Periodic Integration: Further Results on Model Selection and Forecasting
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1996-01-01
textabstractThis paper considers model selection and forecasting issues in two closely related models for nonstationary periodic autoregressive time series [PAR]. Periodically integrated seasonal time series [PIAR] need a periodic differencing filter to remove the stochastic trend. On the other
Antczak, K; Wilczyńska, U
1980-01-01
Part II presents a statistical model devised by the authors for evaluating toxicological analyses results. The model includes: 1. Establishment of a reference value, basing on our own measurements taken by two independent analytical methods. 2. Selection of laboratories -- basing on the deviation of the obtained values from reference ones. 3. On consideration of variance analysis, t-student's test and differences test, subsequent quality controls and particular laboratories have been evaluated.
Müller Schmied, Hannes; Adam, Linda; Döll, Petra; Eisner, Stephanie; Flörke, Martina; Güntner, Andreas; Kynast, Ellen; Portmann, Felix T.; Riedel, Claudia; Schneider, Christoph; Song, Qi; Wattenbach, Martin; Zhang, Jing
2014-05-01
The estimation of global freshwater flows and storages and their dynamics is essential for the assessment of historical and future water availability both for mankind and ecosystems. WaterGAP 2 is a state-of-the-art water model covering the entire global land area (except Antarctica) on a 0.5° by 0.5° grid. WaterGAP consists of a set of water use models and a hydrological model. Five global water use models representing the sectors irrigation, domestic water demand, manufacturing industries, livestock farming and cooling of thermal power plants inform the sub-model GWSWUSE which calculates net water abstractions distinguishing surface water and groundwater sources. Water flows and storages are simulated by the WaterGAP Global Hydrology Model (WGHM). WGHM is calibrated against measured discharge for basins covering around 50 % of global land area. Since the original development of WaterGAP in the late 1990s, new input data and refined process algorithms have led to a significant improvement of the results. We present the current version WaterGAP 2.2 including selected results (e.g. discharge seasonality, water storage) and the global water balance for the time period 1971-2000. In addition, some examples of the application of WaterGAP output, e.g. within the GRACE community and for global environmental assessments are shown, reflecting the importance of global hydrology modeling in our globalized world.
Selection of LHCb Physics Results
Directory of Open Access Journals (Sweden)
Schmidt Burkhard
2013-05-01
Full Text Available LHCb is a dedicated flavour physics experiment at the LHC searching for physics beyond the Standard Model through precision measurements of CP-violating observables and the study of very rare decays of beauty- and charm-flavoured hadrons. In this article a selection of recent LHCb results is presented. Unless otherwise stated, the results are based on an integrated luminosity of 1 fb−1 accumulated during the year 2011 at √s = 7 TeV.
Model Selection for Geostatistical Models
Energy Technology Data Exchange (ETDEWEB)
Hoeting, Jennifer A.; Davis, Richard A.; Merton, Andrew A.; Thompson, Sandra E.
2006-02-01
We consider the problem of model selection for geospatial data. Spatial correlation is typically ignored in the selection of explanatory variables and this can influence model selection results. For example, the inclusion or exclusion of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often used approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also employ the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored.
Liu, Ou Lydia; Minsky, Jennifer; Ling, Guangming; Kyllonen, Patrick
2009-01-01
In an effort to standardize academic application procedures, the authors developed the Standardized Letters of Recommendation (SLR) to capture important cognitive and noncognitive qualities of graduate school candidates. The SLR, which consists of seven scales, is applied to an intern-selection scenario. Both professor ratings (n = 414) during the…
Brown, C; Orford, M; Tzamaloukas, O; Mavrogenis, A P; Miltiadou, D
Inbreeding in a small population of Chios sheep undergoing intense selection for the PrP gene was assessed 10 years after the beginning of a scrapie resistance selection programme. Inbreeding in this stock, already under selection for production traits, was analysed by using pedigree records containing 10,492 animals from 1968 to 2008, and also by genotyping 192 individuals with a panel of 15 microsatellites. Genetic markers indicated a loss of heterozygosity (FIS over all loci was 0.059) and allelic diversity (mean effective number of alleles was 3.075±0.275). The annual rate of inbreeding increased significantly after the start of the scrapie resistance programme, ΔF=0.005 compared with ΔF=0.001 before 1999, and was subjected to several genetic bottlenecks, mainly due to the low initial frequency of resistant animals. However, the mean individual inbreeding coefficient estimated from the pedigree - in this closed stock resembling the case of a rare breed - stood at the level of 4.5 per cent, five generations after the implementation of selection for the PrP gene. The inbreeding coefficient estimated by genetic markers was 4.37 per cent, implying that such a marker panel could be a useful and cost-effective tool for estimating inbreeding in unrecorded populations.
Hulzen, van K.J.E.; Koets, A.P.; Nielen, M.; Heuven, H.C.M.; Arendonk, van J.A.M.; Klinkenberg, D.
2014-01-01
The objective of this study was to model genetic selection for Johne’s disease resistance and to study the effect of different selection strategies on the prevalence in the dairy cattle population. In the Netherlands, a certification-and-surveillance program is in use to reduce prevalence and presen
Selected recent results from AMANDA
Andrés, E; Bai, X; Barouch, G; Barwick, S W; Bay, R C; Becker, K H; Bergström, L; Bertrand, D; Bierenbaum, D; Biron, A; Booth, J; Botner, O; Bouchta, A; Boyce, M M; Carius, S; Chen, A; Chirkin, D; Conrad, J; Cooley, J; Costa, C G S; Cowen, D F; Dailing, J; Dalberg, E; De Young, T R; Desiati, P; Dewulf, J P; Doksus, P; Edsjö, J; Ekstrom, P; Erlandsson, B; Feser, T; Gaug, M; Goldschmidt, A; Goobar, A; Gray, L; Haase, H; Hallgren, A; Halzen, F; Hanson, K; Hardtke, R; He, Y D; Hellwig, M; Heukenkamp, H; Hill, G C; Hulth, P O; Hundertmark, S; Jacobsen, J; Kandhadai, V; Karle, A; Kim, J; Koci, B; Köpke, L; Kowalski, M; Leich, H; Leuthold, M; Lindahl, P; Liubarsky, I; Loaiza, P; Lowder, D M; Ludvig, J; Madsen, J; Marciniewski, P; Matis, H S; Mihályi, A; Mikolajski, T; Miller, T C; Minaeva, Y; Miocinovic, P; Mock, P C; Morse, R; Neunhoffer, T; Newcomer, F M; Niessen, P; Nygren, D R; Ogelman, H; Heros, C P D L; Porrata, R; Price, P B; Rawlins, K; Reed, C; Rhode, W; Richards, A; Richter, S; Martino, J R; Romenesko, P; Ross, D; Rubinstein, H; Sander, H G; Scheider, T; Schmidt, T; Schneider, D; Schneider, E; Schwarzl, R; Silvestri, A; Solarz, M; Spiczak, G M; Spiering, C; Starinsky, N; Steele, D; Steffen, P; Stokstad, R G; Streicher, O; Sun, A; Taboada, I; Thollander, L; Thon, T; Tilav, S; Usechak, N; Donckt, M V; Walck, C; Weinheimer, C; Wiebusch, C; Wischnewski, R; Wissing, H; Woschnagg, K; Wu, W; Yodh, G; Young, S
2001-01-01
We present a selection of results based on data taken in 1997 with the 302-PMT Antarctic Muon and Neutrino Detector Array-B10 ("AMANDA-B10") array. Atmospheric neutrinos created in the northern hemisphere are observed indirectly through their charged current interactions which produce relativistic, Cherenkov-light-emitting upgoing muons in the South Pole ice cap. The reconstructed angular distribution of these events is in good agreement with expectation and demonstrates the viability of this ice-based device as a neutrino telescope. Studies of nearly vertical upgoing muons limit the available parameter space for WIMP dark matter under the assumption that WIMPS are trapped in the earth's gravitational potential well and annihilate with one another near the earth's center.
Antczak, K; Wilczyńska, U
1980-01-01
2 statistical models for evaluation of toxicological studies results have been presented. Model I. after R. Hoschek and H. J. Schittke (2) involves: 1. Elimination of the values deviating from most results-by Grubbs' method (2). 2. Analysis of the differences between the results obtained by the participants of the action and tentatively assumed value. 3. Evaluation of significant differences between the reference value and average value for a given series of measurements. 4. Thorough evaluation of laboratories based on evaluation coefficient fx. Model II after Keppler et al. As a criterion for evaluating the results the authors assumed the median. Individual evaluation of laboratories was performed on the basis of: 1. Adjusted test "t" 2. Linear regression test.
DEFF Research Database (Denmark)
Beaude, Francois; Atayi, A.; Bourmaud, J.-Y.
2013-01-01
The OPTIMATE1 platform focuses on electricity system and market designs modelling in order to assess current and innovative designs in Europe. The current paper describes the results of the first validation studies' conducted with the tool. These studies deal with day-ahead market rules, load fle...
Energy Technology Data Exchange (ETDEWEB)
Schaub, Marcus [Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zuercherstrasse 111, 8903 Birmensdorf (Switzerland)]. E-mail: marcus.schaub@wsl.ch; Emberson, Lisa [Stockholm Environment Institute at York, University of York, York YO10 5DD (United Kingdom); Bueker, Patrick [Stockholm Environment Institute at York, University of York, York YO10 5DD (United Kingdom); Kraeuchi, Norbert [Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zuercherstrasse 111, 8903 Birmensdorf (Switzerland)
2007-02-15
The objective of this study was to establish whether EU and UN-ECE/ICP-Forests monitoring data (i) provide the variables necessary to apply the flux-based modeling methods and (ii) meet the quality criteria necessary to apply the flux-based critical level concept. Application of this model has been possible using environmental data collected from the EU and UN-ECE/ICP-Forests monitoring network in Switzerland and Italy for 2000-2002. The test for data completeness and plausibility resulted in 6 out of a possible total of 20 Fagus sylvatica L. plots being identified as suitable from Switzerland, Italy, Spain, and France. The results show that the collected data allow the identification of different spatial and temporal areas and periods as having higher risk to ozone than those identified using the AOT40 approach. However, it was also apparent that the quality and completeness of the available data may severely limit a complete risk assessment across Europe. - Data sets of the EU and UN-ECE/ICP-Forests monitoring network are examined regarding their suitability for the modeling of ozone uptake in trees in the view of risk assessment.
2006-05-01
interests include feature selection, statistical learning, multivariate statistics, market research, and classification. He may be contacted at...current youth market , and reducing barriers to Army enlistment. Part of the Army Recruiting Initiatives was the creation of a recruiter selection...Selection Model DevelPed by the Openuier Reseach Crate of E...lneSstm Erapseeeng Depce-teo, WViitd Ntt. siliec Academy, NW..t Point, 271 Weau/’itt 21M
Marchenko, Yulia V.
2012-03-01
Sample selection arises often in practice as a result of the partial observability of the outcome of interest in a study. In the presence of sample selection, the observed data do not represent a random sample from the population, even after controlling for explanatory variables. That is, data are missing not at random. Thus, standard analysis using only complete cases will lead to biased results. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. The method was criticized in the literature because of its sensitivity to the normality assumption. In practice, data, such as income or expenditure data, often violate the normality assumption because of heavier tails. We first establish a new link between sample selection models and recently studied families of extended skew-elliptical distributions. Then, this allows us to introduce a selection-t (SLt) model, which models the error distribution using a Student\\'s t distribution. We study its properties and investigate the finite-sample performance of the maximum likelihood estimators for this model. We compare the performance of the SLt model to the conventional Heckman selection-normal (SLN) model and apply it to analyze ambulatory expenditures. Unlike the SLNmodel, our analysis using the SLt model provides statistical evidence for the existence of sample selection bias in these data. We also investigate the performance of the test for sample selection bias based on the SLt model and compare it with the performances of several tests used with the SLN model. Our findings indicate that the latter tests can be misleading in the presence of heavy-tailed data. © 2012 American Statistical Association.
Steigner, D.; Steinbrecher, R.; Rappenglück, B.; Gasche, R.; Hansel, A.; Graus, M.; Lindinger, Ch.
2003-04-01
Biogenic volatile organic compounds (BVOCs) play a crucial role in the formation of photo-oxidants and particles through the diverse BVOC degradation pathways. Yet, current estimations about temporal and spatial BVOC emissions, including the specific BVOC mix are rather vague. This project addresses this issue by: the determination of (a) BVOC net emission rates and (b) primary emissions of BVOCs from the trees and soils. Measurement campaigns were carried out at the Waldstein site in the Fichtelgebirge in 2001 and 2002. Primary emissions of isoprenoids from the soil and from twigs of Norway spruce (Picea abies [L.] Karst.) and stand fluxes of isoprenoids were quantified by means of REA-technique with in situ GC-FID analysis and GC-MS analysis in the laboratory. Moreover, REA-samples obtained by the system were analysed by a PTR-MS. A critical value when using the REA approach is the Businger-Oncley parameter b. For this canopy type a b value of 0.39 (threshold velocity w_o = 0.6) was determined. The PTR-MS data show clear diurnal variations of ambient air mixing ratios of VOC such as isoprene and monoterpenes, but also of oxygenated VOCs such as carbonyls and alcohols and methylvinylketone (MVK) and methacrolein (MAK), products from isoprene degradation. Four selected trees (Picea abies [L.] Karst.) were intensively screened for primary BVOC emission rates. Most abundant species are b-pinene/sabinene and camphene. They show typical diurnal patterns with high emissions during daytime. Soil emissions of NO reached 250 nmol N m-2 s-1 at soil temperatures (in 3 cm depth) of 13^oC and at a relative air humidity of 60%. Ambient air mixing ratios near the soil surface of NO reached values of up to 0.7 ppb. NO_2 and ozone mixing ratios varied between 0.1 to 1.5 ppb and 10 to 37 ppb, respectively. As expected nitrogen oxide emissions rates tend to increase with increasing surface temperature. Isoprenoid emission from the soil was low and in general near the detection limit
Energy Technology Data Exchange (ETDEWEB)
Limpert, Steven; Ghosh, Kunal; Wagner, Hannes; Bowden, Stuart; Honsberg, Christiana; Goodnick, Stephen; Bremner, Stephen; Green, Martin
2014-06-09
We report results from coupled optical and electrical Sentaurus TCAD models of a gallium phosphide (GaP) on silicon electron carrier selective contact (CSC) solar cell. Detailed analyses of current and voltage performance are presented for devices having substrate thicknesses of 10 μm, 50 μm, 100 μm and 150 μm, and with GaP/Si interfacial quality ranging from very poor to excellent. Ultimate potential performance was investigated using optical absorption profiles consistent with light trapping schemes of random pyramids with attached and detached rear reflector, and planar with an attached rear reflector. Results indicate Auger-limited open-circuit voltages up to 787 mV and efficiencies up to 26.7% may be possible for front-contacted devices.
Complexity regularized hydrological model selection
Pande, S.; Arkesteijn, L.; Bastidas, L.A.
2014-01-01
This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available.
Complexity regularized hydrological model selection
Pande, S.; Arkesteijn, L.; Bastidas, L.A.
2014-01-01
This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available.
Individual Influence on Model Selection
Sterba, Sonya K.; Pek, Jolynn
2012-01-01
Researchers in psychology are increasingly using model selection strategies to decide among competing models, rather than evaluating the fit of a given model in isolation. However, such interest in model selection outpaces an awareness that one or a few cases can have disproportionate impact on the model ranking. Though case influence on the fit…
Selected soil thermal conductivity models
Directory of Open Access Journals (Sweden)
Rerak Monika
2017-01-01
Full Text Available The paper presents collected from the literature models of soil thermal conductivity. This is a very important parameter, which allows one to assess how much heat can be transferred from the underground power cables through the soil. The models are presented in table form, thus when the properties of the soil are given, it is possible to select the most accurate method of calculating its thermal conductivity. Precise determination of this parameter results in designing the cable line in such a way that it does not occur the process of cable overheating.
Entropic criterion for model selection
Tseng, Chih-Yuan
2006-10-01
Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why use this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficult to get. Following the logic of inductive inference proposed by Caticha [Relative entropy and inductive inference, in: G. Erickson, Y. Zhai (Eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings, vol. 707, 2004 (available from arXiv.org/abs/physics/0311093)], we show relative entropy to be a unique criterion, which requires no prior information and can be applied to different fields. We examine this criterion by considering a physical problem, simple fluids, and results are promising.
Schmidt-Eisenlohr, F.; Puñal, O.; Klagges, K.; Kirsche, M.
Apart from the general issue of modeling the channel, the PHY and the MAC of wireless networks, there are specific modeling assumptions that are considered for different systems. In this chapter we consider three specific wireless standards and highlight modeling options for them. These are IEEE 802.11 (as example for wireless local area networks), IEEE 802.16 (as example for wireless metropolitan networks) and IEEE 802.15 (as example for body area networks). Each section on these three systems discusses also at the end a set of model implementations that are available today.
Moore, A S; Faisal, A; Gonzalez de Castro, D; Bavetsias, V; Sun, C; Atrash, B; Valenti, M; de Haven Brandon, A; Avery, S; Mair, D; Mirabella, F; Swansbury, J; Pearson, A D J; Workman, P; Blagg, J; Raynaud, F I; Eccles, S A; Linardopoulos, S
2012-07-01
Acquired resistance to selective FLT3 inhibitors is an emerging clinical problem in the treatment of FLT3-ITD(+) acute myeloid leukaemia (AML). The paucity of valid pre-clinical models has restricted investigations to determine the mechanism of acquired therapeutic resistance, thereby limiting the development of effective treatments. We generated selective FLT3 inhibitor-resistant cells by treating the FLT3-ITD(+) human AML cell line MOLM-13 in vitro with the FLT3-selective inhibitor MLN518, and validated the resistant phenotype in vivo and in vitro. The resistant cells, MOLM-13-RES, harboured a new D835Y tyrosine kinase domain (TKD) mutation on the FLT3-ITD(+) allele. Acquired TKD mutations, including D835Y, have recently been identified in FLT3-ITD(+) patients relapsing after treatment with the novel FLT3 inhibitor, AC220. Consistent with this clinical pattern of resistance, MOLM-13-RES cells displayed high relative resistance to AC220 and Sorafenib. Furthermore, treatment of MOLM-13-RES cells with AC220 lead to loss of the FLT3 wild-type allele and the duplication of the FLT3-ITD-D835Y allele. Our FLT3-Aurora kinase inhibitor, CCT137690, successfully inhibited growth of FLT3-ITD-D835Y cells in vitro and in vivo, suggesting that dual FLT3-Aurora inhibition may overcome selective FLT3 inhibitor resistance, in part due to inhibition of Aurora kinase, and may benefit patients with FLT3-mutated AML.
Launch vehicle selection model
Montoya, Alex J.
1990-01-01
Over the next 50 years, humans will be heading for the Moon and Mars to build scientific bases to gain further knowledge about the universe and to develop rewarding space activities. These large scale projects will last many years and will require large amounts of mass to be delivered to Low Earth Orbit (LEO). It will take a great deal of planning to complete these missions in an efficient manner. The planning of a future Heavy Lift Launch Vehicle (HLLV) will significantly impact the overall multi-year launching cost for the vehicle fleet depending upon when the HLLV will be ready for use. It is desirable to develop a model in which many trade studies can be performed. In one sample multi-year space program analysis, the total launch vehicle cost of implementing the program reduced from 50 percent to 25 percent. This indicates how critical it is to reduce space logistics costs. A linear programming model has been developed to answer such questions. The model is now in its second phase of development, and this paper will address the capabilities of the model and its intended uses. The main emphasis over the past year was to make the model user friendly and to incorporate additional realistic constraints that are difficult to represent mathematically. We have developed a methodology in which the user has to be knowledgeable about the mission model and the requirements of the payloads. We have found a representation that will cut down the solution space of the problem by inserting some preliminary tests to eliminate some infeasible vehicle solutions. The paper will address the handling of these additional constraints and the methodology for incorporating new costing information utilizing learning curve theory. The paper will review several test cases that will explore the preferred vehicle characteristics and the preferred period of construction, i.e., within the next decade, or in the first decade of the next century. Finally, the paper will explore the interaction
Model Selection Principles in Misspecified Models
Lv, Jinchi
2010-01-01
Model selection is of fundamental importance to high dimensional modeling featured in many contemporary applications. Classical principles of model selection include the Kullback-Leibler divergence principle and the Bayesian principle, which lead to the Akaike information criterion and Bayesian information criterion when models are correctly specified. Yet model misspecification is unavoidable when we have no knowledge of the true model or when we have the correct family of distributions but miss some true predictor. In this paper, we propose a family of semi-Bayesian principles for model selection in misspecified models, which combine the strengths of the two well-known principles. We derive asymptotic expansions of the semi-Bayesian principles in misspecified generalized linear models, which give the new semi-Bayesian information criteria (SIC). A specific form of SIC admits a natural decomposition into the negative maximum quasi-log-likelihood, a penalty on model dimensionality, and a penalty on model miss...
Bayesian Model Selection and Statistical Modeling
Ando, Tomohiro
2010-01-01
Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The quality of these solutions usually depends on the goodness of the constructed Bayesian model. Realizing how crucial this issue is, many researchers and practitioners have been extensively investigating the Bayesian model selection problem. This book provides comprehensive explanations of the concepts and derivations of the Bayesian approach for model selection and related criteria, including the Bayes factor, the Bayesian information criterion (BIC), the generalized BIC, and the pseudo marginal lik
Introduction. Modelling natural action selection.
Prescott, Tony J; Bryson, Joanna J; Seth, Anil K
2007-09-29
Action selection is the task of resolving conflicts between competing behavioural alternatives. This theme issue is dedicated to advancing our understanding of the behavioural patterns and neural substrates supporting action selection in animals, including humans. The scope of problems investigated includes: (i) whether biological action selection is optimal (and, if so, what is optimized), (ii) the neural substrates for action selection in the vertebrate brain, (iii) the role of perceptual selection in decision-making, and (iv) the interaction of group and individual action selection. A second aim of this issue is to advance methodological practice with respect to modelling natural action section. A wide variety of computational modelling techniques are therefore employed ranging from formal mathematical approaches through to computational neuroscience, connectionism and agent-based modelling. The research described has broad implications for both natural and artificial sciences. One example, highlighted here, is its application to medical science where models of the neural substrates for action selection are contributing to the understanding of brain disorders such as Parkinson's disease, schizophrenia and attention deficit/hyperactivity disorder.
CRUMP 2003 Selected Water Sample Results
U.S. Environmental Protection Agency — Point locations and water sampling results performed in 2003 by the Church Rock Uranium Monitoring Project (CRUMP) a consortium of organizations (Navajo Nation...
Bayesian Evidence and Model Selection
Knuth, Kevin H; Malakar, Nabin K; Mubeen, Asim M; Placek, Ben
2014-01-01
In this paper we review the concept of the Bayesian evidence and its application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Application to several practical examples within the context of signal processing are discussed.
Model Selection for Pion Photoproduction
Landay, J; Fernández-Ramírez, C; Hu, B; Molina, R
2016-01-01
Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the $S$-matrix are implemented to different degree in different approaches, but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the Least Absolute Shrinkage and Selection Operator (LASSO) in combination with criteria from information theory and $K$-fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. The principle is first illustrated with synthetic data, then, its feasibility for real data is demonstrated by analyzing the latest available measu...
Genetic search feature selection for affective modeling
DEFF Research Database (Denmark)
Martínez, Héctor P.; Yannakakis, Georgios N.
2010-01-01
Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...
THE TIME DOMAIN SPECTROSCOPIC SURVEY: VARIABLE SELECTION AND ANTICIPATED RESULTS
Energy Technology Data Exchange (ETDEWEB)
Morganson, Eric; Green, Paul J. [Harvard Smithsonian Center for Astrophysics, 60 Garden St, Cambridge, MA 02138 (United States); Anderson, Scott F.; Ruan, John J. [Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195 (United States); Myers, Adam D. [Department of Physics and Astronomy, University of Wyoming, Laramie, WY 82071 (United States); Eracleous, Michael; Brandt, William Nielsen [Department of Astronomy and Astrophysics, 525 Davey Laboratory, The Pennsylvania State University, University Park, PA 16802 (United States); Kelly, Brandon [Department of Physics, Broida Hall, University of California, Santa Barbara, CA 93106-9530 (United States); Badenes, Carlos [Department of Physics and Astronomy and Pittsburgh Particle Physics, Astrophysics and Cosmology Center (PITT PACC), University of Pittsburgh, 3941 O’Hara St, Pittsburgh, PA 15260 (United States); Bañados, Eduardo [Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg (Germany); Blanton, Michael R. [Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003 (United States); Bershady, Matthew A. [Department of Astronomy, University of Wisconsin, 475 N. Charter St., Madison, WI 53706 (United States); Borissova, Jura [Instituto de Física y Astronomía, Universidad de Valparaíso, Av. Gran Bretaña 1111, Playa Ancha, Casilla 5030, and Millennium Institute of Astrophysics (MAS), Santiago (Chile); Burgett, William S. [GMTO Corp, Suite 300, 251 S. Lake Ave, Pasadena, CA 91101 (United States); Chambers, Kenneth, E-mail: emorganson@cfa.harvard.edu [Institute for Astronomy, University of Hawaii at Manoa, Honolulu, HI 96822 (United States); and others
2015-06-20
We present the selection algorithm and anticipated results for the Time Domain Spectroscopic Survey (TDSS). TDSS is an Sloan Digital Sky Survey (SDSS)-IV Extended Baryon Oscillation Spectroscopic Survey (eBOSS) subproject that will provide initial identification spectra of approximately 220,000 luminosity-variable objects (variable stars and active galactic nuclei across 7500 deg{sup 2} selected from a combination of SDSS and multi-epoch Pan-STARRS1 photometry. TDSS will be the largest spectroscopic survey to explicitly target variable objects, avoiding pre-selection on the basis of colors or detailed modeling of specific variability characteristics. Kernel Density Estimate analysis of our target population performed on SDSS Stripe 82 data suggests our target sample will be 95% pure (meaning 95% of objects we select have genuine luminosity variability of a few magnitudes or more). Our final spectroscopic sample will contain roughly 135,000 quasars and 85,000 stellar variables, approximately 4000 of which will be RR Lyrae stars which may be used as outer Milky Way probes. The variability-selected quasar population has a smoother redshift distribution than a color-selected sample, and variability measurements similar to those we develop here may be used to make more uniform quasar samples in large surveys. The stellar variable targets are distributed fairly uniformly across color space, indicating that TDSS will obtain spectra for a wide variety of stellar variables including pulsating variables, stars with significant chromospheric activity, cataclysmic variables, and eclipsing binaries. TDSS will serve as a pathfinder mission to identify and characterize the multitude of variable objects that will be detected photometrically in even larger variability surveys such as Large Synoptic Survey Telescope.
The Time Domain Spectroscopic Survey: Variable Selection and Anticipated Results
Morganson, Eric; Green, Paul J.; Anderson, Scott F.; Ruan, John J.; Myers, Adam D.; Eracleous, Michael; Kelly, Brandon; Badenes, Carlos; Bañados, Eduardo; Blanton, Michael R.; Bershady, Matthew A.; Borissova, Jura; Brandt, William Nielsen; Burgett, William S.; Chambers, Kenneth; Draper, Peter W.; Davenport, James R. A.; Flewelling, Heather; Garnavich, Peter; Hawley, Suzanne L.; Hodapp, Klaus W.; Isler, Jedidah C.; Kaiser, Nick; Kinemuchi, Karen; Kudritzki, Rolf P.; Metcalfe, Nigel; Morgan, Jeffrey S.; Pâris, Isabelle; Parvizi, Mahmoud; Poleski, Radosław; Price, Paul A.; Salvato, Mara; Shanks, Tom; Schlafly, Eddie F.; Schneider, Donald P.; Shen, Yue; Stassun, Keivan; Tonry, John T.; Walter, Fabian; Waters, Chris Z.
2015-06-01
We present the selection algorithm and anticipated results for the Time Domain Spectroscopic Survey (TDSS). TDSS is an Sloan Digital Sky Survey (SDSS)-IV Extended Baryon Oscillation Spectroscopic Survey (eBOSS) subproject that will provide initial identification spectra of approximately 220,000 luminosity-variable objects (variable stars and active galactic nuclei across 7500 deg2 selected from a combination of SDSS and multi-epoch Pan-STARRS1 photometry. TDSS will be the largest spectroscopic survey to explicitly target variable objects, avoiding pre-selection on the basis of colors or detailed modeling of specific variability characteristics. Kernel Density Estimate analysis of our target population performed on SDSS Stripe 82 data suggests our target sample will be 95% pure (meaning 95% of objects we select have genuine luminosity variability of a few magnitudes or more). Our final spectroscopic sample will contain roughly 135,000 quasars and 85,000 stellar variables, approximately 4000 of which will be RR Lyrae stars which may be used as outer Milky Way probes. The variability-selected quasar population has a smoother redshift distribution than a color-selected sample, and variability measurements similar to those we develop here may be used to make more uniform quasar samples in large surveys. The stellar variable targets are distributed fairly uniformly across color space, indicating that TDSS will obtain spectra for a wide variety of stellar variables including pulsating variables, stars with significant chromospheric activity, cataclysmic variables, and eclipsing binaries. TDSS will serve as a pathfinder mission to identify and characterize the multitude of variable objects that will be detected photometrically in even larger variability surveys such as Large Synoptic Survey Telescope.
A Selective Review of Group Selection in High Dimensional Models
Huang, Jian; Ma, Shuangge
2012-01-01
Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties, and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study.
1991-12-01
WCEHLER PAUL J. MARTO Chair.in, Dean o Research Department of Mathematics unclassified SECURITY CLASSIFICATION OF THIS PAGE Form Approved REPORT...Radiation in the United States, LBL-16344, Lawrence Berkeley Labs. Mikkelsen, T. and R.M. Eckman , 1985: A statistical model for relative dif asion in
Model selection for radiochromic film dosimetry
Méndez, Ignasi
2015-01-01
The purpose of this study was to find the most accurate model for radiochromic film dosimetry by comparing different channel independent perturbation models. A model selection approach based on (algorithmic) information theory was followed, and the results were validated using gamma-index analysis on a set of benchmark test cases. Several questions were addressed: (a) whether incorporating the information of the non-irradiated film, by scanning prior to irradiation, improves the results; (b) whether lateral corrections are necessary when using multichannel models; (c) whether multichannel dosimetry produces better results than single-channel dosimetry; (d) which multichannel perturbation model provides more accurate film doses. It was found that scanning prior to irradiation and applying lateral corrections improved the accuracy of the results. For some perturbation models, increasing the number of color channels did not result in more accurate film doses. Employing Truncated Normal perturbations was found to...
Model selection for pion photoproduction
Landay, J.; Döring, M.; Fernández-Ramírez, C.; Hu, B.; Molina, R.
2017-01-01
Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the S matrix are implemented to a different degree in different approaches; but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the least absolute shrinkage and selection operator (LASSO) in combination with criteria from information theory and K -fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. The principle is first illustrated with synthetic data; then, its feasibility for real data is demonstrated by analyzing the latest available measurements of differential cross sections (d σ /d Ω ), photon-beam asymmetries (Σ ), and target asymmetry differential cross sections (d σT/d ≡T d σ /d Ω ) in the low-energy regime.
MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS
Energy Technology Data Exchange (ETDEWEB)
Asensio Ramos, A.; Manso Sainz, R.; Martinez Gonzalez, M. J.; Socas-Navarro, H. [Instituto de Astrofisica de Canarias, E-38205, La Laguna, Tenerife (Spain); Viticchie, B. [ESA/ESTEC RSSD, Keplerlaan 1, 2200 AG Noordwijk (Netherlands); Orozco Suarez, D., E-mail: aasensio@iac.es [National Astronomical Observatory of Japan, Mitaka, Tokyo 181-8588 (Japan)
2012-04-01
Inferring magnetic and thermodynamic information from spectropolarimetric observations relies on the assumption of a parameterized model atmosphere whose parameters are tuned by comparison with observations. Often, the choice of the underlying atmospheric model is based on subjective reasons. In other cases, complex models are chosen based on objective reasons (for instance, the necessity to explain asymmetries in the Stokes profiles) but it is not clear what degree of complexity is needed. The lack of an objective way of comparing models has, sometimes, led to opposing views of the solar magnetism because the inferred physical scenarios are essentially different. We present the first quantitative model comparison based on the computation of the Bayesian evidence ratios for spectropolarimetric observations. Our results show that there is not a single model appropriate for all profiles simultaneously. Data with moderate signal-to-noise ratios (S/Ns) favor models without gradients along the line of sight. If the observations show clear circular and linear polarization signals above the noise level, models with gradients along the line are preferred. As a general rule, observations with large S/Ns favor more complex models. We demonstrate that the evidence ratios correlate well with simple proxies. Therefore, we propose to calculate these proxies when carrying out standard least-squares inversions to allow for model comparison in the future.
Energy Technology Data Exchange (ETDEWEB)
Saracoglu, B.O.
2016-07-01
The electricity demand in Turkey has been increasing for a while. Hydropower is one of the major electricity generation types to compensate this electricity demand in Turkey. Private investors (domestic and foreign) in the hydropower electricity generation sector have been looking for the most appropriate and satisfactory new private hydropower investment (PHPI) options and opportunities in Turkey. This study aims to present a qualitative multi-attribute decision making (MADM) model, that is easy, straightforward, and fast for the selection of the most satisfactory reasonable PHPI options during the very early investment stages (data and information poorness on projects). The data and information of the PHPI options was gathered from the official records on the official websites. A wide and deep literature review was conducted for the MADM models and for the hydropower industry. The attributes of the model were identified, selected, clustered and evaluated by the expert decision maker (EDM) opinion and by help of an open source search results clustering engine (Carrot2) (helpful for also comprehension). The PHPI options were clustered according to their installed capacities main property to analyze the options in the most appropriate, decidable, informative, understandable and meaningful way. A simple clustering algorithm for the PHPI options was executed in the current study. A template model for the selection of the most satisfactory PHPI options was built in the DEXi (Decision EXpert for Education) and the DEXiTree software. The basic attributes for the selection of the PHPI options were presented and afterwards the aggregate attributes were defined by the bottom-up structuring for the early investment stages. The attributes were also analyzed by help of Carrot2. The most satisfactory PHPI options in Turkey in the big options data set were selected for each PHPI options cluster by the EDM evaluations in the DEXi. (Author)
Adaptive Covariance Estimation with model selection
Biscay, Rolando; Loubes, Jean-Michel
2012-01-01
We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator. We prove that this method is an extension to the matricial regression model of the work by Baraud.
Directory of Open Access Journals (Sweden)
Burak Omer Saracoglu
2016-03-01
Full Text Available Purpose: The electricity demand in Turkey has been increasing for a while. Hydropower is one of the major electricity generation types to compensate this electricity demand in Turkey. Private investors (domestic and foreign in the hydropower electricity generation sector have been looking for the most appropriate and satisfactory new private hydropower investment (PHPI options and opportunities in Turkey. This study aims to present a qualitative multi-attribute decision making (MADM model, that is easy, straightforward, and fast for the selection of the most satisfactory reasonable PHPI options during the very early investment stages (data and information poorness on projects. Design/methodology/approach: The data and information of the PHPI options was gathered from the official records on the official websites. A wide and deep literature review was conducted for the MADM models and for the hydropower industry. The attributes of the model were identified, selected, clustered and evaluated by the expert decision maker (EDM opinion and by help of an open source search results clustering engine (Carrot2 (helpful for also comprehension. The PHPI options were clustered according to their installed capacities main property to analyze the options in the most appropriate, decidable, informative, understandable and meaningful way. A simple clustering algorithm for the PHPI options was executed in the current study. A template model for the selection of the most satisfactory PHPI options was built in the DEXi (Decision EXpert for Education and the DEXiTree software. Findings: The basic attributes for the selection of the PHPI options were presented and afterwards the aggregate attributes were defined by the bottom-up structuring for the early investment stages. The attributes were also analyzed by help of Carrot2. The most satisfactory PHPI options in Turkey in the big options data set were selected for each PHPI options cluster by the EDM evaluations in
Adverse selection model regarding tobacco consumption
Directory of Open Access Journals (Sweden)
Dumitru MARIN
2006-01-01
Full Text Available The impact of introducing a tax on tobacco consumption can be studied trough an adverse selection model. The objective of the model presented in the following is to characterize the optimal contractual relationship between the governmental authorities and the two type employees: smokers and non-smokers, taking into account that the consumers’ decision to smoke or not represents an element of risk and uncertainty. Two scenarios are run using the General Algebraic Modeling Systems software: one without taxes set on tobacco consumption and another one with taxes set on tobacco consumption, based on an adverse selection model described previously. The results of the two scenarios are compared in the end of the paper: the wage earnings levels and the social welfare in case of a smoking agent and in case of a non-smoking agent.
Selective Maintenance Model Considering Time Uncertainty
Le Chen; Zhengping Shu; Yuan Li; Xuezhi Lv
2012-01-01
This study proposes a selective maintenance model for weapon system during mission interval. First, it gives relevant definitions and operational process of material support system. Then, it introduces current research on selective maintenance modeling. Finally, it establishes numerical model for selecting corrective and preventive maintenance tasks, considering time uncertainty brought by unpredictability of maintenance procedure, indetermination of downtime for spares and difference of skil...
Estimation of a multivariate mean under model selection uncertainty
Directory of Open Access Journals (Sweden)
Georges Nguefack-Tsague
2014-05-01
Full Text Available Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty. When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference. In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.
Post-model selection inference and model averaging
Directory of Open Access Journals (Sweden)
Georges Nguefack-Tsague
2011-07-01
Full Text Available Although model selection is routinely used in practice nowadays, little is known about its precise effects on any subsequent inference that is carried out. The same goes for the effects induced by the closely related technique of model averaging. This paper is concerned with the use of the same data first to select a model and then to carry out inference, in particular point estimation and point prediction. The properties of the resulting estimator, called a post-model-selection estimator (PMSE, are hard to derive. Using selection criteria such as hypothesis testing, AIC, BIC, HQ and Cp, we illustrate that, in terms of risk function, no single PMSE dominates the others. The same conclusion holds more generally for any penalised likelihood information criterion. We also compare various model averaging schemes and show that no single one dominates the others in terms of risk function. Since PMSEs can be regarded as a special case of model averaging, with 0-1 random-weights, we propose a connection between the two theories, in the frequentist approach, by taking account of the selection procedure when performing model averaging. We illustrate the point by simulating a simple linear regression model.
Model selection for amplitude analysis
Guegan, Baptiste; Stevens, Justin; Williams, Mike
2015-01-01
Model complexity in amplitude analyses is often a priori under-constrained since the underlying theory permits a large number of amplitudes to contribute to most physical processes. The use of an overly complex model results in reduced predictive power and worse resolution on unknown parameters of interest. Therefore, it is common to reduce the complexity by removing from consideration some subset of the allowed amplitudes. This paper studies a data-driven method for limiting model complexity through regularization during regression in the context of a multivariate (Dalitz-plot) analysis. The regularization technique applied greatly improves the performance. A method is also proposed for obtaining the significance of a resonance in a multivariate amplitude analysis.
Portfolio Selection Model with Derivative Securities
Institute of Scientific and Technical Information of China (English)
王春峰; 杨建林; 蒋祥林
2003-01-01
Traditional portfolio theory assumes that the return rate of portfolio follows normality. However, this assumption is not true when derivative assets are incorporated. In this paper a portfolio selection model is developed based on utility function which can capture asymmetries in random variable distributions. Other realistic conditions are also considered, such as liabilities and integer decision variables. Since the resulting model is a complex mixed-integer nonlinear programming problem, simulated annealing algorithm is applied for its solution. A numerical example is given and sensitivity analysis is conducted for the model.
Bayesian model selection in Gaussian regression
Abramovich, Felix
2009-01-01
We consider a Bayesian approach to model selection in Gaussian linear regression, where the number of predictors might be much larger than the number of observations. From a frequentist view, the proposed procedure results in the penalized least squares estimation with a complexity penalty associated with a prior on the model size. We investigate the optimality properties of the resulting estimator. We establish the oracle inequality and specify conditions on the prior that imply its asymptotic minimaxity within a wide range of sparse and dense settings for "nearly-orthogonal" and "multicollinear" designs.
A Neurodynamical Model for Selective Visual Attention
Institute of Scientific and Technical Information of China (English)
QU Jing-Yi; WANG Ru-Bin; ZHANG Yuan; DU Ying
2011-01-01
A neurodynamical model for selective visual attention considering orientation preference is proposed. Since orientation preference is one of the most important properties of neurons in the primary visual cortex, it should be fully considered besides external stimuli intensity. By tuning the parameter of orientation preference, the regimes of synchronous dynamics associated with the development of the attention focus are studied. The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed. Such dynamics correspond to the partial synchronization mode. Simulation results show that the model can sequentially select objects with different orientation preferences and has a reliable shift of attention from one object to another, which are consistent with the experimental results that neurons with different orientation preferences are laid out in pinwheel patterns.%A neurodynamical model for selective visual attention considering orientation preference is proposed.Since orientation preference is one of the most important properties of neurons in the primary visual cortex,it should be fully considered besides external stimuli intensity.By tuning the parameter of orientation preference,the regimes of synchronous dynamics associated with the development of the attention focus are studied.The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed.Such dynamics correspond to the partial synchronization mode.Simulation results show that the model can sequentially select objects with different orientation preferences and has a reliable shift of attention from one object to another,which are consistent with the experimental results that neurons with different orientation preferences are laid out in pinwheel patterns.Selective visual
Bayesian Constrained-Model Selection for Factor Analytic Modeling
Peeters, Carel F.W.
2016-01-01
My dissertation revolves around Bayesian approaches towards constrained statistical inference in the factor analysis (FA) model. Two interconnected types of restricted-model selection are considered. These types have a natural connection to selection problems in the exploratory FA (EFA) and confirmatory FA (CFA) model and are termed Type I and Type II model selection. Type I constrained-model selection is taken to mean the determination of the appropriate dimensionality of a model. This type ...
Appropriate model selection methods for nonstationary generalized extreme value models
Kim, Hanbeen; Kim, Sooyoung; Shin, Hongjoon; Heo, Jun-Haeng
2017-04-01
Several evidences of hydrologic data series being nonstationary in nature have been found to date. This has resulted in the conduct of many studies in the area of nonstationary frequency analysis. Nonstationary probability distribution models involve parameters that vary over time. Therefore, it is not a straightforward process to apply conventional goodness-of-fit tests to the selection of an appropriate nonstationary probability distribution model. Tests that are generally recommended for such a selection include the Akaike's information criterion (AIC), corrected Akaike's information criterion (AICc), Bayesian information criterion (BIC), and likelihood ratio test (LRT). In this study, the Monte Carlo simulation was performed to compare the performances of these four tests, with regard to nonstationary as well as stationary generalized extreme value (GEV) distributions. Proper model selection ratios and sample sizes were taken into account to evaluate the performances of all the four tests. The BIC demonstrated the best performance with regard to stationary GEV models. In case of nonstationary GEV models, the AIC proved to be better than the other three methods, when relatively small sample sizes were considered. With larger sample sizes, the AIC, BIC, and LRT presented the best performances for GEV models which have nonstationary location and/or scale parameters, respectively. Simulation results were then evaluated by applying all four tests to annual maximum rainfall data of selected sites, as observed by the Korea Meteorological Administration.
Multi-dimensional model order selection
Directory of Open Access Journals (Sweden)
Roemer Florian
2011-01-01
Full Text Available Abstract Multi-dimensional model order selection (MOS techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dimensional decompositions, it is known that the number of main components can be larger when compared to matrix-based decompositions. In this article, we show how to use tensor calculus to extend matrix-based MOS schemes and we also present our proposed multi-dimensional model order selection scheme based on the closed-form PARAFAC algorithm, which is only applicable to multi-dimensional data. In general, as shown by means of simulations, the Probability of correct Detection (PoD of our proposed multi-dimensional MOS schemes is much better than the PoD of matrix-based schemes.
Model selection bias and Freedman's paradox
Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.
2010-01-01
In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.
Selected Logistics Models and Techniques.
1984-09-01
ACCESS PROCEDURE: On-Line System (OLS), UNINET . RCA maintains proprietary control of this model, and the model is available only through a lease...System (OLS), UNINET . RCA maintains proprietary control of this model, and the model is available only through a lease arrangement. • SPONSOR: ASD/ACCC
Entropic Priors and Bayesian Model Selection
Brewer, Brendon J
2009-01-01
We demonstrate that the principle of maximum relative entropy (ME), used judiciously, can ease the specification of priors in model selection problems. The resulting effect is that models that make sharp predictions are disfavoured, weakening the usual Bayesian "Occam's Razor". This is illustrated with a simple example involving what Jaynes called a "sure thing" hypothesis. Jaynes' resolution of the situation involved introducing a large number of alternative "sure thing" hypotheses that were possible before we observed the data. However, in more complex situations, it may not be possible to explicitly enumerate large numbers of alternatives. The entropic priors formalism produces the desired result without modifying the hypothesis space or requiring explicit enumeration of alternatives; all that is required is a good model for the prior predictive distribution for the data. This idea is illustrated with a simple rigged-lottery example, and we outline how this idea may help to resolve a recent debate amongst ...
Genetic search feature selection for affective modeling
DEFF Research Database (Denmark)
Martínez, Héctor P.; Yannakakis, Georgios N.
2010-01-01
Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built...
Efficiently adapting graphical models for selectivity estimation
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2013-01-01
of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing...... cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss......Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent...
The Ouroboros Model, selected facets.
Thomsen, Knud
2011-01-01
The Ouroboros Model features a biologically inspired cognitive architecture. At its core lies a self-referential recursive process with alternating phases of data acquisition and evaluation. Memory entries are organized in schemata. The activation at a time of part of a schema biases the whole structure and, in particular, missing features, thus triggering expectations. An iterative recursive monitor process termed 'consumption analysis' is then checking how well such expectations fit with successive activations. Mismatches between anticipations based on previous experience and actual current data are highlighted and used for controlling the allocation of attention. A measure for the goodness of fit provides feedback as (self-) monitoring signal. The basic algorithm works for goal directed movements and memory search as well as during abstract reasoning. It is sketched how the Ouroboros Model can shed light on characteristics of human behavior including attention, emotions, priming, masking, learning, sleep and consciousness.
The Baikal neutrino experiment: Status, selected physics results, and perspectives
Energy Technology Data Exchange (ETDEWEB)
Aynutdinov, V.; Avrorin, A.; Balkanov, V. [Institute for Nuclear Research, 60th October Anniversary pr. 7a, Moscow 117312 (Russian Federation); Belolaptikov, I. [Joint Institute for Nuclear Research, Dubna (Russian Federation); Budnev, N. [Irkutsk State University, Irkutsk (Russian Federation); Danilchenko, I.; Domogatsky, G.; Doroshenko, A. [Institute for Nuclear Research, 60th October Anniversary pr. 7a, Moscow 117312 (Russian Federation); Dyachok, A. [Irkutsk State University, Irkutsk (Russian Federation); Dzhilkibaev, Zh.-A. [Institute for Nuclear Research, 60th October Anniversary pr. 7a, Moscow 117312 (Russian Federation)], E-mail: djilkib@pcbai10.inr.ruhep.ru; Fialkovsky, S. [Nizhni Novgorod State Technical University, Nizhni Novgorod (Russian Federation); Gaponenko, O. [Institute for Nuclear Research, 60th October Anniversary pr. 7a, Moscow 117312 (Russian Federation); Golubkov, K. [Joint Institute for Nuclear Research, Dubna (Russian Federation); Gress, O.; Gress, T.; Grishin, O. [Irkutsk State University, Irkutsk (Russian Federation); Klabukov, A. [Institute for Nuclear Research, 60th October Anniversary pr. 7a, Moscow 117312 (Russian Federation); Klimov, A. [Kurchatov Institute, Moscow (Russian Federation); Kochanov, A. [Irkutsk State University, Irkutsk (Russian Federation); Konischev, K. [Joint Institute for Nuclear Research, Dubna (Russian Federation)] (and others)
2008-04-01
We review the status of the Baikal neutrino telescope, which is operating in Lake Baikal since 1998 and has been upgraded to the 10 Mton detector NT200+ in 2005. We present selected physics results on searches for upward going neutrinos, relativistic magnetic monopoles and for very high-energy neutrinos. We describe the strategy of creating a detector on the Gigaton (km{sup 3}) scale at Lake Baikal. First steps of activities towards a km{sup 3} Baikal neutrino telescope are discussed.
Random Effect and Latent Variable Model Selection
Dunson, David B
2008-01-01
Presents various methods for accommodating model uncertainty in random effects and latent variable models. This book focuses on frequentist likelihood ratio and score tests for zero variance components. It also focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models
Review and selection of unsaturated flow models
Energy Technology Data Exchange (ETDEWEB)
Reeves, M.; Baker, N.A.; Duguid, J.O. [INTERA, Inc., Las Vegas, NV (United States)
1994-04-04
Since the 1960`s, ground-water flow models have been used for analysis of water resources problems. In the 1970`s, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970`s and well into the 1980`s focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M&O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing.
Model and Variable Selection Procedures for Semiparametric Time Series Regression
Directory of Open Access Journals (Sweden)
Risa Kato
2009-01-01
Full Text Available Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.
Model selection for Gaussian kernel PCA denoising
DEFF Research Database (Denmark)
Jørgensen, Kasper Winther; Hansen, Lars Kai
2012-01-01
We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...
Melody Track Selection Using Discriminative Language Model
Wu, Xiao; Li, Ming; Suo, Hongbin; Yan, Yonghong
In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.
Fuzzy modelling for selecting headgear types.
Akçam, M Okan; Takada, Kenji
2002-02-01
The purpose of this study was to develop a computer-assisted inference model for selecting appropriate types of headgear appliance for orthodontic patients and to investigate its clinical versatility as a decision-making aid for inexperienced clinicians. Fuzzy rule bases were created for degrees of overjet, overbite, and mandibular plane angle variables, respectively, according to subjective criteria based on the clinical experience and knowledge of the authors. The rules were then transformed into membership functions and the geometric mean aggregation was performed to develop the inference model. The resultant fuzzy logic was then tested on 85 cases in which the patients had been diagnosed as requiring headgear appliances. Eight experienced orthodontists judged each of the cases, and decided if they 'agreed', 'accepted', or 'disagreed' with the recommendations of the computer system. Intra-examiner agreements were investigated using repeated judgements of a set of 30 orthodontic cases and the kappa statistic. All of the examiners exceeded a kappa score of 0.7, allowing them to participate in the test run of the validity of the proposed inference model. The examiners' agreement with the system's recommendations was evaluated statistically. The average satisfaction rate of the examiners was 95.6 per cent and, for 83 out of the 85 cases, 97.6 per cent. The majority of the examiners (i.e. six or more out of the eight) were satisfied with the recommendations of the system. Thus, the usefulness of the proposed inference logic was confirmed.
Expert System Model for Educational Personnel Selection
Directory of Open Access Journals (Sweden)
Héctor A. Tabares-Ospina
2013-06-01
Full Text Available The staff selection is a difficult task due to the subjectivity that the evaluation means. This process can be complemented using a system to support decision. This paper presents the implementation of an expert system to systematize the selection process of professors. The management of software development is divided into 4 parts: requirements, design, implementation and commissioning. The proposed system models a specific knowledge through relationships between variables evidence and objective.
Tc-99 Adsorption on Selected Activated Carbons - Batch Testing Results
Energy Technology Data Exchange (ETDEWEB)
Mattigod, Shas V.; Wellman, Dawn M.; Golovich, Elizabeth C.; Cordova, Elsa A.; Smith, Ronald M.
2010-12-01
CH2M HILL Plateau Remediation Company (CHPRC) is currently developing a 200-West Area groundwater pump-and-treat system as the remedial action selected under the Comprehensive Environmental Response, Compensation, and Liability Act Record of Decision for Operable Unit (OU) 200-ZP-1. This report documents the results of treatability tests Pacific Northwest National Laboratory researchers conducted to quantify the ability of selected activated carbon products (or carbons) to adsorb technetium-99 (Tc-99) from 200-West Area groundwater. The Tc-99 adsorption performance of seven activated carbons (J177601 Calgon Fitrasorb 400, J177606 Siemens AC1230AWC, J177609 Carbon Resources CR-1240-AW, J177611 General Carbon GC20X50, J177612 Norit GAC830, J177613 Norit GAC830, and J177617 Nucon LW1230) were evaluated using water from well 299-W19-36. Four of the best performing carbons (J177606 Siemens AC1230AWC, J177609 Carbon Resources CR-1240-AW, J177611 General Carbon GC20X50, and J177613 Norit GAC830) were selected for batch isotherm testing. The batch isotherm tests on four of the selected carbons indicated that under lower nitrate concentration conditions (382 mg/L), Kd values ranged from 6,000 to 20,000 mL/g. In comparison. Under higher nitrate (750 mg/L) conditions, there was a measureable decrease in Tc-99 adsorption with Kd values ranging from 3,000 to 7,000 mL/g. The adsorption data fit both the Langmuir and the Freundlich equations. Supplemental tests were conducted using the two carbons that demonstrated the highest adsorption capacity to resolve the issue of the best fit isotherm. These tests indicated that Langmuir isotherms provided the best fit for Tc-99 adsorption under low nitrate concentration conditions. At the design basis concentration of Tc 0.865 µg/L(14,700 pCi/L), the predicted Kd values from using Langmuir isotherm constants were 5,980 mL/g and 6,870 mL/g for for the two carbons. These Kd values did not meet the target Kd value of 9,000 mL/g. Tests
Parametric or nonparametric? A parametricness index for model selection
Liu, Wei; 10.1214/11-AOS899
2012-01-01
In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is si...
2003-09-01
may be related to enhanced platelet procoagulant activity and annexin V binding.46 Another derivative of gallic acid , bis- muth subgallate, appears to...is described. We studied the effects of nine hemostatic dressings on blood loss using a model of severe venous hemorrhage and hepatic injury in swine...Methods: Swine were treated using one of nine hemostatic dressings. Dress- ings used the following primary active ingredients: microfibrillar
Bayesian variable selection for latent class models.
Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria
2011-09-01
In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.
MODEL SELECTION FOR LOG-LINEAR MODELS OF CONTINGENCY TABLES
Institute of Scientific and Technical Information of China (English)
ZHAO Lincheng; ZHANG Hong
2003-01-01
In this paper, we propose an information-theoretic-criterion-based model selection procedure for log-linear model of contingency tables under multinomial sampling, and establish the strong consistency of the method under some mild conditions. An exponential bound of miss detection probability is also obtained. The selection procedure is modified so that it can be used in practice. Simulation shows that the modified method is valid. To avoid selecting the penalty coefficient in the information criteria, an alternative selection procedure is given.
The genealogy of samples in models with selection.
Neuhauser, C; Krone, S M
1997-02-01
We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models. DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case.
Selected Experimental Results from Heavy-Ion Collisions at LHC
Directory of Open Access Journals (Sweden)
Ranbir Singh
2013-01-01
Full Text Available We review a subset of experimental results from the heavy-ion collisions at the Large Hadron Collider (LHC facility at CERN. Excellent consistency is observed across all the experiments at the LHC (at center of mass energy sNN=2.76 TeV for the measurements such as charged particle multiplicity density, azimuthal anisotropy coefficients, and nuclear modification factor of charged hadrons. Comparison to similar measurements from the Relativistic Heavy Ion Collider (RHIC at lower energy (sNN=200 GeV suggests that the system formed at LHC has a higher energy density and larger system size and lives for a longer time. These measurements are compared to model calculations to obtain physical insights on the properties of matter created at the RHIC and LHC.
A Theoretical Model for Selective Exposure Research.
Roloff, Michael E.; Noland, Mark
This study tests the basic assumptions underlying Fishbein's Model of Attitudes by correlating an individual's selective exposure to types of television programs (situation comedies, family drama, and action/adventure) with the attitudinal similarity between individual attitudes and attitudes characterized on the programs. Twenty-three college…
A mixed model reduction method for preserving selected physical information
Zhang, Jing; Zheng, Gangtie
2017-03-01
A new model reduction method in the frequency domain is presented. By mixedly using the model reduction techniques from both the time domain and the frequency domain, the dynamic model is condensed to selected physical coordinates, and the contribution of slave degrees of freedom is taken as a modification to the model in the form of effective modal mass of virtually constrained modes. The reduced model can preserve the physical information related to the selected physical coordinates such as physical parameters and physical space positions of corresponding structure components. For the cases of non-classical damping, the method is extended to the model reduction in the state space but still only contains the selected physical coordinates. Numerical results are presented to validate the method and show the effectiveness of the model reduction.
Elementary Teachers' Selection and Use of Visual Models
Lee, Tammy D.; Gail Jones, M.
2017-07-01
As science grows in complexity, science teachers face an increasing challenge of helping students interpret models that represent complex science systems. Little is known about how teachers select and use models when planning lessons. This mixed methods study investigated the pedagogical approaches and visual models used by elementary in-service and preservice teachers in the development of a science lesson about a complex system (e.g., water cycle). Sixty-seven elementary in-service and 69 elementary preservice teachers completed a card sort task designed to document the types of visual models (e.g., images) that teachers choose when planning science instruction. Quantitative and qualitative analyses were conducted to analyze the card sort task. Semistructured interviews were conducted with a subsample of teachers to elicit the rationale for image selection. Results from this study showed that both experienced in-service teachers and novice preservice teachers tended to select similar models and use similar rationales for images to be used in lessons. Teachers tended to select models that were aesthetically pleasing and simple in design and illustrated specific elements of the water cycle. The results also showed that teachers were not likely to select images that represented the less obvious dimensions of the water cycle. Furthermore, teachers selected visual models more as a pedagogical tool to illustrate specific elements of the water cycle and less often as a tool to promote student learning related to complex systems.
Gerretzen, Jan; Szymańska, Ewa; Bart, Jacob; Davies, Antony N; van Manen, Henk-Jan; van den Heuvel, Edwin R; Jansen, Jeroen J; Buydens, Lutgarde M C
2016-09-28
The aim of data preprocessing is to remove data artifacts-such as a baseline, scatter effects or noise-and to enhance the contextually relevant information. Many preprocessing methods exist to deliver one or more of these benefits, but which method or combination of methods should be used for the specific data being analyzed is difficult to select. Recently, we have shown that a preprocessing selection approach based on Design of Experiments (DoE) enables correct selection of highly appropriate preprocessing strategies within reasonable time frames. In that approach, the focus was solely on improving the predictive performance of the chemometric model. This is, however, only one of the two relevant criteria in modeling: interpretation of the model results can be just as important. Variable selection is often used to achieve such interpretation. Data artifacts, however, may hamper proper variable selection by masking the true relevant variables. The choice of preprocessing therefore has a huge impact on the outcome of variable selection methods and may thus hamper an objective interpretation of the final model. To enhance such objective interpretation, we here integrate variable selection into the preprocessing selection approach that is based on DoE. We show that the entanglement of preprocessing selection and variable selection not only improves the interpretation, but also the predictive performance of the model. This is achieved by analyzing several experimental data sets of which the true relevant variables are available as prior knowledge. We show that a selection of variables is provided that complies more with the true informative variables compared to individual optimization of both model aspects. Importantly, the approach presented in this work is generic. Different types of models (e.g. PCR, PLS, …) can be incorporated into it, as well as different variable selection methods and different preprocessing methods, according to the taste and experience of
Effect of Genetic Connectedness on the Selection Results of Breeding Pigs
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Two pig populations were simulated with Monte Carlo method; each consisted of 5 boars and 50 sows per generation.Genetic connectedness between herds was established by randomly selecting 1 or 2 boars from one population to mate sows of the other population. Breeding pigs were selected within populations according to animal model BLUP. The benefits of genetic connectedness between herds were examined. The results showed that, the average coefficients of inbreeding decreased, while the cumulative selection responses of populations increased, and the higher response occurred randomly in the two populations at generation 5 with the increase of the genetic connectedness between herds.Selection response was affected by genetic connectedness and trait heritability, the lower heritability and higher connectedness, the better selection results. When the number of exchanged litters between populations per generation was 6 litters, the selection results reached a reflection point; if the number of exchanged litters between populations increased further from this point, neither the increase of the cumulative selection responses nor the decrease of coefficients of inbreeding was significant.
Aerosol model selection and uncertainty modelling by adaptive MCMC technique
Directory of Open Access Journals (Sweden)
M. Laine
2008-12-01
Full Text Available We present a new technique for model selection problem in atmospheric remote sensing. The technique is based on Monte Carlo sampling and it allows model selection, calculation of model posterior probabilities and model averaging in Bayesian way.
The algorithm developed here is called Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ. It uses Markov chain Monte Carlo (MCMC technique and its extension called Reversible Jump MCMC. Both of these techniques have been used extensively in statistical parameter estimation problems in wide area of applications since late 1990's. The novel feature in our algorithm is the fact that it is fully automatic and easy to use.
We show how the AARJ algorithm can be implemented and used for model selection and averaging, and to directly incorporate the model uncertainty. We demonstrate the technique by applying it to the statistical inversion problem of gas profile retrieval of GOMOS instrument on board the ENVISAT satellite. Four simple models are used simultaneously to describe the dependence of the aerosol cross-sections on wavelength. During the AARJ estimation all the models are used and we obtain a probability distribution characterizing how probable each model is. By using model averaging, the uncertainty related to selecting the aerosol model can be taken into account in assessing the uncertainty of the estimates.
On Model Selection Criteria in Multimodel Analysis
Energy Technology Data Exchange (ETDEWEB)
Ye, Ming; Meyer, Philip D.; Neuman, Shlomo P.
2008-03-21
Hydrologic systems are open and complex, rendering them prone to multiple conceptualizations and mathematical descriptions. There has been a growing tendency to postulate several alternative hydrologic models for a site and use model selection criteria to (a) rank these models, (b) eliminate some of them and/or (c) weigh and average predictions and statistics generated by multiple models. This has led to some debate among hydrogeologists about the merits and demerits of common model selection (also known as model discrimination or information) criteria such as AIC [Akaike, 1974], AICc [Hurvich and Tsai, 1989], BIC [Schwartz, 1978] and KIC [Kashyap, 1982] and some lack of clarity about the proper interpretation and mathematical representation of each criterion. In particular, whereas we [Neuman, 2003; Ye et al., 2004, 2005; Meyer et al., 2007] have based our approach to multimodel hydrologic ranking and inference on the Bayesian criterion KIC (which reduces asymptotically to BIC), Poeter and Anderson [2005] and Poeter and Hill [2007] have voiced a preference for the information-theoretic criterion AICc (which reduces asymptotically to AIC). Their preference stems in part from a perception that KIC and BIC require a "true" or "quasi-true" model to be in the set of alternatives while AIC and AICc are free of such an unreasonable requirement. We examine the model selection literature to find that (a) all published rigorous derivations of AIC and AICc require that the (true) model having generated the observational data be in the set of candidate models; (b) though BIC and KIC were originally derived by assuming that such a model is in the set, BIC has been rederived by Cavanaugh and Neath [1999] without the need for such an assumption; (c) KIC reduces to BIC as the number of observations becomes large relative to the number of adjustable model parameters, implying that it likewise does not require the existence of a true model in the set of alternatives; (d) if a true
Pilot-scale tests were conducted to develop a combined nitrogen oxide (NOx) reduction technology using both selective catalytic reduction (SCR) and selective noncatalytic reduction (SNCR). A commercially available vanadium-and titatnium-based composite honeycomb catalyst and enh...
Information-theoretic model selection applied to supernovae data
Biesiada, M
2007-01-01
There are several different theoretical ideas invoked to explain the dark energy with relatively little guidance of which one of them might be right. Therefore the emphasis of ongoing and forthcoming research in this field shifts from estimating specific parameters of cosmological model to the model selection. In this paper we apply information-theoretic model selection approach based on Akaike criterion as an estimator of Kullback-Leibler entropy. In particular, we present the proper way of ranking the competing models based on Akaike weights (in Bayesian language - posterior probabilities of the models). Out of many particular models of dark energy we focus on four: quintessence, quintessence with time varying equation of state, brane-world and generalized Chaplygin gas model and test them on Riess' Gold sample. As a result we obtain that the best model - in terms of Akaike Criterion - is the quintessence model. The odds suggest that although there exist differences in the support given to specific scenario...
[Cardiac transplantation. Selection of patients and long-term results].
Cabrol, C; Gandjbakhch, I; Pavie, A; Bors, V; Cabrol, A; Léger, P; Vaissier, E; Simmoneau, F; Chomette, G; Aupetit, B
1987-12-01
Performed for the first time in the world, in December 1967, by Barnard in Capetown, and for the first time in Europe by our team in April 1968, cardiac transplantation has now 20 years of clinical applications. A best selection of the recipients, a more precise selection of donors, refinements in surgical technique, a better and earlier diagnosis of post-operative complications, more effective therapeutic means especially cyclosporin, have brought us, from 1981, such major improvements that many teams were prompted to resume the procedure. In our experience of more than 400 transplants at La Pitié Hospital, a five-year follow-up shows that 70 p. cent of the patients are alive, having resumed a normal familial, social, professional and often sporting life. Much progress remains to be achieved, but this procedure now seems to be quite common if not routine, only limited by the insufficient number of donors.
Science and Information Conference 2015 : Extended and Selected Results
Kapoor, Supriya; Bhatia, Rahul
2016-01-01
This book is a collection of extended chapters from the selected papers that were published in the proceedings of Science and Information (SAI) Conference 2015. It contains twenty-one chapters in the field of Computational Intelligence, which received highly recommended feedback during SAI Conference 2015 review process. During the three-day event 260 scientists, technology developers, young researcher including PhD students, and industrial practitioners from 56 countries have engaged intensively in presentations, demonstrations, open panel sessions and informal discussions. .
Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai
2006-01-01
The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: 'Are we actually dealing with a convolutive mixture?'. We try to answer this question for EEG data....
Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2006-01-01
The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....
Skewed factor models using selection mechanisms
Kim, Hyoung-Moon
2015-12-21
Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.
Behavioral optimization models for multicriteria portfolio selection
Directory of Open Access Journals (Sweden)
Mehlawat Mukesh Kumar
2013-01-01
Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.
Model selection and comparison for independents sinusoids
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2014-01-01
this method by considering the problem in a full Bayesian framework instead of the approximate formulation, on which the asymptotic MAP criterion is based. This leads to a new model selection and comparison method, the lp-BIC, whose computational complexity is of the same order as the asymptotic MAP criterion......In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve....... Through simulations, we demonstrate that the lp-BIC outperforms the asymptotic MAP criterion and other state of the art methods in terms of model selection, de-noising and prediction performance. The simulation code is available online....
Sensor Optimization Selection Model Based on Testability Constraint
Institute of Scientific and Technical Information of China (English)
YANG Shuming; QIU Jing; LIU Guanjun
2012-01-01
Sensor selection and optimization is one of the important parts in design for testability.To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability,a novel sensor optimization selection model is proposed.Firstly,a universal architecture for sensor selection and optimization is provided.Secondly,a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability.Thirdly,a sensor selection and optimization model for prognostics and health management is constructed,which takes sensor cost as objective finction and the defined testability indexes as constraint conditions.Due to NP-hard property of the model,a generic algorithm is designed to obtain the optimal solution.At last,a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform.The application results and comparison analysis show the proposed model and algorithm are effective and feasible.This approach can be used to select sensors for prognostics and health management of any system.
Sample selection and taste correlation in discrete choice transport modelling
DEFF Research Database (Denmark)
Mabit, Stefan Lindhard
2008-01-01
the question for a broader class of models. It is shown that the original result may be somewhat generalised. Another question investigated is whether mode choice operates as a self-selection mechanism in the estimation of the value of travel time. The results show that self-selection can at least partly...... explain counterintuitive results in value of travel time estimation. However, the results also point at the difficulty of finding suitable instruments for the selection mechanism. Taste heterogeneity is another important aspect of discrete choice modelling. Mixed logit models are designed to capture...... of taste correlation in willingness-to-pay estimation are presented. The first contribution addresses how to incorporate taste correlation in the estimation of the value of travel time for public transport. Given a limited dataset the approach taken is to use theory on the value of travel time as guidance...
Tracking Models for Optioned Portfolio Selection
Liang, Jianfeng
In this paper we study a target tracking problem for the portfolio selection involving options. In particular, the portfolio in question contains a stock index and some European style options on the index. A refined tracking-error-variance methodology is adopted to formulate this problem as a multi-stage optimization model. We derive the optimal solutions based on stochastic programming and optimality conditions. Attention is paid to the structure of the optimal payoff function, which is shown to possess rich properties.
New insights in portfolio selection modeling
Zareei, Abalfazl
2016-01-01
Recent advancements in the field of network theory commence a new line of developments in portfolio selection techniques that stands on the ground of perceiving financial market as a network with assets as nodes and links accounting for various types of relationships among financial assets. In the first chapter, we model the shock propagation mechanism among assets via network theory and provide an approach to construct well-diversified portfolios that are resilient to shock propagation and c...
Robust inference in sample selection models
Zhelonkin, Mikhail
2015-11-20
The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman\\'s two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.
Model Selection in Data Analysis Competitions
DEFF Research Database (Denmark)
Wind, David Kofoed; Winther, Ole
2014-01-01
The use of data analysis competitions for selecting the most appropriate model for a problem is a recent innovation in the field of predictive machine learning. Two of the most well-known examples of this trend was the Netflix Competition and recently the competitions hosted on the online platform...... Kaggle. In this paper, we will state and try to verify a set of qualitative hypotheses about predictive modelling, both in general and in the scope of data analysis competitions. To verify our hypotheses we will look at previous competitions and their outcomes, use qualitative interviews with top...
Interpreting Results from the Multinomial Logit Model
DEFF Research Database (Denmark)
Wulff, Jesper
2015-01-01
This article provides guidelines and illustrates practical steps necessary for an analysis of results from the multinomial logit model (MLM). The MLM is a popular model in the strategy literature because it allows researchers to examine strategic choices with multiple outcomes. However, there see...
Selected results from the Mark II at SPEAR
Energy Technology Data Exchange (ETDEWEB)
Scharre, D.L.
1980-06-01
Recent results on radiative transitions from the psi(3095), charmed meson decay, and the Cabibbo-suppressed decay tau ..-->.. K* ..nu../sub tau/ are reviewed. The results come primarily from the Mark II experiment at SPEAR, but preliminary results from the Crystal Ball experiment on psi radiative transitions are also discussed.
Inflation model selection meets dark radiation
Tram, Thomas; Vallance, Robert; Vennin, Vincent
2017-01-01
We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard ΛCDM model and an extension including dark radiation parametrised by its effective number of relativistic species Neff. Using a minimal dataset (Planck low-l polarisation, temperature power spectrum and lensing reconstruction), we find that the observational status of most inflationary models is unchanged. The exceptions are potentials such as power-law inflation that predict large values for the scalar spectral index that can only be realised when Neff is allowed to vary. Adding baryon acoustic oscillations data and the B-mode data from BICEP2/Keck makes power-law inflation disfavoured, while adding local measurements of the Hubble constant H0 makes power-law inflation slightly favoured compared to the best single-field plateau potentials. This illustrates how the dark radiation solution to the H0 tension would have deep consequences for inflation model selection.
The Markowitz model for portfolio selection
Directory of Open Access Journals (Sweden)
MARIAN ZUBIA ZUBIAURRE
2002-06-01
Full Text Available Since its first appearance, The Markowitz model for portfolio selection has been a basic theoretical reference, opening several new development options. However, practically it has not been used among portfolio managers and investment analysts in spite of its success in the theoretical field. With our paper we would like to show how The Markowitz model may be of great help in real stock markets. Through an empirical study we want to verify the capability of Markowitz’s model to present portfolios with higher profitability and lower risk than the portfolio represented by IBEX-35 and IGBM indexes. Furthermore, we want to test suggested efficiency of these indexes as representatives of market theoretical-portfolio.
Model selection for Poisson processes with covariates
Sart, Mathieu
2011-01-01
We observe $n$ inhomogeneous Poisson processes with covariates and aim at estimating their intensities. To handle this problem, we assume that the intensity of each Poisson process is of the form $s (\\cdot, x)$ where $x$ is the covariate and where $s$ is an unknown function. We propose a model selection approach where the models are used to approximate the multivariate function $s$. We show that our estimator satisfies an oracle-type inequality under very weak assumptions both on the intensities and the models. By using an Hellinger-type loss, we establish non-asymptotic risk bounds and specify them under various kind of assumptions on the target function $s$ such as being smooth or composite. Besides, we show that our estimation procedure is robust with respect to these assumptions.
Information criteria for astrophysical model selection
Liddle, A R
2007-01-01
Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information Criterion (AIC), and those on Bayesian inference such as the Bayesian evidence and Bayesian Information Criterion (BIC). The Deviance Information Criterion combines ideas from both heritages; it is readily computed from Monte Carlo posterior samples and, unlike the AIC and BIC, allows for parameter degeneracy. I describe the properties of the information criteria, and as an example compute them from WMAP3 data for several cosmological models. I find that at present the information theory and Bayesian approaches give significantly different conclusions from that data.
Hydraulic fracture model comparison study: Complete results
Energy Technology Data Exchange (ETDEWEB)
Warpinski, N.R. [Sandia National Labs., Albuquerque, NM (United States); Abou-Sayed, I.S. [Mobil Exploration and Production Services (United States); Moschovidis, Z. [Amoco Production Co. (US); Parker, C. [CONOCO (US)
1993-02-01
Large quantities of natural gas exist in low permeability reservoirs throughout the US. Characteristics of these reservoirs, however, make production difficult and often economic and stimulation is required. Because of the diversity of application, hydraulic fracture design models must be able to account for widely varying rock properties, reservoir properties, in situ stresses, fracturing fluids, and proppant loads. As a result, fracture simulation has emerged as a highly complex endeavor that must be able to describe many different physical processes. The objective of this study was to develop a comparative study of hydraulic-fracture simulators in order to provide stimulation engineers with the necessary information to make rational decisions on the type of models most suited for their needs. This report compares the fracture modeling results of twelve different simulators, some of them run in different modes for eight separate design cases. Comparisons of length, width, height, net pressure, maximum width at the wellbore, average width at the wellbore, and average width in the fracture have been made, both for the final geometry and as a function of time. For the models in this study, differences in fracture length, height and width are often greater than a factor of two. In addition, several comparisons of the same model with different options show a large variability in model output depending upon the options chosen. Two comparisons were made of the same model run by different companies; in both cases the agreement was good. 41 refs., 54 figs., 83 tabs.
Ancestral process and diffusion model with selection
Mano, Shuhei
2008-01-01
The ancestral selection graph in population genetics introduced by Krone and Neuhauser (1997) is an analogue to the coalescent genealogy. The number of ancestral particles, backward in time, of a sample of genes is an ancestral process, which is a birth and death process with quadratic death and linear birth rate. In this paper an explicit form of the number of ancestral particle is obtained, by using the density of the allele frequency in the corresponding diffusion model obtained by Kimura (1955). It is shown that fixation is convergence of the ancestral process to the stationary measure. The time to fixation of an allele is studied in terms of the ancestral process.
Performance results of HESP physical model
Chanumolu, Anantha; Thirupathi, Sivarani; Jones, Damien; Giridhar, Sunetra; Grobler, Deon; Jakobsson, Robert
2017-02-01
As a continuation to the published work on model based calibration technique with HESP(Hanle Echelle Spectrograph) as a case study, in this paper we present the performance results of the technique. We also describe how the open parameters were chosen in the model for optimization, the glass data accuracy and handling the discrepancies. It is observed through simulations that the discrepancies in glass data can be identified but not quantifiable. So having an accurate glass data is important which is possible to obtain from the glass manufacturers. The model's performance in various aspects is presented using the ThAr calibration frames from HESP during its pre-shipment tests. Accuracy of model predictions and its wave length calibration comparison with conventional empirical fitting, the behaviour of open parameters in optimization, model's ability to track instrumental drifts in the spectrum and the double fibres performance were discussed. It is observed that the optimized model is able to predict to a high accuracy the drifts in the spectrum from environmental fluctuations. It is also observed that the pattern in the spectral drifts across the 2D spectrum which vary from image to image is predictable with the optimized model. We will also discuss the possible science cases where the model can contribute.
Selected recent results on charm hadronic decays from BESIII
Muramatsu, Hajime
2015-01-01
I report BESIII preliminary results on: 1 Measurement of sigma(e+e- ->DDbar at Ecm = 3.773 GeV 2 Study of the DDbar production line shape near Ecm = 3.773 GeV 3 The first observation of singly Cabibbo-suppressed decay, D -> omega pi 4 Measurement of BF(Ds+ -> eta' X) and BF(Ds+ -> eta' rho+) .
Improving randomness characterization through Bayesian model selection
R., Rafael Díaz-H; Martínez, Alí M Angulo; U'Ren, Alfred B; Hirsch, Jorge G; Marsili, Matteo; Castillo, Isaac Pérez
2016-01-01
Nowadays random number generation plays an essential role in technology with important applications in areas ranging from cryptography, which lies at the core of current communication protocols, to Monte Carlo methods, and other probabilistic algorithms. In this context, a crucial scientific endeavour is to develop effective methods that allow the characterization of random number generators. However, commonly employed methods either lack formality (e.g. the NIST test suite), or are inapplicable in principle (e.g. the characterization derived from the Algorithmic Theory of Information (ATI)). In this letter we present a novel method based on Bayesian model selection, which is both rigorous and effective, for characterizing randomness in a bit sequence. We derive analytic expressions for a model's likelihood which is then used to compute its posterior probability distribution. Our method proves to be more rigorous than NIST's suite and the Borel-Normality criterion and its implementation is straightforward. We...
Some Results on the Target Set Selection Problem
Chiang, Chun-Ying; Li, Bo-Jr; Wu, Jiaojiao; Yeh, Hong-Gwa
2011-01-01
In this paper we consider a fundamental problem in the area of viral marketing, called T{\\scriptsize ARGET} S{\\scriptsize ET} S{\\scriptsize ELECTION} problem. We study the problem when the underlying graph is a block-cactus graph, a chordal graph or a Hamming graph. We show that if $G$ is a block-cactus graph, then the T{\\scriptsize ARGET} S{\\scriptsize ET} S{\\scriptsize ELECTION} problem can be solved in linear time, which generalizes Chen's result \\cite{chen2009} for trees, and the time complexity is much better than the algorithm in \\cite{treewidth} (for bounded treewidth graphs) when restricted to block-cactus graphs. We show that if the underlying graph $G$ is a chordal graph with thresholds $\\theta(v)\\leq 2$ for each vertex $v$ in $G$, then the problem can be solved in linear time. For a Hamming graph $G$ having thresholds $\\theta(v)=2$ for each vertex $v$ of $G$, we precisely determine an optimal target set $S$ for $(G,\\theta)$. These results partially answer an open problem raised by Dreyer and Robert...
Modeling Malaysia's Energy System: Some Preliminary Results
Directory of Open Access Journals (Sweden)
Ahmad M. Yusof
2011-01-01
Full Text Available Problem statement: The current dynamic and fragile world energy environment necessitates the development of new energy model that solely caters to analyze Malaysias energy scenarios. Approach: The model is a network flow model that traces the flow of energy carriers from its sources (import and mining through some conversion and transformation processes for the production of energy products to final destinations (energy demand sectors. The integration to the economic sectors is done exogeneously by specifying the annual sectoral energy demand levels. The model in turn optimizes the energy variables for a specified objective function to meet those demands. Results: By minimizing the inter temporal petroleum product imports for the crude oil system the annual extraction level of Tapis blend is projected at 579600 barrels per day. The aggregate demand for petroleum products is projected to grow at 2.1% year-1 while motor gasoline and diesel constitute 42 and 38% of the petroleum products demands mix respectively over the 5 year planning period. Petroleum products import is expected to grow at 6.0% year-1. Conclusion: The preliminary results indicate that the model performs as expected. Thus other types of energy carriers such as natural gas, coal and biomass will be added to the energy system for the overall development of Malaysia energy model.
Selected Test Results from the Encell Technology Nickel Iron Battery
Energy Technology Data Exchange (ETDEWEB)
Ferreira, Summer Kamal Rhodes [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Advanced Power Sources R& D; Baca, Wes Edmund [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Advanced Power Sources R& D; Avedikian, Kristan [Encell Technology, Alachua, FL (United States)
2014-09-01
The performance of the Encell Nickel Iron (NiFe) battery was measured. Tests included capacity, capacity as a function of rate, capacity as a function of temperature, charge retention (28-day), efficiency, accelerated life projection, and water refill evaluation. The goal of this work was to evaluate the general performance of the Encell NiFe battery technology for stationary applications and demonstrate the chemistry's capabilities in extreme conditions. Test results have indicated that the Encell NiFe battery technology can provide power levels up to the 6C discharge rate, ampere-hour efficiency above 70%. In summary, the Encell batteries have met performance metrics established by the manufacturer. Long-term cycle tests are not included in this report. A cycle test at elevated temperature was run, funded by the manufacturer, which Encell uses to predict long-term cycling performance, and which passed their prescribed metrics.
Procedures, Resources and Selected Results of the Deep Ecliptic Survey
Buie, M. W.; Millis, R. L.; Wasserman, L. H.; Elliot, J. L.; Kern, S. D.; Clancy, K. B.; Chiang, E. I.; Jordan, A. B.; Meech, K. J.; Wagner, R. M.; Trilling, D. E.
2003-06-01
The Deep Ecliptic Survey is a project whose goal is to survey a large area of the near-ecliptic region to a faint limiting magnitude (R ~ 24) in search of objects in the outer solar system. We are collecting a large homogeneous data sample from the Kitt Peak Mayall 4-m and Cerro Tololo Blanco 4-m telescopes with the Mosaic prime-focus CCD cameras. Our goal is to collect a sample of 500 objects with good orbits to further our understanding of the dynamical structure of the outer solar system. This survey has been in progress since 1998 and is responsible for 272 designated discoveries as of March 2003. We summarize our techniques, highlight recent results, and describe publically available resources.
A physiological production model for cacao : results of model simulations
Zuidema, P.A.; Leffelaar, P.A.
2002-01-01
CASE2 is a physiological model for cocoa (Theobroma cacao L.) growth and yield. This report introduces the CAcao Simulation Engine for water-limited production in a non-technical way and presents simulation results obtained with the model.
A physiological production model for cacao : results of model simulations
Zuidema, P.A.; Leffelaar, P.A.
2002-01-01
CASE2 is a physiological model for cocoa (Theobroma cacao L.) growth and yield. This report introduces the CAcao Simulation Engine for water-limited production in a non-technical way and presents simulation results obtained with the model.
Modelling rainfall erosion resulting from climate change
Kinnell, Peter
2016-04-01
It is well known that soil erosion leads to agricultural productivity decline and contributes to water quality decline. The current widely used models for determining soil erosion for management purposes in agriculture focus on long term (~20 years) average annual soil loss and are not well suited to determining variations that occur over short timespans and as a result of climate change. Soil loss resulting from rainfall erosion is directly dependent on the product of runoff and sediment concentration both of which are likely to be influenced by climate change. This presentation demonstrates the capacity of models like the USLE, USLE-M and WEPP to predict variations in runoff and erosion associated with rainfall events eroding bare fallow plots in the USA with a view to modelling rainfall erosion in areas subject to climate change.
Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romanach, Stephanie; Watling, James I.; Mazzotti, Frank J.
2017-01-01
Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using
Simulation Modeling of Radio Direction Finding Results
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K. Pelikan
1994-12-01
Full Text Available It is sometimes difficult to determine analytically error probabilities of direction finding results for evaluating algorithms of practical interest. Probalistic simulation models are described in this paper that can be to study error performance of new direction finding systems or to geographical modifications of existing configurations.
Inflation Model Selection meets Dark Radiation
Tram, Thomas; Vennin, Vincent
2016-01-01
We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard $\\Lambda\\mathrm{CDM}$ model and an extension including dark radiation parametrised by its effective number of relativistic species $N_\\mathrm{eff}$. We find that the observational status of most inflationary models is unchanged, with the exception of potentials such as power-law inflation that predict a value for the scalar spectral index that is too large in $\\Lambda\\mathrm{CDM}$ but which can be accommodated when $N_\\mathrm{eff}$ is allowed to vary. In this case, cosmic microwave background data indicate that power-law inflation is one of the best models together with plateau potentials. However, contrary to plateau p...
Short-Run Asset Selection using a Logistic Model
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Walter Gonçalves Junior
2011-06-01
Full Text Available Investors constantly look for significant predictors and accurate models to forecast future results, whose occasional efficacy end up being neutralized by market efficiency. Regardless, such predictors are widely used for seeking better (and more unique perceptions. This paper aims to investigate to what extent some of the most notorious indicators have discriminatory power to select stocks, and if it is feasible with such variables to build models that could anticipate those with good performance. In order to do that, logistical regressions were conducted with stocks traded at Bovespa using the selected indicators as explanatory variables. Investigated in this study were the outputs of Bovespa Index, liquidity, the Sharpe Ratio, ROE, MB, size and age evidenced to be significant predictors. Also examined were half-year, logistical models, which were adjusted in order to check the potential acceptable discriminatory power for the asset selection.
Evidence accumulation as a model for lexical selection.
Anders, R; Riès, S; van Maanen, L; Alario, F X
2015-11-01
We propose and demonstrate evidence accumulation as a plausible theoretical and/or empirical model for the lexical selection process of lexical retrieval. A number of current psycholinguistic theories consider lexical selection as a process related to selecting a lexical target from a number of alternatives, which each have varying activations (or signal supports), that are largely resultant of an initial stimulus recognition. We thoroughly present a case for how such a process may be theoretically explained by the evidence accumulation paradigm, and we demonstrate how this paradigm can be directly related or combined with conventional psycholinguistic theory and their simulatory instantiations (generally, neural network models). Then with a demonstrative application on a large new real data set, we establish how the empirical evidence accumulation approach is able to provide parameter results that are informative to leading psycholinguistic theory, and that motivate future theoretical development. Copyright © 2015 Elsevier Inc. All rights reserved.
High-dimensional model estimation and model selection
CERN. Geneva
2015-01-01
I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.
SLAM: A Connectionist Model for Attention in Visual Selection Tasks.
Phaf, R. Hans; And Others
1990-01-01
The SeLective Attention Model (SLAM) performs visual selective attention tasks and demonstrates that object selection and attribute selection are both necessary and sufficient for visual selection. The SLAM is described, particularly with regard to its ability to represent an individual subject performing filtering tasks. (TJH)
Directory of Open Access Journals (Sweden)
Henry de-Graft Acquah
2013-01-01
Full Text Available Information Criteria provides an attractive basis for selecting the best model from a set of competing asymmetric price transmission models or theories. However, little is understood about the sensitivity of the model selection methods to model complexity. This study therefore fits competing asymmetric price transmission models that differ in complexity to simulated data and evaluates the ability of the model selection methods to recover the true model. The results of Monte Carlo experimentation suggest that in general BIC, CAIC and DIC were superior to AIC when the true data generating process was the standard error correction model, whereas AIC was more successful when the true model was the complex error correction model. It is also shown that the model selection methods performed better in large samples for a complex asymmetric data generating process than with a standard asymmetric data generating process. Except for complex models, AIC's performance did not make substantial gains in recovery rates as sample size increased. The research findings demonstrate the influence of model complexity in asymmetric price transmission model comparison and selection.
Tales of the young top quark - Selected recent results from the LHC and Tevatron
Lister, Alison; The ATLAS collaboration
2015-01-01
After a short historical introduction of the discovery of the top quark 20 years ago, a brief motivation was given for continuing to study the heaviest known fundamental particle. Selected recent results from the ATLAS and CMS experiments at the LHC, and the CDF and D0 experiments at the Tevatron are presented. First measurements of the top mass, a fundamental parameter of the Standard Model, are presented. This is followed by stringent tests of the Standard model through the measurement of the \\ttbar\\ inclusive and differential cross-sections as well as the associated production of vector bosons. The spin correlation is used as an example to illustrate the relevance of prevision top quark properly measurements in the search for physics beyond the standard model. Finally some recent results in single top production are presented.
Random effect selection in generalised linear models
DEFF Research Database (Denmark)
Denwood, Matt; Houe, Hans; Forkman, Björn;
We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest...
Selecting an optimal mixed products using grey relationship model
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Farshad Faezy Razi
2013-06-01
Full Text Available This paper presents an integrated supplier selection and inventory management using grey relationship model (GRM as well as multi-objective decision making process. The proposed model of this paper first ranks different suppliers based on GRM technique and then determines the optimum level of inventory by considering different objectives. To show the implementation of the proposed model, we use some benchmark data presented by Talluri and Baker [Talluri, S., & Baker, R. C. (2002. A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operational Research, 141(3, 544-558.]. The preliminary results indicate that the proposed model of this paper is capable of handling different criteria for supplier selection.
Hidden Markov Model for Stock Selection
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Nguyet Nguyen
2015-10-01
Full Text Available The hidden Markov model (HMM is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for stock selection. We first use HMM to make monthly regime predictions for the four macroeconomic variables: inflation (consumer price index (CPI, industrial production index (INDPRO, stock market index (S&P 500 and market volatility (VIX. At the end of each month, we calibrate HMM’s parameters for each of these economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all of the S&P 500 stocks to identify which stock characteristics have been well rewarded during the time periods and assign scores and corresponding weights for each of the stock characteristics. A composite score of each stock is calculated based on the scores and weights of its features. Based on this algorithm, we choose the 50 top ranking stocks to buy. We compare the performances of the portfolio with the benchmark index, S&P 500. With an initial investment of $100 in December 1999, over 15 years, in December 2014, our portfolio had an average gain per annum of 14.9% versus 2.3% for the S&P 500.
TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS: SELECTING OR COMBINING?
Institute of Scientific and Technical Information of China (English)
YU Lean; WANG Shouyang; K. K. Lai; Y.Nakamori
2005-01-01
Various mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this study.The results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.
The Danish national passenger model – Model specification and results
DEFF Research Database (Denmark)
Rich, Jeppe; Hansen, Christian Overgaard
2016-01-01
The paper describes the structure of the new Danish National Passenger model and provides on this basis a general discussion of large-scale model design, cost-damping and model validation. The paper aims at providing three main contributions to the existing literature. Firstly, at the general level......, the paper provides a description of a large-scale forecast model with a discussion of the linkage between population synthesis, demand and assignment. Secondly, the paper gives specific attention to model specification and in particular choice of functional form and cost-damping. Specifically we suggest...... a family of logarithmic spline functions and illustrate how it is applied in the model. Thirdly and finally, we evaluate model sensitivity and performance by evaluating the distance distribution and elasticities. In the paper we present results where the spline-function is compared with more traditional...
Bayesian model selection applied to artificial neural networks used for water resources modeling
Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.
2008-04-01
Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.
The Time Domain Spectroscopic Survey: Variable Object Selection and Anticipated Results
Morganson, Eric; Anderson, Scott F; Ruan, John J; Myers, Adam D; Eracleous, Michael; Kelly, Brandon; Badenes, Carlos; Banados, Eduardo; Blanton, Michael R; Bershady, Matthew A; Borissova, Jura; Brandt, William Nielsen; Burgett, William S; Chambers, Kenneth; Draper, Peter W; Davenport, James R A; Flewelling, Heather; Garnavich, Peter; Hawley, Suzanne L; Hodapp, Klaus W; Isler, Jedidah C; Kaiser, Nick; Kinemuchi, Karen; Kudritzki, Rolf P; Metcalfe, Nigel; Morgan, Jeffrey S; Paris, Isabelle; Parvizi, Mahmoud; Poleski, Radoslaw; Price, Paul A; Salvato, Mara; Shanks, Tom; Schlafly, Eddie F; Schneider, Donald P; Shen, Yue; Stassun, Keivan; Tonry, John T; Walter, Fabian; Waters, Chris Z
2015-01-01
We present the selection algorithm and anticipated results for the Time Domain Spectroscopic Survey (TDSS). TDSS is an SDSS-IV eBOSS subproject that will provide initial identification spectra of approximately 220,000 luminosity-variable objects (variable stars and AGN) across 7,500 square degrees selected from a combination of SDSS and multi-epoch Pan-STARRS1 photometry. TDSS will be the largest spectroscopic survey to explicitly target variable objects, avoiding pre-selection on the basis of colors or detailed modeling of specific variability characteristics. Kernel Density Estimate (KDE) analysis of our target population performed on SDSS Stripe 82 data suggests our target sample will be 95% pure (meaning 95% of objects we select have genuine luminosity variability of a few magnitudes or more). Our final spectroscopic sample will contain roughly 135,000 quasars and 85,000 stellar variables, approximately 4,000 of which will be RR Lyrae stars which may be used as outer Milky Way probes. The variability-sele...
Modelling autophagy selectivity by receptor clustering on peroxisomes
Brown, Aidan I
2016-01-01
When subcellular organelles are degraded by autophagy, typically some, but not all, of each targeted organelle type are degraded. Autophagy selectivity must not only select the correct type of organelle, but must discriminate between individual organelles of the same kind. In the context of peroxisomes, we use computational models to explore the hypothesis that physical clustering of autophagy receptor proteins on the surface of each organelle provides an appropriate all-or-none signal for degradation. The pexophagy receptor proteins NBR1 and p62 are well characterized, though only NBR1 is essential for pexophagy (Deosaran {\\em et al.}, 2013). Extending earlier work by addressing the initial nucleation of NBR1 clusters on individual peroxisomes, we find that larger peroxisomes nucleate NBR1 clusters first and lose them due to competitive coarsening last, resulting in significant size-selectivity favouring large peroxisomes. This effect can explain the increased catalase signal that results from experimental s...
PROPOSAL OF AN EMPIRICAL MODEL FOR SUPPLIERS SELECTION
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Paulo Ávila
2015-03-01
Full Text Available The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, trough the literature review, there were identified five broad suppliers selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. Thereafter, a survey was elaborated and companies were contacted in order to answer which factors have more relevance in their decisions to choose the suppliers. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP method or Simple Multi-Attribute Rating Technique (SMART. The result of the research undertaken by the authors is a reference model that represents a decision making support for the suppliers/partners selection process.
CMS standard model Higgs boson results
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Garcia-Abia Pablo
2013-11-01
Full Text Available In July 2012 CMS announced the discovery of a new boson with properties resembling those of the long-sought Higgs boson. The analysis of the proton-proton collision data recorded by the CMS detector at the LHC, corresponding to integrated luminosities of 5.1 fb−1 at √s = 7 TeV and 19.6 fb−1 at √s = 8 TeV, confirm the Higgs-like nature of the new boson, with a signal strength associated with vector bosons and fermions consistent with the expectations for a standard model (SM Higgs boson, and spin-parity clearly favouring the scalar nature of the new boson. In this note I review the updated results of the CMS experiment.
Autoregressive model selection with simultaneous sparse coefficient estimation
Sang, Hailin
2011-01-01
In this paper we propose a sparse coefficient estimation procedure for autoregressive (AR) models based on penalized conditional maximum likelihood. The penalized conditional maximum likelihood estimator (PCMLE) thus developed has the advantage of performing simultaneous coefficient estimation and model selection. Mild conditions are given on the penalty function and the innovation process, under which the PCMLE satisfies a strong consistency, local $N^{-1/2}$ consistency, and oracle property, respectively, where N is sample size. Two penalty functions, least absolute shrinkage and selection operator (LASSO) and smoothly clipped average deviation (SCAD), are considered as examples, and SCAD is shown to have better performances than LASSO. A simulation study confirms our theoretical results. At the end, we provide an application of our method to a historical price data of the US Industrial Production Index for consumer goods, and the result is very promising.
Model selection for the extraction of movement primitives.
Endres, Dominik M; Chiovetto, Enrico; Giese, Martin A
2013-01-01
A wide range of blind source separation methods have been used in motor control research for the extraction of movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA), independent component analysis (ICA), anechoic demixing, and the time-varying synergy model (d'Avella and Tresch, 2002). However, choosing the parameters of these models, or indeed choosing the type of model, is often done in a heuristic fashion, driven by result expectations as much as by the data. We propose an objective criterion which allows to select the model type, number of primitives and the temporal smoothness prior. Our approach is based on a Laplace approximation to the posterior distribution of the parameters of a given blind source separation model, re-formulated as a Bayesian generative model. We first validate our criterion on ground truth data, showing that it performs at least as good as traditional model selection criteria [Bayesian information criterion, BIC (Schwarz, 1978) and the Akaike Information Criterion (AIC) (Akaike, 1974)]. Then, we analyze human gait data, finding that an anechoic mixture model with a temporal smoothness constraint on the sources can best account for the data.
Model selection for the extraction of movement primitives
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Dominik M Endres
2013-12-01
Full Text Available A wide range of blind source separation methods have been used in motor control research for the extraction of movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA,independent component analysis (ICA, anechoic demixing, and the time-varying synergy model. However, choosing the parameters of these models, or indeed choosing the type of model, is often done in a heuristic fashion, driven by result expectations as much as by the data. We propose an objective criterion which allows to select the model type, number of primitives and the temporal smoothness prior. Our approach is based on a Laplace approximation to the posterior distribution of the parameters of a given blind source separation model, re-formulated as a Bayesian generative model.We first validate our criterion on ground truth data, showing that it performs at least as good as traditional model selection criteria (Bayesian information criterion, BIC and the Akaike Information Criterion (AIC. Then, we analyze human gait data, finding that an anechoic mixture model with a temporal smoothness constraint on the sources can best account for the data.
The detection of observations possibly influential for model selection
Ph.H.B.F. Franses (Philip Hans)
1991-01-01
textabstractModel selection can involve several variables and selection criteria. A simple method to detect observations possibly influential for model selection is proposed. The potentials of this method are illustrated with three examples, each of which is taken from related studies.
Bayesian nonparametric centered random effects models with variable selection.
Yang, Mingan
2013-03-01
In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.
A model-based approach to selection of tag SNPs
Directory of Open Access Journals (Sweden)
Sun Fengzhu
2006-06-01
Full Text Available Abstract Background Single Nucleotide Polymorphisms (SNPs are the most common type of polymorphisms found in the human genome. Effective genetic association studies require the identification of sets of tag SNPs that capture as much haplotype information as possible. Tag SNP selection is analogous to the problem of data compression in information theory. According to Shannon's framework, the optimal tag set maximizes the entropy of the tag SNPs subject to constraints on the number of SNPs. This approach requires an appropriate probabilistic model. Compared to simple measures of Linkage Disequilibrium (LD, a good model of haplotype sequences can more accurately account for LD structure. It also provides a machinery for the prediction of tagged SNPs and thereby to assess the performances of tag sets through their ability to predict larger SNP sets. Results Here, we compute the description code-lengths of SNP data for an array of models and we develop tag SNP selection methods based on these models and the strategy of entropy maximization. Using data sets from the HapMap and ENCODE projects, we show that the hidden Markov model introduced by Li and Stephens outperforms the other models in several aspects: description code-length of SNP data, information content of tag sets, and prediction of tagged SNPs. This is the first use of this model in the context of tag SNP selection. Conclusion Our study provides strong evidence that the tag sets selected by our best method, based on Li and Stephens model, outperform those chosen by several existing methods. The results also suggest that information content evaluated with a good model is more sensitive for assessing the quality of a tagging set than the correct prediction rate of tagged SNPs. Besides, we show that haplotype phase uncertainty has an almost negligible impact on the ability of good tag sets to predict tagged SNPs. This justifies the selection of tag SNPs on the basis of haplotype
Selective experimental review of the Standard Model
Energy Technology Data Exchange (ETDEWEB)
Bloom, E.D.
1985-02-01
Before disussing experimental comparisons with the Standard Model, (S-M) it is probably wise to define more completely what is commonly meant by this popular term. This model is a gauge theory of SU(3)/sub f/ x SU(2)/sub L/ x U(1) with 18 parameters. The parameters are ..cap alpha../sub s/, ..cap alpha../sub qed/, theta/sub W/, M/sub W/ (M/sub Z/ = M/sub W//cos theta/sub W/, and thus is not an independent parameter), M/sub Higgs/; the lepton masses, M/sub e/, M..mu.., M/sub r/; the quark masses, M/sub d/, M/sub s/, M/sub b/, and M/sub u/, M/sub c/, M/sub t/; and finally, the quark mixing angles, theta/sub 1/, theta/sub 2/, theta/sub 3/, and the CP violating phase delta. The latter four parameters appear in the quark mixing matrix for the Kobayashi-Maskawa and Maiani forms. Clearly, the present S-M covers an enormous range of physics topics, and the author can only lightly cover a few such topics in this report. The measurement of R/sub hadron/ is fundamental as a test of the running coupling constant ..cap alpha../sub s/ in QCD. The author will discuss a selection of recent precision measurements of R/sub hadron/, as well as some other techniques for measuring ..cap alpha../sub s/. QCD also requires the self interaction of gluons. The search for the three gluon vertex may be practically realized in the clear identification of gluonic mesons. The author will present a limited review of recent progress in the attempt to untangle such mesons from the plethora q anti q states of the same quantum numbers which exist in the same mass range. The electroweak interactions provide some of the strongest evidence supporting the S-M that exists. Given the recent progress in this subfield, and particularly with the discovery of the W and Z bosons at CERN, many recent reviews obviate the need for further discussion in this report. In attempting to validate a theory, one frequently searches for new phenomena which would clearly invalidate it. 49 references, 28 figures.
Numerical Model based Reliability Estimation of Selective Laser Melting Process
DEFF Research Database (Denmark)
Mohanty, Sankhya; Hattel, Jesper Henri
2014-01-01
Selective laser melting is developing into a standard manufacturing technology with applications in various sectors. However, the process is still far from being at par with conventional processes such as welding and casting, the primary reason of which is the unreliability of the process. While...... of the selective laser melting process. A validated 3D finite-volume alternating-direction-implicit numerical technique is used to model the selective laser melting process, and is calibrated against results from single track formation experiments. Correlation coefficients are determined for process input...... parameters such as laser power, speed, beam profile, etc. Subsequently, uncertainties in the processing parameters are utilized to predict a range for the various outputs, using a Monte Carlo method based uncertainty analysis methodology, and the reliability of the process is established....
Selection of productivity improvement techniques via mathematical modeling
Directory of Open Access Journals (Sweden)
Mahassan M. Khater
2011-07-01
Full Text Available This paper presents a new mathematical model to select an optimal combination of productivity improvement techniques. The proposed model of this paper considers four-stage cycle productivity and the productivity is assumed to be a linear function of fifty four improvement techniques. The proposed model of this paper is implemented for a real-world case study of manufacturing plant. The resulted problem is formulated as a mixed integer programming which can be solved for optimality using traditional methods. The preliminary results of the implementation of the proposed model of this paper indicate that the productivity can be improved through a change on equipments and it can be easily applied for both manufacturing and service industries.
Stationary solutions for metapopulation Moran models with mutation and selection
Constable, George W. A.; McKane, Alan J.
2015-03-01
We construct an individual-based metapopulation model of population genetics featuring migration, mutation, selection, and genetic drift. In the case of a single "island," the model reduces to the Moran model. Using the diffusion approximation and time-scale separation arguments, an effective one-variable description of the model is developed. The effective description bears similarities to the well-mixed Moran model with effective parameters that depend on the network structure and island sizes, and it is amenable to analysis. Predictions from the reduced theory match the results from stochastic simulations across a range of parameters. The nature of the fast-variable elimination technique we adopt is further studied by applying it to a linear system, where it provides a precise description of the slow dynamics in the limit of large time-scale separation.
An integrated model for supplier selection process
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
In today's highly competitive manufacturing environment, the supplier selection process becomes one of crucial activities in supply chain management. In order to select the best supplier(s) it is not only necessary to continuously tracking and benchmarking performance of suppliers but also to make a tradeoff between tangible and intangible factors some of which may conflict. In this paper an integration of case-based reasoning (CBR), analytical network process (ANP) and linear programming (LP) is proposed to solve the supplier selection problem.
Dealing with selection bias in educational transition models
DEFF Research Database (Denmark)
Holm, Anders; Jæger, Mads Meier
2011-01-01
This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational...... transitions to be correlated across transitions. We use simulated and real data to illustrate how the BPSM improves on the traditional Mare model in terms of correcting for selection bias and providing credible estimates of the effect of family background on educational success. We conclude that models which...... account for selection on unobserved variables and high-quality data are both required in order to estimate credible educational transition models....
McGurk, B. J.; Painter, T. H.
2014-12-01
Deterministic snow accumulation and ablation simulation models are widely used by runoff managers throughout the world to predict runoff quantities and timing. Model fitting is typically based on matching modeled runoff volumes and timing with observed flow time series at a few points in the basin. In recent decades, sparse networks of point measurements of the mountain snowpacks have been available to compare with modeled snowpack, but the comparability of results from a snow sensor or course to model polygons of 5 to 50 sq. km is suspect. However, snowpack extent, depth, and derived snow water equivalent have been produced by the NASA/JPL Airborne Snow Observatory (ASO) mission for spring of 20013 and 2014 in the Tuolumne River basin above Hetch Hetchy Reservoir. These high-resolution snowpack data have exposed the weakness in a model calibration based on runoff alone. The U.S. Geological Survey's Precipitation Runoff Modeling System (PRMS) calibration that was based on 30-years of inflow to Hetch Hetchy produces reasonable inflow results, but modeled spatial snowpack location and water quantity diverged significantly from the weekly measurements made by ASO during the two ablation seasons. The reason is that the PRMS model has many flow paths, storages, and water transfer equations, and a calibrated outflow time series can be right for many wrong reasons. The addition of a detailed knowledge of snow extent and water content constrains the model so that it is a better representation of the actual watershed hydrology. The mechanics of recalibrating PRMS to the ASO measurements will be described, and comparisons in observed versus modeled flow for both a small subbasin and the entire Hetch Hetchy basin will be shown. The recalibrated model provided a bitter fit to the snowmelt recession, a key factor for water managers as they balance declining inflows with demand for power generation and ecosystem releases during the final months of snow melt runoff.
Modeling Malaysia's Energy System: Some Preliminary Results
Ahmad M. Yusof
2011-01-01
Problem statement: The current dynamic and fragile world energy environment necessitates the development of new energy model that solely caters to analyze Malaysias energy scenarios. Approach: The model is a network flow model that traces the flow of energy carriers from its sources (import and mining) through some conversion and transformation processes for the production of energy products to final destinations (energy demand sectors). The integration to the economic sectors is done exogene...
Process chain modeling and selection in an additive manufacturing context
DEFF Research Database (Denmark)
Thompson, Mary Kathryn; Stolfi, Alessandro; Mischkot, Michael
2016-01-01
can compete with traditional process chains for small production runs. Combining both types of technology added cost but no benefit in this case. The new process chain model can be used to explain the results and support process selection, but process chain prototyping is still important for rapidly......This paper introduces a new two-dimensional approach to modeling manufacturing process chains. This approach is used to consider the role of additive manufacturing technologies in process chains for a part with micro scale features and no internal geometry. It is shown that additive manufacturing...
Model for personal computer system selection.
Blide, L
1987-12-01
Successful computer software and hardware selection is best accomplished by following an organized approach such as the one described in this article. The first step is to decide what you want to be able to do with the computer. Secondly, select software that is user friendly, well documented, bug free, and that does what you want done. Next, you select the computer, printer and other needed equipment from the group of machines on which the software will run. Key factors here are reliability and compatibility with other microcomputers in your facility. Lastly, you select a reliable vendor who will provide good, dependable service in a reasonable time. The ability to correctly select computer software and hardware is a key skill needed by medical record professionals today and in the future. Professionals can make quality computer decisions by selecting software and systems that are compatible with other computers in their facility, allow for future net-working, ease of use, and adaptability for expansion as new applications are identified. The key to success is to not only provide for your present needs, but to be prepared for future rapid expansion and change in your computer usage as technology and your skills grow.
Engineering Glass Passivation Layers -Model Results
Energy Technology Data Exchange (ETDEWEB)
Skorski, Daniel C.; Ryan, Joseph V.; Strachan, Denis M.; Lepry, William C.
2011-08-08
The immobilization of radioactive waste into glass waste forms is a baseline process of nuclear waste management not only in the United States, but worldwide. The rate of radionuclide release from these glasses is a critical measure of the quality of the waste form. Over long-term tests and using extrapolations of ancient analogues, it has been shown that well designed glasses exhibit a dissolution rate that quickly decreases to a slow residual rate for the lifetime of the glass. The mechanistic cause of this decreased corrosion rate is a subject of debate, with one of the major theories suggesting that the decrease is caused by the formation of corrosion products in such a manner as to present a diffusion barrier on the surface of the glass. Although there is much evidence of this type of mechanism, there has been no attempt to engineer the effect to maximize the passivating qualities of the corrosion products. This study represents the first attempt to engineer the creation of passivating phases on the surface of glasses. Our approach utilizes interactions between the dissolving glass and elements from the disposal environment to create impermeable capping layers. By drawing from other corrosion studies in areas where passivation layers have been successfully engineered to protect the bulk material, we present here a report on mineral phases that are likely have a morphological tendency to encrust the surface of the glass. Our modeling has focused on using the AFCI glass system in a carbonate, sulfate, and phosphate rich environment. We evaluate the minerals predicted to form to determine the likelihood of the formation of a protective layer on the surface of the glass. We have also modeled individual ions in solutions vs. pH and the addition of aluminum and silicon. These results allow us to understand the pH and ion concentration dependence of mineral formation. We have determined that iron minerals are likely to form a complete incrustation layer and we plan
Models of cultural niche construction with selection and assortative mating.
Creanza, Nicole; Fogarty, Laurel; Feldman, Marcus W
2012-01-01
Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.
Models of cultural niche construction with selection and assortative mating.
Directory of Open Access Journals (Sweden)
Nicole Creanza
Full Text Available Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.
Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
Lubke, Gitta H.; Campbell, Ian; McArtor, Dan; Miller, Patrick; Luningham, Justin; van den Berg, Stéphanie Martine
2017-01-01
Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the
Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
Lubke, Gitta H.; Campbell, Ian; McArtor, Dan; Miller, Patrick; Luningham, Justin; Berg, van den Stephanie M.
2016-01-01
Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the
Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
Lubke, Gitta H.; Campbell, Ian; McArtor, Dan; Miller, Patrick; Luningham, Justin; Berg, van den Stephanie M.
2017-01-01
Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the
Model Selection Through Sparse Maximum Likelihood Estimation
Banerjee, Onureena; D'Aspremont, Alexandre
2007-01-01
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added l_1-norm penalty term. The problem as formulated is convex but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can be interpreted as recursive l_1-norm penalized regression. Our second algorithm, based on Nesterov's first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright & Jordan (2006)), we show that these same algorithms can be used to solve an approximate sparse maximum likelihood problem for...
Zhang, Xinyu; Cao, Jiguo; Carroll, Raymond J
2015-03-01
We consider model selection and estimation in a context where there are competing ordinary differential equation (ODE) models, and all the models are special cases of a "full" model. We propose a computationally inexpensive approach that employs statistical estimation of the full model, followed by a combination of a least squares approximation (LSA) and the adaptive Lasso. We show the resulting method, here called the LSA method, to be an (asymptotically) oracle model selection method. The finite sample performance of the proposed LSA method is investigated with Monte Carlo simulations, in which we examine the percentage of selecting true ODE models, the efficiency of the parameter estimation compared to simply using the full and true models, and coverage probabilities of the estimated confidence intervals for ODE parameters, all of which have satisfactory performances. Our method is also demonstrated by selecting the best predator-prey ODE to model a lynx and hare population dynamical system among some well-known and biologically interpretable ODE models.
Feature selection and survival modeling in The Cancer Genome Atlas
Directory of Open Access Journals (Sweden)
Kim H
2013-09-01
Full Text Available Hyunsoo Kim,1 Markus Bredel2 1Department of Pathology, The University of Alabama at Birmingham, Birmingham, AL, USA; 2Department of Radiation Oncology, and Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL, USA Purpose: Personalized medicine is predicated on the concept of identifying subgroups of a common disease for better treatment. Identifying biomarkers that predict disease subtypes has been a major focus of biomedical science. In the era of genome-wide profiling, there is controversy as to the optimal number of genes as an input of a feature selection algorithm for survival modeling. Patients and methods: The expression profiles and outcomes of 544 patients were retrieved from The Cancer Genome Atlas. We compared four different survival prediction methods: (1 1-nearest neighbor (1-NN survival prediction method; (2 random patient selection method and a Cox-based regression method with nested cross-validation; (3 least absolute shrinkage and selection operator (LASSO optimization using whole-genome gene expression profiles; or (4 gene expression profiles of cancer pathway genes. Results: The 1-NN method performed better than the random patient selection method in terms of survival predictions, although it does not include a feature selection step. The Cox-based regression method with LASSO optimization using whole-genome gene expression data demonstrated higher survival prediction power than the 1-NN method, but was outperformed by the same method when using gene expression profiles of cancer pathway genes alone. Conclusion: The 1-NN survival prediction method may require more patients for better performance, even when omitting censored data. Using preexisting biological knowledge for survival prediction is reasonable as a means to understand the biological system of a cancer, unless the analysis goal is to identify completely unknown genes relevant to cancer biology. Keywords: brain, feature selection
Ensemble feature selection integrating elitist roles and quantum game model
Institute of Scientific and Technical Information of China (English)
Weiping Ding; Jiandong Wang; Zhijin Guan; Quan Shi
2015-01-01
To accelerate the selection process of feature subsets in the rough set theory (RST), an ensemble elitist roles based quantum game (EERQG) algorithm is proposed for feature selec-tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles’ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec-tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Final y, the en-semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which wil greatly improve the fea-sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.
Transitions in a genotype selection model driven by coloured noises
Institute of Scientific and Technical Information of China (English)
Wang Can-Jun; Mei Dong-Cheng
2008-01-01
This paper investigates a genotype selection model subjected to both a multiplicative coloured noise and an additive coloured noise with different correlation time T1 and T2 by means of the numerical technique.By directly simulating the Langevin Equation,the following results are obtained.(1) The multiplicative coloured noise dominates,however,the effect of the additive coloured noise is not neglected in the practical gene selection process.The selection rate μ decides that the selection is propitious to gene A haploid or gene B haploid.(2) The additive coloured noise intensity α and the correlation time T2 play opposite roles.It is noted that α and T2 can not separate the single peak,while αcan make the peak disappear and T2 can make the peak be sharp.(3) The multiplicative coloured noise intensity D and the correlation time T1 can induce phase transition,at the same time they play opposite roles and the reentrance phenomenon appears.In this case,it is easy to select one type haploid from the group with increasing D and decreasing T1.
Quality Quandaries- Time Series Model Selection and Parsimony
DEFF Research Database (Denmark)
Bisgaard, Søren; Kulahci, Murat
2009-01-01
Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....
Quality Quandaries- Time Series Model Selection and Parsimony
DEFF Research Database (Denmark)
Bisgaard, Søren; Kulahci, Murat
2009-01-01
Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....
Quantitative magnetospheric models: results and perspectives.
Kuznetsova, M.; Hesse, M.; Gombosi, T.; Csem Team
Global magnetospheric models are indispensable tool that allow multi-point measurements to be put into global context Significant progress is achieved in global MHD modeling of magnetosphere structure and dynamics Medium resolution simulations confirm general topological pictures suggested by Dungey State of the art global models with adaptive grids allow performing simulations with highly resolved magnetopause and magnetotail current sheet Advanced high-resolution models are capable to reproduced transient phenomena such as FTEs associated with formation of flux ropes or plasma bubbles embedded into magnetopause and demonstrate generation of vortices at magnetospheric flanks On the other hand there is still controversy about the global state of the magnetosphere predicted by MHD models to the point of questioning the length of the magnetotail and the location of the reconnection sites within it For example for steady southwards IMF driving condition resistive MHD simulations produce steady configuration with almost stationary near-earth neutral line While there are plenty of observational evidences of periodic loading unloading cycle during long periods of southward IMF Successes and challenges in global modeling of magnetispheric dynamics will be addessed One of the major challenges is to quantify the interaction between large-scale global magnetospheric dynamics and microphysical processes in diffusion regions near reconnection sites Possible solutions to controversies will be discussed
Bayesian Model Selection with Network Based Diffusion Analysis.
Whalen, Andrew; Hoppitt, William J E
2016-01-01
A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed.
Cardinality constrained portfolio selection via factor models
Monge, Juan Francisco
2017-01-01
In this paper we propose and discuss different 0-1 linear models in order to solve the cardinality constrained portfolio problem by using factor models. Factor models are used to build portfolios to track indexes, together with other objectives, also need a smaller number of parameters to estimate than the classical Markowitz model. The addition of the cardinality constraints limits the number of securities in the portfolio. Restricting the number of securities in the portfolio allows us to o...
Auditory-model based robust feature selection for speech recognition.
Koniaris, Christos; Kuropatwinski, Marcin; Kleijn, W Bastiaan
2010-02-01
It is shown that robust dimension-reduction of a feature set for speech recognition can be based on a model of the human auditory system. Whereas conventional methods optimize classification performance, the proposed method exploits knowledge implicit in the auditory periphery, inheriting its robustness. Features are selected to maximize the similarity of the Euclidean geometry of the feature domain and the perceptual domain. Recognition experiments using mel-frequency cepstral coefficients (MFCCs) confirm the effectiveness of the approach, which does not require labeled training data. For noisy data the method outperforms commonly used discriminant-analysis based dimension-reduction methods that rely on labeling. The results indicate that selecting MFCCs in their natural order results in subsets with good performance.
Selecting global climate models for regional climate change studies.
Pierce, David W; Barnett, Tim P; Santer, Benjamin D; Gleckler, Peter J
2009-05-26
Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures.
Selecting global climate models for regional climate change studies
Pierce, David W.; Barnett, Tim P.; Santer, Benjamin D.; Gleckler, Peter J.
2009-01-01
Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures. PMID:19439652
Evidence accumulation as a model for lexical selection
Anders, R.; Riès, S.; van Maanen, L.; Alario, F.-X.
2015-01-01
We propose and demonstrate evidence accumulation as a plausible theoretical and/or empirical model for the lexical selection process of lexical retrieval. A number of current psycholinguistic theories consider lexical selection as a process related to selecting a lexical target from a number of
The Optimal Selection for Restricted Linear Models with Average Estimator
Directory of Open Access Journals (Sweden)
Qichang Xie
2014-01-01
Full Text Available The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing a k-class generalized information criterion (k-GIC, which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.
Multilevel selection in a resource-based model
Ferreira, Fernando Fagundes; Campos, Paulo R. A.
2013-07-01
In the present work we investigate the emergence of cooperation in a multilevel selection model that assumes limiting resources. Following the work by R. J. Requejo and J. Camacho [Phys. Rev. Lett.0031-900710.1103/PhysRevLett.108.038701 108, 038701 (2012)], the interaction among individuals is initially ruled by a prisoner's dilemma (PD) game. The payoff matrix may change, influenced by the resource availability, and hence may also evolve to a non-PD game. Furthermore, one assumes that the population is divided into groups, whose local dynamics is driven by the payoff matrix, whereas an intergroup competition results from the nonuniformity of the growth rate of groups. We study the probability that a single cooperator can invade and establish in a population initially dominated by defectors. Cooperation is strongly favored when group sizes are small. We observe the existence of a critical group size beyond which cooperation becomes counterselected. Although the critical size depends on the parameters of the model, it is seen that a saturation value for the critical group size is achieved. The results conform to the thought that the evolutionary history of life repeatedly involved transitions from smaller selective units to larger selective units.
Muir, William M; Bijma, P; Schinckel, A
2013-06-01
An experiment was conducted comparing multilevel selection in Japanese quail for 43 days weight and survival with birds housed in either kin (K) or random (R) groups. Multilevel selection significantly reduced mortality (6.6% K vs. 8.5% R) and increased weight (1.30 g/MG K vs. 0.13 g/MG R) resulting in response an order of magnitude greater with Kin than Random. Thus, multilevel selection was effective in reducing detrimental social interactions, which contributed to improved weight gain. The observed rates of response did not differ significantly from expected, demonstrating that current theory is adequate to explain multilevel selection response. Based on estimated genetic parameters, group selection would always be superior to any other combination of multilevel selection. Further, near optimal results could be attained using multilevel selection if 20% of the weight was on the group component regardless of group composition. Thus, in nature the conditions for multilevel selection to be effective in bringing about social change maybe common. In terms of a sustainability of breeding programs, multilevel selection is easy to implement and is expected to give near optimal responses with reduced rates of inbreeding as compared to group selection, the only requirement is that animals be housed in kin groups.
Selection of Temporal Lags When Modeling Economic and Financial Processes.
Matilla-Garcia, Mariano; Ojeda, Rina B; Marin, Manuel Ruiz
2016-10-01
This paper suggests new nonparametric statistical tools and procedures for modeling linear and nonlinear univariate economic and financial processes. In particular, the tools presented help in selecting relevant lags in the model description of a general linear or nonlinear time series; that is, nonlinear models are not a restriction. The tests seem to be robust to the selection of free parameters. We also show that the test can be used as a diagnostic tool for well-defined models.
Modeling clicks beyond the first result page
Chuklin, A.; Serdyukov, P.; de Rijke, M.
2013-01-01
Most modern web search engines yield a list of documents of a fixed length (usually 10) in response to a user query. The next ten search results are usually available in one click. These documents either replace the current result page or are appended to the end. Hence, in order to examine more
Modeling clicks beyond the first result page
Chuklin, A.; Serdyukov, P.; de Rijke, M.
2013-01-01
Most modern web search engines yield a list of documents of a fixed length (usually 10) in response to a user query. The next ten search results are usually available in one click. These documents either replace the current result page or are appended to the end. Hence, in order to examine more docu
The Properties of Model Selection when Retaining Theory Variables
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren
Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...
Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos
DEFF Research Database (Denmark)
Ganz, Melanie; Nielsen, Mads; Brandt, Sami
2010-01-01
We propose a statistical generative shape model for archipelago-like structures. These kind of structures occur, for instance, in medical images, where our intention is to model the appearance and shapes of calcifications in x-ray radio graphs. The generative model is constructed by (1) learning...... a patch-based dictionary for possible shapes, (2) building up a time-homogeneous Markov model to model the neighbourhood correlations between the patches, and (3) automatic selection of the model complexity by the minimum description length principle. The generative shape model is proposed...... as a probability distribution of a binary image where the model is intended to facilitate sequential simulation. Our results show that a relatively simple model is able to generate structures visually similar to calcifications. Furthermore, we used the shape model as a shape prior in the statistical segmentation...
Astrophysical Model Selection in Gravitational Wave Astronomy
Adams, Matthew R.; Cornish, Neil J.; Littenberg, Tyson B.
2012-01-01
Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve approximately 5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.
On Optimal Input Design and Model Selection for Communication Channels
Energy Technology Data Exchange (ETDEWEB)
Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL
2013-01-01
In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.
Engineering model development and test results
Wellman, John A.
1993-08-01
The correctability of the primary mirror spherical error in the Wide Field/Planetary Camera (WF/PC) is sensitive to the precise alignment of the incoming aberrated beam onto the corrective elements. Articulating fold mirrors that provide +/- 1 milliradian of tilt in 2 axes are required to allow for alignment corrections in orbit as part of the fix for the Hubble space telescope. An engineering study was made by Itek Optical Systems and the Jet Propulsion Laboratory (JPL) to investigate replacement of fixed fold mirrors within the existing WF/PC optical bench with articulating mirrors. The study contract developed the base line requirements, established the suitability of lead magnesium niobate (PMN) actuators and evaluated several tilt mechanism concepts. Two engineering model articulating mirrors were produced to demonstrate the function of the tilt mechanism to provide +/- 1 milliradian of tilt, packaging within the space constraints and manufacturing techniques including the machining of the invar tilt mechanism and lightweight glass mirrors. The success of the engineering models led to the follow on design and fabrication of 3 flight mirrors that have been incorporated into the WF/PC to be placed into the Hubble Space Telescope as part of the servicing mission scheduled for late 1993.
Muir, W.M.; Bijma, P.; schinckel, A.
2013-01-01
An experiment was conducted comparing multilevel selection in Japanese quail for 43 days weight and survival with birds housed in either kin (K) or random (R) groups. Multilevel selection significantly reduced mortality (6.6% K vs. 8.5% R) and increased weight (1.30 g/MG K vs. 0.13 g/MG R) resulting
Applying a Hybrid MCDM Model for Six Sigma Project Selection
Directory of Open Access Journals (Sweden)
Fu-Kwun Wang
2014-01-01
Full Text Available Six Sigma is a project-driven methodology; the projects that provide the maximum financial benefits and other impacts to the organization must be prioritized. Project selection (PS is a type of multiple criteria decision making (MCDM problem. In this study, we present a hybrid MCDM model combining the decision-making trial and evaluation laboratory (DEMATEL technique, analytic network process (ANP, and the VIKOR method to evaluate and improve Six Sigma projects for reducing performance gaps in each criterion and dimension. We consider the film printing industry of Taiwan as an empirical case. The results show that our study not only can use the best project selection, but can also be used to analyze the gaps between existing performance values and aspiration levels for improving the gaps in each dimension and criterion based on the influential network relation map.
Using multilevel models to quantify heterogeneity in resource selection
Wagner, T.; Diefenbach, D.R.; Christensen, S.A.; Norton, A.S.
2011-01-01
Models of resource selection are being used increasingly to predict or model the effects of management actions rather than simply quantifying habitat selection. Multilevel, or hierarchical, models are an increasingly popular method to analyze animal resource selection because they impose a relatively weak stochastic constraint to model heterogeneity in habitat use and also account for unequal sample sizes among individuals. However, few studies have used multilevel models to model coefficients as a function of predictors that may influence habitat use at different scales or quantify differences in resource selection among groups. We used an example with white-tailed deer (Odocoileus virginianus) to illustrate how to model resource use as a function of distance to road that varies among deer by road density at the home range scale. We found that deer avoidance of roads decreased as road density increased. Also, we used multilevel models with sika deer (Cervus nippon) and white-tailed deer to examine whether resource selection differed between species. We failed to detect differences in resource use between these two species and showed how information-theoretic and graphical measures can be used to assess how resource use may have differed. Multilevel models can improve our understanding of how resource selection varies among individuals and provides an objective, quantifiable approach to assess differences or changes in resource selection. ?? The Wildlife Society, 2011.
Selection of Representative Models for Decision Analysis Under Uncertainty
Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.
2016-03-01
The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.
Continuum model for chiral induced spin selectivity in helical molecules
Energy Technology Data Exchange (ETDEWEB)
Medina, Ernesto [Centro de Física, Instituto Venezolano de Investigaciones Científicas, 21827, Caracas 1020 A (Venezuela, Bolivarian Republic of); Groupe de Physique Statistique, Institut Jean Lamour, Université de Lorraine, 54506 Vandoeuvre-les-Nancy Cedex (France); Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287 (United States); González-Arraga, Luis A. [IMDEA Nanoscience, Cantoblanco, 28049 Madrid (Spain); Finkelstein-Shapiro, Daniel; Mujica, Vladimiro [Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287 (United States); Berche, Bertrand [Centro de Física, Instituto Venezolano de Investigaciones Científicas, 21827, Caracas 1020 A (Venezuela, Bolivarian Republic of); Groupe de Physique Statistique, Institut Jean Lamour, Université de Lorraine, 54506 Vandoeuvre-les-Nancy Cedex (France)
2015-05-21
A minimal model is exactly solved for electron spin transport on a helix. Electron transport is assumed to be supported by well oriented p{sub z} type orbitals on base molecules forming a staircase of definite chirality. In a tight binding interpretation, the spin-orbit coupling (SOC) opens up an effective π{sub z} − π{sub z} coupling via interbase p{sub x,y} − p{sub z} hopping, introducing spin coupled transport. The resulting continuum model spectrum shows two Kramers doublet transport channels with a gap proportional to the SOC. Each doubly degenerate channel satisfies time reversal symmetry; nevertheless, a bias chooses a transport direction and thus selects for spin orientation. The model predicts (i) which spin orientation is selected depending on chirality and bias, (ii) changes in spin preference as a function of input Fermi level and (iii) back-scattering suppression protected by the SO gap. We compute the spin current with a definite helicity and find it to be proportional to the torsion of the chiral structure and the non-adiabatic Aharonov-Anandan phase. To describe room temperature transport, we assume that the total transmission is the result of a product of coherent steps.
Python Program to Select HII Region Models
Miller, Clare; Lamarche, Cody; Vishwas, Amit; Stacey, Gordon J.
2016-01-01
HII regions are areas of singly ionized Hydrogen formed by the ionizing radiaiton of upper main sequence stars. The infrared fine-structure line emissions, particularly Oxygen, Nitrogen, and Neon, can give important information about HII regions including gas temperature and density, elemental abundances, and the effective temperature of the stars that form them. The processes involved in calculating this information from observational data are complex. Models, such as those provided in Rubin 1984 and those produced by Cloudy (Ferland et al, 2013) enable one to extract physical parameters from observational data. However, the multitude of search parameters can make sifting through models tedious. I digitized Rubin's models and wrote a Python program that is able to take observed line ratios and their uncertainties and find the Rubin or Cloudy model that best matches the observational data. By creating a Python script that is user friendly and able to quickly sort through models with a high level of accuracy, this work increases efficiency and reduces human error in matching HII region models to observational data.
Microplasticity of MMC. Experimental results and modelling
Energy Technology Data Exchange (ETDEWEB)
Maire, E. (Groupe d' Etude de Metallurgie Physique et de Physique des Materiaux, INSA, 69 Villeurbanne (France)); Lormand, G. (Groupe d' Etude de Metallurgie Physique et de Physique des Materiaux, INSA, 69 Villeurbanne (France)); Gobin, P.F. (Groupe d' Etude de Metallurgie Physique et de Physique des Materiaux, INSA, 69 Villeurbanne (France)); Fougeres, R. (Groupe d' Etude de Metallurgie Physique et de Physique des Materiaux, INSA, 69 Villeurbanne (France))
1993-11-01
The microplastic behavior of several MMC is investigated by means of tension and compression tests. This behavior is assymetric : the proportional limit is higher in tension than in compression but the work hardening rate is higher in compression. These differences are analysed in terms of maxium of the Tresca's shear stress at the interface (proportional limit) and of the emission of dislocation loops during the cooling (work hardening rate). On another hand, a model is proposed to calculate the value of the yield stress, describing the composite as a material composed of three phases : inclusion, unaffected matrix and matrix surrounding the inclusion having a gradient in the density of the thermally induced dilocations. (orig.).
Methods for model selection in applied science and engineering.
Energy Technology Data Exchange (ETDEWEB)
Field, Richard V., Jr.
2004-10-01
Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be
Estimation and variable selection for generalized additive partial linear models
Wang, Li
2011-08-01
We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.
Stochastic group selection model for the evolution of altruism
Silva, A T C; Silva, Ana T. C.
1999-01-01
We study numerically and analytically a stochastic group selection model in which a population of asexually reproducing individuals, each of which can be either altruist or non-altruist, is subdivided into $M$ reproductively isolated groups (demes) of size $N$. The cost associated with being altruistic is modelled by assigning the fitness $1- \\tau$, with $\\tau \\in [0,1]$, to the altruists and the fitness 1 to the non-altruists. In the case that the altruistic disadvantage $\\tau$ is not too large, we show that the finite $M$ fluctuations are small and practically do not alter the deterministic results obtained for $M \\to \\infty$. However, for large $\\tau$ these fluctuations greatly increase the instability of the altruistic demes to mutations. These results may be relevant to the dynamics of parasite-host systems and, in particular, to explain the importance of mutation in the evolution of parasite virulence.
Bayesian Model Selection for LISA Pathfinder
Karnesis, Nikolaos; Sopuerta, Carlos F; Gibert, Ferran; Armano, Michele; Audley, Heather; Congedo, Giuseppe; Diepholz, Ingo; Ferraioli, Luigi; Hewitson, Martin; Hueller, Mauro; Korsakova, Natalia; Plagnol, Eric; Vitale, and Stefano
2013-01-01
The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the LISA/eLISA concept. The Data Analysis (DA) team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment on-board LPF. These models are used for simulations, but more importantly, they will be used for parameter estimation purposes during flight operations. One of the tasks of the DA team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching to this problem is to recover the essential parameters of the LTP which describe the data. Thus, we want to define the simplest model that efficiently explains the observations. To do so, adopting a Bayesian framework, one has to estimate the so-called Bayes Factor between two competing models. In our analysis, we use three main different methods to estimate...
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Model Selection in Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...
Development of SPAWM: selection program for available watershed models.
Cho, Yongdeok; Roesner, Larry A
2014-01-01
A selection program for available watershed models (also known as SPAWM) was developed. Thirty-three commonly used watershed models were analyzed in depth and classified in accordance to their attributes. These attributes consist of: (1) land use; (2) event or continuous; (3) time steps; (4) water quality; (5) distributed or lumped; (6) subsurface; (7) overland sediment; and (8) best management practices. Each of these attributes was further classified into sub-attributes. Based on user selected sub-attributes, the most appropriate watershed model is selected from the library of watershed models. SPAWM is implemented using Excel Visual Basic and is designed for use by novices as well as by experts on watershed modeling. It ensures that the necessary sub-attributes required by the user are captured and made available in the selected watershed model.
A MODEL SELECTION PROCEDURE IN MIXTURE-PROCESS EXPERIMENTS FOR INDUSTRIAL PROCESS OPTIMIZATION
Directory of Open Access Journals (Sweden)
Márcio Nascimento de Souza Leão
2015-08-01
Full Text Available We present a model selection procedure for use in Mixture and Mixture-Process Experiments. Certain combinations of restrictions on the proportions of the mixture components can result in a very constrained experimental region. This results in collinearity among the covariates of the model, which can make it difficult to fit the model using the traditional method based on the significance of the coefficients. For this reason, a model selection methodology based on information criteria will be proposed for process optimization. Two examples are presented to illustrate this model selection procedure.
Quantile hydrologic model selection and model structure deficiency assessment: 2. Applications
Pande, S.
2013-01-01
Quantile hydrologic model selection and structure deficiency assessment is applied in three case studies. The performance of quantile model selection problem is rigorously evaluated using a model structure on the French Broad river basin data set. The case study shows that quantile model selection
Adapting AIC to conditional model selection
M. van Ommen (Matthijs)
2012-01-01
textabstractIn statistical settings such as regression and time series, we can condition on observed information when predicting the data of interest. For example, a regression model explains the dependent variables $y_1, \\ldots, y_n$ in terms of the independent variables $x_1, \\ldots, x_n$.
A Decision Model for Selecting Participants in Supply Chain
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
In order to satisfy the rapid changing requirements of customers, enterprises must cooperate with each other to form supply chain. The first and the most important stage in the forming of supply chain is the selection of participants. The article proposes a two-staged decision model to select partners. The first stage is the inter company comparison in each business process to select highefficiency candidate based on inside variables. The next stage is to analyse the combination of different candidates in order to select the most perfect partners according to a goal-programming model.
Automation of Endmember Pixel Selection in SEBAL/METRIC Model
Bhattarai, N.; Quackenbush, L. J.; Im, J.; Shaw, S. B.
2015-12-01
The commonly applied surface energy balance for land (SEBAL) and its variant, mapping evapotranspiration (ET) at high resolution with internalized calibration (METRIC) models require manual selection of endmember (i.e. hot and cold) pixels to calibrate sensible heat flux. Current approaches for automating this process are based on statistical methods and do not appear to be robust under varying climate conditions and seasons. In this paper, we introduce a new approach based on simple machine learning tools and search algorithms that provides an automatic and time efficient way of identifying endmember pixels for use in these models. The fully automated models were applied on over 100 cloud-free Landsat images with each image covering several eddy covariance flux sites in Florida and Oklahoma. Observed land surface temperatures at automatically identified hot and cold pixels were within 0.5% of those from pixels manually identified by an experienced operator (coefficient of determination, R2, ≥ 0.92, Nash-Sutcliffe efficiency, NSE, ≥ 0.92, and root mean squared error, RMSE, ≤ 1.67 K). Daily ET estimates derived from the automated SEBAL and METRIC models were in good agreement with their manual counterparts (e.g., NSE ≥ 0.91 and RMSE ≤ 0.35 mm day-1). Automated and manual pixel selection resulted in similar estimates of observed ET across all sites. The proposed approach should reduce time demands for applying SEBAL/METRIC models and allow for their more widespread and frequent use. This automation can also reduce potential bias that could be introduced by an inexperienced operator and extend the domain of the models to new users.
Model selection in systems biology depends on experimental design.
Silk, Daniel; Kirk, Paul D W; Barnes, Chris P; Toni, Tina; Stumpf, Michael P H
2014-06-01
Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.
Modeling HIV-1 drug resistance as episodic directional selection.
Directory of Open Access Journals (Sweden)
Ben Murrell
Full Text Available The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.
Directory of Open Access Journals (Sweden)
Florian Kopp
2014-12-01
Full Text Available Acquiring therapy resistance is one of the major obstacles in the treatment of patients with cancer. The discovery of the cancer stem cell (CSC–specific drug salinomycin raised hope for improved treatment options by targeting therapy-refractory CSCs and mesenchymal cancer cells. However, the occurrence of an acquired salinomycin resistance in tumor cells remains elusive. To study the formation of salinomycin resistance, mesenchymal breast cancer cells were sequentially treated with salinomycin in an in vitro cell culture assay, and the resulting differences in gene expression and salinomycin susceptibility were analyzed. We demonstrated that long-term salinomycin treatment of mesenchymal cancer cells resulted in salinomycin-resistant cells with elevated levels of epithelial markers, such as E-cadherin and miR-200c, a decreased migratory capability, and a higher susceptibility to the classic chemotherapeutic drug doxorubicin. The formation of salinomycin resistance through the acquisition of epithelial traits was further validated by inducing mesenchymal-epithelial transition through an overexpression of miR-200c. The transition from a mesenchymal to a more epithelial-like phenotype of salinomycin-treated tumor cells was moreover confirmed in vivo, using syngeneic and, for the first time, transgenic mouse tumor models. These results suggest that the acquisition of salinomycin resistance through the clonal selection of epithelial-like cancer cells could become exploited for improved cancer therapies by antagonizing the tumor-progressive effects of epithelial-mesenchymal transition.
Kopp, Florian; Hermawan, Adam; Oak, Prajakta Shirish; Ulaganathan, Vijay Kumar; Herrmann, Annika; Elnikhely, Nefertiti; Thakur, Chitra; Xiao, Zhiguang; Knyazev, Pjotr; Ataseven, Beyhan; Savai, Rajkumar; Wagner, Ernst; Roidl, Andreas
2014-12-01
Acquiring therapy resistance is one of the major obstacles in the treatment of patients with cancer. The discovery of the cancer stem cell (CSC)-specific drug salinomycin raised hope for improved treatment options by targeting therapy-refractory CSCs and mesenchymal cancer cells. However, the occurrence of an acquired salinomycin resistance in tumor cells remains elusive. To study the formation of salinomycin resistance, mesenchymal breast cancer cells were sequentially treated with salinomycin in an in vitro cell culture assay, and the resulting differences in gene expression and salinomycin susceptibility were analyzed. We demonstrated that long-term salinomycin treatment of mesenchymal cancer cells resulted in salinomycin-resistant cells with elevated levels of epithelial markers, such as E-cadherin and miR-200c, a decreased migratory capability, and a higher susceptibility to the classic chemotherapeutic drug doxorubicin. The formation of salinomycin resistance through the acquisition of epithelial traits was further validated by inducing mesenchymal-epithelial transition through an overexpression of miR-200c. The transition from a mesenchymal to a more epithelial-like phenotype of salinomycin-treated tumor cells was moreover confirmed in vivo, using syngeneic and, for the first time, transgenic mouse tumor models. These results suggest that the acquisition of salinomycin resistance through the clonal selection of epithelial-like cancer cells could become exploited for improved cancer therapies by antagonizing the tumor-progressive effects of epithelial-mesenchymal transition.
Development of Solar Drying Model for Selected Cambodian Fish Species
Directory of Open Access Journals (Sweden)
Anna Hubackova
2014-01-01
Full Text Available A solar drying was investigated as one of perspective techniques for fish processing in Cambodia. The solar drying was compared to conventional drying in electric oven. Five typical Cambodian fish species were selected for this study. Mean solar drying temperature and drying air relative humidity were 55.6°C and 19.9%, respectively. The overall solar dryer efficiency was 12.37%, which is typical for natural convection solar dryers. An average evaporative capacity of solar dryer was 0.049 kg·h−1. Based on coefficient of determination (R2, chi-square (χ2 test, and root-mean-square error (RMSE, the most suitable models describing natural convection solar drying kinetics were Logarithmic model, Diffusion approximate model, and Two-term model for climbing perch and Nile tilapia, swamp eel and walking catfish and Channa fish, respectively. In case of electric oven drying, the Modified Page 1 model shows the best results for all investigated fish species except Channa fish where the two-term model is the best one. Sensory evaluation shows that most preferable fish is climbing perch, followed by Nile tilapia and walking catfish. This study brings new knowledge about drying kinetics of fresh water fish species in Cambodia and confirms the solar drying as acceptable technology for fish processing.
Development of solar drying model for selected Cambodian fish species.
Hubackova, Anna; Kucerova, Iva; Chrun, Rithy; Chaloupkova, Petra; Banout, Jan
2014-01-01
A solar drying was investigated as one of perspective techniques for fish processing in Cambodia. The solar drying was compared to conventional drying in electric oven. Five typical Cambodian fish species were selected for this study. Mean solar drying temperature and drying air relative humidity were 55.6 °C and 19.9%, respectively. The overall solar dryer efficiency was 12.37%, which is typical for natural convection solar dryers. An average evaporative capacity of solar dryer was 0.049 kg · h(-1). Based on coefficient of determination (R(2)), chi-square (χ(2)) test, and root-mean-square error (RMSE), the most suitable models describing natural convection solar drying kinetics were Logarithmic model, Diffusion approximate model, and Two-term model for climbing perch and Nile tilapia, swamp eel and walking catfish and Channa fish, respectively. In case of electric oven drying, the Modified Page 1 model shows the best results for all investigated fish species except Channa fish where the two-term model is the best one. Sensory evaluation shows that most preferable fish is climbing perch, followed by Nile tilapia and walking catfish. This study brings new knowledge about drying kinetics of fresh water fish species in Cambodia and confirms the solar drying as acceptable technology for fish processing.
Quantum Model for the Selectivity Filter in K$^{+}$ Ion Channel
Cifuentes, A A
2013-01-01
In this work, we present a quantum transport model for the selectivity filter in the KcsA potassium ion channel. This model is fully consistent with the fact that two conduction pathways are involved in the translocation of ions thorough the filter, and we show that the presence of a second path may actually bring advantages for the filter as a result of quantum interference. To highlight interferences and resonances in the model, we consider the selectivity filter to be driven by a controlled time-dependent external field which changes the free energy scenario and consequently the conduction of the ions. In particular, we demonstrate that the two-pathway conduction mechanism is more advantageous for the filter when dephasing in the transient configurations is lower than in the main configurations. As a matter of fact, K$^+$ ions in the main configurations are highly coordinated by oxygen atoms of the filter backbone and this increases noise. Moreover, we also show that, for a wide range of driving frequencie...
Baudry, Jean-Patrick
2012-01-01
The Integrated Completed Likelihood (ICL) criterion has been proposed by Biernacki et al. (2000) in the model-based clustering framework to select a relevant number of classes and has been used by statisticians in various application areas. A theoretical study of this criterion is proposed. A contrast related to the clustering objective is introduced: the conditional classification likelihood. This yields an estimator and a model selection criteria class. The properties of these new procedures are studied and ICL is proved to be an approximation of one of these criteria. We oppose these results to the current leading point of view about ICL, that it would not be consistent. Moreover these results give insights into the class notion underlying ICL and feed a reflection on the class notion in clustering. General results on penalized minimum contrast criteria and on mixture models are derived, which are interesting in their own right.
DEFF Research Database (Denmark)
Kock, Anders Bredahl
2015-01-01
the tuning parameter by Bayesian Information Criterion (BIC) results in consistent model selection. However, it is also shown that the adaptive Lasso has no power against shrinking alternatives of the form c/T if it is tuned to perform consistent model selection. We show that if the adaptive Lasso is tuned...
Asset pricing model selection: Indonesian Stock Exchange
Pasaribu, Rowland Bismark Fernando
2010-01-01
The Capital Asset Pricing Model (CAPM) has dominated finance theory for over thirty years; it suggests that the market beta alone is sufficient to explain stock returns. However evidence shows that the cross-section of stock returns cannot be described solely by the one-factor CAPM. Therefore, the idea is to add other factors in order to complete the beta in explaining the price movements in the stock exchange. The Arbitrage Pricing Theory (APT) has been proposed as the first multifactor succ...
Two-step variable selection in quantile regression models
Directory of Open Access Journals (Sweden)
FAN Yali
2015-06-01
Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions,in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform l1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.
Selection of probability based weighting models for Boolean retrieval system
Energy Technology Data Exchange (ETDEWEB)
Ebinuma, Y. (Japan Atomic Energy Research Inst., Tokai, Ibaraki. Tokai Research Establishment)
1981-09-01
Automatic weighting models based on probability theory were studied if they can be applied to boolean search logics including logical sum. The INIS detabase was used for searching of one particular search formula. Among sixteen models three with good ranking performance were selected. These three models were further applied to searching of nine search formulas in the same database. It was found that two models among them show slightly better average ranking performance while the other model, the simplest one, seems also practical.
Sensitivity of resource selection and connectivity models to landscape definition
Katherine A. Zeller; Kevin McGarigal; Samuel A. Cushman; Paul Beier; T. Winston Vickers; Walter M. Boyce
2017-01-01
Context: The definition of the geospatial landscape is the underlying basis for species-habitat models, yet sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition has received little attention. Objectives: We evaluated the sensitivity of resource selection and connectivity models to four landscape...
On Martingales, Causality, Identifiability and Model Selection
DEFF Research Database (Denmark)
Sokol, Alexander
Ornstein-Uhlenbeck SDEs, where explicit calculations may be made for the postintervention distributions. Chapter 9 concerns identiability of the mixing matrix in ICA. It is a well-known result that identiability of the mixing matrix depends crucially on whether the error distributions are Gaussia...
Directory of Open Access Journals (Sweden)
Leandro R. Monteiro
2005-01-01
Full Text Available In this study, we used a combination of geometric morphometric and evolutionary genetics methods for the inference of possible mechanisms of evolutionary divergence. A sensitivity analysis for the constant-heritability rate test results regarding variation in genetic and demographic parameters was performed, in order to assess the relative influence of uncertainty of parameter estimation on the robustness of test results. As an application, we present a study on body shape variation among populations of the poeciliine fish Poecilia vivipara inhabiting lagoons of the quaternary plains in northern Rio de Janeiro State, Brazil. The sensitivity analysis showed that, in general, the most important parameters are heritability, effective population size and number of generations since divergence. For this specific example, using a conservatively wide range of parameters, the neutral model of genetic drift could not be accepted as a sole cause for the observed magnitude of morphological divergence among populations. A mechanism of directional selection is suggested as the main cause of variation among populations in different habitats and lagoons. The implications of parameter estimation and biological assumptions and consequences are discussed.
A Working Model of Natural Selection Illustrated by Table Tennis
Dinc, Muhittin; Kilic, Selda; Aladag, Caner
2013-01-01
Natural selection is one of the most important topics in biology and it helps to clarify the variety and complexity of organisms. However, students in almost every stage of education find it difficult to understand the mechanism of natural selection and they can develop misconceptions about it. This article provides an active model of natural…
Fluctuating selection models and McDonald-Kreitman type analyses.
Directory of Open Access Journals (Sweden)
Toni I Gossmann
Full Text Available It is likely that the strength of selection acting upon a mutation varies through time due to changes in the environment. However, most population genetic theory assumes that the strength of selection remains constant. Here we investigate the consequences of fluctuating selection pressures on the quantification of adaptive evolution using McDonald-Kreitman (MK style approaches. In agreement with previous work, we show that fluctuating selection can generate evidence of adaptive evolution even when the expected strength of selection on a mutation is zero. However, we also find that the mutations, which contribute to both polymorphism and divergence tend, on average, to be positively selected during their lifetime, under fluctuating selection models. This is because mutations that fluctuate, by chance, to positive selected values, tend to reach higher frequencies in the population than those that fluctuate towards negative values. Hence the evidence of positive adaptive evolution detected under a fluctuating selection model by MK type approaches is genuine since fixed mutations tend to be advantageous on average during their lifetime. Never-the-less we show that methods tend to underestimate the rate of adaptive evolution when selection fluctuates.
Prediction of Farmers’ Income and Selection of Model ARIMA
Institute of Scientific and Technical Information of China (English)
2010-01-01
Based on the research technology of scholars’ prediction of farmers’ income and the data of per capita annual net income in rural households in Henan Statistical Yearbook from 1979 to 2009,it is found that time series of farmers’ income is in accordance with I(2)non-stationary process.The order-determination and identification of the model are achieved by adopting the correlogram-based analytical method of Box-Jenkins.On the basis of comparing a group of model properties with different parameters,model ARIMA(4,2,2)is built up.The testing result shows that the residual error of the selected model is white noise and accords with the normal distribution,which can be used to predict farmers’ income.The model prediction indicates that income in rural households will continue to increase from 2009 to 2012 and will reach the value of 2 282.4,2 502.9,2 686.9 and 2 884.5 respectively.The growth speed will go down from fast to slow with weak sustainability.
The Optimal Portfolio Selection Model under g -Expectation
National Research Council Canada - National Science Library
Li Li
2014-01-01
This paper solves the optimal portfolio selection model under the framework of the prospect theory proposed by Kahneman and Tversky in the 1970s with decision rule replaced by the g -expectation introduced by Peng...
Robust Decision-making Applied to Model Selection
Energy Technology Data Exchange (ETDEWEB)
Hemez, Francois M. [Los Alamos National Laboratory
2012-08-06
The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define each of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical models increases, establishing robustness will become as important as demonstrating accuracy.
Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang
2014-12-01
Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.
Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang
2014-12-01
Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.
SELECTION MOMENTS AND GENERALIZED METHOD OF MOMENTS FOR HETEROSKEDASTIC MODELS
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Constantin ANGHELACHE
2016-06-01
Full Text Available In this paper, the authors describe the selection methods for moments and the application of the generalized moments method for the heteroskedastic models. The utility of GMM estimators is found in the study of the financial market models. The selection criteria for moments are applied for the efficient estimation of GMM for univariate time series with martingale difference errors, similar to those studied so far by Kuersteiner.
Modeling Suspicious Email Detection using Enhanced Feature Selection
2013-01-01
The paper presents a suspicious email detection model which incorporates enhanced feature selection. In the paper we proposed the use of feature selection strategies along with classification technique for terrorists email detection. The presented model focuses on the evaluation of machine learning algorithms such as decision tree (ID3), logistic regression, Na\\"ive Bayes (NB), and Support Vector Machine (SVM) for detecting emails containing suspicious content. In the literature, various algo...
RUC at TREC 2014: Select Resources Using Topic Models
2014-11-01
them being observed (i.e. sampled). To infer the topic Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the...Selection. In CIKM 2009, pages 1277-1286. [10] M. Baillie, M. Carmen, and F. Crestani. A Multiple- Collection Latent Topic Model for Federated...RUC at TREC 2014: Select Resources Using Topic Models Qiuyue Wang, Shaochen Shi, Wei Cao School of Information Renmin University of China Beijing
Robustness and epistasis in mutation-selection models
Wolff, Andrea; Krug, Joachim
2009-09-01
We investigate the fitness advantage associated with the robustness of a phenotype against deleterious mutations using deterministic mutation-selection models of a quasispecies type equipped with a mesa-shaped fitness landscape. We obtain analytic results for the robustness effect which become exact in the limit of infinite sequence length. Thereby, we are able to clarify a seeming contradiction between recent rigorous work and an earlier heuristic treatment based on mapping to a Schrödinger equation. We exploit the quantum mechanical analogy to calculate a correction term for finite sequence lengths and verify our analytic results by numerical studies. In addition, we investigate the occurrence of an error threshold for a general class of epistatic landscapes and show that diminishing epistasis is a necessary but not sufficient condition for error threshold behaviour.
Model catalysis by size-selected cluster deposition
Energy Technology Data Exchange (ETDEWEB)
Anderson, Scott [Univ. of Utah, Salt Lake City, UT (United States)
2015-11-20
This report summarizes the accomplishments during the last four years of the subject grant. Results are presented for experiments in which size-selected model catalysts were studied under surface science and aqueous electrochemical conditions. Strong effects of cluster size were found, and by correlating the size effects with size-dependent physical properties of the samples measured by surface science methods, it was possible to deduce mechanistic insights, such as the factors that control the rate-limiting step in the reactions. Results are presented for CO oxidation, CO binding energetics and geometries, and electronic effects under surface science conditions, and for the electrochemical oxygen reduction reaction, ethanol oxidation reaction, and for oxidation of carbon by water.
A Network Analysis Model for Selecting Sustainable Technology
Directory of Open Access Journals (Sweden)
Sangsung Park
2015-09-01
Full Text Available Most companies develop technologies to improve their competitiveness in the marketplace. Typically, they then patent these technologies around the world in order to protect their intellectual property. Other companies may use patented technologies to develop new products, but must pay royalties to the patent holders or owners. Should they fail to do so, this can result in legal disputes in the form of patent infringement actions between companies. To avoid such situations, companies attempt to research and develop necessary technologies before their competitors do so. An important part of this process is analyzing existing patent documents in order to identify emerging technologies. In such analyses, extracting sustainable technology from patent data is important, because sustainable technology drives technological competition among companies and, thus, the development of new technologies. In addition, selecting sustainable technologies makes it possible to plan their R&D (research and development efficiently. In this study, we propose a network model that can be used to select the sustainable technology from patent documents, based on the centrality and degree of a social network analysis. To verify the performance of the proposed model, we carry out a case study using actual patent data from patent databases.
Selection Criteria in Regime Switching Conditional Volatility Models
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Thomas Chuffart
2015-05-01
Full Text Available A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a regime switching framework. We focus on two types of models: the Logistic Smooth Transition GARCH and the Markov-Switching GARCH models. Simulation experiments reveal that information criteria and loss functions can lead to misspecification ; BIC sometimes indicates the wrong regime switching framework. Depending on the Data Generating Process used in the experiments, great care is needed when choosing a criterion.
A guide to Bayesian model selection for ecologists
Hooten, Mevin B.; Hobbs, N.T.
2015-01-01
The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.
Radial Domany-Kinzel models with mutation and selection
Lavrentovich, Maxim O.; Korolev, Kirill S.; Nelson, David R.
2013-01-01
We study the effect of spatial structure, genetic drift, mutation, and selective pressure on the evolutionary dynamics in a simplified model of asexual organisms colonizing a new territory. Under an appropriate coarse-graining, the evolutionary dynamics is related to the directed percolation processes that arise in voter models, the Domany-Kinzel (DK) model, contact process, and so on. We explore the differences between linear (flat front) expansions and the much less familiar radial (curved front) range expansions. For the radial expansion, we develop a generalized, off-lattice DK model that minimizes otherwise persistent lattice artifacts. With both simulations and analytical techniques, we study the survival probability of advantageous mutants, the spatial correlations between domains of neutral strains, and the dynamics of populations with deleterious mutations. “Inflation” at the frontier leads to striking differences between radial and linear expansions. For a colony with initial radius R0 expanding at velocity v, significant genetic demixing, caused by local genetic drift, occurs only up to a finite time t*=R0/v, after which portions of the colony become causally disconnected due to the inflating perimeter of the expanding front. As a result, the effect of a selective advantage is amplified relative to genetic drift, increasing the survival probability of advantageous mutants. Inflation also modifies the underlying directed percolation transition, introducing novel scaling functions and modifications similar to a finite-size effect. Finally, we consider radial range expansions with deflating perimeters, as might arise from colonization initiated along the shores of an island.
The Use of Evolution in a Central Action Selection Model
Directory of Open Access Journals (Sweden)
F. Montes-Gonzalez
2007-01-01
Full Text Available The use of effective central selection provides flexibility in design by offering modularity and extensibility. In earlier papers we have focused on the development of a simple centralized selection mechanism. Our current goal is to integrate evolutionary methods in the design of non-sequential behaviours and the tuning of specific parameters of the selection model. The foraging behaviour of an animal robot (animat has been modelled in order to integrate the sensory information from the robot to perform selection that is nearly optimized by the use of genetic algorithms. In this paper we present how selection through optimization finally arranges the pattern of presented behaviours for the foraging task. Hence, the execution of specific parts in a behavioural pattern may be ruled out by the tuning of these parameters. Furthermore, the intensive use of colour segmentation from a colour camera for locating a cylinder sets a burden on the calculations carried out by the genetic algorithm.
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
Directory of Open Access Journals (Sweden)
Feipeng Guo
2013-10-01
Full Text Available With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic method for attributes reduction based on rough set theory and principal component analysis was proposed which can reduce multiple attributes into some principal components, yet retaining effective evaluation information. Finally, it used improved BP neural network which has self-learning function to select partners. The empirical analysis on an agricultural enterprise shows that this model is effective and feasible for practical partner selection.
A Hybrid Multiple Criteria Decision Making Model for Supplier Selection
Directory of Open Access Journals (Sweden)
Chung-Min Wu
2013-01-01
Full Text Available The sustainable supplier selection would be the vital part in the management of a sustainable supply chain. In this study, a hybrid multiple criteria decision making (MCDM model is applied to select optimal supplier. The fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP is then used to obtain their weights. To avoid calculation and additional pairwise comparisons of ANP, a technique for order preference by similarity to ideal solution (TOPSIS is used to rank the alternatives. The use of a combination of the fuzzy Delphi method, ANP, and TOPSIS, proposing an MCDM model for supplier selection, and applying these to a real case are the unique features of this study.
Changes in Quail Blastodermal Cell Status as a Result of Selection.
Sawicka, Dorota; Samek, Kamila; Chojnacka-Puchta, Luiza; Witkowski, Andrzej; Knaga, Sebastian; Dębowska, Michalina; Bednarczyk, Marek
2015-01-01
Genetic selection over many years has significantly improved the growth rate of broilers and increased the number of eggs laid by egg laying chicken breeds. Selection has improved desired parameters, but has caused some negative effects as well. Adverse effects of selection may negatively affect embryonic development. The number of live and apoptotic blastodermal cells (BCs) at the X stage of embryogenesis may be a good indicator of changes in selected individuals. In this paper, a comparison of the number of live and apoptotic BCs was made for three lines of quail: Pharaoh (F33), meat-type line, selected for body weight; egg laying line (S33), selected for egg number; and laying line (S22), additionally selected (for 17 generations) for high yolk cholesterol content. Apoptotic BCs were separated by the magnetic activated cell sorting (MACS) method. The percentage of live and apoptotic BCs was different (P ≤ 0.01) for F33 (35.8% and 64.2%, respectively) and S33 (60.0% and 36.4%). The number of apoptotic BCs for F33 embryos (45,098) was higher (P ≤ 0.01) compared to the number of apoptotic BCs for S33 embryos (26,667). The selection for high yolk cholesterol content caused an increase (P ≤ 0.01) in the total number of BCs from 78,403 (S33) to 140,139 (S22). The percentage of apoptotic BCs was lower (P ≤ 0.01) in the S22 line (17.1%) compared to the S33 line (36.4%). The results showed that it is possible to evaluate the effects of selection in the early stage of embryonic development.
Bayesian model evidence for order selection and correlation testing.
Johnston, Leigh A; Mareels, Iven M Y; Egan, Gary F
2011-01-01
Model selection is a critical component of data analysis procedures, and is particularly difficult for small numbers of observations such as is typical of functional MRI datasets. In this paper we derive two Bayesian evidence-based model selection procedures that exploit the existence of an analytic form for the linear Gaussian model class. Firstly, an evidence information criterion is proposed as a model order selection procedure for auto-regressive models, outperforming the commonly employed Akaike and Bayesian information criteria in simulated data. Secondly, an evidence-based method for testing change in linear correlation between datasets is proposed, which is demonstrated to outperform both the traditional statistical test of the null hypothesis of no correlation change and the likelihood ratio test.
A CONCEPTUAL MODEL FOR IMPROVED PROJECT SELECTION AND PRIORITISATION
Directory of Open Access Journals (Sweden)
P. J. Viljoen
2012-01-01
Full Text Available
ENGLISH ABSTRACT: Project portfolio management processes are often designed and operated as a series of stages (or project phases and gates. However, the flow of such a process is often slow, characterised by queues waiting for a gate decision and by repeated work from previous stages waiting for additional information or for re-processing. In this paper the authors propose a conceptual model that applies supply chain and constraint management principles to the project portfolio management process. An advantage of the proposed model is that it provides the ability to select and prioritise projects without undue changes to project schedules. This should result in faster flow through the system.
AFRIKAANSE OPSOMMING: Prosesse om portefeuljes van projekte te bestuur word normaalweg ontwerp en bedryf as ’n reeks fases en hekke. Die vloei deur so ’n proses is dikwels stadig en word gekenmerk deur toue wat wag vir besluite by die hekke en ook deur herwerk van vorige fases wat wag vir verdere inligting of vir herprosessering. In hierdie artikel word ‘n konseptuele model voorgestel. Die model berus op die beginsels van voorsieningskettings sowel as van beperkingsbestuur, en bied die voordeel dat projekte geselekteer en geprioritiseer kan word sonder onnodige veranderinge aan projekskedules. Dit behoort te lei tot versnelde vloei deur die stelsel.
Bayesian Model Selection With Network Based Diffusion Analysis
Directory of Open Access Journals (Sweden)
Andrew eWhalen
2016-04-01
Full Text Available A number of recent studies have used Network Based Diffusion Analysis (NBDA to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA. To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed.
Statistical model selection with “Big Data”
Directory of Open Access Journals (Sweden)
Jurgen A. Doornik
2015-12-01
Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.
Selection Bias in Educational Transition Models: Theory and Empirical Evidence
DEFF Research Database (Denmark)
Holm, Anders; Jæger, Mads
Most studies using Mare’s (1980, 1981) seminal model of educational transitions find that the effect of family background decreases across transitions. Recently, Cameron and Heckman (1998, 2001) have argued that the “waning coefficients” in the Mare model are driven by selection on unobserved...... the United States, United Kingdom, Denmark, and the Netherlands shows that when we take selection into account the effect of family background variables on educational transitions is largely constant across transitions. We also discuss several difficulties in estimating educational transition models which...... variables. This paper, first, explains theoretically how selection on unobserved variables leads to waning coefficients and, second, illustrates empirically how selection leads to biased estimates of the effect of family background on educational transitions. Our empirical analysis using data from...
Multicriteria framework for selecting a process modelling language
Scanavachi Moreira Campos, Ana Carolina; Teixeira de Almeida, Adiel
2016-01-01
The choice of process modelling language can affect business process management (BPM) since each modelling language shows different features of a given process and may limit the ways in which a process can be described and analysed. However, choosing the appropriate modelling language for process modelling has become a difficult task because of the availability of a large number modelling languages and also due to the lack of guidelines on evaluating, and comparing languages so as to assist in selecting the most appropriate one. This paper proposes a framework for selecting a modelling language in accordance with the purposes of modelling. This framework is based on the semiotic quality framework (SEQUAL) for evaluating process modelling languages and a multicriteria decision aid (MCDA) approach in order to select the most appropriate language for BPM. This study does not attempt to set out new forms of assessment and evaluation criteria, but does attempt to demonstrate how two existing approaches can be combined so as to solve the problem of selection of modelling language. The framework is described in this paper and then demonstrated by means of an example. Finally, the advantages and disadvantages of using SEQUAL and MCDA in an integrated manner are discussed.
Selective breeding programme of common carp (Cyprinus carpio L. in Serbia: Preliminary results
Directory of Open Access Journals (Sweden)
Spasić Milan M.
2010-01-01
Full Text Available The aim of this study was to estimate heritability and genetic correlations between weight, length and height of common carp in Serbia (Cyprinus carpio L. during 3-year growth period. The 50 families of common carp were produced in 2007 and used for the estimation of genetic parameters. The fish were measured at tagging for weight, length and height (W0, L0, H0, then during the first autumn (W1, L1, H1 and during the second autumn (W2, L2, H2. Based on univariate models heritability estimates were high for all traits (0.39, 0.34 and 0.45 for W1, L1 and H1, respectively and also for the second production year (0.49, 0.47 and 0.44 for W2, L2 and H2, respectively. The genetic correlations were estimated using multivariate models and they were high between W1 and L1 and H1 (0.81 ± 0.06 and 0.91 ± 0.03 for L1 and H1, respectively, while between H1 and L1 were moderately correlated (0.54 ± 0.12. In the second production year genetic correlations were also high, between W2 and L2 and H2 (0.64 ± 0.09 and 0.74 ± 0.06, respectively, while between length and height they were lower (0.24 ± 0.15. Based on the current results improving growth rate of common carp through genetic selection is expected to be effective.
Directory of Open Access Journals (Sweden)
Luo Arong
2010-08-01
Full Text Available Abstract Background Explicit evolutionary models are required in maximum-likelihood and Bayesian inference, the two methods that are overwhelmingly used in phylogenetic studies of DNA sequence data. Appropriate selection of nucleotide substitution models is important because the use of incorrect models can mislead phylogenetic inference. To better understand the performance of different model-selection criteria, we used 33,600 simulated data sets to analyse the accuracy, precision, dissimilarity, and biases of the hierarchical likelihood-ratio test, Akaike information criterion, Bayesian information criterion, and decision theory. Results We demonstrate that the Bayesian information criterion and decision theory are the most appropriate model-selection criteria because of their high accuracy and precision. Our results also indicate that in some situations different models are selected by different criteria for the same dataset. Such dissimilarity was the highest between the hierarchical likelihood-ratio test and Akaike information criterion, and lowest between the Bayesian information criterion and decision theory. The hierarchical likelihood-ratio test performed poorly when the true model included a proportion of invariable sites, while the Bayesian information criterion and decision theory generally exhibited similar performance to each other. Conclusions Our results indicate that the Bayesian information criterion and decision theory should be preferred for model selection. Together with model-adequacy tests, accurate model selection will serve to improve the reliability of phylogenetic inference and related analyses.
Models of microbiome evolution incorporating host and microbial selection.
Zeng, Qinglong; Wu, Steven; Sukumaran, Jeet; Rodrigo, Allen
2017-09-25
Numerous empirical studies suggest that hosts and microbes exert reciprocal selective effects on their ecological partners. Nonetheless, we still lack an explicit framework to model the dynamics of both hosts and microbes under selection. In a previous study, we developed an agent-based forward-time computational framework to simulate the neutral evolution of host-associated microbial communities in a constant-sized, unstructured population of hosts. These neutral models allowed offspring to sample microbes randomly from parents and/or from the environment. Additionally, the environmental pool of available microbes was constituted by fixed and persistent microbial OTUs and by contributions from host individuals in the preceding generation. In this paper, we extend our neutral models to allow selection to operate on both hosts and microbes. We do this by constructing a phenome for each microbial OTU consisting of a sample of traits that influence host and microbial fitnesses independently. Microbial traits can influence the fitness of hosts ("host selection") and the fitness of microbes ("trait-mediated microbial selection"). Additionally, the fitness effects of traits on microbes can be modified by their hosts ("host-mediated microbial selection"). We simulate the effects of these three types of selection, individually or in combination, on microbiome diversities and the fitnesses of hosts and microbes over several thousand generations of hosts. We show that microbiome diversity is strongly influenced by selection acting on microbes. Selection acting on hosts only influences microbiome diversity when there is near-complete direct or indirect parental contribution to the microbiomes of offspring. Unsurprisingly, microbial fitness increases under microbial selection. Interestingly, when host selection operates, host fitness only increases under two conditions: (1) when there is a strong parental contribution to microbial communities or (2) in the absence of a strong
Testing exclusion restrictions and additive separability in sample selection models
DEFF Research Database (Denmark)
Huber, Martin; Mellace, Giovanni
2014-01-01
Standard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction of these......Standard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction...... of these assumptions by applying the approach of Huber and Mellace (Testing instrument validity for LATE identification based on inequality moment constraints, 2011) (for testing instrument validity under treatment endogeneity) to the sample selection framework. We show that the exclusion restriction and additive...... separability imply two testable inequality constraints that come from both point identifying and bounding the outcome distribution of the subpopulation that is always selected/observed. We apply the tests to two variables for which the exclusion restriction is frequently invoked in female wage regressions: non...
Quantile hydrologic model selection and model structure deficiency assessment: 1. Theory
Pande, S.
2013-01-01
A theory for quantile based hydrologic model selection and model structure deficiency assessment is presented. The paper demonstrates that the degree to which a model selection problem is constrained by the model structure (measured by the Lagrange multipliers of the constraints) quantifies
Quantile hydrologic model selection and model structure deficiency assessment: 1. Theory
Pande, S.
2013-01-01
A theory for quantile based hydrologic model selection and model structure deficiency assessment is presented. The paper demonstrates that the degree to which a model selection problem is constrained by the model structure (measured by the Lagrange multipliers of the constraints) quantifies structur
AN EXPERT SYSTEM MODEL FOR THE SELECTION OF TECHNICAL PERSONNEL
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Emine COŞGUN
2005-03-01
Full Text Available In this study, a model has been developed for the selection of the technical personnel. In the model Visual Basic has been used as user interface, Microsoft Access has been utilized as database system and CLIPS program has been used as expert system program. The proposed model has been developed by utilizing expert system technology. In the personnel selection process, only the pre-evaluation of the applicants has been taken into consideration. Instead of replacing the expert himself, a decision support program has been developed to analyze the data gathered from the job application forms. The attached study will assist the expert to make faster and more accurate decisions.
Novel web service selection model based on discrete group search.
Zhai, Jie; Shao, Zhiqing; Guo, Yi; Zhang, Haiteng
2014-01-01
In our earlier work, we present a novel formal method for the semiautomatic verification of specifications and for describing web service composition components by using abstract concepts. After verification, the instantiations of components were selected to satisfy the complex service performance constraints. However, selecting an optimal instantiation, which comprises different candidate services for each generic service, from a large number of instantiations is difficult. Therefore, we present a new evolutionary approach on the basis of the discrete group search service (D-GSS) model. With regard to obtaining the optimal multiconstraint instantiation of the complex component, the D-GSS model has competitive performance compared with other service selection models in terms of accuracy, efficiency, and ability to solve high-dimensional service composition component problems. We propose the cost function and the discrete group search optimizer (D-GSO) algorithm and study the convergence of the D-GSS model through verification and test cases.
RESULTS OF INTERBANK EXCHANGE RATES FORECASTING USING STATE SPACE MODEL
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Muhammad Kashif
2008-07-01
Full Text Available This study evaluates the performance of three alternative models for forecasting daily interbank exchange rate of U.S. dollar measured in Pak rupees. The simple ARIMA models and complex models such as GARCH-type models and a state space model are discussed and compared. Four different measures are used to evaluate the forecasting accuracy. The main result is the state space model provides the best performance among all the models.
Tumor-Selective Cytotoxicity of Nitidine Results from Its Rapid Accumulation into Mitochondria
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Hironori Iwasaki
2017-01-01
Full Text Available We identified a nitidine- (NTD- accumulating organelle and evaluated the net cytotoxicity of accumulated NTD. To evaluate tumor cell selectivity of the drug, we evaluated its selective cytotoxicity against 39 human cancer cell lines (JFCR39 panel, and the profile was compared with those of known anticancer drugs. Organelle specificity of NTD was visualized using organelle-targeted fluorescent proteins. Real-time analysis of cell growth, proliferation, and cytotoxicity was performed using the xCELLigence system. Selectivity of NTD in the JFCR39 panel was evaluated. Mitochondria-specific accumulation of NTD was observed. Real-time cytotoxicity analysis suggested that the mechanism of NTD-induced cell death is independent of the cell cycle. Short-term treatment indicated that this cytotoxicity only resulted from the accumulation of NTD into the mitochondria. The results from the JFCR39 panel indicated that NTD-mediated cytotoxicity resulted from unique mechanisms compared with those of other known anticancer drugs. These results suggested that the cytotoxicity of NTD is only induced by its accumulation in mitochondria. The drug triggered mitochondrial dysfunction in less than 2 h. Similarity analysis of the selectivity of NTD in 39 tumor cell lines strongly supported the unique tumor cell specificity of NTD. Thus, these features indicate that NTD may be a promising antitumor drug for new combination chemotherapies.
Results of endovascular abdominal aortic aneurysm repair with selective use of the Gore Excluder
Bos, W. T. G. J.; Tielliu, I. F. J.; Van den Dungen, J. J. A. M.; Zeebregts, C. J.; Sondakh, A. O.; Prins, T. R.; Verhoeven, E. L. G.
Aim. To evaluate single center results with selective use of the Gore Excluder stent-graft for elective abdominal aortic aneurysm repair. Methods. Retrospective analysis of a prospective data base. Primary endpoints were technical success, all-cause and aneurysm-related mortality and aneurysm
A comparison of statistical selection strategies for univariate and bivariate log-linear models.
Moses, Tim; Holland, Paul W
2010-11-01
In this study, eight statistical selection strategies were evaluated for selecting the parameterizations of log-linear models used to model the distributions of psychometric tests. The selection strategies included significance tests based on four chi-squared statistics (likelihood ratio, Pearson, Freeman-Tukey, and Cressie-Read) and four additional strategies (Akaike information criterion (AIC), Bayesian information criterion (BIC), consistent Akaike information criterion (CAIC), and a measure attributed to Goodman). The strategies were evaluated in simulations for different log-linear models of univariate and bivariate test-score distributions and two sample sizes. Results showed that all eight selection strategies were most accurate for the largest sample size considered. For univariate distributions, the AIC selection strategy was especially accurate for selecting the correct parameterization of a complex log-linear model and the likelihood ratio chi-squared selection strategy was the most accurate strategy for selecting the correct parameterization of a relatively simple log-linear model. For bivariate distributions, the likelihood ratio chi-squared, Freeman-Tukey chi-squared, BIC, and CAIC selection strategies had similarly high selection accuracies.
Selection of climate change scenario data for impact modelling
DEFF Research Database (Denmark)
Sloth Madsen, M; Fox Maule, C; MacKellar, N
2012-01-01
Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study...... illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make...... the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented...
MODELING RESULTS FROM CESIUM ION EXCHANGE PROCESSING WITH SPHERICAL RESINS
Energy Technology Data Exchange (ETDEWEB)
Nash, C.; Hang, T.; Aleman, S.
2011-01-03
Ion exchange modeling was conducted at the Savannah River National Laboratory to compare the performance of two organic resins in support of Small Column Ion Exchange (SCIX). In-tank ion exchange (IX) columns are being considered for cesium removal at Hanford and the Savannah River Site (SRS). The spherical forms of resorcinol formaldehyde ion exchange resin (sRF) as well as a hypothetical spherical SuperLig{reg_sign} 644 (SL644) are evaluated for decontamination of dissolved saltcake wastes (supernates). Both SuperLig{reg_sign} and resorcinol formaldehyde resin beds can exhibit hydraulic problems in their granular (nonspherical) forms. SRS waste is generally lower in potassium and organic components than Hanford waste. Using VERSE-LC Version 7.8 along with the cesium Freundlich/Langmuir isotherms to simulate the waste decontamination in ion exchange columns, spherical SL644 was found to reduce column cycling by 50% for high-potassium supernates, but sRF performed equally well for the lowest-potassium feeds. Reduced cycling results in reduction of nitric acid (resin elution) and sodium addition (resin regeneration), therefore, significantly reducing life-cycle operational costs. These findings motivate the development of a spherical form of SL644. This work demonstrates the versatility of the ion exchange modeling to study the effects of resin characteristics on processing cycles, rates, and cold chemical consumption. The value of a resin with increased selectivity for cesium over potassium can be assessed for further development.
Fuzzy MCDM Model for Risk Factor Selection in Construction Projects
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Pejman Rezakhani
2012-11-01
Full Text Available Risk factor selection is an important step in a successful risk management plan. There are many risk factors in a construction project and by an effective and systematic risk selection process the most critical risks can be distinguished to have more attention. In this paper through a comprehensive literature survey, most significant risk factors in a construction project are classified in a hierarchical structure. For an effective risk factor selection, a modified rational multi criteria decision making model (MCDM is developed. This model is a consensus rule based model and has the optimization property of rational models. By applying fuzzy logic to this model, uncertainty factors in group decision making such as experts` influence weights, their preference and judgment for risk selection criteria will be assessed. Also an intelligent checking process to check the logical consistency of experts` preferences will be implemented during the decision making process. The solution inferred from this method is in the highest degree of acceptance of group members. Also consistency of individual preferences is checked by some inference rules. This is an efficient and effective approach to prioritize and select risks based on decisions made by group of experts in construction projects. The applicability of presented method is assessed through a case study.
Hyperopt: a Python library for model selection and hyperparameter optimization
Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.
2015-01-01
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.
Empirical evaluation of scoring functions for Bayesian network model selection.
Liu, Zhifa; Malone, Brandon; Yuan, Changhe
2012-01-01
In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike's information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure; the BDeu score is sensitive to the parameter settings; and the fNML performs pretty well on small datasets. We also
A Hybrid Program Projects Selection Model for Nonprofit TV Stations
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Kuei-Lun Chang
2015-01-01
Full Text Available This study develops a hybrid multiple criteria decision making (MCDM model to select program projects for nonprofit TV stations on the basis of managers’ perceptions. By the concept of balanced scorecard (BSC and corporate social responsibility (CSR, we collect criteria for selecting the best program project. Fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Next, considering the interdependence among the selection criteria, analytic network process (ANP is then used to obtain the weights of them. To avoid calculation and additional pairwise comparisons of ANP, technique for order preference by similarity to ideal solution (TOPSIS is used to rank the alternatives. A case study is presented to demonstrate the applicability of the proposed model.
A SUPPLIER SELECTION MODEL FOR SOFTWARE DEVELOPMENT OUTSOURCING
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Hancu Lucian-Viorel
2010-12-01
Full Text Available This paper presents a multi-criteria decision making model used for supplier selection for software development outsourcing on e-marketplaces. This model can be used in auctions. The supplier selection process becomes complex and difficult on last twenty years since the Internet plays an important role in business management. Companies have to concentrate their efforts on their core activities and the others activities should be realized by outsourcing. They can achieve significant cost reduction by using e-marketplaces in their purchase process and by using decision support systems on supplier selection. In the literature were proposed many approaches for supplier evaluation and selection process. The performance of potential suppliers is evaluated using multi criteria decision making methods rather than considering a single factor cost.
Parameter Selection and Performance Analysis of Mobile Terminal Models Based on Unity3D
Institute of Scientific and Technical Information of China (English)
KONG Li-feng; ZHAO Hai-ying; XU Guang-mei
2014-01-01
Mobile platform is now widely seen as a promising multimedia service with a favorable user group and market prospect. To study the influence of mobile terminal models on the quality of scene roaming, a parameter setting platform of mobile terminal models is established to select the parameter selection and performance index on different mobile platforms in this paper. This test platform is established based on model optimality principle, analyzing the performance curve of mobile terminals in different scene models and then deducing the external parameter of model establishment. Simulation results prove that the established test platform is able to analyze the parameter and performance matching list of a mobile terminal model.
Adverse Selection Models with Three States of Nature
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Daniela MARINESCU
2011-02-01
Full Text Available In the paper we analyze an adverse selection model with three states of nature, where both the Principal and the Agent are risk neutral. When solving the model, we use the informational rents and the efforts as variables. We derive the optimal contract in the situation of asymmetric information. The paper ends with the characteristics of the optimal contract and the main conclusions of the model.
Bayesian model selection for constrained multivariate normal linear models
Mulder, J.
2010-01-01
The expectations that researchers have about the structure in the data can often be formulated in terms of equality constraints and/or inequality constraints on the parameters in the model that is used. In a (M)AN(C)OVA model, researchers have expectations about the differences between the
Pedersen, L D; Sørensen, A C; Berg, P
2010-06-01
We used computer simulations to investigate to what extent true inbreeding, i.e. identity-by-descent, is affected by the use of marker-assisted selection (MAS) relative to traditional best linear unbiased predictions (BLUP) selection. The effect was studied by varying the heritability (h(2) = 0.04 vs. 0.25), the marker distance (MAS vs. selection on the gene, GAS), the favourable QTL allele effect (alpha = 0.118 vs. 0.236) and the initial frequency of the favourable QTL allele (p = 0.01 vs. 0.1) in a population resembling the breeding nucleus of a dairy cattle population. The simulated genome consisted of two chromosomes of 100 cM each in addition to a polygenic component. On chromosome 1, a biallelic QTL as well as 4 markers were simulated in linkage disequilibrium. Chromosome 2 was selectively neutral. The results showed that, while reducing pedigree estimated inbreeding, MAS and GAS did not always reduce true inbreeding at the QTL relative to BLUP. MAS and GAS differs from BLUP by increasing the weight on Mendelian sampling terms and thereby lowering inbreeding, while increasing the fixation rate of the favourable QTL allele and thereby increasing inbreeding. The total outcome in terms of inbreeding at the QTL depends on the balance between these two effects. In addition, as a result of hitchhiking, MAS results in extra inbreeding in the region surrounding QTL, which could affect the overall genomic inbreeding.
Yoo, Yon-Sik; Song, Si Young; Yang, Cheol Jung; Ha, Jong Mun; Kim, Yoon Sang
2016-01-01
Purpose The purpose of this study was to compare the clinical outcomes of arthroscopic anatomical double bundle (DB) anterior cruciate ligament (ACL) reconstruction with either selective anteromedial (AM) or posterolateral (PL) bundle reconstruction while preserving a relatively healthy ACL bundle. Materials and Methods The authors evaluated 98 patients with a mean follow-up of 30.8±4.0 months who had undergone DB or selective bundle ACL reconstructions. Of these, 34 cases underwent DB ACL reconstruction (group A), 34 underwent selective AM bundle reconstruction (group B), and 30 underwent selective PL bundle reconstructions (group C). These groups were compared with respect to Lysholm and International Knee Documentation Committee (IKDC) score, side-to-side differences of anterior laxity measured by KT-2000 arthrometer at 30 lbs, and stress radiography and Lachman and pivot shift test results. Pre- and post-operative data were objectively evaluated using a statistical approach. Results The preoperative anterior instability measured by manual stress radiography at 90° of knee flexion in group A was significantly greater than that in groups B and C (all pACL tears offers comparable clinical results to DB reconstruction in complete ACL tears. PMID:27401652
Genetic signatures of natural selection in a model invasive ascidian
Lin, Yaping; Chen, Yiyong; Yi, Changho; Fong, Jonathan J.; Kim, Won; Rius, Marc; Zhan, Aibin
2017-01-01
Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta. Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta. PMID:28266616
IT vendor selection model by using structural equation model & analytical hierarchy process
Maitra, Sarit; Dominic, P. D. D.
2012-11-01
Selecting and evaluating the right vendors is imperative for an organization's global marketplace competitiveness. Improper selection and evaluation of potential vendors can dwarf an organization's supply chain performance. Numerous studies have demonstrated that firms consider multiple criteria when selecting key vendors. This research intends to develop a new hybrid model for vendor selection process with better decision making. The new proposed model provides a suitable tool for assisting decision makers and managers to make the right decisions and select the most suitable vendor. This paper proposes a Hybrid model based on Structural Equation Model (SEM) and Analytical Hierarchy Process (AHP) for long-term strategic vendor selection problems. The five steps framework of the model has been designed after the thorough literature study. The proposed hybrid model will be applied using a real life case study to assess its effectiveness. In addition, What-if analysis technique will be used for model validation purpose.
Pulcini, Céline; Tebano, Gianpiero; Mutters, Nico T; Tacconelli, Evelina; Cambau, Emmanuelle; Kahlmeter, Gunnar; Jarlier, Vincent
2017-02-01
Selective reporting of antibiotic susceptibility test (AST) results is one possible laboratory-based antibiotic stewardship intervention. The primary aim of this study was to identify where and how selective reporting of AST results is implemented in Europe both in inpatient and in outpatient settings. An ESCMID cross-sectional, self-administered, internet-based survey was conducted among all EUCIC (European Committee on Infection Control) or EUCAST (European Committee on Antimicrobial Susceptibility Testing) national representatives in Europe and Israel. Of 38 countries, 36 chose to participate in the survey. Selective reporting of AST results was implemented in 11/36 countries (31%), was partially implemented in 4/36 (11%) and was limited to local initiatives or was not adopted in 21/36 (58%). It was endorsed as standard of care by health authorities in only three countries. The organisation of selective reporting was everywhere discretionally managed by each laboratory, with a pronounced intra- and inter-country variability. The most frequent application was in uncomplicated community-acquired infections, particularly urinary tract and skin and soft-tissue infections. The list of reported antibiotics ranged from a few first-line options, to longer reports where only last-resort antibiotics were hidden. Several barriers to implementation were reported, mainly lack of guidelines, poor system support, insufficient resources, and lack of professionals' capability. In conclusion, selective reporting of AST results is poorly implemented in Europe and is applied with a huge heterogeneity of practices. Development of an international framework, based on existing initiatives and identified barriers, could favour its dissemination as one important element of antibiotic stewardship programmes. Copyright © 2017 Elsevier B.V. and International Society of Chemotherapy. All rights reserved.
Robust model selection and the statistical classification of languages
García, J. E.; González-López, V. A.; Viola, M. L. L.
2012-10-01
In this paper we address the problem of model selection for the set of finite memory stochastic processes with finite alphabet, when the data is contaminated. We consider m independent samples, with more than half of them being realizations of the same stochastic process with law Q, which is the one we want to retrieve. We devise a model selection procedure such that for a sample size large enough, the selected process is the one with law Q. Our model selection strategy is based on estimating relative entropies to select a subset of samples that are realizations of the same law. Although the procedure is valid for any family of finite order Markov models, we will focus on the family of variable length Markov chain models, which include the fixed order Markov chain model family. We define the asymptotic breakdown point (ABDP) for a model selection procedure, and we show the ABDP for our procedure. This means that if the proportion of contaminated samples is smaller than the ABDP, then, as the sample size grows our procedure selects a model for the process with law Q. We also use our procedure in a setting where we have one sample conformed by the concatenation of sub-samples of two or more stochastic processes, with most of the subsamples having law Q. We conducted a simulation study. In the application section we address the question of the statistical classification of languages according to their rhythmic features using speech samples. This is an important open problem in phonology. A persistent difficulty on this problem is that the speech samples correspond to several sentences produced by diverse speakers, corresponding to a mixture of distributions. The usual procedure to deal with this problem has been to choose a subset of the original sample which seems to best represent each language. The selection is made by listening to the samples. In our application we use the full dataset without any preselection of samples. We apply our robust methodology estimating
Selecting Optimal Subset of Features for Student Performance Model
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Hany M. Harb
2012-09-01
Full Text Available Educational data mining (EDM is a new growing research area and the essence of data mining concepts are used in the educational field for the purpose of extracting useful information on the student behavior in the learning process. Classification methods like decision trees, rule mining, and Bayesian network, can be applied on the educational data for predicting the student behavior like performance in an examination. This prediction may help in student evaluation. As the feature selection influences the predictive accuracy of any performance model, it is essential to study elaborately the effectiveness of student performance model in connection with feature selection techniques. The main objective of this work is to achieve high predictive performance by adopting various feature selection techniques to increase the predictive accuracy with least number of features. The outcomes show a reduction in computational time and constructional cost in both training and classification phases of the student performance model.
Experiment selection for the discrimination of semi-quantitative models of dynamical systems
Vatcheva, [No Value; de Jong, H; Bernard, O; Mars, NJI
2006-01-01
Modeling an experimental system often results in a number of alternative models that are all justified by the available experimental data. To discriminate among these models, additional experiments are needed. Existing methods for the selection of discriminatory experiments in statistics and in arti
Financial applications of a Tabu search variable selection model
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Zvi Drezner
2001-01-01
Full Text Available We illustrate how a comparatively new technique, a Tabu search variable selection model [Drezner, Marcoulides and Salhi (1999], can be applied efficiently within finance when the researcher must select a subset of variables from among the whole set of explanatory variables under consideration. Several types of problems in finance, including corporate and personal bankruptcy prediction, mortgage and credit scoring, and the selection of variables for the Arbitrage Pricing Model, require the researcher to select a subset of variables from a larger set. In order to demonstrate the usefulness of the Tabu search variable selection model, we: (1 illustrate its efficiency in comparison to the main alternative search procedures, such as stepwise regression and the Maximum R2 procedure, and (2 show how a version of the Tabu search procedure may be implemented when attempting to predict corporate bankruptcy. We accomplish (2 by indicating that a Tabu Search procedure increases the predictability of corporate bankruptcy by up to 10 percentage points in comparison to Altman's (1968 Z-Score model.
The Properties of Model Selection when Retaining Theory Variables
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren
Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...... set by their statistical significance can be undertaken without affecting the estimator distribution of the theory parameters. This strategy returns the theory-parameter estimates when the theory is correct, yet protects against the theory being under-specified because some w{t} are relevant....
X-Ray Observations of Optically Selected, Radio-quiet Quasars. I. The ASCA Results
George, I. M.; Turner, T. J.; Yaqoob, T.; Netzer, H.; Laor, A.; Mushotzky, R. F.; Nandra, K.; Takahashi, T.
2000-03-01
We present the result of 27 ASCA observations of 26 radio-quiet quasars (RQQs) from the Palomar-Green (PG) survey. The sample is not statistically complete, but it is reasonably representative of RQQs in the PG survey. For many of the sources, the ASCA data are presented here for the first time. All the RQQs were detected except for two objects, both of which contain broad absorption lines in the optical band. We find the variability characteristics of the sources to be consistent with Seyfert 1 galaxies. A power law offers an acceptable description of the time-averaged spectra in the 2-10 keV (quasar frame) band for all but one data set. The best-fitting values of the photon index vary from object to object over the range 1.5~=2 and dispersion σ(Γ2-10)~=0.25. The distribution of Γ2-10 is therefore similar to that observed in other RQ active galactic nuclei (AGNs) and seems to be unrelated to X-ray luminosity. No single model adequately describes the full 0.6-10 keV (observed frame) continuum of all the RQQs. Approximately 50% of the sources can be adequately described by a single power law or by a power law with only very subtle deviations. All but one of the remaining data sets were found to have convex spectra (flattening as one moves to higher energies). The exception is PG 1411+442, in which a substantial column density (NH,z~2x1023 cm-2) obscures ~98% of the continuum. We find only five (maybe six) of 14 objects with z<~0.25 to have ``soft excesses'' at energies <~1 keV, but we find no universal shape for these spectral components. The spectrum of PG 1244+026 contains a rather narrow emission feature centered at an energy ~1 keV (quasar frame). The detection rate of absorption due to ionized material in these RQQs is lower than that seen in Seyfert 1 galaxies. In part, this may be due to selection effects. However, when detected, the absorbers in the RQQs exhibit a similar range of column density and ionization parameter as Seyfert 1 galaxies. We find
Location-based Mobile Relay Selection and Impact of Inaccurate Path Loss Model Parameters
DEFF Research Database (Denmark)
Nielsen, Jimmy Jessen; Madsen, Tatiana Kozlova; Schwefel, Hans-Peter
2010-01-01
In this paper we propose a relay selection scheme which uses collected location information together with a path loss model for relay selection, and analyze the performance impact of mobility and different error causes on this scheme. Performance is evaluated in terms of bit error rate...... in these situations. As the location-based scheme relies on a path loss model to estimate link qualities and select relays, the sensitivity with respect to inaccurate estimates of the unknown path loss model parameters is investigated. The parameter ranges that result in useful performance were found...
Evans, Jason; Sullivan, Jack
2011-01-01
A priori selection of models for use in phylogeny estimation from molecular sequence data is increasingly important as the number and complexity of available models increases. The Bayesian information criterion (BIC) and the derivative decision-theoretic (DT) approaches rely on a conservative approximation to estimate the posterior probability of a given model. Here, we extended the DT method by using reversible jump Markov chain Monte Carlo approaches to directly estimate model probabilities for an extended candidate pool of all 406 special cases of the general time reversible + Γ family. We analyzed 250 diverse data sets in order to evaluate the effectiveness of the BIC approximation for model selection under the BIC and DT approaches. Model choice under DT differed between the BIC approximation and direct estimation methods for 45% of the data sets (113/250), and differing model choice resulted in significantly different sets of trees in the posterior distributions for 26% of the data sets (64/250). The model with the lowest BIC score differed from the model with the highest posterior probability in 30% of the data sets (76/250). When the data indicate a clear model preference, the BIC approximation works well enough to result in the same model selection as with directly estimated model probabilities, but a substantial proportion of biological data sets lack this characteristic, which leads to selection of underparametrized models.
Chain-Wise Generalization of Road Networks Using Model Selection
Bulatov, D.; Wenzel, S.; Häufel, G.; Meidow, J.
2017-05-01
Streets are essential entities of urban terrain and their automatized extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological and semantic aspects. Given a binary image, representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. We propose the fusion of raw segments based on similarity criteria; the output of this process are the so-called chains which better match to the intuitive perception of what a street is. Further, we propose a two-step approach for chain-wise generalization. First, the chain is pre-segmented using circlePeucker and finally, model selection is used to decide whether two neighboring segments should be fused to a new geometric entity. Thereby, we consider both variance-covariance analysis of residuals and model complexity. The results on a complex data-set with many traffic roundabouts indicate the benefits of the proposed procedure.
Bayesian selection of nucleotide substitution models and their site assignments.
Wu, Chieh-Hsi; Suchard, Marc A; Drummond, Alexei J
2013-03-01
Probabilistic inference of a phylogenetic tree from molecular sequence data is predicated on a substitution model describing the relative rates of change between character states along the tree for each site in the multiple sequence alignment. Commonly, one assumes that the substitution model is homogeneous across sites within large partitions of the alignment, assigns these partitions a priori, and then fixes their underlying substitution model to the best-fitting model from a hierarchy of named models. Here, we introduce an automatic model selection and model averaging approach within a Bayesian framework that simultaneously estimates the number of partitions, the assignment of sites to partitions, the substitution model for each partition, and the uncertainty in these selections. This new approach is implemented as an add-on to the BEAST 2 software platform. We find that this approach dramatically improves the fit of the nucleotide substitution model compared with existing approaches, and we show, using a number of example data sets, that as many as nine partitions are required to explain the heterogeneity in nucleotide substitution process across sites in a single gene analysis. In some instances, this improved modeling of the substitution process can have a measurable effect on downstream inference, including the estimated phylogeny, relative divergence times, and effective population size histories.
An Integrated Model For Online shopping, Using Selective Models
Directory of Open Access Journals (Sweden)
Fereshteh Rabiei Dastjerdi
Full Text Available As in traditional shopping, customer acquisition and retention are critical issues in the success of an online store. Many factors impact how, and if, customers accept online shopping. Models presented in recent years, only focus on behavioral or technolo ...
Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H
2017-07-01
Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in
Selecting global climate models for regional climate change studies
Pierce, David W.; Barnett, Tim P.; Santer, Benjamin D.; Gleckler, Peter J.
2009-01-01
Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simula...
Spatial Fleming-Viot models with selection and mutation
Dawson, Donald A
2014-01-01
This book constructs a rigorous framework for analysing selected phenomena in evolutionary theory of populations arising due to the combined effects of migration, selection and mutation in a spatial stochastic population model, namely the evolution towards fitter and fitter types through punctuated equilibria. The discussion is based on a number of new methods, in particular multiple scale analysis, nonlinear Markov processes and their entrance laws, atomic measure-valued evolutions and new forms of duality (for state-dependent mutation and multitype selection) which are used to prove ergodic theorems in this context and are applicable for many other questions and renormalization analysis for a variety of phenomena (stasis, punctuated equilibrium, failure of naive branching approximations, biodiversity) which occur due to the combination of rare mutation, mutation, resampling, migration and selection and make it necessary to mathematically bridge the gap (in the limit) between time and space scales.
Model selection and inference a practical information-theoretic approach
Burnham, Kenneth P
1998-01-01
This book is unique in that it covers the philosophy of model-based data analysis and an omnibus strategy for the analysis of empirical data The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data Kullback-Leibler information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection The maximized log-likelihood function can be bias-corrected to provide an estimate of expected, relative Kullback-Leibler information This leads to Akaike's Information Criterion (AIC) and various extensions and these are relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are ...
A topic evolution model with sentiment and selective attention
Si, Xia-Meng; Wang, Wen-Dong; Zhai, Chun-Qing; Ma, Yan
2017-04-01
Topic evolution is a hybrid dynamics of information propagation and opinion interaction. The dynamics of opinion interaction is inherently interwoven with the dynamics of information propagation in the network, owing to the bidirectional influences between interaction and diffusion. The degree of sentiment determines if the topic can continue to spread from this node, and the selective attention determines the information flow direction and communicatee selection. For this end, we put forward a sentiment-based mixed dynamics model with selective attention, and applied the Bayesian updating rules on it. Our model can indirectly describe the isolated users who seem isolated from a topic due to some reasons even everybody around them has heard about it. Numerical simulations show that, more insiders initially and fewer simultaneous spreaders can lessen the extremism. To promote the topic diffusion or restrain the prevailing of extremism, fewer agents with constructive motivation and more agents with no involving motivation are encouraged.
Xu, Zhiqiang
2017-02-16
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
Second-order model selection in mixture experiments
Energy Technology Data Exchange (ETDEWEB)
Redgate, P.E.; Piepel, G.F.; Hrma, P.R.
1992-07-01
Full second-order models for q-component mixture experiments contain q(q+l)/2 terms, which increases rapidly as q increases. Fitting full second-order models for larger q may involve problems with ill-conditioning and overfitting. These problems can be remedied by transforming the mixture components and/or fitting reduced forms of the full second-order mixture model. Various component transformation and model reduction approaches are discussed. Data from a 10-component nuclear waste glass study are used to illustrate ill-conditioning and overfitting problems that can be encountered when fitting a full second-order mixture model. Component transformation, model term selection, and model evaluation/validation techniques are discussed and illustrated for the waste glass example.
Measuring balance and model selection in propensity score methods
Belitser, S.; Martens, Edwin P.; Pestman, Wiebe R.; Groenwold, Rolf H.H.; De Boer, Anthonius; Klungel, Olaf H.
2011-01-01
Background: Propensity score (PS) methods focus on balancing confounders between groups to estimate an unbiased treatment or exposure effect. However, there is lack of attention in actually measuring, reporting and using the information on balance, for instance for model selection. Objectives: To de
Selecting crop models for decision making in wheat insurance
Castaneda Vera, A.; Leffelaar, P.A.; Alvaro-Fuentes, J.; Cantero-Martinez, C.; Minguez, M.I.
2015-01-01
In crop insurance, the accuracy with which the insurer quantifies the actual risk is highly dependent on the availability on actual yield data. Crop models might be valuable tools to generate data on expected yields for risk assessment when no historical records are available. However, selecting a c
Cross-validation criteria for SETAR model selection
de Gooijer, J.G.
2001-01-01
Three cross-validation criteria, denoted C, C_c, and C_u are proposed for selecting the orders of a self-exciting threshold autoregressive SETAR) model when both the delay and the threshold value are unknown. The derivatioon of C is within a natural cross-validation framework. The crietion C_c is si
Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2011-01-01
’s optimizers are frequently caused by missed correlations between attributes. We present a selectivity estimation approach that does not make the independence assumptions. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution of all...
Selecting crop models for decision making in wheat insurance
Castaneda Vera, A.; Leffelaar, P.A.; Alvaro-Fuentes, J.; Cantero-Martinez, C.; Minguez, M.I.
2015-01-01
In crop insurance, the accuracy with which the insurer quantifies the actual risk is highly dependent on the availability on actual yield data. Crop models might be valuable tools to generate data on expected yields for risk assessment when no historical records are available. However, selecting a
Accurate model selection of relaxed molecular clocks in bayesian phylogenetics.
Baele, Guy; Li, Wai Lok Sibon; Drummond, Alexei J; Suchard, Marc A; Lemey, Philippe
2013-02-01
Recent implementations of path sampling (PS) and stepping-stone sampling (SS) have been shown to outperform the harmonic mean estimator (HME) and a posterior simulation-based analog of Akaike's information criterion through Markov chain Monte Carlo (AICM), in bayesian model selection of demographic and molecular clock models. Almost simultaneously, a bayesian model averaging approach was developed that avoids conditioning on a single model but averages over a set of relaxed clock models. This approach returns estimates of the posterior probability of each clock model through which one can estimate the Bayes factor in favor of the maximum a posteriori (MAP) clock model; however, this Bayes factor estimate may suffer when the posterior probability of the MAP model approaches 1. Here, we compare these two recent developments with the HME, stabilized/smoothed HME (sHME), and AICM, using both synthetic and empirical data. Our comparison shows reassuringly that MAP identification and its Bayes factor provide similar performance to PS and SS and that these approaches considerably outperform HME, sHME, and AICM in selecting the correct underlying clock model. We also illustrate the importance of using proper priors on a large set of empirical data sets.
Rank-based model selection for multiple ions quantum tomography
Guţă, Mădălin; Kypraios, Theodore; Dryden, Ian
2012-10-01
The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the ‘sparsity’ properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods—the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)—two models consisting of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of four ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a four ions experiment aimed at creating a Smolin state of rank 4. By applying the two methods together with the Pearson χ2 test we conclude that the data can be suitably described with a model whose rank is between 7 and 9. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements.
Fuzzy Multi-Objective Decision Model of Supplier Selection with Preference Information
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Supplier selection is a multi-objective decision problem, which must be considered many objectives, someobjectives are qualitative, and others are quantitative. Meanwhile, manufacturer has preference for different suppliers.In this paper, a new multi-objective decision model with preference information of supplier is established. A practicalexample of supplier selection problem utilizing this model is studied. The result demonstrates the feasibility andeffectiveness of the methods proposed in the paper.
A DNA-based system for selecting and displaying the combined result of two input variables
DEFF Research Database (Denmark)
Liu, Huajie; Wang, Jianbang; Song, S
2015-01-01
Oligonucleotide-based technologies for biosensing or bio-regulation produce huge amounts of rich high-dimensional information. There is a consequent need for flexible means to combine diverse pieces of such information to form useful derivative outputs, and to display those immediately. Here we...... demonstrate this capability in a DNA-based system that takes two input numbers, represented in DNA strands, and returns the result of their multiplication, writing this as a number in a display. Unlike a conventional calculator, this system operates by selecting the result from a library of solutions rather...
Selective Media for Actinide Collection and Pre-Concentration: Results of FY 2006 Studies
Energy Technology Data Exchange (ETDEWEB)
Lumetta, Gregg J.; Addleman, Raymond S.; Hay, Benjamin P.; Hubler, Timothy L.; Levitskaia, Tatiana G.; Sinkov, Sergey I.; Snow, Lanee A.; Warner, Marvin G.; Latesky, Stanley L.
2006-11-17
3] > 0.3 M. Preliminary results suggest that the Kl?ui resins can separate Pu(IV) from sample solutions containing high concentrations of competing ions. Conceptual protocols for recovery of the Pu from the resin for subsequent analysis have been proposed, but further work is needed to perfect these techniques. Work on this subject will be continued in FY 2007. Automated laboratory equipment (in conjunction with Task 3 of the NA-22 Automation Project) will be used in FY 2007 to improve the efficiency of these experiments. The sorption of actinide ions on self-assembled monolayer on mesoporous supports materials containing diphosphonate groups was also investigated. These materials also showed a very high affinity for tetravalent actinides, and they also sorbed U(VI) fairly strongly. Computational Ligand Design An extended MM3 molecular mechanics model was developed for calculating the structures of Kl?ui ligand complexes. This laid the groundwork necessary to perform the computer-aided design of bis-Kl?ui architectures tailored for Pu(IV) complexation. Calculated structures of the Kl?ui ligand complexes [Pu(Kl?ui)2(OH2)2]2+ and [Fe(Kl?ui)2]+ indicate a ''bent'' sandwich arrangement of the Kl?ui ligands in the Pu(IV) complex, whereas the Fe(III) complex prefers a ''linear'' octahedral arrangement of the two Kl?ui ligands. This offers the possibility that two Kl?ui ligands can be tethered together to form a material with very high binding affinity for Pu(IV) over Fe(III). The next step in the design process is to use de novo molecule building software (HostDesigner) to identify potential candidate architectures.
The European Integrated Tokamak Modelling (ITM) effort: achievements and first physics results
G.L. Falchetto,; Coster, D.; Coelho, R.; Scott, B. D.; Figini, L.; Kalupin, D.; Nardon, E.; Nowak, S.; L.L. Alves,; Artaud, J. F.; Basiuk, V.; João P.S. Bizarro,; C. Boulbe,; Dinklage, A.; Farina, D.; B. Faugeras,; Ferreira, J.; Figueiredo, A.; Huynh, P.; Imbeaux, F.; Ivanova-Stanik, I.; Jonsson, T.; H.-J. Klingshirn,; Konz, C.; Kus, A.; Marushchenko, N. B.; Pereverzev, G.; M. Owsiak,; Poli, E.; Peysson, Y.; R. Reimer,; Signoret, J.; Sauter, O.; Stankiewicz, R.; Strand, P.; Voitsekhovitch, I.; Westerhof, E.; T. Zok,; Zwingmann, W.; ITM-TF contributors,; ASDEX Upgrade team,; JET-EFDA Contributors,
2014-01-01
A selection of achievements and first physics results are presented of the European Integrated Tokamak Modelling Task Force (EFDA ITM-TF) simulation framework, which aims to provide a standardized platform and an integrated modelling suite of validated numerical codes for the simulation and
Conceptual Incoherence as a Result of the Use of Multiple Historical Models in School Textbooks
Gericke, Niklas M.; Hagberg, Mariana
2010-01-01
This paper explores the occurrence of conceptual incoherence in upper secondary school textbooks resulting from the use of multiple historical models. Swedish biology and chemistry textbooks, as well as a selection of books from English speaking countries, were examined. The purpose of the study was to identify which models are used to represent…
Selective refinement and selection of near-native models in protein structure prediction.
Zhang, Jiong; Barz, Bogdan; Zhang, Jingfen; Xu, Dong; Kosztin, Ioan
2015-10-01
In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target-specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all-atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best-refined models are selected with SMDR by testing their relative stability against gradual heating through all-atom MD simulations. Through extensive testing we have found that Mufold-MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions.
A model selection approach to analysis of variance and covariance.
Alber, Susan A; Weiss, Robert E
2009-06-15
An alternative to analysis of variance is a model selection approach where every partition of the treatment means into clusters with equal value is treated as a separate model. The null hypothesis that all treatments are equal corresponds to the partition with all means in a single cluster. The alternative hypothesis correspond to the set of all other partitions of treatment means. A model selection approach can also be used for a treatment by covariate interaction, where the null hypothesis and each alternative correspond to a partition of treatments into clusters with equal covariate effects. We extend the partition-as-model approach to simultaneous inference for both treatment main effect and treatment interaction with a continuous covariate with separate partitions for the intercepts and treatment-specific slopes. The model space is the Cartesian product of the intercept partition and the slope partition, and we develop five joint priors for this model space. In four of these priors the intercept and slope partition are dependent. We advise on setting priors over models, and we use the model to analyze an orthodontic data set that compares the frictional resistance created by orthodontic fixtures. Copyright (c) 2009 John Wiley & Sons, Ltd.
How many separable sources? Model selection in independent components analysis.
Woods, Roger P; Hansen, Lars Kai; Strother, Stephen
2015-01-01
Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian.
Statistical modelling in biostatistics and bioinformatics selected papers
Peng, Defen
2014-01-01
This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and fu...
Loywyck, V.; Bijma, P.; Pinard-van der Laan, M.H.; Arendonk, van J.A.M.; Verrier, E.
2005-01-01
Selection programmes are mainly concerned with increasing genetic gain. However, short-term progress should not be obtained at the expense of the within-population genetic variability. Different prediction models for the evolution within a small population of the genetic mean of a selected trait, it
How Many Separable Sources? Model Selection In Independent Components Analysis
DEFF Research Database (Denmark)
Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen
2015-01-01
Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysi...... might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian.......Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis....../Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from...
Some results regarding the comparison of the Earth's atmospheric models
Directory of Open Access Journals (Sweden)
Šegan S.
2005-01-01
Full Text Available In this paper we examine air densities derived from our realization of aeronomic atmosphere models based on accelerometer measurements from satellites in a low Earth's orbit (LEO. Using the adapted algorithms we derive comparison parameters. The first results concerning the adjustment of the aeronomic models to the total-density model are given.
Supplier Selection in Virtual Enterprise Model of Manufacturing Supply Network
Kaihara, Toshiya; Opadiji, Jayeola F.
The market-based approach to manufacturing supply network planning focuses on the competitive attitudes of various enterprises in the network to generate plans that seek to maximize the throughput of the network. It is this competitive behaviour of the member units that we explore in proposing a solution model for a supplier selection problem in convergent manufacturing supply networks. We present a formulation of autonomous units of the network as trading agents in a virtual enterprise network interacting to deliver value to market consumers and discuss the effect of internal and external trading parameters on the selection of suppliers by enterprise units.
Efficiency of model selection criteria in flood frequency analysis
Calenda, G.; Volpi, E.
2009-04-01
The estimation of high flood quantiles requires the extrapolation of the probability distributions far beyond the usual sample length, involving high estimation uncertainties. The choice of the probability law, traditionally based on the hypothesis testing, is critical to this point. In this study the efficiency of different model selection criteria, seldom applied in flood frequency analysis, is investigated. The efficiency of each criterion in identifying the probability distribution of the hydrological extremes is evaluated by numerical simulations for different parent distributions, coefficients of variation and skewness, and sample sizes. The compared model selection procedures are the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Anderson Darling Criterion (ADC) recently discussed by Di Baldassarre et al. (2008) and Sample Quantile Criterion (SQC), recently proposed by the authors (Calenda et al., 2009). The SQC is based on the principle of maximising the probability density of the elements of the sample that are considered relevant to the problem, and takes into account both the accuracy and the uncertainty of the estimate. Since the stress is mainly on extreme events, the SQC involves upper-tail probabilities, where the effect of the model assumption is more critical. The proposed index is equal to the sum of logarithms of the inverse of the sample probability density of the observed quantiles. The definition of this index is based on the principle that the more centred is the sample value in respect to its density distribution (accuracy of the estimate) and the less spread is this distribution (uncertainty of the estimate), the greater is the probability density of the sample quantile. Thus, lower values of the index indicate a better performance of the distribution law. This criterion can operate the selection of the optimum distribution among competing probability models that are estimated using different samples. The
Effectiveness of femoral nerve selective block in patients with spasticity: preliminary results.
Albert, Thierry A; Yelnik, Alain; Bonan, Isabelle; Lebreton, Frederique; Bussel, Bernard
2002-05-01
To determine if the vastus intermedius nerve can be blocked by using surface coordinates and to measure the effects of selective nerve block on quadriceps spasticity and immediate gait. Case series. Physical medicine and rehabilitation department of a university hospital. Twelve patients with hemiplegia disabled by quadriceps overactivity. Anesthesic block of the vastus intermedius by using surface coordinates, femoral nerve stimulation before and after block, and surface electrodes recording of the amplitude of the maximum direct motor response of each head of the quadriceps. Assessment of spasticity, voluntary knee extension velocity, speed of gait, and knee flexion when walking. To be effective, the puncture point (.29 of thigh length and 2cm lateral) had to be slightly modified to 1cm laterally from a point situated at 0.2 of the thigh length. A selective block of the vastus intermedius could not be achieved, but a block of the vastus lateralis was always achieved, twice associated with a block of the vastus intermedius, resulting in decreased quadriceps spasticity, no changes in gait parameters, no decrease in voluntary knee extension velocity, and subjective improvement in gait for 3 patients. Selective block of the vastus lateralis with or without the vastus intermedius can be achieved by using surface coordinates without any dramatic effect on knee extension velocity, and it could be useful for phenol or alcohol block or surgical neurotomy. Copyright 2002 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation
Decision support model for selecting and evaluating suppliers in the construction industry
Directory of Open Access Journals (Sweden)
Fernando Schramm
2012-12-01
Full Text Available A structured evaluation of the construction industry's suppliers, considering aspects which make their quality and credibility evident, can be a strategic tool to manage this specific supply chain. This study proposes a multi-criteria decision model for suppliers' selection from the construction industry, as well as an efficient evaluation procedure for the selected suppliers. The model is based on SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranking method and its main contribution is a new approach to structure the process of suppliers' selection, establishing explicit strategic policies on which the company management system relied to make the suppliers selection. This model was applied to a Civil Construction Company in Brazil and the main results demonstrate the efficiency of the proposed model. This study allowed the development of an approach to Construction Industry which was able to provide a better relationship among its managers, suppliers and partners.
Detecting consistent patterns of directional adaptation using differential selection codon models.
Parto, Sahar; Lartillot, Nicolas
2017-06-23
Phylogenetic codon models are often used to characterize the selective regimes acting on protein-coding sequences. Recent methodological developments have led to models explicitly accounting for the interplay between mutation and selection, by modeling the amino acid fitness landscape along the sequence. However, thus far, most of these models have assumed that the fitness landscape is constant over time. Fluctuations of the fitness landscape may often be random or depend on complex and unknown factors. However, some organisms may be subject to systematic changes in selective pressure, resulting in reproducible molecular adaptations across independent lineages subject to similar conditions. Here, we introduce a codon-based differential selection model, which aims to detect and quantify the fine-grained consistent patterns of adaptation at the protein-coding level, as a function of external conditions experienced by the organism under investigation. The model parameterizes the global mutational pressure, as well as the site- and condition-specific amino acid selective preferences. This phylogenetic model is implemented in a Bayesian MCMC framework. After validation with simulations, we applied our method to a dataset of HIV sequences from patients with known HLA genetic background. Our differential selection model detects and characterizes differentially selected coding positions specifically associated with two different HLA alleles. Our differential selection model is able to identify consistent molecular adaptations as a function of repeated changes in the environment of the organism. These models can be applied to many other problems, ranging from viral adaptation to evolution of life-history strategies in plants or animals.
Bayesian model selection in complex linear systems, as illustrated in genetic association studies.
Wen, Xiaoquan
2014-03-01
Motivated by examples from genetic association studies, this article considers the model selection problem in a general complex linear model system and in a Bayesian framework. We discuss formulating model selection problems and incorporating context-dependent a priori information through different levels of prior specifications. We also derive analytic Bayes factors and their approximations to facilitate model selection and discuss their theoretical and computational properties. We demonstrate our Bayesian approach based on an implemented Markov Chain Monte Carlo (MCMC) algorithm in simulations and a real data application of mapping tissue-specific eQTLs. Our novel results on Bayes factors provide a general framework to perform efficient model comparisons in complex linear model systems.
Zarindast, Atousa; Seyed Hosseini, Seyed Mohamad; Pishvaee, Mir Saman
2016-11-01
Robust supplier selection problem, in a scenario-based approach has been proposed, when the demand and exchange rates are subject to uncertainties. First, a deterministic multi-objective mixed integer linear programming is developed; then, the robust counterpart of the proposed mixed integer linear programming is presented using the recent extension in robust optimization theory. We discuss decision variables, respectively, by a two-stage stochastic planning model, a robust stochastic optimization planning model which integrates worst case scenario in modeling approach and finally by equivalent deterministic planning model. The experimental study is carried out to compare the performances of the three models. Robust model resulted in remarkable cost saving and it illustrated that to cope with such uncertainties, we should consider them in advance in our planning. In our case study different supplier were selected due to this uncertainties and since supplier selection is a strategic decision, it is crucial to consider these uncertainties in planning approach.
The Effect of Bathymetric Filtering on Nearshore Process Model Results
2009-01-01
Filtering on Nearshore Process Model Results 6. AUTHOR(S) Nathaniel Plant, Kacey L. Edwards, James M. Kaihatu, Jayaram Veeramony, Yuan-Huang L. Hsu...filtering on nearshore process model results Nathaniel G. Plant **, Kacey L Edwardsb, James M. Kaihatuc, Jayaram Veeramony b, Larry Hsu’’, K. Todd Holland...assimilation efforts that require this information. Published by Elsevier B.V. 1. Introduction Nearshore process models are capable of predicting
Directory of Open Access Journals (Sweden)
Popov F.I.
2010-06-01
Full Text Available The prognosis of flying success is presented to beginning of the flying teaching of students. A prognosis is made on results a complex analysis phsycological - professional selection, physical and training preparation. Each of estimations individually does not give a reliable prediction. Facilities of physical preparation can be used for determination of level of development professionally of important qualities of future pilots. Professionally important qualities need periodic (not rarer than once in a year control after their dynamics the method of the repeated inspections.
Estimating seabed scattering mechanisms via Bayesian model selection.
Steininger, Gavin; Dosso, Stan E; Holland, Charles W; Dettmer, Jan
2014-10-01
A quantitative inversion procedure is developed and applied to determine the dominant scattering mechanism (surface roughness and/or volume scattering) from seabed scattering-strength data. The classification system is based on trans-dimensional Bayesian inversion with the deviance information criterion used to select the dominant scattering mechanism. Scattering is modeled using first-order perturbation theory as due to one of three mechanisms: Interface scattering from a rough seafloor, volume scattering from a heterogeneous sediment layer, or mixed scattering combining both interface and volume scattering. The classification system is applied to six simulated test cases where it correctly identifies the true dominant scattering mechanism as having greater support from the data in five cases; the remaining case is indecisive. The approach is also applied to measured backscatter-strength data where volume scattering is determined as the dominant scattering mechanism. Comparison of inversion results with core data indicates the method yields both a reasonable volume heterogeneity size distribution and a good estimate of the sub-bottom depths at which scatterers occur.
QOS Aware Formalized Model for Semantic Web Service Selection
Directory of Open Access Journals (Sweden)
Divya Sachan
2014-10-01
Full Text Available Selecting the most relevant Web Service according to a client requirement is an onerous task, as innumerous number of functionally same Web Services(WS are listed in UDDI registry. WS are functionally same but their Quality and performance varies as per service providers. A web Service Selection Process involves two major points: Recommending the pertinent Web Service and avoiding unjustifiable web service. The deficiency in keyword based searching is that it doesn’t handle the client request accurately as keyword may have ambiguous meaning on different scenarios. UDDI and search engines all are based on keyword search, which are lagging behind on pertinent Web service selection. So the search mechanism must be incorporated with the Semantic behavior of Web Services. In order to strengthen this approach, the proposed model is incorporated with Quality of Services (QoS based Ranking of semantic web services.
Brito Lopes, Fernando; da Silva, Marcelo Corrêa; Magnabosco, Cláudio Ulhôa; Goncalves Narciso, Marcelo; Sainz, Roberto Daniel
2016-01-01
This research evaluated a multivariate approach as an alternative tool for the purpose of selection regarding expected progeny differences (EPDs). Data were fitted using a multi-trait model and consisted of growth traits (birth weight and weights at 120, 210, 365 and 450 days of age) and carcass traits (longissimus muscle area (LMA), back-fat thickness (BF), and rump fat thickness (RF)), registered over 21 years in extensive breeding systems of Polled Nellore cattle in Brazil. Multivariate analyses were performed using standardized (zero mean and unit variance) EPDs. The k mean method revealed that the best fit of data occurred using three clusters (k = 3) (P multivariate index (LD1) were moderate to high, ranging from 0.48 to 0.97. This reveals that both types of indices give similar results and that the multivariate approach is reliable for the purpose of selection. The alternative tool seems very handy when economic weights are not available or in cases where more rapid identification of the best animals is desired. Interestingly, multivariate analysis allowed forecasting information based on the relationships among breeding values (EPDs). Also, it enabled fine discrimination, rapid data summarization after genetic evaluation, and permitted accounting for maternal ability and the genetic direct potential of the animals. In addition, we recommend the use of longissimus muscle area and subcutaneous fat thickness as selection criteria, to allow estimation of breeding values before the first mating season in order to accelerate the response to individual selection.
Energy Technology Data Exchange (ETDEWEB)
Farrell, Kathryn, E-mail: kfarrell@ices.utexas.edu; Oden, J. Tinsley, E-mail: oden@ices.utexas.edu; Faghihi, Danial, E-mail: danial@ices.utexas.edu
2015-08-15
A general adaptive modeling algorithm for selection and validation of coarse-grained models of atomistic systems is presented. A Bayesian framework is developed to address uncertainties in parameters, data, and model selection. Algorithms for computing output sensitivities to parameter variances, model evidence and posterior model plausibilities for given data, and for computing what are referred to as Occam Categories in reference to a rough measure of model simplicity, make up components of the overall approach. Computational results are provided for representative applications.
Exploratory Bayesian model selection for serial genetics data.
Zhao, Jing X; Foulkes, Andrea S; George, Edward I
2005-06-01
Characterizing the process by which molecular and cellular level changes occur over time will have broad implications for clinical decision making and help further our knowledge of disease etiology across many complex diseases. However, this presents an analytic challenge due to the large number of potentially relevant biomarkers and the complex, uncharacterized relationships among them. We propose an exploratory Bayesian model selection procedure that searches for model simplicity through independence testing of multiple discrete biomarkers measured over time. Bayes factor calculations are used to identify and compare models that are best supported by the data. For large model spaces, i.e., a large number of multi-leveled biomarkers, we propose a Markov chain Monte Carlo (MCMC) stochastic search algorithm for finding promising models. We apply our procedure to explore the extent to which HIV-1 genetic changes occur independently over time.
Predicting artificailly drained areas by means of selective model ensemble
DEFF Research Database (Denmark)
Møller, Anders Bjørn; Beucher, Amélie; Iversen, Bo Vangsø
. The approaches employed include decision trees, discriminant analysis, regression models, neural networks and support vector machines amongst others. Several models are trained with each method, using variously the original soil covariates and principal components of the covariates. With a large ensemble...... out since the mid-19th century, and it has been estimated that half of the cultivated area is artificially drained (Olesen, 2009). A number of machine learning approaches can be used to predict artificially drained areas in geographic space. However, instead of choosing the most accurate model....... The study aims firstly to train a large number of models to predict the extent of artificially drained areas using various machine learning approaches. Secondly, the study will develop a method for selecting the models, which give a good prediction of artificially drained areas, when used in conjunction...
Model Selection Framework for Graph-based data
Caceres, Rajmonda S; Schmidt, Matthew C; Miller, Benjamin A; Campbell, William M
2016-01-01
Graphs are powerful abstractions for capturing complex relationships in diverse application settings. An active area of research focuses on theoretical models that define the generative mechanism of a graph. Yet given the complexity and inherent noise in real datasets, it is still very challenging to identify the best model for a given observed graph. We discuss a framework for graph model selection that leverages a long list of graph topological properties and a random forest classifier to learn and classify different graph instances. We fully characterize the discriminative power of our approach as we sweep through the parameter space of two generative models, the Erdos-Renyi and the stochastic block model. We show that our approach gets very close to known theoretical bounds and we provide insight on which topological features play a critical discriminating role.
Li, Xingfeng; Coyle, Damien; Maguire, Liam; McGinnity, Thomas M; Benali, Habib
2011-07-01
In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.
Selected results of simulation studies in “The Smart Peninsula” project
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Andrzej Kąkol
2012-03-01
Full Text Available “The Intelligent Peninsula” project implementation required the development of a computational model of a medium voltage grid and of a section of a low voltage grid in the Hel Peninsula. The model was used to perform many simulation analyses in the MV grid. The analyses were used to develop MV grid operation control algorithms. The paper presents results of the analyses aimed at verification of a MLDC method-based voltage control algorithm. The paper presents results of the analyses aimed at verification of EC Władysławowo cogeneration plant’s suitability for standalone operation in the Hel Peninsula.
Construction of an extended library of adult male 3D models: rationale and results
Broggio, D.; Beurrier, J.; Bremaud, M.; Desbrée, A.; Farah, J.; Huet, C.; Franck, D.
2011-12-01
In order to best cover the possible extent of heights and weights of male adults the construction of 25 whole body 3D models has been undertaken. Such a library is thought to be useful to specify the uncertainties and relevance of dosimetry calculations carried out with models representing individuals of average body heights and weights. Representative 3D models of Caucasian body types are selected in a commercial database according to their height and weight, and 3D models of the skeleton and internal organs are designed using another commercial dataset. A review of the literature enabled one to fix volume or mass target values for the skeleton, soft organs, skin and fat content of the selected individuals. The composition of the remainder tissue is fixed so that the weight of the voxel models equals the weight of the selected individuals. After mesh and NURBS modelling, volume adjustment of the selected body shapes and additional voxel-based work, 25 voxel models with 109 identified organs or tissue are obtained. Radiation transport calculations are carried out with some of the developed models to illustrate potential uses. The following points are discussed throughout this paper: justification of the fixed or obtained models' features regarding available and relevant literature data; workflow and strategy for major modelling steps; advantages and drawbacks of the obtained library as compared with other works. The construction hypotheses are explained and justified in detail since future calculation results obtained with this library will depend on them.
Wang, Huai-Chun; Susko, Edward; Roger, Andrew J
2014-04-01
Standard protein phylogenetic models use fixed rate matrices of amino acid interchange derived from analyses of large databases. Differences between the stationary amino acid frequencies of these rate matrices from those of a data set of interest are typically adjusted for by matrix multiplication that converts the empirical rate matrix to an exchangeability matrix which is then postmultiplied by the amino acid frequencies in the alignment. The result is a time-reversible rate matrix with stationary amino acid frequencies equal to the data set frequencies. On the basis of population genetics principles, we develop an amino acid substitution-selection model that parameterizes the fitness of an amino acid as the logarithm of the ratio of the frequency of the amino acid to the frequency of the same amino acid under no selection. The model gives rise to a different sequence of matrix multiplications to convert an empirical rate matrix to one that has stationary amino acid frequencies equal to the data set frequencies. We incorporated the substitution-selection model with an improved amino acid class frequency mixture (cF) model to partially take into account site-specific amino acid frequencies in the phylogenetic models. We show that 1) the selection models fit data significantly better than corresponding models without selection for most of the 21 test data sets; 2) both cF and cF selection models favored the phylogenetic trees that were inferred under current sophisticated models and methods for three difficult phylogenetic problems (the positions of microsporidia and breviates in eukaryote phylogeny and the position of the root of the angiosperm tree); and 3) for data simulated under site-specific residue frequencies, the cF selection models estimated trees closer to the generating trees than a standard Г model or cF without selection. We also explored several ways of estimating amino acid frequencies under neutral evolution that are required for these selection
Steel Containment Vessel Model Test: Results and Evaluation
Energy Technology Data Exchange (ETDEWEB)
Costello, J.F.; Hashimote, T.; Hessheimer, M.F.; Luk, V.K.
1999-03-01
A high pressure test of the steel containment vessel (SCV) model was conducted on December 11-12, 1996 at Sandia National Laboratories, Albuquerque, NM, USA. The test model is a mixed-scaled model (1:10 in geometry and 1:4 in shell thickness) of an improved Mark II boiling water reactor (BWR) containment. A concentric steel contact structure (CS), installed over the SCV model and separated at a nominally uniform distance from it, provided a simplified representation of a reactor shield building in the actual plant. The SCV model and contact structure were instrumented with strain gages and displacement transducers to record the deformation behavior of the SCV model during the high pressure test. This paper summarizes the conduct and the results of the high pressure test and discusses the posttest metallurgical evaluation results on specimens removed from the SCV model.
Schöniger, Anneli; Illman, Walter A.; Wöhling, Thomas; Nowak, Wolfgang
2015-12-01
Groundwater modelers face the challenge of how to assign representative parameter values to the studied aquifer. Several approaches are available to parameterize spatial heterogeneity in aquifer parameters. They differ in their conceptualization and complexity, ranging from homogeneous models to heterogeneous random fields. While it is common practice to invest more effort into data collection for models with a finer resolution of heterogeneities, there is a lack of advice which amount of data is required to justify a certain level of model complexity. In this study, we propose to use concepts related to Bayesian model selection to identify this balance. We demonstrate our approach on the characterization of a heterogeneous aquifer via hydraulic tomography in a sandbox experiment (Illman et al., 2010). We consider four increasingly complex parameterizations of hydraulic conductivity: (1) Effective homogeneous medium, (2) geology-based zonation, (3) interpolation by pilot points, and (4) geostatistical random fields. First, we investigate the shift in justified complexity with increasing amount of available data by constructing a model confusion matrix. This matrix indicates the maximum level of complexity that can be justified given a specific experimental setup. Second, we determine which parameterization is most adequate given the observed drawdown data. Third, we test how the different parameterizations perform in a validation setup. The results of our test case indicate that aquifer characterization via hydraulic tomography does not necessarily require (or justify) a geostatistical description. Instead, a zonation-based model might be a more robust choice, but only if the zonation is geologically adequate.
Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection
DEFF Research Database (Denmark)
Bork, Lasse; Møller, Stig Vinther
2015-01-01
We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...
Selection between Linear Factor Models and Latent Profile Models Using Conditional Covariances
Halpin, Peter F.; Maraun, Michael D.
2010-01-01
A method for selecting between K-dimensional linear factor models and (K + 1)-class latent profile models is proposed. In particular, it is shown that the conditional covariances of observed variables are constant under factor models but nonlinear functions of the conditioning variable under latent profile models. The performance of a convenient…
Selection between Linear Factor Models and Latent Profile Models Using Conditional Covariances
Halpin, Peter F.; Maraun, Michael D.
2010-01-01
A method for selecting between K-dimensional linear factor models and (K + 1)-class latent profile models is proposed. In particular, it is shown that the conditional covariances of observed variables are constant under factor models but nonlinear functions of the conditioning variable under latent profile models. The performance of a convenient…
Modeling selective attention using a neuromorphic analog VLSI device.
Indiveri, G
2000-12-01
Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features.
Model Order Selection Rules for Covariance Structure Classification in Radar
Carotenuto, Vincenzo; De Maio, Antonio; Orlando, Danilo; Stoica, Petre
2017-10-01
The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of Model Order Selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules.
Modeling the Effect of Selection History on Pop-Out Visual Search
Tseng, Yuan-Chi; Glaser, Joshua I.; Caddigan, Eamon; Lleras, Alejandro
2014-01-01
While attentional effects in visual selection tasks have traditionally been assigned “top-down” or “bottom-up” origins, more recently it has been proposed that there are three major factors affecting visual selection: (1) physical salience, (2) current goals and (3) selection history. Here, we look further into selection history by investigating Priming of Pop-out (POP) and the Distractor Preview Effect (DPE), two inter-trial effects that demonstrate the influence of recent history on visual search performance. Using the Ratcliff diffusion model, we model observed saccadic selections from an oddball search experiment that included a mix of both POP and DPE conditions. We find that the Ratcliff diffusion model can effectively model the manner in which selection history affects current attentional control in visual inter-trial effects. The model evidence shows that bias regarding the current trial's most likely target color is the most critical parameter underlying the effect of selection history. Our results are consistent with the view that the 3-item color-oddball task used for POP and DPE experiments is best understood as an attentional decision making task. PMID:24595032
Modeling the effect of selection history on pop-out visual search.
Tseng, Yuan-Chi; Glaser, Joshua I; Caddigan, Eamon; Lleras, Alejandro
2014-01-01
While attentional effects in visual selection tasks have traditionally been assigned "top-down" or "bottom-up" origins, more recently it has been proposed that there are three major factors affecting visual selection: (1) physical salience, (2) current goals and (3) selection history. Here, we look further into selection history by investigating Priming of Pop-out (POP) and the Distractor Preview Effect (DPE), two inter-trial effects that demonstrate the influence of recent history on visual search performance. Using the Ratcliff diffusion model, we model observed saccadic selections from an oddball search experiment that included a mix of both POP and DPE conditions. We find that the Ratcliff diffusion model can effectively model the manner in which selection history affects current attentional control in visual inter-trial effects. The model evidence shows that bias regarding the current trial's most likely target color is the most critical parameter underlying the effect of selection history. Our results are consistent with the view that the 3-item color-oddball task used for POP and DPE experiments is best understood as an attentional decision making task.
Results from a new Cocks-Ashby style porosity model
Barton, Nathan
2017-01-01
A new porosity evolution model is described, along with preliminary results. The formulation makes use of a Cocks-Ashby style treatment of porosity kinetics that includes rate dependent flow in the mechanics of porosity growth. The porosity model is implemented in a framework that allows for a variety of strength models to be used for the matrix material, including ones with significant changes in rate sensitivity as a function of strain rate. Results of the effect of changing strain rate sensitivity on porosity evolution are shown. The overall constitutive model update involves the coupled solution of a system of nonlinear equations.
Parameter estimation and model selection in computational biology.
Directory of Open Access Journals (Sweden)
Gabriele Lillacci
2010-03-01
Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
Selected Constitutive Models for Simulating the Hygromechanical Response of Wood
DEFF Research Database (Denmark)
Frandsen, Henrik Lund
-phase transport model. In this paper a so-called multi-Fickian model is revised with respect to the incorporated essential sorption rate model. Based on existing experimental results the sorption rate model is studied. A desorption rate model analogous to the adsorption rate model is proposed. Furthermore......, the boundary conditions are discussed based on discrepancies found for similar research on moisture transport in paper stacks. Paper III: A new sorption hysteresis model suitable for implementation into a numerical method is developed. The prevailing so-called scanning curves are modeled by closed...... in paper III is applied to two different wood species and to bleach-kraft paperboard. Paper V: The sorption hysteresis model is implemented into the multi-Fickian model allowing simultaneous simulation of non-Fickian effects and hysteresis. A key point for this implementation is definition of the condition...
Structure and selection in an autocatalytic binary polymer model
DEFF Research Database (Denmark)
Tanaka, Shinpei; Fellermann, Harold; Rasmussen, Steen
2014-01-01
An autocatalytic binary polymer system is studied as an abstract model for a chemical reaction network capable to evolve. Due to autocatalysis, long polymers appear spontaneously and their concentration is shown to be maintained at the same level as that of monomers. When the reaction starts from....... Stability, fluctuations, and dynamic selection mechanisms are investigated for the involved self-organizing processes. Copyright (C) EPLA, 2014......An autocatalytic binary polymer system is studied as an abstract model for a chemical reaction network capable to evolve. Due to autocatalysis, long polymers appear spontaneously and their concentration is shown to be maintained at the same level as that of monomers. When the reaction starts from...
Velocity selection in the symmetric model of dendritic crystal growth
Barbieri, Angelo; Hong, Daniel C.; Langer, J. S.
1987-01-01
An analytic solution of the problem of velocity selection in a fully nonlocal model of dendritic crystal growth is presented. The analysis uses a WKB technique to derive and evaluate a solvability condition for the existence of steady-state needle-like solidification fronts in the limit of small under-cooling Delta. For the two-dimensional symmetric model with a capillary anisotropy of strength alpha, it is found that the velocity is proportional to (Delta to the 4th) times (alpha exp 7/4). The application of the method in three dimensions is also described.
A simple application of FIC to model selection
Wiggins, Paul A
2015-01-01
We have recently proposed a new information-based approach to model selection, the Frequentist Information Criterion (FIC), that reconciles information-based and frequentist inference. The purpose of this current paper is to provide a simple example of the application of this criterion and a demonstration of the natural emergence of model complexities with both AIC-like ($N^0$) and BIC-like ($\\log N$) scaling with observation number $N$. The application developed is deliberately simplified to make the analysis analytically tractable.
Small populations corrections for selection-mutation models
Jabin, Pierre-Emmanuel
2012-01-01
We consider integro-differential models describing the evolution of a population structured by a quantitative trait. Individuals interact competitively, creating a strong selection pressure on the population. On the other hand, mutations are assumed to be small. Following the formalism of Diekmann, Jabin, Mischler, and Perthame, this creates concentration phenomena, typically consisting in a sum of Dirac masses slowly evolving in time. We propose a modification to those classical models that takes the effect of small populations into accounts and corrects some abnormal behaviours.
Song, Dong; Chan, Rosa H M; Marmarelis, Vasilis Z; Hampson, Robert E; Deadwyler, Sam A; Berger, Theodore W
2007-01-01
Multiple-input multiple-output nonlinear dynamic model of spike train to spike train transformations was previously formulated for hippocampal-cortical prostheses. This paper further described the statistical methods of selecting significant inputs (self-terms) and interactions between inputs (cross-terms) of this Volterra kernel-based model. In our approach, model structure was determined by progressively adding self-terms and cross-terms using a forward stepwise model selection technique. Model coefficients were then pruned based on Wald test. Results showed that the reduced kernel models, which contained much fewer coefficients than the full Volterra kernel model, gave good fits to the novel data. These models could be used to analyze the functional interactions between neurons during behavior.
Results of the Marine Ice Sheet Model Intercomparison Project, MISMIP
Directory of Open Access Journals (Sweden)
F. Pattyn
2012-05-01
Full Text Available Predictions of marine ice-sheet behaviour require models that are able to robustly simulate grounding line migration. We present results of an intercomparison exercise for marine ice-sheet models. Verification is effected by comparison with approximate analytical solutions for flux across the grounding line using simplified geometrical configurations (no lateral variations, no effects of lateral buttressing. Unique steady state grounding line positions exist for ice sheets on a downward sloping bed, while hysteresis occurs across an overdeepened bed, and stable steady state grounding line positions only occur on the downward-sloping sections. Models based on the shallow ice approximation, which does not resolve extensional stresses, do not reproduce the approximate analytical results unless appropriate parameterizations for ice flux are imposed at the grounding line. For extensional-stress resolving "shelfy stream" models, differences between model results were mainly due to the choice of spatial discretization. Moving grid methods were found to be the most accurate at capturing grounding line evolution, since they track the grounding line explicitly. Adaptive mesh refinement can further improve accuracy, including fixed grid models that generally perform poorly at coarse resolution. Fixed grid models, with nested grid representations of the grounding line, are able to generate accurate steady state positions, but can be inaccurate over transients. Only one full-Stokes model was included in the intercomparison, and consequently the accuracy of shelfy stream models as approximations of full-Stokes models remains to be determined in detail, especially during transients.
Selecting, weeding, and weighting biased climate model ensembles
Jackson, C. S.; Picton, J.; Huerta, G.; Nosedal Sanchez, A.
2012-12-01
In the Bayesian formulation, the "log-likelihood" is a test statistic for selecting, weeding, or weighting climate model ensembles with observational data. This statistic has the potential to synthesize the physical and data constraints on quantities of interest. One of the thorny issues for formulating the log-likelihood is how one should account for biases. While in the past we have included a generic discrepancy term, not all biases affect predictions of quantities of interest. We make use of a 165-member ensemble CAM3.1/slab ocean climate models with different parameter settings to think through the issues that are involved with predicting each model's sensitivity to greenhouse gas forcing given what can be observed from the base state. In particular we use multivariate empirical orthogonal functions to decompose the differences that exist among this ensemble to discover what fields and regions matter to the model's sensitivity. We find that the differences that matter are a small fraction of the total discrepancy. Moreover, weighting members of the ensemble using this knowledge does a relatively poor job of adjusting the ensemble mean toward the known answer. This points out the shortcomings of using weights to correct for biases in climate model ensembles created by a selection process that does not emphasize the priorities of your log-likelihood.
An Introduction to Model Selection: Tools and Algorithms
Directory of Open Access Journals (Sweden)
Sébastien Hélie
2006-03-01
Full Text Available Model selection is a complicated matter in science, and psychology is no exception. In particular, the high variance in the object of study (i.e., humans prevents the use of Poppers falsification principle (which is the norm in other sciences. Therefore, the desirability of quantitative psychological models must be assessed by measuring the capacity of the model to fit empirical data. In the present paper, an error measure (likelihood, as well as five methods to compare model fits (the likelihood ratio test, Akaikes information criterion, the Bayesian information criterion, bootstrapping and cross-validation, are presented. The use of each method is illustrated by an example, and the advantages and weaknesses of each method are also discussed.
Selection of key terrain attributes for SOC model
DEFF Research Database (Denmark)
Greve, Mogens Humlekrog; Adhikari, Kabindra; Chellasamy, Menaka
was selected, total 2,514,820 data mining models were constructed by 71 differences grid from 12m to 2304m and 22 attributes, 21 attributes derived by DTM and the original elevation. Relative importance and usage of each attributes in every model were calculated. Comprehensive impact rates of each attribute...... (standh) are the first three key terrain attributes in 5-attributes-model in all resolutions, the rest 2 of 5 attributes are Normal High (NormalH) and Valley Depth (Vall_depth) at the resolution finer than 40m, and Elevation and Channel Base (Chnl_base) coarser than 40m. The models at pixels size at 88m......As an important component of the global carbon pool, soil organic carbon (SOC) plays an important role in the global carbon cycle. SOC pool is the basic information to carry out global warming research, and needs to sustainable use of land resources. Digital terrain attributes are often use...
Unifying models for X-ray selected and Radio selected BL Lac Objects
Fossati, G; Ghisellini, G; Maraschi, L; Brera-Merate, O A
1997-01-01
We discuss alternative interpretations of the differences in the Spectral Energy Distributions (SEDs) of BL Lacs found in complete Radio or X-ray surveys. A large body of observations in different bands suggests that the SEDs of BL Lac objects appearing in X-ray surveys differ from those appearing in radio surveys mainly in having a (synchrotron) spectral cut-off (or break) at much higher frequency. In order to explain the different properties of radio and X-ray selected BL Lacs Giommi and Padovani proposed a model based on a common radio luminosity function. At each radio luminosity, objects with high frequency spectral cut-offs are assumed to be a minority. Nevertheless they dominate the X-ray selected population due to the larger X-ray-to-radio-flux ratio. An alternative model explored here (reminiscent of the orientation models previously proposed) is that the X-ray luminosity function is "primary" and that at each X-ray luminosity a minority of objects has larger radio-to-X-ray flux ratio. The prediction...
Ghosh, P; Bagchi, M C
2009-01-01
With a view to the rational design of selective quinoxaline derivatives, 2D and 3D-QSAR models have been developed for the prediction of anti-tubercular activities. Successful implementation of a predictive QSAR model largely depends on the selection of a preferred set of molecular descriptors that can signify the chemico-biological interaction. Genetic algorithm (GA) and simulated annealing (SA) are applied as variable selection methods for model development. 2D-QSAR modeling using GA or SA based partial least squares (GA-PLS and SA-PLS) methods identified some important topological and electrostatic descriptors as important factor for tubercular activity. Kohonen network and counter propagation artificial neural network (CP-ANN) considering GA and SA based feature selection methods have been applied for such QSAR modeling of Quinoxaline compounds. Out of a variable pool of 380 molecular descriptors, predictive QSAR models are developed for the training set and validated on the test set compounds and a comparative study of the relative effectiveness of linear and non-linear approaches has been investigated. Further analysis using 3D-QSAR technique identifies two models obtained by GA-PLS and SA-PLS methods leading to anti-tubercular activity prediction. The influences of steric and electrostatic field effects generated by the contribution plots are discussed. The results indicate that SA is a very effective variable selection approach for such 3D-QSAR modeling.
The Impact of Varied Discrimination Parameters on Mixed-Format Item Response Theory Model Selection
Whittaker, Tiffany A.; Chang, Wanchen; Dodd, Barbara G.
2013-01-01
Whittaker, Chang, and Dodd compared the performance of model selection criteria when selecting among mixed-format IRT models and found that the criteria did not perform adequately when selecting the more parameterized models. It was suggested by M. S. Johnson that the problems when selecting the more parameterized models may be because of the low…
Martin-StPaul, N. K.; Ay, J. S.; Guillemot, J.; Doyen, L.; Leadley, P.
2014-12-01
Species distribution models (SDMs) are widely used to study and predict the outcome of global changes on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of applications on forest trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8km). We also compared the outputs of the SSDM with outputs of a classical SDM (i.e. Biomod ensemble modelling) in terms of bioclimatic response curves and potential distributions under current climate and climate change scenarios. The shapes of the bioclimatic response curves and the modelled species distribution maps differed markedly between SSDM and classical SDMs, with contrasted patterns according to species and spatial resolutions. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents
The Hierarchical Sparse Selection Model of Visual Crowding
Directory of Open Access Journals (Sweden)
Wesley eChaney
2014-09-01
Full Text Available Because the environment is cluttered, objects rarely appear in isolation. The visual system must therefore attentionally select behaviorally relevant objects from among many irrelevant ones. A limit on our ability to select individual objects is revealed by the phenomenon of visual crowding: an object seen in the periphery, easily recognized in isolation, can become impossible to identify when surrounded by other, similar objects. The neural basis of crowding is hotly debated: while prevailing theories hold that crowded information is irrecoverable – destroyed due to over-integration in early-stage visual processing – recent evidence demonstrates otherwise. Crowding can occur between high-level, configural object representations, and crowded objects can contribute with high precision to judgments about the gist of a group of objects, even when they are individually unrecognizable. While existing models can account for the basic diagnostic criteria of crowding (e.g. specific critical spacing, spatial anisotropies, and temporal tuning, no present model explains how crowding can operate simultaneously at multiple levels in the visual processing hierarchy, including at the level of whole objects. Here, we present a new model of visual crowding— the hierarchical sparse selection (HSS model, which accounts for object-level crowding, as well as a number of puzzling findings in the recent literature. Counter to existing theories, we posit that crowding occurs not due to degraded visual representations in the brain, but due to impoverished sampling of visual representations for the sake of perception. The HSS model unifies findings from a disparate array of visual crowding studies and makes testable predictions about how information in crowded scenes can be accessed.
The hierarchical sparse selection model of visual crowding.
Chaney, Wesley; Fischer, Jason; Whitney, David
2014-01-01
Because the environment is cluttered, objects rarely appear in isolation. The visual system must therefore attentionally select behaviorally relevant objects from among many irrelevant ones. A limit on our ability to select individual objects is revealed by the phenomenon of visual crowding: an object seen in the periphery, easily recognized in isolation, can become impossible to identify when surrounded by other, similar objects. The neural basis of crowding is hotly debated: while prevailing theories hold that crowded information is irrecoverable - destroyed due to over-integration in early stage visual processing - recent evidence demonstrates otherwise. Crowding can occur between high-level, configural object representations, and crowded objects can contribute with high precision to judgments about the "gist" of a group of objects, even when they are individually unrecognizable. While existing models can account for the basic diagnostic criteria of crowding (e.g., specific critical spacing, spatial anisotropies, and temporal tuning), no present model explains how crowding can operate simultaneously at multiple levels in the visual processing hierarchy, including at the level of whole objects. Here, we present a new model of visual crowding-the hierarchical sparse selection (HSS) model, which accounts for object-level crowding, as well as a number of puzzling findings in the recent literature. Counter to existing theories, we posit that crowding occurs not due to degraded visual representations in the brain, but due to impoverished sampling of visual representations for the sake of perception. The HSS model unifies findings from a disparate array of visual crowding studies and makes testable predictions about how information in crowded scenes can be accessed.
Updated Results for the Wake Vortex Inverse Model
Robins, Robert E.; Lai, David Y.; Delisi, Donald P.; Mellman, George R.
2008-01-01
NorthWest Research Associates (NWRA) has developed an Inverse Model for inverting aircraft wake vortex data. The objective of the inverse modeling is to obtain estimates of the vortex circulation decay and crosswind vertical profiles, using time history measurements of the lateral and vertical position of aircraft vortices. The Inverse Model performs iterative forward model runs using estimates of vortex parameters, vertical crosswind profiles, and vortex circulation as a function of wake age. Iterations are performed until a user-defined criterion is satisfied. Outputs from an Inverse Model run are the best estimates of the time history of the vortex circulation derived from the observed data, the vertical crosswind profile, and several vortex parameters. The forward model, named SHRAPA, used in this inverse modeling is a modified version of the Shear-APA model, and it is described in Section 2 of this document. Details of the Inverse Model are presented in Section 3. The Inverse Model was applied to lidar-observed vortex data at three airports: FAA acquired data from San Francisco International Airport (SFO) and Denver International Airport (DEN), and NASA acquired data from Memphis International Airport (MEM). The results are compared with observed data. This Inverse Model validation is documented in Section 4. A summary is given in Section 5. A user's guide for the inverse wake vortex model is presented in a separate NorthWest Research Associates technical report (Lai and Delisi, 2007a).
Fetal Intervention in Right Outflow Tract Obstructive Disease: Selection of Candidates and Results
Gómez Montes, E.; Herraiz, I.; Mendoza, A.; Galindo, A.
2012-01-01
Objectives. To describe the process of selection of candidates for fetal cardiac intervention (FCI) in fetuses diagnosed with pulmonary atresia-critical stenosis with intact ventricular septum (PA/CS-IVS) and report our own experience with FCI for such disease. Methods. We searched our database for cases of PA/CS-IVS prenatally diagnosed in 2003–2012. Data of 38 fetuses were retrieved and analyzed. FCI were offered to 6 patients (2 refused). In the remaining it was not offered due to the presence of either favourable prognostic echocardiographic markers (n = 20) or poor prognostic indicators (n = 12). Results. The outcome of fetuses with PA/CS-IVS was accurately predicted with multiparametric scoring systems. Pulmonary valvuloplasty was technically successful in all 4 fetuses. The growth of the fetal right heart and hemodynamic parameters showed a Gaussian-like behaviour with an improvement in the first weeks and slow worsening as pregnancy advanced, probably indicating a restenosis. Conclusions. The most likely type of circulation after birth may be predicted in the second trimester of pregnancy by means of combining cardiac dimensions and functional parameters. Fetal pulmonary valvuloplasty in midgestation is technically feasible and in well-selected cases may improve right heart growth, fetal hemodynamics, and postnatal outcome. PMID:22928144
Finite element model selection using Particle Swarm Optimization
Mthembu, Linda; Friswell, Michael I; Adhikari, Sondipon
2009-01-01
This paper proposes the application of particle swarm optimization (PSO) to the problem of finite element model (FEM) selection. This problem arises when a choice of the best model for a system has to be made from set of competing models, each developed a priori from engineering judgment. PSO is a population-based stochastic search algorithm inspired by the behaviour of biological entities in nature when they are foraging for resources. Each potentially correct model is represented as a particle that exhibits both individualistic and group behaviour. Each particle moves within the model search space looking for the best solution by updating the parameters values that define it. The most important step in the particle swarm algorithm is the method of representing models which should take into account the number, location and variables of parameters to be updated. One example structural system is used to show the applicability of PSO in finding an optimal FEM. An optimal model is defined as the model that has t...
Directory of Open Access Journals (Sweden)
Fernando Brito Lopes
Full Text Available This research evaluated a multivariate approach as an alternative tool for the purpose of selection regarding expected progeny differences (EPDs. Data were fitted using a multi-trait model and consisted of growth traits (birth weight and weights at 120, 210, 365 and 450 days of age and carcass traits (longissimus muscle area (LMA, back-fat thickness (BF, and rump fat thickness (RF, registered over 21 years in extensive breeding systems of Polled Nellore cattle in Brazil. Multivariate analyses were performed using standardized (zero mean and unit variance EPDs. The k mean method revealed that the best fit of data occurred using three clusters (k = 3 (P < 0.001. Estimates of genetic correlation among growth and carcass traits and the estimates of heritability were moderate to high, suggesting that a correlated response approach is suitable for practical decision making. Estimates of correlation between selection indices and the multivariate index (LD1 were moderate to high, ranging from 0.48 to 0.97. This reveals that both types of indices give similar results and that the multivariate approach is reliable for the purpose of selection. The alternative tool seems very handy when economic weights are not available or in cases where more rapid identification of the best animals is desired. Interestingly, multivariate analysis allowed forecasting information based on the relationships among breeding values (EPDs. Also, it enabled fine discrimination, rapid data summarization after genetic evaluation, and permitted accounting for maternal ability and the genetic direct potential of the animals. In addition, we recommend the use of longissimus muscle area and subcutaneous fat thickness as selection criteria, to allow estimation of breeding values before the first mating season in order to accelerate the response to individual selection.
A model of two-way selection system for human behavior.
Directory of Open Access Journals (Sweden)
Bin Zhou
Full Text Available Two-way selection is a common phenomenon in nature and society. It appears in the processes like choosing a mate between men and women, making contracts between job hunters and recruiters, and trading between buyers and sellers. In this paper, we propose a model of two-way selection system, and present its analytical solution for the expectation of successful matching total and the regular pattern that the matching rate trends toward an inverse proportion to either the ratio between the two sides or the ratio of the state total to the smaller group's people number. The proposed model is verified by empirical data of the matchmaking fairs. Results indicate that the model well predicts this typical real-world two-way selection behavior to the bounded error extent, thus it is helpful for understanding the dynamics mechanism of the real-world two-way selection system.
Whelan, Simon; Allen, James E; Blackburne, Benjamin P; Talavera, David
2015-01-01
Molecular phylogenetics is a powerful tool for inferring both the process and pattern of evolution from genomic sequence data. Statistical approaches, such as maximum likelihood and Bayesian inference, are now established as the preferred methods of inference. The choice of models that a researcher uses for inference is of critical importance, and there are established methods for model selection conditioned on a particular type of data, such as nucleotides, amino acids, or codons. A major limitation of existing model selection approaches is that they can only compare models acting upon a single type of data. Here, we extend model selection to allow comparisons between models describing different types of data by introducing the idea of adapter functions, which project aggregated models onto the originally observed sequence data. These projections are implemented in the program ModelOMatic and used to perform model selection on 3722 families from the PANDIT database, 68 genes from an arthropod phylogenomic data set, and 248 genes from a vertebrate phylogenomic data set. For the PANDIT and arthropod data, we find that amino acid models are selected for the overwhelming majority of alignments; with progressively smaller numbers of alignments selecting codon and nucleotide models, and no families selecting RY-based models. In contrast, nearly all alignments from the vertebrate data set select codon-based models. The sequence divergence, the number of sequences, and the degree of selection acting upon the protein sequences may contribute to explaining this variation in model selection. Our ModelOMatic program is fast, with most families from PANDIT taking fewer than 150 s to complete, and should therefore be easily incorporated into existing phylogenetic pipelines. ModelOMatic is available at https://code.google.com/p/modelomatic/.
A study of early stopping and model selection applied to the papermaking industry.
Edwards, P J; Murray, A F
2000-02-01
This paper addresses the issues of neural network model development and maintenance in the context of a complex task taken from the papermaking industry. In particular, it describes a comparison study of early stopping techniques and model selection, both to optimise neural network models for generalisation performance. The results presented here show that early stopping via use of a Bayesian model evidence measure is a viable way of optimising performance while also making maximum use of all the data. In addition, they show that ten-fold cross-validation performs well as a model selector and as an estimator of prediction accuracy. These results are important in that they show how neural network models may be optimally trained and selected for highly complex industrial tasks where the data are noisy and limited in number.
Generalised Chou-Yang model and recent results
Energy Technology Data Exchange (ETDEWEB)
Fazal-e-Aleem [International Centre for Theoretical Physics, Trieste (Italy); Rashid, H. [Punjab Univ., Lahore (Pakistan). Centre for High Energy Physics
1996-12-31
It is shown that most recent results of E710 and UA4/2 collaboration for the total cross section and {rho} together with earlier measurements give good agreement with measurements for the differential cross section at 546 and 1800 GeV within the framework of Generalised Chou-Yang model. These results are also compared with the predictions of other models. (author) 16 refs.
A Mean-Variance Hybrid-Entropy Model for Portfolio Selection with Fuzzy Returns
Directory of Open Access Journals (Sweden)
Rongxi Zhou
2015-05-01
Full Text Available In this paper, we define the portfolio return as fuzzy average yield and risk as hybrid-entropy and variance to deal with the portfolio selection problem with both random uncertainty and fuzzy uncertainty, and propose a mean-variance hybrid-entropy model (MVHEM. A multi-objective genetic algorithm named Non-dominated Sorting Genetic Algorithm II (NSGA-II is introduced to solve the model. We make empirical comparisons by using the data from the Shanghai and Shenzhen stock exchanges in China. The results show that the MVHEM generally performs better than the traditional portfolio selection models.
Directory of Open Access Journals (Sweden)
A. Pawliczek
2015-10-01
Full Text Available The presented paper deals with the issue of employment and other selected personnel attributes as employees’ affiliations, employees’ benefits, monitoring of employees’ satisfaction, monitoring of work productivity, investments into employees education and obstacles in hiring qualified human resources. The characteristics are benchmarked on the background of enterprise size based on the employees count in the year 2013. The relevant data were collected in Czech industrial enterprises, including metallurgical companies, with the help of university questionnaire research in order to induce synergy effect arising from mutual communication of academy-students-industry. The most important results are presented later in the paper, complemented with discussion based on relevant professional literature sources. The findings suggest that bigger companies check productivity and satisfaction and dismiss employees more frequently, unlike medium companies which do not reduce their workforce and solve the impact of crisis by decreased affiliations, reduced benefits and similar savings.
Directory of Open Access Journals (Sweden)
Ana Pilipović
2014-03-01
Full Text Available Additive manufacturing (AM is increasingly applied in the development projects from the initial idea to the finished product. The reasons are multiple, but what should be emphasised is the possibility of relatively rapid manufacturing of the products of complicated geometry based on the computer 3D model of the product. There are numerous limitations primarily in the number of available materials and their properties, which may be quite different from the properties of the material of the finished product. Therefore, it is necessary to know the properties of the product materials. In AM procedures the mechanical properties of materials are affected by the manufacturing procedure and the production parameters. During SLS procedures it is possible to adjust various manufacturing parameters which are used to influence the improvement of various mechanical and other properties of the products. The paper sets a new mathematical model to determine the influence of individual manufacturing parameters on the polymer product made by selective laser sintering. Old mathematical model is checked by statistical method with central composite plan and it is established that old mathematical model must be expanded with new parameter beam overlay ratio. Verification of new mathematical model and optimization of the processing parameters are made on SLS machine.
Institute of Scientific and Technical Information of China (English)
LIU Ai-hua; DONG Lei; DONG Long-jun
2010-01-01
An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.
Zhou, Yuan; Shi, Tie-Mao; Hu, Yuan-Man; Gao, Chang; Liu, Miao; Song, Lin-Qi
2011-12-01
Based on geographic information system (GIS) technology and multi-objective location-allocation (LA) model, and in considering of four relatively independent objective factors (population density level, air pollution level, urban heat island effect level, and urban land use pattern), an optimized location selection for the urban parks within the Third Ring of Shenyang was conducted, and the selection results were compared with the spatial distribution of existing parks, aimed to evaluate the rationality of the spatial distribution of urban green spaces. In the location selection of urban green spaces in the study area, the factor air pollution was most important, and, compared with single objective factor, the weighted analysis results of multi-objective factors could provide optimized spatial location selection of new urban green spaces. The combination of GIS technology with LA model would be a new approach for the spatial optimizing of urban green spaces.
Selectivity lists of pesticides to beneficial arthropods for IPM programs in carrot--first results.
Hautier, L; Jansen, J-P; Mabon, N; Schiffers, B
2005-01-01
In order to improve IPM programs in carrot, 7 fungicides, 12 herbicides and 9 insecticides commonly used in Belgium were tested for their toxicity towards five beneficial arthropods representative of most important natural enemies encountered in carrot: parasitic wasps - Aphidius rhopalosiphi (De Stefani-Perez) (Hym., Aphidiidae), ladybirds - Adalia bipunctata (L.) (Col., Coccinellidae), hoverfly - Episyrphus balteatus (Dipt.. Syrphidae), rove beetle - Aleochara bilineata (Col., Staphylinidae) and carabid beetle - Bembidion lampros (Col., Carabidae). Initialy, all plant protection products were tested on inert substrate glass plates or sand according to the insect. Products with a corrected mortality (CM) or a parasitism reduction (PR) lower than 30% were kept for the constitution of positive list (green list). The other compounds were further tested on plant for A. rhopalosiphi, A. bipunctata, E. balteatus and soil for B. lampros and A. bilineata. With these extended laboratory tests results, products were listed in toxicity class: green category [CM or PR carrot. Results showed that all fungicides tested were harmless to beneficials except Tebuconazole, which was slightly harmful for A. bipunctata. Herbicides were also harmless for soil beneficials, except Chlorpropham. This product was very toxic on sand towards A. bilineata and must be tested on soil. All soil insecticides tested were very toxic for ground beneficials and considered as non-selective. Their use in IPM is subject to questioning in view of negative impacts on beneficials. Among foliar insecticides, Dimethoate and Deltamethrin are not recommended for IPM because their high toxicity for all beneficials. The other foliar insecticides were more selective; any of them were harmless for all species tested.
Life cycle Prognostic Model Development and Initial Application Results
Energy Technology Data Exchange (ETDEWEB)
Jeffries, Brien; Hines, Wesley; Nam, Alan; Sharp, Michael; Upadhyaya, Belle [The University of Tennessee, Knoxville (United States)
2014-08-15
In order to obtain more accurate Remaining Useful Life (RUL) estimates based on empirical modeling, a Lifecycle Prognostics algorithm was developed that integrates various prognostic models. These models can be categorized into three types based on the type of data they process. The application of multiple models takes advantage of the most useful information available as the system or component operates through its lifecycle. The Lifecycle Prognostics is applied to an impeller test bed, and the initial results serve as a proof of concept.
A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling
Institute of Scientific and Technical Information of China (English)
Ouguan Xu; Yongfeng Fu; Hongye Su; Lijuan Li
2014-01-01
A selective moving window partial least squares (SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene (PX) content. Aiming at the high frequen-cy of model updating in previous recursive PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept unchanged. As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor. The performance of the proposed model is better as compared with that of other PLS-based model. The compro-mise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary, the proposed SMW-PLS method can deal with the slow time-varying processes effectively.
Gray comprehensive assessment and optimal selection of water consumption forecasting model
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A comprehensive assessing method based on the principle of the gray system theory and gray relational grade analysis was put forward to optimize water consumption forecasting models. The method provides a better accuracy for the assessment and the optimal selection of the water consumption forecasting models. The results show that the forecasting model built on this comprehensive assessing method presents better self-adaptability and accuracy in forecasting.
A Reliability Based Model for Wind Turbine Selection
Directory of Open Access Journals (Sweden)
A.K. Rajeevan
2013-06-01
Full Text Available A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.
Refined homology model of monoacylglycerol lipase: toward a selective inhibitor
Bowman, Anna L.; Makriyannis, Alexandros
2009-11-01
Monoacylglycerol lipase (MGL) is primarily responsible for the hydrolysis of 2-arachidonoylglycerol (2-AG), an endocannabinoid with full agonist activity at both cannabinoid receptors. Increased tissue 2-AG levels consequent to MGL inhibition are considered therapeutic against pain, inflammation, and neurodegenerative disorders. However, the lack of MGL structural information has hindered the development of MGL-selective inhibitors. Here, we detail a fully refined homology model of MGL which preferentially identifies MGL inhibitors over druglike noninhibitors. We include for the first time insight into the active-site geometry and potential hydrogen-bonding interactions along with molecular dynamics simulations describing the opening and closing of the MGL helical-domain lid. Docked poses of both the natural substrate and known inhibitors are detailed. A comparison of the MGL active-site to that of the other principal endocannabinoid metabolizing enzyme, fatty acid amide hydrolase, demonstrates key differences which provide crucial insight toward the design of selective MGL inhibitors as potential drugs.
POSSIBILISTIC SHARPE RATIO BASED NOVICE PORTFOLIO SELECTION MODELS
Directory of Open Access Journals (Sweden)
Rupak Bhattacharyya
2013-02-01
Full Text Available This paper uses the concept of possibilistic risk aversion to propose a new approach for portfolio selection in fuzzy environment. Using possibility theory, the possibilistic mean, variance, standard deviation and risk premium of a fuzzy number are established. Possibilistic Sharpe ratio is defined as the ratio of possibilistic risk premium and possibilistic standard deviation of a portfolio. The Sharpe ratio is a measure of the performance of the portfolio compared to the risk taken. The higher the Sharpe ratio, the better the performance of the portfolio is and the greater the profits of taking risk. New models of fuzzy portfolio selection considering the possibilistic Sharpe ratio, return and skewness of the portfolio are considered. The feasibility and effectiveness of the proposed method is illustrated by numerical example extracted from Bombay Stock Exchange (BSE, India and is solved by multiple objective genetic algorithm (MOGA.
Guan, Huade; Bestland, Erick; Zhu, Chuanyu; Zhu, Honglin; Albertsdottir, Dora; Hutson, John; Simmons, Craig T; Ginic-Markovic, Milena; Tao, Xian; Ellis, Amanda V
2010-11-15
Surfactant modified zeolites (SMZs) have the capacity to target various types of water contaminants at relatively low cost and thus are being increasingly considered for use in improving water quality. It is important to know the surfactant loading performance of a zeolite before it is put into application. In this work we compare the loading capacity of a surfactant, hexadecyltrimethylammonium bromide (HDTMA-Br), onto four natural zeolites obtained from specific locations in the USA, Croatia, China, and Australia. The surfactant loading is examined using thermogravimetric analysis (TGA), Fourier transform infrared (FT-IR) spectroscopy, and Raman spectroscopy. We then compare the resulting SMZs performance in removing nitrate from water. Results show that TGA is useful to determine the HDTMA loading capacity on natural zeolites. It is also useful to distinguish between a HDTMA bi-layer and a HDTMA mono-layer on the SMZ surface, which has not been previously reported in the literature. TGA results infer that HDTMA (bi-layer) loading decreases in the order of US zeolite>Croatian zeolite>Chinese zeolite>Australian zeolite. This order of loading explains variation in performance of nitrate removal between the four SMZs. The SMZs remove 8-18 times more nitrate than the raw zeolites. SMZs prepared from the selected US and Croatian zeolites were more efficient in nitrate removal than the two zeolites commercially obtained from Australia and China.
ARCS, The Arcminute Radio Cluster-lens Search - I. Selection Criteria and Initial Results
Phillips, P M; Wilkinson, P N
2000-01-01
We present the results of an unbiased radio search for gravitational lensing events with image separations between 15 and 60 arcsec, which would be associated with clusters of galaxies with masses >10^{13-14}M_{\\sun}. A parent population of 1023 extended radio sources stronger than 35 mJy with stellar optical identifications was selected using the FIRST radio catalogue at 1.4 GHz and the APM optical catalogue. The FIRST catalogue was then searched for companions to the parent sources stronger than 7 mJy and with separation in the range 15 to 60 arcsec. Higher resolution observations of the resulting 38 lens candidates were made with the VLA at 1.4 GHz and 5 GHz, and with MERLIN at 5 GHz in order to test the lens hypothesis in each case. None of our targets was found to be a gravitational lens system. These results provide the best current constraint on the lensing rate for this angular scale, but improved calculations of lensing rates from realistic simulations of the clustering of matter on the relevant scal...
Janczarek, I; Bereznowski, A; Strzelec, K
2013-01-01
The aim of the study was to define the influence of the selected factors (gender, age, transportation time, riding distance and air temperature during the ride) on the cortisol secretion and finding a correlation between the hormone level and the horses' sport results (veterinary parameters and the ride route parameters). The research was performed on 38 Arabian pure breed horses taking part in the endurance rides. The cortisol level was measured with enzyme-immunological method in saliva samples, taken four times from each horse. In order to verify the differences between the mean results the repeated measures design was applied. The significance of the differences between the mean values was determined by the Tukey test. To evaluate the interrelations between the analysed attributes Pearson's correlation analysis was applied. The cortisol level at rest was not affected by any of the analysed factors. In case of other results, the most significant influence (P cortisol level was noted in mares, horses running the longest distances and at the highest temperatures. A significant increase in the cortisol level was noted when the ride distance was longer. There were no clear correlation between the adrenal cortex activity and the veterinary parameters at different riding speed. High cortisol concentration can negatively affect the heart rate (HR) by increasing it, but it can simultaneously stimulate the body to fight dehydration.
A Dual-Stage Two-Phase Model of Selective Attention
Hubner, Ronald; Steinhauser, Marco; Lehle, Carola
2010-01-01
The dual-stage two-phase (DSTP) model is introduced as a formal and general model of selective attention that includes both an early and a late stage of stimulus selection. Whereas at the early stage information is selected by perceptual filters whose selectivity is relatively limited, at the late stage stimuli are selected more efficiently on a…
glmulti: An R Package for Easy Automated Model Selection with (Generalized Linear Models
Directory of Open Access Journals (Sweden)
Vincent Calcagno
2010-10-01
Full Text Available We introduce glmulti, an R package for automated model selection and multi-model inference with glm and related functions. From a list of explanatory variables, the provided function glmulti builds all possible unique models involving these variables and, optionally, their pairwise interactions. Restrictions can be specified for candidate models, by excluding specific terms, enforcing marginality, or controlling model complexity. Models are fitted with standard R functions like glm. The n best models and their support (e.g., (QAIC, (QAICc, or BIC are returned, allowing model selection and multi-model inference through standard R functions. The package is optimized for large candidate sets by avoiding memory limitation, facilitating parallelization and providing, in addition to exhaustive screening, a compiled genetic algorithm method. This article briefly presents the statistical framework and introduces the package, with applications to simulated and real data.
Crow, Cassi L.; Wilson, Jennifer T.; Kunz, James L.
2016-12-01
IntroductionThe Alazán, Apache, Martínez, and San Pedro Creeks in San Antonio, Texas, are part of a network of urban tributaries to the San Antonio River, known locally as the Westside Creeks. The Westside Creeks flow through some of the oldest neighborhoods in San Antonio. The disruption of streambed sediment is anticipated during a planned restoration to improve and restore the environmental condition of 14 miles of channelized sections of the Westside Creeks in San Antonio. These construction activities can create the potential to reintroduce chemicals found in the sediments into the ecosystem where, depending on hydrologic and environmental conditions, they could become bioavailable and toxic to aquatic life. Elevated concentrations of sediment-associated contaminants often are measured in urban areas such as San Antonio, Tex. Contaminants found in sediment can affect the health of aquatic organisms that ingest sediment. The gradual accumulation of trace elements and organic compounds in aquatic organisms can cause various physiological issues and can ultimately result in death of the aquatic organisms; in addition, subsequent ingestion of aquatic organisms can transfer the accumulated contaminants upward through the food chain (a process called biomagnification).The U.S. Geological Survey, in cooperation with the San Antonio River Authority, collected sediment samples and water samples for toxicity testing from sites on the Westside Creeks as part of an initial characterization of selected contaminants in the study area. Samples were collected in January 2014 during base-flow conditions and again in May 2104 after a period of stormwater runoff (poststorm conditions). Sediment samples were analyzed for selected constituents, including trace elements and organic contaminants such as pesticides, brominated flame retardants, polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). In addition, as an indicator of ecological health (and
Mocali, Stefano; Fabiani, Arturo; Butti, Fabrizio; De Meo, Isabella; Bianchetto, Elisa; Landi, Silvia; Montini, Piergiuseppe; Samaden, Stefano; Cantiani, Paolo
2016-04-01
The decay of forest cover and soil erosion is a consequence of continual intensive forest exploitation, such as grazing and wildfires over the centuries. From the end of the eighteenth century up to the mid-1900s, black pine plantations were established throughout the Apennines' range in Italy, to improve forest soil quality. The main aim of this reafforestation was to re-establish the pine as a first cover, pioneer species. A series of thinning activities were therefore planned by foresters when these plantations were designed. The project Selpibiolife (LIFE13 BIO/IT/000282) has the main objective to demonstrate the potential of an innovative silvicultural treatment to enhance soil biodiversity under black pine stands. The monitoring will be carried out by comparing selective and traditional thinning methods (selecting trees from below leaving well-spaced, highest-quality trees) to areas without any silvicultural treatments (e.g. weeding, cleaning, liberation cutting). The monitoring survey was carried out in Pratomagno and Amiata Val D'Orcia areas on the Appennines (Italy) and involved different biotic levels: microorganisms, mesofauna, nematodes and macrofauna (Coleoptera). The results displayed a significant difference between the overall biodiversity of the two areas. In particular, microbial diversity assessed by both biochemical (microbial biomass, microbial respiration, metabolic quotient) and molecular (PCR-DGGE) approaches highlighted different a composition and activity of microbial communities within the two areas before thinning. Furthermore, little but significant differences were observed for mesofauna and nematode community as well which displayed a higher diversity level in Amiata areas compared to Pratomagno. In contrast, Coleoptera showed higher richness values in Pratomagno, where the wood degrader Nebria tibialis specie dominated, compared to Amiata. As expected, a general degraded biodiversity was observed in both areas before thinning.
Selection between foreground models for global 21-cm experiments
Harker, Geraint
2015-01-01
The precise form of the foregrounds for sky-averaged measurements of the 21-cm line during and before the epoch of reionization is unknown. We suggest that the level of complexity in the foreground models used to fit global 21-cm data should be driven by the data, under a Bayesian model selection methodology. A first test of this approach is carried out by applying nested sampling to simplified models of global 21-cm data to compute the Bayesian evidence for the models. If the foregrounds are assumed to be polynomials of order n in log-log space, we can infer the necessity to use n=4 rather than n=3 with <2h of integration with limited frequency coverage, for reasonable values of the n=4 coefficient. Using a higher-order polynomial does not necessarily prevent a significant detection of the 21-cm signal. Even for n=8, we can obtain very strong evidence distinguishing a reasonable model for the signal from a null model with 128h of integration. More subtle features of the signal may, however, be lost if the...
Selection Strategies for Social Influence in the Threshold Model
Karampourniotis, Panagiotis; Szymanski, Boleslaw; Korniss, Gyorgy
The ubiquity of online social networks makes the study of social influence extremely significant for its applications to marketing, politics and security. Maximizing the spread of influence by strategically selecting nodes as initiators of a new opinion or trend is a challenging problem. We study the performance of various strategies for selection of large fractions of initiators on a classical social influence model, the Threshold model (TM). Under the TM, a node adopts a new opinion only when the fraction of its first neighbors possessing that opinion exceeds a pre-assigned threshold. The strategies we study are of two kinds: strategies based solely on the initial network structure (Degree-rank, Dominating Sets, PageRank etc.) and strategies that take into account the change of the states of the nodes during the evolution of the cascade, e.g. the greedy algorithm. We find that the performance of these strategies depends largely on both the network structure properties, e.g. the assortativity, and the distribution of the thresholds assigned to the nodes. We conclude that the optimal strategy needs to combine the network specifics and the model specific parameters to identify the most influential spreaders. Supported in part by ARL NS-CTA, ARO, and ONR.
Selection of models to calculate the LLW source term
Energy Technology Data Exchange (ETDEWEB)
Sullivan, T.M. (Brookhaven National Lab., Upton, NY (United States))
1991-10-01
Performance assessment of a LLW disposal facility begins with an estimation of the rate at which radionuclides migrate out of the facility (i.e., the source term). The focus of this work is to develop a methodology for calculating the source term. In general, the source term is influenced by the radionuclide inventory, the wasteforms and containers used to dispose of the inventory, and the physical processes that lead to release from the facility (fluid flow, container degradation, wasteform leaching, and radionuclide transport). In turn, many of these physical processes are influenced by the design of the disposal facility (e.g., infiltration of water). The complexity of the problem and the absence of appropriate data prevent development of an entirely mechanistic representation of radionuclide release from a disposal facility. Typically, a number of assumptions, based on knowledge of the disposal system, are used to simplify the problem. This document provides a brief overview of disposal practices and reviews existing source term models as background for selecting appropriate models for estimating the source term. The selection rationale and the mathematical details of the models are presented. Finally, guidance is presented for combining the inventory data with appropriate mechanisms describing release from the disposal facility. 44 refs., 6 figs., 1 tab.
A Successive Selection Method for finite element model updating
Gou, Baiyong; Zhang, Weijie; Lu, Qiuhai; Wang, Bo
2016-03-01
Finite Element (FE) model can be updated effectively and efficiently by using the Response Surface Method (RSM). However, it often involves performance trade-offs such as high computational cost for better accuracy or loss of efficiency for lots of design parameter updates. This paper proposes a Successive Selection Method (SSM), which is based on the linear Response Surface (RS) function and orthogonal design. SSM rewrites the linear RS function into a number of linear equations to adjust the Design of Experiment (DOE) after every FE calculation. SSM aims to interpret the implicit information provided by the FE analysis, to locate the Design of Experiment (DOE) points more quickly and accurately, and thereby to alleviate the computational burden. This paper introduces the SSM and its application, describes the solution steps of point selection for DOE in detail, and analyzes SSM's high efficiency and accuracy in the FE model updating. A numerical example of a simply supported beam and a practical example of a vehicle brake disc show that the SSM can provide higher speed and precision in FE model updating for engineering problems than traditional RSM.
Selection Experiments in the Penna Model for Biological Aging
Medeiros, G.; Idiart, M. A.; de Almeida, R. M. C.
We consider the Penna model for biological aging to investigate correlations between early fertility and late life survival rates in populations at equilibrium. We consider inherited initial reproduction ages together with a reproduction cost translated in a probability that mother and offspring die at birth, depending on the mother age. For convenient sets of parameters, the equilibrated populations present genetic variability in what regards both genetically programmed death age and initial reproduction age. In the asexual Penna model, a negative correlation between early life fertility and late life survival rates naturally emerges in the stationary solutions. In the sexual Penna model, selection experiments are performed where individuals are sorted by initial reproduction age from the equilibrated populations and the separated populations are evolved independently. After a transient, a negative correlation between early fertility and late age survival rates also emerges in the sense that populations that start reproducing earlier present smaller average genetically programmed death age. These effects appear due to the age structure of populations in the steady state solution of the evolution equations. We claim that the same demographic effects may be playing an important role in selection experiments in the laboratory.
The Animal Model Determines the Results of Aeromonas Virulence Factors
Romero, Alejandro; Saraceni, Paolo R.; Merino, Susana; Figueras, Antonio; Tomás, Juan M.; Novoa, Beatriz
2016-01-01
The selection of an experimental animal model is of great importance in the study of bacterial virulence factors. Here, a bath infection of zebrafish larvae is proposed as an alternative model to study the virulence factors of Aeromonas hydrophila. Intraperitoneal infections in mice and trout were compared with bath infections in zebrafish larvae using specific mutants. The great advantage of this model is that bath immersion mimics the natural route of infection, and injury to the tail also provides a natural portal of entry for the bacteria. The implication of T3SS in the virulence of A. hydrophila was analyzed using the AH-1::aopB mutant. This mutant was less virulent than the wild-type strain when inoculated into zebrafish larvae, as described in other vertebrates. However, the zebrafish model exhibited slight differences in mortality kinetics only observed using invertebrate models. Infections using the mutant AH-1ΔvapA lacking the gene coding for the surface S-layer suggested that this protein was not totally necessary to the bacteria once it was inside the host, but it contributed to the inflammatory response. Only when healthy zebrafish larvae were infected did the mutant produce less mortality than the wild-type. Variations between models were evidenced using the AH-1ΔrmlB, which lacks the O-antigen lipopolysaccharide (LPS), and the AH-1ΔwahD, which lacks the O-antigen LPS and part of the LPS outer-core. Both mutants showed decreased mortality in all of the animal models, but the differences between them were only observed in injured zebrafish larvae, suggesting that residues from the LPS outer core must be important for virulence. The greatest differences were observed using the AH-1ΔFlaB-J (lacking polar flagella and unable to swim) and the AH-1::motX (non-motile but producing flagella). They were as pathogenic as the wild-type strain when injected into mice and trout, but no mortalities were registered in zebrafish larvae. This study demonstrates
Institute of Scientific and Technical Information of China (English)
郑勋; 李海鹰; 孟令云; 许心越; 陈旭
2015-01-01
An improved social force model based on exit selection is proposed to simulate pedestrians’ microscopic behaviors in subway station. The modification lies in considering three factors of spatial distance, occupant density and exit width. In addition, the problem of pedestrians selecting exit frequently is solved as follows: not changing to other exits in the affected area of one exit, using the probability of remaining preceding exit and invoking function of exit selection after several simulation steps. Pedestrians in subway station have some special characteristics, such as explicit destinations, different familiarities with subway station. Finally, Beijing Zoo Subway Station is taken as an example and the feasibility of the model results is verified through the comparison of the actual data and simulation data. The simulation results show that the improved model can depict the microscopic behaviors of pedestrians in subway station.
Directory of Open Access Journals (Sweden)
João Costa E Silva
Full Text Available Using native trees from near the northern and southern extremities of the relatively continuous eastern distribution of Eucalyptus globulus in Tasmania, we compared the progenies derived from natural open-pollination (OP with those generated from within-region and long-distance outcrossing. Controlled outcrossing amongst eight parents - with four parents from each of the northern and southern regions - was undertaken using a diallel mating scheme. The progeny were planted in two field trials located within the species native range in southern Tasmania, and their survival and diameter growth were monitored over a 13-year-period. The survival and growth performances of all controlled cross types exceeded those of the OP progenies, consistent with inbreeding depression due to a combination of selfing and bi-parental inbreeding. The poorer survival of the northern regional (♀N♂N outcrosses compared with the local southern regional outcrosses (♀S♂S indicated differential selection against the former. Despite this mal-adaptation of the non-local ♀N♂N crosses at both southern sites, the survival of the inter-regional hybrids (♀N♂S and ♀S♂N was never significantly different from that of the local ♀S♂S crosses. Significant site-dependent heterosis was detected for the growth of the surviving long-distance hybrids. This was expressed as mid-parent heterosis, particularly at the more northern planting site. Heterosis increased with age, while the difference between the regional ♀N♂N and ♀S♂S crosses remained insignificant at any age at either site. Nevertheless, the results for growth suggest that the fitness of individuals derived from long-distance crossing may be better at the more northern of the planting sites. Our results demonstrate the potential for early-age assessments of pollen dispersal to underestimate realised gene flow, with local inbreeding under natural open-pollination resulting in selection favouring the
A qualitative model structure sensitivity analysis method to support model selection
Van Hoey, S.; Seuntjens, P.; van der Kwast, J.; Nopens, I.
2014-11-01
The selection and identification of a suitable hydrological model structure is a more challenging task than fitting parameters of a fixed model structure to reproduce a measured hydrograph. The suitable model structure is highly dependent on various criteria, i.e. the modeling objective, the characteristics and the scale of the system under investigation and the available data. Flexible environments for model building are available, but need to be assisted by proper diagnostic tools for model structure selection. This paper introduces a qualitative method for model component sensitivity analysis. Traditionally, model sensitivity is evaluated for model parameters. In this paper, the concept is translated into an evaluation of model structure sensitivity. Similarly to the one-factor-at-a-time (OAT) methods for parameter sensitivity, this method varies the model structure components one at a time and evaluates the change in sensitivity towards the output variables. As such, the effect of model component variations can be evaluated towards different objective functions or output variables. The methodology is presented for a simple lumped hydrological model environment, introducing different possible model building variations. By comparing the effect of changes in model structure for different model objectives, model selection can be better evaluated. Based on the presented component sensitivity analysis of a case study, some suggestions with regard to model selection are formulated for the system under study: (1) a non-linear storage component is recommended, since it ensures more sensitive (identifiable) parameters for this component and less parameter interaction; (2) interflow is mainly important for the low flow criteria; (3) excess infiltration process is most influencing when focussing on the lower flows; (4) a more simple routing component is advisable; and (5) baseflow parameters have in general low sensitivity values, except for the low flow criteria.
Institute of Scientific and Technical Information of China (English)
Thomas Walcher; Bernhard Otto Boehm; Wolfgang Kratzer; Mark Martin Haenle; Martina Kron; Birgit Hay; Richard Andrew Mason; Alexa Friederike Alice von Schmiesing; Armin Imhof; Wolfgang Koenig; Peter Kern
2005-01-01
AIM: To investigate the prevalence, risk factors, and selection of the study population for cholecystolithiasis in an urban population in Germany, in relation to our own findings and to the results in the international literature.METHODS: A total of 2 147 persons (1 111 females,age 42.8±12.7 years; 1 036 males, age 42.3±13.1 years)participating in an investigation on the prevalence of Echinococcus multilocularis were studied for risk factors and prevalence of gallbladder stone disease.Risk factors were assessed by means of a standardized interview and calculation of body mass index (BMI). A diagnostic ultrasound examination of the gallbladder was performed. Data were analyzed by multiple logistic regression, using the SAS statistical software package.RESULTS: Gallbladder stones were detected in 171study participants (8.0%, n = 2 147). Risk factors for the development of gallbladder stone disease included age, sex, BMI, and positive family history. In a separate analysis of female study participants, pregnancy (yes/no)and number of pregnancies did not exert any influence.CONCLUSION: Findings of the present study confirm that age, female sex, BMI, and positive family history are risk factors for the development of gallbladder stone disease. Pregnancy and the number of pregnancies,however, could not be shown to be risk factors. There seem to be no differences in the respective prevalence for gallbladder stone disease in urban and rural populations.
Nirunsuksiri, W; Presland, R B; Brumbaugh, S G; Dale, B A; Fleckman, P
1995-01-13
Ichthyosis vulgaris is an autosomal dominant disorder of keratinization characterized by mild hyperkeratosis and reduced or absent keratohyalin granules in the epidermis. Profilaggrin, a major component of keratohyalin granules, is reduced or absent from the skin of individuals with ichthyosis vulgaris. In this report, we have further characterized the molecular basis of low profilaggrin expression, which occurs in this disease. In situ hybridization revealed little profilaggrin mRNA in ichthyosis vulgaris-affected epidermis. In keratinocytes cultured from the epidermis of affected individuals, the abundance of profilaggrin was reduced to less than 10% of normal controls, while the mRNA level was decreased to 30-60% of controls. Expression of K1 and loricrin, other markers of epidermal differentiation, were not affected. Nuclear run-on assays indicated that the decrease in mRNA levels was not caused by aberrant transcription. Nucleotide sequencing of 5'-upstream, 3'-non-coding, and flanking regions of the profilaggrin gene from ichthyosis vulgaris-affected individuals revealed only minor changes, probably due to genetic polymorphisms. Our results indicate that defective profilaggrin expression in ichthyosis vulgaris is a result of selectively impaired posttranscriptional control.
Observing with a space-borne gamma-ray telescope: selected results from INTEGRAL
Schanne, S
2006-01-01
The International Gamma-Ray Astrophysics Laboratory, i.e. the INTEGRAL satellite of ESA, in orbit since about 3 years, performs gamma-ray observations of the sky in the 15 keV to 8 MeV energy range. Thanks to its imager IBIS, and in particular the ISGRI detection plane based on 16384 CdTe pixels, it achieves an excellent angular resolution (12 arcmin) for point source studies with good continuum spectrum sensitivity. Thanks to its spectrometer SPI, based on 19 germanium detectors maintained at 85 K by a cryogenic system, located inside an active BGO veto shield, it achieves excellent spectral resolution of about 2 keV for 1 MeV photons, which permits astrophysical gamma-ray line studies with good narrow-line sensitivity. In this paper we review some goals of gamma-ray astronomy from space and present the INTEGRAL satellite, in particular its instruments ISGRI and SPI. Ground and in-flight calibration results from SPI are presented, before presenting some selected astrophysical results from INTEGRAL. In partic...
Parametric pattern selection in a reaction-diffusion model.
Directory of Open Access Journals (Sweden)
Michael Stich
Full Text Available We compare spot patterns generated by Turing mechanisms with those generated by replication cascades, in a model one-dimensional reaction-diffusion system. We determine the stability region of spot solutions in parameter space as a function of a natural control parameter (feed-rate where degenerate patterns with different numbers of spots coexist for a fixed feed-rate. While it is possible to generate identical patterns via both mechanisms, we show that replication cascades lead to a wider choice of pattern profiles that can be selected through a tuning of the feed-rate, exploiting hysteresis and directionality effects of the different pattern pathways.
Wyss, Richard; Girman, Cynthia J; LoCasale, Robert J; Brookhart, Alan M; Stürmer, Til
2013-01-01
It is often preferable to simplify the estimation of treatment effects on multiple outcomes by using a single propensity score (PS) model. Variable selection in PS models impacts the efficiency and validity of treatment effects. However, the impact of different variable selection strategies on the estimated treatment effects in settings involving multiple outcomes is not well understood. The authors use simulations to evaluate the impact of different variable selection strategies on the bias and precision of effect estimates to provide insight into the performance of various PS models in settings with multiple outcomes. Simulated studies consisted of dichotomous treatment, two Poisson outcomes, and eight standard-normal covariates. Covariates were selected for the PS models based on their effects on treatment, a specific outcome, or both outcomes. The PSs were implemented using stratification, matching, and weighting (inverse probability treatment weighting). PS models including only covariates affecting a specific outcome (outcome-specific models) resulted in the most efficient effect estimates. The PS model that only included covariates affecting either outcome (generic-outcome model) performed best among the models that simultaneously controlled measured confounding for both outcomes. Similar patterns were observed over the range of parameter values assessed and all PS implementation methods. A single, generic-outcome model performed well compared with separate outcome-specific models in most scenarios considered. The results emphasize the benefit of using prior knowledge to identify covariates that affect the outcome when constructing PS models and support the potential to use a single, generic-outcome PS model when multiple outcomes are being examined. Copyright © 2012 John Wiley & Sons, Ltd.
Meteorological Uncertainty of atmospheric Dispersion model results (MUD)
DEFF Research Database (Denmark)
Havskov Sørensen, Jens; Amstrup, Bjarne; Feddersen, Henrik
The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario...... of the meteorological model results. These uncertainties stem from e.g. limits in meteorological obser-vations used to initialise meteorological forecast series. By perturbing the initial state of an NWP model run in agreement with the available observa-tional data, an ensemble of meteorological forecasts is produced....... However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties...
Meteorological Uncertainty of atmospheric Dispersion model results (MUD)
DEFF Research Database (Denmark)
Havskov Sørensen, Jens; Amstrup, Bjarne; Feddersen, Henrik
The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the ‘most likely...... uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing e.g. the initial state of an NWP model run in agreement with the available observational data, an ensemble......’ dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for long-range atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent...
Mathematical Existence Results for the Doi-Edwards Polymer Model
Chupin, Laurent
2017-01-01
In this paper, we present some mathematical results on the Doi-Edwards model describing the dynamics of flexible polymers in melts and concentrated solutions. This model, developed in the late 1970s, has been used and extensively tested in modeling and simulation of polymer flows. From a mathematical point of view, the Doi-Edwards model consists in a strong coupling between the Navier-Stokes equations and a highly nonlinear constitutive law. The aim of this article is to provide a rigorous proof of the well-posedness of the Doi-Edwards model, namely that it has a unique regular solution. We also prove, which is generally much more difficult for flows of viscoelastic type, that the solution is global in time in the two dimensional case, without any restriction on the smallness of the data.
Linear regression model selection using p-values when the model dimension grows
Pokarowski, Piotr; Teisseyre, Paweł
2012-01-01
We consider a new criterion-based approach to model selection in linear regression. Properties of selection criteria based on p-values of a likelihood ratio statistic are studied for families of linear regression models. We prove that such procedures are consistent i.e. the minimal true model is chosen with probability tending to 1 even when the number of models under consideration slowly increases with a sample size. The simulation study indicates that introduced methods perform promisingly when compared with Akaike and Bayesian Information Criteria.
A selection model for accounting for publication bias in a full network meta-analysis.
Mavridis, Dimitris; Welton, Nicky J; Sutton, Alex; Salanti, Georgia
2014-12-30
Copas and Shi suggested a selection model to explore the potential impact of publication bias via sensitivity analysis based on assumptions for the probability of publication of trials conditional on the precision of their results. Chootrakool et al. extended this model to three-arm trials but did not fully account for the implications of the consistency assumption, and their model is difficult to generalize for complex network structures with more than three treatments. Fitting these selection models within a frequentist setting requires maximization of a complex likelihood function, and identification problems are common. We have previously presented a Bayesian implementation of the selection model when multiple treatments are compared with a common reference treatment. We now present a general model suitable for complex, full network meta-analysis that accounts for consistency when adjusting results for publication bias. We developed a design-by-treatment selection model to describe the mechanism by which studies with different designs (sets of treatments compared in a trial) and precision may be selected for publication. We fit the model in a Bayesian setting because it avoids the numerical problems encountered in the frequentist setting, it is generalizable with respect to the number of treatments and study arms, and it provides a flexible framework for sensitivity analysis using external knowledge. Our model accounts for the additional uncertainty arising from publication bias more successfully compared to the standard Copas model or its previous extensions. We illustrate the methodology using a published triangular network for the failure of vascular graft or arterial patency.
Modeling Results for the ITER Cryogenic Fore Pump
Zhang, Dongsheng
The work presented here is the analysis and modeling of the ITER-Cryogenic Fore Pump (CFP), also called Cryogenic Viscous Compressor (CVC). Unlike common cryopumps that are usually used to create and maintain vacuum, the cryogenic fore pump is designed for ITER to collect and compress hydrogen isotopes during the regeneration process of the torus cryopumps. Different from common cryopumps, the ITER-CFP works in the viscous flow regime. As a result, both adsorption boundary conditions and transport phenomena contribute unique features to the pump performance. In this report, the physical mechanisms of cryopumping are studied, especially the diffusion-adsorption process and these are coupled with the standard equations of species, momentum and energy balance, as well as the equation of state. Numerical models are developed, which include highly coupled non-linear conservation equations of species, momentum, and energy and equation of state. Thermal and kinetic properties are treated as functions of temperature, pressure, and composition of the gas fluid mixture. To solve such a set of equations, a novel numerical technique, identified as the Group-Member numerical technique is proposed. This document presents three numerical models: a transient model, a steady state model, and a hemisphere (or molecular flow) model. The first two models are developed based on analysis of the raw experimental data while the third model is developed as a preliminary study. The modeling results are compared with available experiment data for verification. The models can be used for cryopump design, and can also benefit problems, such as loss of vacuum in a cryomodule or cryogenic desublimation. The scientific and engineering investigation being done here builds connections between Mechanical Engineering and other disciplines, such as Chemical Engineering, Physics, and Chemistry.
Comparison of NASCAP modelling results with lumped circuit analysis
Stang, D. B.; Purvis, C. K.
1980-01-01
Engineering design tools that can be used to predict the development of absolute and differential potentials by realistic spacecraft under geomagnetic substorm conditions are described. Two types of analyses are in use: (1) the NASCAP code, which computes quasistatic charging of geometrically complex objects with multiple surface materials in three dimensions; (2) lumped element equivalent circuit models that are used for analyses of particular spacecraft. The equivalent circuit models require very little computation time, however, they cannot account for effects, such as the formation of potential barriers, that are inherently multidimensional. Steady state potentials of structure and insulation are compared with those resulting from the equivalent circuit model.
The East model: recent results and new progresses
Faggionato, Alessandra; Roberto, Cyril; Toninelli, Cristina
2012-01-01
The East model is a particular one dimensional interacting particle system in which certain transitions are forbidden according to some constraints depending on the configuration of the system. As such it has received particular attention in the physics literature as a special case of a more general class of systems referred to as kinetically constrained models, which play a key role in explaining some features of the dynamics of glasses. In this paper we give an extensive overview of recent rigorous results concerning the equilibrium and non-equilibrium dynamics of the East model together with some new improvements.
Constraining hybrid inflation models with WMAP three-year results
Cardoso, A
2006-01-01
We reconsider the original model of quadratic hybrid inflation in light of the WMAP three-year results and study the possibility of obtaining a spectral index of primordial density perturbations, $n_s$, smaller than one from this model. The original hybrid inflation model naturally predicts $n_s\\geq1$ in the false vacuum dominated regime but it is also possible to have $n_s<1$ when the quadratic term dominates. We therefore investigate whether there is also an intermediate regime compatible with the latest constraints, where the scalar field value during the last 50 e-folds of inflation is less than the Planck scale.
A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios
Yue, Wei; Wang, Yuping
2017-01-01
Due to the important effect of the higher order moments to portfolio returns, the aim of this paper is to make use of the third and fourth moments for fuzzy multi-objective portfolio selection model. Firstly, in order to overcome the low diversity of the obtained solution set and lead to corner solutions for the conventional higher moment portfolio selection models, a new entropy function based on Minkowski measure is proposed as a new objective function and a novel fuzzy multi-objective weighted possibilistic higher order moment portfolio model is presented. Secondly, to solve the proposed model efficiently, a new multi-objective evolutionary algorithm is designed. Thirdly, several portfolio performance evaluation techniques are used to evaluate the performance of the portfolio models. Finally, some experiments are conducted by using the data of Shanghai Stock Exchange and the results indicate the efficiency and effectiveness of the proposed model and algorithm.
Recent MEG Results and Predictive SO(10) Models
Fukuyama, Takeshi
2011-01-01
Recent MEG results of a search for the lepton flavor violating (LFV) muon decay, $\\mu \\to e \\gamma$, show 3 events as the best value for the number of signals in the maximally likelihood fit. Although this result is still far from the evidence/discovery in statistical point of view, it might be a sign of a certain new physics beyond the Standard Model. As has been well-known, supersymmetric (SUSY) models can generate the $\\mu \\to e \\gamma$ decay rate within the search reach of the MEG experiment. A certain class of SUSY grand unified theory (GUT) models such as the minimal SUSY SO(10) model (we call this class of models "predictive SO(10) models") can unambiguously determine fermion Yukawa coupling matrices, in particular, the neutrino Dirac Yukawa matrix. Based on the universal boundary conditions for soft SUSY breaking parameters at the GUT scale, we calculate the rate of the $\\mu \\to e \\gamma$ process by using the completely determined Dirac Yukawa matrix in two examples of predictive SO(10) models. If we ...
BUILDING ROBUST APPEARANCE MODELS USING ON-LINE FEATURE SELECTION
Energy Technology Data Exchange (ETDEWEB)
PORTER, REID B. [Los Alamos National Laboratory; LOVELAND, ROHAN [Los Alamos National Laboratory; ROSTEN, ED [Los Alamos National Laboratory
2007-01-29
In many tracking applications, adapting the target appearance model over time can improve performance. This approach is most popular in high frame rate video applications where latent variables, related to the objects appearance (e.g., orientation and pose), vary slowly from one frame to the next. In these cases the appearance model and the tracking system are tightly integrated, and latent variables are often included as part of the tracking system's dynamic model. In this paper we describe our efforts to track cars in low frame rate data (1 frame/second) acquired from a highly unstable airborne platform. Due to the low frame rate, and poor image quality, the appearance of a particular vehicle varies greatly from one frame to the next. This leads us to a different problem: how can we build the best appearance model from all instances of a vehicle we have seen so far. The best appearance model should maximize the future performance of the tracking system, and maximize the chances of reacquiring the vehicle once it leaves the field of view. We propose an online feature selection approach to this problem and investigate the performance and computational trade-offs with a real-world dataset.
Summary of FY15 results of benchmark modeling activities
Energy Technology Data Exchange (ETDEWEB)
Arguello, J. Guadalupe [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
2015-08-01
Sandia is participating in the third phase of an is a contributing partner to a U.S.-German "Joint Project" entitled "Comparison of current constitutive models and simulation procedures on the basis of model calculations of the thermo-mechanical behavior and healing of rock salt." The first goal of the project is to check the ability of numerical modeling tools to correctly describe the relevant deformation phenomena in rock salt under various influences. Achieving this goal will lead to increased confidence in the results of numerical simulations related to the secure storage of radioactive wastes in rock salt, thereby enhancing the acceptance of the results. These results may ultimately be used to make various assertions regarding both the stability analysis of an underground repository in salt, during the operating phase, and the long-term integrity of the geological barrier against the release of harmful substances into the biosphere, in the post-operating phase.
Directory of Open Access Journals (Sweden)
Chloupek Petr
2011-05-01
Full Text Available Abstract Background Since it is not yet clear whether it is possible to satisfactorily avoid sampling-induced stress interference in poultry, more studies on the pattern of physiological response and detailed quantification of stress connected with the first few minutes of capture and pre-sampling handling in poultry are required. This study focused on detection of changes in the corticosterone level and concentrations of other selected biochemical parameters in broilers handled in two different manners during blood sampling (involving catching, carrying, restraint, and blood collection itself that lasted for various time periods within the interval 30-180 seconds. Methods Stress effects of pre-sampling handling were studied in a group (n = 144 of unsexed ROSS 308 broiler chickens aged 42 d. Handling (catching, carrying, restraint, and blood sampling itself was carried out in a gentle (caught, held and carried carefully in an upright position or rough (caught by the leg, held and carried with lack of care in inverted position manner and lasted for 30 s, 60 s, 90 s, 120 s, 150 s, and 180 s. Plasma corticosterone, albumin, glucose, cholesterol, lactate, triglycerides and total protein were measured in order to assess the stress-induced changes to these biochemical indices following handling in the first few minutes of capture. Results Pre-sampling handling in a rough manner resulted in considerably higher plasma concentrations of all biochemical indices monitored when compared with gentle handling. Concentrations of plasma corticosterone after 150 and 180 s of handling were considerably higher (P Conclusions These results indicate that the pre-sampling procedure may be a considerably stressful procedure for broilers, particularly when carried out with lack of care and exceeding 120 seconds.
Standard Model physics results from ATLAS and CMS
Dordevic, Milos
2015-01-01
The most recent results of Standard Model physics studies in proton-proton collisions at 7 TeV and 8 TeV center-of-mass energy based on data recorded by ATLAS and CMS detectors during the LHC Run I are reviewed. This overview includes studies of vector boson production cross section and properties, results on V+jets production with light and heavy flavours, latest VBS and VBF results, measurement of diboson production with an emphasis on ATGC and QTGC searches, as well as results on inclusive jet cross sections with strong coupling constant measurement and PDF constraints. The outlined results are compared to the prediction of the Standard Model.
Sensor selection of helicopter transmission systems based on physical model and sensitivity analysis
Institute of Scientific and Technical Information of China (English)
Lyu Kehong; Tan Xiaodong; Liu Guanjun; Zhao Chenxu
2014-01-01
In the helicopter transmission systems, it is important to monitor and track the tooth damage evolution using lots of sensors and detection methods. This paper develops a novel approach for sensor selection based on physical model and sensitivity analysis. Firstly, a physical model of tooth damage and mesh stiffness is built. Secondly, some effective condition indicators (CIs) are presented, and the optimal CIs set is selected by comparing their test statistics according to Mann-Kendall test. Afterwards, the selected CIs are used to generate a health indicator (HI) through sen slop estimator. Then, the sensors are selected according to the monotonic relevance and sensitivity to the damage levels. Finally, the proposed method is verified by the simulation and experimental data. The results show that the approach can provide a guide for health monitor-ing of helicopter transmission systems, and it is effective to reduce the test cost and improve the system’s reliability.
Sensor selection of helicopter transmission systems based on physical model and sensitivity analysis
Directory of Open Access Journals (Sweden)
Lyu Kehong
2014-06-01
Full Text Available In the helicopter transmission systems, it is important to monitor and track the tooth damage evolution using lots of sensors and detection methods. This paper develops a novel approach for sensor selection based on physical model and sensitivity analysis. Firstly, a physical model of tooth damage and mesh stiffness is built. Secondly, some effective condition indicators (CIs are presented, and the optimal CIs set is selected by comparing their test statistics according to Mann–Kendall test. Afterwards, the selected CIs are used to generate a health indicator (HI through sen slop estimator. Then, the sensors are selected according to the monotonic relevance and sensitivity to the damage levels. Finally, the proposed method is verified by the simulation and experimental data. The results show that the approach can provide a guide for health monitoring of helicopter transmission systems, and it is effective to reduce the test cost and improve the system’s reliability.
Vendor selection and order allocation using an integrated fuzzy mathematical programming model
Directory of Open Access Journals (Sweden)
Farzaneh Talebi
2015-09-01
Full Text Available In the context of supply chain management, supplier selection plays a key role in reaching desirable production planning. In today's competitive world, many enterprises have focused on selecting the appropriate suppliers in an attempt to reduce purchasing costs and improve quality products and services. Supplier selection is a multi-criteria decision problem, which includes different qualitative and quantitative criteria such as purchase cost, on time delivery, quality of service, etc. In this study, a fuzzy multi-objective mathematical programming model is presented to select appropriate supplier and assign desirable order to different supplies. The proposed model was implemented for an organization by considering 16 different scenarios and the results are compared with two other existing methods.
Bruni, Renato; Cesarone, Francesco; Scozzari, Andrea; Tardella, Fabio
2016-09-01
A large number of portfolio selection models have appeared in the literature since the pioneering work of Markowitz. However, even when computational and empirical results are described, they are often hard to replicate and compare due to the unavailability of the datasets used in the experiments. We provide here several datasets for portfolio selection generated using real-world price values from several major stock markets. The datasets contain weekly return values, adjusted for dividends and for stock splits, which are cleaned from errors as much as possible. The datasets are available in different formats, and can be used as benchmarks for testing the performances of portfolio selection models and for comparing the efficiency of the algorithms used to solve them. We also provide, for these datasets, the portfolios obtained by several selection strategies based on Stochastic Dominance models (see "On Exact and Approximate Stochastic Dominance Strategies for Portfolio Selection" (Bruni et al. [2])). We believe that testing portfolio models on publicly available datasets greatly simplifies the comparison of the different portfolio selection strategies.
Coarsening in an interfacial equation without slope selection revisited: Analytical results
Energy Technology Data Exchange (ETDEWEB)
Guedda, M., E-mail: guedda@u-picardie.f [LAMFA, CNRS UMR 6140, Universite de Picardie Jules Verne, Amiens (France); Trojette, H. [LAMFA, CNRS UMR 6140, Universite de Picardie Jules Verne, Amiens (France)
2010-09-20
In this Letter, we re-examen a one-dimensional model of epitaxial growth that describes pyramidal structures characterized by the absence of a preferred slope [L. Golubovic, Phys. Rev. Lett. 78 (1997) 90]. A similarity approach shows that the typical mound lateral size and the interfacial width growth with time like t{sup 1/2} and t{sup 1/4}, respectively. This result was previously presented by Golubovic. Our contribution provides a mathematical justification for the existence of similarity solutions which correspond to, or predict, the typical coarsening process.
The Selection of ARIMA Models with or without Regressors
DEFF Research Database (Denmark)
Johansen, Søren; Riani, Marco; Atkinson, Anthony C.
We develop a $C_{p}$ statistic for the selection of regression models with stationary and nonstationary ARIMA error term. We derive the asymptotic theory of the maximum likelihood estimators and show they are consistent and asymptotically Gaussian. We also prove that the distribution of the sum...... to noise ratios. A new plot of our time series $C_{p}$ statistic is highly informative about the choice of model....... of squares of one step ahead standardized prediction errors, when the parameters are estimated, differs from the chi-squared distribution by a term which tends to infinity at a lower rate than $\\chi _{n}^{2}$. We further prove that, in the prediction error decomposition, the term involving the sum...
On Model Specification and Selection of the Cox Proportional Hazards Model*
Lin, Chen-Yen; Halabi, Susan
2013-01-01
Prognosis plays a pivotal role in patient management and trial design. A useful prognostic model should correctly identify important risk factors and estimate their effects. In this article, we discuss several challenges in selecting prognostic factors and estimating their effects using the Cox proportional hazards model. Although a flexible semiparametric form, the Cox’s model is not entirely exempt from model misspecification. To minimize possible misspecification, instead of imposing tradi...
Ultrastructural model for size selectivity in glomerular filtration.
Edwards, A; Daniels, B S; Deen, W M
1999-06-01
A theoretical model was developed to relate the size selectivity of the glomerular barrier to the structural characteristics of the individual layers of the capillary wall. Thicknesses and other linear dimensions were evaluated, where possible, from previous electron microscopic studies. The glomerular basement membrane (GBM) was represented as a homogeneous material characterized by a Darcy permeability and by size-dependent hindrance coefficients for diffusion and convection, respectively; those coefficients were estimated from recent data obtained with isolated rat GBM. The filtration slit diaphragm was modeled as a single row of cylindrical fibers of equal radius but nonuniform spacing. The resistances of the remainder of the slit channel, and of the endothelial fenestrae, to macromolecule movement were calculated to be negligible. The slit diaphragm was found to be the most restrictive part of the barrier. Because of that, macromolecule concentrations in the GBM increased, rather than decreased, in the direction of flow. Thus the overall sieving coefficient (ratio of Bowman's space concentration to that in plasma) was predicted to be larger for the intact capillary wall than for a hypothetical structure with no GBM. In other words, because the slit diaphragm and GBM do not act independently, the overall sieving coefficient is not simply the product of those for GBM alone and the slit diaphragm alone. Whereas the calculated sieving coefficients were sensitive to the structural features of the slit diaphragm and to the GBM hindrance coefficients, variations in GBM thickness or filtration slit frequency were predicted to have little effect. The ability of the ultrastructural model to represent fractional clearance data in vivo was at least equal to that of conventional pore models with the same number of adjustable parameters. The main strength of the present approach, however, is that it provides a framework for relating structural findings to the size
Developing a conceptual model for selecting and evaluating online markets
Directory of Open Access Journals (Sweden)
Sadegh Feizollahi
2013-04-01
Full Text Available There are many evidences, which emphasis on the benefits of using new technologies of information and communication in international business and many believe that E-Commerce can help satisfy customer explicit and implicit requirements. Internet shopping is a concept developed after the introduction of electronic commerce. Information technology (IT and its applications, specifically in the realm of the internet and e-mail promoted the development of e-commerce in terms of advertising, motivating and information. However, with the development of new technologies, credit and financial exchange on the internet websites were constructed so to facilitate e-commerce. The proposed study sends a total of 200 questionnaires to the target group (teachers - students - professionals - managers of commercial web sites and it manages to collect 130 questionnaires for final evaluation. Cronbach's alpha test is used for measuring reliability and to evaluate the validity of measurement instruments (questionnaires, and to assure construct validity, confirmatory factor analysis is employed. In addition, in order to analyze the research questions based on the path analysis method and to determine markets selection models, a regular technique is implemented. In the present study, after examining different aspects of e-commerce, we provide a conceptual model for selecting and evaluating online marketing in Iran. These findings provide a consistent, targeted and holistic framework for the development of the Internet market in the country.
Modeling selective elimination of quiescent cancer cells from bone marrow.
Cavnar, Stephen P; Rickelmann, Andrew D; Meguiar, Kaille F; Xiao, Annie; Dosch, Joseph; Leung, Brendan M; Cai Lesher-Perez, Sasha; Chitta, Shashank; Luker, Kathryn E; Takayama, Shuichi; Luker, Gary D
2015-08-01
Patients with many types of malignancy commonly harbor quiescent disseminated tumor cells in bone marrow. These cells frequently resist chemotherapy and may persist for years before proliferating as recurrent metastases. To test for compounds that eliminate quiescent cancer cells, we established a new 384-well 3D spheroid model in which small numbers of cancer cells reversibly arrest in G1/G0 phase of the cell cycle when cultured with bone marrow stromal cells. Using dual-color bioluminescence imaging to selectively quantify viability of cancer and stromal cells in the same spheroid, we identified single compounds and combination treatments that preferentially eliminated quiescent breast cancer cells but not stromal cells. A treatment combination effective against malignant cells in spheroids also eliminated breast cancer cells from bone marrow in a mouse xenograft model. This research establishes a novel screening platform for therapies that selectively target quiescent tumor cells, facilitating identification of new drugs to prevent recurrent cancer. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
DEFF Research Database (Denmark)
Finlay, Chris; Olsen, Nils; Tøffner-Clausen, Lars
th order spline representation with knot points spaced at 0.5 year intervals. The resulting field model is able to consistently fit data from six independent low Earth orbit satellites: Oersted, CHAMP, SAC-C and the three Swarm satellites. As an example, we present comparisons of the excellent model......Ten months of data from ESA's Swarm mission, together with recent ground observatory monthly means, are used to update the CHAOS series of geomagnetic field models with a focus on time-changes of the core field. As for previous CHAOS field models quiet-time, night-side, data selection criteria...
Energy Technology Data Exchange (ETDEWEB)
Barros, Livia F.; Pecequilo, Brigitte R.S.; Aquino, Reginaldo R., E-mail: lfbarros@ipen.b, E-mail: brigitte@ipen.b, E-mail: raquino@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
2011-07-01
The variation of natural radioactivity along the surface of the beach sands of Camburi, located in Vitoria, capital of Espirito Santo, southeastern Brazil, was determined from the contents of {sup 226}Ra, {sup 232}Th and {sup 40}K. Eleven collecting points was selected along all the 6 km extension of the Camburi beach. Sand samples collected from all established points on January 2011 were dried and sealed in standard 100 mL polyethylene flasks and measured by high resolution gamma spectrometry after a 4 weeks ingrowth period, in order to allow the secular equilibrium in the {sup 238}U and {sup 232}Th series. The {sup 226}Ra concentration was determined from the weighted average concentrations of {sup 214}Pb and {sup 214}Bi. The {sup 232}Th concentration was determined from the weighted average concentrations of {sup 228}Ac, {sup 212}Pb and {sup 212}Bi and the {sup 40}K from its single gamma transition. Preliminary results show activity concentrations varying from 5 Bq.kg{sup -1} to {sup 222} Bq.kg{sup -1} for {sup 226}Ra and from 14 Bq.kg{sup -1} to 1074 Bq.kg{sup -'}1 for {sup 232}Th, both with the highest values for Camburi South and Central. For {sup 40}K, the activity concentrations ranged from 14 Bq.kg{sup -1} to 179 Bq.kg{sup -1} and the highest values were obtained for Camburi South. (author)
Energy Technology Data Exchange (ETDEWEB)
Fisk, W.J.; Faulkner, D.; Sullivan, D. [and others
1998-02-17
To test proposed methods for reducing SBS symptoms and to learn about the causes of these symptoms, a double-blind controlled intervention study was designed and implemented. This study utilized two different interventions designed to reduce occupants` exposures to airborne particles: (1) high efficiency filters in the building`s HVAC systems; and (2) thorough cleaning of carpeted floors and fabric-covered chairs with an unusually powerful vacuum cleaner. The study population was the workers on the second and fourth floors of a large office building with mechanical ventilation, air conditioning, and sealed windows. Interventions were implemented on one floor while the occupants on the other floor served as a control group. For the enhanced-filtration intervention, a multiple crossover design was used (a crossover is a repeat of the experiment with the former experimental group as the control group and vice versa). Demographic and health symptom data were collected via an initial questionnaire on the first study week and health symptom data were obtained each week, for eight additional weeks, via weekly questionnaires. A large number of indoor environmental parameters were measured during the study including air temperatures and humidities, carbon dioxide concentrations, particle concentrations, concentrations of several airborne bioaerosols, and concentrations of several microbiologic compounds within the dust sampled from floors and chairs. This report describes the study methods and summarizes the results of selected environmental measurements.
Directory of Open Access Journals (Sweden)
Zieba Jennifer T
2011-06-01
Full Text Available Abstract Background Genetic interactions within hybrids influence their overall fitness. Understanding the details of these interactions can improve our understanding of speciation. One experimental approach is to investigate deviations from Mendelian expectations (segregation distortion in the inheritance of mapped genetic markers. In this study, we used the copepod Tigriopus californicus, a species which exhibits high genetic divergence between populations and a general pattern of reduced fitness in F2 interpopulation hybrids. Previous studies have implicated both nuclear-cytoplasmic and nuclear-nuclear interactions in causing this fitness reduction. We identified and mapped population-diagnostic single nucleotide polymorphisms (SNPs and used these to examine segregation distortion across the genome within F2 hybrids. Results We generated a linkage map which included 45 newly elucidated SNPs and 8 population-diagnostic microsatellites used in previous studies. The map, the first available for the Copepoda, was estimated to cover 75% of the genome and included markers on all 12 T. californicus chromosomes. We observed little segregation distortion in newly hatched F2 hybrid larvae (fewer than 10% of markers at p Conclusion Adult male F2 hybrids between two populations of T. californius exhibit dramatic segregation distortion across the genome. Distorted loci are clustered within specific linkage groups, and the direction of distortion differs between chromosomes. This segregation distortion is due to selection acting between hatching and adulthood.
Elsheikh, A. H.
2013-12-01
Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface flow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam\\'s razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data mismatch of the calibrated model.
Model selection for the North American Breeding Bird Survey: A comparison of methods
Link, William; Sauer, John; Niven, Daniel
2017-01-01
The North American Breeding Bird Survey (BBS) provides data for >420 bird species at multiple geographic scales over 5 decades. Modern computational methods have facilitated the fitting of complex hierarchical models to these data. It is easy to propose and fit new models, but little attention has been given to model selection. Here, we discuss and illustrate model selection using leave-one-out cross validation, and the Bayesian Predictive Information Criterion (BPIC). Cross-validation is enormously computationally intensive; we thus evaluate the performance of the Watanabe-Akaike Information Criterion (WAIC) as a computationally efficient approximation to the BPIC. Our evaluation is based on analyses of 4 models as applied to 20 species covered by the BBS. Model selection based on BPIC provided no strong evidence of one model being consistently superior to the others; for 14/20 species, none of the models emerged as superior. For the remaining 6 species, a first-difference model of population trajectory was always among the best fitting. Our results show that WAIC is not reliable as a surrogate for BPIC. Development of appropriate model sets and their evaluation using BPIC is an important innovation for the analysis of BBS data.
Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao
2017-03-01
Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.
Hybrid Modeling of Flotation Height in Air Flotation Oven Based on Selective Bagging Ensemble Method
Directory of Open Access Journals (Sweden)
Shuai Hou
2013-01-01
Full Text Available The accurate prediction of the flotation height is very necessary for the precise control of the air flotation oven process, therefore, avoiding the scratch and improving production quality. In this paper, a hybrid flotation height prediction model is developed. Firstly, a simplified mechanism model is introduced for capturing the main dynamic behavior of the process. Thereafter, for compensation of the modeling errors existing between actual system and mechanism model, an error compensation model which is established based on the proposed selective bagging ensemble method is proposed for boosting prediction accuracy. In the framework of the selective bagging ensemble method, negative correlation learning and genetic algorithm are imposed on bagging ensemble method for promoting cooperation property between based learners. As a result, a subset of base learners can be selected from the original bagging ensemble for composing a selective bagging ensemble which can outperform the original one in prediction accuracy with a compact ensemble size. Simulation results indicate that the proposed hybrid model has a better prediction performance in flotation height than other algorithms’ performance.
Directory of Open Access Journals (Sweden)
Masoud Ghodrati
Full Text Available Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition.
Ghodrati, Masoud; Khaligh-Razavi, Seyed-Mahdi; Ebrahimpour, Reza; Rajaei, Karim; Pooyan, Mohammad
2012-01-01
Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition.
Relationship Marketing results: proposition of a cognitive mapping model
Directory of Open Access Journals (Sweden)
Iná Futino Barreto
2015-12-01
Full Text Available Objective - This research sought to develop a cognitive model that expresses how marketing professionals understand the relationship between the constructs that define relationship marketing (RM. It also tried to understand, using the obtained model, how objectives in this field are achieved. Design/methodology/approach – Through cognitive mapping, we traced 35 individual mental maps, highlighting how each respondent understands the interactions between RM elements. Based on the views of these individuals, we established an aggregate mental map. Theoretical foundation – The topic is based on a literature review that explores the RM concept and its main elements. Based on this review, we listed eleven main constructs. Findings – We established an aggregate mental map that represents the RM structural model. Model analysis identified that CLV is understood as the final result of RM. We also observed that the impact of most of the RM elements on CLV is brokered by loyalty. Personalization and quality, on the other hand, proved to be process input elements, and are the ones that most strongly impact others. Finally, we highlight that elements that punish customers are much less effective than elements that benefit them. Contributions - The model was able to insert core elements of RM, but absent from most formal models: CLV and customization. The analysis allowed us to understand the interactions between the RM elements and how the end result of RM (CLV is formed. This understanding improves knowledge on the subject and helps guide, assess and correct actions.
Amine modeling for CO2 capture: internals selection.
Karpe, Prakash; Aichele, Clint P
2013-04-16
Traditionally, trays have been the mass-transfer device of choice in amine absorption units. However, the need to process large volumes of flue gas to capture CO2 and the resultant high costs of multiple trains of large trayed columns have prompted process licensors and vendors to investigate alternative mass-transfer devices. These alternatives include third-generation random packings and structured packings. Nevertheless, clear-cut guidelines for selection of packings for amine units are lacking. This paper provides well-defined guidelines and a consistent framework for the choice of mass-transfer devices for amine absorbers and regenerators. This work emphasizes the role played by the flow parameter, a measure of column liquid loading and pressure, in the type of packing selected. In addition, this paper demonstrates the significant economic advantage of packings over trays in terms of capital costs (CAPEX) and operating costs (OPEX).
DEFF Research Database (Denmark)
Luczak, Marcin; Manzato, Simone; Peeters, Bart;
2014-01-01
of model parameters was selected for the model updating process. Design of experiment and response surface method was implemented to find values of model parameters yielding results closest to the experimental. The updated finite element model is producing results more consistent with the measurement...... is to validate finite element model of the modified wind turbine blade section mounted in the flexible support structure accordingly to the experimental results. Bend-twist coupling was implemented by adding angled unidirectional layers on the suction and pressure side of the blade. Dynamic test and simulations...... were performed on a section of a full scale wind turbine blade provided by Vestas Wind Systems A/S. The numerical results are compared to the experimental measurements and the discrepancies are assessed by natural frequency difference and modal assurance criterion. Based on sensitivity analysis, set...
DEFF Research Database (Denmark)
Rørbech, Jakob Thaysen; Vadenbo, Carl; Hellweg, Stefanie
2014-01-01
Resources have received significant attention in recent years resulting in development of a wide range of resource depletion indicators within life cycle assessment (LCA). Understanding the differences in assessment principles used to derive these indicators and the effects on the impact assessment...... results is critical for indicator selection and interpretation of the results. Eleven resource depletion methods were evaluated quantitatively with respect to resource coverage, characterization factors (CF), impact contributions from individual resources, and total impact scores. We included 2247...... groups, according to method focus and modeling approach, to aid method selection within LCA....
Value of the distant future: Model-independent results
Katz, Yuri A.
2017-01-01
This paper shows that the model-independent account of correlations in an interest rate process or a log-consumption growth process leads to declining long-term tails of discount curves. Under the assumption of an exponentially decaying memory in fluctuations of risk-free real interest rates, I derive the analytical expression for an apt value of the long run discount factor and provide a detailed comparison of the obtained result with the outcome of the benchmark risk-free interest rate models. Utilizing the standard consumption-based model with an isoelastic power utility of the representative economic agent, I derive the non-Markovian generalization of the Ramsey discounting formula. Obtained analytical results allowing simple calibration, may augment the rigorous cost-benefit and regulatory impact analysis of long-term environmental and infrastructure projects.
Marginal production in the Gulf of Mexico - II. Model results
Energy Technology Data Exchange (ETDEWEB)
Kaiser, Mark J.; Yu, Yunke [Center for Energy Studies, Louisiana State University, Baton Rouge, LA 70803 (United States)
2010-08-15
In the second part of this two-part article on marginal production in the Gulf of Mexico, we estimate the number of committed assets in water depth less than 1000 ft that are expected to be marginal over a 60-year time horizon. We compute the expected quantity and value of the production and gross revenue streams of the gulf's committed asset inventory circa. January 2007 using a probabilistic model framework. Cumulative hydrocarbon production from the producing inventory is estimated to be 1056 MMbbl oil and 13.3 Tcf gas. Marginal production from the committed asset inventory is expected to contribute 4.1% of total oil production and 5.4% of gas production. A meta-evaluation procedure is adapted to present the results of sensitivity analysis. Model results are discussed along with a description of the model framework and limitations of the analysis. (author)
Selection of Sinopec Lubricating Oil Producing Bases by Using the AHP Model
Institute of Scientific and Technical Information of China (English)
Song Yunchang; Song Zhaozheng; Zheng Chengguo; Jiang Qingzhe; Xu Chunming
2007-01-01
The factors affecting the development of Sinopec lubricating oil were analyzed in this paper,and an analytic hierarchy process (AHP) model for selecting lubricating-oil producing bases was developed. By using this model,nine lubricating oil producing companies under Sinopec were comprehensively evaluated. The evaluation result showed that the Maoming Lubricating Oil Company (Guangdong province),Jingmen Lubricating Oil Company (Hubei province) and Changcheng Lube Oil Company (Beijing) are top three choices,and should be developed preferentially for the development of Sinopec producing bases of lubricating oil in the future. The conclusions provide the theoretical basis for selecting lubricating oil producing bases for decision makers.
A multicriteria decision making model for assessment and selection of an ERP in a logistics context
Pereira, Teresa; Ferreira, Fernanda A.
2017-07-01
The aim of this work is to apply a methodology of decision support based on a multicriteria decision analyses (MCDA) model that allows the assessment and selection of an Enterprise Resource Planning (ERP) in a Portuguese logistics company by Group Decision Maker (GDM). A Decision Support system (DSS) that implements a MCDA - Multicriteria Methodology for the Assessment and Selection of Information Systems / Information Technologies (MMASSI / IT) is used based on its features and facility to change and adapt the model to a given scope. Using this DSS it was obtained the information system that best suited to the decisional context, being this result evaluated through a sensitivity and robustness analysis.
Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies.
Mohammadian-Behbahani, Mohammad-Reza; Kamali-Asl, Ali-Reza
2016-12-01
In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like computation time and general fitting problems. This article proposes an approach based on Artificial Neural Networks (ANNs) for solving these problems. A set of three physiologically and mathematically nested models generated from the Tofts model were assumed: Model I, II and III. These models cover three possible tissue types from normal to malignant. Using 21 experimental arterial input functions and 12 levels of noise, a set of 27,216 time traces were generated. ANN was validated and optimized by the k-fold cross validation technique. An experimental dataset of 20 patients with glioblastoma was applied to ANN and the results were compared to outputs of F-test using Dice index. Optimum neuronal architecture ([6:7:1]) and number of training epochs (50) of the ANN were determined. ANN correctly classified more than 99% of the dataset. Confusion matrices for both ANN and F-test results showed the superior performance of the ANN classifier. The average Dice index (over 20 patients) indicated a 75% similarity between model selection maps of ANN and F-test. ANN improves the model selection process by removing the need for time-consuming, problematic fitting algorithms; as well as the need for hypothesis testing. Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Exact results for car accidents in a traffic model
Huang, Ding-wei
1998-07-01
Within the framework of a recent model for car accidents on single-lane highway traffic, we study analytically the probability of the occurrence of car accidents. Exact results are obtained. Various scaling behaviours are observed. The linear dependence of the occurrence of car accidents on density is understood as the dominance of a single velocity in the distribution.
Liu, Siwei; Rovine, Michael J; Molenaar, Peter C M
2012-03-01
With increasing popularity, growth curve modeling is more and more often considered as the 1st choice for analyzing longitudinal data. Although the growth curve approach is often a good choice, other modeling strategies may more directly answer questions of interest. It is common to see researchers fit growth curve models without considering alterative modeling strategies. In this article we compare 3 approaches for analyzing longitudinal data: repeated measures analysis of variance, covariance pattern models, and growth curve models. As all are members of the general linear mixed model family, they represent somewhat different assumptions about the way individuals change. These assumptions result in different patterns of covariation among the residuals around the fixed effects. In this article, we first indicate the kinds of data that are appropriately modeled by each and use real data examples to demonstrate possible problems associated with the blanket selection of the growth curve model. We then present a simulation that indicates the utility of Akaike information criterion and Bayesian information criterion in the selection of a proper residual covariance structure. The results cast doubt on the popular practice of automatically using growth curve modeling for longitudinal data without comparing the fit of different models. Finally, we provide some practical advice for assessing mean changes in the presence of correlated data.
A structured approach for selecting carbon capture process models : A case study on monoethanolamine
van der Spek, Mijndert; Ramirez, Andrea
2014-01-01
Carbon capture and storage is considered a promising option to mitigate CO2 emissions. This has resulted in many R&D efforts focusing at developing viable carbon capture technologies. During carbon capture technology development, process modeling plays an important role. Selecting an appropriate pro
A structured approach for selecting carbon capture process models : A case study on monoethanolamine
van der Spek, Mijndert; Ramirez, Andrea
2014-01-01
Carbon capture and storage is considered a promising option to mitigate CO2 emissions. This has resulted in many R&D efforts focusing at developing viable carbon capture technologies. During carbon capture technology development, process modeling plays an important role. Selecting an appropriate
A structured approach for selecting carbon capture process models : A case study on monoethanolamine
van der Spek, Mijndert; Ramirez, Andrea
2014-01-01
Carbon capture and storage is considered a promising option to mitigate CO2 emissions. This has resulted in many R&D efforts focusing at developing viable carbon capture technologies. During carbon capture technology development, process modeling plays an important role. Selecting an appropriate pro
Institute of Scientific and Technical Information of China (English)
孟宪锐; 徐永勇; 汪进
2001-01-01
This paper mainly discusses the selection of the technical parameters of fully-mechanized top-coal caving mining using the neural network technique. The comparison between computing results and experiment data shows that the set-up neural network model has high accuracy and decision-making benefit.
Eikeseth, Svein
2016-01-01
B. F. Skinner is one of the most important 20th century psychologists, and the 1981 paper Selection by Consequences is among his most important contributions. In this paper, Skinner integrates evolutionary biology with psychology, sociology and anthropology. More specifically, Skinner shows how selection by consequences operates on the shaping and maintenance of the behavior of the individual (i.e., psychology) as well as...
Directory of Open Access Journals (Sweden)
Sveiczer Akos
2006-03-01
Full Text Available Abstract Background There is considerable controversy concerning the exact growth profile of size parameters during the cell cycle. Linear, exponential and bilinear models are commonly considered, and the same model may not apply for all species. Selection of the most adequate model to describe a given data-set requires the use of quantitative model selection criteria, such as the partial (sequential F-test, the Akaike information criterion and the Schwarz Bayesian information criterion, which are suitable for comparing differently parameterized models in terms of the quality and robustness of the fit but have not yet been used in cell growth-profile studies. Results Length increase data from representative individual fission yeast (Schizosaccharomyces pombe cells measured on time-lapse films have been reanalyzed using these model selection criteria. To fit the data, an extended version of a recently introduced linearized biexponential (LinBiExp model was developed, which makes possible a smooth, continuously differentiable transition between two linear segments and, hence, allows fully parametrized bilinear fittings. Despite relatively small differences, essentially all the quantitative selection criteria considered here indicated that the bilinear model was somewhat more adequate than the exponential model for fitting these fission yeast data. Conclusion A general quantitative framework was introduced to judge the adequacy of bilinear versus exponential models in the description of growth time-profiles. For single cell growth, because of the relatively limited data-range, the statistical evidence is not strong enough to favor one model clearly over the other and to settle the bilinear versus exponential dispute. Nevertheless, for the present individual cell growth data for fission yeast, the bilinear model seems more adequate according to all metrics, especially in the case of wee1Δ cells.
Chloupek, Petr; Bedanova, Iveta; Chloupek, Jan; Vecerek, Vladimir
2011-05-13
Since it is not yet clear whether it is possible to satisfactorily avoid sampling-induced stress interference in poultry, more studies on the pattern of physiological response and detailed quantification of stress connected with the first few minutes of capture and pre-sampling handling in poultry are required. This study focused on detection of changes in the corticosterone level and concentrations of other selected biochemical parameters in broilers handled in two different manners during blood sampling (involving catching, carrying, restraint, and blood collection itself) that lasted for various time periods within the interval 30-180 seconds. Stress effects of pre-sampling handling were studied in a group (n = 144) of unsexed ROSS 308 broiler chickens aged 42 d. Handling (catching, carrying, restraint, and blood sampling itself) was carried out in a gentle (caught, held and carried carefully in an upright position) or rough (caught by the leg, held and carried with lack of care in inverted position) manner and lasted for 30 s, 60 s, 90 s, 120 s, 150 s, and 180 s. Plasma corticosterone, albumin, glucose, cholesterol, lactate, triglycerides and total protein were measured in order to assess the stress-induced changes to these biochemical indices following handling in the first few minutes of capture. Pre-sampling handling in a rough manner resulted in considerably higher plasma concentrations of all biochemical indices monitored when compared with gentle handling. Concentrations of plasma corticosterone after 150 and 180 s of handling were considerably higher (P technique. Concentrations of plasma lactate were also increased by prolonged handling duration. Handling for 90-180 seconds resulted in a highly significant elevation of lactate concentration in comparison with 30 s handling regardless of handling technique. Similarly to corticosterone concentrations, a strong positive correlation was found between plasma lactate and duration of pre-sampling handling
A finite volume alternate direction implicit approach to modeling selective laser melting
DEFF Research Database (Denmark)
Hattel, Jesper Henri; Mohanty, Sankhya
2013-01-01
is proposed for modeling single-layer and few-layers selective laser melting processes. The ADI technique is implemented and applied for two cases involving constant material properties and non-linear material behavior. The ADI FV method consume less time while having comparable accuracy with respect to 3D...... to accurately simulate the process, are constrained by either the size or scale of the model domain. A second challenging aspect involves the inclusion of non-linear material behavior into the 3D implicit FE models. An alternating direction implicit (ADI) method based on a finite volume (FV) formulation......Over the last decade, several studies have attempted to develop thermal models for analyzing the selective laser melting process with a vision to predict thermal stresses, microstructures and resulting mechanical properties of manufactured products. While a holistic model addressing all involved...
Directory of Open Access Journals (Sweden)
Yoshihiro eUesawa
2016-02-01
Full Text Available Random forest (RF is a machine-learning ensemble method with high predictive performance. Majority voting in RF uses the discrimination results in numerous decision trees produced from bootstrapping data. For the same dataset, the bootstrapping process yields different predictive capacities in each generation. As participants in the Toxicology in the 21st Century (Tox21 DATA Challenge 2014, we produced numerous RF models for predicting the structures of compounds that can activate each toxicity-related pathway, and then selected the model with the highest predictive ability. Half of the compounds in the training dataset supplied by the competition organizer were allocated to the validation dataset. The remaining compounds were used in model construction. The charged and uncharged forms of each molecule were calculated using the molecular operating environment (MOE software. Subsequently, the descriptors were computed using MOE, MarvinView, and Dragon. These combined methods yielded over 4,071 descriptors for model construction. Using these descriptors, pattern recognition analyses were performed by RF implemented in JMP Pro (a statistical software package. A hundred to two hundred RF models were generated for each pathway. The predictive performance of each model was tested against the validation dataset, and the best-performing model was selected. In the competition, the latter model selected a best-performing model from the 50% test set that best predicted the structures of compounds that activate the estrogen receptor ligand-binding domain (ER-LBD.
Modeling Results For the ITER Cryogenic Fore Pump. Final Report
Energy Technology Data Exchange (ETDEWEB)
Pfotenhauer, John M. [University of Wisconsin, Madison, WI (United States); Zhang, Dongsheng [University of Wisconsin, Madison, WI (United States)
2014-03-31
A numerical model characterizing the operation of a cryogenic fore-pump (CFP) for ITER has been developed at the University of Wisconsin – Madison during the period from March 15, 2011 through June 30, 2014. The purpose of the ITER-CFP is to separate hydrogen isotopes from helium gas, both making up the exhaust components from the ITER reactor. The model explicitly determines the amount of hydrogen that is captured by the supercritical-helium-cooled pump as a function of the inlet temperature of the supercritical helium, its flow rate, and the inlet conditions of the hydrogen gas flow. Furthermore the model computes the location and amount of hydrogen captured in the pump as a function of time. Throughout the model’s development, and as a calibration check for its results, it has been extensively compared with the measurements of a CFP prototype tested at Oak Ridge National Lab. The results of the model demonstrate that the quantity of captured hydrogen is very sensitive to the inlet temperature of the helium coolant on the outside of the cryopump. Furthermore, the model can be utilized to refine those tests, and suggests methods that could be incorporated in the testing to enhance the usefulness of the measured data.
Assessment of Galileo modal test results for mathematical model verification
Trubert, M.
1984-01-01
The modal test program for the Galileo Spacecraft was completed at the Jet Propulsion Laboratory in the summer of 1983. The multiple sine dwell method was used for the baseline test. The Galileo Spacecraft is a rather complex 2433 kg structure made of a central core on which seven major appendages representing 30 percent of the total mass are attached, resulting in a high modal density structure. The test revealed a strong nonlinearity in several major modes. This nonlinearity discovered in the course of the test necessitated running additional tests at the unusually high response levels of up to about 21 g. The high levels of response were required to obtain a model verification valid at the level of loads for which the spacecraft was designed. Because of the high modal density and the nonlinearity, correlation between the dynamic mathematical model and the test results becomes a difficult task. Significant changes in the pre-test analytical model are necessary to establish confidence in the upgraded analytical model used for the final load verification. This verification, using a test verified model, is required by NASA to fly the Galileo Spacecraft on the Shuttle/Centaur launch vehicle in 1986.
Results and Interpretation of the WFRD ELS Distillation Down-Select Test Data
Delzeit, Lance Dean; Flynn, Michael; Carter, Layne; Long, David A.
2010-01-01
Testing of the Wiped-film Rotating-disk (WFRD) evaporator was conducted in support of the Exploration Life Support Distillation Down-Select Test. The WFRD was constructed at NASA Ames Research Center (ARC) and tested at NASA Marshall Space Flight Center (MSFC). The WFRD was delivered to MSFC in September 2009, and testing of solution #1 and solution #2 immediately following. Solution #1 was composed of humidity condensate and urine, including flush water and pretreatment chemicals. Solution #2 was composed of hygiene water, humidity condensate, and urine, including flush water and pretreatment chemicals. During the testing, the operational parameters of the WFRD were recorded and samples of the feed, brine, and product were collected and analyzed. The steady-state results of processing 414L of feed solution #1 and 1283L of feed solution #2 demonstrated that running the WFRD at a brine temperature of 50 C gave an average production rate of 16.7 L/hr. The specific energy consumption was 80.5W-hr/L. Data Analysis shows that the water recovery rates were 94% and 91%, respectively. The total mass of the WFRD as delivered to MSFC was 300 Kg. The volume of the tests stand rack was 1m width x 0.7m depth x 1.9m height or 1.5 cu m of which about half of the total volume is occupied by equipment. Chemical analysis of the distillate showed an average TOC of 20ppm, a pH of 3.5, and a conductivity of 98 mho/cm. The conductivity of the distillate, compared to the feed, decreased by 98.9%., the total ion concentration decreased by 99.6%, the total organics decreased 98.6%, and the metals were at or below detection limits
Liang, Bowei; Huang, Guofeng; Ding, Luobing; Kang, Liangqi; Sha, Mo; Ding, Zhenqi
2017-01-01
Background: Subsidence and late fusion are commonly observed in anterior subtotal corpectomy and reconstruction for treating thoracolumbar burst fractures. The subsidence rate of this surgical method was reported from 19.6% to 75% in the literatures, which would cause treatment failure. Thus, an improvement of anterior surgery technique should be studied to reduce these complications. Materials and Methods: 130 patients of thoracolumbar burst fractures treated by minimal corpectomy, decompression and U cage, between January 2009 and December 2010 were included in this study. The hospital Ethical Committee approved the protocols. The American Spinal Injury Association (ASIA) scale, visual analog scales, and Oswestry Disability Index (ODI) scores were used for clinical evaluation. The local kyphosis angle, vertebral height (one level above the fractured vertebral to one level below), canal stenosis, and fusion status were used to assess radiological outcome. All complications and demographic data such as number of male/female patients, average age, mode of trauma, burst level involved, mean surgery time and blood lost were reported. Results: 120 patients were followed up for 24 months. Most patients had improvement of at least 1 ASIA grade, and all experienced pain reduction. The mean ODI score steadily decreased after the surgery (P 0.05). The average canal stenosis index was increased from 39% to 99% after surgery. No cage subsidence or implant failure was observed. Conclusions: The clinical outcomes described here suggest that the selective corpectomy and rectangular cage reconstruction can effectively promote solid fusion and eliminate complications related to subsidence or implant failure. PMID:28216750
MHC allele frequency distributions under parasite-driven selection: A simulation model
Directory of Open Access Journals (Sweden)
Radwan Jacek
2010-10-01
Full Text Available Abstract Background The extreme polymorphism that is observed in major histocompatibility complex (MHC genes, which code for proteins involved in recognition of non-self oligopeptides, is thought to result from a pressure exerted by parasites because parasite antigens are more likely to be recognized by MHC heterozygotes (heterozygote advantage and/or by rare MHC alleles (negative frequency-dependent selection. The Ewens-Watterson test (EW is often used to detect selection acting on MHC genes over the recent history of a population. EW is based on the expectation that allele frequencies under balancing selection should be more even than under neutrality. We used computer simulations to investigate whether this expectation holds for selection exerted by parasites on host MHC genes under conditions of heterozygote advantage and negative frequency-dependent selection acting either simultaneously or separately. Results In agreement with simple models of symmetrical overdominance, we found that heterozygote advantage acting alone in populations does, indeed, result in more even allele frequency distributions than expected under neutrality, and this is easily detectable by EW. However, under negative frequency-dependent selection, or under the joint action of negative frequency-dependent selection and heterozygote advantage, distributions of allele frequencies were less predictable: the majority of distributions were indistinguishable from neutral expectations, while the remaining runs resulted in either more even or more skewed distributions than under neutrality. Conclusions Our results indicate that, as long as negative frequency-dependent selection is an important force maintaining MHC variation, the EW test has limited utility in detecting selection acting on these genes.
Model unspecific search in CMS. Results at 8 TeV
Energy Technology Data Exchange (ETDEWEB)
Albert, Andreas; Duchardt, Deborah; Hebbeker, Thomas; Knutzen, Simon; Lieb, Jonas; Meyer, Arnd; Pook, Tobias; Roemer, Jonas [III. Physikalisches Institut A, RWTH Aachen University (Germany)
2016-07-01
In the year 2012, CMS collected a total data set of approximately 20 fb{sup -1} in proton-proton collisions at √(s)=8 TeV. Dedicated searches for physics beyond the standard model are commonly designed with the signatures of a given theoretical model in mind. While this approach allows for an optimised sensitivity to the sought-after signal, it may cause unexpected phenomena to be overlooked. In a complementary approach, the Model Unspecific Search in CMS (MUSiC) analyses CMS data in a general way. Depending on the reconstructed final state objects (e.g. electrons), collision events are sorted into classes. In each of the classes, the distributions of selected kinematic variables are compared to standard model simulation. An automated statistical analysis is performed to quantify the agreement between data and prediction. In this talk, the analysis concept is introduced and selected results of the analysis of the 2012 CMS data set are presented.
Energy Technology Data Exchange (ETDEWEB)
Schaug, J.; Iversen, T.; Pedersen, U. (Norwegian Institute for Air Research, Lillestroem (Norway). Chemical Coordinating Centre of EMEP)
1993-04-01
Comparisons have been made between calculations from the Lagrangian model for acid deposition at Meteorological Synthesizing Centre-West (MSC-W) of EMEP and measurements at EMEP sites. Annual averages of aerosol sulphate, sulphate in precipitation and nitrate in precipitation were calculated and compared for selected sites. Site selection was based on data completeness and on results from EMEP interlaboratory exercises. The comparison for sulphates in precipitation and air led to a model underestimation in the north and model overestimation in a belt through the major source regions in central Europe. The comparisons also indicate irregularities at some sites which may be due to influence from local sources, or the data quality, although this is not substantiated. The model estimates of nitrate in precipitation compare well with the measurements, although some characteristic differences occur also for this component. 21 refs., 11 figs., 2 tabs.
Multicriteria decision group model for the selection of suppliers
Directory of Open Access Journals (Sweden)
Luciana Hazin Alencar
2008-08-01
Full Text Available Several authors have been studying group decision making over the years, which indicates how relevant it is. This paper presents a multicriteria group decision model based on ELECTRE IV and VIP Analysis methods, to those cases where there is great divergence among the decision makers. This model includes two stages. In the first, the ELECTRE IV method is applied and a collective criteria ranking is obtained. In the second, using criteria ranking, VIP Analysis is applied and the alternatives are selected. To illustrate the model, a numerical application in the context of the selection of suppliers in project management is used. The suppliers that form part of the project team have a crucial role in project management. They are involved in a network of connected activities that can jeopardize the success of the project, if they are not undertaken in an appropriate way. The question tackled is how to select service suppliers for a project on behalf of an enterprise that assists the multiple objectives of the decision-makers.Vários autores têm estudado decisão em grupo nos últimos anos, o que indica a relevância do assunto. Esse artigo apresenta um modelo multicritério de decisão em grupo baseado nos métodos ELECTRE IV e VIP Analysis, adequado aos casos em que se tem uma grande divergência entre os decisores. Esse modelo é composto por dois estágios. No primeiro, o método ELECTRE IV é aplicado e uma ordenação dos critérios é obtida. No próximo estágio, com a ordenação dos critérios, o método VIP Analysis é aplicado e as alternativas são selecionadas. Para ilustrar o modelo, uma aplicação numérica no contexto da seleção de fornecedores em projetos é realizada. Os fornecedores que fazem parte da equipe do projeto têm um papel fundamental no gerenciamento de projetos. Eles estão envolvidos em uma rede de atividades conectadas que, caso não sejam executadas de forma apropriada, podem colocar em risco o sucesso do
Improving permafrost distribution modelling using feature selection algorithms
Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail
2016-04-01
The availability of an increasing number of spatial data on the occurrence of mountain permafrost allows the employment of machine learning (ML) classification algorithms for modelling the distribution of the phenomenon. One of the major problems when dealing with high-dimensional dataset is the number of input features (variables) involved. Application of ML classification algorithms to this large number of variables leads to the risk of overfitting, with the consequence of a poor generalization/prediction. For this reason, applying feature selection (FS) techniques helps simplifying the amount of factors required and improves the knowledge on adopted features and their relation with the studied phenomenon. Moreover, taking away irrelevant or redundant variables from the dataset effectively improves the quality of the ML prediction. This research deals with a comparative analysis of permafrost distribution models supported by FS variable importance assessment. The input dataset (dimension = 20-25, 10 m spatial resolution) was constructed using landcover maps, climate data and DEM derived variables (altitude, aspect, slope, terrain curvature, solar radiation, etc.). It was completed with permafrost evidences (geophysical and thermal data and rock glacier inventories) that serve as training permafrost data. Used FS algorithms informed about variables that appeared less statistically important for permafrost presence/absence. Three different algorithms were compared: Information Gain (IG), Correlation-based Feature Selection (CFS) and Random Forest (RF). IG is a filter technique that evaluates the worth of a predictor by measuring the information gain with respect to the permafrost presence/absence. Conversely, CFS is a wrapper technique that evaluates the worth of a subset of predictors by considering the individual predictive ability of each variable along with the degree of redundancy between them. Finally, RF is a ML algorithm that performs FS as part of its
A Model for Selection of Eyespots on Butterfly Wings.
Directory of Open Access Journals (Sweden)
Toshio Sekimura
Full Text Available The development of eyespots on the wing surface of butterflies of the family Nympalidae is one of the most studied examples of biological pattern formation.However, little is known about the mechanism that determines the number and precise locations of eyespots on the wing. Eyespots develop around signaling centers, called foci, that are located equidistant from wing veins along the midline of a wing cell (an area bounded by veins. A fundamental question that remains unsolved is, why a certain wing cell develops an eyespot, while other wing cells do not.We illustrate that the key to understanding focus point selection may be in the venation system of the wing disc. Our main hypothesis is that changes in morphogen concentration along the proximal boundary veins of wing cells govern focus point selection. Based on previous studies, we focus on a spatially two-dimensional reaction-diffusion system model posed in the interior of each wing cell that describes the formation of focus points. Using finite element based numerical simulations, we demonstrate that variation in the proximal boundary condition is sufficient to robustly select whether an eyespot focus point forms in otherwise identical wing cells. We also illustrate that this behavior is robust to small perturbations in the parameters and geometry and moderate levels of noise. Hence, we suggest that an anterior-posterior pattern of morphogen concentration along the proximal vein may be the main determinant of the distribution of focus points on the wing surface. In order to complete our model, we propose a two stage reaction-diffusion system model, in which an one-dimensional surface reaction-diffusion system, posed on the proximal vein, generates the morphogen concentrations that act as non-homogeneous Dirichlet (i.e., fixed boundary conditions for the two-dimensional reaction-diffusion model posed in the wing cells. The two-stage model appears capable of generating focus point distributions
Modeling vertical loads in pools resulting from fluid injection. [BWR
Energy Technology Data Exchange (ETDEWEB)
Lai, W.; McCauley, E.W.
1978-06-15
Table-top model experiments were performed to investigate pressure suppression pool dynamics effects due to a postulated loss-of-coolant accident (LOCA) for the Peachbottom Mark I boiling water reactor containment system. The results guided subsequent conduct of experiments in the /sup 1///sub 5/-scale facility and provided new insight into the vertical load function (VLF). Model experiments show an oscillatory VLF with the download typically double-spiked followed by a more gradual sinusoidal upload. The load function contains a high frequency oscillation superimposed on a low frequency one; evidence from measurements indicates that the oscillations are initiated by fluid dynamics phenomena.
Modeling the Temperature Fields of Copper Powder Melting in the Process of Selective Laser Melting
Saprykin, A. A.; Ibragimov, E. A.; Babakova, E. V.
2016-08-01
Various process variables influence on the quality of the end product when SLM (Selective Laser Melting) synthesizing items of powder materials. The authors of the paper suggest using the model of distributing the temperature fields when forming single tracks and layers of copper powder PMS-1. Relying on the results of modeling it is proposed to reduce melting of powder particles out of the scanning area.
SKANDER, Achraf; Roucoules, Lionel; KLEIN MEYER, Jean-Sébastien
2008-01-01
This research is part of the regional French project IFP2R : " Manufacturing constraints integration in rapid prototyped part design " with IFTS (Higher Technical Formation Institute of Charleville Mézières- France).; The research results presented in this paper are related to the specification of a method and models that tackle the problem of manufacturing processes selection and the integration, as soon as possible, of their constraints in the product modelling (i.e. information synthesis)....
DEFF Research Database (Denmark)
Yang, Ziheng; Nielsen, Rasmus
2008-01-01
Current models of codon substitution are formulated at the levels of nucleotide substitution and do not explicitly consider the separate effects of mutation and selection. They are thus incapable of inferring whether mutation or selection is responsible for evolution at silent sites. Here we...... to examine the null hypothesis that codon usage is due to mutation bias alone, not influenced by natural selection. Application of the test to the mammalian data led to rejection of the null hypothesis in most genes, suggesting that natural selection may be a driving force in the evolution of synonymous...... codon usage in mammals. Estimates of selection coefficients nevertheless suggest that selection on codon usage is weak and most mutations are nearly neutral. The sensitivity of the analysis on the assumed mutation model is discussed....
Multiphysics modeling of selective laser sintering/melting
Ganeriwala, Rishi Kumar
A significant percentage of total global employment is due to the manufacturing industry. However, manufacturing also accounts for nearly 20% of total energy usage in the United States according to the EIA. In fact, manufacturing accounted for 90% of industrial energy consumption and 84% of industry carbon dioxide emissions in 2002. Clearly, advances in manufacturing technology and efficiency are necessary to curb emissions and help society as a whole. Additive manufacturing (AM) refers to a relatively recent group of manufacturing technologies whereby one can 3D print parts, which has the potential to significantly reduce waste, reconfigure the supply chain, and generally disrupt the whole manufacturing industry. Selective laser sintering/melting (SLS/SLM) is one type of AM technology with the distinct advantage of being able to 3D print metals and rapidly produce net shape parts with complicated geometries. In SLS/SLM parts are built up layer-by-layer out of powder particles, which are selectively sintered/melted via a laser. However, in order to produce defect-free parts of sufficient strength, the process parameters (laser power, scan speed, layer thickness, powder size, etc.) must be carefully optimized. Obviously, these process parameters will vary depending on material, part geometry, and desired final part characteristics. Running experiments to optimize these parameters is costly, energy intensive, and extremely material specific. Thus a computational model of this process would be highly valuable. In this work a three dimensional, reduced order, coupled discrete element - finite difference model is presented for simulating the deposition and subsequent laser heating of a layer of powder particles sitting on top of a substrate. Validation is provided and parameter studies are conducted showing the ability of this model to help determine appropriate process parameters and an optimal powder size distribution for a given material. Next, thermal stresses upon
A training set selection strategy for a universal near-infrared quantitative model.
Jia, Yan-Hua; Liu, Xu-Ping; Feng, Yan-Chun; Hu, Chang-Qin
2011-06-01
The purpose of this article is to propose an empirical solution to the problem of how many clusters of complex samples should be selected to construct the training set for a universal near infrared quantitative model based on the Naes method. The sample spectra were hierarchically classified into clusters by Ward's algorithm and Euclidean distance. If the sample spectra were classified into two clusters, the 1/50 of the largest Heterogeneity value in the cluster with larger variation was set as the threshold to determine the total number of clusters. One sample was then randomly selected from each cluster to construct the training set, and the number of samples in training set equaled the number of clusters. In this study, 98 batches of rifampicin capsules with API contents ranging from 50.1% to 99.4% were studied with this strategy. The root mean square errors of cross validation and prediction were 2.54% and 2.31% for the model for rifampicin capsules, respectively. Then, we evaluated this model in terms of outlier diagnostics, accuracy, precision, and robustness. We also used the strategy of training set sample selection to revalidate the models for cefradine capsules, roxithromycin tablets, and erythromycin ethylsuccinate tablets, and the results were satisfactory. In conclusion, all results showed that this training set sample selection strategy assisted in the quick and accurate construction of quantitative models using near-infrared spectroscopy.
Directory of Open Access Journals (Sweden)
Marco Del Giudice
Full Text Available BACKGROUND: Schizophrenia is a mental disorder marked by an evolutionarily puzzling combination of high heritability, reduced reproductive success, and a remarkably stable prevalence. Recently, it has been proposed that sexual selection may be crucially involved in the evolution of schizophrenia. In the sexual selection model (SSM of schizophrenia and schizotypy, schizophrenia represents the negative extreme of a sexually selected indicator of genetic fitness and condition. Schizotypal personality traits are hypothesized to increase the sensitivity of the fitness indicator, thus conferring mating advantages on high-fitness individuals but increasing the risk of schizophrenia in low-fitness individuals; the advantages of successful schzotypy would be mediated by enhanced courtship-related traits such as verbal creativity. Thus, schizotypy-increasing alleles would be maintained by sexual selection, and could be selectively neutral or even beneficial, at least in some populations. However, most empirical studies find that the reduction in fertility experienced by schizophrenic patients is not compensated for by increased fertility in their unaffected relatives. This finding has been interpreted as indicating strong negative selection on schizotypy-increasing alleles, and providing evidence against sexual selection on schizotypy. METHODOLOGY: A simple mathematical model is presented, showing that reduced fertility in the families of schizophrenic patients can coexist with selective neutrality of schizotypy-increasing alleles, or even with positive selection on schizotypy in the general population. If the SSM is correct, studies of patients' families can be expected to underestimate the true fertility associated with schizotypy. SIGNIFICANCE: This paper formally demonstrates that reduced fertility in the families of schizophrenic patients does not constitute evidence against sexual selection on schizotypy-increasing alleles. Futhermore, it suggests
Consistency in Estimation and Model Selection of Dynamic Panel Data Models with Fixed Effects
Directory of Open Access Journals (Sweden)
Guangjie Li
2015-07-01
Full Text Available We examine the relationship between consistent parameter estimation and model selection for autoregressive panel data models with fixed effects. We find that the transformation of fixed effects proposed by Lancaster (2002 does not necessarily lead to consistent estimation of common parameters when some true exogenous regressors are excluded. We propose a data dependent way to specify the prior of the autoregressive coefficient and argue for comparing different model specifications before parameter estimation. Model selection properties of Bayes factors and Bayesian information criterion (BIC are investigated. When model uncertainty is substantial, we recommend the use of Bayesian Model Averaging to obtain point estimators with lower root mean squared errors (RMSE. We also study the implications of different levels of inclusion probabilities by simulations.
Neural Network Model Based Cluster Head Selection for Power Control
Directory of Open Access Journals (Sweden)
Krishan Kumar
2011-01-01
Full Text Available Mobile ad-hoc network has challenge of the limited power to prolong the lifetime of the network, because power is a valuable resource in mobile ad-hoc network. The status of power consumption should be continuously monitored after network deployment. In this paper, we propose coverage aware neural network based power control routing with the objective of maximizing the network lifetime. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage. The simulation results show that the proposed scheme can be used in wide area of applications in mobile ad-hoc network.
Energy Technology Data Exchange (ETDEWEB)
Mack, Robert [Shell International E and P, b.v. Kessler Park 1, Postbus 60, 2280 AB Rijswijk (Netherlands)
2004-07-01
A laboratory program was performed to evaluate the potential of selected martensitic stainless steels for downhole cladding applications. The evaluation of the effects of tubular expansion on mechanical properties, defects, and resistance to environmentally assisted cracking demonstrated that some steels were acceptable for the intended application. The results were used to qualify and select the stainless steel for the intended sweet cladding applications. (authors)
NVC Based Model for Selecting Effective Requirement Elicitation Technique
Directory of Open Access Journals (Sweden)
Md. Rizwan Beg
2012-10-01
Full Text Available Requirement Engineering process starts from gathering of requirements i.e.; requirements elicitation. Requirementselicitation (RE is the base building block for a software project and has very high impact onsubsequent design and builds phases as well. Accurately capturing system requirements is the major factorin the failure of most of software projects. Due to the criticality and impact of this phase, it is very importantto perform the requirements elicitation in no less than a perfect manner. One of the most difficult jobsfor elicitor is to select appropriate technique for eliciting the requirement. Interviewing and Interactingstakeholder during Elicitation process is a communication intensive activity involves Verbal and Nonverbalcommunication (NVC. Elicitor should give emphasis to Non-verbal communication along with verbalcommunication so that requirements recorded more efficiently and effectively. In this paper we proposea model in which stakeholders are classified by observing non-verbal communication and use it as a basefor elicitation technique selection. We also propose an efficient plan for requirements elicitation which intendsto overcome on the constraints, faced by elicitor.
Scaling limits of a model for selection at two scales
Luo, Shishi; Mattingly, Jonathan C.
2017-04-01
The dynamics of a population undergoing selection is a central topic in evolutionary biology. This question is particularly intriguing in the case where selective forces act in opposing directions at two population scales. For example, a fast-replicating virus strain outcompetes slower-replicating strains at the within-host scale. However, if the fast-replicating strain causes host morbidity and is less frequently transmitted, it can be outcompeted by slower-replicating strains at the between-host scale. Here we consider a stochastic ball-and-urn process which models this type of phenomenon. We prove the weak convergence of this process under two natural scalings. The first scaling leads to a deterministic nonlinear integro-partial differential equation on the interval [0,1] with dependence on a single parameter, λ. We show that the fixed points of this differential equation are Beta distributions and that their stability depends on λ and the behavior of the initial data around 1. The second scaling leads to a measure-valued Fleming–Viot process, an infinite dimensional stochastic process that is frequently associated with a population genetics.
Physics-based statistical learning approach to mesoscopic model selection
Taverniers, Søren; Haut, Terry S.; Barros, Kipton; Alexander, Francis J.; Lookman, Turab
2015-11-01
In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. We apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD "training" data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD "test" data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.
Initial CGE Model Results Summary Exogenous and Endogenous Variables Tests
Energy Technology Data Exchange (ETDEWEB)
Edwards, Brian Keith [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Boero, Riccardo [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Rivera, Michael Kelly [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-08-07
The following discussion presents initial results of tests of the most recent version of the National Infrastructure Simulation and Analysis Center Dynamic Computable General Equilibrium (CGE) model developed by Los Alamos National Laboratory (LANL). The intent of this is to test and assess the model’s behavioral properties. The test evaluated whether the predicted impacts are reasonable from a qualitative perspective. This issue is whether the predicted change, be it an increase or decrease in other model variables, is consistent with prior economic intuition and expectations about the predicted change. One of the purposes of this effort is to determine whether model changes are needed in order to improve its behavior qualitatively and quantitatively.
Marker-assisted selection reduces expected inbreeding but can result in large effects of hitchhiking
DEFF Research Database (Denmark)
Pedersen, L D; Sørensen, A C; Berg, P
2010-01-01
We used computer simulations to investigate to what extent true inbreeding, i.e. identity-by-descent, is affected by the use of marker-assisted selection (MAS) relative to traditional best linear unbiased predictions (BLUP) selection. The effect was studied by varying the heritability (h2 = 0.04 vs....... 0.25), the marker distance (MAS vs. selection on the gene, GAS), the favourable QTL allele effect (α = 0.118 vs. 0.236) and the initial frequency of the favourable QTL allele (p = 0.01 vs. 0.1) in a population resembling the breeding nucleus of a dairy cattle population. The simulated genome...
Results of a selective policy for preoperative radiotherapy in rectal cancer surgery.
Gandy; O'Leary; Falk; Roe
2000-01-01
Preoperative radiotherapy (pRT) for rectal cancer may reduce local recurrence and improve survival. This study was undertaken to assess a selective policy of pRT in rectal cancer. The aim was to determine whether patients likely to have involved circumferential margins (CRM) could be reliably selected for pRT using clinical criteria. We have used CRM and delay in surgery as outcome measures. Seventy-nine patients with rectal cancer were assessed for preoperative radiotherapy using clinical criteria. Twelve of 26 (46%) pRT patients had positive CRM compared with three of 53 (5.6%) who did not receive pRT (P benefit from radiotherapy and has avoided excessive delays prior to surgery. However, almost half of the pRT patients did not have involved CRM. With improved imaging techniques we may be able to refine our selection criteria further.
Conceptual Incoherence as a Result of the use of Multiple Historical Models in School Textbooks
Gericke, Niklas M.; Hagberg, Mariana
2010-08-01
This paper explores the occurrence of conceptual incoherence in upper secondary school textbooks resulting from the use of multiple historical models. Swedish biology and chemistry textbooks, as well as a selection of books from English speaking countries, were examined. The purpose of the study was to identify which models are used to represent the phenomenon of gene function in textbooks and to investigate how these models relate to historical scientific models and subject matter contexts. Models constructed for specific use in textbooks were identified using concept mapping. The data were further analyzed by content analysis. The study shows that several different historical models are used in parallel in textbooks to describe gene function. Certain historical models were used more often then others and the most recent scientific views were rarely referred to in the textbooks. Hybrid models were used frequently, i.e. most of the models in the textbooks consisted of a number of components of several historical models. Since the various historical models were developed as part of different scientific frameworks, hybrid models exhibit conceptual incoherence, which may be a source of confusion for students. Furthermore, the use of different historical models was linked to particular subject contexts in the textbooks studied. The results from Swedish and international textbooks were similar, indicating the general applicability of our conclusions.
Research on Site Selection Model of Distribution Center of Agricultural Products
Institute of Scientific and Technical Information of China (English)
2011-01-01
In the light of the practical situation of logistics distribution of agricultural products,we primarily select transportation factor,economic factor,environment factor,and other factors,to establish evaluation index system of site selection of distribution center of agricultural products.And then we adopt the analytic hierarchy process method to calculate weight of site selection of distribution center of agricultural products.Under the circumstance that the evaluation information is interval number,we use uncertain and multiple attribute decision making method to establish site selection model of distribution center of agricultural products.Finally,taking one city as an example,we discuss the application of this model in site selection of distribution center of agricultural products.The results of empirical analysis show that the model we established fully considers the randomness and uncertainty in the process of evaluation,so as to make the results of evaluation more objective,in line with reality.So the effect of evaluation is better as against the former real number evaluation calibration.
The use of vector bootstrapping to improve variable selection precision in Lasso models.
Laurin, Charles; Boomsma, Dorret; Lubke, Gitta
2016-08-01
The Lasso is a shrinkage regression method that is widely used for variable selection in statistical genetics. Commonly, K-fold cross-validation is used to fit a Lasso model. This is sometimes followed by using bootstrap confidence intervals to improve precision in the resulting variable selections. Nesting cross-validation within bootstrapping could provide further improvements in precision, but this has not been investigated systematically. We performed simulation studies of Lasso variable selection precision (VSP) with and without nesting cross-validation within bootstrapping. Data were simulated to represent genomic data under a polygenic model as well as under a model with effect sizes representative of typical GWAS results. We compared these approaches to each other as well as to software defaults for the Lasso. Nested cross-validation had the most precise variable selection at small effect sizes. At larger effect sizes, there was no advantage to nesting. We illustrated the nested approach with empirical data comprising SNPs and SNP-SNP interactions from the most significant SNPs in a GWAS of borderline personality symptoms. In the empirical example, we found that the default Lasso selected low-reliability SNPs and interactions which were excluded by bootstrapping.
Simulating lightning into the RAMS model: implementation and preliminary results
Directory of Open Access Journals (Sweden)
S. Federico
2014-05-01
Full Text Available This paper shows the results of a tailored version of a previously published methodology, designed to simulate lightning activity, implemented into the Regional Atmospheric Modeling System (RAMS. The method gives the flash density at the resolution of the RAMS grid-scale allowing for a detailed analysis of the evolution of simulated lightning activity. The system is applied in detail to two case studies occurred over the Lazio Region, in Central Italy. Simulations are compared with the lightning activity detected by the LINET network. The cases refer to two thunderstorms of different intensity. Results show that the model predicts reasonably well both cases and that the lightning activity is well reproduced especially for the most intense case. However, there are errors in timing and positioning of the convection, whose magnitude depends on the case study, which mirrors in timing and positioning errors of the lightning distribution. To assess objectively the performance of the methodology, standard scores are presented for four additional case studies. Scores show the ability of the methodology to simulate the daily lightning activity for different spatial scales and for two different minimum thresholds of flash number density. The performance decreases at finer spatial scales and for higher thresholds. The comparison of simulated and observed lighting activity is an immediate and powerful tool to assess the model ability to reproduce the intensity and the evolution of the convection. This shows the importance of the use of computationally efficient lightning schemes, such as the one described in this paper, in forecast models.
Variable Selection in the Partially Linear Errors-in-Variables Models for Longitudinal Data
Institute of Scientific and Technical Information of China (English)
Yi-ping YANG; Liu-gen XUE; Wei-hu CHENG
2012-01-01
This paper proposes a new approach for variable selection in partially linear errors-in-variables (EV) models for longitudinal data by penalizing appropriate estimating functions.We apply the SCAD penalty to simultaneously select significant variables and estimate unknown parameters.The rate of convergence and the asymptotic normality of the resulting estimators are established.Furthermore,with proper choice of regularization parameters,we show that the proposed estimators perform as well as the oracle procedure.A new algorithm is proposed for solving penalized estimating equation.The asymptotic results are augmented by a simulation study.
Modeling air quality over China: Results from the Panda project
Katinka Petersen, Anna; Bouarar, Idir; Brasseur, Guy; Granier, Claire; Xie, Ying; Wang, Lili; Wang, Xuemei
2015-04-01
China faces strong air pollution problems related to rapid economic development in the past decade and increasing demand for energy. Air quality monitoring stations often report high levels of particle matter and ozone all over the country. Knowing its long-term health impacts, air pollution became then a pressing problem not only in China but also in other Asian countries. The PANDA project is a result of cooperation between scientists from Europe and China who joined their efforts for a better understanding of the processes controlling air pollution in China, improve methods for monitoring air quality and elaborate indicators in support of European and Chinese policies. A modeling system of air pollution is being setup within the PANDA project and include advanced global (MACC, EMEP) and regional (WRF-Chem, EMEP) meteorological and chemical models to analyze and monitor air quality in China. The poster describes the accomplishments obtained within the first year of the project. Model simulations for January and July 2010 are evaluated with satellite measurements (SCIAMACHY NO2 and MOPITT CO) and in-situ data (O3, CO, NOx, PM10 and PM2.5) observed at several surface stations in China. Using the WRF-Chem model, we investigate the sensitivity of the model performance to emissions (MACCity, HTAPv2), horizontal resolution (60km, 20km) and choice of initial and boundary conditions.
Institute of Scientific and Technical Information of China (English)
Bin Jiang; Chao Yang; Takao Terano
2015-01-01
This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road⁃network congestion problem. We focus on improving those severely congested links. Firstly, a multi⁃agent system is built, where each agent stands for a vehicle, and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent⁃based model captures the nonlinear feedback between vehicle routing behaviors and road⁃network congestion status. Secondly, a hybrid routing selection strategy is provided, which guides the vehicle routes adapting to the real⁃time road⁃network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution, by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road⁃network. Finally, we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And, the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom⁃up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.
Directory of Open Access Journals (Sweden)
Hossam M Zawbaa
Full Text Available Poly-lactide-co-glycolide (PLGA is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP, multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR. The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven.
Directory of Open Access Journals (Sweden)
Naseer Aisha
2011-05-01
Full Text Available Abstract Background There is an increasing recognition that modelling and simulation can assist in the process of designing health care policies, strategies and operations. However, the current use is limited and answers to questions such as what methods to use and when remain somewhat underdeveloped. Aim The aim of this study is to provide a mechanism for decision makers in health services planning and management to compare a broad range of modelling and simulation methods so that they can better select and use them or better commission relevant modelling and simulation work. Methods This paper proposes a modelling and simulation method comparison and selection tool developed from a comprehensive literature review, the research team's extensive expertise and inputs from potential users. Twenty-eight different methods were identified, characterised by their relevance to different application areas, project life cycle stages, types of output and levels of insight, and four input resources required (time, money, knowledge and data. Results The characterisation is presented in matrix forms to allow quick comparison and selection. This paper also highlights significant knowledge gaps in the existing literature when assessing the applicability of particular approaches to health services management, where modelling and simulation skills are scarce let alone money and time. Conclusions A modelling and simulation method comparison and selection tool is developed to assist with the selection of methods appropriate to supporting specific decision making processes. In particular it addresses the issue of which method is most appropriate to which specific health services management problem, what the user might expect to be obtained from the method, and what is required to use the method. In summary, we believe the tool adds value to the scarce existing literature on methods comparison and selection.
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Paulo H. Ferreira
2015-04-01
Full Text Available Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model, a logistic regression with state-dependent sample selection model and a bounded logistic regression model via a large simulation study. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian retail bank portfolio. Our simulation results so far revealed that there is nostatistically significant difference in terms of predictive capacity among the naive logistic regression models, the logistic regression with state-dependent sample selection models and the bounded logistic regression models. However, there is difference between the distributions of the estimated default probabilities from these three statistical modeling techniques, with the naive logistic regression models and the boundedlogistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. Which are common in practice.
Exact results for the one dimensional asymmetric exclusion model
Derrida, B.; Evans, M. R.; Hakim, V.; Pasquier, V.
1993-11-01
The asymmetric exclusion model describes a system of particles hopping in a preferred direction with hard core repulsion. These particles can be thought of as charged particles in a field, as steps of an interface, as cars in a queue. Several exact results concerning the steady state of this system have been obtained recently. The solution consists of representing the weights of the configurations in the steady state as products of non-commuting matrices.
Exact results for the one dimensional asymmetric exclusion model
Energy Technology Data Exchange (ETDEWEB)
Derrida, B.; Evans, M.R.; Pasquier, V. [CEA Centre d`Etudes de Saclay, 91 - Gif-sur-Yvette (France). Service de Physique Theorique; Hakim, V. [Ecole Normale Superieure, 75 - Paris (France)
1993-12-31
The asymmetric exclusion model describes a system of particles hopping in a preferred direction with hard core repulsion. These particles can be thought of as charged particles in a field, as steps of an interface, as cars in a queue. Several exact results concerning the steady state of this system have been obtained recently. The solution consists of representing the weights of the configurations in the steady state as products of non-commuting matrices. (author).
APPLYING LOGISTIC REGRESSION MODEL TO THE EXAMINATION RESULTS DATA
Directory of Open Access Journals (Sweden)
Goutam Saha
2011-01-01
Full Text Available The binary logistic regression model is used to analyze the school examination results(scores of 1002 students. The analysis is performed on the basis of the independent variables viz.gender, medium of instruction, type of schools, category of schools, board of examinations andlocation of schools, where scores or marks are assumed to be dependent variables. The odds ratioanalysis compares the scores obtained in two examinations viz. matriculation and highersecondary.
Analytical results for a three-phase traffic model.
Huang, Ding-wei
2003-10-01
We study analytically a cellular automaton model, which is able to present three different traffic phases on a homogeneous highway. The characteristics displayed in the fundamental diagram can be well discerned by analyzing the evolution of density configurations. Analytical expressions for the traffic flow and shock speed are obtained. The synchronized flow in the intermediate-density region is the result of aggressive driving scheme and determined mainly by the stochastic noise.
Agent-Based vs. Equation-based Epidemiological Models:A Model Selection Case Study
Energy Technology Data Exchange (ETDEWEB)
Sukumar, Sreenivas R [ORNL; Nutaro, James J [ORNL
2012-01-01
This paper is motivated by the need to design model validation strategies for epidemiological disease-spread models. We consider both agent-based and equation-based models of pandemic disease spread and study the nuances and complexities one has to consider from the perspective of model validation. For this purpose, we instantiate an equation based model and an agent based model of the 1918 Spanish flu and we leverage data published in the literature for our case- study. We present our observations from the perspective of each implementation and discuss the application of model-selection criteria to compare the risk in choosing one modeling paradigm to another. We conclude with a discussion of our experience and document future ideas for a model validation framework.
DEFF Research Database (Denmark)
Savietto, D; Cervera, C; Rodenas, L
2014-01-01
This study examined the effect of long-term selection of a maternal rabbit line, solely for a reproductive criterion, on the ability of female rabbits to deal with constrained environmental conditions. Female rabbits from generations 16 and 36 (n=72 and 79, respectively) of a line founded and sel...
Challenges in validating model results for first year ice
Melsom, Arne; Eastwood, Steinar; Xie, Jiping; Aaboe, Signe; Bertino, Laurent
2017-04-01
In order to assess the quality of model results for the distribution of first year ice, a comparison with a product based on observations from satellite-borne instruments has been performed. Such a comparison is not straightforward due to the contrasting algorithms that are used in the model product and the remote sensing product. The implementation of the validation is discussed in light of the differences between this set of products, and validation results are presented. The model product is the daily updated 10-day forecast from the Arctic Monitoring and Forecasting Centre in CMEMS. The forecasts are produced with the assimilative ocean prediction system TOPAZ. Presently, observations of sea ice concentration and sea ice drift are introduced in the assimilation step, but data for sea ice thickness and ice age (or roughness) are not included. The model computes the age of the ice by recording and updating the time passed after ice formation as sea ice grows and deteriorates as it is advected inside the model domain. Ice that is younger than 365 days is classified as first year ice. The fraction of first-year ice is recorded as a tracer in each grid cell. The Ocean and Sea Ice Thematic Assembly Centre in CMEMS redistributes a daily product from the EUMETSAT OSI SAF of gridded sea ice conditions which include "ice type", a representation of the separation of regions between those infested by first year ice, and those infested by multi-year ice. The ice type is parameterized based on data for the gradient ratio GR(19,37) from SSMIS observations, and from the ASCAT backscatter parameter. This product also includes information on ambiguity in the processing of the remote sensing data, and the product's confidence level, which have a strong seasonal dependency.
Directory of Open Access Journals (Sweden)
Dana Kubíčková
2015-12-01
Full Text Available In this paper are presented the results of a study examining the ability of Ohlson’s Logit model assessing and predicting the financial condition development of SMEs in comparison with the other models outcomes. Ohlson´s model was created using logit regression, which allows in the evaluation of the financial situation involve qualitative and discrete variables. The aim of the study is to determine whether the method used to derive the model influences the final assessment of the financial condition and indication of bankruptcy. The solution is based on the comparison of the resulting assessment of these four models, value of which were calculated on the same sample of Czech firms. As compared models were selected Z-score model, derived in the terms of US enterprises, IN05 model, which was derived in the conditions of Czech companies and Taffer´s model, derived in the conditions of UK firms. The sample consisted of 1996 small and medium firms in the manufacturing industry in Czech Republic. Data were obtained from the database of Albertina for the period of the years 2012 and 2013. It was found that the assessment of the firm´s financial situation matches in case of the results of Ohlson´s model and Taffler´s model, greater differences were found between the resulting values of Ohlson´s and Taffler´s model on one side and IN05 and Altman's model on the other side. Ohlson´s model and the Taffler´s model confirmed a good financial situation of companies in about 90 per cent of firms, Altman´s model and IN05 model in about 40 per cent of firms. The influence of the method used to derive the model on the assessment of the financial condition of companies was not proven.
Novel Harmonic Regularization Approach for Variable Selection in Cox’s Proportional Hazards Model
Directory of Open Access Journals (Sweden)
Ge-Jin Chu
2014-01-01
Full Text Available Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex Lq (1/2select key risk factors in the Cox’s proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL, the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods.
Model selection by LASSO methods in a change-point model
Ciuperca, Gabriela
2011-01-01
The paper considers a linear regression model with multiple change-points occurring at unknown times. The LASSO technique is very interesting since it allows the parametric estimation, including the change-points, and automatic variable selection simultaneously. The asymptotic properties of the LASSO-type (which has as particular case the LASSO estimator) and of the adaptive LASSO estimators are studied. For this last estimator the oracle properties are proved. In both cases, a model selection criterion is proposed. Numerical examples are provided showing the performances of the adaptive LASSO estimator compared to the LS estimator.
A simple model of group selection that cannot be analyzed with inclusive fitness
M. van Veelen; S. Luo; B. Simon
2014-01-01
A widespread claim in evolutionary theory is that every group selection model can be recast in terms of inclusive fitness. Although there are interesting classes of group selection models for which this is possible, we show that it is not true in general. With a simple set of group selection models,
Hasanzadeh Nafari, R.; Ngo, T.; Lehman, W.
2015-06-01
Rapid urbanisation, climate change and unsustainable developments are increasing the risk of floods, namely flood frequency and intensity. Flood is a frequent natural hazard that has significant financial consequences for Australia. The emergency response system in Australia is very successful and has saved many lives over the years. However, the preparedness for natural disaster impacts in terms of loss reduction and damage mitigation has been less successful. This study aims to quantify the direct physical damage to residential structures that are prone to flood phenomena in Australia. In this paper, the physical consequences of two floods from Queensland have been simulated, and the results have been compared with the performance of two selected methodologies and one newly derived model. Based on this analysis, the adaptability and applicability of the selected methodologies will be assessed in terms of Australian geographical conditions. Results obtained from the new empirically-based function and non-adapted methodologies indicate that it is apparent that the precision of flood damage models are strongly dependent on selected stage damage curves, and flood damage estimation without model validation results in inaccurate prediction of losses. Therefore, it is very important to be aware of the associated uncertainties in flood risk assessment, especially if models have not been adapted with real damage data.
A Spectroscopic And Photometric Survey Of Selected Near-earth Asteroids: Results From 2008-2012.
Hicks, Michael D.; Lawrence, K. J.; Somers, J.; Teague, S.; Strojia, C.; Dombroski, D.; Davtyan, T.; Barajas, T.; Truong, T.; McCormack, M.; Gerhart, C.; Garcia, K.; Rhoades, H.; Mayes, D.; Shitanishi, J.; Foster, J.; McAuley, A.
2012-10-01
Over the past four years we have used the dual-channel optical spectrometer (DBSP) at the Palomar 200-inch telescope (P200) to collect low-resolution spectroscopy of Near-Earth Asteroids (NEAs) and have been awarded, on average, three nights per semester. Additionally, we have ample access to the JPL Table Mountain 0.6-m (TMO) telescope for time-resolved Bessel BVRI photometry. Undergraduate students from the CURE program (Consortium for Undergraduate Research Experience) have provided a large fraction of the observing effort at TMO. With these two telescopes, we strove to characterize all bright (VEarth asteroids (as potential mission targets). In this paper we will present our observational results for 150 NEAs. Our data products are diverse, and can include taxonomic classification, broad-band colors, rotational period, solar phase behavior and absolute magnitude, and 3-d shape/pole models derived from lightcurve inversion. We will discuss the variability between main-belt and near-Earth spectral families, quantify differences between PHA and background near-Earth populations, and present our first attempts to generate a spectral photometry using solely near-Earth asteroids. This research was funded by NASA. The student participation was supported by the National Science Foundation under REU grant 0852088 to Cal State LA.
Optimal experiment selection for parameter estimation in biological differential equation models
Directory of Open Access Journals (Sweden)
Transtrum Mark K
2012-07-01
Full Text Available Abstract Background Parameter estimation in biological models is a common yet challenging problem. In this work we explore the problem for gene regulatory networks modeled by differential equations with unknown parameters, such as decay rates, reaction rates, Michaelis-Menten constants, and Hill coefficients. We explore the question to what extent parameters can be efficiently estimated by appropriate experimental selection. Results A minimization formulation is used to find the parameter values that best fit the experiment data. When the data is insufficient, the minimization problem often has many local minima that fit the data reasonably well. We show that selecting a new experiment based on the local Fisher Information of one local minimum generates additional data that allows one to successfully discriminate among the many local minima. The parameters can be estimated to high accuracy by iteratively performing minimization and experiment selection. We show that the experiment choices are roughly independent of which local minima is used to calculate the local Fisher Information. Conclusions We show that by an appropriate choice of experiments, one can, in principle, efficiently and accurately estimate all the parameters of gene regulatory network. In addition, we demonstrate that appropriate experiment selection can also allow one to restrict model predictions without constraining the parameters using many fewer experiments. We suggest that predicting model behaviors and inferring parameters represent two different approaches to model calibration with different requirements on data and experimental cost.
Directory of Open Access Journals (Sweden)
Marcin Luczak
2014-01-01
Full Text Available This paper presents selected results and aspects of the multidisciplinary and interdisciplinary research oriented for the experimental and numerical study of the structural dynamics of a bend-twist coupled full scale section of a wind turbine blade structure. The main goal of the conducted research is to validate finite element model of the modified wind turbine blade section mounted in the flexible support structure accordingly to the experimental results. Bend-twist coupling was implemented by adding angled unidirectional layers on the suction and pressure side of the blade. Dynamic test and simulations were performed on a section of a full scale wind turbine blade provided by Vestas Wind Systems A/S. The numerical results are compared to the experimental measurements and the discrepancies are assessed by natural frequency difference and modal assurance criterion. Based on sensitivity analysis, set of model parameters was selected for the model updating process. Design of experiment and response surface method was implemented to find values of model parameters yielding results closest to the experimental. The updated finite element model is producing results more consistent with the measurement outcomes.
Results of Satellite Brightness Modeling Using Kringing Optimized Interpolation
Weeden, C.; Hejduk, M.
At the 2005 AMOS conference, Kriging Optimized Interpolation (KOI) was presented as a tool to model satellite brightness as a function of phase angle and solar declination angle (J.M Okada and M.D. Hejduk). Since November 2005, this method has been used to support the tasking algorithm for all optical sensors in the Space Surveillance Network (SSN). The satellite brightness maps generated by the KOI program are compared to each sensor's ability to detect an object as a function of the brightness of the background sky and angular rate of the object. This will determine if the sensor can technically detect an object based on an explicit calculation of the object's probability of detection. In addition, recent upgrades at Ground-Based Electro Optical Deep Space Surveillance Sites (GEODSS) sites have increased the amount and quality of brightness data collected and therefore available for analysis. This in turn has provided enough data to study the modeling process in more detail in order to obtain the most accurate brightness prediction of satellites. Analysis of two years of brightness data gathered from optical sensors and modeled via KOI solutions are outlined in this paper. By comparison, geo-stationary objects (GEO) were tracked less than non-GEO objects but had higher density tracking in phase angle due to artifices of scheduling. A statistically-significant fit to a deterministic model was possible less than half the time in both GEO and non-GEO tracks, showing that a stochastic model must often be used alone to produce brightness results, but such results are nonetheless serviceable. Within the Kriging solution, the exponential variogram model was the most frequently employed in both GEO and non-GEO tracks, indicating that monotonic brightness variation with both phase and solar declination angle is common and testifying to the suitability to the application of regionalized variable theory to this particular problem. Finally, the average nugget value, or
Shen, Chung-Wei; Chen, Yi-Hau
2015-10-01
Missing observations and covariate measurement error commonly arise in longitudinal data. However, existing methods for model selection in marginal regression analysis of longitudinal data fail to address the potential bias resulting from these issues. To tackle this problem, we propose a new model selection criterion, the Generalized Longitudinal Information Criterion, which is based on an approximately unbiased estimator for the expected quadratic error of a considered marginal model accounting for both data missingness and covariate measurement error. The simulation results reveal that the proposed method performs quite well in the presence of missing data and covariate measurement error. On the contrary, the naive procedures without taking care of such complexity in data may perform quite poorly. The proposed method is applied to data from the Taiwan Longitudinal Study on Aging to assess the relationship of depression with health and social status in the elderly, accommodating measurement error in the covariate as well as missing observations.
Titan Chemistry: Results From A Global Climate Model
Wilson, Eric; West, R. A.; Friedson, A. J.; Oyafuso, F.
2008-09-01
We present results from a 3-dimesional global climate model of Titan's atmosphere and surface. This model, a modified version of NCAR's CAM-3 (Community Atmosphere Model), has been optimized for analysis of Titan's lower atmosphere and surface. With the inclusion of forcing from Saturn's gravitational tides, interaction from the surface, transfer of longwave and shortwave radiation, and parameterization of haze properties, constrained by Cassini observations, a dynamical field is generated, which serves to advect 14 long-lived species. The concentrations of these chemical tracers are also affected by 82 chemical reactions and the photolysis of 21 species, based on the Wilson and Atreya (2004) model, that provide sources and sinks for the advected species along with 23 additional non-advected radicals. In addition, the chemical contribution to haze conversion is parameterized along with the microphysical processes that serve to distribute haze opacity throughout the atmosphere. References Wilson, E.H. and S.K. Atreya, J. Geophys. Res., 109, E06002, 2004.