannowsky, M. [University of Durham, IPPP, Durham (United Kingdom); Thompson, E. [Columbia University, New York, NY (United States); Valery, L. [LPC Clermont-Ferrand, Aubiere Cedex (France); Vos, M. [Instituto de Fisica Corpuscular, IFIC/CSIC-UVEG, Valencia (Spain); Waalewijn, W. [University of Amsterdam, Amsterdam (Netherlands); Wacker, J. [Stanford Institute for Theoretical Physics, Stanford, CA (United States)

    2015-09-15

    Over the past decade, a large number of jet substructure observables have been proposed in the literature, and explored at the LHC experiments. Such observables attempt to utilize the internal structure of jets in order to distinguish those initiated by quarks, gluons, or by boosted heavy objects, such as top quarks and W bosons. This report, originating from and motivated by the BOOST2013 workshop, presents original particle-level studies that aim to improve our understanding of the relationships between jet substructure observables, their complementarity, and their dependence on the underlying jet properties, particularly the jet radius and jet transverse momentum. This is explored in the context of quark/gluon discrimination, boosted W boson tagging and boosted top quark tagging. (orig.)

  20. Stick-slip substructure in rapid tape peeling

    KAUST Repository

    Thoroddsen, Sigurdur T; Nguyen, H. D.; Takehara, K.; Etoh, T. G.

    2010-01-01

    The peeling of adhesive tape is known to proceed with a stick-slip mechanism and produces a characteristic ripping sound. The peeling also produces light and when peeled in a vacuum, even X-rays have been observed, whose emissions are correlated with the slip events. Here we present direct imaging of the detachment zone when Scotch tape is peeled off at high speed from a solid surface, revealing a highly regular substructure, during the slip phase. The typical 4-mm-long slip region has a regular substructure of transverse 220 μm wide slip bands, which fracture sideways at speeds over 300 m/s. The fracture tip emits waves into the detached section of the tape at ∼100 m/s, which promotes the sound, so characteristic of this phenomenon.

  1. Modeling and prediction of Turkey's electricity consumption using Support Vector Regression

    International Nuclear Information System (INIS)

    Kavaklioglu, Kadir

    2011-01-01

    Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)

  2. The importance of calorimetry for highly-boosted jet substructure

    Energy Technology Data Exchange (ETDEWEB)

    Coleman, Evan [Brown U.; Freytsis, Marat [Oregon U.; Hinzmann, Andreas [Hamburg U.; Narain, Meenakshi [Brown U.; Thaler, Jesse [MIT, Cambridge, CTP; Tran, Nhan [Fermilab; Vernieri, Caterina [Fermilab

    2017-09-25

    Jet substructure techniques are playing an essential role in exploring the TeV scale at the Large Hadron Collider (LHC), since they facilitate the efficient reconstruction and identification of highly-boosted objects. Both for the LHC and for future colliders, there is a growing interest in using jet substructure methods based only on charged-particle information. The reason is that silicon-based tracking detectors offer excellent granularity and precise vertexing, which can improve the angular resolution on highly-collimated jets and mitigate the impact of pileup. In this paper, we assess how much jet substructure performance degrades by using track-only information, and we demonstrate physics contexts in which calorimetry is most beneficial. Specifically, we consider five different hadronic final states - W bosons, Z bosons, top quarks, light quarks, gluons - and test the pairwise discrimination power with a multi-variate combination of substructure observables. In the idealized case of perfect reconstruction, we quantify the loss in discrimination performance when using just charged particles compared to using all detected particles. We also consider the intermediate case of using charged particles plus photons, which provides valuable information about neutral pions. In the more realistic case of a segmented calorimeter, we assess the potential performance gains from improving calorimeter granularity and resolution, comparing a CMS-like detector to more ambitious future detector concepts. Broadly speaking, we find large performance gains from neutral-particle information and from improved calorimetry in cases where jet mass resolution drives the discrimination power, whereas the gains are more modest if an absolute mass scale calibration is not required.

  3. Power spectrum of dark matter substructure in strong gravitational lenses

    Science.gov (United States)

    Diaz Rivero, Ana; Cyr-Racine, Francis-Yan; Dvorkin, Cora

    2018-01-01

    Studying the smallest self-bound dark matter structure in our Universe can yield important clues about the fundamental particle nature of dark matter. Galaxy-scale strong gravitational lensing provides a unique way to detect and characterize dark matter substructures at cosmological distances from the Milky Way. Within the cold dark matter (CDM) paradigm, the number of low-mass subhalos within lens galaxies is expected to be large, implying that their contribution to the lensing convergence field is approximately Gaussian and could thus be described by their power spectrum. We develop here a general formalism to compute from first principles the substructure convergence power spectrum for different populations of dark matter subhalos. As an example, we apply our framework to two distinct subhalo populations: a truncated Navarro-Frenk-White subhalo population motivated by standard CDM, and a truncated cored subhalo population motivated by self-interacting dark matter (SIDM). We study in detail how the subhalo abundance, mass function, internal density profile, and concentration affect the amplitude and shape of the substructure power spectrum. We determine that the power spectrum is mostly sensitive to a specific combination of the subhalo abundance and moments of the mass function, as well as to the average tidal truncation scale of the largest subhalos included in the analysis. Interestingly, we show that the asymptotic slope of the substructure power spectrum at large wave number reflects the internal density profile of the subhalos. In particular, the SIDM power spectrum exhibits a characteristic steepening at large wave number absent in the CDM power spectrum, opening the possibility of using this observable, if at all measurable, to discern between these two scenarios.

  4. Spatial Substructure in the M87 Globular Cluster System

    Science.gov (United States)

    Feng, Yuting; Zhang, Yunhao; Guhathakurta, Puragra; Peng, Eric; Lim, Sungsoon

    2018-01-01

    Based on the observation of Next Generation Virgo Cluster Survey (NGVS) project, we obtained the u,g,r,i,z and Ks band photometric information of all the objects in the 2 degree × 2 degree area (Pilot Region) around M87, the major subcluster of Virgo. By adapting an Extreme Deconvolution method, which classifies objects into Globular Clusters (GCs), galaxies and foreground stars with their color and morphology data, we got a purer-than-ever GC distribution map with a depth to gmag=25 in Pilot Region. After masking galaxy GCs, smoothing with a 10arcmin Gaussian kernel and performing a flat field correction, we show the GC density map of M87, and got a good sersic fitting of GC radial distribution with a sersic index~2.2 in the central ellipse part (45arcmin semi major axis area of M87). We quantitatively compared our GC sample with a substructure-free mock data set, which was generated from the smoothed density map as well as the sersic fitting, by calculating the 2 point correlation function (TPCF) value in different parts of the map. After separately performing such comparison with mocks based on different galaxy masking radii which vary from 4 times g band effective radius to 10, we found signals of remarkable spatial enhancement in certain directions in the central ellipse of M87, as well as halo substructures shown as lumpiness and holes in the outer region. We present the estimated scales of these substructures from the TPCF results, and, managed to locate them with a statistical analysis of the pixelized GC map. Apart from all results listed above, we discuss the constant, extra-galactic substructure signal at a scale of ~3kpc, which does not diminish with masking sizes, as the evidence of merging and accretion history of M87.

  5. Prismatic substructure in metals; Prizmaticna substruktura kod metala

    Energy Technology Data Exchange (ETDEWEB)

    Milosavljevic, Dj [Institute of Nuclear Sciences Boris Kidric, Vinca, Beograd (Yugoslavia)

    1965-11-15

    The first step was the study of impurities behaviour during solidification of metals under equilibrium and non-equilibrium conditions. Impurities distribution and their structural shapes are dependent on conditions of solidification. These conditions are directly related to temperature and concentration issues on the solidification surface. Theoretical and experimental evaluated in this paper show the significance of subcooling in formation of cellular sub-structure.

  6. Substructure formation in iron-nickel monocrystals at cellular growth

    International Nuclear Information System (INIS)

    Agapova, E.V.; Tagirova, D.M.

    1984-01-01

    Substructural perfection of Fe-31 wt.% Ni alloy crystals prepared by the Bridgeman method is investigated. Characteristics of banded and cellular structures at different morphology of crystallization front corresponding to the rates of growth (7.0-24.7)x10 -4 cm/s are determined. Position of disorientation axis of banded fragments is shown to depend on orientation of a groWing crystal and its strong fragmentation results in formation of finer cellular structure

  7. Perceived support from a caregiver's social ties predicts subsequent care-recipient health.

    Science.gov (United States)

    Kelley, Dannielle E; Lewis, Megan A; Southwell, Brian G

    2017-12-01

    Most social support research has examined support from an individual patient perspective and does not model the broader social context of support felt by caregivers. Understanding how social support networks may complement healthcare services is critical, considering the aging population, as social support networks may be a valuable resource to offset some of the demands placed on the healthcare system. We sought to identify how caregivers' perceived organizational and interpersonal support from their social support network influences care-recipient health. We created a dyadic dataset of care-recipient and caregivers from the first two rounds of the National Health and Aging Trends survey (2011, 2012) and the first round of the associated National Study of Caregivers survey (2011). Using structural equation modeling, we explored how caregivers' perceived social support is associated with caregiver confidence to provide care, and is associated with care-recipient health outcomes at two time points. All data were analyzed in 2016. Social engagement with members from caregivers' social support networks was positively associated with caregiver confidence, and social engagement and confidence were positively associated with care-recipient health at time 1. Social engagement positively predicted patient health at time 2 controlling for time 1. Conversely, use of organizational support negatively predicted care-recipient health at time 2. Care-recipients experience better health outcomes when caregivers are able to be more engaged with members of their social support network.

  8. Personality predicts perceived availability of social support and satisfaction with social support in women with early stage breast cancer.

    Science.gov (United States)

    Den Oudsten, Brenda L; Van Heck, Guus L; Van der Steeg, Alida F W; Roukema, Jan A; De Vries, Jolanda

    2010-04-01

    This study examines the relationships between personality, on the one hand, and perceived availability of social support (PASS) and satisfaction with received social support (SRSS), on the other hand, in women with early stage breast cancer (BC). In addition, this study examined whether a stressful event (i.e., diagnosis) is associated with quality of life (QOL), when controlling for PASS and SRSS. Women were assessed on PASS and SRSS (World Health Organization QOL assessment instrument-100) before diagnosis (time 1) and 1 (time 2), 3 (time 3), 6 (time 4), 12 (time 5), and 24 months (time 6) after surgical treatment. Personality (neuroticism extraversion openness five-factor inventory and state trait anxiety inventory-trait scale) and fatigue (fatigue assessment scale) were assessed at time 1. Agreeableness and fatigue predicted PASS and SRSS at time 5 and time 6. Trait anxiety had a negative effect on SRSS (ss = -0.22, p personality factors substantially influence the way women with early stage BC perceive social support. Knowledge about these underlying mechanisms of social support is useful for the development of tailor-made interventions. Professionals should be aware of the importance of social support. They should check whether patients have sufficient significant others in their social environment and be sensitive to potential discrepancies patients might experience between availability and adequacy of social support.

  9. Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xiaochen Zhang

    2017-01-01

    Full Text Available To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA and support vector machine (SVM is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap, the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

  10. Facile approach to the fabrication of a micropattern possessing nanoscale substructure.

    Science.gov (United States)

    Ji, Qiang; Jiang, Xuesong; Yin, Jie

    2007-12-04

    On the basis of the combined technologies of photolithography and reaction-induced phase separation (RIPS), a facile approach has been successfully developed for the fabrication of a micropattern possessing nanoscale substructure on the thin film surface. This approach involves three steps. In the first step, a thin film was prepared by spin coating from a solution of a commercial random copolymer, polystyrene-r-poly(methyl methacrylate) (PS-r-PMMA) and a commercial crosslinker, trimethylolpropane triacrylate (TMPTA). In the second step, photolithograph was performed with the thin film using a 250 W high-pressure mercury lamp to produce the micropattern. Finally, the resulting micropattern was annealed at 200 degrees C for a certain time, and reaction-induced phase separation occurred. After soaking in chloroform for 4 h, nanoscale substructure was obtained. The whole processes were traced by atomic force microscopy (AFM), X-ray photoelectron spectrometry (XPS), and Fourier transform infrared (FTIR) spectroscopy, and the results supported the proposed structure.

  11. Lithium-ion battery remaining useful life prediction based on grey support vector machines

    Directory of Open Access Journals (Sweden)

    Xiaogang Li

    2015-12-01

    Full Text Available In this article, an improved grey prediction model is proposed to address low-accuracy prediction issue of grey forecasting model. The first step is using a trigonometric function to transform the original data sequence to smooth the data, which is called smoothness of grey prediction model, and then a grey support vector machine model by integrating the improved grey model with support vector machine is introduced. At the initial stage of the model, trigonometric functions and accumulation generation operation can be used to preprocess the data, which enhances the smoothness of the data and reduces the associated randomness. In addition, support vector machine is implemented to establish a prediction model for the pre-processed data and select the optimal model parameters via genetic algorithms. Finally, the data are restored through the ‘regressive generate’ operation to obtain the forecasting data. To prove that the grey support vector machine model is superior to the other models, the battery life data from the Center for Advanced Life Cycle Engineering are selected, and the presented model is used to predict the remaining useful life of the battery. The predicted result is compared to that of grey model and support vector machines. For a more intuitive comparison of the three models, this article quantifies the root mean square errors for these three different models in the case of different ratio of training samples and prediction samples. The results show that the effect of grey support vector machine model is optimal, and the corresponding root mean square error is only 3.18%.

  12. Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression

    Science.gov (United States)

    Kavitha, A.; Sujatha, C. M.; Ramakrishnan, S.

    2010-01-01

    In this work, prediction of forced expiratory volume in 1 second (FEV1) in pulmonary function test is carried out using the spirometer and support vector regression analysis. Pulmonary function data are measured with flow volume spirometer from volunteers (N=175) using a standard data acquisition protocol. The acquired data are then used to predict FEV1. Support vector machines with polynomial kernel function with four different orders were employed to predict the values of FEV1. The performance is evaluated by computing the average prediction accuracy for normal and abnormal cases. Results show that support vector machines are capable of predicting FEV1 in both normal and abnormal cases and the average prediction accuracy for normal subjects was higher than that of abnormal subjects. Accuracy in prediction was found to be high for a regularization constant of C=10. Since FEV1 is the most significant parameter in the analysis of spirometric data, it appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

  13. Using support vector regression to predict PM10 and PM2.5

    International Nuclear Information System (INIS)

    Weizhen, Hou; Zhengqiang, Li; Yuhuan, Zhang; Hua, Xu; Ying, Zhang; Kaitao, Li; Donghui, Li; Peng, Wei; Yan, Ma

    2014-01-01

    Support vector machine (SVM), as a novel and powerful machine learning tool, can be used for the prediction of PM 10 and PM 2.5 (particulate matter less or equal than 10 and 2.5 micrometer) in the atmosphere. This paper describes the development of a successive over relaxation support vector regress (SOR-SVR) model for the PM 10 and PM 2.5 prediction, based on the daily average aerosol optical depth (AOD) and meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed), which were all measured in Beijing during the year of 2010–2012. The Gaussian kernel function, as well as the k-fold crosses validation and grid search method, are used in SVR model to obtain the optimal parameters to get a better generalization capability. The result shows that predicted values by the SOR-SVR model agree well with the actual data and have a good generalization ability to predict PM 10 and PM 2.5 . In addition, AOD plays an important role in predicting particulate matter with SVR model, which should be included in the prediction model. If only considering the meteorological parameters and eliminating AOD from the SVR model, the prediction results of predict particulate matter will be not satisfying

  14. Predictive Analytics to Support Real-Time Management in Pathology Facilities.

    Science.gov (United States)

    Lessard, Lysanne; Michalowski, Wojtek; Chen Li, Wei; Amyot, Daniel; Halwani, Fawaz; Banerjee, Diponkar

    2016-01-01

    Predictive analytics can provide valuable support to the effective management of pathology facilities. The introduction of new tests and technologies in anatomical pathology will increase the volume of specimens to be processed, as well as the complexity of pathology processes. In order for predictive analytics to address managerial challenges associated with the volume and complexity increases, it is important to pinpoint the areas where pathology managers would most benefit from predictive capabilities. We illustrate common issues in managing pathology facilities with an analysis of the surgical specimen process at the Department of Pathology and Laboratory Medicine (DPLM) at The Ottawa Hospital, which processes all surgical specimens for the Eastern Ontario Regional Laboratory Association. We then show how predictive analytics could be used to support management. Our proposed approach can be generalized beyond the DPLM, contributing to a more effective management of pathology facilities and in turn to quicker clinical diagnoses.

  15. Predictive Analytics to Support Real-Time Management in Pathology Facilities

    Science.gov (United States)

    Lessard, Lysanne; Michalowski, Wojtek; Chen Li, Wei; Amyot, Daniel; Halwani, Fawaz; Banerjee, Diponkar

    2016-01-01

    Predictive analytics can provide valuable support to the effective management of pathology facilities. The introduction of new tests and technologies in anatomical pathology will increase the volume of specimens to be processed, as well as the complexity of pathology processes. In order for predictive analytics to address managerial challenges associated with the volume and complexity increases, it is important to pinpoint the areas where pathology managers would most benefit from predictive capabilities. We illustrate common issues in managing pathology facilities with an analysis of the surgical specimen process at the Department of Pathology and Laboratory Medicine (DPLM) at The Ottawa Hospital, which processes all surgical specimens for the Eastern Ontario Regional Laboratory Association. We then show how predictive analytics could be used to support management. Our proposed approach can be generalized beyond the DPLM, contributing to a more effective management of pathology facilities and in turn to quicker clinical diagnoses. PMID:28269873

  16. Analysis of random response of structure with uncertain parameters. Combination of substructure synthesis method and hierarchy method

    International Nuclear Information System (INIS)

    Iwatsubo, Takuzo; Kawamura, Shozo; Mori, Hiroyuki.

    1995-01-01

    In this paper, the method to obtain the random response of a structure with uncertain parameters is proposed. The proposed method is a combination of the substructure synthesis method and the hierarchy method. The concept of the proposed method is that the hierarchy equation of each substructure is obtained using the hierarchy method, and the hierarchy equation of the overall structure is obtained using the substructure synthesis method. Using the proposed method, the reduced order hierarchy equation can be obtained without analyzing the original whole structure. After the calculation of the mean square value of response, the reliability analysis can be carried out based on the first passage problem and Poisson's excursion rate. As a numerical example of structure, a simple piping system is considered. The damping constant of the support is considered as the uncertainty parameter. Then the random response is calculated using the proposed method. As a result, the proposed method is useful to analyze the random response in terms of the accuracy, computer storage and calculation time. (author)

  17. Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines

    International Nuclear Information System (INIS)

    Niazi, Ali; Jameh-Bozorghi, Saeed; Nori-Shargh, Davood

    2008-01-01

    A quantitative structure-property relationship (QSPR) study is suggested for the prediction of toxicity (IGC 50 ) of nitrobenzenes. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the IGC 50 of nitrobenzenes as a function of molecular structures was established by means of the least squares support vector machines (LS-SVM). This model was applied for the prediction of the toxicity (IGC 50 ) of nitrobenzenes, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.0049 for LS-SVM. Results have shown that the introduction of LS-SVM for quantum chemical descriptors drastically enhances the ability of prediction in QSAR studies superior to multiple linear regression and partial least squares

  18. Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder.

    Science.gov (United States)

    Yavuz, Ahmet Sinan; Sezerman, Osman Ugur

    2014-01-01

    Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism. In this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner. By using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.

  19. The Role of Family Expressed Emotion and Perceived Social Support in Predicting Addiction Relapse

    OpenAIRE

    Atadokht, Akbar; Hajloo, Nader; Karimi, Masoud; Narimani, Mohammad

    2015-01-01

    Background: Emotional conditions governing the family and patients? perceived social support play important roles in the treatment or relapse process of the chronic disease. Objectives: The current study aimed to investigate the role of family expressed emotion and perceived social support in prediction of addiction relapse. Patients and Methods: The descriptive-correlation method was used in the current study. The study population consisted of the individuals referred to the addiction treatm...

  20. "You've Changed": Low Self-Concept Clarity Predicts Lack of Support for Partner Change.

    Science.gov (United States)

    Emery, Lydia F; Gardner, Wendi L; Finkel, Eli J; Carswell, Kathleen L

    2018-03-01

    People often pursue self-change, and having a romantic partner who supports these changes increases relationship satisfaction. However, most existing research focuses only on the experience of the person who is changing. What predicts whether people support their partner's change? People with low self-concept clarity resist self-change, so we hypothesized that they would be unsupportive of their partner's changes. People with low self-concept clarity did not support their partner's change (Study 1a), because they thought they would have to change, too (Study 1b). Low self-concept clarity predicted failing to support a partner's change, but not vice versa (Studies 2 and 3), and only for larger changes (Study 3). Not supporting a partner's change predicted decreases in relationship quality for both members of the couple (Studies 2 and 3). This research underscores the role of partners in self-change, suggesting that failing to support a partner's change may stem from self-concept confusion.

  1. Perceived Threat Associated with Police Officers and Black Men Predicts Support for Policing Policy Reform

    Directory of Open Access Journals (Sweden)

    Allison Louise Skinner

    2016-07-01

    Full Text Available Racial disparities in policing and recent high-profile incidents resulting in the deaths of Black men have ignited a national debate on policing policies. Given evidence that both police officers and Black men may be associated with threat, we examined the impact of perceived threat on support for reformed policing policies. Across three studies we found correlational evidence that perceiving police officers as threatening predicts increased support for reformed policing practices (e.g., limiting the use of lethal force and matching police force demographics to those of the community. In contrast, perceiving Black men as threatening predicted reduced support for policing policy reform. Perceived threat also predicted willingness to sign a petition calling for police reform. Experimental evidence indicated that priming participants to associate Black men with threat could also reduce support for policing policy reform, and this effect was moderated by internal motivation to respond without prejudice. Priming participants to associate police officers with threat did not increase support for policing policy reform. Results indicate that resistance to policing policy reform is associated with perceiving Black men as threatening. Moreover, findings suggest that publicizing racially charged police encounters, which may conjure associations between Black men and threat, could reduce support for policing policy reform.

  2. Sensory predictions during action support perception of imitative reactions across suprasecond delays.

    Science.gov (United States)

    Yon, Daniel; Press, Clare

    2018-04-01

    Perception during action is optimized by sensory predictions about the likely consequences of our movements. Influential theories in social cognition propose that we use the same predictions during interaction, supporting perception of similar reactions in our social partners. However, while our own action outcomes typically occur at short, predictable delays after movement execution, the reactions of others occur at longer, variable delays in the order of seconds. To examine whether we use sensorimotor predictions to support perception of imitative reactions, we therefore investigated the temporal profile of sensory prediction during action in two psychophysical experiments. We took advantage of an influence of prediction on apparent intensity, whereby predicted visual stimuli appear brighter (more intense). Participants performed actions (e.g., index finger lift) and rated the brightness of observed outcomes congruent (index finger lift) or incongruent (middle finger lift) with their movements. Observed action outcomes could occur immediately after execution, or at longer delays likely reflective of those in natural social interaction (1800 or 3600 ms). Consistent with the previous literature, Experiment 1 revealed that congruent action outcomes were rated as brighter than incongruent outcomes. Importantly, this facilitatory perceptual effect was found irrespective of whether outcomes occurred immediately or at delay. Experiment 2 replicated this finding and demonstrated that it was not the result of response bias. These findings therefore suggest that visual predictions generated during action are sufficiently general across time to support our perception of imitative reactions in others, likely generating a range of benefits during social interaction. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Prediction of sport adherence through the influence of autonomy-supportive coaching among spanish adolescent athletes.

    Science.gov (United States)

    Almagro, Bartolomé J; Sáenz-López, Pedro; Moreno, Juan A

    2010-01-01

    The purpose of this study was to test a motivational model of the coach-athlete relationship, based on self-determination theory and on the hierarchical model of intrinsic and extrinsic motivation. The sample comprised of 608 athletes (ages of 12-17 years) completed the following measures: interest in athlete's input, praise for autonomous behavior, perceived autonomy, intrinsic motivation, and the intention to be physically active. Structural equation modeling results demonstrated that interest in athletes' input and praise for autonomous behavior predicted perceived autonomy, and perceived autonomy positively predicted intrinsic motivation. Finally, intrinsic motivation predicted the intention to be physically active in the future. The results are discussed in relation to the importance of the climate of autonomy support created by the coach on intrinsic motivation and adherence to sport by adolescent athletes. Further, the results provide information related to the possible objectives of future interventions for the education of coaches, with the goal of providing them with tools and strategies to favor the development of intrinsic motivation among their athletes. In conclusion, the climate of autonomy support created by the coach can predict the autonomy perceived by the athletes which predicts the intrinsic motivation experienced by the athletes, and therefore, their adherence to athletic practice. Key pointsImportance of the climate of autonomy support created by the coach on intrinsic motivation and adherence to sport by adolescent athletes.Interest in athletes' input and praise for autonomous behavior predicted perceived autonomy, and perceived autonomy positively predicted intrinsic motivation.Intrinsic motivation predicted the intention to be physically active in the future.

  4. Morphology and substructure of lath martensites in dilute Zr--Nb alloys

    International Nuclear Information System (INIS)

    Srivastava, D.; Mukhopadhyay, P.; Banerjee, S.

    2000-01-01

    The morphology and substructure of lath martensites formed in β quenched dilute Zr--Nb alloys are described. The laths are arranged in a nearly parallel manner within any given colony or packet. Packets of alternately twin related laths and clusters of three mutually twin related lath martensite variants have been observed and the twinning plane is of {1 anti 101} H type. With increasing niobium content a continuous transition from large colonies of lath martensites, through smaller lath colonies, to individual plates of the acicular martensites occurs. The lath-lath interface consists of regularly spaced parallel arrays of dislocations of type. The habit plane traces of lath martensite lie close to {334} type poles and the operating lattice invariant shear mode is { anti 1101} H H shear system. This result is consistent with results predicted by the phenomenological theory. The preferred two and three habit plane variant grouping clustering is explained on the basis of self-accommodation effects. (orig.)

  5. Orientation dependence of shock-induced twinning and substructures in a copper bicrystal

    International Nuclear Information System (INIS)

    Cao Fang; Beyerlein, Irene J.; Addessio, Francis L.; Sencer, Bulent H.; Trujillo, Carl P.; Cerreta, Ellen K.; Gray, George T. III

    2010-01-01

    Shock recovery experiments have been conducted to assess the role of shock stress and orientation dependence on substructure evolution and deformation twinning of a [1 0 0]/[011-bar] copper bicrystal. Transmission electron microscopy of the post-shock specimens revealed that well-defined dislocation cell structures developed in both grains and the average cell size decreased with increasing shock pressure from 5 to 10 GPa. Twinning occurred in the [1 0 0] grain, but not the [011-bar] grain, at the 10 GPa shock pressure. The stress and orientation dependence of incipient twinning can be predicted by the stress and orientation conditions required to dissociate slip dislocations into glissile twinning dislocations. The dynamic widths between the two partials are calculated considering the three-dimensional deviatoric stress state induced by the shock as calculated using plane-strain plate impact simulations and the relativistic and drag effects on dislocations moving at high speeds.

  6. An Impulse Based Substructuring approach for impact analysis and load case simulations

    Science.gov (United States)

    Rixen, Daniel J.; van der Valk, Paul L. C.

    2013-12-01

    In the present paper we outline the basic theory of assembling substructures for which the dynamics are described as Impulse Response Functions. The assembly procedure computes the time response of a system by evaluating per substructure the convolution product between the Impulse Response Functions and the applied forces, including the interface forces that are computed to satisfy the interface compatibility. We call this approach the Impulse Based Substructuring method since it transposes to the time domain the Frequency Based Substructuring approach. In the Impulse Based Substructuring technique the Impulse Response Functions of the substructures can be gathered either from experimental tests using a hammer impact or from time-integration of numerical submodels. In this paper the implementation of the method is outlined for the case when the impulse responses of the substructures are computed numerically. A simple bar example is shown in order to illustrate the concept. The Impulse Based Substructuring allows fast evaluation of impact response of a structure when the impulse response of its components is known. It can thus be used to efficiently optimize designs of consumer products by including impact behavior at the early stage of the design, but also for performing substructured simulations of complex structures such as offshore wind turbines.

  7. PREDICTION OF SPORT ADHERENCE THROUGH THE INFLUENCE OF AUTONOMY-SUPPORTIVE COACHING AMONG SPANISH ADOLESCENT ATHLETES

    Directory of Open Access Journals (Sweden)

    Bartolomé J. Almagro

    2010-03-01

    Full Text Available The purpose of this study was to test a motivational model of the coach-athlete relationship, based on self-determination theory and on the hierarchical model of intrinsic and extrinsic motivation. The sample comprised of 608 athletes (ages of 12-17 years completed the following measures: interest in athlete's input, praise for autonomous behavior, perceived autonomy, intrinsic motivation, and the intention to be physically active. Structural equation modeling results demonstrated that interest in athletes' input and praise for autonomous behavior predicted perceived autonomy, and perceived autonomy positively predicted intrinsic motivation. Finally, intrinsic motivation predicted the intention to be physically active in the future. The results are discussed in relation to the importance of the climate of autonomy support created by the coach on intrinsic motivation and adherence to sport by adolescent athletes. Further, the results provide information related to the possible objectives of future interventions for the education of coaches, with the goal of providing them with tools and strategies to favor the development of intrinsic motivation among their athletes. In conclusion, the climate of autonomy support created by the coach can predict the autonomy perceived by the athletes which predicts the intrinsic motivation experienced by the athletes, and therefore, their adherence to athletic practice. Key words: Autonomy support, perceived autonomy, intrinsic motivation, sport adherence

  8. The Roles of Perceived Social Support, Coping, and Loneliness in Predicting Internet Addiction in Adolescents

    Science.gov (United States)

    Çevik, Gülsen Büyüksahin; Yildiz, Mehmet Ali

    2017-01-01

    The current research aims to examine the roles of perceived social support, coping, and loneliness when predicting the Internet addiction in adolescents. The research participants included 300 high school students, with an average age of 16.49 and SD = 1.27, attending schools in a city in Southeastern Anatolian Region during 2015-2016 academic…

  9. The Role of Perceived Social Support and Coping Styles in Predicting Adolescents' Positivity

    Science.gov (United States)

    Çevik, Gülsen Büyüksahin; Yildiz, Mehmet Ali

    2017-01-01

    The current research aims to examine the perceived social support and coping styles predicting positivity. Research participants included 268 adolescents, attending high school, with 147 females (54.9%) and 121 males (45.1%). Adolescents participating in the research were 14 to 18 years old and their average age was 16.12 with SD = 1.01. Research…

  10. Predicting South Korean University Students' Happiness through Social Support and Efficacy Beliefs

    Science.gov (United States)

    Lee, Diane Sookyoung; Padilla, Amado M.

    2016-01-01

    This study investigated the adversity and coping experiences of 198 South Korean university students and takes a cultural lens in understanding how social and individual factors shape their happiness. Hierarchical linear regression analyses suggest that Korean students' perceptions of social support significantly predicted their happiness,…

  11. Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.

    LENUS (Irish Health Repository)

    Mourao-Miranda, J

    2012-05-01

    To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.

  12. Predicting Career Adaptability through Self-Esteem and Social Support: A Research on Young Adults

    Science.gov (United States)

    Ataç, Lale Oral; Dirik, Deniz; Tetik, Hilmiye Türesin

    2018-01-01

    The purpose of this study is to investigate the relationship between career adaptability and self-esteem, and analyze the moderating role of social support in this relationship on a sample of 313 young adults. The results of the study confirm that career adaptability is significantly predicted by self-esteem. Moreover, findings suggest that (1)…

  13. Mentoring Support and Power: A Three Year Predictive Field Study on Protege Networking and Career Success

    Science.gov (United States)

    Blickle, Gerhard; Witzki, Alexander H.; Schneider, Paula B.

    2009-01-01

    Career success of early employees was analyzed from a power perspective and a developmental network perspective. In a predictive field study with 112 employees mentoring support and mentors' power were assessed in the first wave, employees' networking was assessed after two years, and career success (i.e. income and hierarchical position) and…

  14. Development of a Decision Support System to Predict Physicians' Rehabilitation Protocols for Patients with Knee Osteoarthritis

    Science.gov (United States)

    Hawamdeh, Ziad M.; Alshraideh, Mohammad A.; Al-Ajlouni, Jihad M.; Salah, Imad K.; Holm, Margo B.; Otom, Ali H.

    2012-01-01

    To design a medical decision support system (MDSS) that would accurately predict the rehabilitation protocols prescribed by the physicians for patients with knee osteoarthritis (OA) using only their demographic and clinical characteristics. The demographic and clinical variables for 170 patients receiving one of three treatment protocols for knee…

  15. Predicting beta-turns in proteins using support vector machines with fractional polynomials.

    Science.gov (United States)

    Elbashir, Murtada; Wang, Jianxin; Wu, Fang-Xiang; Wang, Lusheng

    2013-11-07

    β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

  16. Using support vector machine to predict beta- and gamma-turns in proteins.

    Science.gov (United States)

    Hu, Xiuzhen; Li, Qianzhong

    2008-09-01

    By using the composite vector with increment of diversity, position conservation scoring function, and predictive secondary structures to express the information of sequence, a support vector machine (SVM) algorithm for predicting beta- and gamma-turns in the proteins is proposed. The 426 and 320 nonhomologous protein chains described by Guruprasad and Rajkumar (Guruprasad and Rajkumar J. Biosci 2000, 25,143) are used for training and testing the predictive model of the beta- and gamma-turns, respectively. The overall prediction accuracy and the Matthews correlation coefficient in 7-fold cross-validation are 79.8% and 0.47, respectively, for the beta-turns. The overall prediction accuracy in 5-fold cross-validation is 61.0% for the gamma-turns. These results are significantly higher than the other algorithms in the prediction of beta- and gamma-turns using the same datasets. In addition, the 547 and 823 nonhomologous protein chains described by Fuchs and Alix (Fuchs and Alix Proteins: Struct Funct Bioinform 2005, 59, 828) are used for training and testing the predictive model of the beta- and gamma-turns, and better results are obtained. This algorithm may be helpful to improve the performance of protein turns' prediction. To ensure the ability of the SVM method to correctly classify beta-turn and non-beta-turn (gamma-turn and non-gamma-turn), the receiver operating characteristic threshold independent measure curves are provided. (c) 2008 Wiley Periodicals, Inc.

  17. Dynamic analysis of clustered building structures using substructures methods

    International Nuclear Information System (INIS)

    Leimbach, K.R.; Krutzik, N.J.

    1989-01-01

    The dynamic substructure approach to the building cluster on a common base mat starts with the generation of Ritz-vectors for each building on a rigid foundation. The base mat plus the foundation soil is subjected to kinematic constraint modes, for example constant, linear, quadratic or cubic constraints. These constraint modes are also imposed on the buildings. By enforcing kinematic compatibility of the complete structural system on the basis of the constraint modes a reduced Ritz model of the complete cluster is obtained. This reduced model can now be analyzed by modal time history or response spectrum methods

  18. Mechanical strenght and niobium and niobium-base alloys substructures

    International Nuclear Information System (INIS)

    Monteiro, W.A.; Andrade, A.H.P. de

    1986-01-01

    Niobium and some of its alloys have been used in several fields of technological applications such as the aerospace, chemical and nuclear industries. This is due to its excelent mechanical stringth at high temperatures and reasonable ductility at low temperatures. In this work, we review the main features of the relationship mechanical strength - substructure in niobium and its alloys, taking into account the presence of impurities, the influence of initial thermal and thermo - mechanical treatments as well as the irradiation by energetic particles. (Author) [pt

  19. A novel representation for apoptosis protein subcellular localization prediction using support vector machine.

    Science.gov (United States)

    Zhang, Li; Liao, Bo; Li, Dachao; Zhu, Wen

    2009-07-21

    Apoptosis, or programmed cell death, plays an important role in development of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on the concept that the position distribution information of amino acids is closely related with the structure and function of proteins, we introduce the concept of distance frequency [Matsuda, S., Vert, J.P., Ueda, N., Toh, H., Akutsu, T., 2005. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 14, 2804-2813] and propose a novel way to calculate distance frequencies. In order to calculate the local features, each protein sequence is separated into p parts with the same length in our paper. Then we use the novel representation of protein sequences and adopt support vector machine to predict subcellular location. The overall prediction accuracy is significantly improved by jackknife test.

  20. Maternal Support of Children's Early Numerical Concept Learning Predicts Preschool and First-Grade Math Achievement.

    Science.gov (United States)

    Casey, Beth M; Lombardi, Caitlin M; Thomson, Dana; Nguyen, Hoa Nha; Paz, Melissa; Theriault, Cote A; Dearing, Eric

    2018-01-01

    The primary goal in this study was to examine maternal support of numerical concepts at 36 months as predictors of math achievement at 4½ and 6-7 years. Observational measures of mother-child interactions (n = 140) were used to examine type of support for numerical concepts. Maternal support that involved labeling the quantities of sets of objects was predictive of later child math achievement. This association was significant for preschool (d = .45) and first-grade math (d = .49), controlling for other forms of numerical support (identifying numerals, one-to-one counting) as well as potential confounding factors. The importance of maternal support of labeling set sizes at 36 months is discussed as a precursor to children's eventual understanding of the cardinal principle. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

  1. Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model

    Directory of Open Access Journals (Sweden)

    Shaojiang Dong

    2014-01-01

    Full Text Available Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.

  2. Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

    Science.gov (United States)

    Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

    2016-02-01

    To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

    Science.gov (United States)

    Santoso, Noviyanti; Wibowo, Wahyu

    2018-03-01

    A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

  4. Guided Iterative Substructure Search (GI-SSS) - A New Trick for an Old Dog.

    Science.gov (United States)

    Weskamp, Nils

    2016-07-01

    Substructure search (SSS) is a fundamental technique supported by various chemical information systems. Many users apply it in an iterative manner: they modify their queries to shape the composition of the retrieved hit sets according to their needs. We propose and evaluate two heuristic extensions of SSS aimed at simplifying these iterative query modifications by collecting additional information during query processing and visualizing this information in an intuitive way. This gives the user a convenient feedback on how certain changes to the query would affect the retrieved hit set and reduces the number of trial-and-error cycles needed to generate an optimal search result. The proposed heuristics are simple, yet surprisingly effective and can be easily added to existing SSS implementations. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Dislocation-Disclination Substructures Formed in FCC Polycrystals Under Large Plastic Deformations: Evolution and Association with Flow Stress

    Science.gov (United States)

    Kozlov, É. V.; Koneva, N. A.; Trishkina, L. I.

    2014-06-01

    The evolution of dislocation substructures formed in polycrystalline Cu-Al and Cu-Mn alloys undergoing large plastic deformations is studied, using transmission electron microscopy. Microband and fragmented substructures are examined. The Al and Mn alloying element concentrations for which the substructures are formed have been found. The mechanisms involved in the formation of the substructures during the substructural evolution in the alloys subjected to deformation have been revealed. Parameters describing the substructures under study have been measured. The dependence of the parameters on the flow stress has been established.

  6. Prediction of backbone dihedral angles and protein secondary structure using support vector machines

    Directory of Open Access Journals (Sweden)

    Hirst Jonathan D

    2009-12-01

    Full Text Available Abstract Background The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure. Results We predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods. Conclusions We have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at http://comp.chem.nottingham.ac.uk/disspred/.

  7. Perceived parenting and social support: can they predict academic achievement in Argentinean college students?

    Science.gov (United States)

    de la Iglesia, Guadalupe; Freiberg Hoffmann, Agustin; Fernández Liporace, Mercedes

    2014-01-01

    The aim of this study was to test the ability to predict academic achievement through the perception of parenting and social support in a sample of 354 Argentinean college students. Their mean age was 23.50 years (standard deviation =2.62 years) and most of them (83.3%) were females. As a prerequisite for admission to college, students are required to pass a series of mandatory core classes and are expected to complete them in two semesters. Delay in completing the curriculum is considered low academic achievement. Parenting was assessed taking into account the mother and the father and considering two dimensions: responsiveness and demandingness. Perceived social support was analyzed considering four sources: parents, teachers, classmates, and best friend or boyfriend/girlfriend. Path analysis showed that, as hypothesized, responsiveness had a positive indirect effect on the perception of social support and enhanced achievement. Demandingness had a different effect in the case of the mother as compared to the father. In the mother model, demandingness had a positive direct effect on achievement. In the case of the father, however, the effect of demandingness had a negative and indirect impact on the perception of social support. Teachers were the only source of perceived social support that significantly predicted achievement. The pathway that belongs to teachers as a source of support was positive and direct. Implications for possible interventions are discussed.

  8. The predictive role of support in the birth experience: A longitudinal cohort study.

    Science.gov (United States)

    Sigurdardottir, Valgerdur Lisa; Gamble, Jennifer; Gudmundsdottir, Berglind; Kristjansdottir, Hildur; Sveinsdottir, Herdis; Gottfredsdottir, Helga

    2017-12-01

    Several risk factors for negative birth experience have been identified, but little is known regarding the influence of social and midwifery support on the birth experience over time. The aim of this study was to describe women's birth experience up to two years after birth and to detect the predictive role of satisfaction with social and midwifery support in the birth experience. A longitudinal cohort study was conducted with a convenience sample of pregnant women from 26 community health care centres. Data was gathered using questionnaires at 11-16 weeks of pregnancy (T1, n=1111), at five to six months (T2, n=765), and at 18-24 months after birth (T3, n=657). Data about sociodemographic factors, reproductive history, birth outcomes, social and midwifery support, depressive symptoms, and birth experience were collected. The predictive role of midwifery support in the birth experience was examined using binary logistic regression. The prevalence of negative birth experience was 5% at T2 and 5.7% at T3. Women who were not satisfied with midwifery support during pregnancy and birth were more likely to have negative birth experience at T2 than women who were satisfied with midwifery support. Operative birth, perception of prolonged birth and being a student predicted negative birth experience at both T2 and T3. Perception of negative birth experience was relatively consistent during the study period and the role of support from midwives during pregnancy and birth had a significant impact on women's perception of birth experience. Copyright © 2017 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

  9. Residuated lattices an algebraic glimpse at substructural logics

    CERN Document Server

    Galatos, Nikolaos; Kowalski, Tomasz; Ono, Hiroakira

    2007-01-01

    The book is meant to serve two purposes. The first and more obvious one is to present state of the art results in algebraic research into residuated structures related to substructural logics. The second, less obvious but equally important, is to provide a reasonably gentle introduction to algebraic logic. At the beginning, the second objective is predominant. Thus, in the first few chapters the reader will find a primer of universal algebra for logicians, a crash course in nonclassical logics for algebraists, an introduction to residuated structures, an outline of Gentzen-style calculi as well as some titbits of proof theory - the celebrated Hauptsatz, or cut elimination theorem, among them. These lead naturally to a discussion of interconnections between logic and algebra, where we try to demonstrate how they form two sides of the same coin. We envisage that the initial chapters could be used as a textbook for a graduate course, perhaps entitled Algebra and Substructural Logics. As the book progresses the f...

  10. Replaceable Substructures for Efficient Part-Based Modeling

    KAUST Repository

    Liu, Han; Vimont, Ulysse; Wand, Michael; Cani, Marie Paule; Hahmann, Stefanie; Rohmer, Damien; Mitra, Niloy J.

    2015-01-01

    A popular mode of shape synthesis involves mixing and matching parts from different objects to form a coherent whole. The key challenge is to efficiently synthesize shape variations that are plausible, both locally and globally. A major obstacle is to assemble the objects with local consistency, i.e., all the connections between parts are valid with no dangling open connections. The combinatorial complexity of this problem limits existing methods in geometric and/or topological variations of the synthesized models. In this work, we introduce replaceable substructures as arrangements of parts that can be interchanged while ensuring boundary consistency. The consistency information is extracted from part labels and connections in the original source models. We present a polynomial time algorithm that discovers such substructures by working on a dual of the original shape graph that encodes inter-part connectivity. We demonstrate the algorithm on a range of test examples producing plausible shape variations, both from a geometric and from a topological viewpoint. © 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

  11. Replaceable Substructures for Efficient Part-Based Modeling

    KAUST Repository

    Liu, Han

    2015-05-01

    A popular mode of shape synthesis involves mixing and matching parts from different objects to form a coherent whole. The key challenge is to efficiently synthesize shape variations that are plausible, both locally and globally. A major obstacle is to assemble the objects with local consistency, i.e., all the connections between parts are valid with no dangling open connections. The combinatorial complexity of this problem limits existing methods in geometric and/or topological variations of the synthesized models. In this work, we introduce replaceable substructures as arrangements of parts that can be interchanged while ensuring boundary consistency. The consistency information is extracted from part labels and connections in the original source models. We present a polynomial time algorithm that discovers such substructures by working on a dual of the original shape graph that encodes inter-part connectivity. We demonstrate the algorithm on a range of test examples producing plausible shape variations, both from a geometric and from a topological viewpoint. © 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

  12. Design and Analysis of Jacket Substructures for Offshore Wind Turbines

    Directory of Open Access Journals (Sweden)

    I-Wen Chen

    2016-04-01

    Full Text Available This study focused on investigating various existing types of offshore jacket substructures along with a proposed twisted-tripod jacket type (modified jacket (MJ-structures. The architectures of the three-leg structure, as well as the patented twisted jacket structure motivated the design of the proposed MJ-structures. The dimensions of the structures were designed iteratively using static stress analysis to ensure that all structures had a similar level of load-carrying capability. The numerical global buckling analyses were performed for all structures after the validation by the scaled-down experiments. The local buckling strength of all compressive members was analyzed using the NORSOK standard. The results showed that the proposed MJ-structures possess excellent structural behavior and few structural nodes and components competitive with the patented twisted jacket structures, while still maintaining the advantages of low material usage similar to the three-leg jacket structures. This study provides alternatives for the initial selection and design of offshore wind turbine substructures for green energy applications.

  13. Prediction and analysis of beta-turns in proteins by support vector machine.

    Science.gov (United States)

    Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

    2003-01-01

    Tight turn has long been recognized as one of the three important features of proteins after the alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns. Analysis and prediction of beta-turns in particular and tight turns in general are very useful for the design of new molecules such as drugs, pesticides, and antigens. In this paper, we introduce a support vector machine (SVM) approach to prediction and analysis of beta-turns. We have investigated two aspects of applying SVM to the prediction and analysis of beta-turns. First, we developed a new SVM method, called BTSVM, which predicts beta-turns of a protein from its sequence. The prediction results on the dataset of 426 non-homologous protein chains by sevenfold cross-validation technique showed that our method is superior to the other previous methods. Second, we analyzed how amino acid positions support (or prevent) the formation of beta-turns based on the "multivariable" classification model of a linear SVM. This model is more general than the other ones of previous statistical methods. Our analysis results are more comprehensive and easier to use than previously published analysis results.

  14. Reservoir Inflow Prediction under GCM Scenario Downscaled by Wavelet Transform and Support Vector Machine Hybrid Models

    Directory of Open Access Journals (Sweden)

    Gusfan Halik

    2015-01-01

    Full Text Available Climate change has significant impacts on changing precipitation patterns causing the variation of the reservoir inflow. Nowadays, Indonesian hydrologist performs reservoir inflow prediction according to the technical guideline of Pd-T-25-2004-A. This technical guideline does not consider the climate variables directly, resulting in significant deviation to the observation results. This research intends to predict the reservoir inflow using the statistical downscaling (SD of General Circulation Model (GCM outputs. The GCM outputs are obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR Reanalysis. A new proposed hybrid SD model named Wavelet Support Vector Machine (WSVM was utilized. It is a combination of the Multiscale Principal Components Analysis (MSPCA and nonlinear Support Vector Machine regression. The model was validated at Sutami Reservoir, Indonesia. Training and testing were carried out using data of 1991–2008 and 2008–2012, respectively. The results showed that MSPCA produced better extracting data than PCA. The WSVM generated better reservoir inflow prediction than the one of technical guideline. Moreover, this research also applied WSVM for future reservoir inflow prediction based on GCM ECHAM5 and scenario SRES A1B.

  15. Support for the Logical Execution Time Model on a Time-predictable Multicore Processor

    DEFF Research Database (Denmark)

    Kluge, Florian; Schoeberl, Martin; Ungerer, Theo

    2016-01-01

    The logical execution time (LET) model increases the compositionality of real-time task sets. Removal or addition of tasks does not influence the communication behavior of other tasks. In this work, we extend a multicore operating system running on a time-predictable multicore processor to support...... the LET model. For communication between tasks we use message passing on a time-predictable network-on-chip to avoid the bottleneck of shared memory. We report our experiences and present results on the costs in terms of memory and execution time....

  16. Prediction on sunspot activity based on fuzzy information granulation and support vector machine

    Science.gov (United States)

    Peng, Lingling; Yan, Haisheng; Yang, Zhigang

    2018-04-01

    In order to analyze the range of sunspots, a combined prediction method of forecasting the fluctuation range of sunspots based on fuzzy information granulation (FIG) and support vector machine (SVM) was put forward. Firstly, employing the FIG to granulate sample data and extract va)alid information of each window, namely the minimum value, the general average value and the maximum value of each window. Secondly, forecasting model is built respectively with SVM and then cross method is used to optimize these parameters. Finally, the fluctuation range of sunspots is forecasted with the optimized SVM model. Case study demonstrates that the model have high accuracy and can effectively predict the fluctuation of sunspots.

  17. THE NEXT GENERATION VIRGO CLUSTER SURVEY. XIX. TOMOGRAPHY OF MILKY WAY SUBSTRUCTURES IN THE NGVS FOOTPRINT

    Energy Technology Data Exchange (ETDEWEB)

    Lokhorst, Deborah; Starkenburg, Else; Navarro, Julio F. [Department of Physics and Astronomy, University of Victoria, Victoria, BC V8P 1A1, Canada (Canada); McConnachie, Alan W.; Ferrarese, Laura; Côté, Patrick; Gwyn, Stephen D. J. [National Research Council, Herzberg Astronomy and Astrophysics, 5071 West Saanich Road, Victoria, BC V9E 2E7 (Canada); Liu, Chengze [Center for Astronomy and Astrophysics, Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240 (China); Peng, Eric W. [Department of Astronomy, Peking University, Beijing 100871 (China); Cuillandre, Jean-Charles [CEA/IRFU/SAP, Laboratoire AIM Paris-Saclay, CNRS/INSU, Université Paris Diderot, Observatoire de Paris, PSL Research University, F-91191 Gif-sur-Yvette Cedex (France); Guhathakurta, Puragra, E-mail: dml@uvic.ca [Department of Astronomy and Astrophysics, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95064 (United States)

    2016-03-10

    The Next Generation Virgo Cluster Survey (NGVS) is a deep u*giz survey targeting the Virgo Cluster of galaxies at 16.5 Mpc. This survey provides high-quality photometry over an ∼100 deg{sup 2} region straddling the constellations of Virgo and Coma Berenices. This sightline through the Milky Way is noteworthy in that it intersects two of the most prominent substructures in the Galactic halo: the Virgo overdensity (VOD) and Sagittarius stellar stream (close to its bifurcation point). In this paper, we use deep u*gi imaging from the NGVS to perform tomography of the VOD and Sagittarius stream using main-sequence turnoff (MSTO) stars as a halo tracer population. The VOD, whose centroid is known to lie at somewhat lower declinations (α ∼ 190°, δ ∼ −5°) than is covered by the NGVS, is nevertheless clearly detected in the NGVS footprint at distances between ∼8 and 25 kpc. By contrast, the Sagittarius stream is found to slice directly across the NGVS field at distances between 25 and 40 kpc, with a density maximum at ≃35 kpc. No evidence is found for new substructures beyond the Sagittarius stream, at least out to a distance of ∼90 kpc—the largest distance to which we can reliably trace the halo using MSTO stars. We find clear evidence for a distance gradient in the Sagittarius stream across the ∼30° of sky covered by the NGVS and its flanking fields. We compare our distance measurements along the stream with those predicted by leading stream models.

  18. THE NEXT GENERATION VIRGO CLUSTER SURVEY. XIX. TOMOGRAPHY OF MILKY WAY SUBSTRUCTURES IN THE NGVS FOOTPRINT

    International Nuclear Information System (INIS)

    Lokhorst, Deborah; Starkenburg, Else; Navarro, Julio F.; McConnachie, Alan W.; Ferrarese, Laura; Côté, Patrick; Gwyn, Stephen D. J.; Liu, Chengze; Peng, Eric W.; Cuillandre, Jean-Charles; Guhathakurta, Puragra

    2016-01-01

    The Next Generation Virgo Cluster Survey (NGVS) is a deep u*giz survey targeting the Virgo Cluster of galaxies at 16.5 Mpc. This survey provides high-quality photometry over an ∼100 deg 2 region straddling the constellations of Virgo and Coma Berenices. This sightline through the Milky Way is noteworthy in that it intersects two of the most prominent substructures in the Galactic halo: the Virgo overdensity (VOD) and Sagittarius stellar stream (close to its bifurcation point). In this paper, we use deep u*gi imaging from the NGVS to perform tomography of the VOD and Sagittarius stream using main-sequence turnoff (MSTO) stars as a halo tracer population. The VOD, whose centroid is known to lie at somewhat lower declinations (α ∼ 190°, δ ∼ −5°) than is covered by the NGVS, is nevertheless clearly detected in the NGVS footprint at distances between ∼8 and 25 kpc. By contrast, the Sagittarius stream is found to slice directly across the NGVS field at distances between 25 and 40 kpc, with a density maximum at ≃35 kpc. No evidence is found for new substructures beyond the Sagittarius stream, at least out to a distance of ∼90 kpc—the largest distance to which we can reliably trace the halo using MSTO stars. We find clear evidence for a distance gradient in the Sagittarius stream across the ∼30° of sky covered by the NGVS and its flanking fields. We compare our distance measurements along the stream with those predicted by leading stream models

  19. Virtual-view PSNR prediction based on a depth distortion tolerance model and support vector machine.

    Science.gov (United States)

    Chen, Fen; Chen, Jiali; Peng, Zongju; Jiang, Gangyi; Yu, Mei; Chen, Hua; Jiao, Renzhi

    2017-10-20

    Quality prediction of virtual-views is important for free viewpoint video systems, and can be used as feedback to improve the performance of depth video coding and virtual-view rendering. In this paper, an efficient virtual-view peak signal to noise ratio (PSNR) prediction method is proposed. First, the effect of depth distortion on virtual-view quality is analyzed in detail, and a depth distortion tolerance (DDT) model that determines the DDT range is presented. Next, the DDT model is used to predict the virtual-view quality. Finally, a support vector machine (SVM) is utilized to train and obtain the virtual-view quality prediction model. Experimental results show that the Spearman's rank correlation coefficient and root mean square error between the actual PSNR and the predicted PSNR by DDT model are 0.8750 and 0.6137 on average, and by the SVM prediction model are 0.9109 and 0.5831. The computational complexity of the SVM method is lower than the DDT model and the state-of-the-art methods.

  20. A Modal-Based Substructure Method Applied to Nonlinear Rotordynamic Systems

    Directory of Open Access Journals (Sweden)

    Helmut J. Holl

    2009-01-01

    Full Text Available The discretisation of rotordynamic systems usually results in a high number of coordinates, so the computation of the solution of the equations of motion is very time consuming. An efficient semianalytic time-integration method combined with a substructure technique is given, which accounts for nonsymmetric matrices and local nonlinearities. The partitioning of the equation of motion into two substructures is performed. Symmetric and linear background systems are defined for each substructure. The excitation of the substructure comes from the given excitation force, the nonlinear restoring force, the induced force due to the gyroscopic and circulatory effects of the substructure under consideration and the coupling force of the substructures. The high effort for the analysis with complex numbers, which is necessary for nonsymmetric systems, is omitted. The solution is computed by means of an integral formulation. A suitable approximation for the unknown coordinates, which are involved in the coupling forces, has to be introduced and the integration results in Green's functions of the considered substructures. Modal analysis is performed for each linear and symmetric background system of the substructure. Modal reduction can be easily incorporated and the solution is calculated iteratively. The numerical behaviour of the algorithm is discussed and compared to other approximate methods of nonlinear structural dynamics for a benchmark problem and a representative example.

  1. Influence of solidification parameters on the cellular sub-structure of tin and some tin alloys

    International Nuclear Information System (INIS)

    Milosavljevic, Dj.

    1965-01-01

    This paper describes an attempt to obtain qualitative data on sub-structure of samples solidified in contact with the cooler. The objective of experiments was to study micro segregation phenomena by investigating the substructure in the solidified sample obtained under experimental conditions which are similar to real solidification conditions

  2. Misoriented dislocation substructures and the fracture of polycrystalline Cu-Al alloys

    Science.gov (United States)

    Koneva, N. A.; Trishkina, L. I.; Cherkasova, T. V.; Kozlov, E. V.

    2016-10-01

    The evolution of the dislocation substructure in polycrystalline Cu-Al alloys with various grain sizes is studied during deformation to failure. A relation between the fracture of the alloys and the forming misorientation dislocation substructures is revealed. Microcracks in the alloy are found to form along grain boundaries and the boundaries of misoriented dislocation cells and microtwins.

  3. Floating substructure flexibility of large-volume 10MW offshore wind turbine platforms in dynamic calculations

    DEFF Research Database (Denmark)

    Borg, Michael; Hansen, Anders Melchior; Bredmose, Henrik

    2016-01-01

    to the extent that it becomes relevant to include in addition to the standard rigid body substructure modes which are typically described through linear radiation-diffraction theory. This paper describes a method for the inclusion of substructural flexibility in aero-hydro-servo-elastic dynamic simulations...

  4. Substructuring in the implicit simulation of single point incremental sheet forming

    NARCIS (Netherlands)

    Hadoush, A.; van den Boogaard, Antonius H.

    2009-01-01

    This paper presents a direct substructuring method to reduce the computing time of implicit simulations of single point incremental forming (SPIF). Substructuring is used to divide the finite element (FE) mesh into several non-overlapping parts. Based on the hypothesis that plastic deformation is

  5. Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression

    International Nuclear Information System (INIS)

    Riaz, Nadeem; Wiersma, Rodney; Mao Weihua; Xing Lei; Shanker, Piyush; Gudmundsson, Olafur; Widrow, Bernard

    2009-01-01

    Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.

  6. Building a Predictive Capability for Decision-Making that Supports MultiPEM

    Energy Technology Data Exchange (ETDEWEB)

    Carmichael, Joshua Daniel [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-11-20

    Multi-phenomenological explosion monitoring (multiPEM) is a developing science that uses multiple geophysical signatures of explosions to better identify and characterize their sources. MultiPEM researchers seek to integrate explosion signatures together to provide stronger detection, parameter estimation, or screening capabilities between different sources or processes. This talk will address forming a predictive capability for screening waveform explosion signatures to support multiPEM.

  7. Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Seizi Someya

    2010-01-01

    Full Text Available Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs. We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

  8. Ductility prediction of substrate-supported metal layers based on rate-independent crystal plasticity theory

    Directory of Open Access Journals (Sweden)

    Akpama Holanyo K.

    2016-01-01

    Full Text Available In this paper, both the bifurcation theory and the initial imperfection approach are used to predict localized necking in substrate-supported metal layers. The self-consistent scale-transition scheme is used to derive the mechanical behavior of a representative volume element of the metal layer from the behavior of its microscopic constituents (the single crystals. The mechanical behavior of the elastomer substrate follows the neo-Hookean hyperelastic model. The adherence between the two layers is assumed to be perfect. Through numerical results, it is shown that the limit strains predicted by the initial imperfection approach tend towards the bifurcation predictions when the size of the geometric imperfection in the metal layer vanishes. Also, it is shown that the addition of an elastomer layer to a metal layer enhances ductility.

  9. Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression

    International Nuclear Information System (INIS)

    Lins, Isis Didier; Droguett, Enrique López; Moura, Márcio das Chagas; Zio, Enrico; Jacinto, Carlos Magno

    2015-01-01

    Data-driven learning methods for predicting the evolution of the degradation processes affecting equipment are becoming increasingly attractive in reliability and prognostics applications. Among these, we consider here Support Vector Regression (SVR), which has provided promising results in various applications. Nevertheless, the predictions provided by SVR are point estimates whereas in order to take better informed decisions, an uncertainty assessment should be also carried out. For this, we apply bootstrap to SVR so as to obtain confidence and prediction intervals, without having to make any assumption about probability distributions and with good performance even when only a small data set is available. The bootstrapped SVR is first verified on Monte Carlo experiments and then is applied to a real case study concerning the prediction of degradation of a component from the offshore oil industry. The results obtained indicate that the bootstrapped SVR is a promising tool for providing reliable point and interval estimates, which can inform maintenance-related decisions on degrading components. - Highlights: • Bootstrap (pairs/residuals) and SVR are used as an uncertainty analysis framework. • Numerical experiments are performed to assess accuracy and coverage properties. • More bootstrap replications does not significantly improve performance. • Degradation of equipment of offshore oil wells is estimated by bootstrapped SVR. • Estimates about the scale growth rate can support maintenance-related decisions

  10. Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

    Science.gov (United States)

    Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

    2012-08-01

    Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

  11. Perceived parenting and social support: can they predict academic achievement in Argentinean college students?

    Directory of Open Access Journals (Sweden)

    de la Iglesia G

    2014-09-01

    Full Text Available Guadalupe de la Iglesia,1,2 Agustin Freiberg Hoffmann,2 Mercedes Fernández Liporace1,2 1National Council of Scientific and Technical Research (CONICET, 2University of Buenos Aires, Buenos Aires, Argentina Abstract: The aim of this study was to test the ability to predict academic achievement through the perception of parenting and social support in a sample of 354 Argentinean college students. Their mean age was 23.50 years (standard deviation =2.62 years and most of them (83.3% were females. As a prerequisite for admission to college, students are required to pass a series of mandatory core classes and are expected to complete them in two semesters. Delay in completing the curriculum is considered low academic achievement. Parenting was assessed taking into account the mother and the father and considering two dimensions: responsiveness and demandingness. Perceived social support was analyzed considering four sources: parents, teachers, classmates, and best friend or boyfriend/girlfriend. Path analysis showed that, as hypothesized, responsiveness had a positive indirect effect on the perception of social support and enhanced achievement. Demandingness had a different effect in the case of the mother as compared to the father. In the mother model, demandingness had a positive direct effect on achievement. In the case of the father, however, the effect of demandingness had a negative and indirect impact on the perception of social support. Teachers were the only source of perceived social support that significantly predicted achievement. The pathway that belongs to teachers as a source of support was positive and direct. Implications for possible interventions are discussed. Keywords: academic achievement, parenting, social support, college

  12. The LabelHash algorithm for substructure matching

    Directory of Open Access Journals (Sweden)

    Bryant Drew H

    2010-11-01

    Full Text Available Abstract Background There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity. Results We present LabelHash, a novel algorithm for matching substructural motifs to large collections of protein structures. The algorithm consists of two phases. In the first phase the proteins are preprocessed in a fashion that allows for instant lookup of partial matches to any motif. In the second phase, partial matches for a given motif are expanded to complete matches. The general applicability of the algorithm is demonstrated with three different case studies. First, we show that we can accurately identify members of the enolase superfamily with a single motif. Next, we demonstrate how LabelHash can complement SOIPPA, an algorithm for motif identification and pairwise substructure alignment. Finally, a large collection of Catalytic Site Atlas motifs is used to benchmark the performance of the algorithm. LabelHash runs very efficiently in parallel; matching a motif against all proteins in the 95% sequence identity filtered non-redundant Protein Data Bank typically takes no more than a few minutes. The LabelHash algorithm is available through a web server and as a suite of standalone programs at http://labelhash.kavrakilab.org. The output of the LabelHash algorithm can be further analyzed with Chimera through a plugin that we developed for this purpose. Conclusions LabelHash is an efficient, versatile algorithm for large-scale substructure matching. When LabelHash is running in parallel, motifs can typically be matched against the entire PDB on the order of minutes. The algorithm is able to identify

  13. Reduction of fatigue loads on jacket substructure through blade design optimization for multimegawatt wind turbines at 50 m water depths

    DEFF Research Database (Denmark)

    NJOMO WANDJI, Wilfried; Pavese, Christian; Natarajan, Anand

    2016-01-01

    This paper addresses the reduction of the fore-aft damage equivalent moment at the tower base for multi-megawatt offshore wind turbines mounted on jacket type substructures at 50 m water depths. The study investigates blade design optimization of a reference 10 MW wind turbine under standard wind...... conditions of onshore sites. The blade geometry and structure is optimized to yield a design that minimizes tower base fatigue loads without significant loss of power production compared to that of the reference setup. The resulting blade design is then mounted on a turbine supported by a jacket and placed...

  14. Predictive based monitoring of nuclear plant component degradation using support vector regression

    International Nuclear Information System (INIS)

    Agarwal, Vivek; Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

    2015-01-01

    Nuclear power plants (NPPs) are large installations comprised of many active and passive assets. Degradation monitoring of all these assets is expensive (labor cost) and highly demanding task. In this paper a framework based on Support Vector Regression (SVR) for online surveillance of critical parameter degradation of NPP components is proposed. In this case, on time replacement or maintenance of components will prevent potential plant malfunctions, and reduce the overall operational cost. In the current work, we apply SVR equipped with a Gaussian kernel function to monitor components. Monitoring includes the one-step-ahead prediction of the component's respective operational quantity using the SVR model, while the SVR model is trained using a set of previous recorded degradation histories of similar components. Predictive capability of the model is evaluated upon arrival of a sensor measurement, which is compared to the component failure threshold. A maintenance decision is based on a fuzzy inference system that utilizes three parameters: (i) prediction evaluation in the previous steps, (ii) predicted value of the current step, (iii) and difference of current predicted value with components failure thresholds. The proposed framework will be tested on turbine blade degradation data.

  15. Freshwater Algal Bloom Prediction by Support Vector Machine in Macau Storage Reservoirs

    Directory of Open Access Journals (Sweden)

    Zhengchao Xie

    2012-01-01

    Full Text Available Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult to model the growth of algae species. Recently, support vector machine (SVM was reported to have advantages of only requiring a small amount of samples, high degree of prediction accuracy, and long prediction period to solve the nonlinear problems. In this study, the SVM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir (MSR are proposed, in which the water parameters of pH, SiO2, alkalinity, bicarbonate (HCO3 -, dissolved oxygen (DO, total nitrogen (TN, UV254, turbidity, conductivity, nitrate, total nitrogen (TN, orthophosphate (PO4 3−, total phosphorus (TP, suspended solid (SS and total organic carbon (TOC selected from the correlation analysis of the 23 monthly water variables were included, with 8-year (2001–2008 data for training and the most recent 3 years (2009–2011 for testing. The modeling results showed that the prediction and forecast powers were estimated as approximately 0.76 and 0.86, respectively, showing that the SVM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir.

  16. BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins

    Directory of Open Access Journals (Sweden)

    MuthuKrishnan Selvaraj

    2016-01-01

    Full Text Available The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM models were developed for predicting HbL proteins based upon amino acid composition (AC, dipeptide composition (DC, hybrid method (AC + DC, and position specific scoring matrix (PSSM. In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM profiles. The average accuracy, standard deviation (SD, false positive rate (FPR, confusion matrix, and receiver operating characteristic (ROC were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

  17. Blood glucose level prediction based on support vector regression using mobile platforms.

    Science.gov (United States)

    Reymann, Maximilian P; Dorschky, Eva; Groh, Benjamin H; Martindale, Christine; Blank, Peter; Eskofier, Bjoern M

    2016-08-01

    The correct treatment of diabetes is vital to a patient's health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models. However, these approaches required a physiological model, representing the human body's response to insulin and glucose intake, or are not directly applicable to mobile platforms (smart phones, tablets). In this paper, we propose an algorithm for mobile platforms to predict blood glucose levels without the need for a physiological model. Using an online software simulator program, we trained a Support Vector Regression (SVR) model and exported the parameter settings to our mobile platform. The prediction accuracy of our mobile platform was evaluated with pre-recorded data of a type 1 diabetes patient. The blood glucose level was predicted with an error of 19 % compared to the true value. Considering the permitted error of commercially used devices of 15 %, our algorithm is the basis for further development of mobile prediction algorithms.

  18. Prediction of Agriculture Drought Using Support Vector Regression Incorporating with Climatology Indices

    Science.gov (United States)

    Tian, Y.; Xu, Y. P.

    2017-12-01

    In this paper, the Support Vector Regression (SVR) model incorporating climate indices and drought indices are developed to predict agriculture drought in Xiangjiang River basin, Central China. The agriculture droughts are presented with the Precipitation-Evapotranspiration Index (SPEI). According to the analysis of the relationship between SPEI with different time scales and soil moisture, it is found that SPEI of six months time scales (SPEI-6) could reflect the soil moisture better than that of three and one month time scale from the drought features including drought duration, severity and peak. Climate forcing like El Niño Southern Oscillation and western Pacific subtropical high (WPSH) are represented by climate indices such as MEI and series indices of WPSH. Ridge Point of WPSH is found to be the key factor that influences the agriculture drought mainly through the control of temperature. Based on the climate indices analysis, the predictions of SPEI-6 are conducted using the SVR model. The results show that the SVR model incorperating climate indices, especially ridge point of WPSH, could improve the prediction accuracy compared to that using drought index only. The improvement was more significant for the prediction of one month lead time than that of three months lead time. However, it needs to be cautious in selection of the input parameters, since adding more useless information could have a counter effect in attaining a better prediction.

  19. Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

    Science.gov (United States)

    Luke, Adam; Vrugt, Jasper A.; AghaKouchak, Amir; Matthew, Richard; Sanders, Brett F.

    2017-07-01

    Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

  20. Spin effects from quark and lepton substructure at future machines

    International Nuclear Information System (INIS)

    Rueckl, R.

    1985-01-01

    If quarks and leptons are composite on a distance scale Λ -1 the physics at energies larger than Λ will provide plenty of evidence for the new level of substructure. However, already at energies below Λ compositeness should become manifest in deviations from the standard model due to form factors, residual interactions and, possibly, new ''light'' states. I discuss the virtue of polarized lepton and hadron beams in searching for new interactions and exemplify the production of excited fermions and bosons focussing on spin properties. The detailed of the contact interactions and the spin of the excited fermions and bosons can give important clues on the basic preon structure and dynamics. Phenomenological studies show that polarization asymmetries and angular distributions of decay products probe most sensitively the chiral properties of contact interactions and the spin of new states. Thus, polarized beams and good angular coverage are of great advantage

  1. Magnetic field influence on substructure formed by electric spark treatment

    International Nuclear Information System (INIS)

    Reza Rahbari, G.; Ivanov, A.N.

    1996-01-01

    The substructure of surface layer (about 10 microns thick) has been studied by x-ray line broadening technique in the samples of plain carbon steel (0.45%C) after electric spark doping with and without magnetic field (MF). The applied spark pulse energy was 0.12 J and MF induction varied from 0 to 0.08 T. The electrode material was the same as that of the treated sample. It has been observed that the MF reduces the tensile residual surface stresses from 660 ± 15MPa (no MF) to 260 ± 15MPa (B=0.053 T). The analysis of x-ray line broadening has revealed only the existence of microstrains, which are dependent of the MF magnitude. The microstrains have been related to the randomly distributed dislocation with the density of about 3x10 sup 11 cm sup -2

  2. An algebraic substructuring using multiple shifts for eigenvalue computations

    International Nuclear Information System (INIS)

    Ko, Jin Hwan; Jung, Sung Nam; Byun, Do Young; Bai, Zhaojun

    2008-01-01

    Algebraic substructuring (AS) is a state-of-the-art method in eigenvalue computations, especially for large-sized problems, but originally it was designed to calculate only the smallest eigenvalues. Recently, an updated version of AS has been introduced to calculate the interior eigenvalues over a specified range by using a shift concept that is referred to as the shifted AS. In this work, we propose a combined method of both AS and the shifted AS by using multiple shifts for solving a considerable number of eigensolutions in a large-sized problem, which is an emerging computational issue of noise or vibration analysis in vehicle design. In addition, we investigated the accuracy of the shifted AS by presenting an error criterion. The proposed method has been applied to the FE model of an automobile body. The combined method yielded a higher efficiency without loss of accuracy in comparison to the original AS

  3. Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved discovery significance relative to existing taggers. The network is trained using an adversarial strategy, resulting in a tagger that learns to balance classification accuracy with decorrelation. As a benchmark scenario, we consider the case where large-radius jets originating from a boosted Z' decay are discriminated from a background of nonresonant quark and gluon jets. We show that in the presence of systematic uncertainties on the background rate, our adversarially-trained, decorrelated tagger considerably outperforms a conventionally trained neural network, despite having a slightly worse signal-background separation power. We generalize the adversarial training technique to include a paramet...

  4. ATLAS Standard Model Measurements Using Jet Grooming and Substructure

    CERN Document Server

    Ucchielli, Giulia; The ATLAS collaboration

    2017-01-01

    Boosted topologies allow to explore Standard Model processes in kinematical regimes never tested before. In such LHC challenging environments, standard reconstruction techniques quickly hit the wall. Targeting hadronic final states means to properly reconstruct energy and multiplicity of the jets in the event. In order to be able to identify the decay product of boosted objects, i.e. W bosons, $t\\bar{t}$ pairs or Higgs produced in association with $t\\bar{t}$ pairs, ATLAS experiment is currently exploiting several algorithms using jet grooming and jet substructure. This contribution will mainly cover the following ATLAS measurements: $t\\bar{t}$ differential cross section production and jet mass using the soft drop procedure. Standard Model measurements offer the perfect field to test the performances of new jet tagging techniques which will become even more important in the search for new physics in highly boosted topologies.”

  5. The role of family expressed emotion and perceived social support in predicting addiction relapse.

    Science.gov (United States)

    Atadokht, Akbar; Hajloo, Nader; Karimi, Masoud; Narimani, Mohammad

    2015-03-01

    Emotional conditions governing the family and patients' perceived social support play important roles in the treatment or relapse process of the chronic disease. The current study aimed to investigate the role of family expressed emotion and perceived social support in prediction of addiction relapse. The descriptive-correlation method was used in the current study. The study population consisted of the individuals referred to the addiction treatment centers in Ardabil from October 2013 to January 2014. The subjects (n = 80) were randomly selected using cluster sampling method. To collect data, expressed emotion test by Cole and Kazaryan, and Multidimensional Scale of Perceived Social Support (MSPSS) were used, and the obtained data was analyzed using the Pearson's correlation coefficient and multiple regression analyses. Results showed a positive relationship between family expressed emotions and the frequency of relapse (r = 0.26, P = 0.011) and a significant negative relationship between perceived social support and the frequency of relapse (r = -0.34, P = 0.001). Multiple regression analysis also showed that perceived social support from family and the family expressed emotions significantly explained 12% of the total variance of relapse frequency. These results have implications for addicted people, their families and professionals working in addiction centers to use the emotional potential of families especially their expressed emotions and the perceived social support of addicts to increase the success rate of addiction treatment.

  6. Support vector machines for prediction and analysis of beta and gamma-turns in proteins.

    Science.gov (United States)

    Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

    2005-04-01

    Tight turns have long been recognized as one of the three important features of proteins, together with alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns and most of the rest are gamma-turns. Analysis and prediction of beta-turns and gamma-turns is very useful for design of new molecules such as drugs, pesticides, and antigens. In this paper we investigated two aspects of applying support vector machine (SVM), a promising machine learning method for bioinformatics, to prediction and analysis of beta-turns and gamma-turns. First, we developed two SVM-based methods, called BTSVM and GTSVM, which predict beta-turns and gamma-turns in a protein from its sequence. When compared with other methods, BTSVM has a superior performance and GTSVM is competitive. Second, we used SVMs with a linear kernel to estimate the support of amino acids for the formation of beta-turns and gamma-turns depending on their position in a protein. Our analysis results are more comprehensive and easier to use than the previous results in designing turns in proteins.

  7. Prediction of hourly PM2.5 using a space-time support vector regression model

    Science.gov (United States)

    Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

    2018-05-01

    Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

  8. Prediction of protein subcellular localization using support vector machine with the choice of proper kernel

    Directory of Open Access Journals (Sweden)

    Al Mehedi Hasan

    2017-07-01

    Full Text Available The prediction of subcellular locations of proteins can provide useful hints for revealing their functions as well as for understanding the mechanisms of some diseases and, finally, for developing novel drugs. As the number of newly discovered proteins has been growing exponentially, laboratory-based experiments to determine the location of an uncharacterized protein in a living cell have become both expensive and time-consuming. Consequently, to tackle these challenges, computational methods are being developed as an alternative to help biologists in selecting target proteins and designing related experiments. However, the success of protein subcellular localization prediction is still a complicated and challenging problem, particularly when query proteins may have multi-label characteristics, i.e. their simultaneous existence in more than one subcellular location, or if they move between two or more different subcellular locations as well. At this point, to get rid of this problem, several types of subcellular localization prediction methods with different levels of accuracy have been proposed. The support vector machine (SVM has been employed to provide potential solutions for problems connected with the prediction of protein subcellular localization. However, the practicability of SVM is affected by difficulties in selecting its appropriate kernel as well as in selecting the parameters of that selected kernel. The literature survey has shown that most researchers apply the radial basis function (RBF kernel to build a SVM based subcellular localization prediction system. Surprisingly, there are still many other kernel functions which have not yet been applied in the prediction of protein subcellular localization. However, the nature of this classification problem requires the application of different kernels for SVM to ensure an optimal result. From this viewpoint, this paper presents the work to apply different kernels for SVM in protein

  9. A dynamic particle filter-support vector regression method for reliability prediction

    International Nuclear Information System (INIS)

    Wei, Zhao; Tao, Tao; ZhuoShu, Ding; Zio, Enrico

    2013-01-01

    Support vector regression (SVR) has been applied to time series prediction and some works have demonstrated the feasibility of its use to forecast system reliability. For accuracy of reliability forecasting, the selection of SVR's parameters is important. The existing research works on SVR's parameters selection divide the example dataset into training and test subsets, and tune the parameters on the training data. However, these fixed parameters can lead to poor prediction capabilities if the data of the test subset differ significantly from those of training. Differently, the novel method proposed in this paper uses particle filtering to estimate the SVR model parameters according to the whole measurement sequence up to the last observation instance. By treating the SVR training model as the observation equation of a particle filter, our method allows updating the SVR model parameters dynamically when a new observation comes. Because of the adaptability of the parameters to dynamic data pattern, the new PF–SVR method has superior prediction performance over that of standard SVR. Four application results show that PF–SVR is more robust than SVR to the decrease of the number of training data and the change of initial SVR parameter values. Also, even if there are trends in the test data different from those in the training data, the method can capture the changes, correct the SVR parameters and obtain good predictions. -- Highlights: •A dynamic PF–SVR method is proposed to predict the system reliability. •The method can adjust the SVR parameters according to the change of data. •The method is robust to the size of training data and initial parameter values. •Some cases based on both artificial and real data are studied. •PF–SVR shows superior prediction performance over standard SVR

  10. Predicting quality of life and self-management from dyadic support and overprotection after myocardial infarction.

    Science.gov (United States)

    Joekes, Katherine; Maes, Stan; Warrens, Matthijs

    2007-11-01

    Using a self-regulatory framework, this study aims to identify how couples perceive a partner's support style after myocardial infarction (MI), and whether this predicts the patient's health-related quality of life (HR-QoL) and self-management (S-M) 9 months later. This longitudinal dyadic study includes 73 couples (86% of patients were men), recruited from two cardiac rehabilitation programmes in the Netherlands. Mean age of patients was 54.8 (SD=9.6) and of partners 52.5 (SD=9.8). Participants were interviewed and completed questionnaires at baseline (T1). Repeat questionnaires were returned by 69 and 67 couples after 3 (T2) and 9 months (T3), respectively. Support by partners is conceptualized in this study as 'active engagement' (AE), which involves the extent to which a partner engages the patient in conversations which focus on emotional support and problem solving. Levels of AE do not change over time, nor do they differ between members of the dyad. Levels of overprotection (OP) diminish with time, whilst patients consistently perceive more OP than partners report providing. Patients' experience of goal hindrance (at T3) due to the MI is associated with a decreased HR-QoL at T3 (controlling for baseline measures). The perception of having a supportive (AE) partner at T1 contributes to enhanced patient HR-QoL at each subsequent time point, although not to physical functioning. Perceiving a partner as overprotective (at T1) predicts worsened physical functioning in patients (at T3). Improvements in S-M at T3 (controlling for baseline measures) are reported by patients whose partner displays active engagement at T1. Cardiac rehabilitation should aim to redress the experience of goal disturbance and advise partners on how to provide support.

  11. Predicting Child Physical Activity and Screen Time: Parental Support for Physical Activity and General Parenting Styles

    Science.gov (United States)

    Crain, A. Lauren; Senso, Meghan M.; Levy, Rona L.; Sherwood, Nancy E.

    2014-01-01

    Objective: To examine relationships between parenting styles and practices and child moderate-to-vigorous physical activity (MVPA) and screen time. Methods: Participants were children (6.9 ± 1.8 years) with a body mass index in the 70–95th percentile and their parents (421 dyads). Parent-completed questionnaires assessed parental support for child physical activity (PA), parenting styles and child screen time. Children wore accelerometers to assess MVPA. Results: Parenting style did not predict MVPA, but support for PA did (positive association). The association between support and MVPA, moreover, varied as a function of permissive parenting. For parents high in permissiveness, the association was positive (greater support was related to greater MVPA and therefore protective). For parents low in permissiveness, the association was neutral; support did not matter. Authoritarian and permissive parenting styles were both associated with greater screen time. Conclusions: Parenting practices and styles should be considered jointly, offering implications for tailored interventions. PMID:24812256

  12. Predicting child physical activity and screen time: parental support for physical activity and general parenting styles.

    Science.gov (United States)

    Langer, Shelby L; Crain, A Lauren; Senso, Meghan M; Levy, Rona L; Sherwood, Nancy E

    2014-07-01

    To examine relationships between parenting styles and practices and child moderate-to-vigorous physical activity (MVPA) and screen time. Participants were children (6.9 ± 1.8 years) with a body mass index in the 70-95th percentile and their parents (421 dyads). Parent-completed questionnaires assessed parental support for child physical activity (PA), parenting styles and child screen time. Children wore accelerometers to assess MVPA. Parenting style did not predict MVPA, but support for PA did (positive association). The association between support and MVPA, moreover, varied as a function of permissive parenting. For parents high in permissiveness, the association was positive (greater support was related to greater MVPA and therefore protective). For parents low in permissiveness, the association was neutral; support did not matter. Authoritarian and permissive parenting styles were both associated with greater screen time. Parenting practices and styles should be considered jointly, offering implications for tailored interventions. © The Author 2014. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  13. Applying a multi-replication framework to support dynamic situation assessment and predictive capabilities

    Science.gov (United States)

    Lammers, Craig; McGraw, Robert M.; Steinman, Jeffrey S.

    2005-05-01

    Technological advances and emerging threats reduce the time between target detection and action to an order of a few minutes. To effectively assist with the decision-making process, C4I decision support tools must quickly and dynamically predict and assess alternative Courses Of Action (COAs) to assist Commanders in anticipating potential outcomes. These capabilities can be provided through the faster-than-real-time predictive simulation of plans that are continuously re-calibrating with the real-time picture. This capability allows decision-makers to assess the effects of re-tasking opportunities, providing the decision-maker with tremendous freedom to make time-critical, mid-course decisions. This paper presents an overview and demonstrates the use of a software infrastructure that supports DSAP capabilities. These DSAP capabilities are demonstrated through the use of a Multi-Replication Framework that supports (1) predictivie simulations using JSAF (Joint Semi-Automated Forces); (2) real-time simulation, also using JSAF, as a state estimation mechanism; and, (3) real-time C4I data updates through TBMCS (Theater Battle Management Core Systems). This infrastructure allows multiple replications of a simulation to be executed simultaneously over a grid faster-than-real-time, calibrated with live data feeds. A cost evaluator mechanism analyzes potential outcomes and prunes simulations that diverge from the real-time picture. In particular, this paper primarily serves to walk a user through the process for using the Multi-Replication Framework providing an enhanced decision aid.

  14. Large-scale ligand-based predictive modelling using support vector machines.

    Science.gov (United States)

    Alvarsson, Jonathan; Lampa, Samuel; Schaal, Wesley; Andersson, Claes; Wikberg, Jarl E S; Spjuth, Ola

    2016-01-01

    The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

  15. The potential of predictive analytics to provide clinical decision support in depression treatment planning.

    Science.gov (United States)

    Kessler, Ronald C

    2018-01-01

    To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD). Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem. Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

  16. Support vector regression model based predictive control of water level of U-tube steam generators

    Energy Technology Data Exchange (ETDEWEB)

    Kavaklioglu, Kadir, E-mail: kadir.kavaklioglu@pau.edu.tr

    2014-10-15

    Highlights: • Water level of U-tube steam generators was controlled in a model predictive fashion. • Models for steam generator water level were built using support vector regression. • Cost function minimization for future optimal controls was performed by using the steepest descent method. • The results indicated the feasibility of the proposed method. - Abstract: A predictive control algorithm using support vector regression based models was proposed for controlling the water level of U-tube steam generators of pressurized water reactors. Steam generator data were obtained using a transfer function model of U-tube steam generators. Support vector regression based models were built using a time series type model structure for five different operating powers. Feedwater flow controls were calculated by minimizing a cost function that includes the level error, the feedwater change and the mismatch between feedwater and steam flow rates. Proposed algorithm was applied for a scenario consisting of a level setpoint change and a steam flow disturbance. The results showed that steam generator level can be controlled at all powers effectively by the proposed method.

  17. Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

    Science.gov (United States)

    Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

    2017-10-01

    In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

  18. BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar

    2011-08-17

    Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

  19. Reservoir rock permeability prediction using support vector regression in an Iranian oil field

    International Nuclear Information System (INIS)

    Saffarzadeh, Sadegh; Shadizadeh, Seyed Reza

    2012-01-01

    Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. It is often measured in the laboratory from reservoir core samples or evaluated from well test data. The prediction of reservoir rock permeability utilizing well log data is important because the core analysis and well test data are usually only available from a few wells in a field and have high coring and laboratory analysis costs. Since most wells are logged, the common practice is to estimate permeability from logs using correlation equations developed from limited core data; however, these correlation formulae are not universally applicable. Recently, support vector machines (SVMs) have been proposed as a new intelligence technique for both regression and classification tasks. The theory has a strong mathematical foundation for dependence estimation and predictive learning from finite data sets. The ultimate test for any technique that bears the claim of permeability prediction from well log data is the accurate and verifiable prediction of permeability for wells where only the well log data are available. The main goal of this paper is to develop the SVM method to obtain reservoir rock permeability based on well log data. (paper)

  20. Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System

    Directory of Open Access Journals (Sweden)

    Ivan Marović

    2017-01-01

    Full Text Available The impact of natural disasters increases every year with more casualties and damage to property and the environment. Therefore, it is important to prevent consequences by implementation of the early warning system (EWS in order to announce the possibility of the harmful phenomena occurrence. In this paper, focus is placed on the implementation of the EWS on the micro location in order to announce possible harmful phenomena occurrence caused by wind. In order to predict such phenomena (wind speed, an artificial neural network (ANN prediction model is developed. The model is developed on the basis of the input data obtained by local meteorological station on the University of Rijeka campus area in the Republic of Croatia. The prediction model is validated and evaluated by visual and common calculation approaches, after which it was found that it is possible to perform very good wind speed prediction for time steps Δt=1 h, Δt=3 h, and Δt=8 h. The developed model is implemented in the EWS as a decision support for improvement of the existing “procedure plan in a case of the emergency caused by stormy wind or hurricane, snow and occurrence of the ice on the University of Rijeka campus.”

  1. BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar; Pugalenthi, Ganesan; Hazrati, Mehrnaz Khodam; Kalies, Kai-Uwe; Martinetz, Thomas

    2011-01-01

    Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

  2. Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Masayuki Yarimizu

    2015-01-01

    Full Text Available Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting Proteins (DIP and UniProtKB, filtered them by removing sequence redundancy, and used them as a dataset for machine learning and assessment of predictive performance. Our prediction method is based on support vector machines (SVMs, and we evaluated several input features suitable for tyrosine kinase for machine learning and compared and analyzed the results. Using sequence pattern information and domain information extracted from sequences as input features, we obtained 0.996 of the area under the receiver operating characteristic curve. This accuracy is higher than that obtained from general protein-protein interaction pair predictions.

  3. Performance Comparison Between Support Vector Regression and Artificial Neural Network for Prediction of Oil Palm Production

    Directory of Open Access Journals (Sweden)

    Mustakim Mustakim

    2016-02-01

    Full Text Available The largest region that produces oil palm in Indonesia has an important role in improving the welfare of society and economy. Oil palm has increased significantly in Riau Province in every period, to determine the production development for the next few years with the functions and benefits of oil palm carried prediction production results that were seen from time series data last 8 years (2005-2013. In its prediction implementation, it was done by comparing the performance of Support Vector Regression (SVR method and Artificial Neural Network (ANN. From the experiment, SVR produced the best model compared with ANN. It is indicated by the correlation coefficient of 95% and 6% for MSE in the kernel Radial Basis Function (RBF, whereas ANN produced only 74% for R2 and 9% for MSE on the 8th experiment with hiden neuron 20 and learning rate 0,1. SVR model generates predictions for next 3 years which increased between 3% - 6% from actual data and RBF model predictions.

  4. A STUDY OF PREDICTING THE NEED FOR VENTILATOR SUPPORT AND OUTCOME IN ORGANOPHOSPHORUS POISONING

    Directory of Open Access Journals (Sweden)

    Kalinga Bommankatte Eranaik

    2017-04-01

    Full Text Available BACKGROUND Organophosphorus compound poisoning is the most common poisonings in India because of easy availability, often requiring ICU care and ventilator support. Clinical research has indicated that respiratory failure is the most important cause of death due to Organophosphorus poisoning. It results in respiratory muscle weakness, pulmonary oedema, respiratory depression, increased secretions and bronchospasm. These complications and death can be prevented with timely Institution of ventilator support. The aim of present study was to identify the factors and predicting the need for ventilator support and outcome. Aim of the Study- To predict the need for ventilator support and outcome in organophosphate poisoning. MATERIALS AND METHODS Seventy consecutive patients admitted with a history of organophosphorus poisoning at KIMS, Hubli were taken for study after considering the inclusion and exclusion criteria. Detailed history, confirmation of poisoning, examination and other than routine investigations serum pseudocholinesterase and arterial blood gas analysis was done. The severity of organophosphorus poisoning was graded as mild, moderate and severe based on the factors which influence the need for ventilator support. RESULTS This study was conducted in 70 patients, out of which 48 (68.6% were male patients and 22 (31.4% were female patients. Among them 37 (53% patients required ventilation and 33 (47% expired. Chlorpyrifos, Dichlorvos and Monocrotophos were most commonly consumed poisons. 74% patients who consumed these compounds required ventilator support and 73% patients expired. 100% of patients presented with pin point pupil, fasciculation score > 4, respiratory rate > 20, GCS score < 7 and severe grade of poisoning required ventilator support and pseudocholinesterase < 900 U/L, 70% of metabolic acidosis and atropine requirement more than 180 mg within 48 hours required ventilator support and associated with high mortality. CONCLUSION

  5. Prediction of Five Softwood Paper Properties from its Density using Support Vector Machine Regression Techniques

    Directory of Open Access Journals (Sweden)

    Esperanza García-Gonzalo

    2016-01-01

    Full Text Available Predicting paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kernel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris and cypress species (Cupressus lusitanica, C. sempervirens, and C. arizonica beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination with two variables: the numerical variable density and the categorical variable species.

  6. Why and when social support predicts older adults' pain-related disability: a longitudinal study.

    Science.gov (United States)

    Matos, Marta; Bernardes, Sónia F; Goubert, Liesbet

    2017-10-01

    Pain-related social support has been shown to be directly associated with pain-related disability, depending on whether it promotes functional autonomy or dependence. However, previous studies mostly relied on cross-sectional methods, precluding conclusions on the temporal relationship between pain-related social support and disability. Also, research on the behavioral and psychological processes that account for such a relationship is scarce. Therefore, this study aimed at investigating the following longitudinally: (1) direct effects of social support for functional autonomy/dependence on pain-related disability, (2) mediating role of physical functioning, pain-related self-efficacy, and fear, and (3) whether pain duration and pain intensity moderate such mediating processes. A total of 168 older adults (Mage = 78.3; SDage = 8.7) participated in a 3-month prospective design, with 3 moments of measurement, with a 6-week lag between them. Participants completed the Formal Social Support for Autonomy and Dependence in Pain Inventory, the Brief Pain Inventory, the 36-SF Health Survey, behavioral tasks from the Senior Fitness Test, the Pain Self-Efficacy Questionnaire, and the Tampa Scale for Kinesiophobia. Moderated mediation analyses showed that formal social support for functional dependence (T1) predicted an increase in pain-related disability (T3), that was mediated by self-reported physical functioning (T2) and by pain-related self-efficacy (T2) at short to moderate pain duration and at low to moderate pain intensity, but not at higher levels. Findings emphasized that social support for functional dependence is a risk factor for pain-related disability and uncovered the "why" and "when" of this relationship. Implications for the design of social support interventions aiming at promoting older adults' healthy aging despite chronic pain are drawn.

  7. Design of a Clinical Decision Support System for Fracture Prediction Using Imbalanced Dataset

    Directory of Open Access Journals (Sweden)

    Yung-Fu Chen

    2018-01-01

    Full Text Available More than 1 billion people suffer from chronic respiratory diseases worldwide, accounting for more than 4 million deaths annually. Inhaled corticosteroid is a popular medication for treating chronic respiratory diseases. Its side effects include decreased bone mineral density and osteoporosis. The aims of this study are to investigate the association of inhaled corticosteroids and fracture and to design a clinical support system for fracture prediction. The data of patients aged 20 years and older, who had visited healthcare centers and been prescribed with inhaled corticosteroids within 2002–2010, were retrieved from the National Health Insurance Research Database (NHIRD. After excluding patients diagnosed with hip fracture or vertebrate fractures before using inhaled corticosteroid, a total of 11645 patients receiving inhaled corticosteroid therapy were included for this study. Among them, 1134 (9.7% were diagnosed with hip fracture or vertebrate fracture. The statistical results showed that demographic information, chronic respiratory diseases and comorbidities, and corticosteroid-related variables (cumulative dose, mean exposed daily dose, follow-up duration, and exposed duration were significantly different between fracture and nonfracture patients. The clinical decision support systems (CDSSs were designed with integrated genetic algorithm (GA and support vector machine (SVM by training and validating the models with balanced training sets obtained by random and cluster-based undersampling methods and testing with the imbalanced NHIRD dataset. Two different objective functions were adopted for obtaining optimal models with best predictive performance. The predictive performance of the CDSSs exhibits a sensitivity of 69.84–77.00% and an AUC of 0.7495–0.7590. It was concluded that long-term use of inhaled corticosteroids may induce osteoporosis and exhibit higher incidence of hip or vertebrate fractures. The accumulated dose of ICS and

  8. Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine

    Directory of Open Access Journals (Sweden)

    Ravindra Kumar

    2017-09-01

    Full Text Available Background The endoplasmic reticulum plays an important role in many cellular processes, which includes protein synthesis, folding and post-translational processing of newly synthesized proteins. It is also the site for quality control of misfolded proteins and entry point of extracellular proteins to the secretory pathway. Hence at any given point of time, endoplasmic reticulum contains two different cohorts of proteins, (i proteins involved in endoplasmic reticulum-specific function, which reside in the lumen of the endoplasmic reticulum, called as endoplasmic reticulum resident proteins and (ii proteins which are in process of moving to the extracellular space. Thus, endoplasmic reticulum resident proteins must somehow be distinguished from newly synthesized secretory proteins, which pass through the endoplasmic reticulum on their way out of the cell. Approximately only 50% of the proteins used in this study as training data had endoplasmic reticulum retention signal, which shows that these signals are not essentially present in all endoplasmic reticulum resident proteins. This also strongly indicates the role of additional factors in retention of endoplasmic reticulum-specific proteins inside the endoplasmic reticulum. Methods This is a support vector machine based method, where we had used different forms of protein features as inputs for support vector machine to develop the prediction models. During training leave-one-out approach of cross-validation was used. Maximum performance was obtained with a combination of amino acid compositions of different part of proteins. Results In this study, we have reported a novel support vector machine based method for predicting endoplasmic reticulum resident proteins, named as ERPred. During training we achieved a maximum accuracy of 81.42% with leave-one-out approach of cross-validation. When evaluated on independent dataset, ERPred did prediction with sensitivity of 72.31% and specificity of 83

  9. Perceived parenting and social support: can they predict academic achievement in Argentinean college students?

    OpenAIRE

    de la Iglesia, Guadalupe; Freiberg Hoffmann, Agustin; Fernández Liporace, Mercedes

    2014-01-01

    Guadalupe de la Iglesia,1,2 Agustin Freiberg Hoffmann,2 Mercedes Fernández Liporace1,2 1National Council of Scientific and Technical Research (CONICET), 2University of Buenos Aires, Buenos Aires, Argentina Abstract: The aim of this study was to test the ability to predict academic achievement through the perception of parenting and social support in a sample of 354 Argentinean college students. Their mean age was 23.50 years (standard deviation =2.62 years) and most of them (83.3%...

  10. Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

    Directory of Open Access Journals (Sweden)

    Nebot

    2012-04-01

    Full Text Available In this research a genetic fuzzy system (GFS is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR methodology and the Linguistic Rule FIR (LR-FIR algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR models and decision support (LR-FIR models. The GFS is evaluated in an e-learning context.

  11. Inspection of Asian Lacquer Substructures by Terahertz Time-Domain Imaging (THz-TDI)

    DEFF Research Database (Denmark)

    Dandolo, Corinna Ludovica Koch; Fukunaga, Kaori; Kohzuma, Yoshei

    2017-01-01

    Lacquering is considered one of the most representative Asian artistic techniques. While the decorative part of lacquerwares is the lacquer itself, their substructures serve as the backbone of the object itself. Very little is known about these hidden substructures. Since lacquerwares are mostly...... by inspecting the substructures of Asian lacquerwares by means of THz time-domain imaging (THz-TDI). Three different kinds of Asian lacquerwares were examined by THz-TDI, and the outcomes have been compared with those obtained by standard X-radiography. THz-TDI provides unique information on lacquerwares...

  12. Dislocation Substructures Formed After Fracture of Deformed Polycrystalline Cu-Al Alloys

    Science.gov (United States)

    Koneva, N. A.; Trishkina, L. I.; Cherkasova, T. V.

    2017-08-01

    The paper deals with the dislocation substructure of polycrystalline FCC alloys modified by plastic deformation at a distance from the area of the specimen fracture. Observations are performed using the transmission electron microscopy. Cu-Al alloys with grain size ranging from 10 to 240 μm are studied in this paper. The parameters of the dislocation substructure are measured and their variation is determined by the increasing distance from the fracture area. It is shown how the grain size influences these processes. The different dislocation substructures which determine the specimen fracture at a mesocscale level are found herein.

  13. Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks

    CERN Document Server

    Altheimer, A; Asquith, L; Brooijmans, G; Butterworth, J; Campanelli, M; Chapleau, B; Cholakian, A E; Chou, J P; Dasgupta, M; Davison, A; Dolen, J; Ellis, S D; Essig, R; Fan, J J; Field, R; Fregoso, A; Gallicchio, J; Gershtein, Y; Gomes, A; Haas, A; Halkiadakis, E; Halyo, V; Hoeche, S; Hook, A; Hornig, A; Huang, P; Izaguirre, E; Jankowiak, M; Kribs, G; Krohn, D; Larkoski, A J; Lath, A; Lee, C; Lee, S J; Loch, P; Maksimovic, P; Martinez, M; Miller, D W; Plehn, T; Prokofiev, K; Rahmat, R; Rappoccio, S; Safonov, A; Salam, G P; Schumann, S; Schwartz, M D; Schwartzman, A; Seymour, M; Shao, J; Sinervo, P; Son, M; Soper, D E; Spannowsky, M; Stewart, I W; Strassler, M; Strauss, E; Takeuchi, M; Thaler, J; Thomas, S; Tweedie, B; Vasquez Sierra, R; Vermilion, C K; Villaplana, M; Vos, M; Wacker, J; Walker, D; Walsh, J R; Wang, L-T; Wilbur, S; Yavin, I; Zhu, W

    2012-01-01

    In this report we review recent theoretical progress and the latest experimental results in jet substructure from the Tevatron and the LHC. We review the status of and outlook for calculation and simulation tools for studying jet substructure. Following up on the report of the Boost 2010 workshop, we present a new set of benchmark comparisons of substructure techniques, focusing on the set of variables and grooming methods that are collectively known as "top taggers". To facilitate further exploration, we have attempted to collect, harmonise, and publish software implementations of these techniques.

  14. Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients.

    Science.gov (United States)

    Thanathornwong, Bhornsawan; Suebnukarn, Siriwan

    2017-10-01

    The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a 'gold standard' to compare with the occlusal force predicted by the multiple regression model. The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R -0.08×G + 0.08×B + 4.74; R 2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients.

  15. Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients

    Science.gov (United States)

    Thanathornwong, Bhornsawan

    2017-01-01

    Objectives The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. Methods We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a ‘gold standard’ to compare with the occlusal force predicted by the multiple regression model. Results The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R –0.08×G + 0.08×B + 4.74; R2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). Conclusions The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients. PMID:29181234

  16. Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin.

    Science.gov (United States)

    Tian, Ye; Xu, Yue-Ping; Wang, Guoqing

    2018-05-01

    Drought can have a substantial impact on the ecosystem and agriculture of the affected region and does harm to local economy. This study aims to analyze the relation between soil moisture and drought and predict agricultural drought in Xiangjiang River basin. The agriculture droughts are presented with the Precipitation-Evapotranspiration Index (SPEI). The Support Vector Regression (SVR) model incorporating climate indices is developed to predict the agricultural droughts. Analysis of climate forcing including El Niño Southern Oscillation and western Pacific subtropical high (WPSH) are carried out to select climate indices. The results show that SPEI of six months time scales (SPEI-6) represents the soil moisture better than that of three and one month time scale on drought duration, severity and peaks. The key factor that influences the agriculture drought is the Ridge Point of WPSH, which mainly controls regional temperature. The SVR model incorporating climate indices, especially ridge point of WPSH, could improve the prediction accuracy compared to that solely using drought index by 4.4% in training and 5.1% in testing measured by Nash Sutcliffe efficiency coefficient (NSE) for three month lead time. The improvement is more significant for the prediction with one month lead (15.8% in training and 27.0% in testing) than that with three months lead time. However, it needs to be cautious in selection of the input parameters, since adding redundant information could have a counter effect in attaining a better prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. The role of social support, family identification, and family constraints in predicting posttraumatic stress after cancer.

    Science.gov (United States)

    Swartzman, Samantha; Sani, Fabio; Munro, Alastair J

    2017-09-01

    We compared social support with other potential psychosocial predictors of posttraumatic stress after cancer. These included family identification, or a sense of belonging to and commonality with family members, and family constraints, or the extent to which family members are closed, judgmental, or unreceptive in conversations about cancer. We also tested the hypothesis that family constraints mediate the relationship between family identification and cancer-related posttraumatic stress. We used a cross-sectional design. Surveys were collected from 205 colorectal cancer survivors in Tayside, Scotland. Both family identification and family constraints were stronger independent predictors of posttraumatic stress than social support. In multivariate analyses, social support was not a significant independent predictor of posttraumatic stress. In addition, there was a significant indirect effect of family identification on posttraumatic stress through family constraints. Numerous studies demonstrate a link between social support and posttraumatic stress. However, experiences within the family may be more important in predicting posttraumatic stress after cancer. Furthermore, a sense of belonging to and commonality with the family may reduce the extent to which cancer survivors experience constraints on conversations about cancer; this may, in turn, reduce posttraumatic stress. Copyright © 2016 John Wiley & Sons, Ltd.

  18. PREDICTIVE MODELS FOR SUPPORT OF INCIDENT MANAGEMENT PROCESS IN IT SERVICE MANAGEMENT

    Directory of Open Access Journals (Sweden)

    Martin SARNOVSKY

    2018-03-01

    Full Text Available ABSTRACT The work presented in this paper is focused on creating of predictive models that help in the process of incident resolution and implementation of IT infrastructure changes to increase the overall support of IT management. Our main objective was to build the predictive models using machine learning algorithms and CRISP-DM methodology. We used the incident and related changes database obtained from the IT environment of the Rabobank Group company, which contained information about the processing of the incidents during the incident management process. We decided to investigate the dependencies between the incident observation on particular infrastructure component and the actual source of the incident as well as the dependency between the incidents and related changes in the infrastructure. We used Random Forests and Gradient Boosting Machine classifiers in the process of identification of incident source as well as in the prediction of possible impact of the observed incident. Both types of models were tested on testing set and evaluated using defined metrics.

  19. Efficient Prediction of Progesterone Receptor Interactome Using a Support Vector Machine Model

    Directory of Open Access Journals (Sweden)

    Ji-Long Liu

    2015-03-01

    Full Text Available Protein-protein interaction (PPI is essential for almost all cellular processes and identification of PPI is a crucial task for biomedical researchers. So far, most computational studies of PPI are intended for pair-wise prediction. Theoretically, predicting protein partners for a single protein is likely a simpler problem. Given enough data for a particular protein, the results can be more accurate than general PPI predictors. In the present study, we assessed the potential of using the support vector machine (SVM model with selected features centered on a particular protein for PPI prediction. As a proof-of-concept study, we applied this method to identify the interactome of progesterone receptor (PR, a protein which is essential for coordinating female reproduction in mammals by mediating the actions of ovarian progesterone. We achieved an accuracy of 91.9%, sensitivity of 92.8% and specificity of 91.2%. Our method is generally applicable to any other proteins and therefore may be of help in guiding biomedical experiments.

  20. Predicting Taxi-Out Time at Congested Airports with Optimization-Based Support Vector Regression Methods

    Directory of Open Access Journals (Sweden)

    Guan Lian

    2018-01-01

    Full Text Available Accurate prediction of taxi-out time is significant precondition for improving the operationality of the departure process at an airport, as well as reducing the long taxi-out time, congestion, and excessive emission of greenhouse gases. Unfortunately, several of the traditional methods of predicting taxi-out time perform unsatisfactorily at congested airports. This paper describes and tests three of those conventional methods which include Generalized Linear Model, Softmax Regression Model, and Artificial Neural Network method and two improved Support Vector Regression (SVR approaches based on swarm intelligence algorithm optimization, which include Particle Swarm Optimization (PSO and Firefly Algorithm. In order to improve the global searching ability of Firefly Algorithm, adaptive step factor and Lévy flight are implemented simultaneously when updating the location function. Six factors are analysed, of which delay is identified as one significant factor in congested airports. Through a series of specific dynamic analyses, a case study of Beijing International Airport (PEK is tested with historical data. The performance measures show that the proposed two SVR approaches, especially the Improved Firefly Algorithm (IFA optimization-based SVR method, not only perform as the best modelling measures and accuracy rate compared with the representative forecast models, but also can achieve a better predictive performance when dealing with abnormal taxi-out time states.

  1. PREDICTED SIZES OF PRESSURE-SUPPORTED HI CLOUDS IN THE OUTSKIRTS OF THE VIRGO CLUSTER

    Energy Technology Data Exchange (ETDEWEB)

    Burkhart, Blakesley; Loeb, Abraham [Harvard-Smithsonian Center for Astrophysics, 60 Garden St. Cambridge, MA (United States)

    2016-06-10

    Using data from the ALFALFA AGES Arecibo HI survey of galaxies and the Virgo cluster X-ray pressure profiles from XMM-Newton , we investigate the possibility that starless dark HI clumps, also known as “dark galaxies,” are supported by external pressure in the surrounding intercluster medium. We find that the starless HI clump masses, velocity dispersions, and positions allow these clumps to be in pressure equilibrium with the X-ray gas near the virial radius of the Virgo cluster. We predict the sizes of these clumps to range from 1 to 10 kpc, in agreement with the range of sizes found for spatially resolved HI starless clumps outside of Virgo. Based on the predicted HI surface density of the Virgo sources, as well as a sample of other similar resolved ALFALFA HI dark clumps with follow-up optical/radio observations, we predict that most of the HI dark clumps are on the cusp of forming stars. These HI sources therefore mark the transition between starless HI clouds and dwarf galaxies with stars.

  2. Inspection of Asian Lacquer Substructures by Terahertz Time-Domain Imaging (THz-TDI)

    Science.gov (United States)

    Dandolo, Corinna Ludovica Koch; Fukunaga, Kaori; Kohzuma, Yoshei; Kiriyama, Kyoko; Matsuda, Kazutaka; Jepsen, Peter Uhd

    2017-04-01

    Lacquering is considered one of the most representative Asian artistic techniques. While the decorative part of lacquerwares is the lacquer itself, their substructures serve as the backbone of the object itself. Very little is known about these hidden substructures. Since lacquerwares are mostly composed of organic materials, such as urushi, wood, carbon black, and fabrics which are very X-ray transparent, standard X-ray radiography has some problems in achieving clear X-ray radiographic images. Therefore, we wanted to contribute to the understanding of the lacquer manufacturing technique by inspecting the substructures of Asian lacquerwares by means of THz time-domain imaging (THz-TDI). Three different kinds of Asian lacquerwares were examined by THz-TDI, and the outcomes have been compared with those obtained by standard X-radiography. THz-TDI provides unique information on lacquerwares substructures, aiding in the comprehension of the manufacturing technology yielding to these precious artefacts.

  3. Analysis of the state of the art of precast concrete bridge substructure systems.

    Science.gov (United States)

    2013-10-01

    Precasting of bridge substructure components holds potential for accelerating the construction of bridges,reducing : impacts to the traveling public on routes adjacent to construction sites, improving bridge durability and hence service : life, and r...

  4. A finite element based substructuring procedure for design analysis of large smart structural systems

    International Nuclear Information System (INIS)

    Ashwin, U; Raja, S; Dwarakanathan, D

    2009-01-01

    A substructuring based design analysis procedure is presented for large smart structural system using the Craig–Bampton method. The smart structural system is distinctively characterized as an active substructure, modelled as a design problem, and a passive substructure, idealized as an analysis problem. Furthermore, a novel thought has been applied by introducing the electro–elastic coupling into the reduction scheme to solve the global structural control problem in a local domain. As an illustration, a smart composite box beam with surface bonded actuators/sensors is considered, and results of the local to global control analysis are presented to show the potential use of the developed procedure. The present numerical scheme is useful for optimally designing the active substructures to study their locations, coupled structure–actuator interaction and provide a solution to the global design of large smart structural systems

  5. Prediction of ttt curves of cold working tool steels using support vector machine model

    Science.gov (United States)

    Pillai, Nandakumar; Karthikeyan, R., Dr.

    2018-04-01

    The cold working tool steels are of high carbon steels with metallic alloy additions which impart higher hardenability, abrasion resistance and less distortion in quenching. The microstructure changes occurring in tool steel during heat treatment is of very much importance as the final properties of the steel depends upon these changes occurred during the process. In order to obtain the desired performance the alloy constituents and its ratio plays a vital role as the steel transformation itself is complex in nature and depends very much upon the time and temperature. The proper treatment can deliver satisfactory results, at the same time process deviation can completely spoil the results. So knowing time temperature transformation (TTT) of phases is very critical which varies for each type depending upon its constituents and proportion range. To obtain adequate post heat treatment properties the percentage of retained austenite should be lower and metallic carbides obtained should be fine in nature. Support vector machine is a computational model which can learn from the observed data and use these to predict or solve using mathematical model. Back propagation feedback network will be created and trained for further solutions. The points on the TTT curve for the known transformations curves are used to plot the curves for different materials. These data will be trained to predict TTT curves for other steels having similar alloying constituents but with different proportion range. The proposed methodology can be used for prediction of TTT curves for cold working steels and can be used for prediction of phases for different heat treatment methods.

  6. Jet substructure and probes of CP violation in Vh production

    Energy Technology Data Exchange (ETDEWEB)

    Godbole, R.M. [Centre for High Energy Physics, Indian Institute of Science,Sir C.V. Raman Road, Bangalore 560012 (India); Miller, D.J. [School of Physics and Astronomy, Scottish Universities Physics Alliance, University of Glasgow,University Avenue, Glasgow G12 8QQ, Scotland (United Kingdom); Mohan, K.A. [Centre for High Energy Physics, Indian Institute of Science,Sir C.V. Raman Road, Bangalore 560012 (India); White, C.D. [School of Physics and Astronomy, Scottish Universities Physics Alliance, University of Glasgow,University Avenue, Glasgow G12 8QQ, Scotland (United Kingdom)

    2015-04-20

    We analyse the hVV (V=W,Z) vertex in a model independent way using Vh production. To that end, we consider possible corrections to the Standard Model Higgs Lagrangian, in the form of higher dimensional operators which parametrise the effects of new physics. In our analysis, we pay special attention to linear observables that can be used to probe CP violation in the same. By considering the associated production of a Higgs boson with a vector boson (W or Z), we use jet substructure methods to define angular observables which are sensitive to new physics effects, including an asymmetry which is linearly sensitive to the presence of CP odd effects. We demonstrate how to use these observables to place bounds on the presence of higher dimensional operators, and quantify these statements using a log likelihood analysis. Our approach allows one to probe separately the hZZ and hWW vertices, involving arbitrary combinations of BSM operators, at the Large Hadron Collider.

  7. Quark substructure approach to 4He charge distribution

    International Nuclear Information System (INIS)

    Wilets, L.; Alberg, M.A.; Pepin, S.; Stancu, F.; Carlson, J.; Koepf, W.

    1997-01-01

    We present a study of the 4 He charge distribution based on realistic nucleonic wave functions and incorporation of quark substructure. Any central depression of the proton point density seen in modern four-body calculations is too small by itself to lead to a correct description of the charge distribution of 4 He if folded with a fixed proton size parameter, as is usually done. We utilize six-quark structures calculated in the chromodielectric model for N-N interactions to find a swelling of the proton size as the internucleon distance decreases. This swelling is a result of the short-range dynamics in the N-N system. Using the independent pair approximation, the corresponding charge distribution of the proton is folded with the two-nucleon distribution generated from Green's function Monte Carlo calculations of the 4 He nucleonic wave function. We obtain a reasonably good fit to the experimental charge distribution of 4 He. Meson-exchange currents have not been included. copyright 1997 The American Physical Society

  8. Solving the Mystery of Galaxy Bulges and Bulge Substructure

    Science.gov (United States)

    Erwin, Peter

    2017-08-01

    Understanding galaxy bulges is crucial for understanding galaxy evolution and the growth of supermassive black holes (SMBHs). Recent studies have shown that at least some - perhaps most - disk-galaxy bulges are actually composite structures, with both classical-bulge (spheroid) and pseudobulge (disky) components; this calls into question the standard practice of using simple, low-resolution bulge/disk decompositions to determine spheroid and SMBH mass functions. We propose WFC3 optical and near-IR imaging of a volume- and mass-limited sample of local disk galaxies to determine the full range of pure-classical, pure-pseudobulge, and composite-bulge frequencies and parameters, including stellar masses for classical bulges, disky pseudobulges, and boxy/peanut-shaped bulges. We will combine this with ground-based spectroscopy to determine the stellar-kinematic and population characteristics of the different substructures revealed by our WFC3 imaging. This will help resolve growing uncertainties about the status and nature of bulges and their relation to SMBH masses, and will provide an essential local-universe reference for understanding bulge (and SMBH) formation and evolution.

  9. Transpiration cooling assisted ablative thermal protection of aerospace substructures

    International Nuclear Information System (INIS)

    Khan, M.B.; Iqbal, N.; Haider, Z.

    2009-01-01

    Ablatives are heat-shielding materials used to protect aerospace substructures. These materials are sacrificial in nature and provide protection primarily through the large endothermic transformation during exposure to hyper thermal environment such as encountered in re-entry modules. The performance of certain ablatives was reported in terms of their TGA/DTA in Advanced Materials-97 (pp 57-65). The focus of this earlier research resided in the consolidation of interface between the refractory inclusion and the host polymeric matrix to improve thermal resistance. In the present work we explore the scope of transpiration cooling in ablative performance through flash evaporation of liquid incorporated in the host EPDM (Ethylene Propylene Diene Monomer) matrix. The compression-molded specimens were exposed separately to plasma flame (15000 C) and oxyacetylene torch (3000 C) and the back face transient temperature is recorded in situ employing a thermocouple/data logger system. Both head on impingement (HOI) and parallel flow (PF) through a central cavity in the ablator were used. It is observed that transpiration cooling is effective and yields (a) rapid thermal equilibrium in the specimen, (b) lower back face temperature and (c) lower ablation rate, compared to conventional ablatives. SEM/EDS analysis is presented to amplify the point. (author)

  10. Revealing dark matter substructure with anisotropies in the diffuse gamma-ray background

    OpenAIRE

    Siegal-Gaskins, Jennifer M.

    2008-01-01

    The majority of gamma-ray emission from Galactic dark matter annihilation is likely to be detected as a contribution to the diffuse gamma-ray background. I show that dark matter substructure in the halo of the Galaxy induces characteristic anisotropies in the diffuse background that could be used to determine the small-scale dark matter distribution. I calculate the angular power spectrum of the emission from dark matter substructure for several models of the subhalo population, and show that...

  11. Observation of lateral substructures in EAS by measurement of the time distribution of atmospheric Cerenkov light

    International Nuclear Information System (INIS)

    Bosia, G.; Navarra, G.; Saavedra, O.

    1975-01-01

    The lateral structure of EAS is derived from the arrival time distribution of atmospheric Cerenkov light assuming a strict correlation between time structure and lateral particle distribution. Results of the Pic du Midi experiment are presented. Substructures in the time distribution of the Cerenkov light can be related to structures in the lateral density distribution of electrons. The frequency (a few %) of substructures can be explained within conventional models of high energy interactions. (orig.) [de

  12. A NEW METHOD TO QUANTIFY X-RAY SUBSTRUCTURES IN CLUSTERS OF GALAXIES

    Energy Technology Data Exchange (ETDEWEB)

    Andrade-Santos, Felipe; Lima Neto, Gastao B.; Lagana, Tatiana F. [Departamento de Astronomia, Instituto de Astronomia, Geofisica e Ciencias Atmosfericas, Universidade de Sao Paulo, Geofisica e Ciencias Atmosfericas, Rua do Matao 1226, Cidade Universitaria, 05508-090 Sao Paulo, SP (Brazil)

    2012-02-20

    We present a new method to quantify substructures in clusters of galaxies, based on the analysis of the intensity of structures. This analysis is done in a residual image that is the result of the subtraction of a surface brightness model, obtained by fitting a two-dimensional analytical model ({beta}-model or Sersic profile) with elliptical symmetry, from the X-ray image. Our method is applied to 34 clusters observed by the Chandra Space Telescope that are in the redshift range z in [0.02, 0.2] and have a signal-to-noise ratio (S/N) greater than 100. We present the calibration of the method and the relations between the substructure level with physical quantities, such as the mass, X-ray luminosity, temperature, and cluster redshift. We use our method to separate the clusters in two sub-samples of high- and low-substructure levels. We conclude, using Monte Carlo simulations, that the method recuperates very well the true amount of substructure for small angular core radii clusters (with respect to the whole image size) and good S/N observations. We find no evidence of correlation between the substructure level and physical properties of the clusters such as gas temperature, X-ray luminosity, and redshift; however, analysis suggest a trend between the substructure level and cluster mass. The scaling relations for the two sub-samples (high- and low-substructure level clusters) are different (they present an offset, i.e., given a fixed mass or temperature, low-substructure clusters tend to be more X-ray luminous), which is an important result for cosmological tests using the mass-luminosity relation to obtain the cluster mass function, since they rely on the assumption that clusters do not present different scaling relations according to their dynamical state.

  13. Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

    Directory of Open Access Journals (Sweden)

    Gabere MN

    2016-06-01

    Full Text Available Musa Nur Gabere,1 Mohamed Aly Hussein,1 Mohammad Azhar Aziz2 1Department of Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 2Colorectal Cancer Research Program, Department of Medical Genomics, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia Purpose: There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC. The selection of important features is a crucial step before training a classifier.Methods: In this study, we built a model that uses support vector machine (SVM to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid.Results: The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF, Bayes net (BN, multilayer perceptron (MLP, naïve Bayes (NB, reduced error pruning tree (REPT, and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP. Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1

  14. Effects of subordinate feedback to the supervisor and participation in decision-making in the prediction of organizational support.

    Science.gov (United States)

    1992-03-01

    The present study tested the hypothesis that participation in decision-making (PDM) and perceived effectiveness of subordinate feedback to the supervisor would contribute unique variance in the prediction of perceptions of organizational support. In ...

  15. Systematic benchmark of substructure search in molecular graphs - From Ullmann to VF2

    Directory of Open Access Journals (Sweden)

    Ehrlich Hans-Christian

    2012-07-01

    Full Text Available Abstract Background Searching for substructures in molecules belongs to the most elementary tasks in cheminformatics and is nowadays part of virtually every cheminformatics software. The underlying algorithms, used over several decades, are designed for the application to general graphs. Applied on molecular graphs, little effort has been spend on characterizing their performance. Therefore, it is not clear how current substructure search algorithms behave on such special graphs. One of the main reasons why such an evaluation was not performed in the past was the absence of appropriate data sets. Results In this paper, we present a systematic evaluation of Ullmann’s and the VF2 subgraph isomorphism algorithms on molecular data. The benchmark set consists of a collection of 1235 SMARTS substructure expressions and selected molecules from the ZINC database. The benchmark evaluates substructures search times for complete database scans as well as individual substructure-molecule pairs. In detail, we focus on the influence of substructure formulation and size, the impact of molecule size, and the ability of both algorithms to be used on multiple cores. Conclusions The results show a clear superiority of the VF2 algorithm in all test scenarios. In general, both algorithms solve most instances in less than one millisecond, which we consider to be acceptable. Still, in direct comparison, the VF2 is most often several folds faster than Ullmann’s algorithm. Additionally, Ullmann’s algorithm shows a surprising number of run time outliers.

  16. Improving substructure identification accuracy of shear structures using virtual control system

    Science.gov (United States)

    Zhang, Dongyu; Yang, Yang; Wang, Tingqiang; Li, Hui

    2018-02-01

    Substructure identification is a powerful tool to identify the parameters of a complex structure. Previously, the authors developed an inductive substructure identification method for shear structures. The identification error analysis showed that the identification accuracy of this method is significantly influenced by the magnitudes of two key structural responses near a certain frequency; if these responses are unfavorable, the method cannot provide accurate estimation results. In this paper, a novel method is proposed to improve the substructure identification accuracy by introducing a virtual control system (VCS) into the structure. A virtual control system is a self-balanced system, which consists of some control devices and a set of self-balanced forces. The self-balanced forces counterbalance the forces that the control devices apply on the structure. The control devices are combined with the structure to form a controlled structure used to replace the original structure in the substructure identification; and the self-balance forces are treated as known external excitations to the controlled structure. By optimally tuning the VCS’s parameters, the dynamic characteristics of the controlled structure can be changed such that the original structural responses become more favorable for the substructure identification and, thus, the identification accuracy is improved. A numerical example of 6-story shear structure is utilized to verify the effectiveness of the VCS based controlled substructure identification method. Finally, shake table tests are conducted on a 3-story structural model to verify the efficacy of the VCS to enhance the identification accuracy of the structural parameters.

  17. Analysis and application of European genetic substructure using 300 K SNP information.

    Directory of Open Access Journals (Sweden)

    Chao Tian

    2008-01-01

    Full Text Available European population genetic substructure was examined in a diverse set of >1,000 individuals of European descent, each genotyped with >300 K SNPs. Both STRUCTURE and principal component analyses (PCA showed the largest division/principal component (PC differentiated northern from southern European ancestry. A second PC further separated Italian, Spanish, and Greek individuals from those of Ashkenazi Jewish ancestry as well as distinguishing among northern European populations. In separate analyses of northern European participants other substructure relationships were discerned showing a west to east gradient. Application of this substructure information was critical in examining a real dataset in whole genome association (WGA analyses for rheumatoid arthritis in European Americans to reduce false positive signals. In addition, two sets of European substructure ancestry informative markers (ESAIMs were identified that provide substantial substructure information. The results provide further insight into European population genetic substructure and show that this information can be used for improving error rates in association testing of candidate genes and in replication studies of WGA scans.

  18. Substructural Regularization With Data-Sensitive Granularity for Sequence Transfer Learning.

    Science.gov (United States)

    Sun, Shichang; Liu, Hongbo; Meng, Jiana; Chen, C L Philip; Yang, Yu

    2018-06-01

    Sequence transfer learning is of interest in both academia and industry with the emergence of numerous new text domains from Twitter and other social media tools. In this paper, we put forward the data-sensitive granularity for transfer learning, and then, a novel substructural regularization transfer learning model (STLM) is proposed to preserve target domain features at substructural granularity in the light of the condition of labeled data set size. Our model is underpinned by hidden Markov model and regularization theory, where the substructural representation can be integrated as a penalty after measuring the dissimilarity of substructures between target domain and STLM with relative entropy. STLM can achieve the competing goals of preserving the target domain substructure and utilizing the observations from both the target and source domains simultaneously. The estimation of STLM is very efficient since an analytical solution can be derived as a necessary and sufficient condition. The relative usability of substructures to act as regularization parameters and the time complexity of STLM are also analyzed and discussed. Comprehensive experiments of part-of-speech tagging with both Brown and Twitter corpora fully justify that our model can make improvements on all the combinations of source and target domains.

  19. An algebraic sub-structuring method for large-scale eigenvalue calculation

    International Nuclear Information System (INIS)

    Yang, C.; Gao, W.; Bai, Z.; Li, X.; Lee, L.; Husbands, P.; Ng, E.

    2004-01-01

    We examine sub-structuring methods for solving large-scale generalized eigenvalue problems from a purely algebraic point of view. We use the term 'algebraic sub-structuring' to refer to the process of applying matrix reordering and partitioning algorithms to divide a large sparse matrix into smaller submatrices from which a subset of spectral components are extracted and combined to provide approximate solutions to the original problem. We are interested in the question of which spectral components one should extract from each sub-structure in order to produce an approximate solution to the original problem with a desired level of accuracy. Error estimate for the approximation to the smallest eigenpair is developed. The estimate leads to a simple heuristic for choosing spectral components (modes) from each sub-structure. The effectiveness of such a heuristic is demonstrated with numerical examples. We show that algebraic sub-structuring can be effectively used to solve a generalized eigenvalue problem arising from the simulation of an accelerator structure. One interesting characteristic of this application is that the stiffness matrix produced by a hierarchical vector finite elements scheme contains a null space of large dimension. We present an efficient scheme to deflate this null space in the algebraic sub-structuring process

  20. Sensitivity of nonlinear photoionization to resonance substructure in collective excitation

    Science.gov (United States)

    Mazza, T.; Karamatskou, A.; Ilchen, M.; Bakhtiarzadeh, S.; Rafipoor, A. J.; O'Keeffe, P.; Kelly, T. J.; Walsh, N.; Costello, J. T.; Meyer, M.; Santra, R.

    2015-01-01

    Collective behaviour is a characteristic feature in many-body systems, important for developments in fields such as magnetism, superconductivity, photonics and electronics. Recently, there has been increasing interest in the optically nonlinear response of collective excitations. Here we demonstrate how the nonlinear interaction of a many-body system with intense XUV radiation can be used as an effective probe for characterizing otherwise unresolved features of its collective response. Resonant photoionization of atomic xenon was chosen as a case study. The excellent agreement between experiment and theory strongly supports the prediction that two distinct poles underlie the giant dipole resonance. Our results pave the way towards a deeper understanding of collective behaviour in atoms, molecules and solid-state systems using nonlinear spectroscopic techniques enabled by modern short-wavelength light sources. PMID:25854939

  1. Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study

    Science.gov (United States)

    Al-Anazi, A. F.; Gates, I. D.

    2010-12-01

    In wells with limited log and core data, porosity, a fundamental and essential property to characterize reservoirs, is challenging to estimate by conventional statistical methods from offset well log and core data in heterogeneous formations. Beyond simple regression, neural networks have been used to develop more accurate porosity correlations. Unfortunately, neural network-based correlations have limited generalization ability and global correlations for a field are usually less accurate compared to local correlations for a sub-region of the reservoir. In this paper, support vector machines are explored as an intelligent technique to correlate porosity to well log data. Recently, support vector regression (SVR), based on the statistical learning theory, have been proposed as a new intelligence technique for both prediction and classification tasks. The underlying formulation of support vector machines embodies the structural risk minimization (SRM) principle which has been shown to be superior to the traditional empirical risk minimization (ERM) principle employed by conventional neural networks and classical statistical methods. This new formulation uses margin-based loss functions to control model complexity independently of the dimensionality of the input space, and kernel functions to project the estimation problem to a higher dimensional space, which enables the solution of more complex nonlinear problem optimization methods to exist for a globally optimal solution. SRM minimizes an upper bound on the expected risk using a margin-based loss function ( ɛ-insensitivity loss function for regression) in contrast to ERM which minimizes the error on the training data. Unlike classical learning methods, SRM, indexed by margin-based loss function, can also control model complexity independent of dimensionality. The SRM inductive principle is designed for statistical estimation with finite data where the ERM inductive principle provides the optimal solution (the

  2. Predicting metabolic syndrome using decision tree and support vector machine methods

    Directory of Open Access Journals (Sweden)

    Farzaneh Karimi-Alavijeh

    2016-06-01

    Full Text Available BACKGROUND: Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. METHODS: This study aims to employ decision tree and support vector machine (SVM to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP, diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs, total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. RESULTS: SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758, 0.74 (0.72 and 0.757 (0.739 in SVM (decision tree method. CONCLUSION: The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most

  3. Predicting metabolic syndrome using decision tree and support vector machine methods.

    Science.gov (United States)

    Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

    2016-05-01

    Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According

  4. Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Chi-Man Vong

    2012-01-01

    Full Text Available Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs, a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.

  5. Selective enantioseparation of levocetirizine via a hollow fiber supported liquid membrane and mass transfer prediction

    International Nuclear Information System (INIS)

    Sunsandee, Niti; Leepipatpiboon, Natchanun; Ramakul, Prakorn

    2013-01-01

    The enantioselective separation of levocetirizine via a hollow fiber supported liquid membrane was examined. O,O'-dibenzoyl-(2R,3R)-tartaric acid ((-)-DBTA) diluted in 1-decanol was used as a chiral selector extractant. The influence of concentrations of feed and stripping phases, and extractant concentration in the membrane phase, was also investigated. A mathematical model focusing on the extraction side of the liquid membrane system was presented to predict the concentration of levocetirizine at different times. The extraction and recovery of levocetirizine from feed phase were 75.00% and 72.00%, respectively. The mass transfer coefficients at aqueous feed boundary layer (k_f) and the organic liquid membrane phase (k_m) were calculated as 2.41x10"2 and 1.89x10"2 cm/s, respectively. The validity of the developed model was evaluated through a comparison with experimental data, and good agreement was obtained

  6. Topological self-organization and prediction learning support both action and lexical chains in the brain.

    Science.gov (United States)

    Chersi, Fabian; Ferro, Marcello; Pezzulo, Giovanni; Pirrelli, Vito

    2014-07-01

    A growing body of evidence in cognitive psychology and neuroscience suggests a deep interconnection between sensory-motor and language systems in the brain. Based on recent neurophysiological findings on the anatomo-functional organization of the fronto-parietal network, we present a computational model showing that language processing may have reused or co-developed organizing principles, functionality, and learning mechanisms typical of premotor circuit. The proposed model combines principles of Hebbian topological self-organization and prediction learning. Trained on sequences of either motor or linguistic units, the network develops independent neuronal chains, formed by dedicated nodes encoding only context-specific stimuli. Moreover, neurons responding to the same stimulus or class of stimuli tend to cluster together to form topologically connected areas similar to those observed in the brain cortex. Simulations support a unitary explanatory framework reconciling neurophysiological motor data with established behavioral evidence on lexical acquisition, access, and recall. Copyright © 2014 Cognitive Science Society, Inc.

  7. Selective enantioseparation of levocetirizine via a hollow fiber supported liquid membrane and mass transfer prediction

    Energy Technology Data Exchange (ETDEWEB)

    Sunsandee, Niti [Government Pharmaceutical Organization, Bangkok (Thailand); Leepipatpiboon, Natchanun [Chulalongkorn University, Bangkok (Thailand); Ramakul, Prakorn [Silpakorn University, Nakhon Pathom (Thailand)

    2013-06-15

    The enantioselective separation of levocetirizine via a hollow fiber supported liquid membrane was examined. O,O'-dibenzoyl-(2R,3R)-tartaric acid ((-)-DBTA) diluted in 1-decanol was used as a chiral selector extractant. The influence of concentrations of feed and stripping phases, and extractant concentration in the membrane phase, was also investigated. A mathematical model focusing on the extraction side of the liquid membrane system was presented to predict the concentration of levocetirizine at different times. The extraction and recovery of levocetirizine from feed phase were 75.00% and 72.00%, respectively. The mass transfer coefficients at aqueous feed boundary layer (k{sub f}) and the organic liquid membrane phase (k{sub m}) were calculated as 2.41x10{sup 2} and 1.89x10{sup 2} cm/s, respectively. The validity of the developed model was evaluated through a comparison with experimental data, and good agreement was obtained.

  8. Boosting the charged Higgs search prospects using jet substructure at the LHC

    Energy Technology Data Exchange (ETDEWEB)

    Li, Jinmian [Center of Excellence for Particle Physics at Terascale, University of Adelaide,Adelaide, 5005 South (Australia); School of Physics, Korea Institute for Advanced Study,Seoul, 130-722 (Korea, Republic of); Patrick, Riley; Sharma, Pankaj; Williams, Anthony G. [Center of Excellence for Particle Physics at Terascale, University of Adelaide,Adelaide, 5005 South (Australia)

    2016-11-28

    Charged Higgs bosons are predicted in variety of theoretically well-motivated new physics models with extended Higgs sectors. In this study, we focus on a type-II two Higgs doublet model (2HDM-II) and consider a heavy charged Higgs with its mass ranging from 500 GeV to 1 TeV as dictated by the b→sγ constraints which render M{sub H{sup ±}}>480 GeV. We study the dominant production mode H{sup ±}t associated production with H{sup ±}→W{sup ±}A being the dominant decay channel when the pseudoscalar A is considerably lighter. For such a heavy charged Higgs, both the decay products W{sup ±} and A are relatively boosted. In such a scenario, we apply the jet substructure analysis of tagging the fat pseudoscalar and W jets in order to eliminate the standard model background efficiently. We perform a detailed detector simulation for the signal and background processes at the 14 TeV LHC. We introduce various kinematical cuts to determine the signal significance for a number of benchmark points with charged Higgs boson mass from 500 GeV to 1 TeV in the W{sup ±}A decay channel. Finally we perform a multivariate analysis utilizing a boosted decision tree algorithm to optimize these significances.

  9. Nuclear substructure reorganization during late stageerythropoiesis is selective and does not involve caspase cleavage ofmajor nuclear substructural proteins

    Energy Technology Data Exchange (ETDEWEB)

    Krauss, Sharon Wald; Lo, Annie J.; Short, Sarah A.; Koury, MarkJ.; Mohandas, Narla; Chasis, Joel Anne

    2005-04-06

    Enucleation, a rare feature of mammalian differentiation, occurs in three cell types: erythroblasts, lens epithelium and keratinocytes. Previous investigations suggest that caspase activation functions in lens epithelial and keratinocyte enucleation, as well as in early erythropoiesis encompassing BFU-E differentiation to proerythroblast. To determine whether caspase activation contributes to later erythropoiesis and whether nuclear substructures other than chromatin reorganize, we analyzed distributions of nuclear subcompartment proteins and assayed for caspase-induced cleavage of subcompartmental target proteins in mouse erythroblasts. We found that patterns of lamin B in the filamentous network interacting with both the nuclear envelope and DNA, nuclear matrix protein NuMA, and splicing factors Sm and SC35 persisted during nuclear condensation, consistent with effective transcription of genes expressed late in differentiation. Thus nuclear reorganization prior to enucleation is selective, allowing maintenance of critical transcriptional processes independent of extensive chromosomal reorganization. Consistent with these data, we found no evidence for caspase-induced cleavage of major nuclear subcompartment proteins during late erythropoiesis, in contrast to what has been observed in early erythropoiesis and in lens epithelial and keratinocyte differentiation. These findings imply that nuclear condensation and extrusion during terminal erythroid differentiation involve novel mechanisms that do not entail major activation of apoptotic machinery.

  10. A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction

    Directory of Open Access Journals (Sweden)

    Daqing Zhang

    2015-01-01

    Full Text Available Blood-brain barrier (BBB is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.

  11. Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Shahrbanoo Goli

    2016-01-01

    Full Text Available The Support Vector Regression (SVR model has been broadly used for response prediction. However, few researchers have used SVR for survival analysis. In this study, a new SVR model is proposed and SVR with different kernels and the traditional Cox model are trained. The models are compared based on different performance measures. We also select the best subset of features using three feature selection methods: combination of SVR and statistical tests, univariate feature selection based on concordance index, and recursive feature elimination. The evaluations are performed using available medical datasets and also a Breast Cancer (BC dataset consisting of 573 patients who visited the Oncology Clinic of Hamadan province in Iran. Results show that, for the BC dataset, survival time can be predicted more accurately by linear SVR than nonlinear SVR. Based on the three feature selection methods, metastasis status, progesterone receptor status, and human epidermal growth factor receptor 2 status are the best features associated to survival. Also, according to the obtained results, performance of linear and nonlinear kernels is comparable. The proposed SVR model performs similar to or slightly better than other models. Also, SVR performs similar to or better than Cox when all features are included in model.

  12. Transport and stability analyses supporting disruption prediction in high beta KSTAR plasmas

    Science.gov (United States)

    Ahn, J.-H.; Sabbagh, S. A.; Park, Y. S.; Berkery, J. W.; Jiang, Y.; Riquezes, J.; Lee, H. H.; Terzolo, L.; Scott, S. D.; Wang, Z.; Glasser, A. H.

    2017-10-01

    KSTAR plasmas have reached high stability parameters in dedicated experiments, with normalized beta βN exceeding 4.3 at relatively low plasma internal inductance li (βN/li>6). Transport and stability analyses have begun on these plasmas to best understand a disruption-free path toward the design target of βN = 5 while aiming to maximize the non-inductive fraction of these plasmas. Initial analysis using the TRANSP code indicates that the non-inductive current fraction in these plasmas has exceeded 50 percent. The advent of KSTAR kinetic equilibrium reconstructions now allows more accurate computation of the MHD stability of these plasmas. Attention is placed on code validation of mode stability using the PEST-3 and resistive DCON codes. Initial evaluation of these analyses for disruption prediction is made using the disruption event characterization and forecasting (DECAF) code. The present global mode kinetic stability model in DECAF developed for low aspect ratio plasmas is evaluated to determine modifications required for successful disruption prediction of KSTAR plasmas. Work supported by U.S. DoE under contract DE-SC0016614.

  13. Prediction of hydrogen and carbon chemical shifts from RNA using database mining and support vector regression

    Energy Technology Data Exchange (ETDEWEB)

    Brown, Joshua D.; Summers, Michael F. [University of Maryland Baltimore County, Howard Hughes Medical Institute (United States); Johnson, Bruce A., E-mail: bruce.johnson@asrc.cuny.edu [University of Maryland Baltimore County, Department of Chemistry and Biochemistry (United States)

    2015-09-15

    The Biological Magnetic Resonance Data Bank (BMRB) contains NMR chemical shift depositions for over 200 RNAs and RNA-containing complexes. We have analyzed the {sup 1}H NMR and {sup 13}C chemical shifts reported for non-exchangeable protons of 187 of these RNAs. Software was developed that downloads BMRB datasets and corresponding PDB structure files, and then generates residue-specific attributes based on the calculated secondary structure. Attributes represent properties present in each sequential stretch of five adjacent residues and include variables such as nucleotide type, base-pair presence and type, and tetraloop types. Attributes and {sup 1}H and {sup 13}C NMR chemical shifts of the central nucleotide are then used as input to train a predictive model using support vector regression. These models can then be used to predict shifts for new sequences. The new software tools, available as stand-alone scripts or integrated into the NMR visualization and analysis program NMRViewJ, should facilitate NMR assignment and/or validation of RNA {sup 1}H and {sup 13}C chemical shifts. In addition, our findings enabled the re-calibration a ring-current shift model using published NMR chemical shifts and high-resolution X-ray structural data as guides.

  14. Suicide literacy predicts the provision of more appropriate support to people experiencing psychological distress.

    Science.gov (United States)

    Cruwys, Tegan; An, Soontae; Chang, Melissa Xue-Ling; Lee, Hannah

    2018-06-01

    Mental health literacy has been hailed as a public health priority to reduce stigma and increase help seeking. We examined the effect of suicide literacy on the type of help provided to those experiencing suicidal ideation. A community sample of 363 Australians were randomly assigned to read one of three messages from a member of their social network (the target). The target reported symptoms consistent with either (1) subclinical distress, (2) clinical depression, or (3) suicidal ideation. Participants were most likely to recommend social support and least likely to recommend professional help. Suicide literacy interacted with the target's presentation, such that participants with higher suicide literacy who considered a suicidal target were less likely to recommend self-help or no action, and more likely to recommend professional help. Suicide literacy was also associated with lower suicide stigma, and unexpectedly, this indirectly predicted more reluctance to recommend professional help. Overall, results indicated that the relationship between mental health literacy, stigma, and provision of help is not straightforward. While suicide literacy was associated with greater sensitivity to a person's risk of suicide, it also predicted fewer recommendations for professional help overall, partly due to the stigma associated with seeking professional help. Copyright © 2018 Elsevier B.V. All rights reserved.

  15. Particle swarm optimization-based least squares support vector regression for critical heat flux prediction

    International Nuclear Information System (INIS)

    Jiang, B.T.; Zhao, F.Y.

    2013-01-01

    Highlights: ► CHF data are collected from the published literature. ► Less training data are used to train the LSSVR model. ► PSO is adopted to optimize the key parameters to improve the model precision. ► The reliability of LSSVR is proved through parametric trends analysis. - Abstract: In view of practical importance of critical heat flux (CHF) for design and safety of nuclear reactors, accurate prediction of CHF is of utmost significance. This paper presents a novel approach using least squares support vector regression (LSSVR) and particle swarm optimization (PSO) to predict CHF. Two available published datasets are used to train and test the proposed algorithm, in which PSO is employed to search for the best parameters involved in LSSVR model. The CHF values obtained by the LSSVR model are compared with the corresponding experimental values and those of a previous method, adaptive neuro fuzzy inference system (ANFIS). This comparison is also carried out in the investigation of parametric trends of CHF. It is found that the proposed method can achieve the desired performance and yields a more satisfactory fit with experimental results than ANFIS. Therefore, LSSVR method is likely to be suitable for other parameters processing such as CHF

  16. Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors.

    Science.gov (United States)

    Zhang, Xia; Amin, Elizabeth Ambrose

    2016-01-01

    Anthrax is a highly lethal, acute infectious disease caused by the rod-shaped, Gram-positive bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), a zinc metalloprotease secreted by the bacilli, plays a key role in anthrax pathogenesis and is chiefly responsible for anthrax-related toxemia and host death, partly via inactivation of mitogen-activated protein kinase kinase (MAPKK) enzymes and consequent disruption of key cellular signaling pathways. Antibiotics such as fluoroquinolones are capable of clearing the bacilli but have no effect on LF-mediated toxemia; LF itself therefore remains the preferred target for toxin inactivation. However, currently no LF inhibitor is available on the market as a therapeutic, partly due to the insufficiency of existing LF inhibitor scaffolds in terms of efficacy, selectivity, and toxicity. In the current work, we present novel support vector machine (SVM) models with high prediction accuracy that are designed to rapidly identify potential novel, structurally diverse LF inhibitor chemical matter from compound libraries. These SVM models were trained and validated using 508 compounds with published LF biological activity data and 847 inactive compounds deposited in the Pub Chem BioAssay database. One model, M1, demonstrated particularly favorable selectivity toward highly active compounds by correctly predicting 39 (95.12%) out of 41 nanomolar-level LF inhibitors, 46 (93.88%) out of 49 inactives, and 844 (99.65%) out of 847 Pub Chem inactives in external, unbiased test sets. These models are expected to facilitate the prediction of LF inhibitory activity for existing molecules, as well as identification of novel potential LF inhibitors from large datasets. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Support vector regression to predict porosity and permeability: Effect of sample size

    Science.gov (United States)

    Al-Anazi, A. F.; Gates, I. D.

    2012-02-01

    Porosity and permeability are key petrophysical parameters obtained from laboratory core analysis. Cores, obtained from drilled wells, are often few in number for most oil and gas fields. Porosity and permeability correlations based on conventional techniques such as linear regression or neural networks trained with core and geophysical logs suffer poor generalization to wells with only geophysical logs. The generalization problem of correlation models often becomes pronounced when the training sample size is small. This is attributed to the underlying assumption that conventional techniques employing the empirical risk minimization (ERM) inductive principle converge asymptotically to the true risk values as the number of samples increases. In small sample size estimation problems, the available training samples must span the complexity of the parameter space so that the model is able both to match the available training samples reasonably well and to generalize to new data. This is achieved using the structural risk minimization (SRM) inductive principle by matching the capability of the model to the available training data. One method that uses SRM is support vector regression (SVR) network. In this research, the capability of SVR to predict porosity and permeability in a heterogeneous sandstone reservoir under the effect of small sample size is evaluated. Particularly, the impact of Vapnik's ɛ-insensitivity loss function and least-modulus loss function on generalization performance was empirically investigated. The results are compared to the multilayer perception (MLP) neural network, a widely used regression method, which operates under the ERM principle. The mean square error and correlation coefficients were used to measure the quality of predictions. The results demonstrate that SVR yields consistently better predictions of the porosity and permeability with small sample size than the MLP method. Also, the performance of SVR depends on both kernel function

  18. Adaptive Encoding of Outcome Prediction by Prefrontal Cortex Ensembles Supports Behavioral Flexibility.

    Science.gov (United States)

    Del Arco, Alberto; Park, Junchol; Wood, Jesse; Kim, Yunbok; Moghaddam, Bita

    2017-08-30

    The prefrontal cortex (PFC) is thought to play a critical role in behavioral flexibility by monitoring action-outcome contingencies. How PFC ensembles represent shifts in behavior in response to changes in these contingencies remains unclear. We recorded single-unit activity and local field potentials in the dorsomedial PFC (dmPFC) of male rats during a set-shifting task that required them to update their behavior, among competing options, in response to changes in action-outcome contingencies. As behavior was updated, a subset of PFC ensembles encoded the current trial outcome before the outcome was presented. This novel outcome-prediction encoding was absent in a control task, in which actions were rewarded pseudorandomly, indicating that PFC neurons are not merely providing an expectancy signal. In both control and set-shifting tasks, dmPFC neurons displayed postoutcome discrimination activity, indicating that these neurons also monitor whether a behavior is successful in generating rewards. Gamma-power oscillatory activity increased before the outcome in both tasks but did not differentiate between expected outcomes, suggesting that this measure is not related to set-shifting behavior but reflects expectation of an outcome after action execution. These results demonstrate that PFC neurons support flexible rule-based action selection by predicting outcomes that follow a particular action. SIGNIFICANCE STATEMENT Tracking action-outcome contingencies and modifying behavior when those contingencies change is critical to behavioral flexibility. We find that ensembles of dorsomedial prefrontal cortex neurons differentiate between expected outcomes when action-outcome contingencies change. This predictive mode of signaling may be used to promote a new response strategy at the service of behavioral flexibility. Copyright © 2017 the authors 0270-6474/17/378363-11$15.00/0.

  19. Neither Basic Life Support knowledge nor self-efficacy are predictive of skills among dental students.

    Science.gov (United States)

    Mac Giolla Phadraig, C; Ho, J D; Guerin, S; Yeoh, Y L; Mohamed Medhat, M; Doody, K; Hwang, S; Hania, M; Boggs, S; Nolan, A; Nunn, J

    2017-08-01

    Basic life support (BLS) is considered a core competence for the graduating dentist. This study aimed to measure BLS knowledge, self-efficacy and skills of undergraduate dental students in Dublin. This study consisted of a cross-sectional survey measuring BLS knowledge and self-efficacy, accompanied by a directly observed BLS skills assessment in a subsample of respondents. Data were collected in January 2014. Bivariate correlations between descriptive and outcome variables (knowledge, self-efficacy and skills) were tested using Pearson's chi-square. We included knowledge and self-efficacy as predictor variables, along with other variables showing association, into a binary logistic regression model with BLS skills as the outcome measure. One hundred and thirty-five students participated. Almost all (n = 133, 98.5%) participants had BLS training within the last 2 years. One hundred and four (77%) felt that they were capable of providing effective BLS (self-efficacy), whilst only 46 (34.1%) scored >80% of knowledge items correct. Amongst the skills (n = 85) subsample, 38.8% (n = 33) were found to pass the BLS skills assessment. Controlling for gender, age and skills assessor, the regression model did not identify a predictive relationship between knowledge or self-efficacy and BLS skills. Neither knowledge nor self-efficacy was predictive of BLS skills. Dental students had low levels of knowledge and skills in BLS. Despite this, their confidence in their ability to perform BLS was high and did not predict actual competence. There is a need for additional hands-on training, focusing on self-efficacy and BLS skills, particularly the use of AED. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  20. Personality predicts perceived availability of social support and satisfaction with social support in women with early stage breast cancer

    NARCIS (Netherlands)

    den Oudsten, Brenda L.; van Heck, Guus L.; van der Steeg, Alida F. W.; Roukema, Jan A.; de Vries, Jolanda

    2010-01-01

    This study examines the relationships between personality, on the one hand, and perceived availability of social support (PASS) and satisfaction with received social support (SRSS), on the other hand, in women with early stage breast cancer (BC). In addition, this study examined whether a stressful

  1. Failure and reliability prediction by support vector machines regression of time series data

    International Nuclear Information System (INIS)

    Chagas Moura, Marcio das; Zio, Enrico; Lins, Isis Didier; Droguett, Enrique

    2011-01-01

    Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques. - Highlights: → Realistic modeling of reliability demands complex mathematical formulations. → SVM is proper when the relation input/output is unknown or very costly to be obtained. → Results indicate the potential of SVM for reliability time series prediction. → Reliability estimates support the establishment of adequate maintenance strategies.

  2. Circulating Biologically Active Adrenomedullin (bio-ADM) Predicts Hemodynamic Support Requirement and Mortality During Sepsis.

    Science.gov (United States)

    Caironi, Pietro; Latini, Roberto; Struck, Joachim; Hartmann, Oliver; Bergmann, Andreas; Maggio, Giuseppe; Cavana, Marco; Tognoni, Gianni; Pesenti, Antonio; Gattinoni, Luciano; Masson, Serge

    2017-08-01

    The biological role of adrenomedullin (ADM), a hormone involved in hemodynamic homeostasis, is controversial in sepsis because administration of either the peptide or an antibody against it may be beneficial. Plasma biologically active ADM (bio-ADM) was assessed on days 1, 2, and 7 after randomization of 956 patients with sepsis or septic shock to albumin or crystalloids for fluid resuscitation in the multicenter Albumin Italian Outcome Sepsis trial. We tested the association of bio-ADM and its time-dependent variation with fluid therapy, vasopressor administration, organ failures, and mortality. Plasma bio-ADM on day 1 (median [Q1-Q3], 110 [59-198] pg/mL) was higher in patients with septic shock, associated with 90-day mortality, multiple organ failures and the average extent of hemodynamic support therapy (fluids and vasopressors), and serum lactate time course over the first week. Moreover, it predicted incident cardiovascular dysfunction in patients without shock at enrollment (OR [95% CI], 1.9 [1.4-2.5]; P sepsis, the circulating, biologically active form of ADM may help individualizing hemodynamic support therapy, while avoiding harmful effects. Its possible pathophysiologic role makes bio-ADM a potential candidate for future targeted therapies. ClinicalTrials.gov; No.: NCT00707122. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

  3. Jet substructure using semi-inclusive jet functions in SCET

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Zhong-Bo [Theoretical Division, Los Alamos National Laboratory,Los Alamos, NM 87545 (United States); Department of Physics and Astronomy, University of California,Los Angeles, CA 90095 (United States); Ringer, Felix; Vitev, Ivan [Theoretical Division, Los Alamos National Laboratory,Los Alamos, NM 87545 (United States)

    2016-11-25

    We propose a new method to evaluate jet substructure observables in inclusive jet measurements, based upon semi-inclusive jet functions in the framework of Soft Collinear Effective Theory (SCET). As a first example, we consider the jet fragmentation function, where a hadron h is identified inside a fully reconstructed jet. We introduce a new semi-inclusive fragmenting jet function G{sub i}{sup h}(z=ω{sub J}/ω,z{sub h}=ω{sub h}/ω{sub J},ω{sub J},R,μ), which depends on the jet radius R and the large light-cone momenta of the parton ‘i’ initiating the jet (ω), the jet (ω{sub J}), and the hadron h (ω{sub h}). The jet fragmentation function can then be expressed as a semi-inclusive observable, in the spirit of actual experimental measurements, rather than as an exclusive one. We demonstrate the consistency of the effective field theory treatment and standard perturbative QCD calculations of this observable at next-to-leading order (NLO). The renormalization group (RG) equation for the semi-inclusive fragmenting jet function G{sub i}{sup h}(z,z{sub h},ω{sub J},R,μ) are also derived and shown to follow exactly the usual timelike DGLAP evolution equations for fragmentation functions. The newly obtained RG equations can be used to perform the resummation of single logarithms of the jet radius parameter R up to next-to-leading logarithmic (NLL{sub R}) accuracy. In combination with the fixed NLO calculation, we obtain NLO+NLL{sub R} results for the hadron distribution inside the jet. We present numerical results for pp→(jet h)X in the new framework, and find excellent agreement with existing LHC experimental data.

  4. Jet substructure using semi-inclusive jet functions in SCET

    International Nuclear Information System (INIS)

    Kang, Zhong-Bo; Ringer, Felix; Vitev, Ivan

    2016-01-01

    We propose a new method to evaluate jet substructure observables in inclusive jet measurements, based upon semi-inclusive jet functions in the framework of Soft Collinear Effective Theory (SCET). As a first example, we consider the jet fragmentation function, where a hadron h is identified inside a fully reconstructed jet. We introduce a new semi-inclusive fragmenting jet function G_i"h(z=ω_J/ω,z_h=ω_h/ω_J,ω_J,R,μ), which depends on the jet radius R and the large light-cone momenta of the parton ‘i’ initiating the jet (ω), the jet (ω_J), and the hadron h (ω_h). The jet fragmentation function can then be expressed as a semi-inclusive observable, in the spirit of actual experimental measurements, rather than as an exclusive one. We demonstrate the consistency of the effective field theory treatment and standard perturbative QCD calculations of this observable at next-to-leading order (NLO). The renormalization group (RG) equation for the semi-inclusive fragmenting jet function G_i"h(z,z_h,ω_J,R,μ) are also derived and shown to follow exactly the usual timelike DGLAP evolution equations for fragmentation functions. The newly obtained RG equations can be used to perform the resummation of single logarithms of the jet radius parameter R up to next-to-leading logarithmic (NLL_R) accuracy. In combination with the fixed NLO calculation, we obtain NLO+NLL_R results for the hadron distribution inside the jet. We present numerical results for pp→(jet h)X in the new framework, and find excellent agreement with existing LHC experimental data.

  5. Discovering Higgs Bosons of the MSSM using Jet Substructure

    International Nuclear Information System (INIS)

    Kribs, Graham D.; Martin, Adam; Roy, Tuhin S.; Spannowsky, Michael

    2010-01-01

    We present a qualitatively new approach to discover Higgs bosons of the MSSM at the LHC using jet substructure techniques applied to boosted Higgs decays. These techniques are ideally suited to the MSSM, since the lightest Higgs boson overwhelmingly decays to b(bar b) throughout the entire parameter space, while the heavier neutral Higgs bosons, if light enough to be produced in a cascade, also predominantly decay to b(bar b). The Higgs production we consider arises from superpartner production where superpartners cascade decay into Higgs bosons. We study this mode of Higgs production for several superpartner hierarchies: m # tilde q#,m # tilde g# > m # tilde W#, # tilde B# > m h + μ; m(tilde q);m # tilde q#,m # tilde g# > m # tilde W#, # tilde B# > m h,H,A + μ; and m # tilde q#,m # tilde g# > m # tilde W# > m h + μ with m # tilde B# ∼ μ. In these cascades, the Higgs bosons are boosted, with pT > 200 GeV a large fraction of the time. Since Higgs bosons appear in cascades originating from squarks and/or gluinos, the cross section for events with at least one Higgs boson can be the same order as squark/gluino production. Given 10 fb -1 of 14 TeV LHC data, with m # tilde q# ∼< 1 TeV, and one of the above superpartner mass hierarchies, our estimate of S√ B of the Higgs signal is sufficiently high that the b(bar b) mode can become the discovery mode of the lightest Higgs boson of the MSSM.

  6. Discovering Higgs bosons of the MSSM using jet substructure

    International Nuclear Information System (INIS)

    Kribs, Graham D.; Roy, Tuhin S.; Spannowsky, Michael; Martin, Adam

    2010-01-01

    We present a qualitatively new approach to discover Higgs bosons of the minimal supersymmetric standard model (MSSM) at the LHC using jet substructure techniques applied to boosted Higgs decays. These techniques are ideally suited to the MSSM, since the lightest Higgs boson overwhelmingly decays to bb throughout the entire parameter space, while the heavier neutral Higgs bosons, if light enough to be produced in a cascade, also predominantly decay to bb. The Higgs production we consider arises from superpartner production where superpartners cascade decay into Higgs bosons. We study this mode of Higgs production for several superpartner hierarchies: m q -tilde, m g -tilde>m W -tilde ,B -tilde>m h +μ; m q -tilde, m g -tilde>m W -tilde ,B -tilde>m h,H,A +μ; and m q -tilde, m g -tilde>m W -tilde>m h +μ with m B -tilde≅μ. In these cascades, the Higgs bosons are boosted, with p T >200 GeV a large fraction of the time. Since Higgses appear in cascades originating from squarks and/or gluinos, the cross section for events with at least one Higgs can be the same order as squark/gluino production. Given 10 fb -1 of 14 TeV LHC data, with m q -tilde < or approx. 1 TeV, and one of the above superpartner mass hierarchies, our estimate of S/√(B) of the Higgs signal is sufficiently high that the bb mode can become the discovery mode of the lightest Higgs boson of the MSSM.

  7. Home Away Home: Better Understanding of the Role of Social Support in Predicting Cross-Cultural Adjustment among International Students

    Science.gov (United States)

    Baba, Yoko; Hosoda, Megumi

    2014-01-01

    Numerous studies have examined international students' adjustment problems, yet, these studies have not explored the mechanisms through which social support operates in the context of stressful events in predicting cross-cultural adjustment among international students. Using Barrera's (1988) models of social support, the present study…

  8. Mass-stiffness substructuring of an elastic metasurface for full transmission beam steering

    Science.gov (United States)

    Lee, Hyuk; Lee, Jun Kyu; Seung, Hong Min; Kim, Yoon Young

    2018-03-01

    The metasurface concept has a significant potential due to its novel wavefront-shaping functionalities that can be critically useful for ultrasonic and solid wave-based applications. To achieve the desired functionalities, elastic metasurfaces should cover full 2π phase shift and also acquire full transmission within subwavelength scale. However, they have not been explored much with respect to the elastic regime, because the intrinsic proportionality of mass-stiffness within the continuum elastic media causes an inevitable trade-off between abrupt phase shift and sufficient transmission. Our goal is to engineer an elastic metasurface that can realize an inverse relation between (amplified) effective mass and (weakened) stiffness in order to satisfy full 2π phase shift as well as full transmission. To achieve this goal, we propose a continuum elastic metasurface unit cell that is decomposed into two substructures, namely a mass-tuning substructure with a local dipolar resonator and a stiffness-tuning substructure composed of non-resonant multiply-perforated slits. We demonstrate analytically, numerically, and experimentally that this unique substructured unit cell can satisfy the required phase shift with high transmission. The substructuring enables independent tuning of the elastic properties over a wide range of values. We use a mass-spring model of the proposed continuum unit cell to investigate the working mechanism of the proposed metasurface. With the designed metasurface consisting of substructured unit cells embedded in an aluminum plate, we demonstrate that our metasurface can successfully realize anomalous steering and focusing of in-plane longitudinal ultrasonic beams. The proposed substructuring concept is expected to provide a new principle for the design of general elastic metasurfaces that can be used to efficiently engineer arbitrary wave profiles.

  9. Decision support system in Predicting the Best teacher with Multi Atribute Decesion Making Weighted Product (MADMWP Method

    Directory of Open Access Journals (Sweden)

    Solikhun Solikhun

    2017-06-01

    Full Text Available Predicting of the best teacher in Indonesia aims to spur the development of the growth and improve the quality of the education. In this paper, the predicting  of the best teacher is implemented based on predefined criteria. To help the predicting process, a decision support system is needed. This paper employs Multi Atribute Decesion Making Weighted Product (MADMWP method. The result of this method is tested some teachers in  junior high school islamic boarding Al-Barokah school, Simalungun, North Sumatera, Indonesia. This system can be used to help in solving problems of the best teacher prediction.

  10. Prediction of First-Order Vessel Responses with Applications to Decision Support Systems

    DEFF Research Database (Denmark)

    Nielsen, Ulrik D.; Iseki, Toshio

    2015-01-01

    The paper presents a practical and simple approach for making vessel response predictions. Features of the procedure include a) predictions which are scaled so to better agree with corresponding true, future values to be measured at the time the predictions apply at; and b) predictions that are a...

  11. The angular power spectrum of the diffuse gamma-ray background as a probe of Galactic dark matter substructure

    OpenAIRE

    Siegal-Gaskins, Jennifer M.

    2009-01-01

    Dark matter annihilation in Galactic substructure produces diffuse gamma-ray emission of remarkably constant intensity across the sky, and in general this signal dominates over the smooth halo signal at angles greater than a few tens of degrees from the Galactic Center. The large-scale isotropy of the emission from substructure suggests that it may be difficult to extract this Galactic dark matter signal from the extragalactic gamma-ray background. I show that dark matter substructure induces...

  12. Predicting hemispheric dominance for language production in healthy individuals using support vector machine.

    Science.gov (United States)

    Zago, Laure; Hervé, Pierre-Yves; Genuer, Robin; Laurent, Alexandre; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie; Joliot, Marc

    2017-12-01

    We used a Support Vector Machine (SVM) classifier to assess hemispheric pattern of language dominance of 47 individuals categorized as non-typical for language from their hemispheric functional laterality index (HFLI) measured on a sentence minus word-list production fMRI-BOLD contrast map. The SVM classifier was trained at discriminating between Dominant and Non-Dominant hemispheric language production activation pattern on a group of 250 participants previously identified as Typicals (HFLI strongly leftward). Then, SVM was applied to each hemispheric language activation pattern of 47 non-typical individuals. The results showed that at least one hemisphere (left or right) was found to be Dominant in every, except 3 individuals, indicating that the "dominant" type of functional organization is the most frequent in non-typicals. Specifically, left hemisphere dominance was predicted in all non-typical right-handers (RH) and in 57.4% of non-typical left-handers (LH). When both hemisphere classifications were jointly considered, four types of brain patterns were observed. The most often predicted pattern (51%) was left-dominant (Dominant left-hemisphere and Non-Dominant right-hemisphere), followed by right-dominant (23%, Dominant right-hemisphere and Non-Dominant left-hemisphere) and co-dominant (19%, 2 Dominant hemispheres) patterns. Co-non-dominant was rare (6%, 2 Non-Dominant hemispheres), but was normal variants of hemispheric specialization. In RH, only left-dominant (72%) and co-dominant patterns were detected, while for LH, all types were found, although with different occurrences. Among the 10 LH with a strong rightward HFLI, 8 had a right-dominant brain pattern. Whole-brain analysis of the right-dominant pattern group confirmed that it exhibited a functional organization strictly mirroring that of left-dominant pattern group. Hum Brain Mapp 38:5871-5889, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  13. Types of Workplace Social Support in the Prediction of Job Satisfaction

    Science.gov (United States)

    Harris, J. Irene; Winskowski, Ann Marie; Engdahl, Brian E.

    2007-01-01

    Research on social support and job satisfaction has yielded mixed results, partly because studies have rarely examined different types of workplace social support, such as collegial support, task support, coaching, and career mentoring. This study identified the relative contributions of different types of social support to job satisfaction and…

  14. AN EXAMINATION OF THE OPTICAL SUBSTRUCTURE OF GALAXY CLUSTERS HOSTING RADIO SOURCES

    International Nuclear Information System (INIS)

    Wing, Joshua D.; Blanton, Elizabeth L.

    2013-01-01

    Using radio sources from the Faint Images of the Radio Sky at Twenty-cm survey, and optical counterparts in the Sloan Digital Sky Survey, we have identified a large number of galaxy clusters. The radio sources within these clusters are driven by active galactic nuclei, and our cluster samples include clusters with bent, and straight, double-lobed radio sources. We also included a single-radio-component comparison sample. We examine these galaxy clusters for evidence of optical substructure, testing the possibility that bent double-lobed radio sources are formed as a result of large-scale cluster mergers. We use a suite of substructure analysis tools to determine the location and extent of substructure visible in the optical distribution of cluster galaxies, and compare the rates of substructure in clusters with different types of radio sources. We found no preference for significant substructure in clusters hosting bent double-lobed radio sources compared to those with other types of radio sources.

  15. DARK MATTER SUBSTRUCTURE DETECTION USING SPATIALLY RESOLVED SPECTROSCOPY OF LENSED DUSTY GALAXIES

    International Nuclear Information System (INIS)

    Hezaveh, Yashar; Holder, Gilbert; Dalal, Neal; Kuhlen, Michael; Marrone, Daniel; Murray, Norman; Vieira, Joaquin

    2013-01-01

    We investigate how strong lensing of dusty, star-forming galaxies (DSFGs) by foreground galaxies can be used as a probe of dark matter halo substructure. We find that spatially resolved spectroscopy of lensed sources allows dramatic improvements to measurements of lens parameters. In particular, we find that modeling of the full, three-dimensional (angular position and radial velocity) data can significantly facilitate substructure detection, increasing the sensitivity of observables to lower mass subhalos. We carry out simulations of lensed dusty sources observed by early ALMA (Cycle 1) and use a Fisher matrix analysis to study the parameter degeneracies and mass detection limits of this method. We find that even with conservative assumptions, it is possible to detect galactic dark matter subhalos of ∼10 8 M ☉ with high significance in most lensed DSFGs. Specifically, we find that in typical DSFG lenses, there is a ∼55% probability of detecting a substructure with M > 10 8 M ☉ with more than 5σ detection significance in each lens, if the abundance of substructure is consistent with previous lensing results. The full ALMA array, with its significantly enhanced sensitivity and resolution, should improve these estimates considerably. Given the sample of ∼100 lenses provided by surveys such as the South Pole Telescope, our understanding of dark matter substructure in typical galaxy halos is poised to improve dramatically over the next few years.

  16. DARK MATTER SUBSTRUCTURE DETECTION USING SPATIALLY RESOLVED SPECTROSCOPY OF LENSED DUSTY GALAXIES

    Energy Technology Data Exchange (ETDEWEB)

    Hezaveh, Yashar; Holder, Gilbert [Department of Physics, McGill University, 3600 Rue University, Montreal, Quebec H3A 2T8 (Canada); Dalal, Neal [Astronomy Department, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801 (United States); Kuhlen, Michael [Theoretical Astrophysics Center, University of California, Berkeley, CA 94720 (United States); Marrone, Daniel [Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721 (United States); Murray, Norman [CITA, University of Toronto, 60 St. George Street, Toronto, ON M5S 3H8 (Canada); Vieira, Joaquin [California Institute of Technology, 1200 East California Blvd, MC 249-17, Pasadena, CA 91125 (United States)

    2013-04-10

    We investigate how strong lensing of dusty, star-forming galaxies (DSFGs) by foreground galaxies can be used as a probe of dark matter halo substructure. We find that spatially resolved spectroscopy of lensed sources allows dramatic improvements to measurements of lens parameters. In particular, we find that modeling of the full, three-dimensional (angular position and radial velocity) data can significantly facilitate substructure detection, increasing the sensitivity of observables to lower mass subhalos. We carry out simulations of lensed dusty sources observed by early ALMA (Cycle 1) and use a Fisher matrix analysis to study the parameter degeneracies and mass detection limits of this method. We find that even with conservative assumptions, it is possible to detect galactic dark matter subhalos of {approx}10{sup 8} M{sub Sun} with high significance in most lensed DSFGs. Specifically, we find that in typical DSFG lenses, there is a {approx}55% probability of detecting a substructure with M > 10{sup 8} M{sub Sun} with more than 5{sigma} detection significance in each lens, if the abundance of substructure is consistent with previous lensing results. The full ALMA array, with its significantly enhanced sensitivity and resolution, should improve these estimates considerably. Given the sample of {approx}100 lenses provided by surveys such as the South Pole Telescope, our understanding of dark matter substructure in typical galaxy halos is poised to improve dramatically over the next few years.

  17. PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Balachandran Manavalan

    2018-03-01

    Full Text Available Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

  18. PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.

    Science.gov (United States)

    Manavalan, Balachandran; Shin, Tae H; Lee, Gwang

    2018-01-01

    Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

  19. How Nanotechnology and Biomedical Engineering Are Supporting the Identification of Predictive Biomarkers in Neuro-Oncology.

    Science.gov (United States)

    Ganau, Mario; Paris, Marco; Syrmos, Nikolaos; Ganau, Laura; Ligarotti, Gianfranco K I; Moghaddamjou, Ali; Prisco, Lara; Ambu, Rossano; Chibbaro, Salvatore

    2018-02-26

    The field of neuro-oncology is rapidly progressing and internalizing many of the recent discoveries coming from research conducted in basic science laboratories worldwide. This systematic review aims to summarize the impact of nanotechnology and biomedical engineering in defining clinically meaningful predictive biomarkers with a potential application in the management of patients with brain tumors. Data were collected through a review of the existing English literature performed on Scopus, MEDLINE, MEDLINE in Process, EMBASE, and/or Cochrane Central Register of Controlled Trials: all available basic science and clinical papers relevant to address the above-stated research question were included and analyzed in this study. Based on the results of this systematic review we can conclude that: (1) the advances in nanotechnology and bioengineering are supporting tremendous efforts in optimizing the methods for genomic, epigenomic and proteomic profiling; (2) a successful translational approach is attempting to identify a growing number of biomarkers, some of which appear to be promising candidates in many areas of neuro-oncology; (3) the designing of Randomized Controlled Trials will be warranted to better define the prognostic value of those biomarkers and biosignatures.

  20. How Nanotechnology and Biomedical Engineering Are Supporting the Identification of Predictive Biomarkers in Neuro-Oncology

    Directory of Open Access Journals (Sweden)

    Mario Ganau

    2018-02-01

    Full Text Available The field of neuro-oncology is rapidly progressing and internalizing many of the recent discoveries coming from research conducted in basic science laboratories worldwide. This systematic review aims to summarize the impact of nanotechnology and biomedical engineering in defining clinically meaningful predictive biomarkers with a potential application in the management of patients with brain tumors. Data were collected through a review of the existing English literature performed on Scopus, MEDLINE, MEDLINE in Process, EMBASE, and/or Cochrane Central Register of Controlled Trials: all available basic science and clinical papers relevant to address the above-stated research question were included and analyzed in this study. Based on the results of this systematic review we can conclude that: (1 the advances in nanotechnology and bioengineering are supporting tremendous efforts in optimizing the methods for genomic, epigenomic and proteomic profiling; (2 a successful translational approach is attempting to identify a growing number of biomarkers, some of which appear to be promising candidates in many areas of neuro-oncology; (3 the designing of Randomized Controlled Trials will be warranted to better define the prognostic value of those biomarkers and biosignatures.

  1. Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Meiping Wang

    2016-01-01

    Full Text Available We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR model-optimized particle swarm optimization (PSO algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR, and artificial neural network (ANN through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.

  2. Predicting supportive behavior of parents and siblings to a family member with intellectual disability living in institutional care.

    Science.gov (United States)

    Rimmerman, Arie; Chen, Ariel

    2012-01-01

    This feasibility study examines whether the theory of planned behavior can predict supportive behavior provided by either parents to their offspring--or adult siblings to their brothers and sisters--with an intellectual disability living in 2 Israeli institutional care facilities. Participants were 67 parents and 63 siblings who were interviewed at baseline regarding their intentions to visit their offspring or sibling in the institutional care facility, to contact the caregiving staff, and to accept visits at home. Parents' and siblings' behavior regarding visitation and supportive behavior was examined after 6 months by caregiving staff. Core findings indicated that subjective norms in siblings and parents predicted frequency of home visits. Perceived behavioral control predicted frequency of contact between siblings and staff. Differences between parents and siblings regarding their supportive behaviors are discussed with respect to social work practice.

  3. Predictive validity of social support relative to psychological well-being in men with spinal cord injury.

    Science.gov (United States)

    Rintala, Diana H

    2013-11-01

    Compare predictive validity (relative to psychological well-being) of long and short versions of 2 measures of social support for persons with spinal cord injury (SCI). Sixty-nine men with SCI completed (a) a long and short version of the Interpersonal Support Evaluation List (ISEL), (b) a structured interview regarding the frequency with which a person receives 11 kinds of support from each of their most important supporters (maximum of 5), and (c) a global measure of the same 11 kinds of support. Approximately 3 years later they completed 4 measures of psychological well-being--the Center for Epidemiologic Studies Depression scale (CESD), the Life Satisfaction Index A (LSIA), the Perceived Stress Scale (PSS), and the Rosenberg Self-Esteem Scale (RSES). Comparisons were made among the social support measures with regard to their ability to predict each of the 4 measures of psychological well-being at a later point in time. The long version of the ISEL had more predictive power than the long version of the structured interview. The long version of the ISEL is a good choice for measuring social support in persons with SCI and the short ISEL may be an acceptable choice when minimizing respondent burden is critical if the number of response options is increased to 4. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  4. The predicting roles of reasons for living and social support on depression, anxiety and stress among young people in Malaysia.

    Science.gov (United States)

    Amit, N; Ibrahim, N; Aga Mohd Jaladin, R; Che Din, N

    2017-10-01

    This research examined the predicting roles of reasons for living and social support on depression, anxiety and stress in Malaysia. This research was carried out on a sample of 263 participants (age range 12-24 years old), from Klang Valley, Selangor. The survey package comprises demographic information, a measure of reasons for living, social support, depression, anxiety and stress. To analyse the data, correlation analysis and a series of linear multiple regression analysis were carried out. Findings showed that there were low negative relationships between all subdomains and the total score of reasons for living and depression. There were also low negative relationships between domain-specific of social support (family and friends) and total social support and depression. In terms of the family alliance, self-acceptance and total score of reasons for living, they were negatively associated with anxiety, whereas family social support was negatively associated with stress. The linear regression analysis showed that only future optimism and family social support found to be the significant predictors for depression. Family alliance and total reasons for living were significant in predicting anxiety, whereas family social support was significant in predicting stress. These findings have the potential to promote awareness related to depression, anxiety, and stress among youth in Malaysia.

  5. Predicting suicide ideation through intrapersonal and interpersonal factors: The interplay of Big-Five personality traits and social support.

    Science.gov (United States)

    Ayub, Nailah

    2015-11-01

    While a specific personality trait may escalate suicide ideation, contextual factors such as social support, when provided effectively, may alleviate the effects of such personality traits. This study examined the moderating role of social support in the relationship between the Big-Five personality traits and suicide ideation. Significant interactions were found between social support and extraversion and emotional stability. Specifically, the relationship between emotional stability and extraversion to suicide ideation was exacerbated when social support was low. Slope analysis showed openness also interacted with low social support. Results were computed for frequency, duration and attitude dimensions of suicide ideation. Extraversion interacted with social support to predict all three dimensions. Social support moderated emotional stability to predict frequency and duration, moderated conscientiousness towards frequency and attitude, and moderated openness towards attitude. The results imply that whereas personality traits may be difficult to alter, social support may play a significant role in saving a life. Psychologists should include family and friends when treating a suicidal youth, guiding them to awareness of one's personality and being more supportive. Copyright © 2015 John Wiley & Sons, Ltd.

  6. Investigation Of Failure Mechanisms In A Wind Turbine Blade Root Sub-Structure

    DEFF Research Database (Denmark)

    Bender, Jens Jakob; Hallett, S.R.; Lindgaard, Esben

    2017-01-01

    and realistic results at the fraction of the cost of a full-scale test. Therefore, this work focuses on testing of sub-structures from the root end of wind turbine blades at the transition from the thick root laminate to the thinner main laminate. Some wind turbine blade manufacturers include pre-cured tapered...... beams in the root to reduce the time required to place the large quantity of material in the mould and to decrease manufacturing defects in these elements. However, this entails the risk of introducing other manufacturing defects during the Vacuum Assisted Resin Transfer Moulding process such as resin...... pockets and fibre wrinkles. Through this work it is sought to determine the effect that these manufacturing defects can have on the strength properties of the sub-structure. The sub-structures used in this work are cut out from actual wind turbine blades, meaning that the manufacturing defects...

  7. Improving jet substructure in ATLAS using unified track and calorimeter information

    CERN Document Server

    Schramm, Steven; The ATLAS collaboration

    2017-01-01

    Jet substructure techniques play a critical role in ATLAS in searches for new physics, are increasingly important in measurements of the Standard Model, and are being utilized in the trigger. To date, ATLAS has mostly focused on the use of calorimeter-based jet substructure, which works well for jets initiated by particles with low to moderate boost, but which lacks the angular resolution needed to resolve the desired substructure in the highly-boosted regime. We will present a novel approach designed to mitigate the calorimeter angular resolution limitations, thus providing superior performance to prior methods. Similar to previous methods, the superior angular resolution of the tracker is combined with information from the calorimeters. However, the new method is fundamentally different, as it correlates low-level objects as tracks and individual energy deposits in the calorimeter, before running any jet finding algorithms. The resulting objects are used as inputs to jet reconstruction, and in turn result i...

  8. Improving jet substructure performance in ATLAS with unified tracking and calorimeter inputs

    CERN Document Server

    Jansky, Roland; The ATLAS collaboration

    2018-01-01

    Jet substructure techniques play a critical role in ATLAS in searches for new physics, and are being utilized in the trigger. They become increasingly important in detailed studies of the Standard Model, among them the inclusive search for the Higgs boson produced with high transverse momentum decaying to a bottom-antibottom quark pair. To date, ATLAS has mostly focused on the use of calorimeter-based jet substructure, which works well for jets initiated by particles with low to moderate boost, but which lacks the angular resolution needed to resolve the desired substructure in the highly-boosted regime. We will present a novel approach designed to mitigate the calorimeter angular resolution limitations, thus providing superior performance to prior methods. Similar to previous methods, the superior angular resolution of the tracker is combined with information from the calorimeters. However, the new method is fundamentally different, as it correlates low-level objects as tracks and individual energy deposits ...

  9. Improving jet substructure performance in ATLAS using Track-CaloClusters

    CERN Document Server

    The ATLAS collaboration

    2017-01-01

    Jet substructure techniques play a critical role in ATLAS in searches for new physics, are increasingly important in measurements of the Standard Model, and are being utilized in the trigger. To date, ATLAS has mostly focused on the use of calorimeter-based jet substructure, which works well for jets initiated by particles with low to moderate boost, but which lacks the angular resolution needed to resolve the desired substructure in the highly-boosted regime. We present a novel approach designed to mitigate the calorimeter angular resolution limitations, thus providing superior performance to prior methods. Similarly to the previously developed combined mass technique, the superior angular resolution of the tracker is combined with information from the calorimeters. However, the new method is fundamentally different, as it correlates low-level objects such as tracks and individual energy deposits in the calorimeter, before running any jet finding algorithms. The resulting objects are used as inputs to jet re...

  10. Two innovative solutions based on fibre concrete blocks designed for building substructure

    Science.gov (United States)

    Pazderka, J.; Hájek, P.

    2017-09-01

    Using of fibers in a high-strength concrete allows reduction of the dimensions of small precast concrete elements, which opens up new ways of solution for traditional construction details in buildings. The paper presents two innovative technical solutions for building substructure: The special shaped plinth block from fibre concrete and the fibre concrete elements for new technical solution of ventilated floor. The main advantages of plinth block from fibre concrete blocks (compared with standard plinth solutions) is: easier and faster assembly, higher durability and thanks to the air cavity between the vertical part of the block, the building substructure reduced moisture level of structures under the waterproofing layer and a comprehensive solution to the final surface of building plinth as well as the surface of adjacent terrain. The ventilated floor based on fibre concrete precast blocks is an attractive structural alternative for tackling the problem of increased moisture in masonry in older buildings, lacking a functional waterproof layer in the substructure.

  11. THEORY AND SIMULATIONS OF REFRACTIVE SUBSTRUCTURE IN RESOLVED SCATTER-BROADENED IMAGES

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, Michael D. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Gwinn, Carl R., E-mail: mjohnson@cfa.harvard.edu [Department of Physics, University of California, Santa Barbara, CA 93106 (United States)

    2015-06-01

    At radio wavelengths, scattering in the interstellar medium distorts the appearance of astronomical sources. Averaged over a scattering ensemble, the result is a blurred image of the source. However, Narayan and Goodman and Goodman and Narayan showed that for an incomplete average, scattering introduces refractive substructure in the image of a point source that is both persistent and wideband. We show that this substructure is quenched but not smoothed by an extended source. As a result, when the scatter-broadening is comparable to or exceeds the unscattered source size, the scattering can introduce spurious compact features into images. In addition, we derive efficient strategies to numerically compute realistic scattered images, and we present characteristic examples from simulations. Our results show that refractive substructure is an important consideration for ongoing missions at the highest angular resolutions, and we discuss specific implications for RadioAstron and the Event Horizon Telescope.

  12. Social support for healthy behaviors: Scale psychometrics and prediction of weight loss among women in a behavioral program

    Science.gov (United States)

    Kiernan, Michaela; Moore, Susan D.; Schoffman, Danielle E.; Lee, Katherine; King, Abby C.; Taylor, C. Barr; Kiernan, Nancy Ellen; Perri, Michael G.

    2015-01-01

    Social support could be a powerful weight-loss treatment moderator or mediator but is rarely assessed. We assessed the psychometric properties, initial levels, and predictive validity of a measure of perceived social support and sabotage from friends and family for healthy eating and physical activity (eight subscales). Overweight/obese women randomized to one of two 6-month, group-based behavioral weight-loss programs (N=267; mean BMI 32.1±3.5; 66.3% White) completed subscales at baseline, and weight loss was assessed at 6 months. Internal consistency, discriminant validity, and content validity were excellent for support subscales and adequate for sabotage subscales; qualitative responses revealed novel deliberate instances not reflected in current sabotage items. Most women (>75%) “never” or “rarely” experienced support from friends or family. Using non-parametric classification methods, we identified two subscales—support from friends for healthy eating and support from family for physical activity—that predicted three clinically meaningful subgroups who ranged in likelihood of losing ≥5% of initial weight at 6 months. Women who “never” experienced family support were least likely to lose weight (45.7% lost weight) whereas women who experienced both frequent friend and family support were more likely to lose weight (71.6% lost weight). Paradoxically, women who “never” experienced friend support were most likely to lose weight (80.0% lost weight), perhaps because the group-based programs provided support lacking from friendships. Psychometrics for support subscales were excellent; initial support was rare; and the differential roles of friend versus family support could inform future targeted weight-loss interventions to subgroups at risk. PMID:21996661

  13. Combined prediction model for supply risk in nuclear power equipment manufacturing industry based on support vector machine and decision tree

    International Nuclear Information System (INIS)

    Shi Chunsheng; Meng Dapeng

    2011-01-01

    The prediction index for supply risk is developed based on the factor identifying of nuclear equipment manufacturing industry. The supply risk prediction model is established with the method of support vector machine and decision tree, based on the investigation on 3 important nuclear power equipment manufacturing enterprises and 60 suppliers. Final case study demonstrates that the combination model is better than the single prediction model, and demonstrates the feasibility and reliability of this model, which provides a method to evaluate the suppliers and measure the supply risk. (authors)

  14. Structural optimization of the fibre-reinforced composite substructure in a three-unit dental bridge.

    Science.gov (United States)

    Shi, Li; Fok, Alex S L

    2009-06-01

    Failures of fixed partial dentures (FPDs) made of fibre-reinforced composites (FRC) have been reported in many clinical and in vitro studies. The types of failure include debonding at the composite-tooth interface, delamination of the veneering material from the FRC substructure and fracture of the pontic. The design of the FRC substructure, i.e. the position and orientation of the fibres, will affect the fracture resistance of the FPD. The purpose of this study was to find an optimal arrangement of the FRC substructure, by means of structural optimization, which could minimize the failure-initiating stresses in a three-unit FPD. A structural optimization method mimicking biological adaptive growth was developed for orthotropic materials such as FRC and incorporated into the finite element (FE) program ABAQUS. Using the program, optimization of the fibre positions and directions in a three-unit FPD was carried out, the aim being to align the fibre directions with those of the maximum principal stresses. The optimized design was then modeled and analyzed to verify the improvements in mechanical performance of the FPD. Results obtained from the optimization suggested that the fibres should be placed at the bottom of the pontic, forming a U-shape substructure that extended into the connectors linking the teeth and the pontic. FE analyses of the optimized design indicated stress reduction in both the veneering composite and at the interface between the veneer and the FRC substructure. The optimized design obtained using FE-based structural optimization can potentially improve the fracture resistance of FPDs by reducing some of the failure-initiating stresses. Optimization methods can therefore be a useful tool to provide sound scientific guidelines for the design of FRC substructures in FPDs.

  15. Earthquake analysis of structures including structure-soil interaction by a substructure method

    International Nuclear Information System (INIS)

    Chopra, A.K.; Guttierrez, J.A.

    1977-01-01

    A general substructure method for analysis of response of nuclear power plant structures to earthquake ground motion, including the effects of structure-soil interaction, is summarized. The method is applicable to complex structures idealized as finite element systems and the soil region treated as either a continuum, for example as a viscoelastic halfspace, or idealized as a finite element system. The halfspace idealization permits reliable analysis for sites where essentially similar soils extend to large depths and there is no rigid boundary such as soil-rock interface. For sites where layers of soft soil are underlain by rock at shallow depth, finite element idealization of the soil region is appropriate; in this case, the direct and substructure methods would lead to equivalent results but the latter provides the better alternative. Treating the free field motion directly as the earthquake input in the substructure method eliminates the deconvolution calculations and the related assumption -regarding type and direction of earthquake waves- required in the direct method. The substructure method is computationally efficient because the two substructures-the structure and the soil region- are analyzed separately; and, more important, it permits taking advantage of the important feature that response to earthquake ground motion is essentially contained in the lower few natural modes of vibration of the structure on fixed base. For sites where essentially similar soils extend to large depths and there is no obvious rigid boundary such as a soil-rock interface, numerical results for earthquake response of a nuclear reactor structure are presented to demonstrate that the commonly used finite element method may lead to unacceptable errors; but the substructure method leads to reliable results

  16. A composite experimental dynamic substructuring method based on partitioned algorithms and localized Lagrange multipliers

    Science.gov (United States)

    Abbiati, Giuseppe; La Salandra, Vincenzo; Bursi, Oreste S.; Caracoglia, Luca

    2018-02-01

    Successful online hybrid (numerical/physical) dynamic substructuring simulations have shown their potential in enabling realistic dynamic analysis of almost any type of non-linear structural system (e.g., an as-built/isolated viaduct, a petrochemical piping system subjected to non-stationary seismic loading, etc.). Moreover, owing to faster and more accurate testing equipment, a number of different offline experimental substructuring methods, operating both in time (e.g. the impulse-based substructuring) and frequency domains (i.e. the Lagrange multiplier frequency-based substructuring), have been employed in mechanical engineering to examine dynamic substructure coupling. Numerous studies have dealt with the above-mentioned methods and with consequent uncertainty propagation issues, either associated with experimental errors or modelling assumptions. Nonetheless, a limited number of publications have systematically cross-examined the performance of the various Experimental Dynamic Substructuring (EDS) methods and the possibility of their exploitation in a complementary way to expedite a hybrid experiment/numerical simulation. From this perspective, this paper performs a comparative uncertainty propagation analysis of three EDS algorithms for coupling physical and numerical subdomains with a dual assembly approach based on localized Lagrange multipliers. The main results and comparisons are based on a series of Monte Carlo simulations carried out on a five-DoF linear/non-linear chain-like systems that include typical aleatoric uncertainties emerging from measurement errors and excitation loads. In addition, we propose a new Composite-EDS (C-EDS) method to fuse both online and offline algorithms into a unique simulator. Capitalizing from the results of a more complex case study composed of a coupled isolated tank-piping system, we provide a feasible way to employ the C-EDS method when nonlinearities and multi-point constraints are present in the emulated system.

  17. Analysis of the forced vibration test of the Hualien large scale soil-structure interaction model using a flexible volume substructuring method

    International Nuclear Information System (INIS)

    Tang, H.T.; Nakamura, N.

    1995-01-01

    A 1/4-scale cylindrical reactor containment model was constructed in Hualien, Taiwan for foil-structure interaction (SSI) effect evaluation and SSI analysis procedure verification. Forced vibration tests were executed before backfill (FVT-1) and after backfill (FVT-2) to characterize soil-structure system characteristics under low excitations. A number of organizations participated in the pre-test blind prediction and post-test correlation analyses of the forced vibration test using various industry familiar methods. In the current study, correlation analyses were performed using a three-dimensional flexible volume substructuring method. The results are reported and soil property sensitivities are evaluated in the paper. (J.P.N.)

  18. RaCon - decision maker's support for RAdiation CONsequences prediction and for crisis management optimization

    International Nuclear Information System (INIS)

    Svanda, J.; Tschiesche, J.; Fiser, V.

    2003-01-01

    Full text: Emergencies, especially in nuclear accidents, put high demands an intervening personnel and on decision makers. There are lot of things to do in time stress and lack of information and data are usually not 'good' enough to justify implementation of interventions and countermeasures in a simple way. Computerized tools play important role in this process and the quality of user interface and unambiguous presentation of results are the dominant issues for mitigation of an accident. The RaCon (Radiological Consequences) system, developed by NRI Rez, is one of the representatives of advanced supporting tools, which allows to predict, what can happen during emergencies accompanied by real or possible release of radioactive substances and how to response on it. System is presented by databases of input and output data and by the program tool for fast prognostic evaluation of urgent emergency countermeasures at nuclear facilities radiation accidents. System is based on fast evaluation of radiation doses to population and emergency teams after an accidental release of radioactive material into atmosphere in early phase of accident and near region around the facilities. System is designed to process all available data and to communicate with user in a 'simple' way that can reduce misunderstanding and misinterpretation. The aim of the tool is to give prediction of urgent countermeasures as fast as possible before the radioactive cloud has come when the countermeasures are the most effective. Database of the most probable source terms for individual nuclear installation, calculated by advanced qualified codes, is integral part of the software. Most important outputs are maps presentations of affected area, table presentation of settlement with doses to population exceeding limits for countermeasures given by 'Czech Regulatory Authority' and table presentation of dose rates and doses in defined location and time for mobile monitoring and emergency teams. Proposals of

  19. AREVA modeling and predictive capacities to support PWR fuel assembly upgrading

    International Nuclear Information System (INIS)

    Canat, J. N.; Mollard, P.; Gentet, G.; Uyeda, G.

    2008-01-01

    The first goal of the fuel designer is to closely address the customers' expectations, with the aim of providing them in the shortest possible time a flawless product fully addressing their needs. However, the designer knows from experience that designing a new fuel assembly is a task which always lasts a long time. Depending on the extent and innovative dimension of the performed changes, development and qualification of new products have lasted from a few years to as much as roughly 15 years. Experience feedback proves that developing and qualifying a cladding material is the longest-term process, requiring the determination of its behavior laws under irradiation and also under accident conditions. Regarding fuel assembly structure, new development generally requires the irradiation of Lead Test Assemblies during a period of time representative of the fuel operating conditions. This explains the critical importance of high powered, top quality modeling to adequately support the fuel assembly design development and the behavior assessment. Advanced calculation codes and methods, improved modeling tools and test facilities, are key contributors to reinforced reliability, robustness, thermal hydraulic performance and maneuverability of nuclear fuel under ever more demanding operational conditions. Sophisticated, high powered modeling tools and representative test capacities are cutting the time necessary for AREVA to develop a new product, license it and load it in the core of a reactor. This trend towards greater modeling capability has been backed up by the upgrading of computing means over the last few years, allowing the consideration of a large number of factors and a higher accuracy in the representation of the modeled phenomena. This article details how predictive tools currently play a more and more important role in the design developments engaged by AREVA. They have led to a more physical approach to finding technical solutions and allowed their analytical

  20. Substructure based modeling of nickel single crystals cycled at low plastic strain amplitudes

    Science.gov (United States)

    Zhou, Dong

    In this dissertation a meso-scale, substructure-based, composite single crystal model is fully developed from the simple uniaxial model to the 3-D finite element method (FEM) model with explicit substructures and further with substructure evolution parameters, to simulate the completely reversed, strain controlled, low plastic strain amplitude cyclic deformation of nickel single crystals. Rate-dependent viscoplasticity and Armstrong-Frederick type kinematic hardening rules are applied to substructures on slip systems in the model to describe the kinematic hardening behavior of crystals. Three explicit substructure components are assumed in the composite single crystal model, namely "loop patches" and "channels" which are aligned in parallel in a "vein matrix," and persistent slip bands (PSBs) connected in series with the vein matrix. A magnetic domain rotation model is presented to describe the reverse magnetostriction of single crystal nickel. Kinematic hardening parameters are obtained by fitting responses to experimental data in the uniaxial model, and the validity of uniaxial assumption is verified in the 3-D FEM model with explicit substructures. With information gathered from experiments, all control parameters in the model including hardening parameters, volume fraction of loop patches and PSBs, and variation of Young's modulus etc. are correlated to cumulative plastic strain and/or plastic strain amplitude; and the whole cyclic deformation history of single crystal nickel at low plastic strain amplitudes is simulated in the uniaxial model. Then these parameters are implanted in the 3-D FEM model to simulate the formation of PSB bands. A resolved shear stress criterion is set to trigger the formation of PSBs, and stress perturbation in the specimen is obtained by several elements assigned with PSB material properties a priori. Displacement increment, plastic strain amplitude control and overall stress-strain monitor and output are carried out in the user

  1. VARIATION OF SUBSTRUCTURES OF PEARLITIC HEAT RESISTANT STEEL AFTER HIGH TEMPERATURE AGING

    Institute of Scientific and Technical Information of China (English)

    R.C.Yang; K.Chen; H.X.Feng; H.Wang

    2004-01-01

    The observations of dislocations, substructures and other microstructural details were conducted mainly by means of transmission electron microscope (TEM) and scanning electron microscope (SEM) for 12Cr1Mo V pearlitic heat-resistant steel. It is shown that during the high temperature long-term aging, the disordered and jumbled phasetransformed dislocations caused by normalized cooling are recovered and rearranged into cell substructures, and then the dislocation density is reduced gradually. Finally a low density linear dislocation configuration and a stabler dislocation network are formed and ferritic grains grow considerably.

  2. On mechanism of substructure formation in SmS during isomorphic phase transformations

    International Nuclear Information System (INIS)

    Aptekar', I.L.; Ivanov, V.I.; Tonkov, E.Yu.; Shmyt'ko, I.M.

    1986-01-01

    X-ray diffraction study of substructure characteristics of SmS samples subjected to treatment at different temrerature and pressure in media with different viscosity ( graphite, silicon oil) for realization of P-M-P transformations ( p-semiconductor phase, M - high pressure phase) is performed. It is assumed that with M - phase formation P - matrix volume relaxation delays, therefore the new phase particles occupy smaller volume than the initial matrix which causes the M - phase disorientation. The difference between the phase transformation rate and deformation rate under the pressure in media with various viscosity results in arising different substructural characteristics

  3. Performance of Jet Substructure Techniques and Boosted Object Identification in ATLAS

    CERN Document Server

    Lacey, J; The ATLAS collaboration

    2014-01-01

    ATLAS has implemented and commissioned many new jet substructure techniques to aid in the identification and interpretation of hadronic final states originating from Lorentz-boosted heavy particles produced at the LHC. These techniques include quantum jets, jet charge, jet shapes, quark/gluon, boosted boson and top quark tagging, along with grooming methods such as pruning, trimming, and filtering. These techniques have been validated using the large 2012 ATLAS dataset. Presented here is a summary of the state of the art jet substructure and tagging techniques developed in ATLAS, their performance and recent results.

  4. ALMA OBSERVATIONS OF Ly α BLOB 1: HALO SUBSTRUCTURE ILLUMINATED FROM WITHIN

    Energy Technology Data Exchange (ETDEWEB)

    Geach, J. E. [Centre for Astrophysics Research, University of Hertfordshire, Hatfield, AL10 9AB (United Kingdom); Narayanan, D. [Dept. of Physics and Astronomy, Haverford College, PA 19041 (United States); Matsuda, Y.; Ao, Y.; Kubo, M. [National Astronomical Observatory of Japan, Osawa, Mitaka, Tokyo 181-8588 (Japan); Hayes, M. [Stockholm University, Dept. of Astronomy and Oskar Klein Centre for Cosmoparticle Physics, SE-10691, Stockholm (Sweden); Mas-Ribas, Ll.; Dijkstra, M. [Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029 Blindern, NO-0315 Oslo (Norway); Steidel, C. C. [California Institute of Technology, 1216 East California Boulevard, MS 249-17, Pasadena, CA 91125 (United States); Chapman, S. C. [Dept. of Physics and Atmospheric Science, Dalhousie University, Halifax, NS B3H 4R2 (Canada); Feldmann, R. [Dept. of Astronomy, University of California Berkeley, CA 94720 (United States); Avison, A. [UK ALMA Regional Centre Node, Manchester (United Kingdom); Agertz, O. [Dept. of Physics, University of Surrey, GU2 7XH, Surrey (United Kingdom); Birkinshaw, M.; Bremer, M. N. [H. H. Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol, BS8 1TL (United Kingdom); Clements, D. L. [Astrophysics Group, Imperial College London, Blackett Laboratory, Prince Consort Road, London SW7 2AZ (United Kingdom); Dannerbauer, H. [Instituto de Astrofísica de Canarias, La Laguna, Tenerife (Spain); Farrah, D. [Dept. of Physics, Virginia Tech, Blacksburg, VA 24061 (United States); Harrison, C. M. [Centre for Extragalactic Astronomy, Dept. of Physics, Durham University, South Road, Durham, DH1 3LE (United Kingdom); Michałowski, M. J., E-mail: j.geach@herts.ac.uk [Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ (United Kingdom); and others

    2016-11-20

    We present new Atacama Large Millimeter/Submillimeter Array (ALMA) 850 μ m continuum observations of the original Ly α Blob (LAB) in the SSA22 field at z = 3.1 (SSA22-LAB01). The ALMA map resolves the previously identified submillimeter source into three components with a total flux density of S {sub 850} = 1.68 ± 0.06 mJy, corresponding to a star-formation rate of ∼150 M {sub ⊙} yr{sup -1}. The submillimeter sources are associated with several faint ( m ≈ 27 mag) rest-frame ultraviolet sources identified in Hubble Space Telescope Imaging Spectrograph (STIS) clear filter imaging ( λ ≈ 5850 Å). One of these companions is spectroscopically confirmed with the Keck Multi-Object Spectrometer For Infra-Red Exploration to lie within 20 projected kpc and 250 km s{sup -1} of one of the ALMA components. We postulate that some of these STIS sources represent a population of low-mass star-forming satellites surrounding the central submillimeter sources, potentially contributing to their growth and activity through accretion. Using a high-resolution cosmological zoom simulation of a 10{sup 13} M {sub ⊙} halo at z = 3, including stellar, dust, and Ly α radiative transfer, we can model the ALMA+STIS observations and demonstrate that Ly α photons escaping from the central submillimeter sources are expected to resonantly scatter in neutral hydrogen, the majority of which is predicted to be associated with halo substructure. We show how this process gives rise to extended Ly α emission with similar surface brightness and morphology to observed giant LABs.

  5. Predicting the Filial Behaviors of Chinese-Malaysian Adolescents from Perceived Parental Investments, Filial Emotions, and Parental Warmth and Support

    Science.gov (United States)

    Cheah, Charissa S. L.; Ozdemir, Sevgi Bayram; Leung, Christy Y. Y.

    2012-01-01

    The present study examined the mediating role of perceived parental warmth and support in predicting Chinese Malaysian adolescents' filial behaviors from their age, perceived parental investments, and positive filial emotions toward their parents. The effects of these predictors were examined separately for mothers and fathers. Participants…

  6. Predicting Early Spelling: The Contribution of Children's Early Literacy, Private Speech during Spelling, Behavioral Regulation, and Parental Spelling Support

    Science.gov (United States)

    Aram, Dorit; Abiri, Shimrit; Elad, Lili

    2014-01-01

    The present study aimed to extend understanding of preschoolers' early spelling using the Vygotskian ("Mind in society: the development of higher psychological processes," Cambridge, Harvard University Press, 1978) paradigm of child development. We assessed the contribution of maternal spelling support in predicting children's word…

  7. Coping Styles, Social Support, Relational Self-Construal, and Resilience in Predicting Students' Adjustment to University Life

    Science.gov (United States)

    Rahat, Enes; Ilhan, Tahsin

    2016-01-01

    The purpose of the present study is to investigate how well coping styles, social support, relational self-construal, and resilience characteristics predict first year university students' ability to adjust to university life. Participants consisted of 527 at-risk students attending a state university in Turkey. The Personal Information Form, Risk…

  8. Measurement of jet substructure observables in $\\mathrm{t \\bar t}$ events from pp collisions at $\\sqrt{s}=13~\\mathrm{TeV}$

    CERN Document Server

    CMS Collaboration

    2018-01-01

    A measurement of differential jet substructure observables is presented using $\\mathrm{t \\bar t}$ lepton+jets events from proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$ recorded by the CMS experiment at the LHC in 2016 corresponding to an integrated luminosity of $35.9~\\mathrm{fb^{-1}}$. Multiple jet substructure variables, such as the particle multiplicity, width, eccentricity, $p_\\mathrm{T}$ dispersion, N-subjettiness ratios, generalized angularities, and energy correlation functions, are measured for inclusive jets, as well as for identified bottom, light-quark, and gluon jets from the $\\mathrm{t \\bar t}$ final state. The results are unfolded to the stable-particle level and compared to predictions from POWHEG interfaced with PYTHIA 8 and HERWIG 7.1, as well as from SHERPA 2 and DIRE. A reasonable agreement between the data and the Monte Carlo predictions is found. From a comparison of the jet width distribution to the prediction, it is shown that a lower value of the effective strong coupling in t...

  9. Application of support vector regression (SVR) for stream flow prediction on the Amazon basin

    CSIR Research Space (South Africa)

    Du Toit, Melise

    2016-10-01

    Full Text Available regression technique is used in this study to analyse historical stream flow occurrences and predict stream flow values for the Amazon basin. Up to twelve month predictions are made and the coefficient of determination and root-mean-square error are used...

  10. Support vector machine in prediction of building energy demand using pseudo dynamic approach

    NARCIS (Netherlands)

    Paudel, S.; Nguyen, H.P.; Kling, W.L.; Elmitri, Mohamed; Lacarriere, B.; Corre, le O.

    2015-01-01

    Building’s energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal

  11. The Role of Teachers' Support in Predicting Students' Motivation and Achievement Outcomes in Physical Education

    Science.gov (United States)

    Zhang, Tao; Solmon, Melinda A.; Gu, Xiangli

    2012-01-01

    Examining how teachers' beliefs and behaviors predict students' motivation and achievement outcomes in physical education is an area of increasing research interest. Guided by the expectancy-value model and self-determination theory, the major purpose of this study was to examine the predictive strength of teachers' autonomy, competence, and…

  12. Assessment of First- and Second-Order Wave-Excitation Load Models for Cylindrical Substructures: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Pereyra, Brandon; Wendt, Fabian; Robertson, Amy; Jonkman, Jason

    2017-03-09

    The hydrodynamic loads on an offshore wind turbine's support structure present unique engineering challenges for offshore wind. Two typical approaches used for modeling these hydrodynamic loads are potential flow (PF) and strip theory (ST), the latter via Morison's equation. This study examines the first- and second-order wave-excitation surge forces on a fixed cylinder in regular waves computed by the PF and ST approaches to (1) verify their numerical implementations in HydroDyn and (2) understand when the ST approach breaks down. The numerical implementation of PF and ST in HydroDyn, a hydrodynamic time-domain solver implemented as a module in the FAST wind turbine engineering tool, was verified by showing the consistency in the first- and second-order force output between the two methods across a range of wave frequencies. ST is known to be invalid at high frequencies, and this study investigates where the ST solution diverges from the PF solution. Regular waves across a range of frequencies were run in HydroDyn for a monopile substructure. As expected, the solutions for the first-order (linear) wave-excitation loads resulting from these regular waves are similar for PF and ST when the diameter of the cylinder is small compared to the length of the waves (generally when the diameter-to-wavelength ratio is less than 0.2). The same finding applies to the solutions for second-order wave-excitation loads, but for much smaller diameter-to-wavelength ratios (based on wavelengths of first-order waves).

  13. Hubble space telescope/advanced camera for surveys confirmation of the dark substructure in A520

    International Nuclear Information System (INIS)

    Jee, M. J.; Hoekstra, H.; Mahdavi, A.; Babul, A.

    2014-01-01

    We present a weak-lensing study of the cluster A520 based on Advanced Camera for Surveys (ACS) data. The excellent data quality provides a mean source density of ∼109 arcmin –2 , which improves both resolution and significance of the mass reconstruction compared to a previous study based on Wide Field Planetary Camera 2 (WFPC2) images. We take care in removing instrumental effects such as the charge trailing due to radiation damage of the detector and the position-dependent point-spread function. This new ACS analysis confirms the previous claims that a substantial amount of dark mass is present between two luminous subclusters where we observe very little light. The centroid of the dark peak in the current ACS analysis is offset to the southwest by ∼1' with respect to the centroid from the WFPC2 analysis. Interestingly, this new centroid is in better agreement with the location where the X-ray emission is strongest, and the mass-to-light ratio estimated with this centroid is much higher (813 ± 78 M ☉ /L R☉ ) than the previous value; the aperture mass with the WFPC2 centroid provides a consistent mass. Although we cannot provide a definite explanation for the dark peak, we discuss a revised scenario, wherein dark matter with a more conventional range (σ DM /m DM < 1 cm 2 g –1 ) of self-interacting cross-section can lead to the detection of this dark substructure. If supported by detailed numerical simulations, this hypothesis opens up the possibility that the A520 system can be used to establish a lower limit of the self-interacting cross-section of dark matter.

  14. Towards Accurate Prediction of Unbalance Response, Oil Whirl and Oil Whip of Flexible Rotors Supported by Hydrodynamic Bearings

    Directory of Open Access Journals (Sweden)

    Rob Eling

    2016-09-01

    Full Text Available Journal bearings are used to support rotors in a wide range of applications. In order to ensure reliable operation, accurate analyses of these rotor-bearing systems are crucial. Coupled analysis of the rotor and the journal bearing is essential in the case that the rotor is flexible. The accuracy of prediction of the model at hand depends on its comprehensiveness. In this study, we construct three bearing models of increasing modeling comprehensiveness and use these to predict the response of two different rotor-bearing systems. The main goal is to evaluate the correlation with measurement data as a function of modeling comprehensiveness: 1D versus 2D pressure prediction, distributed versus lumped thermal model, Newtonian versus non-Newtonian fluid description and non-mass-conservative versus mass-conservative cavitation description. We conclude that all three models predict the existence of critical speeds and whirl for both rotor-bearing systems. However, the two more comprehensive models in general show better correlation with measurement data in terms of frequency and amplitude. Furthermore, we conclude that a thermal network model comprising temperature predictions of the bearing surroundings is essential to obtain accurate predictions. The results of this study aid in developing accurate and computationally-efficient models of flexible rotors supported by plain journal bearings.

  15. Predictive value of age for coping: the role of self-efficacy, social support satisfaction and perceived stress.

    Science.gov (United States)

    Trouillet, Raphaël; Gana, Kamel; Lourel, Marcel; Fort, Isabelle

    2009-05-01

    The present study was prompted by the lack of agreement on how coping changes with age. We postulate that the effect of age on coping is mediated by coping resources, such as self-efficacy, perceived stress and social support satisfaction. The participants in the study were community dwelling and aged between 22 and 88 years old. Data were collected using the General Self Efficacy Scale, the Social Support Questionnaire, the Perceived Stress Scale, the Geriatric Depression Scale, the Social Readjustment Rating Scale (life-events) and the Way of Coping Checklist. We performed path analyses for two competitive structural models: M1 (age does not directly affect coping processes) and M2 (age directly affects coping processes). Our results supported a modified version of M2. Age was not found to predict either of two coping strategies: problem-focused coping is predicted by self-efficacy and social support satisfaction; emotion-focused coping is predicted by social support satisfaction and perceived stress. Changes in coping over the lifespan reflect the effectiveness with which a person's adaptive processes deal with age-associated changes in self-referred beliefs and environment perception.

  16. Simulated big sagebrush regeneration supports predicted changes at the trailing and leading edges of distribution shifts

    Science.gov (United States)

    Schlaepfer, Daniel R.; Taylor, Kyle A.; Pennington, Victoria E.; Nelson, Kellen N.; Martin, Trace E.; Rottler, Caitlin M.; Lauenroth, William K.; Bradford, John B.

    2015-01-01

    Many semi-arid plant communities in western North America are dominated by big sagebrush. These ecosystems are being reduced in extent and quality due to economic development, invasive species, and climate change. These pervasive modifications have generated concern about the long-term viability of sagebrush habitat and sagebrush-obligate wildlife species (notably greater sage-grouse), highlighting the need for better understanding of the future big sagebrush distribution, particularly at the species' range margins. These leading and trailing edges of potential climate-driven sagebrush distribution shifts are likely to be areas most sensitive to climate change. We used a process-based regeneration model for big sagebrush, which simulates potential germination and seedling survival in response to climatic and edaphic conditions and tested expectations about current and future regeneration responses at trailing and leading edges that were previously identified using traditional species distribution models. Our results confirmed expectations of increased probability of regeneration at the leading edge and decreased probability of regeneration at the trailing edge below current levels. Our simulations indicated that soil water dynamics at the leading edge became more similar to the typical seasonal ecohydrological conditions observed within the current range of big sagebrush ecosystems. At the trailing edge, an increased winter and spring dryness represented a departure from conditions typically supportive of big sagebrush. Our results highlighted that minimum and maximum daily temperatures as well as soil water recharge and summer dry periods are important constraints for big sagebrush regeneration. Overall, our results confirmed previous predictions, i.e., we see consistent changes in areas identified as trailing and leading edges; however, we also identified potential local refugia within the trailing edge, mostly at sites at higher elevation. Decreasing

  17. Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method

    International Nuclear Information System (INIS)

    Sun Zhong-Hua; Jiang Fan

    2010-01-01

    In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using 0 and 1. So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites. We also design a new group of physical and chemical descriptors to characterize the binding sites. The new descriptors are more effective, with an averaging procedure used. Our test shows that much better prediction results can be obtained by the support vector regression (SVR) method than by the support vector classification method. (rapid communication)

  18. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks

    International Nuclear Information System (INIS)

    Li Qiong; Meng Qinglin; Cai Jiejin; Yoshino, Hiroshi; Mochida, Akashi

    2009-01-01

    This study presents four modeling techniques for the prediction of hourly cooling load in the building. In addition to the traditional back propagation neural network (BPNN), the radial basis function neural network (RBFNN), general regression neural network (GRNN) and support vector machine (SVM) are considered. All the prediction models have been applied to an office building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is based on the root mean square error (RMSE) and mean relative error (MRE). The simulation results demonstrate that the four discussed models can be effective for building cooling load prediction. The SVM and GRNN methods can achieve better accuracy and generalization than the BPNN and RBFNN methods

  19. LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines.

    Science.gov (United States)

    Xu, Jingting; Hu, Hong; Dai, Yang

    The identification of enhancers is a challenging task. Various types of epigenetic information including histone modification have been utilized in the construction of enhancer prediction models based on a diverse panel of machine learning schemes. However, DNA methylation profiles generated from the whole genome bisulfite sequencing (WGBS) have not been fully explored for their potential in enhancer prediction despite the fact that low methylated regions (LMRs) have been implied to be distal active regulatory regions. In this work, we propose a prediction framework, LMethyR-SVM, using LMRs identified from cell-type-specific WGBS DNA methylation profiles and a weighted support vector machine learning framework. In LMethyR-SVM, the set of cell-type-specific LMRs is further divided into three sets: reliable positive, like positive and likely negative, according to their resemblance to a small set of experimentally validated enhancers in the VISTA database based on an estimated non-parametric density distribution. Then, the prediction model is obtained by solving a weighted support vector machine. We demonstrate the performance of LMethyR-SVM by using the WGBS DNA methylation profiles derived from the human embryonic stem cell type (H1) and the fetal lung fibroblast cell type (IMR90). The predicted enhancers are highly conserved with a reasonable validation rate based on a set of commonly used positive markers including transcription factors, p300 binding and DNase-I hypersensitive sites. In addition, we show evidence that the large fraction of the LMethyR-SVM predicted enhancers are not predicted by ChromHMM in H1 cell type and they are more enriched for the FANTOM5 enhancers. Our work suggests that low methylated regions detected from the WGBS data are useful as complementary resources to histone modification marks in developing models for the prediction of cell-type-specific enhancers.

  20. Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sang Youn; Moon, Sung Kyoung; Hwang, Sung Il; Sung, Chang Kyu; Cho, Jeong Yeon; Kim, Seung Hyup; Lee, Hak Jong [Seoul National University College of Medicine, Seoul (Korea, Republic of); Jung, Dae Chul [National Cancer Center, Ilsan (Korea, Republic of); Lee, Ji Won [Kangwon National University College of Medicine, Chuncheon (Korea, Republic of)

    2011-10-15

    The purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models. Five hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05). The AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer. Te performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.

  1. Revealing dark matter substructure with anisotropies in the diffuse gamma-ray background

    Energy Technology Data Exchange (ETDEWEB)

    Siegal-Gaskins, Jennifer M, E-mail: jsg@kicp.uchicago.edu [Kavli Institute for Cosmological Physics and Department of Physics, University of Chicago, 5640 S. Ellis Avenue, Chicago, IL 60637 (United States)

    2008-10-15

    The majority of gamma-ray emission from galactic dark matter annihilation is likely to be detected as a contribution to the diffuse gamma-ray background. I show that dark matter substructure in the halo of the Galaxy induces characteristic anisotropies in the diffuse background that could be used to determine the small-scale dark matter distribution. I calculate the angular power spectrum of the emission from dark matter substructure for several models of the subhalo population and show that features in the power spectrum can be used to infer the presence of substructure. The shape of the power spectrum is largely unaffected by the subhalo radial distribution and mass function, and for many scenarios I find that a measurement of the angular power spectrum by Fermi will be able to constrain the abundance of substructure. An anti-biased subhalo radial distribution is shown to produce emission that differs significantly in intensity and large-scale angular dependence from that of a subhalo distribution which traces the smooth dark matter halo, potentially impacting the detectability of the dark matter signal for a variety of targets and methods.

  2. Substructures in DAFT/FADA survey clusters based on XMM and optical data

    Science.gov (United States)

    Durret, F.; DAFT/FADA Team

    2014-07-01

    The DAFT/FADA survey was initiated to perform weak lensing tomography on a sample of 90 massive clusters in the redshift range [0.4,0.9] with HST imaging available. The complementary deep multiband imaging constitutes a high quality imaging data base for these clusters. In X-rays, we have analysed the XMM-Newton and/or Chandra data available for 32 clusters, and for 23 clusters we fit the X-ray emissivity with a beta-model and subtract it to search for substructures in the X-ray gas. This study was coupled with a dynamical analysis for the 18 clusters with at least 15 spectroscopic galaxy redshifts in the cluster range, based on a Serna & Gerbal (SG) analysis. We detected ten substructures in eight clusters by both methods (X-rays and SG). The percentage of mass included in substructures is found to be roughly constant with redshift, with values of 5-15%. Most of the substructures detected both in X-rays and with the SG method are found to be relatively recent infalls, probably at their first cluster pericenter approach.

  3. Substructure method of soil-structure interaction analysis for earthquake loadings

    Energy Technology Data Exchange (ETDEWEB)

    Park, H. G.; Joe, Y. H. [Industrial Development Research Center, Univ. of Incheon, Incheon (Korea, Republic of)

    1997-07-15

    Substructure method has been preferably adopted for soil-structure interaction analysis because of its simplicity and economy in practical application. However, substructure method has some limitation in application and does not always give reliable results especially for embedded structures or layered soil conditions. The objective of this study to validate the reliability of the soil-structure interaction analysis results by the proposed substructure method using lumped-parameter model and suggest a method of seismic design of nuclear power plant structures with specific design conditions. In this study, theoretic background and modeling technique of soil-structure interaction phenomenon have been reviewed and an analysis technique based on substructure method using lumped-parameter model has been suggested. The practicality and reliability of the proposed method have been validated through the application of the method to the seismic analysis of the large-scale seismic test models. A technical guide for practical application and evaluation of the proposed method have been also provided through the various type parametric.

  4. DASS: efficient discovery and p-value calculation of substructures in unordered data.

    Science.gov (United States)

    Hollunder, Jens; Friedel, Maik; Beyer, Andreas; Workman, Christopher T; Wilhelm, Thomas

    2007-01-01

    Pattern identification in biological sequence data is one of the main objectives of bioinformatics research. However, few methods are available for detecting patterns (substructures) in unordered datasets. Data mining algorithms mainly developed outside the realm of bioinformatics have been adapted for that purpose, but typically do not determine the statistical significance of the identified patterns. Moreover, these algorithms do not exploit the often modular structure of biological data. We present the algorithm DASS (Discovery of All Significant Substructures) that first identifies all substructures in unordered data (DASS(Sub)) in a manner that is especially efficient for modular data. In addition, DASS calculates the statistical significance of the identified substructures, for sets with at most one element of each type (DASS(P(set))), or for sets with multiple occurrence of elements (DASS(P(mset))). The power and versatility of DASS is demonstrated by four examples: combinations of protein domains in multi-domain proteins, combinations of proteins in protein complexes (protein subcomplexes), combinations of transcription factor target sites in promoter regions and evolutionarily conserved protein interaction subnetworks. The program code and additional data are available at http://www.fli-leibniz.de/tsb/DASS

  5. Revealing dark matter substructure with anisotropies in the diffuse gamma-ray background

    International Nuclear Information System (INIS)

    Siegal-Gaskins, Jennifer M

    2008-01-01

    The majority of gamma-ray emission from galactic dark matter annihilation is likely to be detected as a contribution to the diffuse gamma-ray background. I show that dark matter substructure in the halo of the Galaxy induces characteristic anisotropies in the diffuse background that could be used to determine the small-scale dark matter distribution. I calculate the angular power spectrum of the emission from dark matter substructure for several models of the subhalo population and show that features in the power spectrum can be used to infer the presence of substructure. The shape of the power spectrum is largely unaffected by the subhalo radial distribution and mass function, and for many scenarios I find that a measurement of the angular power spectrum by Fermi will be able to constrain the abundance of substructure. An anti-biased subhalo radial distribution is shown to produce emission that differs significantly in intensity and large-scale angular dependence from that of a subhalo distribution which traces the smooth dark matter halo, potentially impacting the detectability of the dark matter signal for a variety of targets and methods

  6. MS2Analyzer: A Software for Small Molecule Substructure Annotations from Accurate Tandem Mass Spectra

    Science.gov (United States)

    2015-01-01

    Systematic analysis and interpretation of the large number of tandem mass spectra (MS/MS) obtained in metabolomics experiments is a bottleneck in discovery-driven research. MS/MS mass spectral libraries are small compared to all known small molecule structures and are often not freely available. MS2Analyzer was therefore developed to enable user-defined searches of thousands of spectra for mass spectral features such as neutral losses, m/z differences, and product and precursor ions from MS/MS spectra in MSP/MGF files. The software is freely available at http://fiehnlab.ucdavis.edu/projects/MS2Analyzer/. As the reference query set, 147 literature-reported neutral losses and their corresponding substructures were collected. This set was tested for accuracy of linking neutral loss analysis to substructure annotations using 19 329 accurate mass tandem mass spectra of structurally known compounds from the NIST11 MS/MS library. Validation studies showed that 92.1 ± 6.4% of 13 typical neutral losses such as acetylations, cysteine conjugates, or glycosylations are correct annotating the associated substructures, while the absence of mass spectra features does not necessarily imply the absence of such substructures. Use of this tool has been successfully demonstrated for complex lipids in microalgae. PMID:25263576

  7. Updating failure probability of a welded joint in offshore wind turbine substructures

    DEFF Research Database (Denmark)

    Mai, Quang A.; Sørensen, John Dalsgaard; Rigo, Philippe

    2016-01-01

    The operation and maintenance cost of offshore wind turbine substructures contributes significantly in the cost of a kWh. That cost may be lowered by application of reliability- and risk based maintenance strategies and reliability updating based on inspections performed during the design lifetim...

  8. Morality as the Substructure of Social Justice: Religion in Education as a Case in Point

    Science.gov (United States)

    Potgieter, Ferdinand J.

    2011-01-01

    Moral issues and principles do not only emerge in cases of conflict among, for instance, religious communities or political parties; indeed they form the moral substructure of notions of social justice. During periods of conflict each opponent claims justice for his/her side and bases the claim on certain principles. In this article, reference is…

  9. Substructure analysis techniques and automation. [to eliminate logistical data handling and generation chores

    Science.gov (United States)

    Hennrich, C. W.; Konrath, E. J., Jr.

    1973-01-01

    A basic automated substructure analysis capability for NASTRAN is presented which eliminates most of the logistical data handling and generation chores that are currently associated with the method. Rigid formats are proposed which will accomplish this using three new modules, all of which can be added to level 16 with a relatively small effort.

  10. Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures.

    Science.gov (United States)

    Zhang, Bijun; Vogt, Martin; Maggiora, Gerald M; Bajorath, Jürgen

    2015-10-01

    Chemical space networks (CSNs) have recently been introduced as an alternative to other coordinate-free and coordinate-based chemical space representations. In CSNs, nodes represent compounds and edges pairwise similarity relationships. In addition, nodes are annotated with compound property information such as biological activity. CSNs have been applied to view biologically relevant chemical space in comparison to random chemical space samples and found to display well-resolved topologies at low edge density levels. The way in which molecular similarity relationships are assessed is an important determinant of CSN topology. Previous CSN versions were based on numerical similarity functions or the assessment of substructure-based similarity. Herein, we report a new CSN design that is based upon combined numerical and substructure similarity evaluation. This has been facilitated by calculating numerical similarity values on the basis of maximum common substructures (MCSs) of compounds, leading to the introduction of MCS-based CSNs (MCS-CSNs). This CSN design combines advantages of continuous numerical similarity functions with a robust and chemically intuitive substructure-based assessment. Compared to earlier version of CSNs, MCS-CSNs are characterized by a further improved organization of local compound communities as exemplified by the delineation of drug-like subspaces in regions of biologically relevant chemical space.

  11. Precast concrete elements for accelerated bridge construction : laboratory testing of precast substructure components, Boone County bridge.

    Science.gov (United States)

    2009-01-01

    Vol. 1-1: In July 2006, construction began on an accelerated bridge project in Boone County, Iowa that was composed of precast substructure : elements and an innovative, precast deck panel system. The superstructure system consisted of full-depth dec...

  12. Ring and jet study on the azimuthal substructure of pions at CERN ...

    Indian Academy of Sciences (India)

    structures in the emission of secondary charged hadrons coming from 32S–Ag/Br interactions at 200 A GeV/c. Nuclear photographic emulsion technique has been employed to collect the experimental data. The presence of such substructures, ...

  13. Microdistribution of phases and substructure of the composite electrolytic self-lubricating copper-molybdenite coating

    International Nuclear Information System (INIS)

    Pribysh, I.Z.; Bakakin, G.N.; Borzyak, A.G.; Sajfullin, R.S.

    1978-01-01

    The influence of MoS 2 particles on the substructure of a copper matrix was studied, and their location in the composition was established. It is shown that the presence of molybdenite causes a variation in the conditions of electrical crystallization of copper. The optimum composition has been found, which is used as a self-lubricating coating for friction machine parts

  14. Substructures developed during creep and cyclic tests of type 304 stainless steel (heat 9T2796)

    International Nuclear Information System (INIS)

    Swindeman, R.W.; Bhargava, R.K.; Sikka, V.K.; Moteff, J.

    1977-09-01

    Substructures developed in tested specimens of a reference heat of type 304 stainless steel (heat 9T2796) are examined. Data include dislocation densities, cell and subgrain sizes, and carbide precipitate sizes. Testing conditions range for temperatures from 482 to 649 0 C, for stresses from 28 to 241 MPa, and for times from 4 to 15,000 hr. As expected, it is observed that temperature, stress, and time have strong influences on substructure. The change in the dislocation density is too small to measure for conditions which produce less than 1 percent monotonic strain. No cells form, and the major alteration of substructure is the precipitation of M 23 C 6 carbides on grain boundaries, on twin boundaries, and on some dislocations. At stresses ranging from 69 to 172 MPa and at temperatures ranging from 482 to 593 0 C, the dislocation density increases with increasing stress and is generally higher than expected from studies made at higher temperatures. Dislocations are arranged in fine networks stabilized by carbides. At stresses above 172 MPa and temperatures to 649 0 C, the dislocation density is too great to measure. Cells develop which are finer in size than cells developed at similar stresses but at higher temperatures. Dislocation densities and cell sizes for cyclic specimens are comparable to data for creep-tested specimens. On the basis of the observed substructures, recommendations are made regarding further studies which would assist in the development of constitutive equations for high-temperature inelastic analysis of reactor components

  15. Prefabricated floor panels composed of fiber reinforced concrete and a steel substructure

    DEFF Research Database (Denmark)

    Lárusson, Lárus H.; Fischer, Gregor; Jönsson, Jeppe

    2013-01-01

    This paper reports on a study on prefabricated composite and modular floor deck panels composed of relatively thin fiber reinforced concrete slabs connected to steel substructures. The study focuses on the design, manufacturing, structural improvements and behavior of the floor systems during...

  16. Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

    Directory of Open Access Journals (Sweden)

    Peek Andrew S

    2007-06-01

    Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid

  17. Settlement Prediction of Road Soft Foundation Using a Support Vector Machine (SVM Based on Measured Data

    Directory of Open Access Journals (Sweden)

    Yu Huiling

    2016-01-01

    Full Text Available The suppor1t vector machine (SVM is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. SVM is a new machine learning method based on the statistical learning theory. A case study based on road foundation engineering project shows that the forecast results are in good agreement with the measured data. The SVM model is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model and hyperbola method. Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly. The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well. It will provide a new method to predict foundation settlement.

  18. Using high-throughput literature mining to support read-across predictions of skin sensitization (WC)

    Science.gov (United States)

    Read-across predictions require high quality measured data for source analogues. These data are typically retrieved from structured databases, but biomedical literature data are often untapped because current literature mining approaches are resource intensive. Our high-throughpu...

  19. Social support and social norms: do both contribute to predicting leisure-time exercise?

    Science.gov (United States)

    Okun, Morris A; Ruehlman, Linda; Karoly, Paul; Lutz, Rafer; Fairholme, Chris; Schaub, Rachel

    2003-01-01

    To clarify the contribution of social support and social norms to exercise behavior. A sample of 363 college students completed a questionnaire that assessed social support and social negativity from friends, descriptive and injunctive social norms related to friends, perceived behavioral control, attitude, intention, and leisure-time exercise. Esteem social support was the strongest predictor of total and strenuous leisure-time exercise (P leisure-time exercise. Social support and social norms contribute independently to our understanding of variation in the frequency of strenuous leisure-time exercise.

  20. A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

    Directory of Open Access Journals (Sweden)

    Fuqiang Sun

    2017-01-01

    Full Text Available Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

  1. Hybrid biomembrane substructure determination by contrast-variation analysis

    DEFF Research Database (Denmark)

    Gutberlet, T.; Steitz, R.; Howse, J.

    2002-01-01

    Sandwiched composites that consist of a fluid lipid bilayer associated with a suitable support can serve as a model membrane for biophysical studies. As a precondition of their formation, both support and membrane have to fit each other and the composite has to exhibit a sufficient stability if e...

  2. Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

    International Nuclear Information System (INIS)

    Chen, Kuilin; Yu, Jie

    2014-01-01

    Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations

  3. Smoking behaviors and attitudes during adolescence prospectively predict support for tobacco control policies in adulthood.

    Science.gov (United States)

    Macy, Jonathan T; Chassin, Laurie; Presson, Clark C

    2012-07-01

    Several cross-sectional studies have examined factors associated with support for tobacco control policies. The current study utilized a longitudinal design to test smoking status and attitude toward smoking measured in adolescence as prospective predictors of support for tobacco control policies measured in adulthood. Participants (N = 4,834) were from a longitudinal study of a Midwestern community-based sample. Hierarchical multiple regression analyses tested adolescent smoking status and attitude toward smoking as prospective predictors (after controlling for sociodemographic factors, adult smoking status, and adult attitude toward smoking) of support for regulation of smoking in public places, discussion of the dangers of smoking in public schools, prohibiting smoking in bars, eliminating smoking on television and in movies, prohibiting smoking in restaurants, and increasing taxes on cigarettes. Participants who smoked during adolescence demonstrated more support for discussion of the dangers of smoking in public schools and less support for increasing taxes on cigarettes but only among those who smoked as adults. Those with more positive attitudes toward smoking during adolescence demonstrated less support as adults for prohibiting smoking in bars and eliminating smoking on television and in movies. Moreover, a significant interaction indicated that those with more positive attitudes toward smoking as adolescents demonstrated less support as adults for prohibiting smoking in restaurants, but only if they became parents as adults. This study's findings suggest that interventions designed to deter adolescent smoking may have future benefits in increasing support for tobacco control policies.

  4. Emotional Support and Expectations from Parents, Teachers, and Peers Predict Adolescent Competence at School

    Science.gov (United States)

    Wentzel, Kathryn R.; Russell, Shannon; Baker, Sandra

    2016-01-01

    We examined perceived emotional support and expectations from parents, teachers, and classmates in relation to Mexican American adolescents' (n = 398) social behavior and academic functioning. Results of regression analyses indicated that direct associations between emotional support and expectations differ as a function of source and domain;…

  5. Perceived social support interacts with prenatal depression to predict birth outcomes.

    Science.gov (United States)

    Nylen, Kimberly J; O'Hara, Michael W; Engeldinger, Jane

    2013-08-01

    Prenatal depression has been linked to adverse reproductive outcomes including preterm labor and delivery, and low birth weight. Social support also has been linked to birth outcomes, and may buffer infants from the adverse impact of maternal depression. In this prospective study, 235 pregnant women completed questionnaires about depression and social support. Clinical interviews were administered to assess for DSM-IV axis I disorders. Following delivery, birth outcomes were obtained from medical records. Babies of depressed mothers weighed less, were born earlier and had lower Apgar scores than babies of nondepressed mothers. Depressed women had smaller social support networks and were less satisfied with support from social networks. We found no direct associations between perceived social support and birth weight. However, depressed women who rated their partners as less supportive had babies who were born earlier and had lower Apgar scores than depressed mothers with higher perceived partner support. Women's perception of partner support appears to buffer infants of depressed mothers from potential adverse outcomes. These results are notable in light of the low-risk nature of our sample and point to the need for continued depression screening in pregnant women and a broader view of risk for adverse birth outcomes. The results also suggest a possible means of intervention that may ultimately lead to reductions in adverse birth outcomes.

  6. Development of Multivariable Models to Predict and Benchmark Transfusion in Elective Surgery Supporting Patient Blood Management.

    Science.gov (United States)

    Hayn, Dieter; Kreiner, Karl; Ebner, Hubert; Kastner, Peter; Breznik, Nada; Rzepka, Angelika; Hofmann, Axel; Gombotz, Hans; Schreier, Günter

    2017-06-14

    Blood transfusion is a highly prevalent procedure in hospitalized patients and in some clinical scenarios it has lifesaving potential. However, in most cases transfusion is administered to hemodynamically stable patients with no benefit, but increased odds of adverse patient outcomes and substantial direct and indirect cost. Therefore, the concept of Patient Blood Management has increasingly gained importance to pre-empt and reduce transfusion and to identify the optimal transfusion volume for an individual patient when transfusion is indicated. It was our aim to describe, how predictive modeling and machine learning tools applied on pre-operative data can be used to predict the amount of red blood cells to be transfused during surgery and to prospectively optimize blood ordering schedules. In addition, the data derived from the predictive models should be used to benchmark different hospitals concerning their blood transfusion patterns. 6,530 case records obtained for elective surgeries from 16 centers taking part in two studies conducted in 2004-2005 and 2009-2010 were analyzed. Transfused red blood cell volume was predicted using random forests. Separate models were trained for overall data, for each center and for each of the two studies. Important characteristics of different models were compared with one another. Our results indicate that predictive modeling applied prior surgery can predict the transfused volume of red blood cells more accurately (correlation coefficient cc = 0.61) than state of the art algorithms (cc = 0.39). We found significantly different patterns of feature importance a) in different hospitals and b) between study 1 and study 2. We conclude that predictive modeling can be used to benchmark the importance of different features on the models derived with data from different hospitals. This might help to optimize crucial processes in a specific hospital, even in other scenarios beyond Patient Blood Management.

  7. QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

    Directory of Open Access Journals (Sweden)

    Rachid Darnag

    2017-02-01

    Full Text Available Support vector machines (SVM represent one of the most promising Machine Learning (ML tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR models using molecular descriptors. Multiple linear regression (MLR and artificial neural networks (ANNs were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated.

  8. Real-time prediction of respiratory motion using a cascade structure of an extended Kalman filter and support vector regression.

    Science.gov (United States)

    Hong, S-M; Bukhari, W

    2014-07-07

    The motion of thoracic and abdominal tumours induced by respiratory motion often exceeds 20 mm, and can significantly compromise dose conformality. Motion-adaptive radiotherapy aims to deliver a conformal dose distribution to the tumour with minimal normal tissue exposure by compensating for the tumour motion. This adaptive radiotherapy, however, requires the prediction of the tumour movement that can occur over the system latency period. In general, motion prediction approaches can be classified into two groups: model-based and model-free. Model-based approaches utilize a motion model in predicting respiratory motion. These approaches are computationally efficient and responsive to irregular changes in respiratory motion. Model-free approaches do not assume an explicit model of motion dynamics, and predict future positions by learning from previous observations. Artificial neural networks (ANNs) and support vector regression (SVR) are examples of model-free approaches. In this article, we present a prediction algorithm that combines a model-based and a model-free approach in a cascade structure. The algorithm, which we call EKF-SVR, first employs a model-based algorithm (named LCM-EKF) to predict the respiratory motion, and then uses a model-free SVR algorithm to estimate and correct the error of the LCM-EKF prediction. Extensive numerical experiments based on a large database of 304 respiratory motion traces are performed. The experimental results demonstrate that the EKF-SVR algorithm successfully reduces the prediction error of the LCM-EKF, and outperforms the model-free ANN and SVR algorithms in terms of prediction accuracy across lookahead lengths of 192, 384, and 576 ms.

  9. Real-time prediction of respiratory motion using a cascade structure of an extended Kalman filter and support vector regression

    International Nuclear Information System (INIS)

    Hong, S-M; Bukhari, W

    2014-01-01

    The motion of thoracic and abdominal tumours induced by respiratory motion often exceeds 20 mm, and can significantly compromise dose conformality. Motion-adaptive radiotherapy aims to deliver a conformal dose distribution to the tumour with minimal normal tissue exposure by compensating for the tumour motion. This adaptive radiotherapy, however, requires the prediction of the tumour movement that can occur over the system latency period. In general, motion prediction approaches can be classified into two groups: model-based and model-free. Model-based approaches utilize a motion model in predicting respiratory motion. These approaches are computationally efficient and responsive to irregular changes in respiratory motion. Model-free approaches do not assume an explicit model of motion dynamics, and predict future positions by learning from previous observations. Artificial neural networks (ANNs) and support vector regression (SVR) are examples of model-free approaches. In this article, we present a prediction algorithm that combines a model-based and a model-free approach in a cascade structure. The algorithm, which we call EKF–SVR, first employs a model-based algorithm (named LCM–EKF) to predict the respiratory motion, and then uses a model-free SVR algorithm to estimate and correct the error of the LCM–EKF prediction. Extensive numerical experiments based on a large database of 304 respiratory motion traces are performed. The experimental results demonstrate that the EKF–SVR algorithm successfully reduces the prediction error of the LCM–EKF, and outperforms the model-free ANN and SVR algorithms in terms of prediction accuracy across lookahead lengths of 192, 384, and 576 ms. (paper)

  10. Automated System Checkout to Support Predictive Maintenance for the Reusable Launch Vehicle

    Science.gov (United States)

    Patterson-Hine, Ann; Deb, Somnath; Kulkarni, Deepak; Wang, Yao; Lau, Sonie (Technical Monitor)

    1998-01-01

    The Propulsion Checkout and Control System (PCCS) is a predictive maintenance software system. The real-time checkout procedures and diagnostics are designed to detect components that need maintenance based on their condition, rather than using more conventional approaches such as scheduled or reliability centered maintenance. Predictive maintenance can reduce turn-around time and cost and increase safety as compared to conventional maintenance approaches. Real-time sensor validation, limit checking, statistical anomaly detection, and failure prediction based on simulation models are employed. Multi-signal models, useful for testability analysis during system design, are used during the operational phase to detect and isolate degraded or failed components. The TEAMS-RT real-time diagnostic engine was developed to utilize the multi-signal models by Qualtech Systems, Inc. Capability of predicting the maintenance condition was successfully demonstrated with a variety of data, from simulation to actual operation on the Integrated Propulsion Technology Demonstrator (IPTD) at Marshall Space Flight Center (MSFC). Playback of IPTD valve actuations for feature recognition updates identified an otherwise undetectable Main Propulsion System 12 inch prevalve degradation. The algorithms were loaded into the Propulsion Checkout and Control System for further development and are the first known application of predictive Integrated Vehicle Health Management to an operational cryogenic testbed. The software performed successfully in real-time, meeting the required performance goal of 1 second cycle time.

  11. Patent Analysis for Supporting Merger and Acquisition (M&A) Prediction: A Data Mining Approach

    Science.gov (United States)

    Wei, Chih-Ping; Jiang, Yu-Syun; Yang, Chin-Sheng

    M&A plays an increasingly important role in the contemporary business environment. Companies usually conduct M&A to pursue complementarity from other companies for preserving and/or extending their competitive advantages. For the given bidder company, a critical first step to the success of M&A activities is the appropriate selection of target companies. However, existing studies on M&A prediction incur several limitations, such as the exclusion of technological variables in M&A prediction models and the omission of the profile of the respective bidder company and its compatibility with candidate target companies. In response to these limitations, we propose an M&A prediction technique which not only encompasses technological variables derived from patent analysis as prediction indictors but also takes into account the profiles of both bidder and candidate target companies when building an M&A prediction model. We collect a set of real-world M&A cases to evaluate the proposed technique. The evaluation results are encouraging and will serve as a basis for future studies.

  12. Prediction of Quality of Life of Non–Insulin-Dependent Diabetic Patients Based on Perceived Social Support

    Directory of Open Access Journals (Sweden)

    Hossein Shareh

    2012-04-01

    Full Text Available Background: The objective of this study was to predic quality of life based on perceived social support components in non–insulin-dependent diabetic patients.Materials and Method: Fifty patients with non–insulin-dependent diabetes mellitus from Al-Zahra diabetic center in Shiraz participated in a cross-sectional study via survey instrument. All subjects completed multidimensional scale of perceived social support (MSPSS and world health organization quality of life- brief (WHOQOL-BREF questionnaires. Results: On the basis of stepwise multiple regression analysis friends and family dimensions of perceived social support were the best predictors of the quality of life and its dimensions (p<0.01.Conclusion: Friends and family dimensions of perceived social support have significant contributions in predicting quality of life of patients with non–insulin-dependent diabetes mellitus.

  13. Predicting the filial behaviors of Chinese-Malaysian adolescents from perceived parental investments, filial emotions, and parental warmth and support.

    Science.gov (United States)

    Cheah, Charissa S L; Bayram Özdemir, Sevgi; Leung, Christy Y Y

    2012-06-01

    The present study examined the mediating role of perceived parental warmth and support in predicting Chinese Malaysian adolescents' filial behaviors from their age, perceived parental investments, and positive filial emotions toward their parents. The effects of these predictors were examined separately for mothers and fathers. Participants included 122 Chinese adolescents (M = 13.14 years; SD = 2.22) in Malaysia. Adolescents' perceived parental investments, filial emotions, and warmth and support from each parent were positively, and age was negatively associated with their filial behaviors. No gender differences were found. Perceived maternal warmth and support significantly mediated the effect of age, perceived investments from, and filial emotions toward mothers on adolescents' filial behaviors, but perceived paternal warmth and support did not have a mediating role. The present study sheds light on the unique maternal versus paternal filial role, and important familial processes in Chinese-Malaysian children and adolescents from a cultural perspective. Published by Elsevier Ltd.

  14. The Role of Perceived Teacher's Support and Motivational Orientation in Prediction of Metacognitive Awareness of Reading Strategies in Learning English

    Directory of Open Access Journals (Sweden)

    Zohreh Kazemi

    2016-08-01

    Full Text Available This study aims to determine the role of perceived teacher support and motivational orientation in predicting metacognitive awareness of reading strategies in learning the English language. The sample included 425 male and female students, studying in the elementary schools in the city of Birjand, eastern Iran, in the 2014-2015 academic year. Three different types of questionnaires were distributed among these students. The questionnaires were, respectively, about the students’ perception of teacher support (Zaki, 2007, motivational orientation for English learning (Sheikholeslami, 2005, and metacognitive awareness of the study methods (Mokhtari & Richard, 2002. Multiple regression analysis was applied to analyze the obtained data. It was found that there was a direct and significant correlation between teacher support variable, and intrinsic motivation, overall reading strategies, problem-solving strategies, reading support strategies, and metacognitive awareness. Additionally, there was an inverse and significant correlation with the non-motivation variable. Furthermore, no significant correlation was observed between the teacher support variable and the extrinsic motivation variable. A direct and significant relationship was, however, spotted between intrinsic motivation, and extrinsic motivation, overall reading strategies, problem-solving strategies, reading support strategies,and metacognitive awareness; and an inverse and significant relationship was noticed between the intrinsic motivation and non-motivation variables. Moreover, there existed a direct and significant relationship between extrinsic motivation, and overall reading strategies, problem-solving strategies, reading support strategies, metacognitive awareness and it had an inverse and significant relationship with non-motivation variable. The findings demonstrated that the components of perceived teacher support and motivational orientation (extrinsic motivation, intrinsic

  15. Predicting Hepatotoxicity of Drug Metabolites Via an Ensemble Approach Based on Support Vector Machine

    Science.gov (United States)

    Lu, Yin; Liu, Lili; Lu, Dong; Cai, Yudong; Zheng, Mingyue; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

    2017-11-20

    Drug-induced liver injury (DILI) is a major cause of drug withdrawal. The chemical properties of the drug, especially drug metabolites, play key roles in DILI. Our goal is to construct a QSAR model to predict drug hepatotoxicity based on drug metabolites. 64 hepatotoxic drug metabolites and 3,339 non-hepatotoxic drug metabolites were gathered from MDL Metabolite Database. Considering the imbalance of the dataset, we randomly split the negative samples and combined each portion with all the positive samples to construct individually balanced datasets for constructing independent classifiers. Then, we adopted an ensemble approach to make prediction based on the results of all individual classifiers and applied the minimum Redundancy Maximum Relevance (mRMR) feature selection method to select the molecular descriptors. Eventually, for the drugs in the external test set, a Bayesian inference method was used to predict the hepatotoxicity of a drug based on its metabolites. The model showed the average balanced accuracy=78.47%, sensitivity =74.17%, and specificity=82.77%. Five molecular descriptors characterizing molecular polarity, intramolecular bonding strength, and molecular frontier orbital energy were obtained. When predicting the hepatotoxicity of a drug based on all its metabolites, the sensitivity, specificity and balanced accuracy were 60.38%, 70.00%, and 65.19%, respectively, indicating that this method is useful for identifying the hepatotoxicity of drugs. We developed an in silico model to predict hepatotoxicity of drug metabolites. Moreover, Bayesian inference was applied to predict the hepatotoxicity of a drug based on its metabolites which brought out valuable high sensitivity and specificity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  16. Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

    Science.gov (United States)

    Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

    2014-12-01

    Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  17. Effect of Soil-Structure Interaction on Seismic Performance of Long-Span Bridge Tested by Dynamic Substructuring Method

    Directory of Open Access Journals (Sweden)

    Zhenyun Tang

    2017-01-01

    Full Text Available Because of the limitations of testing facilities and techniques, the seismic performance of soil-structure interaction (SSI system can only be tested in a quite small scale model in laboratory. Especially for long-span bridge, a smaller tested model is required when SSI phenomenon is considered in the physical test. The scale effect resulting from the small scale model is always coupled with the dynamic performance, so that the seismic performance of bridge considering SSI effect cannot be uncovered accurately by the traditional testing method. This paper presented the implementation of real-time dynamic substructuring (RTDS, involving the combined use of shake table array and computational engines for the seismic simulation of SSI. In RTDS system, the bridge with soil-foundation system is divided into physical and numerical substructures, in which the bridge is seen as physical substructures and the remaining part is seen as numerical substructures. The interface response between the physical and numerical substructures is imposed by shake table and resulting reaction force is fed back to the computational engine. The unique aspect of the method is to simulate the SSI systems subjected to multisupport excitation in terms of a larger physical model. The substructuring strategy and the control performance associated with the real-time substructuring testing for SSI were performed. And the influence of SSI on a long-span bridge was tested by this novel testing method.

  18. Experimental prediction of tube support interaction characteristics in steam generators: Volume 2, Westinghouse Model 51 flow entrance region: Topical report

    International Nuclear Information System (INIS)

    Haslinger, K.H.

    1988-06-01

    Tube-to-tube support interaction characterisitics were determined experimentally on a single tube, multi-span geometry, representative of the Westinghouse Model 51 steam generator economizer design. Results, in part, became input for an autoclave type wear test program on steam generator tubes, performed by Kraftwerk Union (KWU). More importantly, the test data reported here have been used to validate two analytical wear prediction codes; the WECAN code, which was developed by Westinghouse, and the ABAQUS code which has been enhanced for EPRI by Foster Wheeler to enable simulation of gap conditions (including fluid film effects) for various support geometries

  19. Low perceived social support predicts later depression but not social phobia in middle adolescence

    OpenAIRE

    V??n?nen, Juha-Matti; Marttunen, Mauri; Helminen, Mika; Kaltiala-Heino, Riittakerttu

    2014-01-01

    Social phobia and depression are common and highly comorbid disorders in adolescence. There is a lack of studies on possible psychosocial shared risk factors for these disorders. The current study examined if low social support is a shared risk factor for both disorders among adolescent girls and boys. This study is a part of the Adolescent Mental Health Cohort Study's two-year follow-up. We studied cross-sectional and longitudinal associations of perceived social support with social phobia, ...

  20. Low perceived social support predicts later depression but not social phobia in middle adolescence

    OpenAIRE

    Väänänen, Juha-Matti; Marttunen, Mauri; Helminen, Mika; Kaltiala-Heino, Riittakerttu

    2014-01-01

    Abstract Social phobia and depression are common and highly comorbid disorders in adolescence. There is a lack of studies on possible psychosocial shared risk factors for these disorders. The current study examined if low social support is a shared risk factor for both disorders among adolescent girls and boys. This study is a part of the Adolescent Mental Health Cohort Study's two-year follow-up. We studied cross-sectional and longitudinal associations of perceived social support with so...

  1. Coupled Simulations of Wind Turbines and Offshore Support Structures : Strategies based on the Dynamic Substructuring Paradigm

    NARCIS (Netherlands)

    Van der Valk, P.L.C.

    2014-01-01

    Large scale offshore wind power has been recognized as a key technology to increase the share of renewable energy. However, as this energy source is currently still relatively expensive, efforts are made to significantly reduce its costs. Cost reductions are to be achieved, for instance, by

  2. Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

    DEFF Research Database (Denmark)

    King, Zachary A.; O'Brien, Edward J.; Feist, Adam M.

    2017-01-01

    metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli...

  3. Targeting chronic illness together. Health plans support providers through predictive analytics.

    Science.gov (United States)

    Raths, David

    2012-05-01

    Health insurers that used to focus on individual claims are now looking across their entire membership to better define the appropriate level of financial risk to assign and to drive the right kinds of interventions. Many are making a determined effort to share their predictive analytics findings with clinicians.

  4. Prediction of Composition and Emission Characteristics of Articles in Support of Exposure Assessment

    Science.gov (United States)

    The risk to humans from chemicals in consumer products is dependent on both hazard and exposure. The prediction and quantification of near-field (i.e., indoor) chemical exposure from household articles such as furniture and building materials is an ongoing effort. As opposed to (...

  5. OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records.

    Science.gov (United States)

    Chen, Jonathan H; Podchiyska, Tanya; Altman, Russ B

    2016-03-01

    To answer a "grand challenge" in clinical decision support, the authors produced a recommender system that automatically data-mines inpatient decision support from electronic medical records (EMR), analogous to Netflix or Amazon.com's product recommender. EMR data were extracted from 1 year of hospitalizations (>18K patients with >5.4M structured items including clinical orders, lab results, and diagnosis codes). Association statistics were counted for the ∼1.5K most common items to drive an order recommender. The authors assessed the recommender's ability to predict hospital admission orders and outcomes based on initial encounter data from separate validation patients. Compared to a reference benchmark of using the overall most common orders, the recommender using temporal relationships improves precision at 10 recommendations from 33% to 38% (P < 10(-10)) for hospital admission orders. Relative risk-based association methods improve inverse frequency weighted recall from 4% to 16% (P < 10(-16)). The framework yields a prediction receiver operating characteristic area under curve (c-statistic) of 0.84 for 30 day mortality, 0.84 for 1 week need for ICU life support, 0.80 for 1 week hospital discharge, and 0.68 for 30-day readmission. Recommender results quantitatively improve on reference benchmarks and qualitatively appear clinically reasonable. The method assumes that aggregate decision making converges appropriately, but ongoing evaluation is necessary to discern common behaviors from "correct" ones. Collaborative filtering recommender algorithms generate clinical decision support that is predictive of real practice patterns and clinical outcomes. Incorporating temporal relationships improves accuracy. Different evaluation metrics satisfy different goals (predicting likely events vs. "interesting" suggestions). Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government

  6. Predicting Chinese human resource managers' strategic competence : roles of identity, career variety, organizational support and career adaptability.

    OpenAIRE

    Guan, Y.; Yang, W.; Zhou, X.; Tian, Z.; Eves, A.

    2016-01-01

    Based on career construction theory, the predictors of human resource managers' strategic competence in the Chinese context were examined. Results from a survey administered to Chinese HR managers (N = 220) showed that professional identification, career variety and organizational support for strategic human resource management positively predicted Chinese human resource managers' strategic competence. In addition, career adaptability served as a significant mediator for the above relations. ...

  7. A glucose model based on support vector regression for the prediction of hypoglycemic events under free-living conditions.

    Science.gov (United States)

    Georga, Eleni I; Protopappas, Vasilios C; Ardigò, Diego; Polyzos, Demosthenes; Fotiadis, Dimitrios I

    2013-08-01

    The prevention of hypoglycemic events is of paramount importance in the daily management of insulin-treated diabetes. The use of short-term prediction algorithms of the subcutaneous (s.c.) glucose concentration may contribute significantly toward this direction. The literature suggests that, although the recent glucose profile is a prominent predictor of hypoglycemia, the overall patient's context greatly impacts its accurate estimation. The objective of this study is to evaluate the performance of a support vector for regression (SVR) s.c. glucose method on hypoglycemia prediction. We extend our SVR model to predict separately the nocturnal events during sleep and the non-nocturnal (i.e., diurnal) ones over 30-min and 60-min horizons using information on recent glucose profile, meals, insulin intake, and physical activities for a hypoglycemic threshold of 70 mg/dL. We also introduce herein additional variables accounting for recurrent nocturnal hypoglycemia due to antecedent hypoglycemia, exercise, and sleep. SVR predictions are compared with those from two other machine learning techniques. The method is assessed on a dataset of 15 patients with type 1 diabetes under free-living conditions. Nocturnal hypoglycemic events are predicted with 94% sensitivity for both horizons and with time lags of 5.43 min and 4.57 min, respectively. As concerns the diurnal events, when physical activities are not considered, the sensitivity is 92% and 96% for a 30-min and 60-min horizon, respectively, with both time lags being less than 5 min. However, when such information is introduced, the diurnal sensitivity decreases by 8% and 3%, respectively. Both nocturnal and diurnal predictions show a high (>90%) precision. Results suggest that hypoglycemia prediction using SVR can be accurate and performs better in most diurnal and nocturnal cases compared with other techniques. It is advised that the problem of hypoglycemia prediction should be handled differently for nocturnal

  8. Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012

    International Nuclear Information System (INIS)

    Altheimer, A.; Thompson, E.N.; Arce, A.; Bjergaard, D.; Asquith, L.; Backus Mayes, J.; Hook, A.; Izaguirre, E.; Jankowiak, M.; Larkoski, A.; Nef, P.; Schwartzman, A.; Swiatlowski, M.; Wacker, J.; Kuutmann, E.B.; Berger, J.; Bryngemark, L.; Buckley, A.; Debenedetti, C.; Butterworth, J.; Campanelli, M.; Davison, A.; Cacciari, M.; Carli, T.; Roeck, A. de; Chala, M.; Chapleau, B.; Chen, C.; Chou, J.P.; Cornelissen, T.; Fleischmann, S.; Curtin, D.; Dasgupta, M.; Almeida Dias, F. de; De Cosa, A.; Doglioni, C.; Guescini, F.; Ellis, S.D.; Hornig, A.; Scholtz, J.; Fassi, F.; Hoz, S.G. de la; Kaci, M.; Oliver Garcia, E.; Rodrigo, G.; Salt, J.; Sanchez Martinez, V.; Villaplana, M.; Vos, M.; Ferrando, J.; Kar, D.; Nordstrom, K.; Freytsis, M.; Gonzalez Silva, M.L.; Han, Z.; Lopez Mateos, D.; Schwartz, M.D.; Juknevich, J.; Kasieczka, G.; Plehn, T.; Schaetzel, S.; Takeuchi, M.; Kogler, R.; Loch, P.; Marzani, S.; Spannowsky, M.; Masetti, L.; Mateu, V.; Stewart, I.; Thaler, J.; Miller, D.W.; Mishra, K.; Tran, N.V.; Penwell, J.; Pilot, J.; Rappoccio, S.; Rizzi, A.; Safonov, A.; Salam, G.P.; Schioppa, M.; Schmidt, A.; Segala, M.; Son, M.; Soyez, G.; Strom, D.; Vermilion, C.; Walsh, J.

    2014-01-01

    This report of the BOOST2012 workshop presents the results of four working groups that studied key aspects of jet substructure. We discuss the potential of first-principle QCD calculations to yield a precise description of the substructure of jets and study the accuracy of state-of-the-art Monte Carlo tools. Limitations of the experiments' ability to resolve substructure are evaluated, with a focus on the impact of additional (pile-up) proton proton collisions on jet substructure performance in future LHC operating scenarios. A final section summarizes the lessons learnt from jet substructure analyses in searches for new physics in the production of boosted top quarks. (orig.)

  9. Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012

    Energy Technology Data Exchange (ETDEWEB)

    Altheimer, A.; Thompson, E.N. [Columbia University, Nevis Laboratory, Irvington, NY (United States); Arce, A.; Bjergaard, D. [Duke University, Durham, NC (United States); Asquith, L. [Argonne National Laboratory, Lemont, IL (United States); Backus Mayes, J.; Hook, A.; Izaguirre, E.; Jankowiak, M.; Larkoski, A.; Nef, P.; Schwartzman, A.; Swiatlowski, M.; Wacker, J. [SLAC National Accelerator Laboratory, Menlo Park, CA (United States); Kuutmann, E.B. [Deutsches Elektronen-Synchrotron, DESY, Zeuthen (Germany); Humboldt University, Berlin (Germany); Berger, J. [Cornell University, Ithaca, NY (United States); Bryngemark, L. [Lund University, Lund (Sweden); Buckley, A.; Debenedetti, C. [University of Edinburgh, Edinburgh (United Kingdom); Butterworth, J.; Campanelli, M.; Davison, A. [University College London, London (United Kingdom); Cacciari, M. [CERN, Geneva 23 (Switzerland); Carli, T.; Roeck, A. de [LPTHE, UPMC Univ. Paris 6 et CNRS UMR, Paris (France); Chala, M. [CAFPE and Univ. of Granada, Granada (Spain); Chapleau, B. [McGill University, Montreal, QC (Canada); Chen, C. [Iowa State University, Ames, IA (United States); Chou, J.P. [Rutgers University, Piscataway, NJ (United States); Cornelissen, T.; Fleischmann, S. [Bergische Universitaet Wuppertal, Wuppertal (Germany); Curtin, D. [YITP, Stony Brook University, Stony Brook, NY (United States); Dasgupta, M. [University of Manchester, Manchester (United Kingdom); Almeida Dias, F. de [UNESP-Universidade Estadual Paulista, Sao Paulo (Brazil); De Cosa, A. [INFN, Naples (Italy); University of Naples, Naples (Italy); Doglioni, C.; Guescini, F. [University of Geneva, Geneva 4 (Switzerland); Ellis, S.D.; Hornig, A.; Scholtz, J. [University of Washington, Seattle, WA (United States); Fassi, F.; Hoz, S.G. de la; Kaci, M.; Oliver Garcia, E.; Rodrigo, G.; Salt, J.; Sanchez Martinez, V.; Villaplana, M.; Vos, M. [Instituto de Fisica Corpuscular, IFIC/CSIC-UVEG, Valencia (Spain); Ferrando, J.; Kar, D.; Nordstrom, K. [University of Glasgow, Glasgow (United Kingdom); Freytsis, M. [University of California, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); University of California, Berkeley, CA (United States); Gonzalez Silva, M.L. [Universidad de Buenos Aires, Buenos Aires (Argentina); Han, Z.; Lopez Mateos, D.; Schwartz, M.D. [Harvard University, Cambridge, MA (United States); Juknevich, J. [Weizmann Institute, Rehovot (Israel); Kasieczka, G.; Plehn, T.; Schaetzel, S.; Takeuchi, M. [Universitaet Heidelberg, Heidelberg (Germany); Kogler, R. [Universitaet Hamburg, Hamburg (Germany); Loch, P. [University of Arizona, Tucson, AZ (United States); Marzani, S.; Spannowsky, M. [IPPP, University of Durham, Durham (United Kingdom); Masetti, L. [Universitaet Mainz, Mainz (Germany); Mateu, V.; Stewart, I.; Thaler, J. [MIT, Cambridge, MA (United States); Miller, D.W. [University of Chicago, Chicago, IL (United States); Mishra, K.; Tran, N.V. [Fermi National Accelerator Laboratory, Batavia, IL (United States); Penwell, J. [Indiana University, Bloomington, IN (United States); Pilot, J. [University of California, Davis, CA (United States); Rappoccio, S. [Johns Hopkins University, Baltimore, MD (United States); University at Buffalo, State University of New York, Buffalo, NY (United States); Rizzi, A. [INFN, Pisa (Italy); University of Pisa, Pisa (Italy); Safonov, A. [Texas A and M University, College Station, TX (United States); Salam, G.P. [CERN, Geneva 23 (Switzerland); LPTHE, UPMC Univ. Paris 6 et CNRS UMR, Paris (France); Schioppa, M. [INFN, Rende (IT); University of Calabria, Rende (IT); Schmidt, A. [Universitaet Hamburg, Hamburg (DE); Universitaet Heidelberg, Heidelberg (DE); Segala, M. [Brown University, Richmond, RI (US); Son, M. [Yale University, New Haven, CT (US); Soyez, G. [CEA Saclay, Gif-sur-Yvette (FR); Strom, D. [University of Illinois, Chicago, IL (US); Vermilion, C. [University of California, Lawrence Berkeley National Laboratory, Berkeley, CA (US); Walsh, J. [University of California, Berkeley, CA (US)

    2014-03-15

    This report of the BOOST2012 workshop presents the results of four working groups that studied key aspects of jet substructure. We discuss the potential of first-principle QCD calculations to yield a precise description of the substructure of jets and study the accuracy of state-of-the-art Monte Carlo tools. Limitations of the experiments' ability to resolve substructure are evaluated, with a focus on the impact of additional (pile-up) proton proton collisions on jet substructure performance in future LHC operating scenarios. A final section summarizes the lessons learnt from jet substructure analyses in searches for new physics in the production of boosted top quarks. (orig.)

  10. The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

    Directory of Open Access Journals (Sweden)

    Xiaomin Xu

    2015-01-01

    Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

  11. Application of Artificial Neural Network and Support Vector Machines in Predicting Metabolizable Energy in Compound Feeds for Pigs.

    Science.gov (United States)

    Ahmadi, Hamed; Rodehutscord, Markus

    2017-01-01

    In the nutrition literature, there are several reports on the use of artificial neural network (ANN) and multiple linear regression (MLR) approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM) method as a new alternative approach to MLR and ANN models is still not fully investigated. The MLR, ANN, and SVM models were developed to predict metabolizable energy (ME) content of compound feeds for pigs based on the German energy evaluation system from analyzed contents of crude protein (CP), ether extract (EE), crude fiber (CF), and starch. A total of 290 datasets from standardized digestibility studies with compound feeds was provided from several institutions and published papers, and ME was calculated thereon. Accuracy and precision of developed models were evaluated, given their produced prediction values. The results revealed that the developed ANN [ R 2  = 0.95; root mean square error (RMSE) = 0.19 MJ/kg of dry matter] and SVM ( R 2  = 0.95; RMSE = 0.21 MJ/kg of dry matter) models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR ( R 2  = 0.89; RMSE = 0.27 MJ/kg of dry matter). The developed ANN and SVM models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR; however, there were not obvious differences between performance of ANN and SVM models. Thus, SVM model may also be considered as a promising tool for modeling the relationship between chemical composition and ME of compound feeds for pigs. To provide the readers and nutritionist with the easy and rapid tool, an Excel ® calculator, namely, SVM_ME_pig, was created to predict the metabolizable energy values in compound feeds for pigs using developed support vector machine model.

  12. Periodic and chaotic psychological stress variations as predicted by a social support buffered response model

    Science.gov (United States)

    Field, Richard J.; Gallas, Jason A. C.; Schuldberg, David

    2017-08-01

    Recent work has introduced social dynamic models of people's stress-related processes, some including amelioration of stress symptoms by support from others. The effects of support may be ;direct;, depending only on the level of support, or ;buffering;, depending on the product of the level of support and level of stress. We focus here on the nonlinear buffering term and use a model involving three variables (and 12 control parameters), including stress as perceived by the individual, physical and psychological symptoms, and currently active social support. This model is quantified by a set of three nonlinear differential equations governing its stationary-state stability, temporal evolution (sometimes oscillatory), and how each variable affects the others. Chaos may appear with periodic forcing of an environmental stress parameter. Here we explore this model carefully as the strength and amplitude of this forcing, and an important psychological parameter relating to self-kindling in the stress response, are varied. Three significant observations are made: 1. There exist many complex but orderly regions of periodicity and chaos, 2. there are nested regions of increasing number of peaks per cycle that may cascade to chaos, and 3. there are areas where more than one state, e.g., a period-2 oscillation and chaos, coexist for the same parameters; which one is reached depends on initial conditions.

  13. Less thought, more punishment: need for cognition predicts support for punitive responses to crime.

    Science.gov (United States)

    Sargent, Michael J

    2004-11-01

    Three studies examined the relationship between need for cognition and support for punitive responses to crime. The results of Study 1 (N = 110) indicated that individuals high in need for cognition were less supportive of punitive measures than their low need for cognition counterparts. This finding was replicated in Study 2 (N = 1,807), which employed a nationally representative probability sample and included a more extensive battery of control variables. The purpose of Study 3 (N = 255) was to identify a third variable that might explain this relationship. This final study's results suggest that attributional complexity mediates the relationship between need for cognition and punitiveness. High need for cognition individuals are less supportive of punitive measures because they endorse more complex attributions for human behavior than their low need for cognition peers.

  14. Improving corrosion resistance of post-tensioned substructures emphasizing high performance grouts

    Science.gov (United States)

    Schokker, Andrea Jeanne

    The use of post-tensioning in bridges can provide durability and structural benefits to the system while expediting the construction process. When post-tensioning is combined with precast elements, traffic interference can be greatly reduced through rapid construction. Post-tensioned concrete substructure elements such as bridge piers, hammerhead bents, and straddle bents have become more prevalent in recent years. Chloride induced corrosion of steel in concrete is one of the most costly forms of corrosion each year. Coastal substructure elements are exposed to seawater by immersion or spray, and inland bridges may also be at risk due to the application of deicing salts. Corrosion protection of the post-tensioning system is vital to the integrity of the structure because loss of post-tensioning can result in catastrophic failure. Documentation for durability design of the grout, ducts, and anchorage systems is very limited. The objective of this research is to evaluate the effectiveness of corrosion protection measures for post-tensioned concrete substructures by designing and testing specimens representative of typical substructure elements using state-of-the-art practices in aggressive chloride exposure environments. This was accomplished through exposure testing of twenty-seven large-scale beam specimens and ten large-scale column specimens. High performance grout for post-tensioning tendon injection was also developed through a series of fresh property tests, accelerated exposure tests, and a large-scale pumping test to simulate field conditions. A high performance fly ash grout was developed for applications with small vertical rises, and a high performance anti-bleed grout was developed for applications involving large vertical rises such as tall bridge piers. Long-term exposure testing of the beam and column specimens is ongoing, but preliminary findings indicate increased corrosion protection with increasing levels of post-tensioning, although traditional

  15. Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems

    OpenAIRE

    Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

    2016-01-01

    It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed...

  16. The predictive factors for perceived social support among cancer patients and caregiver burden of their family caregivers in Turkish population.

    Science.gov (United States)

    Oven Ustaalioglu, Basak; Acar, Ezgi; Caliskan, Mecit

    2018-03-01

    We aimed to identify the predictive factors for the perceived family social support among cancer patients and caregiver burden of their family caregivers. Participants were 302 cancer patients and their family caregivers. Family social support scale was used for cancer patients, burden interview was used for family caregivers.All subjects also completed Beck depression invantery. The related socio-demographical factors with perceived social support (PSS) and caregiver burden were evaluated by correlation analysis. To find independent factors predicting caregiver burden and PSS, logistic regression analysis were conducted. Depression scores was higher among patients than their family caregivers (12.5 vs. 8). PSS was lower in depressed patients (p Family caregiver burden were also higher in depressive groups (p family caregiver role was negatively correlated (p caregiver burden. Presence of depression was the independent predictor for both, lower PSS for patients and higher burden for caregivers. The results of this study is noteworthy because it may help for planning any supportive care program not only for patients but together with their caregiver at the same time during chemotherapy period in Turkish population.

  17. Learned Social Hopelessness: The Role of Explanatory Style in Predicting Social Support during Adolescence

    Science.gov (United States)

    Ciarrochi, Joseph; Heaven, Patrick C. L.

    2008-01-01

    Background: Almost no research has examined the impact of explanatory style on social adjustment. We hypothesised that adolescents with a pessimistic style would be less likely to develop and maintain social support networks. Methods: Seven hundred and nineteen students (351 males and 366 females; 2 unknown; M[subscript AGE] = 12.28, SD = 0.49)…

  18. Social Support and Coping Styles in Predicting Suicide Probability among Turkish Adolescents

    Science.gov (United States)

    Cenkseven-Önder, Fulya

    2018-01-01

    This study aims to investigate whether the perceived social support and coping styles are predictors of suicide probability by gender. The study was conducted with 445 high schools students, 227 girls, and 218 boys. The participants were aged between 14 and 18, and their average age was 15.90. Data were collected through the "Multidimensional…

  19. Using Emotional Intelligence and Social Support to Predict Job Performance of Health Educators

    Science.gov (United States)

    Branscum, Paul; Haider, Taj; Brown, David; Sharma, Manoj

    2016-01-01

    Background: The theory of emotional intelligence (EI) has been developed to evaluate and highlight the importance of emotional health, especially on job performance. Purpose: No study has examined EI's role on the performance of public health educators; therefore, this study examined the role of EI and social support on the performance of health…

  20. Predicting Preschoolers' Attachment Security from Fathers' Involvement, Internal Working Models, and Use of Social Support

    Science.gov (United States)

    Newland, Lisa A.; Coyl, Diana D.; Freeman, Harry

    2008-01-01

    Associations between preschoolers' attachment security, fathers' involvement (i.e. parenting behaviors and consistency) and fathering context (i.e. fathers' internal working models (IWMs) and use of social support) were examined in a subsample of 102 fathers, taken from a larger sample of 235 culturally diverse US families. The authors predicted…

  1. Examining the Correlation between Perceived Social Support in Adolescence and Bullying in Terms of Prediction

    Science.gov (United States)

    Isiklar, Abdullah; Sar, Ali Haydar; Celik, Aslihan

    2012-01-01

    This research was carried out to examine perceived social support in adolescence and bullying. 112 females and 171 males (in total 283) attending different types of high schools were used in this research. The sample group includes students who were referred to guidance and counseling service as bullies. According to the research results; when…

  2. Soil moisture prediction to support management in semiarid wetlands during drying episodes

    NARCIS (Netherlands)

    Aguilera, Héctor; Moreno, Luis; Wesseling, Jan G.; Jiménez-Hernández, María E.; Castaño, Silvino

    2016-01-01

    Wetlands supported by groundwater in semiarid regions are extremely vulnerable to the impacts of droughts, particularly anthropized systems. During drying periods, soil water content arises as the controlling factor for environmental and ecological disturbances such as the spread of invasive

  3. Early Student Support to Investigate the Role of Sea Ice Albedo Feedback in Sea Ice Predictions

    Science.gov (United States)

    2015-09-30

    supports Brandon Ray, who just completed his second year of graduate studies. Cecilia Bitz, the PI, manages the project and supervises the graduate...www.arcus.org/sipn REFERENCES Bitz, C.M., K.M. Shell, P.R. Gent, D. Bailey, G. Danabasoglu, K.C. Armour , M. M. Holland, and J.T. Kiehl, 2012

  4. Factors Predicting Sustainability of the Schoolwide Positive Behavior Intervention Support Model

    Science.gov (United States)

    Chitiyo, Jonathan; May, Michael E.

    2018-01-01

    The Schoolwide Positive Behavior Intervention Support model (SWPBIS) continues to gain widespread use across schools in the United States and abroad. Despite its widespread implementation, little research has examined factors that influence its sustainability. Informed by Rogers's diffusion theory, this study examined school personnel's…

  5. Factors Predicting Oncology Care Providers' Behavioral Intention to Adopt Clinical Decision Support Systems

    Science.gov (United States)

    Wolfenden, Andrew

    2012-01-01

    The purpose of this quantitative correlation study was to examine the predictors of user behavioral intention on the decision of oncology care providers to adopt or reject the clinical decision support system. The Unified Theory of Acceptance and Use of Technology (UTAUT) formed the foundation of the research model and survey instrument. The…

  6. Predictive modeling of interfacial damage in substructured steels: application to martensitic microstructures

    NARCIS (Netherlands)

    Maresca, F.; Kouznetsova, V.; Geers, M.G.D.

    2016-01-01

    Metallic composite phases, like martensite present in conventional steels and new generation high strength steels exhibit microscale, locally lamellar microstructures characterized by alternating layers of phases or crystallographic variants. The layers can be sub-micron down to a few nanometers

  7. Do parents' support behaviours predict whether or not their children get sufficient sleep? A cross-sectional study.

    Science.gov (United States)

    Pyper, Evelyn; Harrington, Daniel; Manson, Heather

    2017-05-24

    Sleep is an essential component of healthy cognitive and physical development. Lack of sleep may put children at risk for a variety of mental and physical health outcomes, including overweight, obesity and related chronic diseases. Given that children's sleep duration has decreased in recent decades, there is a need to understand the determinants of child sleep, including the role of parental support behaviours. This study aims to determine the relative contribution of different types of parental support behaviours for predicting the likelihood that children meet recently established Canadian sleep guidelines. Data were collected using Computer Assisted Telephone Interviews (CATI) of parents or guardians with at least one child under the age of 18 living in Ontario, Canada. To align with sleep guidelines, parents included in this analysis had at least one child between 5 and 17 years of age (n = 1622). Two multivariable logistic regression models were built to predict whether or not parents reported their child was meeting sleep guidelines - one for weekday sleep and another for sleep on weekends. Independent variables included parent and child age and gender, motivational and regulatory parental support behaviours, and socio-demographic characteristics. On weekdays, enforcing rules about child bedtime was a significant positive predictor of children meeting sleep guidelines (OR: 1.59; 95% CI: 1.03-2.44); while encouraging the child to go to bed at a specific time was a significant negative predictor of child meeting sleep guidelines (OR: 0.29; 95% CI: 0.13-0.65). On weekends, none of the parental support behaviours contributed significantly to the predictions of child sleep. For both weekdays and weekends, the child's age group was an important predictor of children meeting sleep guidelines. The contribution of parental support behaviours to predictions of children meeting sleep guidelines varied with the type of support provided, and weekend versus weekday

  8. Do parents’ support behaviours predict whether or not their children get sufficient sleep? A cross-sectional study

    Directory of Open Access Journals (Sweden)

    Evelyn Pyper

    2017-05-01

    Full Text Available Abstract Background Sleep is an essential component of healthy cognitive and physical development. Lack of sleep may put children at risk for a variety of mental and physical health outcomes, including overweight, obesity and related chronic diseases. Given that children’s sleep duration has decreased in recent decades, there is a need to understand the determinants of child sleep, including the role of parental support behaviours. This study aims to determine the relative contribution of different types of parental support behaviours for predicting the likelihood that children meet recently established Canadian sleep guidelines. Methods Data were collected using Computer Assisted Telephone Interviews (CATI of parents or guardians with at least one child under the age of 18 living in Ontario, Canada. To align with sleep guidelines, parents included in this analysis had at least one child between 5 and 17 years of age (n = 1622. Two multivariable logistic regression models were built to predict whether or not parents reported their child was meeting sleep guidelines – one for weekday sleep and another for sleep on weekends. Independent variables included parent and child age and gender, motivational and regulatory parental support behaviours, and socio-demographic characteristics. Results On weekdays, enforcing rules about child bedtime was a significant positive predictor of children meeting sleep guidelines (OR: 1.59; 95% CI: 1.03–2.44; while encouraging the child to go to bed at a specific time was a significant negative predictor of child meeting sleep guidelines (OR: 0.29; 95% CI: 0.13–0.65. On weekends, none of the parental support behaviours contributed significantly to the predictions of child sleep. For both weekdays and weekends, the child’s age group was an important predictor of children meeting sleep guidelines. Conclusions The contribution of parental support behaviours to predictions of children meeting sleep

  9. Predicting microscopic extrauterine spread of endometrial carcinoma with MRI to support less invasive therapy

    Energy Technology Data Exchange (ETDEWEB)

    Oishi Tanaka, Yumiko; Nishida, Masato; Minami, Rie; Yamaguchi, Masayuki; Itai, Yuji [Tsukuba Univ., Ibaraki (Japan). Inst. of Clinical Medicine; Yoshizako, Takeshi

    2000-06-01

    Magnetic resonance imaging (MRI) provides precise staging of endometrial carcinoma. However, we have sometimes experienced patients with microscopic extrauterine extension in whom MRI showed the disease as being limited to the uterus. We studied indirect MRI signs for microscopic extrauterine spread of endometrial carcinoma which outwardly seemed to be limited to within the uterus. MRI studies and the clinical records of 100 patients with surgically proven endometrial carcinoma were retrospectively reviewed. We evaluated: MRI staging, tumor growing at the orifices of the fallopian tube in the uterine fundus, hydrosalpinx, and ascites, in each MRI study. Surgical specimens showed that 12 of the 100 patients had extrauterine spread, with 1 patient showing both ovarian extension and omental metastasis; there ovarian extension in 3, extension to the fallopian tubes in 3, omental metastasis in 1, and positive peritoneal cytology in 4. Tumor growing at the orifices of the fallopian tubes with deep myometrial invasion showed higher accuracy for predicting microscopic intrauterine spread (82.0%) although it was not significantly different from the accuracy of deep myometrial invasion anywhere within the uterus (75.0%). However, tumor growing at the orifices of the fallopian tubes in patients with stage Ia disease showed a high negative predictive value (89.7%). Hydrosalpinx had the highest specificity (98.9%) and accuracy (88.0%); however, it did not seem to be practical because it was observed in only 2 patients. Ascites in postmenopausal patients showed higher specificity (93.5%), although it was not considered to be useful in the premenopausal patients. Tumor extension at the orifices of the fallopian tubes in patients with stage Ia disease, and ascites in postmenopausal patients on MRI seemed to be predictive factors for microscopic extrauterine spread. (author)

  10. Prediction of Hydrologic Characteristics for Ungauged Catchments to Support Hydroecological Modeling

    Science.gov (United States)

    Bond, Nick R.; Kennard, Mark J.

    2017-11-01

    Hydrologic variability is a fundamental driver of ecological processes and species distribution patterns within river systems, yet the paucity of gauges in many catchments means that streamflow data are often unavailable for ecological survey sites. Filling this data gap is an important challenge in hydroecological research. To address this gap, we first test the ability to spatially extrapolate hydrologic metrics calculated from gauged streamflow data to ungauged sites as a function of stream distance and catchment area. Second, we examine the ability of statistical models to predict flow regime metrics based on climate and catchment physiographic variables. Our assessment focused on Australia's largest catchment, the Murray-Darling Basin (MDB). We found that hydrologic metrics were predictable only between sites within ˜25 km of one another. Beyond this, correlations between sites declined quickly. We found less than 40% of fish survey sites from a recent basin-wide monitoring program (n = 777 sites) to fall within this 25 km range, thereby greatly limiting the ability to utilize gauge data for direct spatial transposition of hydrologic metrics to biological survey sites. In contrast, statistical model-based transposition proved effective in predicting ecologically relevant aspects of the flow regime (including metrics describing central tendency, high- and low-flows intermittency, seasonality, and variability) across the entire gauge network (median R2 ˜ 0.54, range 0.39-0.94). Modeled hydrologic metrics thus offer a useful alternative to empirical data when examining biological survey data from ungauged sites. More widespread use of these statistical tools and modeled metrics could expand our understanding of flow-ecology relationships.

  11. Water demand prediction using artificial neural networks and support vector regression

    CSIR Research Space (South Africa)

    Msiza, IS

    2008-11-01

    Full Text Available Neural Networks and Support Vector Regression Ishmael S. Msiza1, Fulufhelo V. Nelwamondo1,2, Tshilidzi Marwala3 . 1Modelling and Digital Science, CSIR, Johannesburg,SOUTH AFRICA 2Graduate School of Arts and Sciences, Harvard University, Cambridge..., Massachusetts, USA 3School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, SOUTH AFRICA Email: imsiza@csir.co.za, nelwamon@fas.harvard.edu, tshilidzi.marwala@wits.ac.za Abstract— Computational Intelligence techniques...

  12. EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

    OpenAIRE

    Bennett, Casey; Doub, Tom; Selove, Rebecca

    2012-01-01

    Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. ...

  13. 3D Representative Volume Element Reconstruction of Fiber Composites via Orientation Tensor and Substructure Features

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yi; Chen, Wei; Xu, Hongyi; Jin, Xuejun

    2016-01-01

    To provide a seamless integration of manufacturing processing simulation and fiber microstructure modeling, two new stochastic 3D microstructure reconstruction methods are proposed for two types of random fiber composites: random short fiber composites, and Sheet Molding Compounds (SMC) chopped fiber composites. A Random Sequential Adsorption (RSA) algorithm is first developed to embed statistical orientation information into 3D RVE reconstruction of random short fiber composites. For the SMC composites, an optimized Voronoi diagram based approach is developed for capturing the substructure features of SMC chopped fiber composites. The proposed methods are distinguished from other reconstruction works by providing a way of integrating statistical information (fiber orientation tensor) obtained from material processing simulation, as well as capturing the multiscale substructures of the SMC composites.

  14. Fast and accurate protein substructure searching with simulated annealing and GPUs

    Directory of Open Access Journals (Sweden)

    Stivala Alex D

    2010-09-01

    Full Text Available Abstract Background Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif searching. Results We developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU. Conclusions The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.

  15. Seasonal variations of radon concentrations in single-family houses with different sub-structures

    DEFF Research Database (Denmark)

    Majborn, B.

    1992-01-01

    Seasonal variations of indoor radon concentrations have been studied in 70 single-family houses selected according to the type of sub-structure and the type of soil underneath the house. Five categories of sub-structure were included - slab-on-grade, crawl space, basement, and combinations...... of basement with slab-on-grade or crawl space. Half of the houses are located on clayey till and the other half on glaciofluvial gravel. In each house radon was measured in a living room and a bedroom, in the basement if present, and in the crawl space if present and accessible. The measurements were made...... with track detectors on a quarterly basis throughout a year. For living rooms and bedrooms the seasonal variations range from being highly significant for the slab-on-grade houses to being insignificant for the crawl space houses. For basements and crawl spaces the geometric mean radon concentrations do...

  16. Performance of large-R jets and jet substructure reconstruction with the ATLAS detector

    CERN Document Server

    The ATLAS collaboration

    2012-01-01

    This paper presents the application of techniques to study jet substructure. The performance of modified jet algorithms for a variety of jet types and event topologies is investigated. Properties of jets subjected to the mass-drop filtering, trimming and pruning algorithms are found to have a reduced sensitivity to multiple proton-proton interactions and exhibit improved stability at high luminosity. Monte Carlo studies of the signal-background discrimination with jet grooming in new physics searches based on jet invariant mass and jet substructure properties are also presented. The application of jet trimming is shown to improve the robustness of large-R jet measurements, reduce sensitivity to the superfluous effects due to the intense environment of the high luminosity LHC, and improve the physics potential of searches for heavy boosted objects. The analyses presented in this note use the full 2011 ATLAS dataset, corresponding to an integrated luminosity of 4.7 \\pm 0.2 fb−1 .

  17. Development and Demonstration of a Magnesium-Intensive Vehicle Front-End Substructure

    Energy Technology Data Exchange (ETDEWEB)

    Logan, Stephen D. [United States Automotive Materials Partnership LLC, Southfield, MI (United States); Forsmark, Joy H. [United States Automotive Materials Partnership LLC, Southfield, MI (United States); Osborne, Richard [United States Automotive Materials Partnership LLC, Southfield, MI (United States)

    2016-07-01

    This project is the final phase (designated Phase III) of an extensive, nine-year effort with the objectives of developing a knowledge base and enabling technologies for the design, fabrication and performance evaluation of magnesium-intensive automotive front-end substructures intended to partially or completely replace all-steel comparators, providing a weight savings approaching 50% of the baseline. Benefits of extensive vehicle weight reduction in terms of fuel economy increase, extended vehicle range, vehicle performance and commensurate reductions in greenhouse gas emissions are well known. An exemplary vehicle substructure considered by the project is illustrated in Figure 1, along with the exterior vehicle appearance. This unibody front-end “substructure” is one physical objective of the ultimate design and engineering aspects established at the outset of the larger collective effort.

  18. Deliverable D74.2. Probabilistic analysis methods for support structures

    DEFF Research Database (Denmark)

    Gintautas, Tomas

    2018-01-01

    Relevant Description: Report describing the probabilistic analysis for offshore substructures and results attained. This includes comparison with experimental data and with conventional design. Specific targets: 1) Estimate current reliability level of support structures 2) Development of basis...... for probabilistic calculations and evaluation of reliability for offshore support structures (substructures) 3) Development of a probabilistic model for stiffness and strength of soil parameters and for modeling geotechnical load bearing capacity 4) Comparison between probabilistic analysis and deterministic...

  19. Weak lensing study of 16 DAFT/FADA clusters: Substructures and filaments

    Science.gov (United States)

    Martinet, Nicolas; Clowe, Douglas; Durret, Florence; Adami, Christophe; Acebrón, Ana; Hernandez-García, Lorena; Márquez, Isabel; Guennou, Loic; Sarron, Florian; Ulmer, Mel

    2016-05-01

    While our current cosmological model places galaxy clusters at the nodes of a filament network (the cosmic web), we still struggle to detect these filaments at high redshifts. We perform a weak lensing study for a sample of 16 massive, medium-high redshift (0.4 DAFT/FADA survey, which are imaged in at least three optical bands with Subaru/Suprime-Cam or CFHT/MegaCam. We estimate the cluster masses using an NFW fit to the shear profile measured in a KSB-like method, adding our contribution to the calibration of the observable-mass relation required for cluster abundance cosmological studies. We compute convergence maps and select structures within these maps, securing their detection with noise resampling techniques. Taking advantage of the large field of view of our data, we study cluster environment, adding information from galaxy density maps at the cluster redshift and from X-ray images when available. We find that clusters show a large variety of weak lensing maps at large scales and that they may all be embedded in filamentary structures at megaparsec scale. We classify these clusters in three categories according to the smoothness of their weak lensing contours and to the amount of substructures: relaxed (~7%), past mergers (~21.5%), and recent or present mergers (~71.5%). The fraction of clusters undergoing merging events observationally supports the hierarchical scenario of cluster growth, and implies that massive clusters are strongly evolving at the studied redshifts. Finally, we report the detection of unusually elongated structures in CLJ0152, MACSJ0454, MACSJ0717, A851, BMW1226, MACSJ1621, and MS1621. This study is based on observations obtained with MegaCam, a joint project of CFHT and CEA/IRFU, at the Canada-France-Hawaii Telescope (CFHT), which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii

  20. Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure

    CERN Document Server

    Dolen, James; Marzani, Simone; Rappoccio, Salvatore; Tran, Nhan

    2016-01-01

    We explore the scale-dependence and correlations of jet substructure observables to improve upon existing techniques in the identification of highly Lorentz-boosted objects. Modified observables are designed to remove correlations from existing theoretically well-understood observables, providing practical advantages for experimental measurements and searches for new phenomena. We study such observables in $W$ jet tagging and provide recommendations for observables based on considerations beyond signal and background efficiencies.

  1. Comparison of Shade of Ceramic with Three Different Zirconia Substructures using Spectrophotometer.

    Science.gov (United States)

    Habib, Syed Rashid; Shiddi, Ibraheem F Al

    2015-02-01

    This study assessed how changing the Zirconia (Zr) substructure affected the color samples after they have been overlaid by the same shade of veneering ceramic. Three commercial Zr materials were tested in this study: Prettau(®) Zirconia (ZirKonZahn, Italy), Cercon (Dentsply, Germany) and InCoris ZI (Sirona, Germany). For each system, 15 disk-shaped specimens (10 × 1 mm) were fabricated. Three shades of A1, A2 and A3.5 of porcelain (IPS e.MaxCeram, IvoclarVivadent, USA) were used for layering the specimens. Five specimens from each type of Zr were layered with same shade of ceramic. Color measurements were recorderd by a spectrophotometer Color-Eye(®) 7000A (X-Rite, Grand Rapids, MI). Mean values of L, a, b color coordinates and ΔE were recorded and comparisons were made. Differences in the ΔE were recorded for the same porcelain shade with different Zr substructures and affected the color of the specimens (p < 0.01, ANOVA). The maximum difference between the ΔE values for the A1, A2 and A3.5 shades with three types of Zr substructures was found to be 1.59, 1.69 and 1.45 respectively. Multiple comparisons of the ΔE with PostHoc Tukey test revealed a statistically significant difference (p < 0.05) between the three types of Zr, except between Type 2 Zr and Type 3 Zr for the Shade A1. The mean values of L, a, b and ΔE for the Prettau(®) Zirconia substructure were found to be the least among the three types. The brand of Zr used influences the final color of the all ceramic Zr based restorations and this has clinical significance.

  2. Substructuring preconditioners for an h-p domain decomposition method with interior penalty mortaring

    KAUST Repository

    Antonietti, P. F.; Ayuso Dios, Blanca; Bertoluzza, S.; Pennacchio, M.

    2014-01-01

    We propose and study an iterative substructuring method for an h-p Nitsche-type discretization, following the original approach introduced in Bramble et al. Math. Comp. 47(175):103–134, (1986) for conforming methods. We prove quasi-optimality with respect to the mesh size and the polynomial degree for the proposed preconditioner. Numerical experiments assess the performance of the preconditioner and verify the theory. © 2014, Springer-Verlag Italia.

  3. Observation of the substructure in the electron bunch on the ACO storage ring

    International Nuclear Information System (INIS)

    Bergher, M.; Velghe, M.; Mialocq, J.P.

    1984-09-01

    In the future, one interesting point of the SRFEL at Orsay will be the microtemporal analysis of the laser beam correlated with that of the electron bunch. In a first time, we have only analysed the temporal structure of the electron bunch with an Electrophotonic streak camera. The first results seem to indicate that the bunch is not an homogeneous bunch but presents a substructure. We discuss with details this data

  4. AFM friction and adhesion mapping of the substructures of human hair cuticles

    International Nuclear Information System (INIS)

    Smith, James R.; Tsibouklis, John; Nevell, Thomas G.; Breakspear, Steven

    2013-01-01

    Using atomic force microscopy, values of the microscale friction coefficient, the tip (silicon nitride) - surface adhesion force and the corresponding adhesion energy, for the substructures that constitute the surface of human hair (European brown hair) have been determined from Amonton plots. The values, mapped for comparison with surface topography, corresponded qualitatively with the substructures’ plane surface characteristics. Localised maps and values of the frictional coefficient, extracted avoiding scale edge effects, are likely to inform the formulation of hair-care products and treatments.

  5. Thinking outside the ROCs: Designing decorrelated taggers (DDT) for jet substructure

    International Nuclear Information System (INIS)

    Dolen, James; Harris, Philip; Marzani, Simone; Rappoccio, Salvatore; Tran, Nhan

    2016-01-01

    Here, we explore the scale-dependence and correlations of jet substructure observables to improve upon existing techniques in the identification of highly Lorentz-boosted objects. Modified observables are designed to remove correlations from existing theoretically well-understood observables, providing practical advantages for experimental measurements and searches for new phenomena. We study such observables in W jet tagging and provide recommendations for observables based on considerations beyond signal and background efficiencies

  6. Substructuring preconditioners for an h-p domain decomposition method with interior penalty mortaring

    KAUST Repository

    Antonietti, P. F.

    2014-05-13

    We propose and study an iterative substructuring method for an h-p Nitsche-type discretization, following the original approach introduced in Bramble et al. Math. Comp. 47(175):103–134, (1986) for conforming methods. We prove quasi-optimality with respect to the mesh size and the polynomial degree for the proposed preconditioner. Numerical experiments assess the performance of the preconditioner and verify the theory. © 2014, Springer-Verlag Italia.

  7. Support vector machine learning model for the prediction of sentinel node status in patients with cutaneous melanoma.

    Science.gov (United States)

    Mocellin, Simone; Ambrosi, Alessandro; Montesco, Maria Cristina; Foletto, Mirto; Zavagno, Giorgio; Nitti, Donato; Lise, Mario; Rossi, Carlo Riccardo

    2006-08-01

    Currently, approximately 80% of melanoma patients undergoing sentinel node biopsy (SNB) have negative sentinel lymph nodes (SLNs), and no prediction system is reliable enough to be implemented in the clinical setting to reduce the number of SNB procedures. In this study, the predictive power of support vector machine (SVM)-based statistical analysis was tested. The clinical records of 246 patients who underwent SNB at our institution were used for this analysis. The following clinicopathologic variables were considered: the patient's age and sex and the tumor's histological subtype, Breslow thickness, Clark level, ulceration, mitotic index, lymphocyte infiltration, regression, angiolymphatic invasion, microsatellitosis, and growth phase. The results of SVM-based prediction of SLN status were compared with those achieved with logistic regression. The SLN positivity rate was 22% (52 of 234). When the accuracy was > or = 80%, the negative predictive value, positive predictive value, specificity, and sensitivity were 98%, 54%, 94%, and 77% and 82%, 41%, 69%, and 93% by using SVM and logistic regression, respectively. Moreover, SVM and logistic regression were associated with a diagnostic error and an SNB percentage reduction of (1) 1% and 60% and (2) 15% and 73%, respectively. The results from this pilot study suggest that SVM-based prediction of SLN status might be evaluated as a prognostic method to avoid the SNB procedure in 60% of patients currently eligible, with a very low error rate. If validated in larger series, this strategy would lead to obvious advantages in terms of both patient quality of life and costs for the health care system.

  8. COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.

    Science.gov (United States)

    Zhang, Huiling; Huang, Qingsheng; Bei, Zhendong; Wei, Yanjie; Floudas, Christodoulos A

    2016-03-01

    In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/. © 2016 Wiley Periodicals, Inc.

  9. Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines.

    Science.gov (United States)

    Majid, Abdul; Ali, Safdar; Iqbal, Mubashar; Kausar, Nabeela

    2014-03-01

    This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  10. Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing

    Directory of Open Access Journals (Sweden)

    Valentina Ciullo

    2018-05-01

    Full Text Available The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still debated. Using graph theoretical analysis applied to structural connectivity data, we tested the extent of brain connectivity properties associated with spatio-temporal predictive performance across 29 healthy subjects. Participants detected visual targets appearing at one out of three locations after one out of three intervals; expectations about stimulus location (spatial condition or onset (temporal condition were induced by valid or invalid symbolic cues. Connectivity matrices and centrality/segregation measures, expressing the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation, were calculated from whole-brain tractography and cortico-subcortical parcellation.Results: Response preparedness to cued stimuli relied on different structural connectivity networks for the temporal and spatial domains. Significant covariance was observed between centrality measures of regions within a subcortical-fronto-parietal-occipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit. When the potential for functional segregation was tested, we found highly clustered structural connectivity across the right superior, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting. Conversely, when the interaction between

  11. Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing.

    Science.gov (United States)

    Ciullo, Valentina; Vecchio, Daniela; Gili, Tommaso; Spalletta, Gianfranco; Piras, Federica

    2018-01-01

    The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still debated. Using graph theoretical analysis applied to structural connectivity data, we tested the extent of brain connectivity properties associated with spatio-temporal predictive performance across 29 healthy subjects. Participants detected visual targets appearing at one out of three locations after one out of three intervals; expectations about stimulus location (spatial condition) or onset (temporal condition) were induced by valid or invalid symbolic cues. Connectivity matrices and centrality/segregation measures, expressing the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation, were calculated from whole-brain tractography and cortico-subcortical parcellation. Results: Response preparedness to cued stimuli relied on different structural connectivity networks for the temporal and spatial domains. Significant covariance was observed between centrality measures of regions within a subcortical-fronto-parietal-occipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit. When the potential for functional segregation was tested, we found highly clustered structural connectivity across the right superior, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting. Conversely, when the interaction between explicit and

  12. A substructure method to compute the 3D fluid-structure interaction during blowdown

    International Nuclear Information System (INIS)

    Guilbaud, D.; Axisa, F.; Gantenbein, F.; Gibert, R.J.

    1983-08-01

    The waves generated by a sudden rupture of a PWR primary pipe have an important mechanical effect on the internal structures of the vessel. This fluid-structure interaction has a strong 3D aspect. 3D finite element explicit methods can be applied. These methods take into account the non linearities of the problem but the calculation is heavy and expensive. We describe in this paper another type of method based on a substructure procedure: the vessel, internals and contained fluid are axisymmetrically described (AQUAMODE computer code). The pipes and contained fluid are monodimensionaly described (TEDEL-FLUIDE Computer Code). These substructures are characterized by their natural modes. Then, they are connected to another (connection of both structural and fluid nodes) the TRISTANA Computer Code. This method allows to compute correctly and cheaply the 3D fluid-structure effects. The treatment of certain non linearities is difficult because of the modal characterization of the substructures. However variations of contact conditions versus time can be introduced. We present here some validation tests and comparison with experimental results of the litterature

  13. Some sub-structures of many-particle correlation in nuclei

    Energy Technology Data Exchange (ETDEWEB)

    Chang, C; Chao, W; Li, K

    1977-01-01

    The coherent structures of two phonons were proposed as the sub-structure ..cap alpha..' of four-particle clusters for the light nuclei. In the same way the sub-structure ..beta../sup +/ of four-hole clusters can also be given. Based on this the sub-structures between particle clusters and hole clusters in /sup 16/O and /sup 18/O were chosen as examples for investigation. It is found that there is a very strong repulsive force between them. Therefore the loose structure between particle cluster and hole cluster is of the lowest energy state. In this way, the deformations of these states were explained from the microscopic structures. Moreover, these structures can coherently strengthen the E2 transition. Further in order to study the particle correlation in the medium nuclei, the L-S coupling coherent structure is extended to the pseudo L-S coupling coherent structure and the expressions are given in the j-j coupling representation. Some preliminary analyses are made for the nuclei around /sup 56/Ni by using these structures.

  14. Automatic identification of mobile and rigid substructures in molecular dynamics simulations and fractional structural fluctuation analysis.

    Directory of Open Access Journals (Sweden)

    Leandro Martínez

    Full Text Available The analysis of structural mobility in molecular dynamics plays a key role in data interpretation, particularly in the simulation of biomolecules. The most common mobility measures computed from simulations are the Root Mean Square Deviation (RMSD and Root Mean Square Fluctuations (RMSF of the structures. These are computed after the alignment of atomic coordinates in each trajectory step to a reference structure. This rigid-body alignment is not robust, in the sense that if a small portion of the structure is highly mobile, the RMSD and RMSF increase for all atoms, resulting possibly in poor quantification of the structural fluctuations and, often, to overlooking important fluctuations associated to biological function. The motivation of this work is to provide a robust measure of structural mobility that is practical, and easy to interpret. We propose a Low-Order-Value-Optimization (LOVO strategy for the robust alignment of the least mobile substructures in a simulation. These substructures are automatically identified by the method. The algorithm consists of the iterative superposition of the fraction of structure displaying the smallest displacements. Therefore, the least mobile substructures are identified, providing a clearer picture of the overall structural fluctuations. Examples are given to illustrate the interpretative advantages of this strategy. The software for performing the alignments was named MDLovoFit and it is available as free-software at: http://leandro.iqm.unicamp.br/mdlovofit.

  15. Earthquake analysis of structures including structure-soil interaction by a substructure method

    International Nuclear Information System (INIS)

    Chopra, A.K.; Guttierrez, J.A.

    1977-01-01

    A general substructure method for analysis of response of nuclear power plant structures to earthquake ground motion, including the effects of structure-soil interaction, is summarized. The method is applicable to complex structures idealized as finite element systems and the soil region treated as either a continuum, for example as a viscoelastic halfspace, or idealized as a finite element system. The halfspace idealization permits reliable analysis for sites where essentially similar soils extend to large depths and there is no rigid boundary such as soil-rock interface. For sites where layers of soft soil are underlain by rock at shallow depth, finite element idealization of the soil region is appropriate; in this case, the direct and substructure methods would lead to equivalent results but the latter provides the better alternative. Treating the free field motion directly as the earthquake input in the substructure eliminates the deconvolution calculations and the related assumption-regarding type and direction of earthquake waves-required in the direct method. (Auth.)

  16. CLOSE STELLAR ENCOUNTERS IN YOUNG, SUBSTRUCTURED, DISSOLVING STAR CLUSTERS: STATISTICS AND EFFECTS ON PLANETARY SYSTEMS

    International Nuclear Information System (INIS)

    Craig, Jonathan; Krumholz, Mark R.

    2013-01-01

    Both simulations and observations indicate that stars form in filamentary, hierarchically clustered associations, most of which disperse into their galactic field once feedback destroys their parent clouds. However, during their early evolution in these substructured environments, stars can undergo close encounters with one another that might have significant impacts on their protoplanetary disks or young planetary systems. We perform N-body simulations of the early evolution of dissolving, substructured clusters with a wide range of properties, with the aim of quantifying the expected number and orbital element distributions of encounters as a function of cluster properties. We show that the presence of substructure both boosts the encounter rate and modifies the distribution of encounter velocities compared to what would be expected for a dynamically relaxed cluster. However, the boost only lasts for a dynamical time, and as a result the overall number of encounters expected remains low enough that gravitational stripping is unlikely to be a significant effect for the vast majority of star-forming environments in the Galaxy. We briefly discuss the implications of this result for models of the origin of the solar system, and of free-floating planets. We also provide tabulated encounter rates and orbital element distributions suitable for inclusion in population synthesis models of planet formation in a clustered environment.

  17. CLOSE STELLAR ENCOUNTERS IN YOUNG, SUBSTRUCTURED, DISSOLVING STAR CLUSTERS: STATISTICS AND EFFECTS ON PLANETARY SYSTEMS

    Energy Technology Data Exchange (ETDEWEB)

    Craig, Jonathan; Krumholz, Mark R., E-mail: krumholz@ucolick.org [Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064 (United States)

    2013-06-01

    Both simulations and observations indicate that stars form in filamentary, hierarchically clustered associations, most of which disperse into their galactic field once feedback destroys their parent clouds. However, during their early evolution in these substructured environments, stars can undergo close encounters with one another that might have significant impacts on their protoplanetary disks or young planetary systems. We perform N-body simulations of the early evolution of dissolving, substructured clusters with a wide range of properties, with the aim of quantifying the expected number and orbital element distributions of encounters as a function of cluster properties. We show that the presence of substructure both boosts the encounter rate and modifies the distribution of encounter velocities compared to what would be expected for a dynamically relaxed cluster. However, the boost only lasts for a dynamical time, and as a result the overall number of encounters expected remains low enough that gravitational stripping is unlikely to be a significant effect for the vast majority of star-forming environments in the Galaxy. We briefly discuss the implications of this result for models of the origin of the solar system, and of free-floating planets. We also provide tabulated encounter rates and orbital element distributions suitable for inclusion in population synthesis models of planet formation in a clustered environment.

  18. Close Stellar Encounters in Young, Substructured, Dissolving Star Clusters: Statistics and Effects on Planetary Systems

    Science.gov (United States)

    Craig, Jonathan; Krumholz, Mark R.

    2013-06-01

    Both simulations and observations indicate that stars form in filamentary, hierarchically clustered associations, most of which disperse into their galactic field once feedback destroys their parent clouds. However, during their early evolution in these substructured environments, stars can undergo close encounters with one another that might have significant impacts on their protoplanetary disks or young planetary systems. We perform N-body simulations of the early evolution of dissolving, substructured clusters with a wide range of properties, with the aim of quantifying the expected number and orbital element distributions of encounters as a function of cluster properties. We show that the presence of substructure both boosts the encounter rate and modifies the distribution of encounter velocities compared to what would be expected for a dynamically relaxed cluster. However, the boost only lasts for a dynamical time, and as a result the overall number of encounters expected remains low enough that gravitational stripping is unlikely to be a significant effect for the vast majority of star-forming environments in the Galaxy. We briefly discuss the implications of this result for models of the origin of the solar system, and of free-floating planets. We also provide tabulated encounter rates and orbital element distributions suitable for inclusion in population synthesis models of planet formation in a clustered environment.

  19. Prediction Models for Licensure Examination Performance using Data Mining Classifiers for Online Test and Decision Support System

    Directory of Open Access Journals (Sweden)

    Ivy M. Tarun

    2017-05-01

    Full Text Available This study focuse d on two main points: the generation of licensure examination performan ce prediction models; and the development of a Decision Support System. In this study, data mining classifiers were used to generate the models using WEKA (Waikato Environment for Knowledge Analysis. These models were integrated into the Decision Support System as default models to support decision making as far as appropriate interventions during review sessions are concerned. The system developed mainly involves the repeated generation of MR models for performance prediction and also provides a Mock Boar d Exam for the reviewees to take. From the models generated, it is established that the General Weighted Average of the reviewees in their General Education subjects, the result of the Mock Board Exam and the instance when the reviewee is conducting a sel f - review are good predictors of the licensure examination performance. Further , it is concluded that the General Weighted Average of the reviewees in their Major or Content courses is the best predictor of licensure examination performance. Based from the evaluation results of the system , the system satisfied its implied functions and is efficient, usable, reliable and portable. Hence, it can already be used not as a substitute to the face - to - face review sessions but to enhance the reviewees’ licensure exa mination review and allow initial identification of those who are likely to have difficulty in passing the licensure examination, therefore providing sufficient time and opportunities for appropriate interventions.

  20. On-line prediction of BWR transients in support of plant operation and safety analyses

    International Nuclear Information System (INIS)

    Wulff, W.; Cheng, H.S.; Lekach, S.V.; Mallen, A.N.

    1983-01-01

    A combination of advanced modeling techniques and modern, special-purpose peripheral minicomputer technology is presented which affords realistic predictions of plant transient and severe off-normal events in LWR power plants through on-line simulations at a speed ten times greater than actual process speeds. Results are shown for a BWR plant simulation. The mathematical models account for nonequilibrium, nonhomogeneous two-phase flow effects in the coolant, for acoustical effects in the steam line and for the dynamics of the recirculation loop and feed-water train. Point kinetics incorporate reactivity feedback for void fraction, for fuel temperature, and for coolant temperature. Control systems and trip logic are simulated for the nuclear steam supply system

  1. A Fine-Grained API Link Prediction Approach Supporting CMDA Mashup Recommendation

    Science.gov (United States)

    Zhang, J.; Bao, Q.; Lee, T. J.; Ramachandran, R.; Lee, S.; Pan, L.; Gatlin, P. N.; Maskey, M.

    2017-12-01

    Service (API) discovery and recommendation is key to the wide spread of service oriented architecture and service oriented software engineering. Service recommendation typically relies on service linkage prediction calculated by the semantic distances (or similarities) among services based on their collection of inherent attributes. Given a specific context (mashup goal), however, different attributes may contribute differently to a service linkage. In this work, instead of training a model for all attributes as a whole, a novel approach is presented to simultaneously train separate models for individual attributes. Our contributions are summarized in three-fold. First is that we have developed a scalable attribute-level data model, featuring scalability and extensibility. We have extended Multiplicative Attribute Graph (MAG) model to represent node profiles featuring rich categorical attributes, while relaxing its constraint of requiring a priori knowledge of predefined attributes. LDA is leveraged to dynamically identify attributes based on attribute modeling, and multiple Gaussian fit is applied to find global optimal values. The second contribution is that we have seamlessly integrated the latent relationships between API attributes as well as observed network structure based on historical API usage data. Such a layered information model enables us to predict the probability of a link between two APIs based on their attribute link affinities carrying a variety of information including meta data, semantic data, historical usage data, as well as crowdsourcing user comments and annotations. The third contribution is that we have developed a finegrained context-aware mashup-API recommendation technique. On top of individual models trained for separate attributes, a dedicated layer is trained to represent the latent attribute distribution regarding mashup purpose, i.e., sensitivity of attributes to context. Thus, given the description of an intended mashup, the

  2. QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method.

    Science.gov (United States)

    Golmohammadi, H; Dashtbozorgi, Z

    2016-12-01

    In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liquid chromatography based on molecular descriptors calculated from the optimized structures. Diverse kinds of molecular descriptors were calculated to encode the molecular structures of compounds, such as geometric, thermodynamic, electrostatic and quantum mechanical descriptors. The variable selection method of ERM was employed to select an optimum subset of descriptors. The five descriptors selected using ERM were used as inputs of SVM to predict the polarity parameter of organic compounds in methanol. The coefficient of determination, r 2 , between experimental and predicted polarity parameters for the prediction set by ERM and SVM were 0.952 and 0.982, respectively. Acceptable results specified that the ERM approach is a very effective method for variable selection and the predictive aptitude of the SVM model is superior to those obtained by ERM. The obtained results demonstrate that SVM can be used as a substitute influential modeling tool for QSPR studies.

  3. Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity

    Directory of Open Access Journals (Sweden)

    Xin Yi Ng

    2015-01-01

    Full Text Available This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM- LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.

  4. Integrating principal component analysis and vector quantization with support vector regression for sulfur content prediction in HDS process

    Directory of Open Access Journals (Sweden)

    Shokri Saeid

    2015-01-01

    Full Text Available An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR was developed and the effects of integrating Vector Quantization (VQ with Principle Component Analysis (PCA were studied on the assessment of this soft sensor. First, in pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR was better than (PCA-SVR in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE= 0.0668 and R2= 0.995 in comparison with investigated models.

  5. A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

    Science.gov (United States)

    Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

    2014-08-01

    Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Harnessing Facebook for Smoking Reduction and Cessation Interventions: Facebook User Engagement and Social Support Predict Smoking Reduction.

    Science.gov (United States)

    Kim, Sunny Jung; Marsch, Lisa A; Brunette, Mary F; Dallery, Jesse

    2017-05-23

    Social media technologies offer a novel opportunity for scalable health interventions that can facilitate user engagement and social support, which in turn may reinforce positive processes for behavior change. By using principles from health communication and social support literature, we implemented a Facebook group-based intervention that targeted smoking reduction and cessation. This study hypothesized that participants' engagement with and perceived social support from our Facebook group intervention would predict smoking reduction. We recruited 16 regular smokers who live in the United States and who were motivated in quitting smoking at screening. We promoted message exposure as well as engagement and social support systems throughout the intervention. For message exposure, we posted prevalidated, antismoking messages (such as national antismoking campaigns) on our smoking reduction and cessation Facebook group. For engagement and social support systems, we delivered a high degree of engagement and social support systems during the second and third week of the intervention and a low degree of engagement and social support systems during the first and fourth week. A total of six surveys were conducted via Amazon Mechanical Turk (MTurk) at baseline on a weekly basis and at a 2-week follow-up. Of the total 16 participants, most were female (n=13, 81%), white (n=15, 94%), and between 25 and 50 years of age (mean 34.75, SD 8.15). There was no study attrition throughout the 6-time-point baseline, weekly, and follow-up surveys. We generated Facebook engagement and social support composite scores (mean 19.19, SD 24.35) by combining the number of likes each participant received and the number of comments or wall posts each participant posted on our smoking reduction and cessation Facebook group during the intervention period. The primary outcome was smoking reduction in the past 7 days measured at baseline and at the two-week follow-up. Compared with the baseline

  7. Harnessing Facebook for Smoking Reduction and Cessation Interventions: Facebook User Engagement and Social Support Predict Smoking Reduction

    Science.gov (United States)

    Marsch, Lisa A; Brunette, Mary F; Dallery, Jesse

    2017-01-01

    Background Social media technologies offer a novel opportunity for scalable health interventions that can facilitate user engagement and social support, which in turn may reinforce positive processes for behavior change. Objective By using principles from health communication and social support literature, we implemented a Facebook group–based intervention that targeted smoking reduction and cessation. This study hypothesized that participants’ engagement with and perceived social support from our Facebook group intervention would predict smoking reduction. Methods We recruited 16 regular smokers who live in the United States and who were motivated in quitting smoking at screening. We promoted message exposure as well as engagement and social support systems throughout the intervention. For message exposure, we posted prevalidated, antismoking messages (such as national antismoking campaigns) on our smoking reduction and cessation Facebook group. For engagement and social support systems, we delivered a high degree of engagement and social support systems during the second and third week of the intervention and a low degree of engagement and social support systems during the first and fourth week. A total of six surveys were conducted via Amazon Mechanical Turk (MTurk) at baseline on a weekly basis and at a 2-week follow-up. Results Of the total 16 participants, most were female (n=13, 81%), white (n=15, 94%), and between 25 and 50 years of age (mean 34.75, SD 8.15). There was no study attrition throughout the 6-time-point baseline, weekly, and follow-up surveys. We generated Facebook engagement and social support composite scores (mean 19.19, SD 24.35) by combining the number of likes each participant received and the number of comments or wall posts each participant posted on our smoking reduction and cessation Facebook group during the intervention period. The primary outcome was smoking reduction in the past 7 days measured at baseline and at the two

  8. Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Chenzhong Cao

    2009-07-01

    Full Text Available Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT and murine local lymph node assay (LLNA are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.

  9. Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

    Science.gov (United States)

    Yuan, Hua; Huang, Jianping; Cao, Chenzhong

    2009-01-01

    Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers. PMID:19742136

  10. A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction.

    Science.gov (United States)

    Qiu, Shibin; Lane, Terran

    2009-01-01

    The cell defense mechanism of RNA interference has applications in gene function analysis and promising potentials in human disease therapy. To effectively silence a target gene, it is desirable to select appropriate initiator siRNA molecules having satisfactory silencing capabilities. Computational prediction for silencing efficacy of siRNAs can assist this screening process before using them in biological experiments. String kernel functions, which operate directly on the string objects representing siRNAs and target mRNAs, have been applied to support vector regression for the prediction and improved accuracy over numerical kernels in multidimensional vector spaces constructed from descriptors of siRNA design rules. To fully utilize information provided by string and numerical data, we propose to unify the two in a kernel feature space by devising a multiple kernel regression framework where a linear combination of the kernels is used. We formulate the multiple kernel learning into a quadratically constrained quadratic programming (QCQP) problem, which although yields global optimal solution, is computationally demanding and requires a commercial solver package. We further propose three heuristics based on the principle of kernel-target alignment and predictive accuracy. Empirical results demonstrate that multiple kernel regression can improve accuracy, decrease model complexity by reducing the number of support vectors, and speed up computational performance dramatically. In addition, multiple kernel regression evaluates the importance of constituent kernels, which for the siRNA efficacy prediction problem, compares the relative significance of the design rules. Finally, we give insights into the multiple kernel regression mechanism and point out possible extensions.

  11. Prediction of Tourism Demand in Iran by Using Artificial Neural Network (ANN and Supporting Vector Machine (SVR

    Directory of Open Access Journals (Sweden)

    Seyedehelham Sadatiseyedmahalleh

    2016-02-01

    Full Text Available This research examines and proves this effectiveness connected with artificial neural networks (ANNs as an alternative approach to the use of Support Vector Machine (SVR in the tourism research. This method can be used for the tourism industry to define the turism’s demands in Iran. The outcome reveals the use of ANNs in tourism research might result in better quotations when it comes to prediction bias and accuracy. Even more applications of ANNs in the context of tourism demand evaluation is needed to establish and validate the effects.

  12. Automatic evidence quality prediction to support evidence-based decision making.

    Science.gov (United States)

    Sarker, Abeed; Mollá, Diego; Paris, Cécile

    2015-06-01

    Evidence-based medicine practice requires practitioners to obtain the best available medical evidence, and appraise the quality of the evidence when making clinical decisions. Primarily due to the plethora of electronically available data from the medical literature, the manual appraisal of the quality of evidence is a time-consuming process. We present a fully automatic approach for predicting the quality of medical evidence in order to aid practitioners at point-of-care. Our approach extracts relevant information from medical article abstracts and utilises data from a specialised corpus to apply supervised machine learning for the prediction of the quality grades. Following an in-depth analysis of the usefulness of features (e.g., publication types of articles), they are extracted from the text via rule-based approaches and from the meta-data associated with the articles, and then applied in the supervised classification model. We propose the use of a highly scalable and portable approach using a sequence of high precision classifiers, and introduce a simple evaluation metric called average error distance (AED) that simplifies the comparison of systems. We also perform elaborate human evaluations to compare the performance of our system against human judgments. We test and evaluate our approaches on a publicly available, specialised, annotated corpus containing 1132 evidence-based recommendations. Our rule-based approach performs exceptionally well at the automatic extraction of publication types of articles, with F-scores of up to 0.99 for high-quality publication types. For evidence quality classification, our approach obtains an accuracy of 63.84% and an AED of 0.271. The human evaluations show that the performance of our system, in terms of AED and accuracy, is comparable to the performance of humans on the same data. The experiments suggest that our structured text classification framework achieves evaluation results comparable to those of human performance

  13. Eradicating BVD, reviewing Irish programme data and model predictions to support prospective decision making.

    Science.gov (United States)

    Thulke, H-H; Lange, M; Tratalos, J A; Clegg, T A; McGrath, G; O'Grady, L; O'Sullivan, P; Doherty, M L; Graham, D A; More, S J

    2018-02-01

    Bovine Viral Diarrhoea is an infectious production disease of major importance in many cattle sectors of the world. The infection is predominantly transmitted by animal contact. Postnatal infections are transient, leading to immunologically protected cattle. However, for a certain window of pregnancy, in utero infection of the foetus results in persistently infected (PI) calves being the major risk of BVD spread, but also an efficient target for controlling the infection. There are two acknowledged strategies to identify PI animals for removal: tissue tag testing (direct; also known as the Swiss model) and serological screening (indirect by interpreting the serological status of the herd; the Scandinavian model). Both strategies are effective in reducing PI prevalence and herd incidence. During the first four years of the Irish national BVD eradication programme (2013-16), it has been mandatory for all newborn calves to be tested using tissue tag testing. During this period, PI incidence has substantially declined. In recent times, there has been interest among stakeholders in a change to an indirect testing strategy, with potential benefit to the overall programme, particularly with respect to cost to farmers. Advice was sought on the usefulness of implementing the necessary changes. Here we review available data from the national eradication programme and strategy performance predictions from an expert system model to quantify expected benefits of the strategy change from strategic, budgetary and implementation points of view. Key findings from our work include (i) drawbacks associated with changes to programme implementation, in particular the loss of epidemiological information to allow real-time monitoring of eradication progress or to reliably predict time to eradication, (ii) the fact that only 25% of the herds in the Irish cattle sector (14% beef, 78% dairy herds) would benefit financially from a change to serosurveillance, with half of these participants

  14. Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle

    Directory of Open Access Journals (Sweden)

    Nazira Mammadova

    2013-01-01

    Full Text Available This study presented a potentially useful alternative approach to ascertain the presence of subclinical and clinical mastitis in dairy cows using support vector machine (SVM techniques. The proposed method detected mastitis in a cross-sectional representative sample of Holstein dairy cattle milked using an automatic milking system. The study used such suspected indicators of mastitis as lactation rank, milk yield, electrical conductivity, average milking duration, and control season as input data. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the control period. Cattle were judged to be healthy or infected based on those somatic cell counts. This study undertook a detailed scrutiny of the SVM methodology, constructing and examining a model which showed 89% sensitivity, 92% specificity, and 50% error in mastitis detection.

  15. Prediction of Support Reaction Forces of ITA via Response Spectrum Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kwak, Jin Sung; Jeong, Joon Ho; Lee, Sang Jin; Oh, Jin Ho; Lee, Jong Min [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    The irradiation targets are transferred along pipes between TTS (Target Transfer Station) and ITA (Irradiation Tube Assembly) by hydraulic forces. The ITA corresponds to the vertical guide tube for irradiation targets inside a reactor, and it penetrates the reactor structure. Because the ITA is classified into seismic category II, its structural integrity must be evaluated by the seismic analysis. To approach more realistic problem, the interaction between the ITA and the reactor structure must be considered. However, this paper is focused on the preliminary analysis, and it is simplified that only the response of the ITA caused by earthquake affects the reactor structure. The response of the ITA is predicted by the spectrum response analysis based on the FDRS (Floor Design Response Spectra) of KJRR. Finally, the reaction forces corresponding to the load transfer into the reactor structure are estimated by using ANSYS. In this study, the reaction forces due to the earthquake are estimated by the response spectrum analysis. For the saving computational time and resource required, the FE model with beam element is constructed, and it is confirmed that the accuracy of the solution is acceptable by comparing the results of the solid model.

  16. Predicting DNA binding proteins using support vector machine with hybrid fractal features.

    Science.gov (United States)

    Niu, Xiao-Hui; Hu, Xue-Hai; Shi, Feng; Xia, Jing-Bo

    2014-02-21

    DNA-binding proteins play a vitally important role in many biological processes. Prediction of DNA-binding proteins from amino acid sequence is a significant but not fairly resolved scientific problem. Chaos game representation (CGR) investigates the patterns hidden in protein sequences, and visually reveals previously unknown structure. Fractal dimensions (FD) are good tools to measure sizes of complex, highly irregular geometric objects. In order to extract the intrinsic correlation with DNA-binding property from protein sequences, CGR algorithm, fractal dimension and amino acid composition are applied to formulate the numerical features of protein samples in this paper. Seven groups of features are extracted, which can be computed directly from the primary sequence, and each group is evaluated by the 10-fold cross-validation test and Jackknife test. Comparing the results of numerical experiments, the group of amino acid composition and fractal dimension (21-dimension vector) gets the best result, the average accuracy is 81.82% and average Matthew's correlation coefficient (MCC) is 0.6017. This resulting predictor is also compared with existing method DNA-Prot and shows better performances. © 2013 The Authors. Published by Elsevier Ltd All rights reserved.

  17. Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems.

    Science.gov (United States)

    Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

    2016-01-01

    It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning.

  18. Perceived discrimination predicts increased support for political rights and life satisfaction mediated by ethnic identity: A longitudinal analysis.

    Science.gov (United States)

    Stronge, Samantha; Sengupta, Nikhil K; Barlow, Fiona Kate; Osborne, Danny; Houkamau, Carla A; Sibley, Chris G

    2016-07-01

    The aim of the current research is to test predictions derived from the rejection-identification model and research on collective action using cross-sectional (Study 1) and longitudinal (Study 2) methods. Specifically, an integration of these 2 literatures suggests that recognition of discrimination can have simultaneous positive relationships with well-being and engagement in collective action via the formation of a strong ingroup identity. We test these predictions in 2 studies using data from a large national probability sample of Māori (the indigenous peoples of New Zealand), collected as part of the New Zealand Attitudes and Values Study (Ns for Study 1 and 2 were 1,981 and 1,373, respectively). Consistent with the extant research, Study 1 showed that perceived discrimination was directly linked with decreased life satisfaction, but indirectly linked with increased life satisfaction through higher levels of ethnic identification. Perceived discrimination was also directly linked with increased support for Māori rights and indirectly linked with increased support for Māori rights through higher levels of ethnic identification. Study 2 replicated these findings using longitudinal data and identified multiple bidirectional paths between perceived discrimination, ethnic identity, well-being, and support for collective action. These findings replicate and extend the rejection-identification model in a novel cultural context by demonstrating via cross-sectional (Study 1) and longitudinal (Study 2) analyses that the recognition of discrimination can both motivate support for political rights and increase well-being by strengthening ingroup identity. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  19. Meteorological Predictions in Support of the Mars Science Laboratory Entry, Descent and Landing

    Science.gov (United States)

    Rothchild, A.; Rafkin, S. C.; Pielke, R. A., Sr.

    2010-12-01

    The Mars Science Laboratory (MSL) entry, descent, and landing (EDL) system employs a standard parachute strategy followed by a new sky crane concept where the rover is lowered to the ground via a tether from a hovering entry vehicle. As with previous missions, EDL system performance is sensitive to atmospheric conditions. While some observations characterizing the mean, large-scale atmospheric temperature and density data are available, there is effectively no information on the atmospheric conditions and variability at the scale that directly affects the spacecraft. In order to evaluate EDL system performance and to assess landing hazards and risk, it is necessary to simulate the atmosphere with a model that provides data at the appropriate spatial and temporal scales. Models also permit the study of the impact of the highly variable atmospheric dust loading on temperature, density and winds. There are four potential MSL landing sites: Mawrth Valle (22.3 N, 16.5W) , Gale Crater (5.4S, 137.7E), Holden Crater (26.1S, 34W), and Eberswalde Crater (24S, 33W). The final selection of the landing site will balance potential science return against landing and operational risk. Atmospheric modeling studies conducted with the Mars Regional Atmospheric Modeling System (MRAMS) is an integral part of the selection process. At each of the landing sites, a variety of simulations are conducted. The first type of simulations provide baseline predictions under nominal atmospheric dust loading conditions within the landing site window of ~Ls 150-170. The second type of simulation explores situations with moderate and high global atmospheric dust loading. The final type of simulation investigates the impact of local dust disturbances at the landing site. Mean and perturbation fields from each type of simulation at each of the potential landing sites are presented in comparison with the engineering performance limitations for the MSL EDL system. Within the lowest scale height, winds

  20. Seasonal prediction of winter extreme precipitation over Canada by support vector regression

    Directory of Open Access Journals (Sweden)

    Z. Zeng

    2011-01-01

    Full Text Available For forecasting the maximum 5-day accumulated precipitation over the winter season at lead times of 3, 6, 9 and 12 months over Canada from 1950 to 2007, two nonlinear and two linear regression models were used, where the models were support vector regression (SVR (nonlinear and linear versions, nonlinear Bayesian neural network (BNN and multiple linear regression (MLR. The 118 stations were grouped into six geographic regions by K-means clustering. For each region, the leading principal components of the winter maximum 5-d accumulated precipitation anomalies were the predictands. Potential predictors included quasi-global sea surface temperature anomalies and 500 hPa geopotential height anomalies over the Northern Hemisphere, as well as six climate indices (the Niño-3.4 region sea surface temperature, the North Atlantic Oscillation, the Pacific-North American teleconnection, the Pacific Decadal Oscillation, the Scandinavia pattern, and the East Atlantic pattern. The results showed that in general the two robust SVR models tended to have better forecast skills than the two non-robust models (MLR and BNN, and the nonlinear SVR model tended to forecast slightly better than the linear SVR model. Among the six regions, the Prairies region displayed the highest forecast skills, and the Arctic region the second highest. The strongest nonlinearity was manifested over the Prairies and the weakest nonlinearity over the Arctic.

  1. Structure and substructure analysis of DAFT/FADA galaxy clusters in the [0.4-0.9] redshift range

    Science.gov (United States)

    Guennou, L.; Adami, C.; Durret, F.; Lima Neto, G. B.; Ulmer, M. P.; Clowe, D.; LeBrun, V.; Martinet, N.; Allam, S.; Annis, J.; Basa, S.; Benoist, C.; Biviano, A.; Cappi, A.; Cypriano, E. S.; Gavazzi, R.; Halliday, C.; Ilbert, O.; Jullo, E.; Just, D.; Limousin, M.; Márquez, I.; Mazure, A.; Murphy, K. J.; Plana, H.; Rostagni, F.; Russeil, D.; Schirmer, M.; Slezak, E.; Tucker, D.; Zaritsky, D.; Ziegler, B.

    2014-01-01

    Context. The DAFT/FADA survey is based on the study of ~90 rich (masses found in the literature >2 × 1014 M⊙) and moderately distant clusters (redshifts 0.4 DAFT/FADA survey for which XMM-Newton and/or a sufficient number of galaxy redshifts in the cluster range are available, with the aim of detecting substructures and evidence for merging events. These properties are discussed in the framework of standard cold dark matter (ΛCDM) cosmology. Methods: In X-rays, we analysed the XMM-Newton data available, fit a β-model, and subtracted it to identify residuals. We used Chandra data, when available, to identify point sources. In the optical, we applied a Serna & Gerbal (SG) analysis to clusters with at least 15 spectroscopic galaxy redshifts available in the cluster range. We discuss the substructure detection efficiencies of both methods. Results: XMM-Newton data were available for 32 clusters, for which we derive the X-ray luminosity and a global X-ray temperature for 25 of them. For 23 clusters we were able to fit the X-ray emissivity with a β-model and subtract it to detect substructures in the X-ray gas. A dynamical analysis based on the SG method was applied to the clusters having at least 15 spectroscopic galaxy redshifts in the cluster range: 18 X-ray clusters and 11 clusters with no X-ray data. The choice of a minimum number of 15 redshifts implies that only major substructures will be detected. Ten substructures were detected both in X-rays and by the SG method. Most of the substructures detected both in X-rays and with the SG method are probably at their first cluster pericentre approach and are relatively recent infalls. We also find hints of a decreasing X-ray gas density profile core radius with redshift. Conclusions: The percentage of mass included in substructures was found to be roughly constant with redshift values of 5-15%, in agreement both with the general CDM framework and with the results of numerical simulations. Galaxies in substructures

  2. Regression model of support vector machines for least squares prediction of crystallinity of cracking catalysts by infrared spectroscopy

    International Nuclear Information System (INIS)

    Comesanna Garcia, Yumirka; Dago Morales, Angel; Talavera Bustamante, Isneri

    2010-01-01

    The recently introduction of the least squares support vector machines method for regression purposes in the field of Chemometrics has provided several advantages to linear and nonlinear multivariate calibration methods. The objective of the paper was to propose the use of the least squares support vector machine as an alternative multivariate calibration method for the prediction of the percentage of crystallinity of fluidized catalytic cracking catalysts, by means of Fourier transform mid-infrared spectroscopy. A linear kernel was used in the calculations of the regression model. The optimization of its gamma parameter was carried out using the leave-one-out cross-validation procedure. The root mean square error of prediction was used to measure the performance of the model. The accuracy of the results obtained with the application of the method is in accordance with the uncertainty of the X-ray powder diffraction reference method. To compare the generalization capability of the developed method, a comparison study was carried out, taking into account the results achieved with the new model and those reached through the application of linear calibration methods. The developed method can be easily implemented in refinery laboratories

  3. No sympathy for the devil: attributing psychopathic traits to capital murderers also predicts support for executing them.

    Science.gov (United States)

    Edens, John F; Davis, Karen M; Fernandez Smith, Krissie; Guy, Laura S

    2013-04-01

    Mental health evidence concerning antisocial and psychopathic traits appears to be introduced frequently in capital murder trials in the United States to argue that defendants are a "continuing threat" to society and thus worthy of execution. Using a simulation design, the present research examined how layperson perceptions of the psychopathic traits exhibited by a capital defendant would impact their attitudes about whether he should receive a death sentence. Across three studies (total N = 362), ratings of a defendant's perceived level of psychopathy strongly predicted support for executing him. The vast majority of the predictive utility was attributable to interpersonal and affective traits historically associated with psychopathy rather than traits associated with a criminal and socially deviant lifestyle. A defendant's perceived lack of remorse in particular was influential, although perceptions of grandiose self-worth and a manipulative interpersonal style also contributed incrementally to support for a death sentence. These results highlight how attributions regarding socially undesirable personality traits can have a pronounced negative impact on layperson attitudes toward persons who are perceived to exhibit these characteristics. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  4. Modeling and Predicting the Electrical Conductivity of Composite Cathode for Solid Oxide Fuel Cell by Using Support Vector Regression

    Science.gov (United States)

    Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J.

    2012-07-01

    The electrical conductivity of solid oxide fuel cell (SOFC) cathode is one of the most important indices affecting the efficiency of SOFC. In order to improve the performance of fuel cell system, it is advantageous to have accurate model with which one can predict the electrical conductivity. In this paper, a model utilizing support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter optimization was established to modeling and predicting the electrical conductivity of Ba0.5Sr0.5Co0.8Fe0.2 O3-δ-xSm0.5Sr0.5CoO3-δ (BSCF-xSSC) composite cathode under two influence factors, including operating temperature (T) and SSC content (x) in BSCF-xSSC composite cathode. The leave-one-out cross validation (LOOCV) test result by SVR strongly supports that the generalization ability of SVR model is high enough. The absolute percentage error (APE) of 27 samples does not exceed 0.05%. The mean absolute percentage error (MAPE) of all 30 samples is only 0.09% and the correlation coefficient (R2) as high as 0.999. This investigation suggests that the hybrid PSO-SVR approach may be not only a promising and practical methodology to simulate the properties of fuel cell system, but also a powerful tool to be used for optimal designing or controlling the operating process of a SOFC system.

  5. Knowledge-based Fragment Binding Prediction

    Science.gov (United States)

    Tang, Grace W.; Altman, Russ B.

    2014-01-01

    Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. PMID:24762971

  6. A carbon risk prediction model for Chinese heavy-polluting industrial enterprises based on support vector machine

    International Nuclear Information System (INIS)

    Zhou, Zhifang; Xiao, Tian; Chen, Xiaohong; Wang, Chang

    2016-01-01

    Chinese heavy-polluting industrial enterprises, especially petrochemical or chemical industry, labeled low carbon efficiency and high emission load, are facing the tremendous pressure of emission reduction under the background of global shortage of energy supply and constrain of carbon emission. However, due to the limited amount of theoretic and practical research in this field, problems like lacking prediction indicators or models, and the quantified standard of carbon risk remain unsolved. In this paper, the connotation of carbon risk and an assessment index system for Chinese heavy-polluting industrial enterprises (eg. coal enterprise, petrochemical enterprises, chemical enterprises et al.) based on support vector machine are presented. By using several heavy-polluting industrial enterprises’ related data, SVM model is trained to predict the carbon risk level of a specific enterprise, which allows the enterprise to identify and manage its carbon risks. The result shows that this method can predict enterprise’s carbon risk level in an efficient, accurate way with high practical application and generalization value.

  7. River flow prediction using hybrid models of support vector regression with the wavelet transform, singular spectrum analysis and chaotic approach

    Science.gov (United States)

    Baydaroğlu, Özlem; Koçak, Kasım; Duran, Kemal

    2018-06-01

    Prediction of water amount that will enter the reservoirs in the following month is of vital importance especially for semi-arid countries like Turkey. Climate projections emphasize that water scarcity will be one of the serious problems in the future. This study presents a methodology for predicting river flow for the subsequent month based on the time series of observed monthly river flow with hybrid models of support vector regression (SVR). Monthly river flow over the period 1940-2012 observed for the Kızılırmak River in Turkey has been used for training the method, which then has been applied for predictions over a period of 3 years. SVR is a specific implementation of support vector machines (SVMs), which transforms the observed input data time series into a high-dimensional feature space (input matrix) by way of a kernel function and performs a linear regression in this space. SVR requires a special input matrix. The input matrix was produced by wavelet transforms (WT), singular spectrum analysis (SSA), and a chaotic approach (CA) applied to the input time series. WT convolutes the original time series into a series of wavelets, and SSA decomposes the time series into a trend, an oscillatory and a noise component by singular value decomposition. CA uses a phase space formed by trajectories, which represent the dynamics producing the time series. These three methods for producing the input matrix for the SVR proved successful, while the SVR-WT combination resulted in the highest coefficient of determination and the lowest mean absolute error.

  8. PubMed-supported clinical term weighting approach for improving inter-patient similarity measure in diagnosis prediction.

    Science.gov (United States)

    Chan, Lawrence Wc; Liu, Ying; Chan, Tao; Law, Helen Kw; Wong, S C Cesar; Yeung, Andy Ph; Lo, K F; Yeung, S W; Kwok, K Y; Chan, William Yl; Lau, Thomas Yh; Shyu, Chi-Ren

    2015-06-02

    Similarity-based retrieval of Electronic Health Records (EHRs) from large clinical information systems provides physicians the evidence support in making diagnoses or referring examinations for the suspected cases. Clinical Terms in EHRs represent high-level conceptual information and the similarity measure established based on these terms reflects the chance of inter-patient disease co-occurrence. The assumption that clinical terms are equally relevant to a disease is unrealistic, reducing the prediction accuracy. Here we propose a term weighting approach supported by PubMed search engine to address this issue. We collected and studied 112 abdominal computed tomography imaging examination reports from four hospitals in Hong Kong. Clinical terms, which are the image findings related to hepatocellular carcinoma (HCC), were extracted from the reports. Through two systematic PubMed search methods, the generic and specific term weightings were established by estimating the conditional probabilities of clinical terms given HCC. Each report was characterized by an ontological feature vector and there were totally 6216 vector pairs. We optimized the modified direction cosine (mDC) with respect to a regularization constant embedded into the feature vector. Equal, generic and specific term weighting approaches were applied to measure the similarity of each pair and their performances for predicting inter-patient co-occurrence of HCC diagnoses were compared by using Receiver Operating Characteristics (ROC) analysis. The Areas under the curves (AUROCs) of similarity scores based on equal, generic and specific term weighting approaches were 0.735, 0.728 and 0.743 respectively (p PubMed. Our findings suggest that the optimized similarity measure with specific term weighting to EHRs can improve significantly the accuracy for predicting the inter-patient co-occurrence of diagnosis when compared with equal and generic term weighting approaches.

  9. Social justice in education: how the function of selection in educational institutions predicts support for (non)egalitarian assessment practices

    Science.gov (United States)

    Autin, Frédérique; Batruch, Anatolia; Butera, Fabrizio

    2015-01-01

    Educational institutions are considered a keystone for the establishment of a meritocratic society. They supposedly serve two functions: an educational function that promotes learning for all, and a selection function that sorts individuals into different programs, and ultimately social positions, based on individual merit. We study how the function of selection relates to support for assessment practices known to harm vs. benefit lower status students, through the perceived justice principles underlying these practices. We study two assessment practices: normative assessment—focused on ranking and social comparison, known to hinder the success of lower status students—and formative assessment—focused on learning and improvement, known to benefit lower status students. Normative assessment is usually perceived as relying on an equity principle, with rewards being allocated based on merit and should thus appear as positively associated with the function of selection. Formative assessment is usually perceived as relying on corrective justice that aims to ensure equality of outcomes by considering students’ needs, which makes it less suitable for the function of selection. A questionnaire measuring these constructs was administered to university students. Results showed that believing that education is intended to select the best students positively predicts support for normative assessment, through increased perception of its reliance on equity, and negatively predicts support for formative assessment, through reduced perception of its ability to establish corrective justice. This study suggests that the belief in the function of selection as inherent to educational institutions can contribute to the reproduction of social inequalities by preventing change from assessment practices known to disadvantage lower-status student, namely normative assessment, to more favorable practices, namely formative assessment, and by promoting matching beliefs in justice

  10. Social justice in education: how the function of selection in educational institutions predicts support for (non)egalitarian assessment practices.

    Science.gov (United States)

    Autin, Frédérique; Batruch, Anatolia; Butera, Fabrizio

    2015-01-01

    Educational institutions are considered a keystone for the establishment of a meritocratic society. They supposedly serve two functions: an educational function that promotes learning for all, and a selection function that sorts individuals into different programs, and ultimately social positions, based on individual merit. We study how the function of selection relates to support for assessment practices known to harm vs. benefit lower status students, through the perceived justice principles underlying these practices. We study two assessment practices: normative assessment-focused on ranking and social comparison, known to hinder the success of lower status students-and formative assessment-focused on learning and improvement, known to benefit lower status students. Normative assessment is usually perceived as relying on an equity principle, with rewards being allocated based on merit and should thus appear as positively associated with the function of selection. Formative assessment is usually perceived as relying on corrective justice that aims to ensure equality of outcomes by considering students' needs, which makes it less suitable for the function of selection. A questionnaire measuring these constructs was administered to university students. Results showed that believing that education is intended to select the best students positively predicts support for normative assessment, through increased perception of its reliance on equity, and negatively predicts support for formative assessment, through reduced perception of its ability to establish corrective justice. This study suggests that the belief in the function of selection as inherent to educational institutions can contribute to the reproduction of social inequalities by preventing change from assessment practices known to disadvantage lower-status student, namely normative assessment, to more favorable practices, namely formative assessment, and by promoting matching beliefs in justice principles.

  11. Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs.

    Directory of Open Access Journals (Sweden)

    Kyle A McQuisten

    2009-10-01

    Full Text Available Exogenous short interfering RNAs (siRNAs induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs, General Linear Models (GLMs and Support Vector Machines (SVMs. Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are

  12. Chronic Pain Predicting Reciprocity of Support Among Vulnerable, Predominantly African-American Persons Living with HIV/AIDS.

    Science.gov (United States)

    Mitchell, Mary M; Isenberg, Sarina R; Maragh-Bass, Allysha C; Knowlton, Amy R

    2018-06-01

    Among persons living with HIV/AIDS (PLHIV), approximately two-thirds report moderate to severe pain. Chronic pain can negatively affect PLHIVs' health behaviors and outcomes by interfering with their reciprocity (mutual exchange) of support in their caregiving relationships, which has been found to be associated with PLHIVs' antiretroviral adherence and viral suppression. Data were longitudinal (baseline, 6- and 12-month follow-up) from 383 PLHIV who were formerly or currently using drugs. Utilizing a longitudinal lagged fixed effects structural equation model, we found that never having pain in the past 6 months was predictive of increased reciprocity of support. Sub-analyses by care relationship type revealed never having pain was a significant predictor of greater reciprocity for sexual partner caregiving dyads, but not for kin or friend caregiving dyads. Our study emphasizes the importance of pain management in quality caregiving relationships characterized by reciprocity, which has consistently been found to be associated with stronger, more supportive caregiving relationships and better quality of life. Our findings suggest the importance of pain management intervention for improving reciprocity between vulnerable PLHIVs and their primary caregivers, especially between PLHIVs and caregivers who are current or former sexual partners.

  13. Method for predicting enzyme-catalyzed reactions

    Science.gov (United States)

    Hlavacek, William S.; Unkefer, Clifford J.; Mu, Fangping; Unkefer, Pat J.

    2013-03-19

    The reactivity of given metabolites is assessed using selected empirical atomic properties in the potential reaction center. Metabolic reactions are represented as biotransformation rules. These rules are generalized from the patterns in reactions. These patterns are not unique to reactants but are widely distributed among metabolites. Using a metabolite database, potential substructures are identified in the metabolites for a given biotransformation. These substructures are divided into reactants or non-reactants, depending on whether they participate in the biotransformation or not. Each potential substructure is then modeled using descriptors of the topological and electronic properties of atoms in the potential reaction center; molecular properties can also be used. A Support Vector Machine (SVM) or classifier is trained to classify a potential reactant as a true or false reactant using these properties.

  14. Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

    Science.gov (United States)

    Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

    2017-10-03

    Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.

  15. Convergence properties of halo merger trees; halo and substructure merger rates across cosmic history

    Science.gov (United States)

    Poole, Gregory B.; Mutch, Simon J.; Croton, Darren J.; Wyithe, Stuart

    2017-12-01

    We introduce GBPTREES: an algorithm for constructing merger trees from cosmological simulations, designed to identify and correct for pathological cases introduced by errors or ambiguities in the halo finding process. GBPTREES is built upon a halo matching method utilizing pseudo-radial moments constructed from radially sorted particle ID lists (no other information is required) and a scheme for classifying merger tree pathologies from networks of matches made to-and-from haloes across snapshots ranging forward-and-backward in time. Focusing on SUBFIND catalogues for this work, a sweep of parameters influencing our merger tree construction yields the optimal snapshot cadence and scanning range required for converged results. Pathologies proliferate when snapshots are spaced by ≲0.128 dynamical times; conveniently similar to that needed for convergence of semi-analytical modelling, as established by Benson et al. Total merger counts are converged at the level of ∼5 per cent for friends-of-friends (FoF) haloes of size np ≳ 75 across a factor of 512 in mass resolution, but substructure rates converge more slowly with mass resolution, reaching convergence of ∼10 per cent for np ≳ 100 and particle mass mp ≲ 109 M⊙. We present analytic fits to FoF and substructure merger rates across nearly all observed galactic history (z ≤ 8.5). While we find good agreement with the results presented by Fakhouri et al. for FoF haloes, a slightly flatter dependence on merger ratio and increased major merger rates are found, reducing previously reported discrepancies with extended Press-Schechter estimates. When appropriately defined, substructure merger rates show a similar mass ratio dependence as FoF rates, but with stronger mass and redshift dependencies for their normalization.

  16. Halo substructure in the SDSS-Gaia catalogue: streams and clumps

    Science.gov (United States)

    Myeong, G. C.; Evans, N. W.; Belokurov, V.; Amorisco, N. C.; Koposov, S. E.

    2018-04-01

    We use the Sloan Digital Sky Survey (SDSS)-Gaia Catalogue to identify six new pieces of halo substructure. SDSS-Gaia is an astrometric catalogue that exploits SDSS data release 9 to provide first epoch photometry for objects in the Gaia source catalogue. We use a version of the catalogue containing 245 316 stars with all phase-space coordinates within a heliocentric distance of ˜10 kpc. We devise a method to assess the significance of halo substructures based on their clustering in velocity space. The two most substantial structures are multiple wraps of a stream which has undergone considerable phase mixing (S1, with 94 members) and a kinematically cold stream (S2, with 61 members). The member stars of S1 have a median position of (X, Y, Z) = (8.12, -0.22, 2.75) kpc and a median metallicity of [Fe/H] = -1.78. The stars of S2 have median coordinates (X, Y, Z) = (8.66, 0.30, 0.77) kpc and a median metallicity of [Fe/H] = -1.91. They lie in velocity space close to some of the stars in the stream reported by Helmi et al. By modelling, we estimate that both structures had progenitors with virial masses ≈1010M⊙ and infall times ≳ 9 Gyr ago. Using abundance matching, these correspond to stellar masses between 106 and 107M⊙. These are somewhat larger than the masses inferred through the mass-metallicity relation by factors of 5 to 15. Additionally, we identify two further substructures (S3 and S4 with 55 and 40 members) and two clusters or moving group (C1 and C2 with 24 and 12) members. In all six cases, clustering in kinematics is found to correspond to clustering in both configuration space and metallicity, adding credence to the reliability of our detections.

  17. Search for vector-like quarks using jet substructure techniques with the CMS experiment

    International Nuclear Information System (INIS)

    Nowatschin, Dominik

    2017-01-01

    In this thesis, a search for pair produced vector-like T quarks in pp collision data from the LRC at √(s)=13 TeV is presented. The data were collected with the CMS detector and correspond to an integrated luminosity of up to 2.6 fb"-"1. Vector-like quarks are hypothetical new particles predicted by many extensions of the Standard Model in which the Higgs boson is a composite state of an unknown strong interaction. Vector-like T quarks are assumed to decay via three different decay modes to either bW, tZ or tH, with branching fractions that are not fixed and can vary depending on the particular model featuring vector-like quarks. This search focuses on decays of the T anti T system in which at least one muon or electron is present in the final state, and in which at least one of the T quarks decays to a top quark and a Higgs boson. As the T quarks are expected to be quite heavy, their decay products are significantly Lorentz-boasted in the reference frame of the T anti T system. The subsequent decay products of the Higgs boson are then emitted with a very small angle between them. This search is optimised for the main decay channel of the Higgs boson to two bottom quarks and attempts to reconstruct the two b quarks within a single jet with a large cone size. Dedicated jet substructure techniques, in combination with algorithms to identify jets originating from the fragmentation of a b quark, are then used to reconstruct the entire H→b anti b decay. The event categories of this search are also combined with the categories of a search for pair-produced T quarks that focuses on the T→bW decay. This approach provides a high sensitivity to T anti T production for many different combinations of branching fractions to the three possible decay modes. No excess of the data above the expected background contribution from the Standard Model is observed in any of the final event categories. Upper limits on the T anti T production cross section are calculated at 95

  18. Search for vector-like quarks using jet substructure techniques with the CMS experiment

    Energy Technology Data Exchange (ETDEWEB)

    Nowatschin, Dominik

    2017-07-03

    In this thesis, a search for pair produced vector-like T quarks in pp collision data from the LRC at √(s)=13 TeV is presented. The data were collected with the CMS detector and correspond to an integrated luminosity of up to 2.6 fb{sup -1}. Vector-like quarks are hypothetical new particles predicted by many extensions of the Standard Model in which the Higgs boson is a composite state of an unknown strong interaction. Vector-like T quarks are assumed to decay via three different decay modes to either bW, tZ or tH, with branching fractions that are not fixed and can vary depending on the particular model featuring vector-like quarks. This search focuses on decays of the T anti T system in which at least one muon or electron is present in the final state, and in which at least one of the T quarks decays to a top quark and a Higgs boson. As the T quarks are expected to be quite heavy, their decay products are significantly Lorentz-boasted in the reference frame of the T anti T system. The subsequent decay products of the Higgs boson are then emitted with a very small angle between them. This search is optimised for the main decay channel of the Higgs boson to two bottom quarks and attempts to reconstruct the two b quarks within a single jet with a large cone size. Dedicated jet substructure techniques, in combination with algorithms to identify jets originating from the fragmentation of a b quark, are then used to reconstruct the entire H→b anti b decay. The event categories of this search are also combined with the categories of a search for pair-produced T quarks that focuses on the T→bW decay. This approach provides a high sensitivity to T anti T production for many different combinations of branching fractions to the three possible decay modes. No excess of the data above the expected background contribution from the Standard Model is observed in any of the final event categories. Upper limits on the T anti T production cross section are calculated at 95

  19. On substructuring algorithms and solution techniques for the numerical approximation of partial differential equations

    Science.gov (United States)

    Gunzburger, M. D.; Nicolaides, R. A.

    1986-01-01

    Substructuring methods are in common use in mechanics problems where typically the associated linear systems of algebraic equations are positive definite. Here these methods are extended to problems which lead to nonpositive definite, nonsymmetric matrices. The extension is based on an algorithm which carries out the block Gauss elimination procedure without the need for interchanges even when a pivot matrix is singular. Examples are provided wherein the method is used in connection with finite element solutions of the stationary Stokes equations and the Helmholtz equation, and dual methods for second-order elliptic equations.

  20. Chemical composition of stars in kinematical substructures of the galactic disk

    Directory of Open Access Journals (Sweden)

    Gorbaneva T.I.

    2012-02-01

    Full Text Available The Y, Zr, La, Ce, Nd , Sm and Eu abundances were found in LTE approach, and the abundance of Ba was computed in NLTE approximation for 280 FGK dwarfs in the region of metallicity of − 1<[Fe]< + 0.3. The selection of stars belonging to thin and thick disks and the stream Hercules was made on kinematic criteria. The analysis of enrichment of the different substructures of the Galaxy with α-element (Mg, Si, the iron peak (Ni and neutron-capture elements was carried out.