Sample records for model predicts large

  1. Adaptive Gaussian Predictive Process Models for Large Spatial Datasets (United States)

    Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.


    Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is determined by a judicious choice of “knots” or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial-temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. PMID:22298952

  2. Large-area dry bean yield prediction modeling in Mexico (United States)

    Given the importance of dry bean in Mexico, crop yield predictions before harvest are valuable for authorities of the agricultural sector, in order to define support for producers. The aim of this study was to develop an empirical model to estimate the yield of dry bean at the regional level prior t...

  3. Effects of uncertainty in model predictions of individual tree volume on large area volume estimates (United States)

    Ronald E. McRoberts; James A. Westfall


    Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees. However, the uncertainty in the model predictions is generally ignored with the result that the precision of the large area volume estimates is overestimated. The primary study objective was to estimate the effects of model...

  4. Development of estrogen receptor beta binding prediction model using large sets of chemicals. (United States)

    Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao


    We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

  5. Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments. (United States)

    Kaplan, David; Lee, Chansoon


    This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model's posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.

  6. Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

    Directory of Open Access Journals (Sweden)

    Zhaosheng Yang


    Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

  7. Ensemble modeling to predict habitat suitability for a large-scale disturbance specialist. (United States)

    Latif, Quresh S; Saab, Victoria A; Dudley, Jonathan G; Hollenbeck, Jeff P


    To conserve habitat for disturbance specialist species, ecologists must identify where individuals will likely settle in newly disturbed areas. Habitat suitability models can predict which sites at new disturbances will most likely attract specialists. Without validation data from newly disturbed areas, however, the best approach for maximizing predictive accuracy can be unclear (Northwestern U.S.A.). We predicted habitat suitability for nesting Black-backed Woodpeckers (Picoides arcticus; a burned-forest specialist) at 20 recently (≤6 years postwildfire) burned locations in Montana using models calibrated with data from three locations in Washington, Oregon, and Idaho. We developed 8 models using three techniques (weighted logistic regression, Maxent, and Mahalanobis D (2) models) and various combinations of four environmental variables describing burn severity, the north-south orientation of topographic slope, and prefire canopy cover. After translating model predictions into binary classifications (0 = low suitability to unsuitable, 1 = high to moderate suitability), we compiled "ensemble predictions," consisting of the number of models (0-8) predicting any given site as highly suitable. The suitability status for 40% of the area burned by eastside Montana wildfires was consistent across models and therefore robust to uncertainty in the relative accuracy of particular models and in alternative ecological hypotheses they described. Ensemble predictions exhibited two desirable properties: (1) a positive relationship with apparent rates of nest occurrence at calibration locations and (2) declining model agreement outside surveyed environments consistent with our reduced confidence in novel (i.e., "no-analogue") environments. Areas of disagreement among models suggested where future surveys could help validate and refine models for an improved understanding of Black-backed Woodpecker nesting habitat relationships. Ensemble predictions presented here can help guide

  8. Flexible non-linear predictive models for large-scale wind turbine diagnostics

    DEFF Research Database (Denmark)

    Bach-Andersen, Martin; Rømer-Odgaard, Bo; Winther, Ole


    We demonstrate how flexible non-linear models can provide accurate and robust predictions on turbine component temperature sensor data using data-driven principles and only a minimum of system modeling. The merits of different model architectures are evaluated using data from a large set...... of turbines operating under diverse conditions. We then go on to test the predictive models in a diagnostic setting, where the output of the models are used to detect mechanical faults in rotor bearings. Using retrospective data from 22 actual rotor bearing failures, the fault detection performance...... of the models are quantified using a structured framework that provides the metrics required for evaluating the performance in a fleet wide monitoring setup. It is demonstrated that faults are identified with high accuracy up to 45 days before a warning from the hard-threshold warning system....

  9. Prediction Model of Machining Failure Trend Based on Large Data Analysis (United States)

    Li, Jirong


    The mechanical processing has high complexity, strong coupling, a lot of control factors in the machining process, it is prone to failure, in order to improve the accuracy of fault detection of large mechanical equipment, research on fault trend prediction requires machining, machining fault trend prediction model based on fault data. The characteristics of data processing using genetic algorithm K mean clustering for machining, machining feature extraction which reflects the correlation dimension of fault, spectrum characteristics analysis of abnormal vibration of complex mechanical parts processing process, the extraction method of the abnormal vibration of complex mechanical parts processing process of multi-component spectral decomposition and empirical mode decomposition Hilbert based on feature extraction and the decomposition results, in order to establish the intelligent expert system for the data base, combined with large data analysis method to realize the machining of the Fault trend prediction. The simulation results show that this method of fault trend prediction of mechanical machining accuracy is better, the fault in the mechanical process accurate judgment ability, it has good application value analysis and fault diagnosis in the machining process.

  10. Economic Model Predictive Control for Large-Scale and Distributed Energy Systems

    DEFF Research Database (Denmark)

    Standardi, Laura

    In this thesis, we consider control strategies for large and distributed energy systems that are important for the implementation of smart grid technologies.  An electrical grid has to ensure reliability and avoid long-term interruptions in the power supply. Moreover, the share of Renewable Energy...... Sources (RESs) in the smart grids is increasing. These energy sources bring uncertainty to the production due to their fluctuations. Hence,smart grids need suitable control systems that are able to continuously balance power production and consumption.  We apply the Economic Model Predictive Control (EMPC......) strategy to optimise the economic performances of the energy systems and to balance the power production and consumption. In the case of large-scale energy systems, the electrical grid connects a high number of power units. Because of this, the related control problem involves a high number of variables...

  11. Nonlinear Model-Based Predictive Control applied to Large Scale Cryogenic Facilities

    CERN Document Server

    Blanco Vinuela, Enrique; de Prada Moraga, Cesar


    The thesis addresses the study, analysis, development, and finally the real implementation of an advanced control system for the 1.8 K Cooling Loop of the LHC (Large Hadron Collider) accelerator. The LHC is the next accelerator being built at CERN (European Center for Nuclear Research), it will use superconducting magnets operating below a temperature of 1.9 K along a circumference of 27 kilometers. The temperature of these magnets is a control parameter with strict operating constraints. The first control implementations applied a procedure that included linear identification, modelling and regulation using a linear predictive controller. It did improve largely the overall performance of the plant with respect to a classical PID regulator, but the nature of the cryogenic processes pointed out the need of a more adequate technique, such as a nonlinear methodology. This thesis is a first step to develop a global regulation strategy for the overall control of the LHC cells when they will operate simultaneously....

  12. Model Predictive Control for Flexible Power Consumption of Large-Scale Refrigeration Systems

    DEFF Research Database (Denmark)

    Shafiei, Seyed Ehsan; Stoustrup, Jakob; Rasmussen, Henrik


    A model predictive control (MPC) scheme is introduced to directly control the electrical power consumption of large-scale refrigeration systems. Deviation from the baseline of the consumption is corresponded to the storing and delivering of thermal energy. By virtue of such correspondence......, the control method can be employed for regulating power services in the smart grid. The proposed scheme contains the control of cooling capacity as well as optimizing the efficiency factor of the system, which is in general a nonconvex optimization problem. By introducing a fictitious manipulated variable......, and novel incorporation of the evaporation temperature set-point into optimization problem, the convex optimization problem is formulated within the MPC scheme. The method is applied to a simulation benchmark of large-scale refrigeration systems including several medium and low temperature cold reservoirs....

  13. Predicting large wildfires across western North America by modeling seasonal variation in soil water balance. (United States)

    Waring, Richard H; Coops, Nicholas C

    A lengthening of the fire season, coupled with higher temperatures, increases the probability of fires throughout much of western North America. Although regional variation in the frequency of fires is well established, attempts to predict the occurrence of fire at a spatial resolution soil water reserves were coupled more directly to maximum leaf area index (LAI max ) and stomatal behavior. In an earlier publication, we used LAI max and a process-based forest growth model to derive and map the maximum available soil water storage capacity (ASW max ) of forested lands in western North America at l km resolution. To map large fires, we used data products acquired from NASA's Moderate Resolution Imaging Spectroradiometers (MODIS) over the period 2000-2009. To establish general relationships that incorporate the major biophysical processes that control evaporation and transpiration as well as the flammability of live and dead trees, we constructed a decision tree model (DT). We analyzed seasonal variation in the relative availability of soil water ( fASW ) for the years 2001, 2004, and 2007, representing respectively, low, moderate, and high rankings of areas burned. For these selected years, the DT predicted where forest fires >1 km occurred and did not occur at ~100,000 randomly located pixels with an average accuracy of 69 %. Extended over the decade, the area predicted burnt varied by as much as 50 %. The DT identified four seasonal combinations, most of which included exhaustion of ASW during the summer as critical; two combinations involving antecedent conditions the previous spring or fall accounted for 86 % of the predicted fires. The approach introduced in this paper can help identify forested areas where management efforts to reduce fire hazards might prove most beneficial.

  14. Prospective large-scale field study generates predictive model identifying major contributors to colony losses.

    Directory of Open Access Journals (Sweden)

    Merav Gleit Kielmanowicz


    Full Text Available Over the last decade, unusually high losses of colonies have been reported by beekeepers across the USA. Multiple factors such as Varroa destructor, bee viruses, Nosema ceranae, weather, beekeeping practices, nutrition, and pesticides have been shown to contribute to colony losses. Here we describe a large-scale controlled trial, in which different bee pathogens, bee population, and weather conditions across winter were monitored at three locations across the USA. In order to minimize influence of various known contributing factors and their interaction, the hives in the study were not treated with antibiotics or miticides. Additionally, the hives were kept at one location and were not exposed to potential stress factors associated with migration. Our results show that a linear association between load of viruses (DWV or IAPV in Varroa and bees is present at high Varroa infestation levels (>3 mites per 100 bees. The collection of comprehensive data allowed us to draw a predictive model of colony losses and to show that Varroa destructor, along with bee viruses, mainly DWV replication, contributes to approximately 70% of colony losses. This correlation further supports the claim that insufficient control of the virus-vectoring Varroa mite would result in increased hive loss. The predictive model also indicates that a single factor may not be sufficient to trigger colony losses, whereas a combination of stressors appears to impact hive health.

  15. Prospective Large-Scale Field Study Generates Predictive Model Identifying Major Contributors to Colony Losses (United States)

    Kielmanowicz, Merav Gleit; Inberg, Alex; Lerner, Inbar Maayan; Golani, Yael; Brown, Nicholas; Turner, Catherine Louise; Hayes, Gerald J. R.; Ballam, Joan M.


    Over the last decade, unusually high losses of colonies have been reported by beekeepers across the USA. Multiple factors such as Varroa destructor, bee viruses, Nosema ceranae, weather, beekeeping practices, nutrition, and pesticides have been shown to contribute to colony losses. Here we describe a large-scale controlled trial, in which different bee pathogens, bee population, and weather conditions across winter were monitored at three locations across the USA. In order to minimize influence of various known contributing factors and their interaction, the hives in the study were not treated with antibiotics or miticides. Additionally, the hives were kept at one location and were not exposed to potential stress factors associated with migration. Our results show that a linear association between load of viruses (DWV or IAPV) in Varroa and bees is present at high Varroa infestation levels (>3 mites per 100 bees). The collection of comprehensive data allowed us to draw a predictive model of colony losses and to show that Varroa destructor, along with bee viruses, mainly DWV replication, contributes to approximately 70% of colony losses. This correlation further supports the claim that insufficient control of the virus-vectoring Varroa mite would result in increased hive loss. The predictive model also indicates that a single factor may not be sufficient to trigger colony losses, whereas a combination of stressors appears to impact hive health. PMID:25875764

  16. Ensemble modeling to predict habitat suitability for a large-scale disturbance specialist (United States)

    Quresh S. Latif; Victoria A. Saab; Jonathan G. Dudley; Jeff P. Hollenbeck


    To conserve habitat for disturbance specialist species, ecologists must identify where individuals will likely settle in newly disturbed areas. Habitat suitability models can predict which sites at new disturbances will most likely attract specialists. Without validation data from newly disturbed areas, however, the best approach for maximizing predictive accuracy can...

  17. Enhancements to the Water Erosion Prediction Project (WEPP) for modeling large snow-dominated mountainous forest watersheds (United States)

    Anurag Srivastava; Joan Q. Wu; William J. Elliot; Erin S. Brooks


    The Water Erosion Prediction Project (WEPP) model, originally developed for hillslope and small watershed applications, simulates complex interactive processes influencing erosion. Recent incorporations to the model have improved the subsurface hydrology components for forest applications. Incorporation of channel routing has made the WEPP model well suited for large...

  18. Distributed Model Predictive Control over Multiple Groups of Vehicles in Highway Intelligent Space for Large Scale System


    Tang Xiaofeng; Gao Feng; Xu Guoyan; Ding Nenggen; Cai Yao; Liu Jian Xing


    The paper presents the three time warning distances for solving the large scale system of multiple groups of vehicles safety driving characteristics towards highway tunnel environment based on distributed model prediction control approach. Generally speaking, the system includes two parts. First, multiple vehicles are divided into multiple groups. Meanwhile, the distributed model predictive control approach is proposed to calculate the information framework of each group. Each group of optimi...

  19. Time-Varying Scheme for Noncentralized Model Predictive Control of Large-Scale Systems

    Directory of Open Access Journals (Sweden)

    Alfredo Núñez


    Full Text Available The noncentralized model predictive control (NC-MPC framework in this paper refers to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we propose suitable online methods to decide which information is shared and how this information is used between the different local predictive controllers operating in a decentralized, distributed, and/or hierarchical way. Evaluating all the possible structures of the NC-MPC controller leads to a combinatorial optimization problem. Therefore, we also propose heuristic reduction methods, to keep the number of NC-MPC problems tractable to be solved. To show the benefits of the proposed framework, a case study of a set of coupled water tanks is presented.

  20. Prediction model of potential hepatocarcinogenicity of rat hepatocarcinogens using a large-scale toxicogenomics database

    International Nuclear Information System (INIS)

    Uehara, Takeki; Minowa, Yohsuke; Morikawa, Yuji; Kondo, Chiaki; Maruyama, Toshiyuki; Kato, Ikuo; Nakatsu, Noriyuki; Igarashi, Yoshinobu; Ono, Atsushi; Hayashi, Hitomi; Mitsumori, Kunitoshi; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro


    The present study was performed to develop a robust gene-based prediction model for early assessment of potential hepatocarcinogenicity of chemicals in rats by using our toxicogenomics database, TG-GATEs (Genomics-Assisted Toxicity Evaluation System developed by the Toxicogenomics Project in Japan). The positive training set consisted of high- or middle-dose groups that received 6 different non-genotoxic hepatocarcinogens during a 28-day period. The negative training set consisted of high- or middle-dose groups of 54 non-carcinogens. Support vector machine combined with wrapper-type gene selection algorithms was used for modeling. Consequently, our best classifier yielded prediction accuracies for hepatocarcinogenicity of 99% sensitivity and 97% specificity in the training data set, and false positive prediction was almost completely eliminated. Pathway analysis of feature genes revealed that the mitogen-activated protein kinase p38- and phosphatidylinositol-3-kinase-centered interactome and the v-myc myelocytomatosis viral oncogene homolog-centered interactome were the 2 most significant networks. The usefulness and robustness of our predictor were further confirmed in an independent validation data set obtained from the public database. Interestingly, similar positive predictions were obtained in several genotoxic hepatocarcinogens as well as non-genotoxic hepatocarcinogens. These results indicate that the expression profiles of our newly selected candidate biomarker genes might be common characteristics in the early stage of carcinogenesis for both genotoxic and non-genotoxic carcinogens in the rat liver. Our toxicogenomic model might be useful for the prospective screening of hepatocarcinogenicity of compounds and prioritization of compounds for carcinogenicity testing. - Highlights: →We developed a toxicogenomic model to predict hepatocarcinogenicity of chemicals. →The optimized model consisting of 9 probes had 99% sensitivity and 97% specificity.

  1. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. (United States)

    Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola


    Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

  2. Large-scale assessment of the zebrafish embryo as a possible predictive model in toxicity testing.

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    Shaukat Ali

    Full Text Available BACKGROUND: In the drug discovery pipeline, safety pharmacology is a major issue. The zebrafish has been proposed as a model that can bridge the gap in this field between cell assays (which are cost-effective, but low in data content and rodent assays (which are high in data content, but less cost-efficient. However, zebrafish assays are only likely to be useful if they can be shown to have high predictive power. We examined this issue by assaying 60 water-soluble compounds representing a range of chemical classes and toxicological mechanisms. METHODOLOGY/PRINCIPAL FINDINGS: Over 20,000 wild-type zebrafish embryos (including controls were cultured individually in defined buffer in 96-well plates. Embryos were exposed for a 96 hour period starting at 24 hours post fertilization. A logarithmic concentration series was used for range-finding, followed by a narrower geometric series for LC(50 determination. Zebrafish embryo LC(50 (log mmol/L, and published data on rodent LD(50 (log mmol/kg, were found to be strongly correlated (using Kendall's rank correlation tau and Pearson's product-moment correlation. The slope of the regression line for the full set of compounds was 0.73403. However, we found that the slope was strongly influenced by compound class. Thus, while most compounds had a similar toxicity level in both species, some compounds were markedly more toxic in zebrafish than in rodents, or vice versa. CONCLUSIONS: For the substances examined here, in aggregate, the zebrafish embryo model has good predictivity for toxicity in rodents. However, the correlation between zebrafish and rodent toxicity varies considerably between individual compounds and compound class. We discuss the strengths and limitations of the zebrafish model in light of these findings.

  3. Adapting SWAT hillslope erosion model to predict sediment concentrations and yields in large Basins. (United States)

    Vigiak, Olga; Malagó, Anna; Bouraoui, Fayçal; Vanmaercke, Matthias; Poesen, Jean


    The Soil and Water Assessment Tool (SWAT) is used worldwide for water quality assessment and planning. This paper aimed to assess and adapt SWAT hillslope sediment yield model (Modified Universal Soil Loss Equation, MUSLE) for applications in large basins, i.e. when spatial data is coarse and model units are large; and to develop a robust sediment calibration method for large regions. The Upper Danube Basin (132,000km(2)) was used as case study representative of large European Basins. The MUSLE was modified to reduce sensitivity of sediment yields to the Hydrologic Response Unit (HRU) size, and to identify appropriate algorithms for estimating hillslope length (L) and slope-length factor (LS). HRUs gross erosion was broadly calibrated against plot data and soil erosion map estimates. Next, mean annual SWAT suspended sediment concentrations (SSC, mg/L) were calibrated and validated against SSC data at 55 gauging stations (622 station-years). SWAT annual specific sediment yields in subbasin reaches (RSSY, t/km(2)/year) were compared to yields measured at 33 gauging stations (87station-years). The best SWAT configuration combined a MUSLE equation modified by the introduction of a threshold area of 0.01km(2) where L and LS were estimated with flow accumulation algorithms. For this configuration, the SSC residual interquartile was less than +/-15mg/L both for the calibration (1995-2004) and the validation (2005-2009) periods. The mean SSC percent bias for 1995-2009 was 24%. RSSY residual interquartile was within +/-10t/km(2)/year, with a mean RSSY percent bias of 12%. Residuals showed no bias with respect to drainage area, slope, or spatial distribution. The use of multiple data types at multiple sites enabled robust simulation of sediment concentrations and yields of the region. The MUSLE modifications are recommended for use in large basins. Based on SWAT simulations, we present a sediment budget for the Upper Danube Basin. Copyright © 2015. Published by Elsevier B.V.

  4. Predicting Soil Infiltration and Horizon Thickness for a Large-Scale Water Balance Model in an Arid Environment

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    Tadaomi Saito


    Full Text Available Prediction of soil characteristics over large areas is desirable for environmental modeling. In arid environments, soil characteristics often show strong ecological connectivity with natural vegetation, specifically biomass and/or canopy cover, suggesting that the soil characteristics may be predicted from vegetation data. The objective of this study was to predict soil infiltration characteristics and horizon (soil layer thickness using vegetation data for a large-scale water balance model in an arid region. Double-ring infiltrometer tests (at 23 sites, horizon thickness measurements (58 sites and vegetation surveys (35 sites were conducted in a 30 km × 50 km area in Western Australia during 1999 to 2003. The relationships between soil parameters and vegetation data were evaluated quantitatively by simple linear regression. The parameters for initial-term infiltration had strong and positive correlations with biomass and canopy coverage (R2 = 0.64 − 0.81. The horizon thickness also had strong positive correlations with vegetation properties (R2 = 0.53 − 0.67. These results suggest that the soil infiltration parameters and horizon thickness can be spatially predicted by properties of vegetation using their linear regression based equations and vegetation maps. The background and reasons of the strong ecological connectivity between soil and vegetation in this region were also considered.

  5. The potential of large studies for building genetic risk prediction models (United States)

    NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

  6. A general method for assessing the effects of uncertainty in individual-tree volume model predictions on large-area volume estimates with a subtropical forest illustration (United States)

    Ronald E. McRoberts; Paolo Moser; Laio Zimermann Oliveira; Alexander C. Vibrans


    Forest inventory estimates of tree volume for large areas are typically calculated by adding the model predictions of volumes for individual trees at the plot level, calculating the mean over plots, and expressing the result on a per unit area basis. The uncertainty in the model predictions is generally ignored, with the result that the precision of the large-area...

  7. Development and Validation of a Computational Model for Predicting the Behavior of Plumes from Large Solid Rocket Motors (United States)

    Wells, Jason E.; Black, David L.; Taylor, Casey L.


    Exhaust plumes from large solid rocket motors fired at ATK's Promontory test site carry particulates to high altitudes and typically produce deposits that fall on regions downwind of the test area. As populations and communities near the test facility grow, ATK has become increasingly concerned about the impact of motor testing on those surrounding communities. To assess the potential impact of motor testing on the community and to identify feasible mitigation strategies, it is essential to have a tool capable of predicting plume behavior downrange of the test stand. A software package, called PlumeTracker, has been developed and validated at ATK for this purpose. The code is a point model that offers a time-dependent, physics-based description of plume transport and precipitation. The code can utilize either measured or forecasted weather data to generate plume predictions. Next-Generation Radar (NEXRAD) data and field observations from twenty-three historical motor test fires at Promontory were collected to test the predictive capability of PlumeTracker. Model predictions for plume trajectories and deposition fields were found to correlate well with the collected dataset.

  8. 5D Modelling: An Efficient Approach for Creating Spatiotemporal Predictive 3D Maps of Large-Scale Cultural Resources (United States)

    Doulamis, A.; Doulamis, N.; Ioannidis, C.; Chrysouli, C.; Grammalidis, N.; Dimitropoulos, K.; Potsiou, C.; Stathopoulou, E.-K.; Ioannides, M.


    Outdoor large-scale cultural sites are mostly sensitive to environmental, natural and human made factors, implying an imminent need for a spatio-temporal assessment to identify regions of potential cultural interest (material degradation, structuring, conservation). On the other hand, in Cultural Heritage research quite different actors are involved (archaeologists, curators, conservators, simple users) each of diverse needs. All these statements advocate that a 5D modelling (3D geometry plus time plus levels of details) is ideally required for preservation and assessment of outdoor large scale cultural sites, which is currently implemented as a simple aggregation of 3D digital models at different time and levels of details. The main bottleneck of such an approach is its complexity, making 5D modelling impossible to be validated in real life conditions. In this paper, a cost effective and affordable framework for 5D modelling is proposed based on a spatial-temporal dependent aggregation of 3D digital models, by incorporating a predictive assessment procedure to indicate which regions (surfaces) of an object should be reconstructed at higher levels of details at next time instances and which at lower ones. In this way, dynamic change history maps are created, indicating spatial probabilities of regions needed further 3D modelling at forthcoming instances. Using these maps, predictive assessment can be made, that is, to localize surfaces within the objects where a high accuracy reconstruction process needs to be activated at the forthcoming time instances. The proposed 5D Digital Cultural Heritage Model (5D-DCHM) is implemented using open interoperable standards based on the CityGML framework, which also allows the description of additional semantic metadata information. Visualization aspects are also supported to allow easy manipulation, interaction and representation of the 5D-DCHM geometry and the respective semantic information. The open source 3DCity

  9. Reduced Order Modeling for Prediction and Control of Large-Scale Systems.

    Energy Technology Data Exchange (ETDEWEB)

    Kalashnikova, Irina [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Mathematics; Arunajatesan, Srinivasan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Aerosciences Dept.; Barone, Matthew Franklin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Aerosciences Dept.; van Bloemen Waanders, Bart Gustaaf [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Uncertainty Quantification and Optimization Dept.; Fike, Jeffrey A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Component Science and Mechanics Dept.


    This report describes work performed from June 2012 through May 2014 as a part of a Sandia Early Career Laboratory Directed Research and Development (LDRD) project led by the first author. The objective of the project is to investigate methods for building stable and efficient proper orthogonal decomposition (POD)/Galerkin reduced order models (ROMs): models derived from a sequence of high-fidelity simulations but having a much lower computational cost. Since they are, by construction, small and fast, ROMs can enable real-time simulations of complex systems for onthe- spot analysis, control and decision-making in the presence of uncertainty. Of particular interest to Sandia is the use of ROMs for the quantification of the compressible captive-carry environment, simulated for the design and qualification of nuclear weapons systems. It is an unfortunate reality that many ROM techniques are computationally intractable or lack an a priori stability guarantee for compressible flows. For this reason, this LDRD project focuses on the development of techniques for building provably stable projection-based ROMs. Model reduction approaches based on continuous as well as discrete projection are considered. In the first part of this report, an approach for building energy-stable Galerkin ROMs for linear hyperbolic or incompletely parabolic systems of partial differential equations (PDEs) using continuous projection is developed. The key idea is to apply a transformation induced by the Lyapunov function for the system, and to build the ROM in the transformed variables. It is shown that, for many PDE systems including the linearized compressible Euler and linearized compressible Navier-Stokes equations, the desired transformation is induced by a special inner product, termed the “symmetry inner product”. Attention is then turned to nonlinear conservation laws. A new transformation and corresponding energy-based inner product for the full nonlinear compressible Navier

  10. Distributed Model Predictive Control over Multiple Groups of Vehicles in Highway Intelligent Space for Large Scale System

    Directory of Open Access Journals (Sweden)

    Tang Xiaofeng


    Full Text Available The paper presents the three time warning distances for solving the large scale system of multiple groups of vehicles safety driving characteristics towards highway tunnel environment based on distributed model prediction control approach. Generally speaking, the system includes two parts. First, multiple vehicles are divided into multiple groups. Meanwhile, the distributed model predictive control approach is proposed to calculate the information framework of each group. Each group of optimization performance considers the local optimization and the neighboring subgroup of optimization characteristics, which could ensure the global optimization performance. Second, the three time warning distances are studied based on the basic principles used for highway intelligent space (HIS and the information framework concept is proposed according to the multiple groups of vehicles. The math model is built to avoid the chain avoidance of vehicles. The results demonstrate that the proposed highway intelligent space method could effectively ensure driving safety of multiple groups of vehicles under the environment of fog, rain, or snow.

  11. Computational Techniques for Model Predictive Control of Large-Scale Systems with Continuous-Valued and Discrete-Valued Inputs

    Directory of Open Access Journals (Sweden)

    Koichi Kobayashi


    Full Text Available We propose computational techniques for model predictive control of large-scale systems with both continuous-valued control inputs and discrete-valued control inputs, which are a class of hybrid systems. In the proposed method, we introduce the notion of virtual control inputs, which are obtained by relaxing discrete-valued control inputs to continuous variables. In online computation, first, we find continuous-valued control inputs and virtual control inputs minimizing a cost function. Next, using the obtained virtual control inputs, only discrete-valued control inputs at the current time are computed in each subsystem. In addition, we also discuss the effect of quantization errors. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method enables us to reduce and decentralize the computation load.

  12. Stochastic backscatter modelling for the prediction of pollutant removal from an urban street canyon: A large-eddy simulation (United States)

    O'Neill, J. J.; Cai, X.-M.; Kinnersley, R.


    The large-eddy simulation (LES) approach has recently exhibited its appealing capability of capturing turbulent processes inside street canyons and the urban boundary layer aloft, and its potential for deriving the bulk parameters adopted in low-cost operational urban dispersion models. However, the thin roof-level shear layer may be under-resolved in most LES set-ups and thus sophisticated subgrid-scale (SGS) parameterisations may be required. In this paper, we consider the important case of pollutant removal from an urban street canyon of unit aspect ratio (i.e. building height equal to street width) with the external flow perpendicular to the street. We show that by employing a stochastic SGS model that explicitly accounts for backscatter (energy transfer from unresolved to resolved scales), the pollutant removal process is better simulated compared with the use of a simpler (fully dissipative) but widely-used SGS model. The backscatter induces additional mixing within the shear layer which acts to increase the rate of pollutant removal from the street canyon, giving better agreement with a recent wind-tunnel experiment. The exchange velocity, an important parameter in many operational models that determines the mass transfer between the urban canopy and the external flow, is predicted to be around 15% larger with the backscatter SGS model; consequently, the steady-state mean pollutant concentration within the street canyon is around 15% lower. A database of exchange velocities for various other urban configurations could be generated and used as improved input for operational street canyon models.

  13. Water and salt balance modelling to predict the effects of land-use changes in forested catchments. 3. The large catchment model (United States)

    Sivapalan, Murugesu; Viney, Neil R.; Jeevaraj, Charles G.


    This paper presents an application of a long-term, large catchment-scale, water balance model developed to predict the effects of forest clearing in the south-west of Western Australia. The conceptual model simulates the basic daily water balance fluxes in forested catchments before and after clearing. The large catchment is divided into a number of sub-catchments (1-5 km2 in area), which are taken as the fundamental building blocks of the large catchment model. The responses of the individual subcatchments to rainfall and pan evaporation are conceptualized in terms of three inter-dependent subsurface stores A, B and F, which are considered to represent the moisture states of the subcatchments. Details of the subcatchment-scale water balance model have been presented earlier in Part 1 of this series of papers. The response of any subcatchment is a function of its local moisture state, as measured by the local values of the stores. The variations of the initial values of the stores among the subcatchments are described in the large catchment model through simple, linear equations involving a number of similarity indices representing topography, mean annual rainfall and level of forest clearing.The model is applied to the Conjurunup catchment, a medium-sized (39·6 km2) catchment in the south-west of Western Australia. The catchment has been heterogeneously (in space and time) cleared for bauxite mining and subsequently rehabilitated. For this application, the catchment is divided into 11 subcatchments. The model parameters are estimated by calibration, by comparing observed and predicted runoff values, over a 18 year period, for the large catchment and two of the subcatchments. Excellent fits are obtained.

  14. A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions

    Directory of Open Access Journals (Sweden)

    Jose eGonzalez-Vargas


    Full Text Available Humans can efficiently walk across a large variety of terrains and locomotion conditions with little or no mental effort. It has been hypothesized that the nervous system simplifies neuromuscular control by using muscle synergies, thus organizing multi-muscle activity into a small number of coordinative co-activation modules. In the present study we investigated how muscle modularity is structured across a large repertoire of locomotion conditions including five different speeds and five different ground elevations. For this we have used the non-negative matrix factorization technique in order to explain EMG experimental data with a low-dimensional set of four motor components. In this context each motor components is composed of a non-negative factor and the associated muscle weightings. Furthermore, we have investigated if the proposed descriptive analysis of muscle modularity could be translated into a predictive model that could: 1 Estimate how motor components modulate across locomotion speeds and ground elevations. This implies not only estimating the non-negative factors temporal characteristics, but also the associated muscle weighting variations. 2 Estimate how the resulting muscle excitations modulate across novel locomotion conditions and subjects.The results showed three major distinctive features of muscle modularity: 1 the number of motor components was preserved across all locomotion conditions, 2 the non-negative factors were consistent in shape and timing across all locomotion conditions, and 3 the muscle weightings were modulated as distinctive functions of locomotion speed and ground elevation. Results also showed that the developed predictive model was able to reproduce well the muscle modularity of un-modeled data, i.e. novel subjects and conditions. Muscle weightings were reconstructed with a cross-correlation factor greater than 70% and a root mean square error less than 0.10. Furthermore, the generated muscle excitations

  15. Comparing large-scale hydrological model predictions with observed streamflow in the Pacific Northwest: effects of climate and groundwater (United States)

    Mohammad Safeeq; Guillaume S. Mauger; Gordon E. Grant; Ivan Arismendi; Alan F. Hamlet; Se-Yeun Lee


    Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse (1/16°) and fine (1/120°) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In...

  16. Coupling a Mesoscale Numerical Weather Prediction Model with Large-Eddy Simulation for Realistic Wind Plant Aerodynamics Simulations (Poster)

    Energy Technology Data Exchange (ETDEWEB)

    Draxl, C.; Churchfield, M.; Mirocha, J.; Lee, S.; Lundquist, J.; Michalakes, J.; Moriarty, P.; Purkayastha, A.; Sprague, M.; Vanderwende, B.


    Wind plant aerodynamics are influenced by a combination of microscale and mesoscale phenomena. Incorporating mesoscale atmospheric forcing (e.g., diurnal cycles and frontal passages) into wind plant simulations can lead to a more accurate representation of microscale flows, aerodynamics, and wind turbine/plant performance. Our goal is to couple a numerical weather prediction model that can represent mesoscale flow [specifically the Weather Research and Forecasting model] with a microscale LES model (OpenFOAM) that can predict microscale turbulence and wake losses.

  17. The "AQUASCOPE" simplified model for predicting 89, 90Sr, 131l and 134, 137Cs in surface waters after a large-scale radioactive fallout

    NARCIS (Netherlands)

    Smith, J.T.; Belova, N.V.; Bulgakov, A.A.; Comans, R.N.J.; Konoplev, A.V.; Kudelsky, A.V.; Madruga, M.J.; Voitsekhovitch, O.V.; Zibolt, G.


    Simplified dynamic models have been developed for predicting the concentrations of radiocesium, radiostrontium, and 131I in surface waters and freshwater fish following a large-scale radioactive fallout. The models are intended to give averaged estimates for radionuclides in water bodies and in fish

  18. A comparison of large-scale climate signals and the North American Multi-Model Ensemble (NMME) for drought prediction in China (United States)

    Xu, Lei; Chen, Nengcheng; Zhang, Xiang


    Drought is an extreme natural disaster that can lead to huge socioeconomic losses. Drought prediction ahead of months is helpful for early drought warning and preparations. In this study, we developed a statistical model, two weighted dynamic models and a statistical-dynamic (hybrid) model for 1-6 month lead drought prediction in China. Specifically, statistical component refers to climate signals weighting by support vector regression (SVR), dynamic components consist of the ensemble mean (EM) and Bayesian model averaging (BMA) of the North American Multi-Model Ensemble (NMME) climatic models, and the hybrid part denotes a combination of statistical and dynamic components by assigning weights based on their historical performances. The results indicate that the statistical and hybrid models show better rainfall predictions than NMME-EM and NMME-BMA models, which have good predictability only in southern China. In the 2011 China winter-spring drought event, the statistical model well predicted the spatial extent and severity of drought nationwide, although the severity was underestimated in the mid-lower reaches of Yangtze River (MLRYR) region. The NMME-EM and NMME-BMA models largely overestimated rainfall in northern and western China in 2011 drought. In the 2013 China summer drought, the NMME-EM model forecasted the drought extent and severity in eastern China well, while the statistical and hybrid models falsely detected negative precipitation anomaly (NPA) in some areas. Model ensembles such as multiple statistical approaches, multiple dynamic models or multiple hybrid models for drought predictions were highlighted. These conclusions may be helpful for drought prediction and early drought warnings in China.

  19. Large scale model predictions on the effect of GDL thermal conductivity and porosity on PEM fuel cell performance

    Directory of Open Access Journals (Sweden)

    Obaid ur Rehman


    Full Text Available The performance of proton exchange membrane (PEM fuel cell majorly relies on properties of gas diffusion layer (GDL which supports heat and mass transfer across the membrane electrode assembly. A novel approach is adopted in this work to analyze the activity of GDL during fuel cell operation on a large-scale model. The model with mesh size of 1.3 million computational cells for 50 cm2 active area was simulated by parallel computing technique via computer cluster. Grid independence study showed less than 5% deviation in criterion parameter as mesh size was increased to 1.8 million cells. Good approximation was achieved as model was validated with the experimental data for Pt loading of 1 mg cm-2. The results showed that GDL with higher thermal conductivity prevented PEM from drying and led to improved protonic conduction. GDL with higher porosity enhanced the reaction but resulted in low output voltage which demonstrated the effect of contact resistance. In addition, reduced porosity under the rib regions was significant which resulted in lower gas diffusion and heat and water accumulation.

  20. The Large-scale Coronal Structure of the 2017 August 21 Great American Eclipse: An Assessment of Solar Surface Flux Transport Model Enabled Predictions and Observations (United States)

    Nandy, Dibyendu; Bhowmik, Prantika; Yeates, Anthony R.; Panda, Suman; Tarafder, Rajashik; Dash, Soumyaranjan


    On 2017 August 21, a total solar eclipse swept across the contiguous United States, providing excellent opportunities for diagnostics of the Sun’s corona. The Sun’s coronal structure is notoriously difficult to observe except during solar eclipses; thus, theoretical models must be relied upon for inferring the underlying magnetic structure of the Sun’s outer atmosphere. These models are necessary for understanding the role of magnetic fields in the heating of the corona to a million degrees and the generation of severe space weather. Here we present a methodology for predicting the structure of the coronal field based on model forward runs of a solar surface flux transport model, whose predicted surface field is utilized to extrapolate future coronal magnetic field structures. This prescription was applied to the 2017 August 21 solar eclipse. A post-eclipse analysis shows good agreement between model simulated and observed coronal structures and their locations on the limb. We demonstrate that slow changes in the Sun’s surface magnetic field distribution driven by long-term flux emergence and its evolution governs large-scale coronal structures with a (plausibly cycle-phase dependent) dynamical memory timescale on the order of a few solar rotations, opening up the possibility for large-scale, global corona predictions at least a month in advance.

  1. Integrating SMOS brightness temperatures with a new conceptual spatially distributed hydrological model for improving flood and drought predictions at large scale. (United States)

    Hostache, Renaud; Rains, Dominik; Chini, Marco; Lievens, Hans; Verhoest, Niko E. C.; Matgen, Patrick


    Motivated by climate change and its impact on the scarcity or excess of water in many parts of the world, several agencies and research institutions have taken initiatives in monitoring and predicting the hydrologic cycle at a global scale. Such a monitoring/prediction effort is important for understanding the vulnerability to extreme hydrological events and for providing early warnings. This can be based on an optimal combination of hydro-meteorological models and remote sensing, in which satellite measurements can be used as forcing or calibration data or for regularly updating the model states or parameters. Many advances have been made in these domains and the near future will bring new opportunities with respect to remote sensing as a result of the increasing number of spaceborn sensors enabling the large scale monitoring of water resources. Besides of these advances, there is currently a tendency to refine and further complicate physically-based hydrologic models to better capture the hydrologic processes at hand. However, this may not necessarily be beneficial for large-scale hydrology, as computational efforts are therefore increasing significantly. As a matter of fact, a novel thematic science question that is to be investigated is whether a flexible conceptual model can match the performance of a complex physically-based model for hydrologic simulations at large scale. In this context, the main objective of this study is to investigate how innovative techniques that allow for the estimation of soil moisture from satellite data can help in reducing errors and uncertainties in large scale conceptual hydro-meteorological modelling. A spatially distributed conceptual hydrologic model has been set up based on recent developments of the SUPERFLEX modelling framework. As it requires limited computational efforts, this model enables early warnings for large areas. Using as forcings the ERA-Interim public dataset and coupled with the CMEM radiative transfer model

  2. Applied the additive hazard model to predict the survival time of patient with diffuse large B- cell lymphoma and determine the effective genes, using microarray data

    Directory of Open Access Journals (Sweden)

    Arefa Jafarzadeh Kohneloo


    Full Text Available Background: Recent studies have shown that effective genes on survival time of cancer patients play an important role as a risk factor or preventive factor. Present study was designed to determine effective genes on survival time for diffuse large B-cell lymphoma patients and predict the survival time using these selected genes. Materials & Methods: Present study is a cohort study was conducted on 40 patients with diffuse large B-cell lymphoma. For these patients, 2042 gene expression was measured. In order to predict the survival time, the composition of the semi-parametric additive survival model with two gene selection methods elastic net and lasso were used. Two methods were evaluated by plotting area under the ROC curve over time and calculating the integral of this curve. Results: Based on our findings, the elastic net method identified 10 genes, and Lasso-Cox method identified 7 genes. GENE3325X increased the survival time (P=0.006, Whereas GENE3980X and GENE377X reduced the survival time (P=0.004. These three genes were selected as important genes in both methods. Conclusion: This study showed that the elastic net method outperformed the common Lasso method in terms of predictive power. Moreover, apply the additive model instead Cox regression and using microarray data is usable way for predict the survival time of patients.

  3. Development and evaluation of a prediction model for underestimated invasive breast cancer in women with ductal carcinoma in situ at stereotactic large core needle biopsy.

    Directory of Open Access Journals (Sweden)

    Suzanne C E Diepstraten

    Full Text Available BACKGROUND: We aimed to develop a multivariable model for prediction of underestimated invasiveness in women with ductal carcinoma in situ at stereotactic large core needle biopsy, that can be used to select patients for sentinel node biopsy at primary surgery. METHODS: From the literature, we selected potential preoperative predictors of underestimated invasive breast cancer. Data of patients with nonpalpable breast lesions who were diagnosed with ductal carcinoma in situ at stereotactic large core needle biopsy, drawn from the prospective COBRA (Core Biopsy after RAdiological localization and COBRA2000 cohort studies, were used to fit the multivariable model and assess its overall performance, discrimination, and calibration. RESULTS: 348 women with large core needle biopsy-proven ductal carcinoma in situ were available for analysis. In 100 (28.7% patients invasive carcinoma was found at subsequent surgery. Nine predictors were included in the model. In the multivariable analysis, the predictors with the strongest association were lesion size (OR 1.12 per cm, 95% CI 0.98-1.28, number of cores retrieved at biopsy (OR per core 0.87, 95% CI 0.75-1.01, presence of lobular cancerization (OR 5.29, 95% CI 1.25-26.77, and microinvasion (OR 3.75, 95% CI 1.42-9.87. The overall performance of the multivariable model was poor with an explained variation of 9% (Nagelkerke's R(2, mediocre discrimination with area under the receiver operating characteristic curve of 0.66 (95% confidence interval 0.58-0.73, and fairly good calibration. CONCLUSION: The evaluation of our multivariable prediction model in a large, clinically representative study population proves that routine clinical and pathological variables are not suitable to select patients with large core needle biopsy-proven ductal carcinoma in situ for sentinel node biopsy during primary surgery.

  4. Large-scale linear programs in planning and prediction. (United States)


    Large-scale linear programs are at the core of many traffic-related optimization problems in both planning and prediction. Moreover, many of these involve significant uncertainty, and hence are modeled using either chance constraints, or robust optim...

  5. Large scale model testing

    International Nuclear Information System (INIS)

    Brumovsky, M.; Filip, R.; Polachova, H.; Stepanek, S.


    Fracture mechanics and fatigue calculations for WWER reactor pressure vessels were checked by large scale model testing performed using large testing machine ZZ 8000 (with a maximum load of 80 MN) at the SKODA WORKS. The results are described from testing the material resistance to fracture (non-ductile). The testing included the base materials and welded joints. The rated specimen thickness was 150 mm with defects of a depth between 15 and 100 mm. The results are also presented of nozzles of 850 mm inner diameter in a scale of 1:3; static, cyclic, and dynamic tests were performed without and with surface defects (15, 30 and 45 mm deep). During cyclic tests the crack growth rate in the elastic-plastic region was also determined. (author). 6 figs., 2 tabs., 5 refs

  6. Predicting Surface Runoff from Catchment to Large Region

    Directory of Open Access Journals (Sweden)

    Hongxia Li


    Full Text Available Predicting surface runoff from catchment to large region is a fundamental and challenging task in hydrology. This paper presents a comprehensive review for various studies conducted for improving runoff predictions from catchment to large region in the last several decades. This review summarizes the well-established methods and discusses some promising approaches from the following four research fields: (1 modeling catchment, regional and global runoff using lumped conceptual rainfall-runoff models, distributed hydrological models, and land surface models, (2 parameterizing hydrological models in ungauged catchments, (3 improving hydrological model structure, and (4 using new remote sensing precipitation data.

  7. Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

    Directory of Open Access Journals (Sweden)

    Chunrong Mi


    Full Text Available Species distribution models (SDMs have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha, n = 33, White-naped Crane (Grus vipio, n = 40, and Black-necked Crane (Grus nigricollis, n = 75 in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model, Random Forest, CART (Classification and Regression Tree and Maxent (Maximum Entropy Models. In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC and true skill statistic (TSS were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid

  8. Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence. (United States)

    Mi, Chunrong; Huettmann, Falk; Guo, Yumin; Han, Xuesong; Wen, Lijia


    Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane ( Grus monacha , n  = 33), White-naped Crane ( Grus vipio , n  = 40), and Black-necked Crane ( Grus nigricollis , n  = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid

  9. Using a novel flood prediction model and GIS automation to measure the valley and channel morphology of large river networks (United States)

    Traditional methods for measuring river valley and channel morphology require intensive ground-based surveys which are often expensive, time consuming, and logistically difficult to implement. The number of surveys required to assess the hydrogeomorphic structure of large river n...

  10. A comparison of modeling techniques to predict juvenile 0+ fish species occurrences in a large river system


    Leclere, J.; Oberdorff, Thierry; Belliard, J.; Leprieur, Fabien


    Even if European river management and restoration are largely supported by the use of reliable tools, these tools are most often "generalist" and provide only initial leads of alteration sources. Acknowledging that young-of-the-year (YOY) fish assemblages are highly dependent on riverine habitat conditions, the development of a YOY-based tool might be very useful or even essential in the design and implementation of conservation or restoration plan of large rivers, in measuring more straight-...

  11. Cultural Resource Predictive Modeling (United States)


    refining formal, inductive predictive models is the quality of the archaeological and environmental data. To build models efficiently, relevant...geomorphology, and historic information . Lessons Learned: The original model was focused on the identification of prehistoric resources. This...system but uses predictive modeling informally . For example, there is no probability for buried archaeological deposits on the Burton Mesa, but there is

  12. From GenBank to GBIF: Phylogeny-Based Predictive Niche Modeling Tests Accuracy of Taxonomic Identifications in Large Occurrence Data Repositories. (United States)

    Smith, B Eugene; Johnston, Mark K; Lücking, Robert


    Accuracy of taxonomic identifications is crucial to data quality in online repositories of species occurrence data, such as the Global Biodiversity Information Facility (GBIF), which have accumulated several hundred million records over the past 15 years. These data serve as basis for large scale analyses of macroecological and biogeographic patterns and to document environmental changes over time. However, taxonomic identifications are often unreliable, especially for non-vascular plants and fungi including lichens, which may lack critical revisions of voucher specimens. Due to the scale of the problem, restudy of millions of collections is unrealistic and other strategies are needed. Here we propose to use verified, georeferenced occurrence data of a given species to apply predictive niche modeling that can then be used to evaluate unverified occurrences of that species. Selecting the charismatic lichen fungus, Usnea longissima, as a case study, we used georeferenced occurrence records based on sequenced specimens to model its predicted niche. Our results suggest that the target species is largely restricted to a narrow range of boreal and temperate forest in the Northern Hemisphere and that occurrence records in GBIF from tropical regions and the Southern Hemisphere do not represent this taxon, a prediction tested by comparison with taxonomic revisions of Usnea for these regions. As a novel approach, we employed Principal Component Analysis on the environmental grid data used for predictive modeling to visualize potential ecogeographical barriers for the target species; we found that tropical regions conform a strong barrier, explaining why potential niches in the Southern Hemisphere were not colonized by Usnea longissima and instead by morphologically similar species. This approach is an example of how data from two of the most important biodiversity repositories, GenBank and GBIF, can be effectively combined to remotely address the problem of inaccuracy of

  13. Dynameomics: data-driven methods and models for utilizing large-scale protein structure repositories for improving fragment-based loop prediction. (United States)

    Rysavy, Steven J; Beck, David A C; Daggett, Valerie


    Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment-based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, ∼ 25-75% of the best predictions came from the Dynameomics set, resulting in lower main chain root-mean-square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at © 2014 The Protein Society.

  14. Predictive modeling of complications. (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P


    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  15. Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation (United States)

    Qin, Changbo; Jia, Yangwen; Su, Z.(Bob); Zhou, Zuhao; Qiu, Yaqin; Suhui, Shen


    This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems. PMID:27879946

  16. Archaeological predictive model set. (United States)


    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  17. Evolution and application of a pseudo-multi-zone model for the prediction of NOx emissions from large-scale diesel engines at various operating conditions

    International Nuclear Information System (INIS)

    Savva, Nicholas S.; Hountalas, Dimitrios T.


    Highlights: • Development of a simplified simulation model for NO x formation during combustion. • Application of the proposed model on large-scale two and four-stroke diesel engines. • Experimental data from stationary and ship main and auxiliary engines were used. • The model captures the trend of NO x as engine power and fuel injection timing varies. • The model is recommended for research and practical use in maritime and power industry. - Abstract: Emissions regulations for heavy-duty diesel units used in maritime and power generation applications have become very strict the last years. Hence, the industry is enforced to limit specific gaseous and particulate emissions (NO x , SO x , CO x , PM and HC) depending on the regulations. Among numerous methods, simulation models are extensively used to support the development of techniques used for the control of emitted pollutants. This is very important for large-scale engines due to the extremely high cost of the experimental investigation resulting from the size of the engines and the test equipment involved. Beyond this, simulation models can also be used to support NO x monitoring, since on-board verification techniques are to become mandatory for the marine industry in the near future. Last but not least, simulation models can also be used for model-based control applications to support the operation of both in-cylinder and after-treatment techniques. Currently, the major controlled pollutant for both marine and stationary applications is NO x . For this reason, in the present work, authors focus on the development and application of a simplified NO x model with special emphasis on its ability to predict the effect of operating conditions on NO x for both two and four-stroke diesel engines. To accomplish this, an existing well validated simplified NO x model has been modified to enhance its physical background and applied on 16 different large-scale diesel engines utilizing 18 different sets of

  18. Wind power prediction models (United States)

    Levy, R.; Mcginness, H.


    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  19. Large scale composting model


    Henon , Florent; Debenest , Gérald; Tremier , Anne; Quintard , Michel; Martel , Jean-Luc; Duchalais , Guy


    International audience; One way to treat the organic wastes accordingly to the environmental policies is to develop biological treatment like composting. Nevertheless, this development largely relies on the quality of the final product and as a consequence on the quality of the biological activity during the treatment. Favourable conditions (oxygen concentration, temperature and moisture content) in the waste bed largely contribute to the establishment of a good aerobic biological activity an...

  20. Zephyr - the prediction models

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg


    utilities as partners and users. The new models are evaluated for five wind farms in Denmark as well as one wind farm in Spain. It is shown that the predictions based on conditional parametric models are superior to the predictions obatined by state-of-the-art parametric models.......This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Danish...

  1. Development and application of a remote sensing-based salinity prediction model for a large estuarine lake in the US Gulf of Mexico coast (United States)

    Wang, Fugui; Xu, Y. Jun


    SummarySalinity in estuaries is highly variable due to river discharge, tidal motion, and winds. Information on the spatial and temporal changes in salinity can provide important ecological indications, but accurate monitoring of the space-time variability for a large estuary is often costly and time-consuming. This study applied remote sensing techniques to develop a salinity prediction model for Lake Pontchartrain, a large estuarine lake located in the Northern Gulf of Mexico, USA. "Ground truth" salinity was measured along two transects across the lake and near the shoreline. Water-leaving reflectance from the measurement locations was extracted from Landsat Thematic Mapper (TM) images pre-processed through "banding" noise reduction and radiometrical correction approaches. Ordinary least square and ridge regression methods were performed to identify model parameters and to determine relationships between salinity and reflectance. Salinity in the lake on eight dates was predicted with the developed model. Difference in salinity level and patterns, and impacts of Hurricanes Katrina and Rita on salinity were assessed with ANOVA and Fuzzy Similarity methods. The results showed that the model achieved a high power in prediction of the lake salinity ( R2 = 0.89 and RMSE of validation = 0.27). Reflectance from TM bands 1, 2, and 4 was positively correlated to salinity levels and explained 1.9%, 20.3%, and 10.2% variance in salinity levels. Reflectance from bands 3 and 5 was negatively correlated to salinity and explained 34.1% and 31.2% variance. Under normal circumstances without the impacts of hurricanes, the lake salinity presented two patterns with average salinity level of 5.5 ppt. After Katrina's landfall, the average was significantly increased by 1.1 ppt and the spatial patterns were altered. The pattern on 30 August 2005 was the most dissimilar one as compared to the two normal patterns, and then followed by the patterns on 9 and 25 October, and 7 September

  2. Computational fluid dynamics model for predicting flow of viscous fluids in a large fermentor with hydrofoil flow impellers and internal cooling coils (United States)

    Kelly; Humphrey


    Considerable debate has occurred over the use of hydrofoil impellers in large-scale fermentors to improve mixing and mass transfer in highly viscous non-Newtonian systems. Using a computational fluid dynamics software package (Fluent, version 4.30) extensive calculations were performed to study the effect of impeller speed (70-130 rpm), broth rheology (value of power law flow behavior index from 0.2 to 0.6), and distance between the cooling coil bank and the fermentor wall (6-18 in.) on flow near the perimeter of a large (75-m3) fermentor equipped with A315 impellers. A quadratic model utilizing the data was developed in an attempt to correlate the effect of A315 impeller speed, power law flow behavior index, and distance between the cooling coil bank and the fermentor wall on the average axial velocity in the coil bank-wall region. The results suggest that there is a potential for slow or stagnant flow in the coil bank-wall region which could result in poor oxygen and heat transfer for highly viscous fermentations. The results also indicate that there is the potential for slow or stagnant flow in the region between the top impeller and the gas headspace when flow through the coil bank-wall region is slow. Finally, a simple guideline was developed to allow fermentor design engineers to predict the degree of flow behind a bank of helical cooling coils in a large fermentor with hydrofoil flow impellers.

  3. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)


    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  4. Coupling the Weather Research and Forecasting (WRF) model and Large Eddy Simulations with Actuator Disk Model: predictions of wind farm power production (United States)

    Garcia Cartagena, Edgardo Javier; Santoni, Christian; Ciri, Umberto; Iungo, Giacomo Valerio; Leonardi, Stefano


    A large-scale wind farm operating under realistic atmospheric conditions is studied by coupling a meso-scale and micro-scale models. For this purpose, the Weather Research and Forecasting model (WRF) is coupled with an in-house LES solver for wind farms. The code is based on a finite difference scheme, with a Runge-Kutta, fractional step and the Actuator Disk Model. The WRF model has been configured using seven one-way nested domains where the child domain has a mesh size one third of its parent domain. A horizontal resolution of 70 m is used in the innermost domain. A section from the smallest and finest nested domain, 7.5 diameters upwind of the wind farm is used as inlet boundary condition for the LES code. The wind farm consists in six-turbines aligned with the mean wind direction and streamwise spacing of 10 rotor diameters, (D), and 2.75D in the spanwise direction. Three simulations were performed by varying the velocity fluctuations at the inlet: random perturbations, precursor simulation, and recycling perturbation method. Results are compared with a simulation on the same wind farm with an ideal uniform wind speed to assess the importance of the time varying incoming wind velocity. Numerical simulations were performed at TACC (Grant CTS070066). This work was supported by NSF, (Grant IIA-1243482 WINDINSPIRE).

  5. Melanoma risk prediction models

    Directory of Open Access Journals (Sweden)

    Nikolić Jelena


    Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were

  6. Prospective detection of large prediction errors: a hypothesis testing approach

    International Nuclear Information System (INIS)

    Ruan, Dan


    Real-time motion management is important in radiotherapy. In addition to effective monitoring schemes, prediction is required to compensate for system latency, so that treatment can be synchronized with tumor motion. However, it is difficult to predict tumor motion at all times, and it is critical to determine when large prediction errors may occur. Such information can be used to pause the treatment beam or adjust monitoring/prediction schemes. In this study, we propose a hypothesis testing approach for detecting instants corresponding to potentially large prediction errors in real time. We treat the future tumor location as a random variable, and obtain its empirical probability distribution with the kernel density estimation-based method. Under the null hypothesis, the model probability is assumed to be a concentrated Gaussian centered at the prediction output. Under the alternative hypothesis, the model distribution is assumed to be non-informative uniform, which reflects the situation that the future position cannot be inferred reliably. We derive the likelihood ratio test (LRT) for this hypothesis testing problem and show that with the method of moments for estimating the null hypothesis Gaussian parameters, the LRT reduces to a simple test on the empirical variance of the predictive random variable. This conforms to the intuition to expect a (potentially) large prediction error when the estimate is associated with high uncertainty, and to expect an accurate prediction when the uncertainty level is low. We tested the proposed method on patient-derived respiratory traces. The 'ground-truth' prediction error was evaluated by comparing the prediction values with retrospective observations, and the large prediction regions were subsequently delineated by thresholding the prediction errors. The receiver operating characteristic curve was used to describe the performance of the proposed hypothesis testing method. Clinical implication was represented by miss

  7. Electro-thermal Modeling for Junction Temperature Cycling-Based Lifetime Prediction of a Press-Pack IGBT 3L-NPC-VSC Applied to Large Wind Turbines

    DEFF Research Database (Denmark)

    Senturk, Osman Selcuk; Munk-Nielsen, Stig; Teodorescu, Remus


    Reliability is a critical criterion for multi-MW wind turbines, which are being employed with increasing numbers in wind power plants, since they operate under harsh conditions and have high maintenance cost due to their remote locations. In this study, the wind turbine grid-side converter...... prediction, the converter electro-thermal model including electrical, power loss, and dynamical thermal models is developed with the main focus on the thermal modeling regarding converter topology, switch technology, and physical structure. Moreover, these models are simplified for their practical...... implementation in computation platforms. Finally, the converter lifetimes for wind power profiles are predicted using the IGBT lifetime model available. Hence, the developed electrothermal model’s suitability for the lifetime predictions is shown....

  8. Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications

    DEFF Research Database (Denmark)

    Sun, Shu; Rappaport, Theodore S.; Thomas, Timothy


    This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha–beta–gamma (ABG) model, the close-in (CI) free-space reference distance model, and the CI model with a frequency-weighted path loss...... the accuracy and sensitivity of these models using measured data from 30 propagation measurement data sets from 2 to 73 GHz over distances ranging from 4 to 1238 m. A series of sensitivity analyses of the three models shows that the physically based two-parameter CI model and three-parameter CIF model offer...

  9. Large Hadron Collider (LHC) phenomenology, operational challenges and theoretical predictions

    CERN Document Server

    Gilles, Abelin R


    The Large Hadron Collider (LHC) is the highest-energy particle collider ever constructed and is considered "one of the great engineering milestones of mankind." It was built by the European Organization for Nuclear Research (CERN) from 1998 to 2008, with the aim of allowing physicists to test the predictions of different theories of particle physics and high-energy physics, and particularly prove or disprove the existence of the theorized Higgs boson and of the large family of new particles predicted by supersymmetric theories. In this book, the authors study the phenomenology, operational challenges and theoretical predictions of LHC. Topics discussed include neutral and charged black hole remnants at the LHC; the modified statistics approach for the thermodynamical model of multiparticle production; and astroparticle physics and cosmology in the LHC era.

  10. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences


    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  11. A design-build-test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinantS. cerevisiaeand improves predictive capabilities of large-scale kinetic models. (United States)

    Miskovic, Ljubisa; Alff-Tuomala, Susanne; Soh, Keng Cher; Barth, Dorothee; Salusjärvi, Laura; Pitkänen, Juha-Pekka; Ruohonen, Laura; Penttilä, Merja; Hatzimanikatis, Vassily


    Recent advancements in omics measurement technologies have led to an ever-increasing amount of available experimental data that necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models. These models can help us to uncover new insights into cellular physiology and also to assist in the rational design of bioreactor or fermentation processes. Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework for the construction of large-scale kinetic models can be used as guidance for formulating alternative metabolic engineering strategies. We used ORACLE in a metabolic engineering problem: improvement of the xylose uptake rate during mixed glucose-xylose consumption in a recombinant Saccharomyces cerevisiae strain. Using the data from bioreactor fermentations, we characterized network flux and concentration profiles representing possible physiological states of the analyzed strain. We then identified enzymes that could lead to improved flux through xylose transporters (XTR). For some of the identified enzymes, including hexokinase (HXK), we could not deduce if their control over XTR was positive or negative. We thus performed a follow-up experiment, and we found out that HXK2 deletion improves xylose uptake rate. The data from the performed experiments were then used to prune the kinetic models, and the predictions of the pruned population of kinetic models were in agreement with the experimental data collected on the HXK2 -deficient S. cerevisiae strain. We present a design-build-test cycle composed of modeling efforts and experiments with a glucose-xylose co-utilizing recombinant S. cerevisiae and its HXK2 -deficient mutant that allowed us to uncover interdependencies between upper glycolysis and xylose uptake pathway. Through this cycle, we also obtained kinetic models with improved prediction capabilities. The present study demonstrates the potential of integrated "modeling

  12. Predicting Positive and Negative Relationships in Large Social Networks.

    Directory of Open Access Journals (Sweden)

    Guan-Nan Wang

    Full Text Available In a social network, users hold and express positive and negative attitudes (e.g. support/opposition towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM. Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.

  13. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik


    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...

  14. Improving Prediction Accuracy of a Rate-Based Model of an MEA-Based Carbon Capture Process for Large-Scale Commercial Deployment

    Directory of Open Access Journals (Sweden)

    Xiaobo Luo


    Full Text Available Carbon capture and storage (CCS technology will play a critical role in reducing anthropogenic carbon dioxide (CO2 emission from fossil-fired power plants and other energy-intensive processes. However, the increment of energy cost caused by equipping a carbon capture process is the main barrier to its commercial deployment. To reduce the capital and operating costs of carbon capture, great efforts have been made to achieve optimal design and operation through process modeling, simulation, and optimization. Accurate models form an essential foundation for this purpose. This paper presents a study on developing a more accurate rate-based model in Aspen Plus® for the monoethanolamine (MEA-based carbon capture process by multistage model validations. The modeling framework for this process was established first. The steady-state process model was then developed and validated at three stages, which included a thermodynamic model, physical properties calculations, and a process model at the pilot plant scale, covering a wide range of pressures, temperatures, and CO2 loadings. The calculation correlations of liquid density and interfacial area were updated by coding Fortran subroutines in Aspen Plus®. The validation results show that the correlation combination for the thermodynamic model used in this study has higher accuracy than those of three other key publications and the model prediction of the process model has a good agreement with the pilot plant experimental data. A case study was carried out for carbon capture from a 250 MWe combined cycle gas turbine (CCGT power plant. Shorter packing height and lower specific duty were achieved using this accurate model.

  15. Constituent models and large transverse momentum reactions

    International Nuclear Information System (INIS)

    Brodsky, S.J.


    The discussion of constituent models and large transverse momentum reactions includes the structure of hard scattering models, dimensional counting rules for large transverse momentum reactions, dimensional counting and exclusive processes, the deuteron form factor, applications to inclusive reactions, predictions for meson and photon beams, the charge-cubed test for the e/sup +-/p → e/sup +-/γX asymmetry, the quasi-elastic peak in inclusive hadronic reactions, correlations, and the multiplicity bump at large transverse momentum. Also covered are the partition method for bound state calculations, proofs of dimensional counting, minimal neutralization and quark--quark scattering, the development of the constituent interchange model, and the A dependence of high transverse momentum reactions

  16. Computational Modeling of Large Wildfires: A Roadmap

    KAUST Repository

    Coen, Janice L.


    Wildland fire behavior, particularly that of large, uncontrolled wildfires, has not been well understood or predicted. Our methodology to simulate this phenomenon uses high-resolution dynamic models made of numerical weather prediction (NWP) models coupled to fire behavior models to simulate fire behavior. NWP models are capable of modeling very high resolution (< 100 m) atmospheric flows. The wildland fire component is based upon semi-empirical formulas for fireline rate of spread, post-frontal heat release, and a canopy fire. The fire behavior is coupled to the atmospheric model such that low level winds drive the spread of the surface fire, which in turn releases sensible heat, latent heat, and smoke fluxes into the lower atmosphere, feeding back to affect the winds directing the fire. These coupled dynamic models capture the rapid spread downwind, flank runs up canyons, bifurcations of the fire into two heads, and rough agreement in area, shape, and direction of spread at periods for which fire location data is available. Yet, intriguing computational science questions arise in applying such models in a predictive manner, including physical processes that span a vast range of scales, processes such as spotting that cannot be modeled deterministically, estimating the consequences of uncertainty, the efforts to steer simulations with field data ("data assimilation"), lingering issues with short term forecasting of weather that may show skill only on the order of a few hours, and the difficulty of gathering pertinent data for verification and initialization in a dangerous environment. © 2010 IEEE.

  17. Spatiotemporal property and predictability of large-scale human mobility (United States)

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


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

  18. The predictability of large-scale wind-driven flows

    Directory of Open Access Journals (Sweden)

    A. Mahadevan


    Full Text Available The singular values associated with optimally growing perturbations to stationary and time-dependent solutions for the general circulation in an ocean basin provide a measure of the rate at which solutions with nearby initial conditions begin to diverge, and hence, a measure of the predictability of the flow. In this paper, the singular vectors and singular values of stationary and evolving examples of wind-driven, double-gyre circulations in different flow regimes are explored. By changing the Reynolds number in simple quasi-geostrophic models of the wind-driven circulation, steady, weakly aperiodic and chaotic states may be examined. The singular vectors of the steady state reveal some of the physical mechanisms responsible for optimally growing perturbations. In time-dependent cases, the dominant singular values show significant variability in time, indicating strong variations in the predictability of the flow. When the underlying flow is weakly aperiodic, the dominant singular values co-vary with integral measures of the large-scale flow, such as the basin-integrated upper ocean kinetic energy and the transport in the western boundary current extension. Furthermore, in a reduced gravity quasi-geostrophic model of a weakly aperiodic, double-gyre flow, the behaviour of the dominant singular values may be used to predict a change in the large-scale flow, a feature not shared by an analogous two-layer model. When the circulation is in a strongly aperiodic state, the dominant singular values no longer vary coherently with integral measures of the flow. Instead, they fluctuate in a very aperiodic fashion on mesoscale time scales. The dominant singular vectors then depend strongly on the arrangement of mesoscale features in the flow and the evolved forms of the associated singular vectors have relatively short spatial scales. These results have several implications. In weakly aperiodic, periodic, and stationary regimes, the mesoscale energy

  19. Model of large pool fires

    International Nuclear Information System (INIS)

    Fay, J.A.


    A two zone entrainment model of pool fires is proposed to depict the fluid flow and flame properties of the fire. Consisting of combustion and plume zones, it provides a consistent scheme for developing non-dimensional scaling parameters for correlating and extrapolating pool fire visible flame length, flame tilt, surface emissive power, and fuel evaporation rate. The model is extended to include grey gas thermal radiation from soot particles in the flame zone, accounting for emission and absorption in both optically thin and thick regions. A model of convective heat transfer from the combustion zone to the liquid fuel pool, and from a water substrate to cryogenic fuel pools spreading on water, provides evaporation rates for both adiabatic and non-adiabatic fires. The model is tested against field measurements of large scale pool fires, principally of LNG, and is generally in agreement with experimental values of all variables

  20. Modelling and control of large cryogenic refrigerator

    International Nuclear Information System (INIS)

    Bonne, Francois


    This manuscript is concern with both the modeling and the derivation of control schemes for large cryogenic refrigerators. The particular case of those which are submitted to highly variable pulsed heat load is studied. A model of each object that normally compose a large cryo-refrigerator is proposed. The methodology to gather objects model into the model of a subsystem is presented. The manuscript also shows how to obtain a linear equivalent model of the subsystem. Based on the derived models, advances control scheme are proposed. Precisely, a linear quadratic controller for warm compression station working with both two and three pressures state is derived, and a predictive constrained one for the cold-box is obtained. The particularity of those control schemes is that they fit the computing and data storage capabilities of Programmable Logic Controllers (PLC) with are well used in industry. The open loop model prediction capability is assessed using experimental data. Developed control schemes are validated in simulation and experimentally on the 400W1.8K SBT's cryogenic test facility and on the CERN's LHC warm compression station. (author) [fr

  1. Coupling hydrodynamic modeling and empirical measures of bed mobility to predict the risk of scour and fill of salmon redds in a large regulated river (United States)

    May, Christine L.; Pryor, Bonnie; Lisle, Thomas E.; Lang, Margaret


    In order to assess the risk of scour and fill of spawning redds during floods, an understanding of the relations among river discharge, bed mobility, and scour and fill depths in areas of the streambed heavily utilized by spawning salmon is needed. Our approach coupled numerical flow modeling and empirical data from the Trinity River, California, to quantify spatially explicit zones of differential bed mobility and to identify specific areas where scour and fill is deep enough to impact redd viability. Spatial patterns of bed mobility, based on model-predicted Shields stress, indicate that a zone of full mobility was limited to a central core that expanded with increasing flow strength. The likelihood and maximum depth of measured scour increased with increasing modeled Shields stress. Because redds were preferentially located in coarse substrate in shallow areas with close proximity to the stream banks, they were less likely to become mobilized or to risk deep scour during high-flow events but were more susceptible to sediment deposition.

  2. [ACG model can predict large consumers of health care. Health care resources can be used more wisely, individuals at risk can receive better care]. (United States)

    Fredriksson, Martin; Edenström, Marcus; Lundahl, Anneth; Björkman, Lars


    We describe a method, which uses already existent administrative data to identify individuals with a high risk of a large need of healthcare in the coming year. The model is based on the ACG (Adjusted Clinical Groups) system to identify the high-risk patients. We have set up a model where we combine the ACG system stratification analysis tool RUB (Resource Utilization Band) and Probability High Total Cost >0.5. We tested the method with historical data, using 2 endpoints, either >19 physical visits anywhere in the healthcare system in the coming 12 months or more than 2 hospital admissions in the coming 12 months. In the region of Västra Götaland with 1.6 million inhabitants, 5.6% of the population had >19 physical visits during a 12 month period and 1.2% more than 2 hospital admissions. Our model identified approximately 24,000 individuals of whom 25.7% had >19 physical visits and 11.6% had more than 2 hospital admissions in the coming 12 months. We now plan a small test in ten primary care centers to evaluate if the model should be introduced in the entire Västra Götaland region.

  3. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA


    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation......, then rival strategies can still be compared based on repeated bootstraps of the same data. Often, however, the overall performance of rival strategies is similar and it is thus difficult to decide for one model. Here, we investigate the variability of the prediction models that results when the same...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...

  4. Heterozygosity in an isolated population of a large mammal founded by four individuals is predicted by an individual-based genetic model.

    Directory of Open Access Journals (Sweden)

    Jaana Kekkonen

    Full Text Available Within-population genetic diversity is expected to be dramatically reduced if a population is founded by a low number of individuals. Three females and one male white-tailed deer Odocoileus virginianus, a North American species, were successfully introduced in Finland in 1934 and the population has since been growing rapidly, but remained in complete isolation from other populations.Based on 14 microsatellite loci, the expected heterozygosity H was 0.692 with a mean allelic richness (AR of 5.36, which was significantly lower than what was found in Oklahoma, U.S.A. (H = 0.742; AR = 9.07, demonstrating that a bottleneck occurred. Observed H was in line with predictions from an individual-based model where the genealogy of the males and females in the population were tracked and the population's demography was included.Our findings provide a rare within-population empirical test of the founder effect and suggest that founding a population by a small number of individuals need not have a dramatic impact on heterozygosity in an iteroparous species.

  5. Bootstrap prediction and Bayesian prediction under misspecified models


    Fushiki, Tadayoshi


    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  6. Predictability of extreme values in geophysical models

    Directory of Open Access Journals (Sweden)

    A. E. Sterk


    Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.

  7. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik


    The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... the performance of HIRLAM in particular with respect to wind predictions. To estimate the performance of the model two spatial resolutions (0,5 Deg. and 0.2 Deg.) and different sets of HIRLAM variables were used to predict wind speed and energy production. The predictions of energy production for the wind farms...... are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production...

  8. Multiplexed Predictive Control of a Large Commercial Turbofan Engine (United States)

    Richter, hanz; Singaraju, Anil; Litt, Jonathan S.


    Model predictive control is a strategy well-suited to handle the highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. However, it has thus far been infeasible to implement model predictive control in engine control applications, because of the combination of model complexity and the time allotted for the control update calculation. In this paper, a multiplexed implementation is proposed that dramatically reduces the computational burden of the quadratic programming optimization that must be solved online as part of the model-predictive-control algorithm. Actuator updates are calculated sequentially and cyclically in a multiplexed implementation, as opposed to the simultaneous optimization taking place in conventional model predictive control. Theoretical aspects are discussed based on a nominal model, and actual computational savings are demonstrated using a realistic commercial engine model.


    African Journals Online (AJOL)


    Jul 2, 2012 ... Linear MPC. 1. Uses linear model: ˙x = Ax + Bu. 2. Quadratic cost function: F = xT Qx + uT Ru. 3. Linear constraints: Hx + Gu < 0. 4. Quadratic program. Nonlinear MPC. 1. Nonlinear model: ˙x = f(x, u). 2. Cost function can be nonquadratic: F = (x, u). 3. Nonlinear constraints: h(x, u) < 0. 4. Nonlinear program.

  10. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria


    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  11. Melanoma Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Predictive models of moth development (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  13. Predictive Models and Computational Embryology (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  14. Flash flood prediction in large dams using neural networks (United States)

    Múnera Estrada, J. C.; García Bartual, R.


    A flow forecasting methodology is presented as a support tool for flood management in large dams. The practical and efficient use of hydrological real-time measurements is necessary to operate early warning systems for flood disasters prevention, either in natural catchments or in those regulated with reservoirs. In this latter case, the optimal dam operation during flood scenarios should reduce the downstream risks, and at the same time achieve a compromise between different goals: structural security, minimize predictions uncertainty and water resources system management objectives. Downstream constraints depend basically on the geomorphology of the valley, the critical flow thresholds for flooding, the land use and vulnerability associated with human settlements and their economic activities. A dam operation during a flood event thus requires appropriate strategies depending on the flood magnitude and the initial freeboard at the reservoir. The most important difficulty arises from the inherently stochastic character of peak rainfall intensities, their strong spatial and temporal variability, and the highly nonlinear response of semiarid catchments resulting from initial soil moisture condition and the dominant flow mechanisms. The practical integration of a flow prediction model in a real-time system should include combined techniques of pre-processing, data verification and completion, assimilation of information and implementation of real time filters depending on the system characteristics. This work explores the behaviour of real-time flood forecast algorithms based on artificial neural networks (ANN) techniques, in the River Meca catchment (Huelva, Spain), regulated by El Sancho dam. The dam is equipped with three Taintor gates of 12x6 meters. The hydrological data network includes five high-resolution automatic pluviometers (dt=10 min) and three high precision water level sensors in the reservoir. A cross correlation analysis between precipitation data

  15. Predictions models with neural nets

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný


    Full Text Available The contribution is oriented to basic problem trends solution of economic pointers, using neural networks. Problems include choice of the suitable model and consequently configuration of neural nets, choice computational function of neurons and the way prediction learning. The contribution contains two basic models that use structure of multilayer neural nets and way of determination their configuration. It is postulate a simple rule for teaching period of neural net, to get most credible prediction.Experiments are executed with really data evolution of exchange rate Kč/Euro. The main reason of choice this time series is their availability for sufficient long period. In carry out of experiments the both given basic kind of prediction models with most frequent use functions of neurons are verified. Achieve prediction results are presented as in numerical and so in graphical forms.

  16. Predicting intracranial hemorrhage after traumatic brain injury in low and middle-income countries: A prognostic model based on a large, multi-center, international cohort

    Directory of Open Access Journals (Sweden)

    Subaiya Saleena


    Full Text Available Abstract Background Traumatic brain injury (TBI affects approximately 10 million people annually, of which intracranial hemorrhage is a devastating sequelae, occurring in one-third to half of cases. Patients in low and middle-income countries (LMIC are twice as likely to die following TBI as compared to those in high-income countries. Diagnostic capabilities and treatment options for intracranial hemorrhage are limited in LMIC as there are fewer computed tomography (CT scanners and neurosurgeons per patient as in high-income countries. Methods The Medical Research Council CRASH-1 trial was utilized to build this model. The study cohort included all patients from LMIC who received a CT scan of the brain (n = 5669. Prognostic variables investigated included age, sex, time from injury to randomization, pupil reactivity, cause of injury, seizure and the presence of major extracranial injury. Results There were five predictors that were included in the final model; age, Glasgow Coma Scale, pupil reactivity, the presence of a major extracranial injury and time from injury to presentation. The model demonstrated good discrimination and excellent calibration (c-statistic 0.71. A simplified risk score was created for clinical settings to estimate the percentage risk of intracranial hemorrhage among TBI patients. Conclusion Simple prognostic models can be used in LMIC to estimate the risk of intracranial hemorrhage among TBI patients. Combined with clinical judgment this may facilitate risk stratification, rapid transfer to higher levels of care and treatment in resource-poor settings.

  17. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo


    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  18. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.


    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  19. A large-scale evaluation of computational protein function prediction. (United States)

    Radivojac, Predrag; Clark, Wyatt T; Oron, Tal Ronnen; Schnoes, Alexandra M; Wittkop, Tobias; Sokolov, Artem; Graim, Kiley; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa; Pandey, Gaurav; Yunes, Jeffrey M; Talwalkar, Ameet S; Repo, Susanna; Souza, Michael L; Piovesan, Damiano; Casadio, Rita; Wang, Zheng; Cheng, Jianlin; Fang, Hai; Gough, Julian; Koskinen, Patrik; Törönen, Petri; Nokso-Koivisto, Jussi; Holm, Liisa; Cozzetto, Domenico; Buchan, Daniel W A; Bryson, Kevin; Jones, David T; Limaye, Bhakti; Inamdar, Harshal; Datta, Avik; Manjari, Sunitha K; Joshi, Rajendra; Chitale, Meghana; Kihara, Daisuke; Lisewski, Andreas M; Erdin, Serkan; Venner, Eric; Lichtarge, Olivier; Rentzsch, Robert; Yang, Haixuan; Romero, Alfonso E; Bhat, Prajwal; Paccanaro, Alberto; Hamp, Tobias; Kaßner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Boehm, Ariane; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas A; Kaufmann, Stefanie; Kiening, Michael; Krompass, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Björne, Jari; Salakoski, Tapio; Wong, Andrew; Shatkay, Hagit; Gatzmann, Fanny; Sommer, Ingolf; Wass, Mark N; Sternberg, Michael J E; Škunca, Nives; Supek, Fran; Bošnjak, Matko; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Kourmpetis, Yiannis A I; van Dijk, Aalt D J; ter Braak, Cajo J F; Zhou, Yuanpeng; Gong, Qingtian; Dong, Xinran; Tian, Weidong; Falda, Marco; Fontana, Paolo; Lavezzo, Enrico; Di Camillo, Barbara; Toppo, Stefano; Lan, Liang; Djuric, Nemanja; Guo, Yuhong; Vucetic, Slobodan; Bairoch, Amos; Linial, Michal; Babbitt, Patricia C; Brenner, Steven E; Orengo, Christine; Rost, Burkhard; Mooney, Sean D; Friedberg, Iddo


    Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

  20. Advances in Modelling of Large Scale Coastal Evolution

    NARCIS (Netherlands)

    Stive, M.J.F.; De Vriend, H.J.


    The attention for climate change impact on the world's coastlines has established large scale coastal evolution as a topic of wide interest. Some more recent advances in this field, focusing on the potential of mathematical models for the prediction of large scale coastal evolution, are discussed.

  1. What do saliency models predict? (United States)

    Koehler, Kathryn; Guo, Fei; Zhang, Sheng; Eckstein, Miguel P.


    Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements. PMID:24618107

  2. Very Large System Dynamics Models - Lessons Learned

    Energy Technology Data Exchange (ETDEWEB)

    Jacob J. Jacobson; Leonard Malczynski


    This paper provides lessons learned from developing several large system dynamics (SD) models. System dynamics modeling practice emphasize the need to keep models small so that they are manageable and understandable. This practice is generally reasonable and prudent; however, there are times that large SD models are necessary. This paper outlines two large SD projects that were done at two Department of Energy National Laboratories, the Idaho National Laboratory and Sandia National Laboratories. This paper summarizes the models and then discusses some of the valuable lessons learned during these two modeling efforts.

  3. Predicting the survival time for diffuse large B-cell lymphoma using microarray data


    Khoshhali, Mehri; Mahjub, Hossein; Saidijam, Massoud; Poorolajal, Jalal; Soltanian, Ali Reza


    The present study was conducted to predict survival time in patients with diffuse large B-cell lymphoma, DLBCL, based on microarray data using Cox regression model combined with seven dimension reduction methods. This historical cohort included 2042 gene expression measurements from 40 patients with DLBCL. In order to predict survival, a combination of Cox regression model was used with seven methods for dimension reduction or shrinkage including univariate selection, forward stepwise selecti...

  4. Large-scale prediction of drug-target relationships

    DEFF Research Database (Denmark)

    Kuhn, Michael; Campillos, Mónica; González, Paula


    , but also provides a more global view on drug-target relations. Here we review recent attempts to apply large-scale computational analyses to predict novel interactions of drugs and targets from molecular and cellular features. In this context, we quantify the family-dependent probability of two proteins...... to bind the same ligand as function of their sequence similarity. We finally discuss how phenotypic data could help to expand our understanding of the complex mechanisms of drug action....

  5. Finding Correlation and Predicting System Behavior in Large IT Infrastructure


    Hussain, Shahbaz


    Modern IT development infrastructure has a large number of components that must be monitored, for instance servers and network components. Various system-metrics (build time, CPU utilization, queries time etc.) are gathered to monitor system performance. In practice, it is extremely difficult for a system administrator to observe a correlation between several systemmetrics and predict a target system-metric based on highly correlated system-metrics without machine learning support. The experi...

  6. Modeling of Carbon Tetrachloride Flow and Transport in the Subsurface of the 200 West Disposal Sites: Large-Scale Model Configuration and Prediction of Future Carbon Tetrachloride Distribution Beneath the 216-Z-9 Disposal Site

    Energy Technology Data Exchange (ETDEWEB)

    Oostrom, Mart; Thorne, Paul D.; Zhang, Z. F.; Last, George V.; Truex, Michael J.


    Three-dimensional simulations considered migration of dense, nonaqueous phase liquid (DNAPL) consisting of CT and co disposed organics in the subsurface as a function of the properties and distribution of subsurface sediments and of the properties and disposal history of the waste. Simulations of CT migration were conducted using the Water-Oil-Air mode of Subsurface Transport Over Multiple Phases (STOMP) simulator. A large-scale model was configured to model CT and waste water discharge from the major CT and waste-water disposal sites.

  7. Comparison of two ordinal prediction models

    DEFF Research Database (Denmark)

    Kattan, Michael W; Gerds, Thomas A


    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  8. Large Scale Computations in Air Pollution Modelling

    DEFF Research Database (Denmark)

    Zlatev, Z.; Brandt, J.; Builtjes, P. J. H.

    Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...

  9. Characterizing Attention with Predictive Network Models. (United States)

    Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M


    Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases

    International Nuclear Information System (INIS)

    Nigsch, Florian; Mitchell, John B.O.


    The combination of models for protein target prediction with large databases containing toxicological information for individual molecules allows the derivation of 'toxiclogical' profiles, i.e., to what extent are molecules of known toxicity predicted to interact with a set of protein targets. To predict protein targets of drug-like and toxic molecules, we built a computational multiclass model using the Winnow algorithm based on a dataset of protein targets derived from the MDL Drug Data Report. A 15-fold Monte Carlo cross-validation using 50% of each class for training, and the remaining 50% for testing, provided an assessment of the accuracy of that model. We retained the 3 top-ranking predictions and found that in 82% of all cases the correct target was predicted within these three predictions. The first prediction was the correct one in almost 70% of cases. A model built on the whole protein target dataset was then used to predict the protein targets for 150 000 molecules from the MDL Toxicity Database. We analysed the frequency of the predictions across the panel of protein targets for experimentally determined toxicity classes of all molecules. This allowed us to identify clusters of proteins related by their toxicological profiles, as well as toxicities that are related. Literature-based evidence is provided for some specific clusters to show the relevance of the relationships identified

  11. Link Prediction via Sparse Gaussian Graphical Model

    Directory of Open Access Journals (Sweden)

    Liangliang Zhang


    Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.

  12. Solving large linear systems in an implicit thermohaline ocean model

    NARCIS (Netherlands)

    de Niet, Arie Christiaan


    The climate on earth is largely determined by the global ocean circulation. Hence it is important to predict how the flow will react to perturbation by for example melting icecaps. To answer questions about the stability of the global ocean flow, a computer model has been developed that is able to

  13. Regularization modeling for large-eddy simulation

    NARCIS (Netherlands)

    Geurts, Bernardus J.; Holm, D.D.


    A new modeling approach for large-eddy simulation (LES) is obtained by combining a "regularization principle" with an explicit filter and its inversion. This regularization approach allows a systematic derivation of the implied subgrid model, which resolves the closure problem. The central role of

  14. Large-scale prediction of long disordered regions in proteins using random forests

    Directory of Open Access Journals (Sweden)

    Norton Raymond S


    Full Text Available Abstract Background Many proteins contain disordered regions that lack fixed three-dimensional (3D structure under physiological conditions but have important biological functions. Prediction of disordered regions in protein sequences is important for understanding protein function and in high-throughput determination of protein structures. Machine learning techniques, including neural networks and support vector machines have been widely used in such predictions. Predictors designed for long disordered regions are usually less successful in predicting short disordered regions. Combining prediction of short and long disordered regions will dramatically increase the complexity of the prediction algorithm and make the predictor unsuitable for large-scale applications. Efficient batch prediction of long disordered regions alone is of greater interest in large-scale proteome studies. Results A new algorithm, IUPforest-L, for predicting long disordered regions using the random forest learning model is proposed in this paper. IUPforest-L is based on the Moreau-Broto auto-correlation function of amino acid indices (AAIs and other physicochemical features of the primary sequences. In 10-fold cross validation tests, IUPforest-L can achieve an area of 89.5% under the receiver operating characteristic (ROC curve. Compared with existing disorder predictors, IUPforest-L has high prediction accuracy and is efficient for predicting long disordered regions in large-scale proteomes. Conclusion The random forest model based on the auto-correlation functions of the AAIs within a protein fragment and other physicochemical features could effectively detect long disordered regions in proteins. A new predictor, IUPforest-L, was developed to batch predict long disordered regions in proteins, and the server can be accessed from

  15. Models for large superconducting toroidal magnet systems

    International Nuclear Information System (INIS)

    Arendt, F.; Brechna, H.; Erb, J.; Komarek, P.; Krauth, H.; Maurer, W.


    Prior to the design of large GJ toroidal magnet systems it is appropriate to procure small scale models, which can simulate their pertinent properties and allow to investigate their relevant phenomena. The important feature of the model is to show under which circumstances the system performance can be extrapolated to large magnets. Based on parameters such as the maximum magnetic field and the current density, the maximum tolerable magneto-mechanical stresses, a simple method of designing model magnets is presented. It is shown how pertinent design parameters are changed when the toroidal dimensions are altered. In addition some conductor cost estimations are given based on reactor power output and wall loading

  16. Prediction of Thermal Environment in a Large Space Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Hyun-Jung Yoon


    Full Text Available Since the thermal environment of large space buildings such as stadiums can vary depending on the location of the stands, it is important to divide them into different zones and evaluate their thermal environment separately. The thermal environment can be evaluated using physical values measured with the sensors, but the occupant density of the stadium stands is high, which limits the locations available to install the sensors. As a method to resolve the limitations of installing the sensors, we propose a method to predict the thermal environment of each zone in a large space. We set six key thermal factors affecting the thermal environment in a large space to be predicted factors (indoor air temperature, mean radiant temperature, and clothing and the fixed factors (air velocity, metabolic rate, and relative humidity. Using artificial neural network (ANN models and the outdoor air temperature and the surface temperature of the interior walls around the stands as input data, we developed a method to predict the three thermal factors. Learning and verification datasets were established using STAR CCM+ (2016.10, Siemens PLM software, Plano, TX, USA. An analysis of each model’s prediction results showed that the prediction accuracy increased with the number of learning data points. The thermal environment evaluation process developed in this study can be used to control heating, ventilation, and air conditioning (HVAC facilities in each zone in a large space building with sufficient learning by ANN models at the building testing or the evaluation stage.

  17. Seasonal predictability of Kiremt rainfall in coupled general circulation models (United States)

    Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen


    The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

  18. Predictive Models for Carcinogenicity and Mutagenicity ... (United States)

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  19. Web tools for predictive toxicology model building. (United States)

    Jeliazkova, Nina


    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  20. The research of the quantitative prediction of the deposits concentrated regions of the large and super-large sized mineral deposits in China

    International Nuclear Information System (INIS)

    Zhao Zhenyu; Wang Shicheng


    By the general theory and method of mineral resources prognosis of synthetic information, the locative and quantitative prediction of the large and super-large sized mineral deposits of solid resources of 1 : 5,000,000 are developed in china. The deposit concentrated regions is model unit, the anomaly concentrated regions is prediction unit. The mineral prognosis of synthetic information is developed on GIS platform. The technical route and work method of looking for the large and super-large sized mineral resources and basic principle of compiling attribute table of the variables and the response variables are mentioned. In research of prediction of resources quantity, the locative and quantitative prediction are processed by separately the quantification theory Ⅲ and the corresponding characteristic analysis, two methods are compared. It is very important for resources prediction of western ten provinces in china, it is helpful. (authors)

  1. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu


    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  2. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

    NARCIS (Netherlands)

    Li, T.; Hasegawa, T.; Yin, X.; Zhu, Y.; Boote, K.; Adam, M.; Bregaglio, S.; Buis, S.; Confalonieri, R.; Fumoto, T.; Gaydon, D.; Marcaida III, M.; Nakagawa, H.; Oriol, P.; Ruane, A.C.; Ruget, F.; Singh, B.; Singh, U.; Tang, L.; Yoshida, H.; Zhang, Z.; Bouman, B.


    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We

  3. Predicting and Modeling RNA Architecture (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice


    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  4. Large Mammalian Animal Models of Heart Disease

    Directory of Open Access Journals (Sweden)

    Paula Camacho


    Full Text Available Due to the biological complexity of the cardiovascular system, the animal model is an urgent pre-clinical need to advance our knowledge of cardiovascular disease and to explore new drugs to repair the damaged heart. Ideally, a model system should be inexpensive, easily manipulated, reproducible, a biological representative of human disease, and ethically sound. Although a larger animal model is more expensive and difficult to manipulate, its genetic, structural, functional, and even disease similarities to humans make it an ideal model to first consider. This review presents the commonly-used large animals—dog, sheep, pig, and non-human primates—while the less-used other large animals—cows, horses—are excluded. The review attempts to introduce unique points for each species regarding its biological property, degrees of susceptibility to develop certain types of heart diseases, and methodology of induced conditions. For example, dogs barely develop myocardial infarction, while dilated cardiomyopathy is developed quite often. Based on the similarities of each species to the human, the model selection may first consider non-human primates—pig, sheep, then dog—but it also depends on other factors, for example, purposes, funding, ethics, and policy. We hope this review can serve as a basic outline of large animal models for cardiovascular researchers and clinicians.

  5. Improving Prediction of Large-scale Regime Transitions (United States)

    Gyakum, J. R.; Roebber, P.; Bosart, L. F.; Honor, A.; Bunker, E.; Low, Y.; Hart, J.; Bliankinshtein, N.; Kolly, A.; Atallah, E.; Huang, Y.


    Cool season atmospheric predictability over the CONUS on subseasonal times scales (1-4 weeks) is critically dependent upon the structure, configuration, and evolution of the North Pacific jet stream (NPJ). The NPJ can be perturbed on its tropical side on synoptic time scales by recurving and transitioning tropical cyclones (TCs) and on subseasonal time scales by longitudinally varying convection associated with the Madden-Julian Oscillation (MJO). Likewise, the NPJ can be perturbed on its poleward side on synoptic time scales by midlatitude and polar disturbances that originate over the Asian continent. These midlatitude and polar disturbances can often trigger downstream Rossby wave propagation across the North Pacific, North America, and the North Atlantic. The project team is investigating the following multiscale processes and features: the spatiotemporal distribution of cyclone clustering over the Northern Hemisphere; cyclone clustering as influenced by atmospheric blocking and the phases and amplitudes of the major teleconnection indices, ENSO and the MJO; composite and case study analyses of representative cyclone clustering events to establish the governing dynamics; regime change predictability horizons associated with cyclone clustering events; Arctic air mass generation and modification; life cycles of the MJO; and poleward heat and moisture transports of subtropical air masses. A critical component of the study is weather regime classification. These classifications are defined through: the spatiotemporal clustering of surface cyclogenesis; a general circulation metric combining data at 500-hPa and the dynamic tropopause; Self Organizing Maps (SOM), constructed from dynamic tropopause and 850 hPa equivalent potential temperature data. The resultant lattice of nodes is used to categorize synoptic classes and their predictability, as well as to determine the robustness of the CFSv2 model climate relative to observations. Transition pathways between these

  6. Managing large-scale models: DBS

    International Nuclear Information System (INIS)


    A set of fundamental management tools for developing and operating a large scale model and data base system is presented. Based on experience in operating and developing a large scale computerized system, the only reasonable way to gain strong management control of such a system is to implement appropriate controls and procedures. Chapter I discusses the purpose of the book. Chapter II classifies a broad range of generic management problems into three groups: documentation, operations, and maintenance. First, system problems are identified then solutions for gaining management control are disucssed. Chapters III, IV, and V present practical methods for dealing with these problems. These methods were developed for managing SEAS but have general application for large scale models and data bases

  7. Modeling capillary forces for large displacements

    NARCIS (Netherlands)

    Mastrangeli, M.; Arutinov, G.; Smits, E.C.P.; Lambert, P.


    Originally applied to the accurate, passive positioning of submillimetric devices, recent works proved capillary self-alignment as effective also for larger components and relatively large initial offsets. In this paper, we describe an analytic quasi-static model of 1D capillary restoring forces

  8. Pronunciation Modeling for Large Vocabulary Speech Recognition (United States)

    Kantor, Arthur


    The large pronunciation variability of words in conversational speech is one of the major causes of low accuracy in automatic speech recognition (ASR). Many pronunciation modeling approaches have been developed to address this problem. Some explicitly manipulate the pronunciation dictionary as well as the set of the units used to define the…

  9. Generation and analysis of large reliability models (United States)

    Palumbo, Daniel L.; Nicol, David M.


    An effort has been underway for several years at NASA's Langley Research Center to extend the capability of Markov modeling techniques for reliability analysis to the designers of highly reliable avionic systems. This effort has been focused in the areas of increased model abstraction and increased computational capability. The reliability model generator (RMG), a software tool which uses as input a graphical, object-oriented block diagram of the system, is discussed. RMG uses an automated failure modes-effects analysis algorithm to produce the reliability model from the graphical description. Also considered is the ASSURE software tool, a parallel processing program which uses the ASSIST modeling language and SURE semi-Markov solution technique. An executable failure modes-effects analysis is used by ASSURE. The successful combination of the power of graphical representation, automated model generation, and parallel computation leads to the conclusion that large system architectures can now be analyzed.

  10. Dark radiation in LARGE volume models (United States)

    Cicoli, Michele; Conlon, Joseph P.; Quevedo, Fernando


    We consider reheating driven by volume modulus decays in the LARGE volume scenario. Such reheating always generates nonzero dark radiation through the decays to the axion partner, while the only competitive visible sector decays are Higgs pairs via the Giudice-Masiero term. In the framework of sequestered models where the cosmological moduli problem is absent, the simplest model with a shift-symmetric Higgs sector generates 1.56≤ΔNeff≤1.74. For more general cases, the known experimental bounds on ΔNeff strongly constrain the parameters and matter content of the models.

  11. A prediction model for Clostridium difficile recurrence

    Directory of Open Access Journals (Sweden)

    Francis D. LaBarbera


    Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.

  12. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D


    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  13. Iowa calibration of MEPDG performance prediction models. (United States)


    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  14. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.


    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  15. Large-scale multimedia modeling applications

    International Nuclear Information System (INIS)

    Droppo, J.G. Jr.; Buck, J.W.; Whelan, G.; Strenge, D.L.; Castleton, K.J.; Gelston, G.M.


    Over the past decade, the US Department of Energy (DOE) and other agencies have faced increasing scrutiny for a wide range of environmental issues related to past and current practices. A number of large-scale applications have been undertaken that required analysis of large numbers of potential environmental issues over a wide range of environmental conditions and contaminants. Several of these applications, referred to here as large-scale applications, have addressed long-term public health risks using a holistic approach for assessing impacts from potential waterborne and airborne transport pathways. Multimedia models such as the Multimedia Environmental Pollutant Assessment System (MEPAS) were designed for use in such applications. MEPAS integrates radioactive and hazardous contaminants impact computations for major exposure routes via air, surface water, ground water, and overland flow transport. A number of large-scale applications of MEPAS have been conducted to assess various endpoints for environmental and human health impacts. These applications are described in terms of lessons learned in the development of an effective approach for large-scale applications

  16. On spinfoam models in large spin regime

    International Nuclear Information System (INIS)

    Han, Muxin


    We study the semiclassical behavior of Lorentzian Engle–Pereira–Rovelli–Livine (EPRL) spinfoam model, by taking into account the sum over spins in the large spin regime. We also employ the method of stationary phase analysis with parameters and the so-called, almost analytic machinery, in order to find the asymptotic behavior of the contributions from all possible large spin configurations in the spinfoam model. The spins contributing the sum are written as J f = λj f , where λ is a large parameter resulting in an asymptotic expansion via stationary phase approximation. The analysis shows that at least for the simplicial Lorentzian geometries (as spinfoam critical configurations), they contribute the leading order approximation of spinfoam amplitude only when their deficit angles satisfy γ Θ-ring f ≤λ −1/2 mod 4πZ. Our analysis results in a curvature expansion of the semiclassical low energy effective action from the spinfoam model, where the UV modifications of Einstein gravity appear as subleading high-curvature corrections. (paper)

  17. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  18. Limits of predictability for large-scale urban vehicular mobility


    Li, Yong; Jin, Depeng; Hui, Pan; Wang, Zhaocheng; Chen, Sheng


    Key challenges in vehicular transportation and communication systems are understanding vehicular mobility and utilizing mobility prediction, which are vital for both solving the congestion problem and helping to build efficient vehicular communication networking. Most of the existing works mainly focus on designing algorithms for mobility prediction and exploring utilization of these algorithms. However, the crucial questions of how much the mobility is predictable and how the mobility predic...

  19. Predictive Models for Normal Fetal Cardiac Structures. (United States)

    Krishnan, Anita; Pike, Jodi I; McCarter, Robert; Fulgium, Amanda L; Wilson, Emmanuel; Donofrio, Mary T; Sable, Craig A


    Clinicians rely on age- and size-specific measures of cardiac structures to diagnose cardiac disease. No universally accepted normative data exist for fetal cardiac structures, and most fetal cardiac centers do not use the same standards. The aim of this study was to derive predictive models for Z scores for 13 commonly evaluated fetal cardiac structures using a large heterogeneous population of fetuses without structural cardiac defects. The study used archived normal fetal echocardiograms in representative fetuses aged 12 to 39 weeks. Thirteen cardiac dimensions were remeasured by a blinded echocardiographer from digitally stored clips. Studies with inadequate imaging views were excluded. Regression models were developed to relate each dimension to estimated gestational age (EGA) by dates, biparietal diameter, femur length, and estimated fetal weight by the Hadlock formula. Dimension outcomes were transformed (e.g., using the logarithm or square root) as necessary to meet the normality assumption. Higher order terms, quadratic or cubic, were added as needed to improve model fit. Information criteria and adjusted R 2 values were used to guide final model selection. Each Z-score equation is based on measurements derived from 296 to 414 unique fetuses. EGA yielded the best predictive model for the majority of dimensions; adjusted R 2 values ranged from 0.72 to 0.893. However, each of the other highly correlated (r > 0.94) biometric parameters was an acceptable surrogate for EGA. In most cases, the best fitting model included squared and cubic terms to introduce curvilinearity. For each dimension, models based on EGA provided the best fit for determining normal measurements of fetal cardiac structures. Nevertheless, other biometric parameters, including femur length, biparietal diameter, and estimated fetal weight provided results that were nearly as good. Comprehensive Z-score results are available on the basis of highly predictive models derived from gestational

  20. Modelling large-scale hydrogen infrastructure development

    International Nuclear Information System (INIS)

    De Groot, A.; Smit, R.; Weeda, M.


    In modelling a possible H2 infrastructure development the following questions are answered in this presentation: How could the future demand for H2 develop in the Netherlands?; and In which year and where would it be economically viable to construct a H2 infrastructure in the Netherlands? Conclusions are that: A model for describing a possible future H2 infrastructure is successfully developed; The model is strongly regional and time dependent; Decrease of fuel cell cost appears to be a sensitive parameter for development of H2 demand; Cost-margin between large-scale and small-scale H2 production is a main driver for development of a H2 infrastructure; A H2 infrastructure seems economically viable in the Netherlands starting from the year 2022

  1. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay


    Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

  2. Particle production at large transverse momentum and hard collision models

    International Nuclear Information System (INIS)

    Ranft, G.; Ranft, J.


    The majority of the presently available experimental data is consistent with hard scattering models. Therefore the hard scattering model seems to be well established. There is good evidence for jets in large transverse momentum reactions as predicted by these models. The overall picture is however not yet well enough understood. We mention only the empirical hard scattering cross section introduced in most of the models, the lack of a deep theoretical understanding of the interplay between quark confinement and jet production, and the fact that we are not yet able to discriminate conclusively between the many proposed hard scattering models. The status of different hard collision models discussed in this paper is summarized. (author)

  3. The Next Page Access Prediction Using Makov Model


    Deepti Razdan


    Predicting the next page to be accessed by the Webusers has attracted a large amount of research. In this paper, anew web usage mining approach is proposed to predict next pageaccess. It is proposed to identify similar access patterns from weblog using K-mean clustering and then Markov model is used forprediction for next page accesses. The tightness of clusters isimproved by setting similarity threshold while forming clusters.In traditional recommendation models, clustering by nonsequentiald...

  4. Large animal models for stem cell therapy. (United States)

    Harding, John; Roberts, R Michael; Mirochnitchenko, Oleg


    The field of regenerative medicine is approaching translation to clinical practice, and significant safety concerns and knowledge gaps have become clear as clinical practitioners are considering the potential risks and benefits of cell-based therapy. It is necessary to understand the full spectrum of stem cell actions and preclinical evidence for safety and therapeutic efficacy. The role of animal models for gaining this information has increased substantially. There is an urgent need for novel animal models to expand the range of current studies, most of which have been conducted in rodents. Extant models are providing important information but have limitations for a variety of disease categories and can have different size and physiology relative to humans. These differences can preclude the ability to reproduce the results of animal-based preclinical studies in human trials. Larger animal species, such as rabbits, dogs, pigs, sheep, goats, and non-human primates, are better predictors of responses in humans than are rodents, but in each case it will be necessary to choose the best model for a specific application. There is a wide spectrum of potential stem cell-based products that can be used for regenerative medicine, including embryonic and induced pluripotent stem cells, somatic stem cells, and differentiated cellular progeny. The state of knowledge and availability of these cells from large animals vary among species. In most cases, significant effort is required for establishing and characterizing cell lines, comparing behavior to human analogs, and testing potential applications. Stem cell-based therapies present significant safety challenges, which cannot be addressed by traditional procedures and require the development of new protocols and test systems, for which the rigorous use of larger animal species more closely resembling human behavior will be required. In this article, we discuss the current status and challenges of and several major directions

  5. Loan Default Prediction on Large Imbalanced Data Using Random Forests


    Hong Wang; Lifeng Zhou


    In this paper, we propose an improved random forest algorithm which allocates weights to decision trees in the forest during tree aggregation for prediction and their weights are easily calculated based on out-of-bag errors in training. We compare the performance of our proposed algorithm and the original one on loan default prediction datasets. We also use these two algorithms to create two kinds of balanced random forests to deal with imbalanced data problem. Experiments results show that ...

  6. Calibration of PMIS pavement performance prediction models. (United States)


    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  7. Predictive Model Assessment for Count Data

    National Research Council Canada - National Science Library

    Czado, Claudia; Gneiting, Tilmann; Held, Leonhard


    .... In case studies, we critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. Key words: Calibration...

  8. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp


    deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs......) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described....... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...

  9. ARMA modelling of neutron stochastic processes with large measurement noise

    International Nuclear Information System (INIS)

    Zavaljevski, N.; Kostic, Lj.; Pesic, M.


    An autoregressive moving average (ARMA) model of the neutron fluctuations with large measurement noise is derived from langevin stochastic equations and validated using time series data obtained during prompt neutron decay constant measurements at the zero power reactor RB in Vinca. Model parameters are estimated using the maximum likelihood (ML) off-line algorithm and an adaptive pole estimation algorithm based on the recursive prediction error method (RPE). The results show that subcriticality can be determined from real data with high measurement noise using much shorter statistical sample than in standard methods. (author)

  10. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy


    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  11. Predictive models for arteriovenous fistula maturation. (United States)

    Al Shakarchi, Julien; McGrogan, Damian; Van der Veer, Sabine; Sperrin, Matthew; Inston, Nicholas


    Haemodialysis (HD) is a lifeline therapy for patients with end-stage renal disease (ESRD). A critical factor in the survival of renal dialysis patients is the surgical creation of vascular access, and international guidelines recommend arteriovenous fistulas (AVF) as the gold standard of vascular access for haemodialysis. Despite this, AVFs have been associated with high failure rates. Although risk factors for AVF failure have been identified, their utility for predicting AVF failure through predictive models remains unclear. The objectives of this review are to systematically and critically assess the methodology and reporting of studies developing prognostic predictive models for AVF outcomes and assess them for suitability in clinical practice. Electronic databases were searched for studies reporting prognostic predictive models for AVF outcomes. Dual review was conducted to identify studies that reported on the development or validation of a model constructed to predict AVF outcome following creation. Data were extracted on study characteristics, risk predictors, statistical methodology, model type, as well as validation process. We included four different studies reporting five different predictive models. Parameters identified that were common to all scoring system were age and cardiovascular disease. This review has found a small number of predictive models in vascular access. The disparity between each study limits the development of a unified predictive model.

  12. Model Predictive Control Fundamentals | Orukpe | Nigerian Journal ...

    African Journals Online (AJOL)

    Model Predictive Control (MPC) has developed considerably over the last two decades, both within the research control community and in industries. MPC strategy involves the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, ...

  13. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp


    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optim...

  14. Large-scale genomic prediction using singular value decomposition of the genotype matrix. (United States)

    Ødegård, Jørgen; Indahl, Ulf; Strandén, Ismo; Meuwissen, Theo H E


    For marker effect models and genomic animal models, computational requirements increase with the number of loci and the number of genotyped individuals, respectively. In the latter case, the inverse genomic relationship matrix (GRM) is typically needed, which is computationally demanding to compute for large datasets. Thus, there is a great need for dimensionality-reduction methods that can analyze massive genomic data. For this purpose, we developed reduced-dimension singular value decomposition (SVD) based models for genomic prediction. Fast SVD is performed by analyzing different chromosomes/genome segments in parallel and/or by restricting SVD to a limited core of genotyped individuals, producing chromosome- or segment-specific principal components (PC). Given a limited effective population size, nearly all the genetic variation can be effectively captured by a limited number of PC. Genomic prediction can then be performed either by PC ridge regression (PCRR) or by genomic animal models using an inverse GRM computed from the chosen PC (PCIG). In the latter case, computation of the inverse GRM will be feasible for any number of genotyped individuals and can be readily produced row- or element-wise. Using simulated data, we show that PCRR and PCIG models, using chromosome-wise SVD of a core sample of individuals, are appropriate for genomic prediction in a larger population, and results in virtually identical predicted breeding values as the original full-dimension genomic model (r = 1.000). Compared with other algorithms (e.g. algorithm for proven and young animals, APY), the (chromosome-wise SVD-based) PCRR and PCIG models were more robust to size of the core sample, giving nearly identical results even down to 500 core individuals. The method was also successfully tested on a large multi-breed dataset. SVD can be used for dimensionality reduction of large genomic datasets. After SVD, genomic prediction using dense genomic data and many genotyped individuals


    Energy Technology Data Exchange (ETDEWEB)

    Boubert, Douglas; Evans, N. Wyn, E-mail:, E-mail: [Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA (United Kingdom)


    We predict the distribution of hypervelocity stars (HVSs) ejected from the Large Magellanic Cloud (LMC), under the assumption that the dwarf galaxy hosts a central massive black hole (MBH). For the majority of stars ejected from the LMC, the orbital velocity of the LMC has contributed a significant fraction of their galactic rest-frame velocity, leading to a dipole density distribution on the sky. We quantify the dipole using spherical harmonic analysis and contrast with the monopole expected for HVSs ejected from the Galactic center (GC). There is a tendril in the density distribution that leads the LMC, which is coincident with the well-known and unexplained clustering of HVSs in the constellations of Leo and Sextans. Our model is falsifiable since it predicts that Gaia will reveal a large density of HVSs in the southern hemisphere.

  16. Rheology Effects on Predicted Fiber Orientation and Elastic Properties in Large Scale Polymer Composite Additive Manufacturing

    Directory of Open Access Journals (Sweden)

    Zhaogui Wang


    Full Text Available Short fiber-reinforced polymers have recently been introduced to large-scale additive manufacturing to improve the mechanical performances of printed-parts. As the short fiber polymer composite is extruded and deposited on a moving platform, velocity gradients within the melt orientate the suspended fibers, and the final orientation directly affects material properties in the solidified extrudate. This paper numerically evaluates melt rheology effects on predicted fiber orientation and elastic properties of printed-composites in three steps. First, the steady-state isothermal axisymmetric nozzle melt flow is computed, which includes the prediction of die swell just outside the nozzle exit. Simulations are performed with ANSYS-Polyflow, where we consider the effect of various rheology models on the computed outcomes. Here, we include Newtonian, generalized Newtonian, and viscoelastic rheology models to represent the melt flow. Fiber orientation is computed using Advani–Tucker fiber orientation tensors. Finally, elastic properties in the extrudate are evaluated based from predicted fiber orientation distributions. Calculations show that the Phan–Thien–Tanner (PTT model yields the lowest fiber principal alignment among considered rheology models. Furthermore, the cross section averaged elastic properties indicate a strong transversely isotropic behavior in these composites, where generalized Newtonian models yield higher principal Young’s modulus, while the viscoelastic fluid models result in higher shear moduli.

  17. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database. (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M


    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  18. Hybrid approaches to physiologic modeling and prediction (United States)

    Olengü, Nicholas O.; Reifman, Jaques


    This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.

  19. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models (United States)

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.


    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

  20. North Atlantic climate model bias influence on multiyear predictability (United States)

    Wu, Y.; Park, T.; Park, W.; Latif, M.


    The influences of North Atlantic biases on multiyear predictability of unforced surface air temperature (SAT) variability are examined in the Kiel Climate Model (KCM). By employing a freshwater flux correction over the North Atlantic to the model, which strongly alleviates both North Atlantic sea surface salinity (SSS) and sea surface temperature (SST) biases, the freshwater flux-corrected integration depicts significantly enhanced multiyear SAT predictability in the North Atlantic sector in comparison to the uncorrected one. The enhanced SAT predictability in the corrected integration is due to a stronger and more variable Atlantic Meridional Overturning Circulation (AMOC) and its enhanced influence on North Atlantic SST. Results obtained from preindustrial control integrations of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the findings obtained from the KCM: models with large North Atlantic biases tend to have a weak AMOC influence on SAT and exhibit a smaller SAT predictability over the North Atlantic sector.

  1. Evaluating the Predictive Value of Growth Prediction Models (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.


    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  2. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil


    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  3. A Computational Model for Predicting Gas Breakdown (United States)

    Gill, Zachary


    Pulsed-inductive discharges are a common method of producing a plasma. They provide a mechanism for quickly and efficiently generating a large volume of plasma for rapid use and are seen in applications including propulsion, fusion power, and high-power lasers. However, some common designs see a delayed response time due to the plasma forming when the magnitude of the magnetic field in the thruster is at a minimum. New designs are difficult to evaluate due to the amount of time needed to construct a new geometry and the high monetary cost of changing the power generation circuit. To more quickly evaluate new designs and better understand the shortcomings of existing designs, a computational model is developed. This model uses a modified single-electron model as the basis for a Mathematica code to determine how the energy distribution in a system changes with regards to time and location. By analyzing this energy distribution, the approximate time and location of initial plasma breakdown can be predicted. The results from this code are then compared to existing data to show its validity and shortcomings. Missouri S&T APLab.

  4. A Note on Sequence Prediction over Large Alphabets

    Directory of Open Access Journals (Sweden)

    Travis Gagie


    Full Text Available Building on results from data compression, we prove nearly tight bounds on how well sequences of length n can be predicted in terms of the size σ of the alphabet and the length k of the context considered when making predictions. We compare the performance achievable by an adaptive predictor with no advance knowledge of the sequence, to the performance achievable by the optimal static predictor using a table listing the frequency of each (k + 1-tuple in the sequence. We show that, if the elements of the sequence are chosen uniformly at random, then an adaptive predictor can compete in the expected case if k ≤ logσ n – 3 – ε, for a constant ε > 0, but not if k ≥ logσ n.

  5. Risk avoidance in sympatric large carnivores: reactive or predictive? (United States)

    Broekhuis, Femke; Cozzi, Gabriele; Valeix, Marion; McNutt, John W; Macdonald, David W


    1. Risks of predation or interference competition are major factors shaping the distribution of species. An animal's response to risk can either be reactive, to an immediate risk, or predictive, based on preceding risk or past experiences. The manner in which animals respond to risk is key in understanding avoidance, and hence coexistence, between interacting species. 2. We investigated whether cheetahs (Acinonyx jubatus), known to be affected by predation and competition by lions (Panthera leo) and spotted hyaenas (Crocuta crocuta), respond reactively or predictively to the risks posed by these larger carnivores. 3. We used simultaneous spatial data from Global Positioning System (GPS) radiocollars deployed on all known social groups of cheetahs, lions and spotted hyaenas within a 2700 km(2) study area on the periphery of the Okavango Delta in northern Botswana. The response to risk of encountering lions and spotted hyaenas was explored on three levels: short-term or immediate risk, calculated as the distance to the nearest (contemporaneous) lion or spotted hyaena, long-term risk, calculated as the likelihood of encountering lions and spotted hyaenas based on their cumulative distributions over a 6-month period and habitat-associated risk, quantified by the habitat used by each of the three species. 4. We showed that space and habitat use by cheetahs was similar to that of lions and, to a lesser extent, spotted hyaenas. However, cheetahs avoided immediate risks by positioning themselves further from lions and spotted hyaenas than predicted by a random distribution. 5. Our results suggest that cheetah spatial distribution is a hierarchical process, first driven by resource acquisition and thereafter fine-tuned by predator avoidance; thus suggesting a reactive, rather than a predictive, response to risk. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

  6. Black holes from large N singlet models (United States)

    Amado, Irene; Sundborg, Bo; Thorlacius, Larus; Wintergerst, Nico


    The emergent nature of spacetime geometry and black holes can be directly probed in simple holographic duals of higher spin gravity and tensionless string theory. To this end, we study time dependent thermal correlation functions of gauge invariant observables in suitably chosen free large N gauge theories. At low temperature and on short time scales the correlation functions encode propagation through an approximate AdS spacetime while interesting departures emerge at high temperature and on longer time scales. This includes the existence of evanescent modes and the exponential decay of time dependent boundary correlations, both of which are well known indicators of bulk black holes in AdS/CFT. In addition, a new time scale emerges after which the correlation functions return to a bulk thermal AdS form up to an overall temperature dependent normalization. A corresponding length scale was seen in equal time correlation functions in the same models in our earlier work.

  7. Model for predicting mountain wave field uncertainties (United States)

    Damiens, Florentin; Lott, François; Millet, Christophe; Plougonven, Riwal


    Studying the propagation of acoustic waves throughout troposphere requires knowledge of wind speed and temperature gradients from the ground up to about 10-20 km. Typical planetary boundary layers flows are known to present vertical low level shears that can interact with mountain waves, thereby triggering small-scale disturbances. Resolving these fluctuations for long-range propagation problems is, however, not feasible because of computer memory/time restrictions and thus, they need to be parameterized. When the disturbances are small enough, these fluctuations can be described by linear equations. Previous works by co-authors have shown that the critical layer dynamics that occur near the ground produces large horizontal flows and buoyancy disturbances that result in intense downslope winds and gravity wave breaking. While these phenomena manifest almost systematically for high Richardson numbers and when the boundary layer depth is relatively small compare to the mountain height, the process by which static stability affects downslope winds remains unclear. In the present work, new linear mountain gravity wave solutions are tested against numerical predictions obtained with the Weather Research and Forecasting (WRF) model. For Richardson numbers typically larger than unity, the mesoscale model is used to quantify the effect of neglected nonlinear terms on downslope winds and mountain wave patterns. At these regimes, the large downslope winds transport warm air, a so called "Foehn" effect than can impact sound propagation properties. The sensitivity of small-scale disturbances to Richardson number is quantified using two-dimensional spectral analysis. It is shown through a pilot study of subgrid scale fluctuations of boundary layer flows over realistic mountains that the cross-spectrum of mountain wave field is made up of the same components found in WRF simulations. The impact of each individual component on acoustic wave propagation is discussed in terms of

  8. A Global Model for Bankruptcy Prediction. (United States)

    Alaminos, David; Del Castillo, Agustín; Fernández, Manuel Ángel


    The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.

  9. Predicting the breeding success of large raptors in arid southern ...

    African Journals Online (AJOL)

    Raptors are often priorities for conservation efforts and breeding success is a target measure for assessing their conservation status. The breeding success of large raptors in arid southern Africa is thought to be higher in years of high rainfall. While this correlation has been found in several studies, it has not yet been shown ...

  10. Creep Rupture Life Prediction Based on Analysis of Large Creep Deformation

    Directory of Open Access Journals (Sweden)

    YE Wenming


    Full Text Available A creep rupture life prediction method for high temperature component was proposed. The method was based on a true stress-strain elastoplastic creep constitutive model and the large deformation finite element analysis method. This method firstly used the high-temperature tensile stress-strain curve expressed by true stress and strain and the creep curve to build materials' elastoplastic and creep constitutive model respectively, then used the large deformation finite element method to calculate the deformation response of high temperature component under a given load curve, finally the creep rupture life was determined according to the change trend of the responsive curve.The method was verified by durable test of TC11 titanium alloy notched specimens under 500 ℃, and was compared with the three creep rupture life prediction methods based on the small deformation analysis. Results show that the proposed method can accurately predict the high temperature creep response and long-term life of TC11 notched specimens, and the accuracy is better than that of the methods based on the average effective stress of notch ligament, the bone point stress and the fracture strain of the key point, which are all based on small deformation finite element analysis.

  11. Prediction Model for Gastric Cancer Incidence in Korean Population. (United States)

    Eom, Bang Wool; Joo, Jungnam; Kim, Sohee; Shin, Aesun; Yang, Hye-Ryung; Park, Junghyun; Choi, Il Ju; Kim, Young-Woo; Kim, Jeongseon; Nam, Byung-Ho


    Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea. Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope. During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women). In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

  12. Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery. (United States)

    Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan


    Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

  13. Prediction Model for Gastric Cancer Incidence in Korean Population.

    Directory of Open Access Journals (Sweden)

    Bang Wool Eom

    Full Text Available Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.During a median of 11.4 years of follow-up, 19,465 (1.4% and 5,579 (0.7% newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women.In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

  14. Fingerprint verification prediction model in hand dermatitis. (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah


    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  15. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    CERN. Geneva


    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  16. Interior Noise Predictions in the Preliminary Design of the Large Civil Tiltrotor (LCTR2) (United States)

    Grosveld, Ferdinand W.; Cabell, Randolph H.; Boyd, David D.


    A prediction scheme was established to compute sound pressure levels in the interior of a simplified cabin model of the second generation Large Civil Tiltrotor (LCTR2) during cruise conditions, while being excited by turbulent boundary layer flow over the fuselage, or by tiltrotor blade loading and thickness noise. Finite element models of the cabin structure, interior acoustic space, and acoustically absorbent (poro-elastic) materials in the fuselage were generated and combined into a coupled structural-acoustic model. Fluctuating power spectral densities were computed according to the Efimtsov turbulent boundary layer excitation model. Noise associated with the tiltrotor blades was predicted in the time domain as fluctuating surface pressures and converted to power spectral densities at the fuselage skin finite element nodes. A hybrid finite element (FE) approach was used to compute the low frequency acoustic cabin response over the frequency range 6-141 Hz with a 1 Hz bandwidth, and the Statistical Energy Analysis (SEA) approach was used to predict the interior noise for the 125-8000 Hz one-third octave bands.

  17. Predictive Model of Systemic Toxicity (SOT) (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  18. Testicular Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Pancreatic Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. Colorectal Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  1. Prostate Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  2. Bladder Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  3. Esophageal Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  4. Cervical Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  5. Breast Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Lung Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Liver Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Ovarian Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

    Directory of Open Access Journals (Sweden)

    Milan Vukićević


    Full Text Available Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.

  10. Posterior Predictive Model Checking in Bayesian Networks (United States)

    Crawford, Aaron


    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  11. Global Bedload Flux Modeling and Analysis in Large Rivers (United States)

    Islam, M. T.; Cohen, S.; Syvitski, J. P.


    Proper sediment transport quantification has long been an area of interest for both scientists and engineers in the fields of geomorphology, and management of rivers and coastal waters. Bedload flux is important for monitoring water quality and for sustainable development of coastal and marine bioservices. Bedload measurements, especially for large rivers, is extremely scarce across time, and many rivers have never been monitored. Bedload measurements in rivers, is particularly acute in developing countries where changes in sediment yields is high. The paucity of bedload measurements is the result of 1) the nature of the problem (large spatial and temporal uncertainties), and 2) field costs including the time-consuming nature of the measurement procedures (repeated bedform migration tracking, bedload samplers). Here we present a first of its kind methodology for calculating bedload in large global rivers (basins are >1,000 km. Evaluation of model skill is based on 113 bedload measurements. The model predictions are compared with an empirical model developed from the observational dataset in an attempt to evaluate the differences between a physically-based numerical model and a lumped relationship between bedload flux and fluvial and basin parameters (e.g., discharge, drainage area, lithology). The initial study success opens up various applications to global fluvial geomorphology (e.g. including the relationship between suspended sediment (wash load) and bedload). Simulated results with known uncertainties offers a new research product as a valuable resource for the whole scientific community.

  12. Multiple Steps Prediction with Nonlinear ARX Models


    Zhang, Qinghua; Ljung, Lennart


    NLARX (NonLinear AutoRegressive with eXogenous inputs) models are frequently used in black-box nonlinear system identication. Though it is easy to make one step ahead prediction with such models, multiple steps prediction is far from trivial. The main difficulty is that in general there is no easy way to compute the mathematical expectation of an output conditioned by past measurements. An optimal solution would require intensive numerical computations related to nonlinear filltering. The pur...

  13. Modeling of large-scale oxy-fuel combustion processes

    DEFF Research Database (Denmark)

    Yin, Chungen


    Quite some studies have been conducted in order to implement oxy-fuel combustion with flue gas recycle in conventional utility boilers as an effective effort of carbon capture and storage. However, combustion under oxy-fuel conditions is significantly different from conventional air-fuel firing......, among which radiative heat transfer under oxy-fuel conditions is one of the fundamental issues. This paper demonstrates the nongray-gas effects in modeling of large-scale oxy-fuel combustion processes. Oxy-fuel combustion of natural gas in a 609MW utility boiler is numerically studied, in which...... calculation of the oxy-fuel WSGGM remarkably over-predicts the radiative heat transfer to the furnace walls and under-predicts the gas temperature at the furnace exit plane, which also result in a higher incomplete combustion in the gray calculation. Moreover, the gray and non-gray calculations of the same...

  14. Model complexity control for hydrologic prediction (United States)

    Schoups, G.; van de Giesen, N. C.; Savenije, H. H. G.


    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore needed. We compare three model complexity control methods for hydrologic prediction, namely, cross validation (CV), Akaike's information criterion (AIC), and structural risk minimization (SRM). Results show that simulation of water flow using non-physically-based models (polynomials in this case) leads to increasingly better calibration fits as the model complexity (polynomial order) increases. However, prediction uncertainty worsens for complex non-physically-based models because of overfitting of noisy data. Incorporation of physically based constraints into the model (e.g., storage-discharge relationship) effectively bounds prediction uncertainty, even as the number of parameters increases. The conclusion is that overparameterization and equifinality do not lead to a continued increase in prediction uncertainty, as long as models are constrained by such physical principles. Complexity control of hydrologic models reduces parameter equifinality and identifies the simplest model that adequately explains the data, thereby providing a means of hydrologic generalization and classification. SRM is a promising technique for this purpose, as it (1) provides analytic upper bounds on prediction uncertainty, hence avoiding the computational burden of CV, and (2) extends the applicability of classic methods such as AIC to finite data. The main hurdle in applying SRM is the need for an a priori estimation of the complexity of the hydrologic model, as measured by its Vapnik-Chernovenkis (VC) dimension. Further research is needed in this area.

  15. Quantifying predictive accuracy in survival models. (United States)

    Lirette, Seth T; Aban, Inmaculada


    For time-to-event outcomes in medical research, survival models are the most appropriate to use. Unlike logistic regression models, quantifying the predictive accuracy of these models is not a trivial task. We present the classes of concordance (C) statistics and R 2 statistics often used to assess the predictive ability of these models. The discussion focuses on Harrell's C, Kent and O'Quigley's R 2 , and Royston and Sauerbrei's R 2 . We present similarities and differences between the statistics, discuss the software options from the most widely used statistical analysis packages, and give a practical example using the Worcester Heart Attack Study dataset.

  16. Predictive power of nuclear-mass models

    Directory of Open Access Journals (Sweden)

    Yu. A. Litvinov


    Full Text Available Ten different theoretical models are tested for their predictive power in the description of nuclear masses. Two sets of experimental masses are used for the test: the older set of 2003 and the newer one of 2011. The predictive power is studied in two regions of nuclei: the global region (Z, N ≥ 8 and the heavy-nuclei region (Z ≥ 82, N ≥ 126. No clear correlation is found between the predictive power of a model and the accuracy of its description of the masses.

  17. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  18. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S


    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  19. Comparing National Water Model Inundation Predictions with Hydrodynamic Modeling (United States)

    Egbert, R. J.; Shastry, A.; Aristizabal, F.; Luo, C.


    The National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts, runoff, and other variables for 2.7 million reaches along the National Hydrography Dataset for the continental United States. NWM applies Muskingum-Cunge channel routing which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain better estimates of streamflow and stage in rivers especially for applications such as flood inundation mapping. Simulation Program for River NeTworks (SPRNT) is a fully dynamic model for large scale river networks that solves the full nonlinear Saint-Venant equations for 1D flow and stage height in river channel networks with non-uniform bathymetry. For the current work, the steady-state version of the SPRNT model was leveraged. An evaluation on SPRNT's and NWM's abilities to predict inundation was conducted for the record flood of Hurricane Matthew in October 2016 along the Neuse River in North Carolina. This event was known to have been influenced by backwater effects from the Hurricane's storm surge. Retrospective NWM discharge predictions were converted to stage using synthetic rating curves. The stages from both models were utilized to produce flood inundation maps using the Height Above Nearest Drainage (HAND) method which uses the local relative heights to provide a spatial representation of inundation depths. In order to validate the inundation produced by the models, Sentinel-1A synthetic aperture radar data in the VV and VH polarizations along with auxiliary data was used to produce a reference inundation map. A preliminary, binary comparison of the inundation maps to the reference, limited to the five HUC-12 areas of Goldsboro, NC, yielded that the flood inundation accuracies for NWM and SPRNT were 74.68% and 78.37%, respectively. The differences for all the relevant test statistics including accuracy, true positive rate, true negative rate, and positive predictive value were found

  20. Electric vehicle charge planning using Economic Model Predictive Control

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Poulsen, Niels K.; Madsen, Henrik


    Economic Model Predictive Control (MPC) is very well suited for controlling smart energy systems since electricity price and demand forecasts are easily integrated in the controller. Electric vehicles (EVs) are expected to play a large role in the future Smart Grid. They are expected to provide...

  1. Consensus models to predict endocrine disruption for all ... (United States)

    Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

  2. Mathematical formulation to predict the harmonics of the superconducting Large Hadron Collider magnets

    Directory of Open Access Journals (Sweden)

    Nicholas Sammut


    Full Text Available CERN is currently assembling the LHC (Large Hadron Collider that will accelerate and bring in collision 7 TeV protons for high energy physics. Such a superconducting magnet-based accelerator can be controlled only when the field errors of production and installation of all magnetic elements are known to the required accuracy. The ideal way to compensate the field errors obviously is to have direct diagnostics on the beam. For the LHC, however, a system solely based on beam feedback may be too demanding. The present baseline for the LHC control system hence requires an accurate forecast of the magnetic field and the multipole field errors to reduce the burden on the beam-based feedback. The field model is the core of this magnetic prediction system, that we call the field description for the LHC (FIDEL. The model will provide the forecast of the magnetic field at a given time, magnet operating current, magnet ramp rate, magnet temperature, and magnet powering history. The model is based on the identification and physical decomposition of the effects that contribute to the total field in the magnet aperture of the LHC dipoles. Each effect is quantified using data obtained from series measurements, and modeled theoretically or empirically depending on the complexity of the physical phenomena involved. This paper presents the developments of the new finely tuned magnetic field model and, using the data accumulated through series tests to date, evaluates its accuracy and predictive capabilities over a sector of the machine.

  3. Uncertainty quantification for large-scale ocean circulation predictions.

    Energy Technology Data Exchange (ETDEWEB)

    Safta, Cosmin; Debusschere, Bert J.; Najm, Habib N.; Sargsyan, Khachik


    Uncertainty quantificatio in climate models is challenged by the sparsity of the available climate data due to the high computational cost of the model runs. Another feature that prevents classical uncertainty analyses from being easily applicable is the bifurcative behavior in the climate data with respect to certain parameters. A typical example is the Meridional Overturning Circulation in the Atlantic Ocean. The maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO{sub 2} forcing. We develop a methodology that performs uncertainty quantificatio in the presence of limited data that have discontinuous character. Our approach is two-fold. First we detect the discontinuity location with a Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve location in presence of arbitrarily distributed input parameter values. Furthermore, we developed a spectral approach that relies on Polynomial Chaos (PC) expansions on each sides of the discontinuity curve leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification and propagation. The methodology is tested on synthetic examples of discontinuous data with adjustable sharpness and structure.

  4. Protein homology model refinement by large-scale energy optimization. (United States)

    Park, Hahnbeom; Ovchinnikov, Sergey; Kim, David E; DiMaio, Frank; Baker, David


    Proteins fold to their lowest free-energy structures, and hence the most straightforward way to increase the accuracy of a partially incorrect protein structure model is to search for the lowest-energy nearby structure. This direct approach has met with little success for two reasons: first, energy function inaccuracies can lead to false energy minima, resulting in model degradation rather than improvement; and second, even with an accurate energy function, the search problem is formidable because the energy only drops considerably in the immediate vicinity of the global minimum, and there are a very large number of degrees of freedom. Here we describe a large-scale energy optimization-based refinement method that incorporates advances in both search and energy function accuracy that can substantially improve the accuracy of low-resolution homology models. The method refined low-resolution homology models into correct folds for 50 of 84 diverse protein families and generated improved models in recent blind structure prediction experiments. Analyses of the basis for these improvements reveal contributions from both the improvements in conformational sampling techniques and the energy function.

  5. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  6. Predictive Validation of an Influenza Spread Model (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian


    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  7. Predictive modeling of coupled multi-physics systems: I. Theory

    International Nuclear Information System (INIS)

    Cacuci, Dan Gabriel


    Highlights: • We developed “predictive modeling of coupled multi-physics systems (PMCMPS)”. • PMCMPS reduces predicted uncertainties in predicted model responses and parameters. • PMCMPS treats efficiently very large coupled systems. - Abstract: This work presents an innovative mathematical methodology for “predictive modeling of coupled multi-physics systems (PMCMPS).” This methodology takes into account fully the coupling terms between the systems but requires only the computational resources that would be needed to perform predictive modeling on each system separately. The PMCMPS methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution based on a priori known mean values and uncertainties characterizing the parameters and responses for both multi-physics models. This “maximum entropy”-approximate a priori distribution is combined, using Bayes’ theorem, with the “likelihood” provided by the multi-physics simulation models. Subsequently, the posterior distribution thus obtained is evaluated using the saddle-point method to obtain analytical expressions for the optimally predicted values for the multi-physics models parameters and responses along with corresponding reduced uncertainties. Noteworthy, the predictive modeling methodology for the coupled systems is constructed such that the systems can be considered sequentially rather than simultaneously, while preserving exactly the same results as if the systems were treated simultaneously. Consequently, very large coupled systems, which could perhaps exceed available computational resources if treated simultaneously, can be treated with the PMCMPS methodology presented in this work sequentially and without any loss of generality or information, requiring just the resources that would be needed if the systems were treated sequentially

  8. Seismic pattern recognition techniques to predict large eruptions at the Popocatépetl, Mexico, volcano (United States)

    Novelo-Casanova, D. A.; Valdés-González, C.


    Using pattern recognition techniques, we formulate a simple prediction rule for a retrospective prediction of the three last largest eruptions of the Popocatépetl, Mexico, volcano that occurred on 23 April-30 June 1997 (Eruption 1; VEI ~ 2-3); 11 December 2000-23 January 2001 (Eruption 2; VEI ~ 3-4) and 7 June-4 September 2002 (Eruption 3; explosive dome extrusion and destruction phase). Times of Increased Probability (TIP) were estimated from the seismicity recorded by the local seismic network from 1 January 1995 to 31 December 2005. A TIP is issued when a cluster of seismic events occurs under our algorithm considerations in a temporal window several days (or weeks) prior to large volcanic activity providing sufficient time to organize an effective alert strategy. The best predictions of the three analyzed eruptions were obtained when averaging seismicity rate over a 5-day window with a threshold value of 12 events and declaring an alarm for 45 days. A TIP was issued about six weeks before Eruption 1. TIPs were detected about one and four weeks before Eruptions 2 and 3, respectively. According to our objectives, in all cases, the observed TIPs would have allowed the development of an effective civil protection strategy. Although, under our model considerations the three eruptive events were successfully predicted, one false alarm was also issued by our algorithm. An analysis of the epicentral and depth distribution of the local seismicity used by our prediction rule reveals that successful TIPs were issued from microearthquakes that took place below and towards SE of the crater. On the contrary, the seismicity that issued the observed false alarm was concentrated below the summit of the volcano. We conclude that recording of precursory seismicity below and SE of the crater together with detection of TIPs as described here, could become an important tool to predict future large eruptions at Popocatépetl. Although our model worked well for events that occurred

  9. Prediction skill of rainstorm events over India in the TIGGE weather prediction models (United States)

    Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.


    Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

  10. Modeling Human Behavior at a Large Scale (United States)


    online messages, along with text analysis of those messages, enables us to predict the progress of a contagion from person to person at a population scale...tation, we represent probabilities and likelihoods with their log-counterparts to avoid arithmetic underflow. At testing time, we are interested in...patterns of people taking taxis, rating movies, choosing a cell phone provider, or sharing music are best explained and predicted by the habits of

  11. Posterior predictive checking of multiple imputation models. (United States)

    Nguyen, Cattram D; Lee, Katherine J; Carlin, John B


    Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the model and the data. The aim of this study was to evaluate the performance of the posterior predictive p-value as an imputation diagnostic. Using simulation methods, we deliberately misspecified imputation models to determine whether posterior predictive p-values were effective in identifying these problems. When estimating the regression parameter of interest, we found that more extreme p-values were associated with poorer imputation model performance, although the results highlighted that traditional thresholds for classical p-values do not apply in this context. A shortcoming of the PPC method was its reduced ability to detect misspecified models with increasing amounts of missing data. Despite the limitations of posterior predictive p-values, they appear to have a valuable place in the imputer's toolkit. In addition to automated checking using p-values, we recommend imputers perform graphical checks and examine other summaries of the test quantity distribution. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Prediction of welding residual distortions of large structures using a local/global approach

    International Nuclear Information System (INIS)

    Duan, Y. G.; Bergheau, J. M.; Vincent, Y.; Boitour, F.; Leblond, J. B.


    Prediction of welding residual distortions is more difficult than that of the microstructure and residual stresses. On the one hand, a fine mesh (often 3D) has to be used in the heat affected zone for the sake of the sharp variations of thermal, metallurgical and mechanical fields in this region. On the other hand, the whole structure is required to be meshed for the calculation of residual distortions. But for large structures, a 3D mesh is inconceivable caused by the costs of the calculation. Numerous methods have been developed to reduce the size of models. A local/global approach has been proposed to determine the welding residual distortions of large structures. The plastic strains and the microstructure due to welding are supposed can be determined from a local 3D model which concerns only the weld and its vicinity. They are projected as initial strains into a global 3D model which consists of the whole structure and obviously much less fine in the welded zone than the local model. The residual distortions are then calculated using a simple elastic analysis, which makes this method particularly effective in an industrial context. The aim of this article is to present the principle of the local/global approach then show the capacity of this method in an industrial context and finally study the definition of the local model

  13. A simple formula for insertion loss prediction of large acoustical enclosures using statistical energy analysis method

    Directory of Open Access Journals (Sweden)

    Kim Hyun-Sil


    Full Text Available Insertion loss prediction of large acoustical enclosures using Statistical Energy Analysis (SEA method is presented. The SEA model consists of three elements: sound field inside the enclosure, vibration energy of the enclosure panel, and sound field outside the enclosure. It is assumed that the space surrounding the enclosure is sufficiently large so that there is no energy flow from the outside to the wall panel or to air cavity inside the enclosure. The comparison of the predicted insertion loss to the measured data for typical large acoustical enclosures shows good agreements. It is found that if the critical frequency of the wall panel falls above the frequency region of interest, insertion loss is dominated by the sound transmission loss of the wall panel and averaged sound absorption coefficient inside the enclosure. However, if the critical frequency of the wall panel falls into the frequency region of interest, acoustic power from the sound radiation by the wall panel must be added to the acoustic power from transmission through the panel.

  14. Life Prediction of Large Lithium-Ion Battery Packs with Active and Passive Balancing

    Energy Technology Data Exchange (ETDEWEB)

    Shi, Ying [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Smith, Kandler A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zane, Regan [Utah State University; Anderson, Dyche [Ford Motor Company


    Lithium-ion battery packs take a major part of large-scale stationary energy storage systems. One challenge in reducing battery pack cost is to reduce pack size without compromising pack service performance and lifespan. Prognostic life model can be a powerful tool to handle the state of health (SOH) estimate and enable active life balancing strategy to reduce cell imbalance and extend pack life. This work proposed a life model using both empirical and physical-based approaches. The life model described the compounding effect of different degradations on the entire cell with an empirical model. Then its lower-level submodels considered the complex physical links between testing statistics (state of charge level, C-rate level, duty cycles, etc.) and the degradation reaction rates with respect to specific aging mechanisms. The hybrid approach made the life model generic, robust and stable regardless of battery chemistry and application usage. The model was validated with a custom pack with both passive and active balancing systems implemented, which created four different aging paths in the pack. The life model successfully captured the aging trajectories of all four paths. The life model prediction errors on capacity fade and resistance growth were within +/-3% and +/-5% of the experiment measurements.

  15. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus


    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  16. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas


    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  17. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti


    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  18. Validating modeled turbulent heat fluxes across large freshwater surfaces (United States)

    Lofgren, B. M.; Fujisaki-Manome, A.; Gronewold, A.; Anderson, E. J.; Fitzpatrick, L.; Blanken, P.; Spence, C.; Lenters, J. D.; Xiao, C.; Charusambot, U.


    Turbulent fluxes of latent and sensible heat are important physical processes that influence the energy and water budgets of the Great Lakes. Validation and improvement of bulk flux algorithms to simulate these turbulent heat fluxes are critical for accurate prediction of hydrodynamics, water levels, weather, and climate over the region. Here we consider five heat flux algorithms from several model systems; the Finite-Volume Community Ocean Model, the Weather Research and Forecasting model, and the Large Lake Thermodynamics Model, which are used in research and operational environments and concentrate on different aspects of the Great Lakes' physical system, but interface at the lake surface. The heat flux algorithms were isolated from each model and driven by meteorological data from over-lake stations in the Great Lakes Evaporation Network. The simulation results were compared with eddy covariance flux measurements at the same stations. All models show the capacity to the seasonal cycle of the turbulent heat fluxes. Overall, the Coupled Ocean Atmosphere Response Experiment algorithm in FVCOM has the best agreement with eddy covariance measurements. Simulations with the other four algorithms are overall improved by updating the parameterization of roughness length scales of temperature and humidity. Agreement between modelled and observed fluxes notably varied with geographical locations of the stations. For example, at the Long Point station in Lake Erie, observed fluxes are likely influenced by the upwind land surface while the simulations do not take account of the land surface influence, and therefore the agreement is worse in general.

  19. Are animal models predictive for humans?

    Directory of Open Access Journals (Sweden)

    Greek Ray


    Full Text Available Abstract It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics.

  20. Recent Successes and Remaining Challenges in Predicting Phosphorus Loading to Surface Waters at Large Scales (United States)

    Harrison, J.; Metson, G.; Beusen, A.


    Over the past century humans have greatly accelerated phosphorus (P) flows from land to aquatic ecosystems, causing eutrophication and associated effects such as harmful algal blooms and hypoxia. Effectively addressing this challenge requires understanding geographic and temporal distribution of aquatic P loading, knowledge of major controls on P loading, and the relative importance of various potential P sources. The Global (N)utrient (E)xport from (W)ater(S)heds) NEWS model and recent improvements and extensions of this modeling system can be used to generate this understanding. This presentation will focus on insights global NEWS models grant into past, present, and potential future P sources and sinks, with a focus on the world's large rivers. Early results suggest: 1) that while aquatic P loading is globally dominated by particulate forms, dissolved P can be locally dominant; 2) that P loading has increased substantially at the global scale, but unevenly between world regions, with hotspots in South and East Asia; 3) that P loading is likely to continue to increase globally, but decrease in certain regions that are actively pursuing proactive P management; and 4) that point sources, especially in urban centers, play an important (even dominant) role in determining loads of dissolved inorganic P. Despite these insights, substantial unexplained variance remains when model predictions and measurements are compared at global and regional scales, for example within the U.S. Disagreements between model predictions and measurements suggest opportunities for model improvement. In particular, explicit inclusion of soil characteristics and the concept of temporal P legacies in future iterations of NEWS (and other) models may help improve correspondence between models and measurements.

  1. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico


    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  2. Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges

    Directory of Open Access Journals (Sweden)

    Ranjith Gopalakrishnan


    Full Text Available Generating accurate and unbiased wall-to-wall canopy height maps from airborne lidar data for large regions is useful to forest scientists and natural resource managers. However, mapping large areas often involves using lidar data from different projects, with varying acquisition parameters. In this work, we address the important question of whether one can accurately model canopy heights over large areas of the Southeastern US using a very heterogeneous dataset of small-footprint, discrete-return airborne lidar data (with 76 separate lidar projects. A unique aspect of this effort is the use of nationally uniform and extensive field data (~1800 forested plots from the Forest Inventory and Analysis (FIA program of the US Forest Service. Preliminary results are quite promising: Over all lidar projects, we observe a good correlation between the 85th percentile of lidar heights and field-measured height (r = 0.85. We construct a linear regression model to predict subplot-level dominant tree heights from distributional lidar metrics (R2 = 0.74, RMSE = 3.0 m, n = 1755. We also identify and quantify the importance of several factors (like heterogeneity of vegetation, point density, the predominance of hardwoods or softwoods, the average height of the forest stand, slope of the plot, and average scan angle of lidar acquisition that influence the efficacy of predicting canopy heights from lidar data. For example, a subset of plots (coefficient of variation of vegetation heights <0.2 significantly reduces the RMSE of our model from 3.0–2.4 m (~20% reduction. We conclude that when all these elements are factored into consideration, combining data from disparate lidar projects does not preclude robust estimation of canopy heights.

  3. Verification and improvement of a predictive model for radionuclide migration

    International Nuclear Information System (INIS)

    Miller, C.W.; Benson, L.V.; Carnahan, C.L.


    Prediction of the rates of migration of contaminant chemical species in groundwater flowing through toxic waste repositories is essential to the assessment of a repository's capability of meeting standards for release rates. A large number of chemical transport models, of varying degrees of complexity, have been devised for the purpose of providing this predictive capability. In general, the transport of dissolved chemical species through a water-saturated porous medium is influenced by convection, diffusion/dispersion, sorption, formation of complexes in the aqueous phase, and chemical precipitation. The reliability of predictions made with the models which omit certain of these processes is difficult to assess. A numerical model, CHEMTRN, has been developed to determine which chemical processes govern radionuclide migration. CHEMTRN builds on a model called MCCTM developed previously by Lichtner and Benson

  4. Model predictive controller design of hydrocracker reactors


    GÖKÇE, Dila


    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  5. Working Towards a Risk Prediction Model for Neural Tube Defects (United States)

    Agopian, A.J.; Lupo, Philip J.; Tinker, Sarah C.; Canfield, Mark A.; Mitchell, Laura E.


    BACKGROUND Several risk factors have been consistently associated with neural tube defects (NTDs). However, the predictive ability of these risk factors in combination has not been evaluated. METHODS To assess the predictive ability of established risk factors for NTDs, we built predictive models using data from the National Birth Defects Prevention Study, which is a large, population-based study of nonsyndromic birth defects. Cases with spina bifida or anencephaly, or both (n = 1239), and controls (n = 8494) were randomly divided into separate training (75% of cases and controls) and validation (remaining 25%) samples. Multivariable logistic regression models were constructed with the training samples. The predictive ability of these models was evaluated in the validation samples by assessing the area under the receiver operator characteristic curves. An ordinal predictive risk index was also constructed and evaluated. In addition, the ability of classification and regression tree (CART) analysis to identify subgroups of women at increased risk for NTDs in offspring was evaluated. RESULTS The predictive ability of the multivariable models was poor (area under the receiver operating curve: 0.55 for spina bifida only, 0.59 for anencephaly only, and 0.56 for anencephaly and spina bifida combined). The predictive abilities of the ordinal risk indexes and CART models were also low. CONCLUSION Current established risk factors for NTDs are insufficient for population-level prediction of a women’s risk for having affected offspring. Identification of genetic risk factors and novel nongenetic risk factors will be critical to establishing models, with good predictive ability, for NTDs. PMID:22253139

  6. Chaotic advection at large Péclet number: Electromagnetically driven experiments, numerical simulations, and theoretical predictions

    Energy Technology Data Exchange (ETDEWEB)

    Figueroa, Aldo [Facultad de Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209 (Mexico); Meunier, Patrice; Villermaux, Emmanuel [Aix-Marseille Univ., CNRS, Centrale Marseille, IRPHE, Marseille F-13384 (France); Cuevas, Sergio; Ramos, Eduardo [Instituto de Energías Renovables, Universidad Nacional Autónoma de México, A.P. 34, Temixco, Morelos 62580 (Mexico)


    We present a combination of experiment, theory, and modelling on laminar mixing at large Péclet number. The flow is produced by oscillating electromagnetic forces in a thin electrolytic fluid layer, leading to oscillating dipoles, quadrupoles, octopoles, and disordered flows. The numerical simulations are based on the Diffusive Strip Method (DSM) which was recently introduced (P. Meunier and E. Villermaux, “The diffusive strip method for scalar mixing in two-dimensions,” J. Fluid Mech. 662, 134–172 (2010)) to solve the advection-diffusion problem by combining Lagrangian techniques and theoretical modelling of the diffusion. Numerical simulations obtained with the DSM are in reasonable agreement with quantitative dye visualization experiments of the scalar fields. A theoretical model based on log-normal Probability Density Functions (PDFs) of stretching factors, characteristic of homogeneous turbulence in the Batchelor regime, allows to predict the PDFs of scalar in agreement with numerical and experimental results. This model also indicates that the PDFs of scalar are asymptotically close to log-normal at late stages, except for the large concentration levels which correspond to low stretching factors.

  7. Large-Eddy Simulation of a High Reynolds Number Flow Around a Cylinder Including Aeroacoustic Predictions (United States)

    Spyropoulos, Evangelos T.; Holmes, Bayard S.


    The dynamic subgrid-scale model is employed in large-eddy simulations of flow over a cylinder at a Reynolds number, based on the diameter of the cylinder, of 90,000. The Centric SPECTRUM(trademark) finite element solver is used for the analysis. The far field sound pressure is calculated from Lighthill-Curle's equation using the computed fluctuating pressure at the surface of the cylinder. The sound pressure level at a location 35 diameters away from the cylinder and at an angle of 90 deg with respect to the wake's downstream axis was found to have a peak value of approximately 110 db. Slightly smaller peak values were predicted at the 60 deg and 120 deg locations. A grid refinement study suggests that the dynamic model demands mesh refinement beyond that used here.

  8. Multi-Model Ensemble Wake Vortex Prediction (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.


    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  9. Predicting the evolution of large cholera outbreaks: lessons learnt from the Haiti case study (United States)

    Bertuzzo, Enrico; Mari, Lorenzo; Righetto, Lorenzo; Knox, Allyn; Finger, Flavio; Casagrandi, Renato; Gatto, Marino; Rodriguez-Iturbe, Ignacio; Rinaldo, Andrea


    Mathematical models can provide key insights into the course of an ongoing epidemic, potentially aiding real-time emergency management in allocating health care resources and possibly anticipating the impact of alternative interventions. Spatially explicit models of waterborne disease are made routinely possible by widespread data mapping of hydrology, road network, population distribution, and sanitation. Here, we study the ex-post reliability of predictions of the ongoing Haiti cholera outbreak. Our model consists of a set of dynamical equations (SIR-like, i.e. subdivided into the compartments of Susceptible, Infected and Recovered individuals) describing a connected network of human communities where the infection results from the exposure to excess concentrations of pathogens in the water, which are, in turn, driven by hydrologic transport through waterways and by mobility of susceptible and infected individuals. Following the evidence of a clear correlation between rainfall events and cholera resurgence, we test a new mechanism explicitly accounting for rainfall as a driver of enhanced disease transmission by washout of open-air defecation sites or cesspool overflows. A general model for Haitian epidemic cholera and the related uncertainty is thus proposed and applied to the dataset of reported cases now available. The model allows us to draw predictions on longer-term epidemic cholera in Haiti from multi-season Monte Carlo runs, carried out up to January 2014 by using a multivariate Poisson rainfall generator, with parameters varying in space and time. Lessons learned and open issues are discussed and placed in perspective. We conclude that, despite differences in methods that can be tested through model-guided field validation, mathematical modeling of large-scale outbreaks emerges as an essential component of future cholera epidemic control.

  10. Thermodynamic modeling of activity coefficient and prediction of solubility: Part 1. Predictive models. (United States)

    Mirmehrabi, Mahmoud; Rohani, Sohrab; Perry, Luisa


    A new activity coefficient model was developed from excess Gibbs free energy in the form G(ex) = cA(a) x(1)(b)...x(n)(b). The constants of the proposed model were considered to be function of solute and solvent dielectric constants, Hildebrand solubility parameters and specific volumes of solute and solvent molecules. The proposed model obeys the Gibbs-Duhem condition for activity coefficient models. To generalize the model and make it as a purely predictive model without any adjustable parameters, its constants were found using the experimental activity coefficient and physical properties of 20 vapor-liquid systems. The predictive capability of the proposed model was tested by calculating the activity coefficients of 41 binary vapor-liquid equilibrium systems and showed good agreement with the experimental data in comparison with two other predictive models, the UNIFAC and Hildebrand models. The only data used for the prediction of activity coefficients, were dielectric constants, Hildebrand solubility parameters, and specific volumes of the solute and solvent molecules. Furthermore, the proposed model was used to predict the activity coefficient of an organic compound, stearic acid, whose physical properties were available in methanol and 2-butanone. The predicted activity coefficient along with the thermal properties of the stearic acid were used to calculate the solubility of stearic acid in these two solvents and resulted in a better agreement with the experimental data compared to the UNIFAC and Hildebrand predictive models.


    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro


    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  12. A revised prediction model for natural conception. (United States)

    Bensdorp, Alexandra J; van der Steeg, Jan Willem; Steures, Pieternel; Habbema, J Dik F; Hompes, Peter G A; Bossuyt, Patrick M M; van der Veen, Fulco; Mol, Ben W J; Eijkemans, Marinus J C


    One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis was to assess whether additional predictors can refine the Hunault model and extend its applicability. Consecutive subfertile couples with unexplained and mild male subfertility presenting in fertility clinics were asked to participate in a prospective cohort study. We constructed a multivariable prediction model with the predictors from the Hunault model and new potential predictors. The primary outcome, natural conception leading to an ongoing pregnancy, was observed in 1053 women of the 5184 included couples (20%). All predictors of the Hunault model were selected into the revised model plus an additional seven (woman's body mass index, cycle length, basal FSH levels, tubal status,history of previous pregnancies in the current relationship (ongoing pregnancies after natural conception, fertility treatment or miscarriages), semen volume, and semen morphology. Predictions from the revised model seem to concur better with observed pregnancy rates compared with the Hunault model; c-statistic of 0.71 (95% CI 0.69 to 0.73) compared with 0.59 (95% CI 0.57 to 0.61). Copyright © 2017. Published by Elsevier Ltd.

  13. Random Coefficient Logit Model for Large Datasets

    NARCIS (Netherlands)

    C. Hernández-Mireles (Carlos); D. Fok (Dennis)


    textabstractWe present an approach for analyzing market shares and products price elasticities based on large datasets containing aggregate sales data for many products, several markets and for relatively long time periods. We consider the recently proposed Bayesian approach of Jiang et al [Jiang,

  14. Hydrogen combustion modelling in large-scale geometries

    International Nuclear Information System (INIS)

    Studer, E.; Beccantini, A.; Kudriakov, S.; Velikorodny, A.


    Hydrogen risk mitigation issues based on catalytic recombiners cannot exclude flammable clouds to be formed during the course of a severe accident in a Nuclear Power Plant. Consequences of combustion processes have to be assessed based on existing knowledge and state of the art in CFD combustion modelling. The Fukushima accidents have also revealed the need for taking into account the hydrogen explosion phenomena in risk management. Thus combustion modelling in a large-scale geometry is one of the remaining severe accident safety issues. At present day there doesn't exist a combustion model which can accurately describe a combustion process inside a geometrical configuration typical of the Nuclear Power Plant (NPP) environment. Therefore the major attention in model development has to be paid on the adoption of existing approaches or creation of the new ones capable of reliably predicting the possibility of the flame acceleration in the geometries of that type. A set of experiments performed previously in RUT facility and Heiss Dampf Reactor (HDR) facility is used as a validation database for development of three-dimensional gas dynamic model for the simulation of hydrogen-air-steam combustion in large-scale geometries. The combustion regimes include slow deflagration, fast deflagration, and detonation. Modelling is based on Reactive Discrete Equation Method (RDEM) where flame is represented as an interface separating reactants and combustion products. The transport of the progress variable is governed by different flame surface wrinkling factors. The results of numerical simulation are presented together with the comparisons, critical discussions and conclusions. (authors)

  15. Effective models of new physics at the Large Hadron Collider

    International Nuclear Information System (INIS)

    Llodra-Perez, J.


    With the start of the Large Hadron Collider runs, in 2010, particle physicists will be soon able to have a better understanding of the electroweak symmetry breaking. They might also answer to many experimental and theoretical open questions raised by the Standard Model. Surfing on this really favorable situation, we will first present in this thesis a highly model-independent parametrization in order to characterize the new physics effects on mechanisms of production and decay of the Higgs boson. This original tool will be easily and directly usable in data analysis of CMS and ATLAS, the huge generalist experiments of LHC. It will help indeed to exclude or validate significantly some new theories beyond the Standard Model. In another approach, based on model-building, we considered a scenario of new physics, where the Standard Model fields can propagate in a flat six-dimensional space. The new spatial extra-dimensions will be compactified on a Real Projective Plane. This orbifold is the unique six-dimensional geometry which possesses chiral fermions and a natural Dark Matter candidate. The scalar photon, which is the lightest particle of the first Kaluza-Klein tier, is stabilized by a symmetry relic of the six dimension Lorentz invariance. Using the current constraints from cosmological observations and our first analytical calculation, we derived a characteristic mass range around few hundred GeV for the Kaluza-Klein scalar photon. Therefore the new states of our Universal Extra-Dimension model are light enough to be produced through clear signatures at the Large Hadron Collider. So we used a more sophisticated analysis of particle mass spectrum and couplings, including radiative corrections at one-loop, in order to establish our first predictions and constraints on the expected LHC phenomenology. (author)

  16. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen


    Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  17. Modelling language evolution: Examples and predictions (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan


    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  18. Identification and Prediction of Large Pedestrian Flow in Urban Areas Based on a Hybrid Detection Approach

    Directory of Open Access Journals (Sweden)

    Kaisheng Zhang


    Full Text Available Recently, population density has grown quickly with the increasing acceleration of urbanization. At the same time, overcrowded situations are more likely to occur in populous urban areas, increasing the risk of accidents. This paper proposes a synthetic approach to recognize and identify the large pedestrian flow. In particular, a hybrid pedestrian flow detection model was constructed by analyzing real data from major mobile phone operators in China, including information from smartphones and base stations (BS. With the hybrid model, the Log Distance Path Loss (LDPL model was used to estimate the pedestrian density from raw network data, and retrieve information with the Gaussian Progress (GP through supervised learning. Temporal-spatial prediction of the pedestrian data was carried out with Machine Learning (ML approaches. Finally, a case study of a real Central Business District (CBD scenario in Shanghai, China using records of millions of cell phone users was conducted. The results showed that the new approach significantly increases the utility and capacity of the mobile network. A more reasonable overcrowding detection and alert system can be developed to improve safety in subway lines and other hotspot landmark areas, such as the Bundle, People’s Square or Disneyland, where a large passenger flow generally exists.

  19. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik


    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....... and controlled have thus become essential factors for efficient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona...

  20. A prediction model for assessing residential radon concentration in Switzerland

    International Nuclear Information System (INIS)

    Hauri, Dimitri D.; Huss, Anke; Zimmermann, Frank; Kuehni, Claudia E.; Röösli, Martin


    Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th–90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40–111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69–215 Bq/m³) in the medium category, and 219 Bq/m³ (108–427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be

  1. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C


    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior......One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view...

  2. Intra prediction based on Markov process modeling of images. (United States)

    Kamisli, Fatih


    In recent video coding standards, intraprediction of a block of pixels is performed by copying neighbor pixels of the block along an angular direction inside the block. Each block pixel is predicted from only one or few directionally aligned neighbor pixels of the block. Although this is a computationally efficient approach, it ignores potentially useful correlation of other neighbor pixels of the block. To use this correlation, a general linear prediction approach is proposed, where each block pixel is predicted using a weighted sum of all neighbor pixels of the block. The disadvantage of this approach is the increased complexity because of the large number of weights. In this paper, we propose an alternative approach to intraprediction, where we model image pixels with a Markov process. The Markov process model accounts for the ignored correlation in standard intraprediction methods, but uses few neighbor pixels and enables a computationally efficient recursive prediction algorithm. Compared with the general linear prediction approach that has a large number of independent weights, the Markov process modeling approach uses a much smaller number of independent parameters and thus offers significantly reduced memory or computation requirements, while achieving similar coding gains with offline computed parameters.

  3. Predictive analytics can support the ACO model. (United States)

    Bradley, Paul


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

  4. Predictive modeling in homogeneous catalysis: a tutorial

    NARCIS (Netherlands)

    Maldonado, A.G.; Rothenberg, G.


    Predictive modeling has become a practical research tool in homogeneous catalysis. It can help to pinpoint ‘good regions’ in the catalyst space, narrowing the search for the optimal catalyst for a given reaction. Just like any other new idea, in silico catalyst optimization is accepted by some

  5. Model predictive control of smart microgrids

    DEFF Research Database (Denmark)

    Hu, Jiefeng; Zhu, Jianguo; Guerrero, Josep M.


    required to realise high-performance of distributed generations and will realise innovative control techniques utilising model predictive control (MPC) to assist in coordinating the plethora of generation and load combinations, thus enable the effective exploitation of the clean renewable energy sources...

  6. Feedback model predictive control by randomized algorithms

    NARCIS (Netherlands)

    Batina, Ivo; Stoorvogel, Antonie Arij; Weiland, Siep


    In this paper we present a further development of an algorithm for stochastic disturbance rejection in model predictive control with input constraints based on randomized algorithms. The algorithm presented in our work can solve the problem of stochastic disturbance rejection approximately but with

  7. A Robustly Stabilizing Model Predictive Control Algorithm (United States)

    Ackmece, A. Behcet; Carson, John M., III


    A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

  8. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob


    This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...

  9. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp


    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations ...

  10. Large animal models for vaccine development and testing. (United States)

    Gerdts, Volker; Wilson, Heather L; Meurens, Francois; van Drunen Littel-van den Hurk, Sylvia; Wilson, Don; Walker, Stewart; Wheler, Colette; Townsend, Hugh; Potter, Andrew A


    The development of human vaccines continues to rely on the use of animals for research. Regulatory authorities require novel vaccine candidates to undergo preclinical assessment in animal models before being permitted to enter the clinical phase in human subjects. Substantial progress has been made in recent years in reducing and replacing the number of animals used for preclinical vaccine research through the use of bioinformatics and computational biology to design new vaccine candidates. However, the ultimate goal of a new vaccine is to instruct the immune system to elicit an effective immune response against the pathogen of interest, and no alternatives to live animal use currently exist for evaluation of this response. Studies identifying the mechanisms of immune protection; determining the optimal route and formulation of vaccines; establishing the duration and onset of immunity, as well as the safety and efficacy of new vaccines, must be performed in a living system. Importantly, no single animal model provides all the information required for advancing a new vaccine through the preclinical stage, and research over the last two decades has highlighted that large animals more accurately predict vaccine outcome in humans than do other models. Here we review the advantages and disadvantages of large animal models for human vaccine development and demonstrate that much of the success in bringing a new vaccine to market depends on choosing the most appropriate animal model for preclinical testing. © The Author 2015. Published by Oxford University Press on behalf of the Institute for Laboratory Animal Research. All rights reserved. For permissions, please email:

  11. Disease prediction models and operational readiness.

    Directory of Open Access Journals (Sweden)

    Courtney D Corley

    Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology

  12. Caries risk assessment models in caries prediction

    Directory of Open Access Journals (Sweden)

    Amila Zukanović


    Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.

  13. Multi-fidelity uncertainty quantification in large-scale predictive simulations of turbulent flow (United States)

    Geraci, Gianluca; Jofre-Cruanyes, Lluis; Iaccarino, Gianluca


    The performance characterization of complex engineering systems often relies on accurate, but computationally intensive numerical simulations. It is also well recognized that in order to obtain a reliable numerical prediction the propagation of uncertainties needs to be included. Therefore, Uncertainty Quantification (UQ) plays a fundamental role in building confidence in predictive science. Despite the great improvement in recent years, even the more advanced UQ algorithms are still limited to fairly simplified applications and only moderate parameter dimensionality. Moreover, in the case of extremely large dimensionality, sampling methods, i.e. Monte Carlo (MC) based approaches, appear to be the only viable alternative. In this talk we describe and compare a family of approaches which aim to accelerate the convergence of standard MC simulations. These methods are based on hierarchies of generalized numerical resolutions (multi-level) or model fidelities (multi-fidelity), and attempt to leverage the correlation between Low- and High-Fidelity (HF) models to obtain a more accurate statistical estimator without introducing additional HF realizations. The performance of these methods are assessed on an irradiated particle laden turbulent flow (PSAAP II solar energy receiver). This investigation was funded by the United States Department of Energy's (DoE) National Nuclear Security Administration (NNSA) under the Predicitive Science Academic Alliance Program (PSAAP) II at Stanford University.

  14. The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing

    Directory of Open Access Journals (Sweden)

    Peng Zhang


    Full Text Available Ebola virus disease (EVD distinguishes its feature as high infectivity and mortality. Thus, it is urgent for governments to draw up emergency plans against Ebola. However, it is hard to predict the possible epidemic situations in practice. Luckily, in recent years, computational experiments based on artificial society appeared, providing a new approach to study the propagation of EVD and analyze the corresponding interventions. Therefore, the rationality of artificial society is the key to the accuracy and reliability of experiment results. Individuals’ behaviors along with travel mode directly affect the propagation among individuals. Firstly, artificial Beijing is reconstructed based on geodemographics and machine learning is involved to optimize individuals’ behaviors. Meanwhile, Ebola course model and propagation model are built, according to the parameters in West Africa. Subsequently, propagation mechanism of EVD is analyzed, epidemic scenario is predicted, and corresponding interventions are presented. Finally, by simulating the emergency responses of Chinese government, the conclusion is finally drawn that Ebola is impossible to outbreak in large scale in the city of Beijing.

  15. Small- and large-signal modeling of InP HBTs in transferred-substrate technology

    DEFF Research Database (Denmark)

    Johansen, Tom Keinicke; Rudolph, Matthias; Jensen, Thomas


    a direct parameter extraction methodology dedicated to III–V based HBTs. It is shown that the modeling of measured S-parameters can be improved in the millimeter-wave frequency range by augmenting the small-signal model with a description of AC current crowding. The extracted elements of the small......-signal model structure are employed as a starting point for the extraction of a large-signal model. The developed large-signal model for the TS-HBTs accurately predicts the DC over temperature and small-signal performance over bias as well as the large-signal performance at millimeter-wave frequencies....

  16. Large Animal Stroke Models vs. Rodent Stroke Models, Pros and Cons, and Combination? (United States)

    Cai, Bin; Wang, Ning


    Stroke is a leading cause of serious long-term disability worldwide and the second leading cause of death in many countries. Long-time attempts to salvage dying neurons via various neuroprotective agents have failed in stroke translational research, owing in part to the huge gap between animal stroke models and stroke patients, which also suggests that rodent models have limited predictive value and that alternate large animal models are likely to become important in future translational research. The genetic background, physiological characteristics, behavioral characteristics, and brain structure of large animals, especially nonhuman primates, are analogous to humans, and resemble humans in stroke. Moreover, relatively new regional imaging techniques, measurements of regional cerebral blood flow, and sophisticated physiological monitoring can be more easily performed on the same animal at multiple time points. As a result, we can use large animal stroke models to decrease the gap and promote translation of basic science stroke research. At the same time, we should not neglect the disadvantages of the large animal stroke model such as the significant expense and ethical considerations, which can be overcome by rodent models. Rodents should be selected as stroke models for initial testing and primates or cats are desirable as a second species, which was recommended by the Stroke Therapy Academic Industry Roundtable (STAIR) group in 2009.

  17. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)


    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  18. Genetic models of homosexuality: generating testable predictions (United States)

    Gavrilets, Sergey; Rice, William R


    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism. PMID:17015344

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

    Directory of Open Access Journals (Sweden)

    Sungho Won


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

  20. A statistical model for predicting muscle performance (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  1. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.


    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  2. Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

    Energy Technology Data Exchange (ETDEWEB)

    Rossi, R; Gallagher, B; Neville, J; Henderson, K


    Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.

  3. Predicting viscous-range velocity gradient dynamics in large-eddy simulations of turbulence (United States)

    Johnson, Perry; Meneveau, Charles


    The details of small-scale turbulence are not directly accessible in large-eddy simulations (LES), posing a modeling challenge because many important micro-physical processes depend strongly on the dynamics of turbulence in the viscous range. Here, we introduce a method for coupling existing stochastic models for the Lagrangian evolution of the velocity gradient tensor with LES to simulate unresolved dynamics. The proposed approach is implemented in LES of turbulent channel flow and detailed comparisons with DNS are carried out. An application to modeling the fate of deformable, small (sub-Kolmogorov) droplets at negligible Stokes number and low volume fraction with one-way coupling is carried out. These results illustrate the ability of the proposed model to predict the influence of small scale turbulence on droplet micro-physics in the context of LES. This research was made possible by a graduate Fellowship from the National Science Foundation and by a Grant from The Gulf of Mexico Research Initiative.

  4. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.


    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  5. Holonic Modelling of Large Scale Geographic Environments (United States)

    Mekni, Mehdi; Moulin, Bernard

    In this paper, we propose a novel approach to model Virtual Geographic Environments (VGE) which uses the holonic approach as a computational geographic methodology and holarchy as organizational principle. Our approach allows to automatically build VGE using data provided by Geographic Information Systems (GIS) and enables an explicit representation of the geographic environment for Situated Multi-Agent Systems (SMAS) in which agents are situated and with which they interact. In order to take into account geometric, topologic, and semantic characteristics of the geographic environment, we propose the use of the holonic approach to build the environment holarchy. We illustrate our holonic model using two different environments: an urban environment and a natural environment.

  6. Data Quality Enhanced Prediction Model for Massive Plant Data

    Energy Technology Data Exchange (ETDEWEB)

    Park, Moon-Ghu [Nuclear Engr. Sejong Univ., Seoul (Korea, Republic of); Kang, Seong-Ki [Monitoring and Diagnosis, Suwon (Korea, Republic of); Shin, Hajin [Saint Paul Preparatory Seoul, Seoul (Korea, Republic of)


    This paper introduces an integrated signal preconditioning and model prediction mainly by kernel functions. The performance and benefits of the methods are demonstrated by a case study with measurement data from a power plant and its components transient data. The developed methods will be applied as a part of monitoring massive or big data platform where human experts cannot detect the fault behaviors due to too large size of the measurements. Recent extensive efforts for on-line monitoring implementation insists that a big surprise in the modeling for predicting process variables was the extent of data quality problems in measurement data especially for data-driven modeling. Bad data for training will be learned as normal and can make significant degrade in prediction performance. For this reason, the quantity and quality of measurement data in modeling phase need special care. Bad quality data must be removed from training sets to the bad data considered as normal system behavior. This paper presented an integrated structure of supervisory system for monitoring the plants or sensors performance. The quality of the data-driven model is improved with a bilateral kernel filter for preprocessing of the noisy data. The prediction module is also based on kernel regression having the same basis with noise filter. The model structure is optimized by a grouping process with nonlinear Hoeffding correlation function.

  7. Data Quality Enhanced Prediction Model for Massive Plant Data

    International Nuclear Information System (INIS)

    Park, Moon-Ghu; Kang, Seong-Ki; Shin, Hajin


    This paper introduces an integrated signal preconditioning and model prediction mainly by kernel functions. The performance and benefits of the methods are demonstrated by a case study with measurement data from a power plant and its components transient data. The developed methods will be applied as a part of monitoring massive or big data platform where human experts cannot detect the fault behaviors due to too large size of the measurements. Recent extensive efforts for on-line monitoring implementation insists that a big surprise in the modeling for predicting process variables was the extent of data quality problems in measurement data especially for data-driven modeling. Bad data for training will be learned as normal and can make significant degrade in prediction performance. For this reason, the quantity and quality of measurement data in modeling phase need special care. Bad quality data must be removed from training sets to the bad data considered as normal system behavior. This paper presented an integrated structure of supervisory system for monitoring the plants or sensors performance. The quality of the data-driven model is improved with a bilateral kernel filter for preprocessing of the noisy data. The prediction module is also based on kernel regression having the same basis with noise filter. The model structure is optimized by a grouping process with nonlinear Hoeffding correlation function

  8. Numerical comparisons of ground motion predictions with kinematic rupture modeling (United States)

    Yuan, Y. O.; Zurek, B.; Liu, F.; deMartin, B.; Lacasse, M. D.


    Recent advances in large-scale wave simulators allow for the computation of seismograms at unprecedented levels of detail and for areas sufficiently large to be relevant to small regional studies. In some instances, detailed information of the mechanical properties of the subsurface has been obtained from seismic exploration surveys, well data, and core analysis. Using kinematic rupture modeling, this information can be used with a wave propagation simulator to predict the ground motion that would result from an assumed fault rupture. The purpose of this work is to explore the limits of wave propagation simulators for modeling ground motion in different settings, and in particular, to explore the numerical accuracy of different methods in the presence of features that are challenging to simulate such as topography, low-velocity surface layers, and shallow sources. In the main part of this work, we use a variety of synthetic three-dimensional models and compare the relative costs and benefits of different numerical discretization methods in computing the seismograms of realistic-size models. The finite-difference method, the discontinuous-Galerkin method, and the spectral-element method are compared for a range of synthetic models having different levels of complexity such as topography, large subsurface features, low-velocity surface layers, and the location and characteristics of fault ruptures represented as an array of seismic sources. While some previous studies have already demonstrated that unstructured-mesh methods can sometimes tackle complex problems (Moczo et al.), we investigate the trade-off between unstructured-mesh methods and regular-grid methods for a broad range of models and source configurations. Finally, for comparison, our direct simulation results are briefly contrasted with those predicted by a few phenomenological ground-motion prediction equations, and a workflow for accurately predicting ground motion is proposed.

  9. Competency-Based Model for Predicting Construction Project Managers Performance


    Dainty, A. R. J.; Cheng, M.; Moore, D. R.


    Using behavioral competencies to influence human resource management decisions is gaining popularity in business organizations. This study identifies the core competencies associated with the construction management role and further, develops a predictive model to inform human resource selection and development decisions within large construction organizations. A range of construction managers took part in behavioral event interviews where staffs were asked to recount critical management inci...

  10. Scaling predictive modeling in drug development with cloud computing. (United States)

    Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola


    Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

  11. In silico modeling to predict drug-induced phospholipidosis

    International Nuclear Information System (INIS)

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G.; Sadrieh, Nakissa


    Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL

  12. Disease Prediction Models and Operational Readiness

    Energy Technology Data Exchange (ETDEWEB)

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.


    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  13. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars


    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from, together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  14. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.


    values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  15. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.


    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  16. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)


    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  17. Predictive modelling of evidence informed teaching


    Zhang, Dell; Brown, C.


    In this paper, we analyse the questionnaire survey data collected from 79 English primary schools about the situation of evidence informed teaching, where the evidences could come from research journals or conferences. Specifically, we build a predictive model to see what external factors could help to close the gap between teachers’ belief and behaviour in evidence informed teaching, which is the first of its kind to our knowledge. The major challenge, from the data mining perspective, is th...

  18. A Predictive Model for Cognitive Radio (United States)


    response in a given situation. Vadde et al. interest and produce a model for prediction of the response. have applied response surface methodology and...34 2000. [3] K. K. Vadde and V. R. Syrotiuk, "Factor interaction on service configurations to those that best meet our communication delivery in mobile ad...resulting set of configurations randomly or apply additional 2004. screening criteria. [4] K. K. Vadde , M.-V. R. Syrotiuk, and D. C. Montgomery

  19. PEEX Modelling Platform for Seamless Environmental Prediction (United States)

    Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku


    The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.

  20. Tectonic predictions with mantle convection models (United States)

    Coltice, Nicolas; Shephard, Grace E.


    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

  1. Predictive Modeling of the CDRA 4BMS (United States)

    Coker, Robert F.; Knox, James C.


    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  2. Improving Predictive Modeling in Pediatric Drug Development: Pharmacokinetics, Pharmacodynamics, and Mechanistic Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Slikker, William; Young, John F.; Corley, Rick A.; Dorman, David C.; Conolly, Rory B.; Knudsen, Thomas; Erstad, Brian L.; Luecke, Richard H.; Faustman, Elaine M.; Timchalk, Chuck; Mattison, Donald R.


    A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

  3. RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures. (United States)

    Miao, Zhichao; Adamiak, Ryszard W; Blanchet, Marc-Frédérick; Boniecki, Michal; Bujnicki, Janusz M; Chen, Shi-Jie; Cheng, Clarence; Chojnowski, Grzegorz; Chou, Fang-Chieh; Cordero, Pablo; Cruz, José Almeida; Ferré-D'Amaré, Adrian R; Das, Rhiju; Ding, Feng; Dokholyan, Nikolay V; Dunin-Horkawicz, Stanislaw; Kladwang, Wipapat; Krokhotin, Andrey; Lach, Grzegorz; Magnus, Marcin; Major, François; Mann, Thomas H; Masquida, Benoît; Matelska, Dorota; Meyer, Mélanie; Peselis, Alla; Popenda, Mariusz; Purzycka, Katarzyna J; Serganov, Alexander; Stasiewicz, Juliusz; Szachniuk, Marta; Tandon, Arpit; Tian, Siqi; Wang, Jian; Xiao, Yi; Xu, Xiaojun; Zhang, Jinwei; Zhao, Peinan; Zok, Tomasz; Westhof, Eric


    This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at © 2015 Miao et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  4. Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using Genetic Programming (United States)

    Kashid, Satishkumar S.; Maity, Rajib


    SummaryPrediction of Indian Summer Monsoon Rainfall (ISMR) is of vital importance for Indian economy, and it has been remained a great challenge for hydro-meteorologists due to inherent complexities in the climatic systems. The Large-scale atmospheric circulation patterns from tropical Pacific Ocean (ENSO) and those from tropical Indian Ocean (EQUINOO) are established to influence the Indian Summer Monsoon Rainfall. The information of these two large scale atmospheric circulation patterns in terms of their indices is used to model the complex relationship between Indian Summer Monsoon Rainfall and the ENSO as well as EQUINOO indices. However, extracting the signal from such large-scale indices for modeling such complex systems is significantly difficult. Rainfall predictions have been done for 'All India' as one unit, as well as for five 'homogeneous monsoon regions of India', defined by Indian Institute of Tropical Meteorology. Recent 'Artificial Intelligence' tool 'Genetic Programming' (GP) has been employed for modeling such problem. The Genetic Programming approach is found to capture the complex relationship between the monthly Indian Summer Monsoon Rainfall and large scale atmospheric circulation pattern indices - ENSO and EQUINOO. Research findings of this study indicate that GP-derived monthly rainfall forecasting models, that use large-scale atmospheric circulation information are successful in prediction of All India Summer Monsoon Rainfall with correlation coefficient as good as 0.866, which may appears attractive for such a complex system. A separate analysis is carried out for All India Summer Monsoon rainfall for India as one unit, and five homogeneous monsoon regions, based on ENSO and EQUINOO indices of months of March, April and May only, performed at end of month of May. In this case, All India Summer Monsoon Rainfall could be predicted with 0.70 as correlation coefficient with somewhat lesser Correlation Coefficient (C.C.) values for different

  5. Geometric algorithms for electromagnetic modeling of large scale structures (United States)

    Pingenot, James

    With the rapid increase in the speed and complexity of integrated circuit designs, 3D full wave and time domain simulation of chip, package, and board systems becomes more and more important for the engineering of modern designs. Much effort has been applied to the problem of electromagnetic (EM) simulation of such systems in recent years. Major advances in boundary element EM simulations have led to O(n log n) simulations using iterative methods and advanced Fast. Fourier Transform (FFT), Multi-Level Fast Multi-pole Methods (MLFMM), and low-rank matrix compression techniques. These advances have been augmented with an explosion of multi-core and distributed computing technologies, however, realization of the full scale of these capabilities has been hindered by cumbersome and inefficient geometric processing. Anecdotal evidence from industry suggests that users may spend around 80% of turn-around time manipulating the geometric model and mesh. This dissertation addresses this problem by developing fast and efficient data structures and algorithms for 3D modeling of chips, packages, and boards. The methods proposed here harness the regular, layered 2D nature of the models (often referred to as "2.5D") to optimize these systems for large geometries. First, an architecture is developed for efficient storage and manipulation of 2.5D models. The architecture gives special attention to native representation of structures across various input models and special issues particular to 3D modeling. The 2.5D structure is then used to optimize the mesh systems First, circuit/EM co-simulation techniques are extended to provide electrical connectivity between objects. This concept is used to connect independently meshed layers, allowing simple and efficient 2D mesh algorithms to be used in creating a 3D mesh. Here, adaptive meshing is used to ensure that the mesh accurately models the physical unknowns (current and charge). Utilizing the regularized nature of 2.5D objects and

  6. A Traffic Prediction Model for Self-Adapting Routing Overlay Network in Publish/Subscribe System

    Directory of Open Access Journals (Sweden)

    Meng Chi


    Full Text Available In large-scale location-based service, an ideal situation is that self-adapting routing strategies use future traffic data as input to generate a topology which could adapt to the changing traffic well. In the paper, we propose a traffic prediction model for the broker in publish/subscribe system, which can predict the traffic of the link in future by neural network. We first introduced our traffic prediction model and then described the model integration. Finally, the experimental results show that our traffic prediction model could predict the traffic of link well.

  7. Predictive assessment of models for dynamic functional connectivity. (United States)

    Nielsen, Søren F V; Schmidt, Mikkel N; Madsen, Kristoffer H; Mørup, Morten


    In neuroimaging, it has become evident that models of dynamic functional connectivity (dFC), which characterize how intrinsic brain organization changes over time, can provide a more detailed representation of brain function than traditional static analyses. Many dFC models in the literature represent functional brain networks as a meta-stable process with a discrete number of states; however, there is a lack of consensus on how to perform model selection and learn the number of states, as well as a lack of understanding of how different modeling assumptions influence the estimated state dynamics. To address these issues, we consider a predictive likelihood approach to model assessment, where models are evaluated based on their predictive performance on held-out test data. Examining several prominent models of dFC (in their probabilistic formulations) we demonstrate our framework on synthetic data, and apply it on two real-world examples: a face recognition EEG experiment and resting-state fMRI. Our results evidence that both EEG and fMRI are better characterized using dynamic modeling approaches than by their static counterparts, but we also demonstrate that one must be cautious when interpreting dFC because parameter settings and modeling assumptions, such as window lengths and emission models, can have a large impact on the estimated states and consequently on the interpretation of the brain dynamics. Copyright © 2018 Elsevier Inc. All rights reserved.

  8. Predictive Modeling by the Cerebellum Improves Proprioception (United States)

    Bhanpuri, Nasir H.; Okamura, Allison M.


    Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance. PMID:24005283

  9. Prediction of Chemical Function: Model Development and ... (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  10. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K


    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  11. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif


    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  12. A generative model for predicting terrorist incidents (United States)

    Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger


    A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations


    Directory of Open Access Journals (Sweden)

    Narciso Ysac Avila Serrano


    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  14. Flood management: prediction of microbial contamination in large-scale floods in urban environments. (United States)

    Taylor, Jonathon; Lai, Ka Man; Davies, Mike; Clifton, David; Ridley, Ian; Biddulph, Phillip


    With a changing climate and increased urbanisation, the occurrence and the impact of flooding is expected to increase significantly. Floods can bring pathogens into homes and cause lingering damp and microbial growth in buildings, with the level of growth and persistence dependent on the volume and chemical and biological content of the flood water, the properties of the contaminating microbes, and the surrounding environmental conditions, including the restoration time and methods, the heat and moisture transport properties of the envelope design, and the ability of the construction material to sustain the microbial growth. The public health risk will depend on the interaction of these complex processes and the vulnerability and susceptibility of occupants in the affected areas. After the 2007 floods in the UK, the Pitt review noted that there is lack of relevant scientific evidence and consistency with regard to the management and treatment of flooded homes, which not only put the local population at risk but also caused unnecessary delays in the restoration effort. Understanding the drying behaviour of flooded buildings in the UK building stock under different scenarios, and the ability of microbial contaminants to grow, persist, and produce toxins within these buildings can help inform recovery efforts. To contribute to future flood management, this paper proposes the use of building simulations and biological models to predict the risk of microbial contamination in typical UK buildings. We review the state of the art with regard to biological contamination following flooding, relevant building simulation, simulation-linked microbial modelling, and current practical considerations in flood remediation. Using the city of London as an example, a methodology is proposed that uses GIS as a platform to integrate drying models and microbial risk models with the local building stock and flood models. The integrated tool will help local governments, health authorities

  15. Environmental Impacts of Large Scale Biochar Application Through Spatial Modeling (United States)

    Huber, I.; Archontoulis, S.


    In an effort to study the environmental (emissions, soil quality) and production (yield) impacts of biochar application at regional scales we coupled the APSIM-Biochar model with the pSIMS parallel platform. So far the majority of biochar research has been concentrated on lab to field studies to advance scientific knowledge. Regional scale assessments are highly needed to assist decision making. The overall objective of this simulation study was to identify areas in the USA that have the most gain environmentally from biochar's application, as well as areas which our model predicts a notable yield increase due to the addition of biochar. We present the modifications in both APSIM biochar and pSIMS components that were necessary to facilitate these large scale model runs across several regions in the United States at a resolution of 5 arcminutes. This study uses the AgMERRA global climate data set (1980-2010) and the Global Soil Dataset for Earth Systems modeling as a basis for creating its simulations, as well as local management operations for maize and soybean cropping systems and different biochar application rates. The regional scale simulation analysis is in progress. Preliminary results showed that the model predicts that high quality soils (particularly those common to Iowa cropping systems) do not receive much, if any, production benefit from biochar. However, soils with low soil organic matter ( 0.5%) do get a noteworthy yield increase of around 5-10% in the best cases. We also found N2O emissions to be spatial and temporal specific; increase in some areas and decrease in some other areas due to biochar application. In contrast, we found increases in soil organic carbon and plant available water in all soils (top 30 cm) due to biochar application. The magnitude of these increases (% change from the control) were larger in soil with low organic matter (below 1.5%) and smaller in soils with high organic matter (above 3%) and also dependent on biochar

  16. Advective transport in heterogeneous aquifers: Are proxy models predictive? (United States)

    Fiori, A.; Zarlenga, A.; Gotovac, H.; Jankovic, I.; Volpi, E.; Cvetkovic, V.; Dagan, G.


    We examine the prediction capability of two approximate models (Multi-Rate Mass Transfer (MRMT) and Continuous Time Random Walk (CTRW)) of non-Fickian transport, by comparison with accurate 2-D and 3-D numerical simulations. Both nonlocal in time approaches circumvent the need to solve the flow and transport equations by using proxy models to advection, providing the breakthrough curves (BTC) at control planes at any x, depending on a vector of five unknown parameters. Although underlain by different mechanisms, the two models have an identical structure in the Laplace Transform domain and have the Markovian property of independent transitions. We show that also the numerical BTCs enjoy the Markovian property. Following the procedure recommended in the literature, along a practitioner perspective, we first calibrate the parameters values by a best fit with the numerical BTC at a control plane at x1, close to the injection plane, and subsequently use it for prediction at further control planes for a few values of σY2≤8. Due to a similar structure and Markovian property, the two methods perform equally well in matching the numerical BTC. The identified parameters are generally not unique, making their identification somewhat arbitrary. The inverse Gaussian model and the recently developed Multi-Indicator Model (MIM), which does not require any fitting as it relates the BTC to the permeability structure, are also discussed. The application of the proxy models for prediction requires carrying out transport field tests of large plumes for a long duration.

  17. Nonlinear mixed-effects modeling: individualization and prediction. (United States)

    Olofsen, Erik; Dinges, David F; Van Dongen, Hans P A


    The development of biomathematical models for the prediction of fatigue and performance relies on statistical techniques to analyze experimental data and model simulations. Statistical models of empirical data have adjustable parameters with a priori unknown values. Interindividual variability in estimates of those values requires a form of smoothing. This traditionally consists of averaging observations across subjects, or fitting a model to the data of individual subjects first and subsequently averaging the parameter estimates. However, the standard errors of the parameter estimates are assessed inaccurately by such averaging methods. The reason is that intra- and inter-individual variabilities are intertwined. They can be separated by mixed-effects modeling in which model predictions are not only determined by fixed effects (usually constant parameters or functions of time) but also by random effects, describing the sampling of subject-specific parameter values from probability distributions. By estimating the parameters of the distributions of the random effects, mixed-effects models can describe experimental observations involving multiple subjects properly (i.e., yielding correct estimates of the standard errors) and parsimoniously (i.e., estimating no more parameters than necessary). Using a Bayesian approach, mixed-effects models can be "individualized" as observations are acquired that capture the unique characteristics of the individual at hand. Mixed-effects models, therefore, have unique advantages in research on human neurobehavioral functions, which frequently show large inter-individual differences. To illustrate this we analyzed laboratory neurobehavioral performance data acquired during sleep deprivation, using a nonlinear mixed-effects model. The results serve to demonstrate the usefulness of mixed-effects modeling for data-driven development of individualized predictive models of fatigue and performance.

  18. Long-Term Calculations with Large Air Pollution Models

    DEFF Research Database (Denmark)

    Ambelas Skjøth, C.; Bastrup-Birk, A.; Brandt, J.


    Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...

  19. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.


    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  20. An Anisotropic Hardening Model for Springback Prediction

    International Nuclear Information System (INIS)

    Zeng, Danielle; Xia, Z. Cedric


    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test

  1. Using radar altimetry to update a large-scale hydrological model of the Brahmaputra river basin

    DEFF Research Database (Denmark)

    Finsen, F.; Milzow, Christian; Smith, R.


    of the Brahmaputra is excellent (17 high-quality virtual stations from ERS-2, 6 from Topex and 10 from Envisat are available for the Brahmaputra). In this study, altimetry data are used to update a large-scale Budyko-type hydrological model of the Brahmaputra river basin in real time. Altimetry measurements...... improved model performance considerably. The Nash-Sutcliffe model efficiency increased from 0.77 to 0.83. Real-time river basin modelling using radar altimetry has the potential to improve the predictive capability of large-scale hydrological models elsewhere on the planet....

  2. Constituent rearrangement model and large transverse momentum reactions

    International Nuclear Information System (INIS)

    Igarashi, Yuji; Imachi, Masahiro; Matsuoka, Takeo; Otsuki, Shoichiro; Sawada, Shoji.


    In this chapter, two models based on the constituent rearrangement picture for large p sub( t) phenomena are summarized. One is the quark-junction model, and the other is the correlating quark rearrangement model. Counting rules of the models apply to both two-body reactions and hadron productions. (author)

  3. Toward Bicycle Demand Prediction of Large-Scale Bicycle-Sharing System


    HAN, Yufei; COME, Etienne; OUKHELLOU, Latifa


    We focus on predicting demands of bicycle usage in Velib system of Paris, which is a large-scale bicycle sharing service covering the whole Paris and its near suburbs. In this system, bicycle demand of each station usually correlates with historical Velib usage records at both spatial and temporal scale. The spatio-temporal correlation acts as an important factor affecting bicycle demands in the system. Thus it is a necessary information source for predicting bicycle demand of each station ac...

  4. Improving the local relevance of large scale water demand predictions: the way forward (United States)

    Bernhard, Jeroen; Reynaud, Arnaud; de Roo, Ad


    use and water prices. Subsequently, econometric estimates allow us to make a monetary valuation of water and identify the dominant drivers of domestic and industrial water demand per country. Combined with socio-economic, demographic and climate scenarios we made predictions for future Europe. Since this is a first attempt we obtained mixed results between countries when it comes to data availability and therefore model uncertainty. For some countries we have been able to develop robust predictions based on vast amounts of data while some other countries proved more challenging. We do feel however, that large scale predictions based on regional data are the way forward to provide relevant scientific policy support. In order to improve on our work it is imperative to further expand our database of consistent regional data. We are looking forward to any kind of input and would be very interested in sharing our data to collaborate towards a better understanding of the water use system.

  5. [Endometrial cancer: Predictive models and clinical impact]. (United States)

    Bendifallah, Sofiane; Ballester, Marcos; Daraï, Emile


    In France, in 2015, endometrial cancer (CE) is the first gynecological cancer in terms of incidence and the fourth cause of cancer of the woman. About 8151 new cases and nearly 2179 deaths have been reported. Treatments (surgery, external radiotherapy, brachytherapy and chemotherapy) are currently delivered on the basis of an estimation of the recurrence risk, an estimation of lymph node metastasis or an estimate of survival probability. This risk is determined on the basis of prognostic factors (clinical, histological, imaging, biological) taken alone or grouped together in the form of classification systems, which are currently insufficient to account for the evolutionary and prognostic heterogeneity of endometrial cancer. For endometrial cancer, the concept of mathematical modeling and its application to prediction have developed in recent years. These biomathematical tools have opened a new era of care oriented towards the promotion of targeted therapies and personalized treatments. Many predictive models have been published to estimate the risk of recurrence and lymph node metastasis, but a tiny fraction of them is sufficiently relevant and of clinical utility. The optimization tracks are multiple and varied, suggesting the possibility in the near future of a place for these mathematical models. The development of high-throughput genomics is likely to offer a more detailed molecular characterization of the disease and its heterogeneity. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  6. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.


    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  7. Predictions of models for environmental radiological assessment

    International Nuclear Information System (INIS)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando


    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for 137 Cs and 60 Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  8. Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness. (United States)

    Li, Jin; Tran, Maggie; Siwabessy, Justy


    Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to 'small p and large n' problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

  9. Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness (United States)

    Li, Jin; Tran, Maggie; Siwabessy, Justy


    Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

  10. A transport model for prediction of wildfire behavior

    Energy Technology Data Exchange (ETDEWEB)

    Linn, R.R.


    Wildfires are a threat to human life and property, yet they are an unavoidable part of nature. In the past people have tried to predict wildfire behavior through the use of point functional models but have been unsuccessful at adequately predicting the gross behavior of the broad spectrum of fires that occur in nature. The majority of previous models do not have self-determining propagation rates. The author uses a transport approach to represent this complicated problem and produce a model that utilizes a self-determining propagation rate. The transport approach allows one to represent a large number of environments including transition regions such as those with nonhomogeneous vegetation and terrain. Some of the most difficult features to treat are the imperfectly known boundary conditions and the fine scale structure that is unresolvable, such as the specific location of the fuel or the precise incoming winds. The author accounts for the microscopic details of a fire with macroscopic resolution by dividing quantities into mean and fluctuating parts similar to what is done in traditional turbulence modelling. The author develops a complicated model that includes the transport of multiple gas species, such as oxygen and volatile hydrocarbons, and tracks the depletion of various fuels and other stationary solids and liquids. From this model the author also forms a simplified local burning model with which he performs a number of simulations for the purpose of demonstrating the properties of a self-determining transport-based wildfire model.


    Directory of Open Access Journals (Sweden)



    Full Text Available Stock market crashes have been a constant subject of interest among capital market researchers. Crashes’ behavior has been largely studied, but the problem that remained unsolved until recently, was that of a prediction algorithm. Stock market crashes are complex and global events, rarely taking place on a singular national capital market. They usually occur simultaneously on several if not most capital markets, implying important losses among the investors. Investments made within various stock markets have an extremely important role within the global economy, influencing people’s lives in many ways. Presently, stock market crashes are being studied with great interest, not only because of the necessity of a deep understanding of the phenomenon, but also because of the fact that these crashes belong to the so-called category of “extreme phenomena”. Those are the main reasons that determined scientists to try building mathematical models for crashes prediction. Such a model was built by Professor Didier Sornette, inspired and adapted from an earthquake detection model. Still, the model keeps many characteristics of its predecessor, not being fully adapted to the economic realities and demands, or to the stock market’s characteristics. This paper attempts to test the utility of the model in predicting Bucharest Stock Exchange’s price falls, as well as the possibility of it being successfully used by investors.

  12. Parameterization of Fire Injection Height in Large Scale Transport Model (United States)

    Paugam, R.; Wooster, M.; Atherton, J.; Val Martin, M.; Freitas, S.; Kaiser, J. W.; Schultz, M. G.


    The parameterization of fire injection height in global chemistry transport model is currently a subject of debate in the atmospheric community. The approach usually proposed in the literature is based on relationships linking injection height and remote sensing products like the Fire Radiative Power (FRP) which can measure active fire properties. In this work we present an approach based on the Plume Rise Model (PRM) developed by Freitas et al (2007, 2010). This plume model is already used in different host models (e.g. WRF, BRAMS). In its original version, the fire is modeled by: a convective heat flux (CHF; pre-defined by the land cover and evaluated as a fixed part of the total heat released) and a plume radius (derived from the GOES Wildfire-ABBA product) which defines the fire extension where the CHF is homogeneously distributed. Here in our approach the Freitas model is modified, in particular we added (i) an equation for mass conservation, (ii) a scheme to parameterize horizontal entrainment/detrainment, and (iii) a new initialization module which estimates the sensible heat released by the fire on the basis of measured FRP rather than fuel cover type. FRP and Active Fire (AF) area necessary for the initialization of the model are directly derived from a modified version of the Dozier algorithm applied to the MOD14 product. An optimization (using the simulating annealing method) of this new version of the PRM is then proposed based on fire plume characteristics derived from the official MISR plume height project and atmospheric profiles extracted from the ECMWF analysis. The data set covers the main fire region (Africa, Siberia, Indonesia, and North and South America) and is set up to (i) retain fires where plume height and FRP can be easily linked (i.e. avoid large fire cluster where individual plume might interact), (ii) keep fire which show decrease of FRP and AF area after MISR overpass (i.e. to minimize effect of the time period needed for the plume to

  13. Research on Fault Prediction of Distribution Network Based on Large Data

    Directory of Open Access Journals (Sweden)

    Jinglong Zhou


    Full Text Available With the continuous development of information technology and the improvement of distribution automation level. Especially, the amount of on-line monitoring and statistical data is increasing, and large data is used data distribution system, describes the technology to collect, data analysis and data processing of the data distribution system. The artificial neural network mining algorithm and the large data are researched in the fault diagnosis and prediction of the distribution network.

  14. Development of a Generic Creep-Fatigue Life Prediction Model (United States)

    Goswami, Tarun


    The objective of this research proposal is to further compile creep-fatigue data of steel alloys and superalloys used in military aircraft engines and/or rocket engines and to develop a statistical multivariate equation. The newly derived model will be a probabilistic fit to all the data compiled from various sources. Attempts will be made to procure the creep-fatigue data from NASA Glenn Research Center and other sources to further develop life prediction models for specific alloy groups. In a previous effort [1-3], a bank of creep-fatigue data has been compiled and tabulated under a range of known test parameters. These test parameters are called independent variables, namely; total strain range, strain rate, hold time, and temperature. The present research attempts to use these variables to develop a multivariate equation, which will be a probabilistic equation fitting a large database. The data predicted by the new model will be analyzed using the normal distribution fits, the closer the predicted lives are with the experimental lives (normal line 1 to 1 fit) the better the prediction. This will be evaluated in terms of a coefficient of correlation, R 2 as well. A multivariate equation developed earlier [3] has the following form, where S, R, T, and H have specific meaning discussed later.

  15. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models. (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F


    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  16. Using a Person-Environment Fit Model to Predict Job Involvement and Organizational Commitment. (United States)

    Blau, Gary J.


    Using a sample of registered nurses (N=228) from a large urban hospital, this longitudinal study tested the applicability of a person-environment fit model for predicting job involvement and organizational commitment. Results indicated the proposed person-environment fit model is useful for predicting job involvement, but not organizational…

  17. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.


    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  18. Mathematical models for indoor radon prediction

    International Nuclear Information System (INIS)

    Malanca, A.; Pessina, V.; Dallara, G.


    It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

  19. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang


    presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...... recovery on the track quality after tamping operation and (5) Tamping machine operation factors. A Danish railway track between Odense and Fredericia with 57.2 km of length is applied for a time period of two to four years in the proposed maintenance model. The total cost can be reduced with up to 50...

  20. An Operational Model for the Prediction of Jet Blast (United States)


    This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...

  1. Forced versus coupled dynamics in Earth system modelling and prediction

    Directory of Open Access Journals (Sweden)

    B. Knopf


    Full Text Available We compare coupled nonlinear climate models and their simplified forced counterparts with respect to predictability and phase space topology. Various types of uncertainty plague climate change simulation, which is, in turn, a crucial element of Earth System modelling. Since the currently preferred strategy for simulating the climate system, or the Earth System at large, is the coupling of sub-system modules (representing, e.g. atmosphere, oceans, global vegetation, this paper explicitly addresses the errors and indeterminacies generated by the coupling procedure. The focus is on a comparison of forced dynamics as opposed to fully, i.e. intrinsically, coupled dynamics. The former represents a particular type of simulation, where the time behaviour of one complex systems component is prescribed by data or some other external information source. Such a simplifying technique is often employed in Earth System models in order to save computing resources, in particular when massive model inter-comparisons need to be carried out. Our contribution to the debate is based on the investigation of two representative model examples, namely (i a low-dimensional coupled atmosphere-ocean simulator, and (ii a replica-like simulator embracing corresponding components.Whereas in general the forced version (ii is able to mimic its fully coupled counterpart (i, we show in this paper that for a considerable fraction of parameter- and state-space, the two approaches qualitatively differ. Here we take up a phenomenon concerning the predictability of coupled versus forced models that was reported earlier in this journal: the observation that the time series of the forced version display artificial predictive skill. We present an explanation in terms of nonlinear dynamical theory. In particular we observe an intermittent version of artificial predictive skill, which we call on-off synchronization, and trace it back to the appearance of unstable periodic orbits. We also

  2. Predicting the effect of fire on large-scale vegetation patterns in North America. (United States)

    Donald McKenzie; David L. Peterson; Ernesto. Alvarado


    Changes in fire regimes are expected across North America in response to anticipated global climatic changes. Potential changes in large-scale vegetation patterns are predicted as a result of altered fire frequencies. A new vegetation classification was developed by condensing Kuchler potential natural vegetation types into aggregated types that are relatively...

  3. Failure analysis and life prediction of a large, complex plate fin heat exchanger

    CSIR Research Space (South Africa)

    Carter, P


    Full Text Available Failure analysis and life prediction of a large, complex fin plate heat exchanger required metallurgical analysis, at the beginning of 1993, inter-stream leaks were found in two aluminium plate fin heat exchangers in parallel operation at a...

  4. Continuous-Discrete Time Prediction-Error Identification Relevant for Linear Model Predictive Control

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay


    A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...... model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model...

  5. Large scale stochastic spatio-temporal modelling with PCRaster

    NARCIS (Netherlands)

    Karssenberg, D.J.; Drost, N.; Schmitz, O.; Jong, K. de; Bierkens, M.F.P.


    PCRaster is a software framework for building spatio-temporal models of land surface processes ( Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations are available to model

  6. An accurate and simple large signal model of HEMT

    DEFF Research Database (Denmark)

    Liu, Qing


    A large-signal model of discrete HEMTs (high-electron-mobility transistors) has been developed. It is simple and suitable for SPICE simulation of hybrid digital ICs. The model parameters are extracted by using computer programs and data provided by the manufacturer. Based on this model, a hybrid...

  7. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    In this thesis, we consider control strategies for flexible distributed energy resources in the future intelligent energy system – the Smart Grid. The energy system is a large-scale complex network with many actors and objectives in different hierarchical layers. Specifically the power system must...... significantly. A Smart Grid calls for flexible consumers that can adjust their consumption based on the amount of green energy in the grid. This requires coordination through new large-scale control and optimization algorithms. Trading of flexibility is key to drive power consumption in a sustainable direction....... In Denmark, we expect that distributed energy resources such as heat pumps, and batteries in electric vehicles will mobilize part of the needed flexibility. Our primary objectives in the thesis were threefold: 1.Simulate the components in the power system based on simple models from literature (e.g. heat...

  8. Data-Driven Modeling and Prediction of Arctic Sea Ice (United States)

    Kondrashov, Dmitri; Chekroun, Mickael; Ghil, Michael


    We present results of data-driven predictive analyses of sea ice over the main Arctic regions. Our approach relies on the Multilayer Stochastic Modeling (MSM) framework of Kondrashov, Chekroun and Ghil [Physica D, 2015] and it leads to probabilistic prognostic models of sea ice concentration (SIC) anomalies on seasonal time scales. This approach is applied to monthly time series of state-of-the-art data-adaptive decompositions of SIC and selected climate variables over the Arctic. We evaluate the predictive skill of MSM models by performing retrospective forecasts with "no-look ahead" for up to 6-months ahead. It will be shown in particular that the memory effects included intrinsically in the formulation of our non-Markovian MSM models allow for improvements of the prediction skill of large-amplitude SIC anomalies in certain Arctic regions on the one hand, and of September Sea Ice Extent, on the other. Further improvements allowed by the MSM framework will adopt a nonlinear formulation and explore next-generation data-adaptive decompositions, namely modification of Principal Oscillation Patterns (POPs) and rotated Multichannel Singular Spectrum Analysis (M-SSA).

  9. Economic decision making and the application of nonparametric prediction models (United States)

    Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.


    Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.

  10. Gene prediction in metagenomic fragments: a large scale machine learning approach. (United States)

    Hoff, Katharina J; Tech, Maike; Lingner, Thomas; Daniel, Rolf; Morgenstern, Burkhard; Meinicke, Peter


    Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Large scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the

  11. Gene prediction in metagenomic fragments: A large scale machine learning approach

    Directory of Open Access Journals (Sweden)

    Morgenstern Burkhard


    Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

  12. Methods for Prediction of Steel Temperature Curve in the Whole Process of a Localized Fire in Large Spaces

    Directory of Open Access Journals (Sweden)

    Zhang Guowei


    Full Text Available Based on a full-scale bookcase fire experiment, a fire development model is proposed for the whole process of localized fires in large-space buildings. We found that for localized fires in large-space buildings full of wooden combustible materials the fire growing phases can be simplified into a t2 fire with a 0.0346 kW/s2 fire growth coefficient. FDS technology is applied to study the smoke temperature curve for a 2 MW to 25 MW fire occurring within a large space with a height of 6 m to 12 m and a building area of 1 500 m2 to 10 000 m2 based on the proposed fire development model. Through the analysis of smoke temperature in various fire scenarios, a new approach is proposed to predict the smoke temperature curve. Meanwhile, a modified model of steel temperature development in localized fire is built. In the modified model, the localized fire source is treated as a point fire source to evaluate the flame net heat flux to steel. The steel temperature curve in the whole process of a localized fire could be accurately predicted by the above findings. These conclusions obtained in this paper could provide valuable reference to fire simulation, hazard assessment, and fire protection design.

  13. Nonconvex Model Predictive Control for Commercial Refrigeration

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Larsen, Lars F.S.; Jørgensen, John Bagterp


    function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimization method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out...... the iterations, which is more than fast enough to run in real-time. We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost...... capacity associated with large penetration of intermittent renewable energy sources in a future smart grid....

  14. Analytical model for local scour prediction around hydrokinetic turbine foundations (United States)

    Musa, M.; Heisel, M.; Hill, C.; Guala, M.


    Marine and Hydrokinetic renewable energy is an emerging sustainable and secure technology which produces clean energy harnessing water currents from mostly tidal and fluvial waterways. Hydrokinetic turbines are typically anchored at the bottom of the channel, which can be erodible or non-erodible. Recent experiments demonstrated the interactions between operating turbines and an erodible surface with sediment transport, resulting in a remarkable localized erosion-deposition pattern significantly larger than those observed by static in-river construction such as bridge piers, etc. Predicting local scour geometry at the base of hydrokinetic devices is extremely important during foundation design, installation, operation, and maintenance (IO&M), and long-term structural integrity. An analytical modeling framework is proposed applying the phenomenological theory of turbulence to the flow structures that promote the scouring process at the base of a turbine. The evolution of scour is directly linked to device operating conditions through the turbine drag force, which is inferred to locally dictate the energy dissipation rate in the scour region. The predictive model is validated using experimental data obtained at the University of Minnesota's St. Anthony Falls Laboratory (SAFL), covering two sediment mobility regimes (clear water and live bed), different turbine designs, hydraulic parameters, grain size distribution and bedform types. The model is applied to a potential prototype scale deployment in the lower Mississippi River, demonstrating its practical relevance and endorsing the feasibility of hydrokinetic energy power plants in large sandy rivers. Multi-turbine deployments are further studied experimentally by monitoring both local and non-local geomorphic effects introduced by a twelve turbine staggered array model installed in a wide channel at SAFL. Local scour behind each turbine is well captured by the theoretical predictive model. However, multi

  15. Regularization modeling for large-eddy simulation of diffusion flames

    NARCIS (Netherlands)

    Geurts, Bernardus J.; Wesseling, P.; Oñate, E.; Périaux, J.

    We analyze the evolution of a diffusion flame in a turbulent mixing layer using large-eddy simulation. The large-eddy simulation includes Leray regularization of the convective transport and approximate inverse filtering to represent the chemical source terms. The Leray model is compared to the more

  16. Predictive modeling: potential application in prevention services. (United States)

    Wilson, Moira L; Tumen, Sarah; Ota, Rissa; Simmers, Anthony G


    In 2012, the New Zealand Government announced a proposal to introduce predictive risk models (PRMs) to help professionals identify and assess children at risk of abuse or neglect as part of a preventive early intervention strategy, subject to further feasibility study and trialing. The purpose of this study is to examine technical feasibility and predictive validity of the proposal, focusing on a PRM that would draw on population-wide linked administrative data to identify newborn children who are at high priority for intensive preventive services. Data analysis was conducted in 2013 based on data collected in 2000-2012. A PRM was developed using data for children born in 2010 and externally validated for children born in 2007, examining outcomes to age 5 years. Performance of the PRM in predicting administratively recorded substantiations of maltreatment was good compared to the performance of other tools reviewed in the literature, both overall, and for indigenous Māori children. Some, but not all, of the children who go on to have recorded substantiations of maltreatment could be identified early using PRMs. PRMs should be considered as a potential complement to, rather than a replacement for, professional judgment. Trials are needed to establish whether risks can be mitigated and PRMs can make a positive contribution to frontline practice, engagement in preventive services, and outcomes for children. Deciding whether to proceed to trial requires balancing a range of considerations, including ethical and privacy risks and the risk of compounding surveillance bias. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  17. Nuclear spectroscopy in large shell model spaces: recent advances

    International Nuclear Information System (INIS)

    Kota, V.K.B.


    Three different approaches are now available for carrying out nuclear spectroscopy studies in large shell model spaces and they are: (i) the conventional shell model diagonalization approach but taking into account new advances in computer technology; (ii) the recently introduced Monte Carlo method for the shell model; (iii) the spectral averaging theory, based on central limit theorems, in indefinitely large shell model spaces. The various principles, recent applications and possibilities of these three methods are described and the similarity between the Monte Carlo method and the spectral averaging theory is emphasized. (author). 28 refs., 1 fig., 5 tabs

  18. Quantitative Prediction of Beef Quality Using Visible and NIR Spectroscopy with Large Data Samples Under Industry Conditions (United States)

    Qiao, T.; Ren, J.; Craigie, C.; Zabalza, J.; Maltin, Ch.; Marshall, S.


    It is well known that the eating quality of beef has a significant influence on the repurchase behavior of consumers. There are several key factors that affect the perception of quality, including color, tenderness, juiciness, and flavor. To support consumer repurchase choices, there is a need for an objective measurement of quality that could be applied to meat prior to its sale. Objective approaches such as offered by spectral technologies may be useful, but the analytical algorithms used remain to be optimized. For visible and near infrared (VISNIR) spectroscopy, Partial Least Squares Regression (PLSR) is a widely used technique for meat related quality modeling and prediction. In this paper, a Support Vector Machine (SVM) based machine learning approach is presented to predict beef eating quality traits. Although SVM has been successfully used in various disciplines, it has not been applied extensively to the analysis of meat quality parameters. To this end, the performance of PLSR and SVM as tools for the analysis of meat tenderness is evaluated, using a large dataset acquired under industrial conditions. The spectral dataset was collected using VISNIR spectroscopy with the wavelength ranging from 350 to 1800 nm on 234 beef M. longissimus thoracis steaks from heifers, steers, and young bulls. As the dimensionality with the VISNIR data is very high (over 1600 spectral bands), the Principal Component Analysis (PCA) technique was applied for feature extraction and data reduction. The extracted principal components (less than 100) were then used for data modeling and prediction. The prediction results showed that SVM has a greater potential to predict beef eating quality than PLSR, especially for the prediction of tenderness. The infl uence of animal gender on beef quality prediction was also investigated, and it was found that beef quality traits were predicted most accurately in beef from young bulls.

  19. Heuristic Modeling for TRMM Lifetime Predictions (United States)

    Jordan, P. S.; Sharer, P. J.; DeFazio, R. L.


    Analysis time for computing the expected mission lifetimes of proposed frequently maneuvering, tightly altitude constrained, Earth orbiting spacecraft have been significantly reduced by means of a heuristic modeling method implemented in a commercial-off-the-shelf spreadsheet product (QuattroPro) running on a personal computer (PC). The method uses a look-up table to estimate the maneuver frequency per month as a function of the spacecraft ballistic coefficient and the solar flux index, then computes the associated fuel use by a simple engine model. Maneuver frequency data points are produced by means of a single 1-month run of traditional mission analysis software for each of the 12 to 25 data points required for the table. As the data point computations are required only a mission design start-up and on the occasion of significant mission redesigns, the dependence on time consuming traditional modeling methods is dramatically reduced. Results to date have agreed with traditional methods to within 1 to 1.5 percent. The spreadsheet approach is applicable to a wide variety of Earth orbiting spacecraft with tight altitude constraints. It will be particularly useful to such missions as the Tropical Rainfall Measurement Mission scheduled for launch in 1997, whose mission lifetime calculations are heavily dependent on frequently revised solar flux predictions.

  20. A dynamic programming approach for quickly estimating large network-based MEV models

    DEFF Research Database (Denmark)

    Mai, Tien; Frejinger, Emma; Fosgerau, Mogens


    by a rooted, directed graph where each node without successor is an alternative. We formulate a family of MEV models as dynamic discrete choice models on graphs of correlation structures and show that the dynamic models are consistent with MEV theory and generalize the network MEV model (Daly and Bierlaire......We propose a way to estimate a family of static Multivariate Extreme Value (MEV) models with large choice sets in short computational time. The resulting model is also straightforward and fast to use for prediction. Following Daly and Bierlaire (2006), the correlation structure is defined...

  1. Which method predicts recidivism best?: A comparison of statistical, machine learning, and data mining predictive models


    Tollenaar, N.; van der Heijden, P.G.M.


    Using criminal population conviction histories of recent offenders, prediction mod els are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining and machine learning provide an improvement in predictive performance over classical statistical methods, namely logistic regression and linear discrim inant analysis. These models are compared ...

  2. Phenological indices of avian reproduction: cryptic shifts and prediction across large spatial and temporal scales. (United States)

    Gullett, Philippa; Hatchwell, Ben J; Robinson, Robert A; Evans, Karl L


    Climate change-induced shifts in phenology have important demographic consequences, and are frequently used to assess species' sensitivity to climate change. Therefore, developing accurate phenological predictions is an important step in modeling species' responses to climate change. The ability of such phenological models to predict effects at larger spatial and temporal scales has rarely been assessed. It is also not clear whether the most frequently used phenological index, namely the average date of a phenological event across a population, adequately captures phenological shifts in the distribution of events across the season. We use the long-tailed tit Aegithalos caudatus (Fig. 1) as a case study to explore these issues. We use an intensive 17-year local study to model mean breeding date and test the capacity of this local model to predict phenology at larger spatial and temporal scales. We assess whether local models of breeding initiation, termination, and renesting reveal phenological shifts and responses to climate not detected by a standard phenological index, that is, population average lay date. These models take predation timing/intensity into account. The locally-derived model performs well at predicting phenology at the national scale over several decades, at both high and low temperatures. In the local model, a trend toward warmer Aprils is associated with a significant advance in termination dates, probably in response to phenological shifts in food supply. This results in a 33% reduction in breeding season length over 17 years - a substantial loss of reproductive opportunity that is not detected by the index of population average lay date. We show that standard phenological indices can fail to detect patterns indicative of negative climatic effects, potentially biasing assessments of species' vulnerability to climate change. More positively, we demonstrate the potential of detailed local studies for developing broader-scale predictive models of

  3. A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers. (United States)

    Oh, Jeeheh; Makar, Maggie; Fusco, Christopher; McCaffrey, Robert; Rao, Krishna; Ryan, Erin E; Washer, Laraine; West, Lauren R; Young, Vincent B; Guttag, John; Hooper, David C; Shenoy, Erica S; Wiens, Jenna


    OBJECTIVE An estimated 293,300 healthcare-associated cases of Clostridium difficile infection (CDI) occur annually in the United States. To date, research has focused on developing risk prediction models for CDI that work well across institutions. However, this one-size-fits-all approach ignores important hospital-specific factors. We focus on a generalizable method for building facility-specific models. We demonstrate the applicability of the approach using electronic health records (EHR) from the University of Michigan Hospitals (UM) and the Massachusetts General Hospital (MGH). METHODS We utilized EHR data from 191,014 adult admissions to UM and 65,718 adult admissions to MGH. We extracted patient demographics, admission details, patient history, and daily hospitalization details, resulting in 4,836 features from patients at UM and 1,837 from patients at MGH. We used L2 regularized logistic regression to learn the models, and we measured the discriminative performance of the models on held-out data from each hospital. RESULTS Using the UM and MGH test data, the models achieved area under the receiver operating characteristic curve (AUROC) values of 0.82 (95% confidence interval [CI], 0.80-0.84) and 0.75 ( 95% CI, 0.73-0.78), respectively. Some predictive factors were shared between the 2 models, but many of the top predictive factors differed between facilities. CONCLUSION A data-driven approach to building models for estimating daily patient risk for CDI was used to build institution-specific models at 2 large hospitals with different patient populations and EHR systems. In contrast to traditional approaches that focus on developing models that apply across hospitals, our generalizable approach yields risk-stratification models tailored to an institution. These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies. Infect Control Hosp Epidemiol 2018;39:425-433.

  4. Prehospital Acute Stroke Severity Scale to Predict Large Artery Occlusion: Design and Comparison With Other Scales. (United States)

    Hastrup, Sidsel; Damgaard, Dorte; Johnsen, Søren Paaske; Andersen, Grethe


    We designed and validated a simple prehospital stroke scale to identify emergent large vessel occlusion (ELVO) in patients with acute ischemic stroke and compared the scale to other published scales for prediction of ELVO. A national historical test cohort of 3127 patients with information on intracranial vessel status (angiography) before reperfusion therapy was identified. National Institutes of Health Stroke Scale (NIHSS) items with the highest predictive value of occlusion of a large intracranial artery were identified, and the most optimal combination meeting predefined criteria to ensure usefulness in the prehospital phase was determined. The predictive performance of Prehospital Acute Stroke Severity (PASS) scale was compared with other published scales for ELVO. The PASS scale was composed of 3 NIHSS scores: level of consciousness (month/age), gaze palsy/deviation, and arm weakness. In derivation of PASS 2/3 of the test cohort was used and showed accuracy (area under the curve) of 0.76 for detecting large arterial occlusion. Optimal cut point ≥2 abnormal scores showed: sensitivity=0.66 (95% CI, 0.62-0.69), specificity=0.83 (0.81-0.85), and area under the curve=0.74 (0.72-0.76). Validation on 1/3 of the test cohort showed similar performance. Patients with a large artery occlusion on angiography with PASS ≥2 had a median NIHSS score of 17 (interquartile range=6) as opposed to PASS <2 with a median NIHSS score of 6 (interquartile range=5). The PASS scale showed equal performance although more simple when compared with other scales predicting ELVO. The PASS scale is simple and has promising accuracy for prediction of ELVO in the field. © 2016 American Heart Association, Inc.

  5. Prediction of Fecal Nitrogen and Fecal Phosphorus Content for Lactating Dairy Cows in Large-scale Dairy Farms

    Directory of Open Access Journals (Sweden)

    QU Qing-bo


    Full Text Available To facilitate efficient and sustainable manure management and reduce potential pollution, it's necessary for precise prediction of fecal nutrient content. The aim of this study is to build prediction models of fecal nitrogen and phosphorus content by the factors of dietary nutrient composition, days in milk, milk yield and body weight of Chinese Holstein lactating dairy cows. 20 kinds of dietary nutrient composition and 60 feces samples were collected from lactating dairy cows from 7 large-scale dairy farms in Tianjin City; The fecal nitrogen and phosphorus content were analyzed. The whole data set was divided into training data set and testing data set. The training data set, including 14 kinds of dietary nutrient composition and 48 feces samples, was used to develop prediction models. The relationship between fecal nitrogen or phosphorus content and dietary nutrient composition was illustrated by means of correlation and regression analysis using SAS software. The results showed that fecal nitrogen(FN content was highly positively correlated with organic matter intake(OMI and crude fat intake(CFi, and correlation coefficients were 0. 836 and 0. 705, respectively. Negative correlation coefficient was found between fecal phosphorus(FP content and body weight(BW, and the correlation coefficient was -0.525. Among different approaches to develop prediction models, the results indicated that determination coefficients of multiple linear regression equations were higher than those of simple linear regression equations. Specially, fecal nitrogen content was excellently predicted by milk yield(MY, days in milk(DIM, organic matter intake(OMI and nitrogen intake(NI, and the model was as follows:y=0.43+0.29×MY+0.02×DIM+0.92×OMI-13.01×NI (R2=0.96. Accordingly, the highest determination coefficient of prediction equation of FP content was 0.62, when body weight(BW, phosphorus intake(PI and nitrogen intake(NI were combined as predictors. The prediction

  6. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim


    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  7. Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables (United States)

    Mortensen, Eric; Wu, Shu; Notaro, Michael; Vavrus, Stephen; Montgomery, Rob; De Piérola, José; Sánchez, Carlos; Block, Paul


    Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

  8. QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles. (United States)

    Liu, Huanxiang; Papa, Ester; Gramatica, Paola


    A large number of environmental chemicals, known as endocrine-disrupting chemicals, are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones, and such chemicals may pose a serious threat to the health of humans and wildlife. They are thought to act through a variety of mechanisms, mainly estrogen-receptor-mediated mechanisms of toxicity. However, it is practically impossible to perform thorough toxicological tests on all potential xenoestrogens, and thus, the quantitative structure--activity relationship (QSAR) provides a promising method for the estimation of a compound's estrogenic activity. Here, QSAR models of the estrogen receptor binding affinity of a large data set of heterogeneous chemicals have been built using theoretical molecular descriptors, giving full consideration to the new OECD principles in regulation for QSAR acceptability, during model construction and assessment. An unambiguous multiple linear regression (MLR) algorithm was used to build the models, and model predictive ability was validated by both internal and external validation. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicate that the proposed QSAR model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.

  9. Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables

    Directory of Open Access Journals (Sweden)

    E. Mortensen


    Full Text Available Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January–March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño–Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit–miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet–dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

  10. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.


    The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...

  11. Comparison of mixed layer models predictions with experimental data

    Energy Technology Data Exchange (ETDEWEB)

    Faggian, P.; Riva, G.M. [CISE Spa, Divisione Ambiente, Segrate (Italy); Brusasca, G. [ENEL Spa, CRAM, Milano (Italy)


    The temporal evolution of the PBL vertical structure for a North Italian rural site, situated within relatively large agricultural fields and almost flat terrain, has been investigated during the period 22-28 June 1993 by experimental and modellistic point of view. In particular, the results about a sunny day (June 22) and a cloudy day (June 25) are presented in this paper. Three schemes to estimate mixing layer depth have been compared, i.e. Holzworth (1967), Carson (1973) and Gryning-Batchvarova models (1990), which use standard meteorological observations. To estimate their degree of accuracy, model outputs were analyzed considering radio-sounding meteorological profiles and stability atmospheric classification criteria. Besides, the mixed layer depths prediction were compared with the estimated values obtained by a simple box model, whose input requires hourly measures of air concentrations and ground flux of {sup 222}Rn. (LN)

  12. Model Predictive Vibration Control Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures

    CERN Document Server

    Takács, Gergely


    Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: ·         the implementation of ...

  13. Modelling and measurements of wakes in large wind farms

    DEFF Research Database (Denmark)

    Barthelmie, Rebecca Jane; Rathmann, Ole; Frandsen, Sten Tronæs


    The paper presents research conducted in the Flow workpackage of the EU funded UPWIND project which focuses on improving models of flow within and downwind of large wind farms in complex terrain and offshore. The main activity is modelling the behaviour of wind turbine wakes in order to improve p...

  14. Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice

    NARCIS (Netherlands)

    Callot, Laurent A.F.; Kock, Anders B.; Medeiros, Marcelo C.


    We consider modeling and forecasting large realized covariance matrices by penalized vector autoregressive models. We consider Lasso-type estimators to reduce the dimensionality and provide strong theoretical guarantees on the forecast capability of our procedure. We show that we can forecast

  15. On the predictiveness of single-field inflationary models (United States)

    Burgess, C. P.; Patil, Subodh P.; Trott, Michael


    We re-examine the predictiveness of single-field inflationary models and discuss how an unknown UV completion can complicate determining inflationary model parameters from observations, even from precision measurements. Besides the usual naturalness issues associated with having a shallow inflationary potential, we describe another issue for inflation, namely, unknown UV physics modifies the running of Standard Model (SM) parameters and thereby introduces uncertainty into the potential inflationary predictions. We illustrate this point using the minimal Higgs Inflationary scenario, which is arguably the most predictive single-field model on the market, because its predictions for A S , r and n s are made using only one new free parameter beyond those measured in particle physics experiments, and run up to the inflationary regime. We find that this issue can already have observable effects. At the same time, this UV-parameter dependence in the Renormalization Group allows Higgs Inflation to occur (in principle) for a slightly larger range of Higgs masses. We comment on the origin of the various UV scales that arise at large field values for the SM Higgs, clarifying cut off scale arguments by further developing the formalism of a non-linear realization of SU L (2) × U(1) in curved space. We discuss the interesting fact that, outside of Higgs Inflation, the effect of a non-minimal coupling to gravity, even in the SM, results in a non-linear EFT for the Higgs sector. Finally, we briefly comment on post BICEP2 attempts to modify the Higgs Inflation scenario.

  16. Large eddy simulation of spanwise rotating turbulent channel flow with dynamic variants of eddy viscosity model (United States)

    Jiang, Zhou; Xia, Zhenhua; Shi, Yipeng; Chen, Shiyi


    A fully developed spanwise rotating turbulent channel flow has been numerically investigated utilizing large-eddy simulation. Our focus is to assess the performances of the dynamic variants of eddy viscosity models, including dynamic Vreman's model (DVM), dynamic wall adapting local eddy viscosity (DWALE) model, dynamic σ (Dσ ) model, and the dynamic volumetric strain-stretching (DVSS) model, in this canonical flow. The results with dynamic Smagorinsky model (DSM) and direct numerical simulations (DNS) are used as references. Our results show that the DVM has a wrong asymptotic behavior in the near wall region, while the other three models can correctly predict it. In the high rotation case, the DWALE can get reliable mean velocity profile, but the turbulence intensities in the wall-normal and spanwise directions show clear deviations from DNS data. DVSS exhibits poor predictions on both the mean velocity profile and turbulence intensities. In all three cases, Dσ performs the best.

  17. In Silico Prediction of Scaffold/Matrix Attachment Regions in Large Genomic Sequences


    Frisch, Matthias; Frech, Kornelie; Klingenhoff, Andreas; Cartharius, Kerstin; Liebich, Ines; Werner, Thomas


    Scaffold/matrix attachment regions (S/MARs) are essential regulatory DNA elements of eukaryotic cells. They are major determinants of locus control of gene expression and can shield gene expression from position effects. Experimental detection of S/MARs requires substantial effort and is not suitable for large-scale screening of genomic sequences. In silico prediction of S/MARs can provide a crucial first selection step to reduce the number of candidates. We used experimentally defined S/MAR ...

  18. Including investment risk in large-scale power market models

    DEFF Research Database (Denmark)

    Lemming, Jørgen Kjærgaard; Meibom, P.


    can be included in large-scale partial equilibrium models of the power market. The analyses are divided into a part about risk measures appropriate for power market investors and a more technical part about the combination of a risk-adjustment model and a partial-equilibrium model. To illustrate...... the analyses quantitatively, a framework based on an iterative interaction between the equilibrium model and a separate risk-adjustment module was constructed. To illustrate the features of the proposed modelling approach we examined how uncertainty in demand and variable costs affects the optimal choice...

  19. Large scale experiments as a tool for numerical model development

    DEFF Research Database (Denmark)

    Kirkegaard, Jens; Hansen, Erik Asp; Fuchs, Jesper


    for improvement of the reliability of physical model results. This paper demonstrates by examples that numerical modelling benefits in various ways from experimental studies (in large and small laboratory facilities). The examples range from very general hydrodynamic descriptions of wave phenomena to specific......Experimental modelling is an important tool for study of hydrodynamic phenomena. The applicability of experiments can be expanded by the use of numerical models and experiments are important for documentation of the validity of numerical tools. In other cases numerical tools can be applied...... hydrodynamic interaction with structures. The examples also show that numerical model development benefits from international co-operation and sharing of high quality results....

  20. Active Exploration of Large 3D Model Repositories. (United States)

    Gao, Lin; Cao, Yan-Pei; Lai, Yu-Kun; Huang, Hao-Zhi; Kobbelt, Leif; Hu, Shi-Min


    With broader availability of large-scale 3D model repositories, the need for efficient and effective exploration becomes more and more urgent. Existing model retrieval techniques do not scale well with the size of the database since often a large number of very similar objects are returned for a query, and the possibilities to refine the search are quite limited. We propose an interactive approach where the user feeds an active learning procedure by labeling either entire models or parts of them as "like" or "dislike" such that the system can automatically update an active set of recommended models. To provide an intuitive user interface, candidate models are presented based on their estimated relevance for the current query. From the methodological point of view, our main contribution is to exploit not only the similarity between a query and the database models but also the similarities among the database models themselves. We achieve this by an offline pre-processing stage, where global and local shape descriptors are computed for each model and a sparse distance metric is derived that can be evaluated efficiently even for very large databases. We demonstrate the effectiveness of our method by interactively exploring a repository containing over 100 K models.

  1. Numerical prediction of unburned carbon levels in large pulverized coal utility boilers

    Energy Technology Data Exchange (ETDEWEB)

    Javier Pallares; Inmaculada Arauzo; Luis Ignacio Diez [University of Zaragoza, Zaragoza (Spain). Centre of Research for Energy Resources and Consumptions (CIRCE), Thermal Division


    Advanced combustion kinetics models are of widespread use to predict carbon losses from coal combustion. However, those models cannot completely capture the complexity of the real phenomena affecting the fluid flow in a full-scale utility boiler, such as burner-to-burner interactions and bottom hopper vortexes or reversed-flows, and usually underpredict carbon in ash values. The use of CFD codes offers a more detailed treatment of the fluid dynamics involved in the boiler. However, most of them do not incorporate advanced kinetics submodels for char oxidation. In this paper, rank-dependent correlations and ash inhibition submodel have been coupled to a commercial CFD code, significantly improving carbon in ash predictions. Results from the simulation of the ASM Brescia power plant (Italy) for three different South-American coals are compared against plant laboratory values, using either the popular single film combustion model or the modified combustion model discussed in this paper. 24 refs., 7 figs., 6 tabs.

  2. Model Predictive Control for an Industrial SAG Mill

    DEFF Research Database (Denmark)

    Ohan, Valeriu; Steinke, Florian; Metzger, Michael


    We discuss Model Predictive Control (MPC) based on ARX models and a simple lower order disturbance model. The advantage of this MPC formulation is that it has few tuning parameters and is based on an ARX prediction model that can readily be identied using standard technologies from system identic...

  3. Recent Advances in Detailed Chemical Kinetic Models for Large Hydrocarbon and Biodiesel Transportation Fuels

    Energy Technology Data Exchange (ETDEWEB)

    Westbrook, C K; Pitz, W J; Curran, H J; Herbinet, O; Mehl, M


    n-Hexadecane and 2,2,4,4,6,8,8-heptamethylnonane represent the primary reference fuels for diesel that are used to determine cetane number, a measure of the ignition property of diesel fuel. With the development of chemical kinetics models for these two primary reference fuels for diesel, a new capability is now available to model diesel fuel ignition. Also, we have developed chemical kinetic models for a whole series of large n-alkanes and a large iso-alkane to represent these chemical classes in fuel surrogates for conventional and future fuels. Methyl decanoate and methyl stearate are large methyl esters that are closely related to biodiesel fuels, and kinetic models for these molecules have also been developed. These chemical kinetic models are used to predict the effect of the fuel molecule size and structure on ignition characteristics under conditions found in internal combustion engines.

  4. Uncertainties in spatially aggregated predictions from a logistic regression model

    NARCIS (Netherlands)

    Horssen, P.W. van; Pebesma, E.J.; Schot, P.P.


    This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The

  5. Dealing with missing predictor values when applying clinical prediction models.

    NARCIS (Netherlands)

    Janssen, K.J.; Vergouwe, Y.; Donders, A.R.T.; Harrell Jr, F.E.; Chen, Q.; Grobbee, D.E.; Moons, K.G.


    BACKGROUND: Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with

  6. Modeling and Simulation Techniques for Large-Scale Communications Modeling

    National Research Council Canada - National Science Library

    Webb, Steve


    .... Tests of random number generators were also developed and applied to CECOM models. It was found that synchronization of random number strings in simulations is easy to implement and can provide significant savings for making comparative studies. If synchronization is in place, then statistical experiment design can be used to provide information on the sensitivity of the output to input parameters. The report concludes with recommendations and an implementation plan.

  7. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen


    Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

  8. Predictive Models of Cognitive Outcomes of Developmental Insults (United States)

    Chan, Yupo; Bouaynaya, Nidhal; Chowdhury, Parimal; Leszczynska, Danuta; Patterson, Tucker A.; Tarasenko, Olga


    Representatives of Arkansas medical, research and educational institutions have gathered over the past four years to discuss the relationship between functional developmental perturbations and their neurological consequences. We wish to track the effect on the nervous system by developmental perturbations over time and across species. Except for perturbations, the sequence of events that occur during neural development was found to be remarkably conserved across mammalian species. The tracking includes consequences on anatomical regions and behavioral changes. The ultimate goal is to develop a predictive model of long-term genotypic and phenotypic outcomes that includes developmental insults. Such a model can subsequently be fostered into an educated intervention for therapeutic purposes. Several datasets were identified to test plausible hypotheses, ranging from evoked potential datasets to sleep-disorder datasets. An initial model may be mathematical and conceptual. However, we expect to see rapid progress as large-scale gene expression studies in the mammalian brain permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions. These genes ultimately control behavior. By using a validated model we endeavor to make useful predictions.

  9. Predictive capabilities of various constitutive models for arterial tissue. (United States)

    Schroeder, Florian; Polzer, Stanislav; Slažanský, Martin; Man, Vojtěch; Skácel, Pavel


    Aim of this study is to validate some constitutive models by assessing their capabilities in describing and predicting uniaxial and biaxial behavior of porcine aortic tissue. 14 samples from porcine aortas were used to perform 2 uniaxial and 5 biaxial tensile tests. Transversal strains were furthermore stored for uniaxial data. The experimental data were fitted by four constitutive models: Holzapfel-Gasser-Ogden model (HGO), model based on generalized structure tensor (GST), Four-Fiber-Family model (FFF) and Microfiber model. Fitting was performed to uniaxial and biaxial data sets separately and descriptive capabilities of the models were compared. Their predictive capabilities were assessed in two ways. Firstly each model was fitted to biaxial data and its accuracy (in term of R 2 and NRMSE) in prediction of both uniaxial responses was evaluated. Then this procedure was performed conversely: each model was fitted to both uniaxial tests and its accuracy in prediction of 5 biaxial responses was observed. Descriptive capabilities of all models were excellent. In predicting uniaxial response from biaxial data, microfiber model was the most accurate while the other models showed also reasonable accuracy. Microfiber and FFF models were capable to reasonably predict biaxial responses from uniaxial data while HGO and GST models failed completely in this task. HGO and GST models are not capable to predict biaxial arterial wall behavior while FFF model is the most robust of the investigated constitutive models. Knowledge of transversal strains in uniaxial tests improves robustness of constitutive models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Optimization of large-scale heterogeneous system-of-systems models.

    Energy Technology Data Exchange (ETDEWEB)

    Parekh, Ojas; Watson, Jean-Paul; Phillips, Cynthia Ann; Siirola, John; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Lee, Herbert K. H. (University of California, Santa Cruz, Santa Cruz, CA); Hart, William Eugene; Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Woodruff, David L. (University of California, Davis, Davis, CA)


    Decision makers increasingly rely on large-scale computational models to simulate and analyze complex man-made systems. For example, computational models of national infrastructures are being used to inform government policy, assess economic and national security risks, evaluate infrastructure interdependencies, and plan for the growth and evolution of infrastructure capabilities. A major challenge for decision makers is the analysis of national-scale models that are composed of interacting systems: effective integration of system models is difficult, there are many parameters to analyze in these systems, and fundamental modeling uncertainties complicate analysis. This project is developing optimization methods to effectively represent and analyze large-scale heterogeneous system of systems (HSoS) models, which have emerged as a promising approach for describing such complex man-made systems. These optimization methods enable decision makers to predict future system behavior, manage system risk, assess tradeoffs between system criteria, and identify critical modeling uncertainties.

  11. Estimation and Inference for Very Large Linear Mixed Effects Models


    Gao, K.; Owen, A. B.


    Linear mixed models with large imbalanced crossed random effects structures pose severe computational problems for maximum likelihood estimation and for Bayesian analysis. The costs can grow as fast as $N^{3/2}$ when there are N observations. Such problems arise in any setting where the underlying factors satisfy a many to many relationship (instead of a nested one) and in electronic commerce applications, the N can be quite large. Methods that do not account for the correlation structure can...

  12. Spectra of operators in large N tensor models (United States)

    Bulycheva, Ksenia; Klebanov, Igor R.; Milekhin, Alexey; Tarnopolsky, Grigory


    We study the operators in the large N tensor models, focusing mostly on the fermionic quantum mechanics with O (N )3 symmetry which may be either global or gauged. In the model with global symmetry, we study the spectra of bilinear operators, which are in either the symmetric traceless or the antisymmetric representation of one of the O (N ) groups. In the symmetric traceless case, the spectrum of scaling dimensions is the same as in the Sachdev-Ye-Kitaev (SYK) model with real fermions; it includes the h =2 zero mode. For the operators antisymmetric in the two indices, the scaling dimensions are the same as in the additional sector found in the complex tensor and SYK models; the lowest h =0 eigenvalue corresponds to the conserved O (N ) charges. A class of singlet operators may be constructed from contracted combinations of m symmetric traceless or antisymmetric two-particle operators. Their two-point functions receive contributions from m melonic ladders. Such multiple ladders are a new phenomenon in the tensor model, which does not seem to be present in the SYK model. The more typical 2 k -particle operators do not receive any ladder corrections and have quantized large N scaling dimensions k /2 . We construct pictorial representations of various singlet operators with low k . For larger k , we use available techniques to count the operators and show that their number grows as 2kk !. As a consequence, the theory has a Hagedorn phase transition at the temperature which approaches zero in the large N limit. We also study the large N spectrum of low-lying operators in the Gurau-Witten model, which has O (N )6 symmetry. We argue that it corresponds to one of the generalized SYK models constructed by Gross and Rosenhaus. Our paper also includes studies of the invariants in large N tensor integrals with various symmetries.

  13. Modelling and transient stability of large wind farms

    DEFF Research Database (Denmark)

    Akhmatov, Vladislav; Knudsen, Hans; Nielsen, Arne Hejde


    by a physical model of grid-connected windmills. The windmill generators ate conventional induction generators and the wind farm is ac-connected to the power system. Improvements-of short-term voltage stability in case of failure events in the external power system are treated with use of conventional generator...... technology. This subject is treated as a parameter study with respect to the windmill electrical and mechanical parameters and with use of control strategies within the conventional generator technology. Stability improvements on the wind farm side of the connection point lead to significant reduction......The paper is dealing-with modelling and short-term Voltage stability considerations of large wind farms. A physical model of a large offshore wind farm consisting of a large number of windmills is implemented in the dynamic simulation tool PSS/E. Each windmill in the wind farm is represented...

  14. The BREAST-V: a unifying predictive formula for volume assessment in small, medium, and large breasts. (United States)

    Longo, Benedetto; Farcomeni, Alessio; Ferri, Germano; Campanale, Antonella; Sorotos, Micheal; Santanelli, Fabio


    Breast volume assessment enhances preoperative planning of both aesthetic and reconstructive procedures, helping the surgeon in the decision-making process of shaping the breast. Numerous methods of breast size determination are currently reported but are limited by methodologic flaws and variable estimations. The authors aimed to develop a unifying predictive formula for volume assessment in small to large breasts based on anthropomorphic values. Ten anthropomorphic breast measurements and direct volumes of 108 mastectomy specimens from 88 women were collected prospectively. The authors performed a multivariate regression to build the optimal model for development of the predictive formula. The final model was then internally validated. A previously published formula was used as a reference. Mean (±SD) breast weight was 527.9 ± 227.6 g (range, 150 to 1250 g). After model selection, sternal notch-to-nipple, inframammary fold-to-nipple, and inframammary fold-to-fold projection distances emerged as the most important predictors. The resulting formula (the BREAST-V) showed an adjusted R of 0.73. The estimated expected absolute error on new breasts is 89.7 g (95 percent CI, 62.4 to 119.1 g) and the expected relative error is 18.4 percent (95 percent CI, 12.9 to 24.3 percent). Application of reference formula on the sample yielded worse predictions than those derived by the formula, showing an R of 0.55. The BREAST-V is a reliable tool for predicting small to large breast volumes accurately for use as a complementary device in surgeon evaluation. An app entitled BREAST-V for both iOS and Android devices is currently available for free download in the Apple App Store and Google Play Store. Diagnostic, II.

  15. Predictive models for moving contact line flows (United States)

    Rame, Enrique; Garoff, Stephen


    Modeling flows with moving contact lines poses the formidable challenge that the usual assumptions of Newtonian fluid and no-slip condition give rise to a well-known singularity. This singularity prevents one from satisfying the contact angle condition to compute the shape of the fluid-fluid interface, a crucial calculation without which design parameters such as the pressure drop needed to move an immiscible 2-fluid system through a solid matrix cannot be evaluated. Some progress has been made for low Capillary number spreading flows. Combining experimental measurements of fluid-fluid interfaces very near the moving contact line with an analytical expression for the interface shape, we can determine a parameter that forms a boundary condition for the macroscopic interface shape when Ca much les than l. This parameter, which plays the role of an "apparent" or macroscopic dynamic contact angle, is shown by the theory to depend on the system geometry through the macroscopic length scale. This theoretically established dependence on geometry allows this parameter to be "transferable" from the geometry of the measurement to any other geometry involving the same material system. Unfortunately this prediction of the theory cannot be tested on Earth.

  16. Modeling the Effect of Climate Change on Large Fire Size, Counts, and Intensities Using the Large Fire Simulator (FSim) (United States)

    Riley, K. L.; Haas, J. R.; Finney, M.; Abatzoglou, J. T.


    Changes in climate can be expected to cause changes in wildfire activity due to a combination of shifts in weather (temperature, precipitation, relative humidity, wind speed and direction) and vegetation. Changes in vegetation could include type conversions, altered forest structure, and shifts in species composition, the effects of which could be mitigated or exacerbated by management activities. Further, changes in suppression response and effectiveness may alter potential wildfire activity, as well as the consequences of wildfire. Feedbacks among these factors are extremely complex and uncertain. The ability to anticipate changes driven by fire weather (largely outside of human control) can lead to development of fire and fuel management strategies aimed at mitigating current and future risk. Therefore, in this study we focus on isolating the effects of climate-induced changes in weather on wildfire activity. Specifically, we investigated the effect of changes in weather on fire activity in the Canadian Rockies ecoregion, which encompasses Glacier National Park and several large wilderness areas to the south. To model the ignition, growth, and containment of wildfires, we used the Large Fire Simulator (FSim), which we coupled with current and projected future climatic conditions. Weather streams were based on data from 14 downscaled Global Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) using the Representative Concentration Pathways (RCP) 45 and 85 for the years 2040-2060. While all GCMs indicate increases in temperature for this area, which would be expected to exacerbate fire activity, precipitation predictions for the summer wildfire season are more variable, ranging from a decrease of approximately 50 mm to an increase of approximately 50 mm. Windspeeds are generally predicted to decrease, which would reduce rates of spread and fire intensity. The net effect of these weather changes on the size, number, and intensity

  17. A hierarchical causal modeling for large industrial plants supervision

    International Nuclear Information System (INIS)

    Dziopa, P.; Leyval, L.


    A supervision system has to analyse the process current state and the way it will evolve after a modification of the inputs or disturbance. It is proposed to base this analysis on a hierarchy of models, witch differ by the number of involved variables and the abstraction level used to describe their temporal evolution. In a first step, special attention is paid to causal models building, from the most abstract one. Once the hierarchy of models has been build, the most detailed model parameters are estimated. Several models of different abstraction levels can be used for on line prediction. These methods have been applied to a nuclear reprocessing plant. The abstraction level could be chosen on line by the operator. Moreover when an abnormal process behaviour is detected a more detailed model is automatically triggered in order to focus the operator attention on the suspected subsystem. (authors). 11 refs., 11 figs

  18. Developmental prediction model for early alcohol initiation in Dutch adolescents

    NARCIS (Netherlands)

    Geels, L.M.; Vink, J.M.; Beijsterveldt, C.E.M. van; Bartels, M.; Boomsma, D.I.


    Objective: Multiple factors predict early alcohol initiation in teenagers. Among these are genetic risk factors, childhood behavioral problems, life events, lifestyle, and family environment. We constructed a developmental prediction model for alcohol initiation below the Dutch legal drinking age

  19. A Review of Hemolysis Prediction Models for Computational Fluid Dynamics. (United States)

    Yu, Hai; Engel, Sebastian; Janiga, Gábor; Thévenin, Dominique


    Flow-induced hemolysis is a crucial issue for many biomedical applications; in particular, it is an essential issue for the development of blood-transporting devices such as left ventricular assist devices, and other types of blood pumps. In order to estimate red blood cell (RBC) damage in blood flows, many models have been proposed in the past. Most models have been validated by their respective authors. However, the accuracy and the validity range of these models remains unclear. In this work, the most established hemolysis models compatible with computational fluid dynamics of full-scale devices are described and assessed by comparing two selected reference experiments: a simple rheometric flow and a more complex hemodialytic flow through a needle. The quantitative comparisons show very large deviations concerning hemolysis predictions, depending on the model and model parameter. In light of the current results, two simple power-law models deliver the best compromise between computational efficiency and obtained accuracy. Finally, hemolysis has been computed in an axial blood pump. The reconstructed geometry of a HeartMate II shows that hemolysis occurs mainly at the tip and leading edge of the rotor blades, as well as at the leading edge of the diffusor vanes. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

  20. Exposing earth surface process model simulations to a large audience (United States)

    Overeem, I.; Kettner, A. J.; Borkowski, L.; Russell, E. L.; Peddicord, H.


    The Community Surface Dynamics Modeling System (CSDMS) represents a diverse group of >1300 scientists who develop and apply numerical models to better understand the Earth's surface. CSDMS has a mandate to make the public more aware of model capabilities and therefore started sharing state-of-the-art surface process modeling results with large audiences. One platform to reach audiences outside the science community is through museum displays on 'Science on a Sphere' (SOS). Developed by NOAA, SOS is a giant globe, linked with computers and multiple projectors and can display data and animations on a sphere. CSDMS has developed and contributed model simulation datasets for the SOS system since 2014, including hydrological processes, coastal processes, and human interactions with the environment. Model simulations of a hydrological and sediment transport model (WBM-SED) illustrate global river discharge patterns. WAVEWATCH III simulations have been specifically processed to show the impacts of hurricanes on ocean waves, with focus on hurricane Katrina and super storm Sandy. A large world dataset of dams built over the last two centuries gives an impression of the profound influence of humans on water management. Given the exposure of SOS, CSDMS aims to contribute at least 2 model datasets a year, and will soon provide displays of global river sediment fluxes and changes of the sea ice free season along the Arctic coast. Over 100 facilities worldwide show these numerical model displays to an estimated 33 million people every year. Datasets storyboards, and teacher follow-up materials associated with the simulations, are developed to address common core science K-12 standards. CSDMS dataset documentation aims to make people aware of the fact that they look at numerical model results, that underlying models have inherent assumptions and simplifications, and that limitations are known. CSDMS contributions aim to familiarize large audiences with the use of numerical

  1. Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.

    Directory of Open Access Journals (Sweden)

    Ivo D Dinov

    Full Text Available A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI. The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data.Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i introduce methods for rebalancing imbalanced cohorts, (ii utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model

  2. Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations. (United States)

    Dinov, Ivo D; Heavner, Ben; Tang, Ming; Glusman, Gustavo; Chard, Kyle; Darcy, Mike; Madduri, Ravi; Pa, Judy; Spino, Cathie; Kesselman, Carl; Foster, Ian; Deutsch, Eric W; Price, Nathan D; Van Horn, John D; Ames, Joseph; Clark, Kristi; Hood, Leroy; Hampstead, Benjamin M; Dauer, William; Toga, Arthur W


    A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches


    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev


    Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

  4. Thermal Storage Power Balancing with Model Predictive Control

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik


    . The total power consumption of all loads is controlled indirectly through a real-time price. The MPC incorporates forecasts of the power production and disturbances that influence the loads, e.g. time-varying weather forecasts, in order to react ahead of time. A simulation scenario demonstrates......The method described in this paper balances power production and consumption with a large number of thermal loads. Linear controllers are used for the loads to track a temperature set point, while Model Predictive Control (MPC) and model estimation of the load behavior are used for coordination...... that the method allows for the integration of flexible thermal loads in a smart energy system in which consumption follows the changing production....

  5. Performance Prediction for Large-Scale Nuclear Waste Repositories: Final Report

    International Nuclear Information System (INIS)

    Glassley, W E; Nitao, J J; Grant, W; Boulos, T N; Gokoffski, M O; Johnson, J W; Kercher, J R; Levatin, J A; Steefel, C I


    The goal of this project was development of a software package capable of utilizing terascale computational platforms for solving subsurface flow and transport problems important for disposal of high level nuclear waste materials, as well as for DOE-complex clean-up and stewardship efforts. We sought to develop a tool that would diminish reliance on abstracted models, and realistically represent the coupling between subsurface fluid flow, thermal effects and chemical reactions that both modify the physical framework of the rock materials and which change the rock mineralogy and chemistry of the migrating fluid. Providing such a capability would enhance realism in models and increase confidence in long-term predictions of performance. Achieving this goal also allows more cost-effective design and execution of monitoring programs needed to evaluate model results. This goal was successfully accomplished through the development of a new simulation tool (NUFT-C). This capability allows high resolution modeling of complex coupled thermal-hydrological-geochemical processes in the saturated and unsaturated zones of the Earth's crust. The code allows consideration of virtually an unlimited number of chemical species and minerals in a multi-phase, non-isothermal environment. Because the code is constructed to utilize the computational power of the tera-scale IBM ASCI computers, simulations that encompass large rock volumes and complex chemical systems can now be done without sacrificing spatial or temporal resolution. The code is capable of doing one-, two-, and three-dimensional simulations, allowing unprecedented evaluation of the evolution of rock properties and mineralogical and chemical change as a function of time. The code has been validated by comparing results of simulations to laboratory-scale experiments, other benchmark codes, field scale experiments, and observations in natural systems. The results of these exercises demonstrate that the physics and chemistry

  6. Disinformative data in large-scale hydrological modelling

    Directory of Open Access Journals (Sweden)

    A. Kauffeldt


    Full Text Available Large-scale hydrological modelling has become an important tool for the study of global and regional water resources, climate impacts, and water-resources management. However, modelling efforts over large spatial domains are fraught with problems of data scarcity, uncertainties and inconsistencies between model forcing and evaluation data. Model-independent methods to screen and analyse data for such problems are needed. This study aimed at identifying data inconsistencies in global datasets using a pre-modelling analysis, inconsistencies that can be disinformative for subsequent modelling. The consistency between (i basin areas for different hydrographic datasets, and (ii between climate data (precipitation and potential evaporation and discharge data, was examined in terms of how well basin areas were represented in the flow networks and the possibility of water-balance closure. It was found that (i most basins could be well represented in both gridded basin delineations and polygon-based ones, but some basins exhibited large area discrepancies between flow-network datasets and archived basin areas, (ii basins exhibiting too-high runoff coefficients were abundant in areas where precipitation data were likely affected by snow undercatch, and (iii the occurrence of basins exhibiting losses exceeding the potential-evaporation limit was strongly dependent on the potential-evaporation data, both in terms of numbers and geographical distribution. Some inconsistencies may be resolved by considering sub-grid variability in climate data, surface-dependent potential-evaporation estimates, etc., but further studies are needed to determine the reasons for the inconsistencies found. Our results emphasise the need for pre-modelling data analysis to identify dataset inconsistencies as an important first step in any large-scale study. Applying data-screening methods before modelling should also increase our chances to draw robust conclusions from subsequent

  7. Coupled SWAT-MODFLOW Model Development for Large Basins (United States)

    Aliyari, F.; Bailey, R. T.; Tasdighi, A.


    Water management in semi-arid river basins requires allocating water resources between urban, industrial, energy, and agricultural sectors, with the latter competing for necessary irrigation water to sustain crop yield. Competition between these sectors will intensify due to changes in climate and population growth. In this study, the recently developed SWAT-MODFLOW coupled hydrologic model is modified for application in a large managed river basin that provides both surface water and groundwater resources for urban and agricultural areas. Specific modifications include the linkage of groundwater pumping and irrigation practices and code changes to allow for the large number of SWAT hydrologic response units (HRU) required for a large river basin. The model is applied to the South Platte River Basin (SPRB), a 56,980 km2 basin in northeastern Colorado dominated by large urban areas along the front range of the Rocky Mountains and agriculture regions to the east. Irregular seasonal and annual precipitation and 150 years of urban and agricultural water management history in the basin provide an ideal test case for the SWAT-MODFLOW model. SWAT handles land surface and soil zone processes whereas MODFLOW handles groundwater flow and all sources and sinks (pumping, injection, bedrock inflow, canal seepage, recharge areas, groundwater/surface water interaction), with recharge and stream stage provided by SWAT. The model is tested against groundwater levels, deep percolation estimates, and stream discharge. The model will be used to quantify spatial groundwater vulnerability in the basin under scenarios of climate change and population growth.

  8. Machine learning models in breast cancer survival prediction. (United States)

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin


    Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

  9. Predictability in models of the atmospheric circulation

    NARCIS (Netherlands)

    Houtekamer, P.L.


    It will be clear from the above discussions that skill forecasts are still in their infancy. Operational skill predictions do not exist. One is still struggling to prove that skill predictions, at any range, have any quality at all. It is not clear what the statistics of the analysis error


    Directory of Open Access Journals (Sweden)

    A. I. Hinojosa

    Full Text Available Abstract In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC, based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.

  11. Large N scalars: From glueballs to dynamical Higgs models (United States)

    Sannino, Francesco


    We construct effective Lagrangians, and corresponding counting schemes, valid to describe the dynamics of the lowest lying large N stable massive composite state emerging in strongly coupled theories. The large N counting rules can now be employed when computing quantum corrections via an effective Lagrangian description. The framework allows for systematic investigations of composite dynamics of a non-Goldstone nature. Relevant examples are the lightest glueball states emerging in any Yang-Mills theory. We further apply the effective approach and associated counting scheme to composite models at the electroweak scale. To illustrate the formalism we consider the possibility that the Higgs emerges as the lightest glueball of a new composite theory; the large N scalar meson in models of dynamical electroweak symmetry breaking; the large N pseudodilaton useful also for models of near-conformal dynamics. For each of these realizations we determine the leading N corrections to the electroweak precision parameters. The results nicely elucidate the underlying large N dynamics and can be used to confront first principle lattice results featuring composite scalars with a systematic effective approach.

  12. Shear wave elastography of thyroid nodules for the prediction of malignancy in a large scale study. (United States)

    Park, Ah Young; Son, Eun Ju; Han, Kyunghwa; Youk, Ji Hyun; Kim, Jeong-Ah; Park, Cheong Soo


    The purpose of this study is to validate the usefulness of shear wave elastography (SWE) in predicting thyroid malignancy with a large-scale quantitative SWE data. This restrospective study included 476 thyroid nodules in 453 patients who underwent gray-scale US and SWE before US-guided fine-needle aspiration biopsy (US-FNA) or surgical excision were included. Gray-scale findings and SWE elasticity indices (EIs) were retrospectively reviewed and compared between benign and malignant thyroid nodules. The optimal cut-off values of EIs for predicting malignancy were determined. The diagnostic performances of gray-scale US and SWE for predicting malignancy were analyzed. The diagnostic performance was compared between the gray-scale US findings only and the combined use of gray-scale US findings with SWEs. All EIs of malignant thyroid nodules were significantly higher than those of benign nodules (p≤.001). The optimal cut-off value of each EI for predicting malignancy was 85.2kPa of Emean, 94.0kPa of Emax, 54.0kPa of Emin. Emean (OR 3.071, p=.005) and Emax (OR 3.015, p=.003) were the independent predictors of thyroid malignancy. Combined use of gray-scale US findings and each EI showed elevated sensitivity (95.0-95.5% vs 92.9%, p≤.005) and AUC (0.820-0.834 vs 0.769, p≤.005) for predicting malignancy, compared with the use of only gray-scale US findings. Quantitative parameters of SWE were the independent predictors of thyroid malignancy and SWE evaluation combined with gray-scale US was adjunctive to the diagnostic performance of gray-scale US for predicting thyroid malignancy. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  13. Required Collaborative Work in Online Courses: A Predictive Modeling Approach (United States)

    Smith, Marlene A.; Kellogg, Deborah L.


    This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…

  14. Models for predicting compressive strength and water absorption of ...

    African Journals Online (AJOL)

    This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...

  15. Analysing earthquake slip models with the spatial prediction comparison test

    KAUST Repository

    Zhang, L.


    Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.

  16. Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

    International Nuclear Information System (INIS)

    Cao Dongsheng; Liu Shao; Xu Qingsong; Lu Hongmei; Huang Jianhua; Hu Qiannan; Liang Yizeng


    Highlights: ► Drug–target interactions are predicted using an extended SAR methodology. ► A drug–target interaction is regarded as an event triggered by many factors. ► Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. ► Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug–target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug–target interactions in a timely manner. In this article, we aim at extending current structure–activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug–target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug–target interactions, and show a general compatibility between the new scheme and current SAR

  17. Application of Logic Models in a Large Scientific Research Program (United States)

    O'Keefe, Christine M.; Head, Richard J.


    It is the purpose of this article to discuss the development and application of a logic model in the context of a large scientific research program within the Commonwealth Scientific and Industrial Research Organisation (CSIRO). CSIRO is Australia's national science agency and is a publicly funded part of Australia's innovation system. It conducts…

  18. Searches for phenomena beyond the Standard Model at the Large ...

    Indian Academy of Sciences (India)

    metry searches at the LHC is thus the channel with large missing transverse momentum and jets of high transverse momentum. No excess above the expected SM background is observed and limits are set on supersymmetric models. Figures 1 and 2 show the limits from ATLAS [11] and CMS [12]. In addition to setting limits ...

  19. A stochastic large deformation model for computational anatomy

    DEFF Research Database (Denmark)

    Arnaudon, Alexis; Holm, Darryl D.; Pai, Akshay Sadananda Uppinakudru


    In the study of shapes of human organs using computational anatomy, variations are found to arise from inter-subject anatomical differences, disease-specific effects, and measurement noise. This paper introduces a stochastic model for incorporating random variations into the Large Deformation...

  20. A numerical shoreline model for shorelines with large curvature

    DEFF Research Database (Denmark)

    Kærgaard, Kasper Hauberg; Fredsøe, Jørgen


    This paper presents a new numerical model for shoreline change which can be used to model the evolution of shorelines with large curvature. The model is based on a one-line formulation in terms of coordinates which follow the shape of the shoreline, instead of the more common approach where the two...... orthogonal horizontal directions are used. The volume error in the sediment continuity equation which is thereby introduced is removed through an iterative procedure. The model treats the shoreline changes by computing the sediment transport in a 2D coastal area model, and then integrating the sediment...... transport field across the coastal profile to obtain the longshore sediment transport variation along the shoreline. The model is used to compute the evolution of a shoreline with a 90° change in shoreline orientation; due to this drastic change in orientation a migrating shoreline spit develops...

  1. Extreme value prediction of the wave-induced vertical bending moment in large container ships

    DEFF Research Database (Denmark)

    Andersen, Ingrid Marie Vincent; Jensen, Jørgen Juncher


    increase the extreme hull girder response significantly. Focus in the present paper is on the influence of the hull girder flexibility on the extreme response amidships, namely the wave-induced vertical bending moment (VBM) in hogging, and the prediction of the extreme value of the same. The analysis...... in the present paper is based on time series of full scale measurements from three large container ships of 8600, 9400 and 14000 TEU. When carrying out the extreme value estimation the peak-over-threshold (POT) method combined with an appropriate extreme value distribution is applied. The choice of a proper...... threshold level as well as the statistical correlation between clustered peaks influence the extreme value prediction and are taken into consideration in the present paper....

  2. Comprehensive model for predicting elemental composition of coal pyrolysis products

    Energy Technology Data Exchange (ETDEWEB)

    Ricahrds, Andrew P. [Brigham Young Univ., Provo, UT (United States); Shutt, Tim [Brigham Young Univ., Provo, UT (United States); Fletcher, Thomas H. [Brigham Young Univ., Provo, UT (United States)


    Large-scale coal combustion simulations depend highly on the accuracy and utility of the physical submodels used to describe the various physical behaviors of the system. Coal combustion simulations depend on the particle physics to predict product compositions, temperatures, energy outputs, and other useful information. The focus of this paper is to improve the accuracy of devolatilization submodels, to be used in conjunction with other particle physics models. Many large simulations today rely on inaccurate assumptions about particle compositions, including that the volatiles that are released during pyrolysis are of the same elemental composition as the char particle. Another common assumption is that the char particle can be approximated by pure carbon. These assumptions will lead to inaccuracies in the overall simulation. There are many factors that influence pyrolysis product composition, including parent coal composition, pyrolysis conditions (including particle temperature history and heating rate), and others. All of these factors are incorporated into the correlations to predict the elemental composition of the major pyrolysis products, including coal tar, char, and light gases.

  3. Multilevel method for modeling large-scale networks.

    Energy Technology Data Exchange (ETDEWEB)

    Safro, I. M. (Mathematics and Computer Science)


    Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

  4. Large scale Bayesian nuclear data evaluation with consistent model defects

    International Nuclear Information System (INIS)

    Schnabel, G


    The aim of nuclear data evaluation is the reliable determination of cross sections and related quantities of the atomic nuclei. To this end, evaluation methods are applied which combine the information of experiments with the results of model calculations. The evaluated observables with their associated uncertainties and correlations are assembled into data sets, which are required for the development of novel nuclear facilities, such as fusion reactors for energy supply, and accelerator driven systems for nuclear waste incineration. The efficiency and safety of such future facilities is dependent on the quality of these data sets and thus also on the reliability of the applied evaluation methods. This work investigated the performance of the majority of available evaluation methods in two scenarios. The study indicated the importance of an essential component in these methods, which is the frequently ignored deficiency of nuclear models. Usually, nuclear models are based on approximations and thus their predictions may deviate from reliable experimental data. As demonstrated in this thesis, the neglect of this possibility in evaluation methods can lead to estimates of observables which are inconsistent with experimental data. Due to this finding, an extension of Bayesian evaluation methods is proposed to take into account the deficiency of the nuclear models. The deficiency is modeled as a random function in terms of a Gaussian process and combined with the model prediction. This novel formulation conserves sum rules and allows to explicitly estimate the magnitude of model deficiency. Both features are missing in available evaluation methods so far. Furthermore, two improvements of existing methods have been developed in the course of this thesis. The first improvement concerns methods relying on Monte Carlo sampling. A Metropolis-Hastings scheme with a specific proposal distribution is suggested, which proved to be more efficient in the studied scenarios than the

  5. Automatic prediction of catalytic residues by modeling residue structural neighborhood

    Directory of Open Access Journals (Sweden)

    Passerini Andrea


    Full Text Available Abstract Background Prediction of catalytic residues is a major step in characterizing the function of enzymes. In its simpler formulation, the problem can be cast into a binary classification task at the residue level, by predicting whether the residue is directly involved in the catalytic process. The task is quite hard also when structural information is available, due to the rather wide range of roles a functional residue can play and to the large imbalance between the number of catalytic and non-catalytic residues. Results We developed an effective representation of structural information by modeling spherical regions around candidate residues, and extracting statistics on the properties of their content such as physico-chemical properties, atomic density, flexibility, presence of water molecules. We trained an SVM classifier combining our features with sequence-based information and previously developed 3D features, and compared its performance with the most recent state-of-the-art approaches on different benchmark datasets. We further analyzed the discriminant power of the information provided by the presence of heterogens in the residue neighborhood. Conclusions Our structure-based method achieves consistent improvements on all tested datasets over both sequence-based and structure-based state-of-the-art approaches. Structural neighborhood information is shown to be responsible for such results, and predicting the presence of nearby heterogens seems to be a promising direction for further improvements.

  6. Predictive modeling of reactive wetting and metal joining.

    Energy Technology Data Exchange (ETDEWEB)

    van Swol, Frank B.


    The performance, reproducibility and reliability of metal joints are complex functions of the detailed history of physical processes involved in their creation. Prediction and control of these processes constitutes an intrinsically challenging multi-physics problem involving heating and melting a metal alloy and reactive wetting. Understanding this process requires coupling strong molecularscale chemistry at the interface with microscopic (diffusion) and macroscopic mass transport (flow) inside the liquid followed by subsequent cooling and solidification of the new metal mixture. The final joint displays compositional heterogeneity and its resulting microstructure largely determines the success or failure of the entire component. At present there exists no computational tool at Sandia that can predict the formation and success of a braze joint, as current capabilities lack the ability to capture surface/interface reactions and their effect on interface properties. This situation precludes us from implementing a proactive strategy to deal with joining problems. Here, we describe what is needed to arrive at a predictive modeling and simulation capability for multicomponent metals with complicated phase diagrams for melting and solidification, incorporating dissolutive and composition-dependent wetting.

  7. Predicting the Feasibility of Correcting Mechanical Axis in Large Varus Deformities With Unicompartmental Knee Arthroplasty. (United States)

    Kleeblad, Laura J; van der List, Jelle P; Pearle, Andrew D; Fragomen, Austin T; Rozbruch, S Robert


    Due to disappointing historical outcomes of unicompartmental knee arthroplasty (UKA), Kozinn and Scott proposed strict selection criteria, including preoperative varus alignment of ≤15°, to improve the outcomes of UKA. No studies to date, however, have assessed the feasibility of correcting large preoperative varus deformities with UKA surgery. The study goals were therefore to (1) assess to what extent patients with large varus deformities could be corrected and (2) determine radiographic parameters to predict sufficient correction. In 200 consecutive robotic-arm assisted medial UKA patients with large preoperative varus deformities (≥7°), the mechanical axis angle (MAA) and joint line convergence angle (JLCA) were measured on hip-knee-ankle radiographs. It was assessed what number of patients were corrected to optimal (≤4°) and acceptable (5°-7°) alignment, and whether the feasibility of this correction could be predicted using an estimated MAA (eMAA, preoperative MAA-JLCA) using regression analyses. Mean preoperative MAA was 10° of varus (range, 7°-18°), JLCA was 5° (1°-12°), postoperative MAA was 4° of varus (-3° to 8°), and correction was 6° (1°-14°). Postoperative optimal alignment was achieved in 62% and acceptable alignment in 36%. The eMAA was a significant predictor for optimal postoperative alignment, when corrected for age and gender (P varus deformities (7°-18°) could be considered candidates for medial UKA, as 98% was corrected to optimal or acceptable alignment, although cautious approach is needed in deformities >15°. Furthermore, it was noted that the feasibility of achieving optimal alignment could be predicted using the preoperative MAA, JLCA, and age. Published by Elsevier Inc.

  8. Regression models for predicting anthropometric measurements of ...

    African Journals Online (AJOL)

    measure anthropometric dimensions to predict difficult-to-measure dimensions required for ergonomic design of school furniture. A total of 143 students aged between 16 and 18 years from eight public secondary schools in Ogbomoso, Nigeria ...


    African Journals Online (AJOL)

    direction (σx) had a maximum value of 375MPa (tensile) and minimum value of ... These results shows that the residual stresses obtained by prediction from the finite element method are in fair agreement with the experimental results.

  10. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara


    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...... visualization to improve our understanding of the different attained performances, effectively compiling all the conducted experiments in a meaningful way. We complete our study with an entropy-based analysis that highlights the uncertainty handling properties provided by the GP, crucial for prediction tasks...

  11. Prediction for Major Adverse Outcomes in Cardiac Surgery: Comparison of Three Prediction Models

    Directory of Open Access Journals (Sweden)

    Cheng-Hung Hsieh


    Conclusion: The Parsonnet score performed as well as the logistic regression models in predicting major adverse outcomes. The Parsonnet score appears to be a very suitable model for clinicians to use in risk stratification of cardiac surgery.

  12. Predictive Modeling of Defibrillation utilizing Hexahedral and Tetrahedral Finite Element Models: Recent Advances (United States)

    Triedman, John K.; Jolley, Matthew; Stinstra, Jeroen; Brooks, Dana H.; MacLeod, Rob


    ICD implants may be complicated by body size and anatomy. One approach to this problem has been the adoption of creative, extracardiac implant strategies using standard ICD components. Because data on safety or efficacy of such ad hoc implant strategies is lacking, we have developed image-based finite element models (FEMs) to compare electric fields and expected defibrillation thresholds (DFTs) using standard and novel electrode locations. In this paper, we review recently published studies by our group using such models, and progress in meshing strategies to improve efficiency and visualization. Our preliminary observations predict that they may be large changes in DFTs with clinically relevant variations of electrode placement. Extracardiac ICDs of various lead configurations are predicted to be effective in both children and adults. This approach may aid both ICD development and patient-specific optimization of electrode placement, but the simplified nature of current models dictates further development and validation prior to clinical or industrial utilization. PMID:18817926

  13. Experimentally derived model to predict permeability behavior of mudstones (United States)

    Schneider, J.; Flemings, P. B.; Day-Stirrat, R.; Germaine, J. T.


    We use uniaxial consolidation experiments to analyze the permeability evolution during consolidation for mudstones with varying composition to develop a predictive permeability model for mudstones. We admixed silt-sized silica to dry, natural Boston Blue Clay (BBC) powder in five different mass ratios. The result is mixtures of silty clay and clayey silt with percentages of clay-sized particles varying between 36 % and 57 %. To recreate natural conditions yet remove variability and soil disturbance, we resedimented all mixtures to a total stress of 100 kPa. We then loaded them to a vertical effective stress of 2.4 MPa in an uniaxial, constant-rate-of-strain consolidation device. We show that vertical permeability increases exponentially with void ratio and decreasing clay content. There is an order of magnitude difference in permeability at a given void ratio for clay contents varying from 36 % to 57 % (by mass). We developed a model that predicts the permeability of silt-clay mixtures based on knowledge of the composition and void ratio alone. The model assumes that flow occurs through the clay-matrix. Thus, the effective permeability is controlled by the void ratio of the clay fraction. At a given stress level, the clay void ratio increases with silt content: large pores are preserved in silty samples due to stress-bridging which does not allow the clay particles to consolidate. Mudstones are important to practical and fundamental programs. They are a key cap rock for subsurface hydrocarbons and geologic storage of CO2. Over the last decade, large amounts of natural gas have been produced from mudstone (shale) gas fields.

  14. Development of a Methodology for Predicting Forest Area for Large-Area Resource Monitoring (United States)

    William H. Cooke


    The U.S. Department of Agriculture, Forest Service, Southcm Research Station, appointed a remote-sensing team to develop an image-processing methodology for mapping forest lands over large geographic areds. The team has presented a repeatable methodology, which is based on regression modeling of Advanced Very High Resolution Radiometer (AVHRR) and Landsat Thematic...

  15. Large deflection of viscoelastic beams using fractional derivative model

    International Nuclear Information System (INIS)

    Bahranini, Seyed Masoud Sotoodeh; Eghtesad, Mohammad; Ghavanloo, Esmaeal; Farid, Mehrdad


    This paper deals with large deflection of viscoelastic beams using a fractional derivative model. For this purpose, a nonlinear finite element formulation of viscoelastic beams in conjunction with the fractional derivative constitutive equations has been developed. The four-parameter fractional derivative model has been used to describe the constitutive equations. The deflected configuration for a uniform beam with different boundary conditions and loads is presented. The effect of the order of fractional derivative on the large deflection of the cantilever viscoelastic beam, is investigated after 10, 100, and 1000 hours. The main contribution of this paper is finite element implementation for nonlinear analysis of viscoelastic fractional model using the storage of both strain and stress histories. The validity of the present analysis is confirmed by comparing the results with those found in the literature.

  16. Engineering Large Animal Species to Model Human Diseases. (United States)

    Rogers, Christopher S


    Animal models are an important resource for studying human diseases. Genetically engineered mice are the most commonly used species and have made significant contributions to our understanding of basic biology, disease mechanisms, and drug development. However, they often fail to recreate important aspects of human diseases and thus can have limited utility as translational research tools. Developing disease models in species more similar to humans may provide a better setting in which to study disease pathogenesis and test new treatments. This unit provides an overview of the history of genetically engineered large animals and the techniques that have made their development possible. Factors to consider when planning a large animal model, including choice of species, type of modification and methodology, characterization, production methods, and regulatory compliance, are also covered. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

  17. From Predictive Models to Instructional Policies (United States)

    Rollinson, Joseph; Brunskill, Emma


    At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…

  18. Zone modelling of the thermal performances of a large-scale bloom reheating furnace

    International Nuclear Information System (INIS)

    Tan, Chee-Keong; Jenkins, Joana; Ward, John; Broughton, Jonathan; Heeley, Andy


    This paper describes the development and comparison of a two- (2D) and three-dimensional (3D) mathematical models, based on the zone method of radiation analysis, to simulate the thermal performances of a large bloom reheating furnace. The modelling approach adopted in the current paper differs from previous work since it takes into account the net radiation interchanges between the top and bottom firing sections of the furnace and also allows for enthalpy exchange due to the flows of combustion products between these sections. The models were initially validated at two different furnace throughput rates using experimental and plant's model data supplied by Tata Steel. The results to-date demonstrated that the model predictions are in good agreement with measured heating profiles of the blooms encountered in the actual furnace. It was also found no significant differences between the predictions from the 2D and 3D models. Following the validation, the 2D model was then used to assess the impact of the furnace responses to changing throughput rate. It was found that the potential furnace response to changing throughput rate influences the settling time of the furnace to the next steady state operation. Overall the current work demonstrates the feasibility and practicality of zone modelling and its potential for incorporation into a model based furnace control system. - Highlights: ► 2D and 3D zone models of large-scale bloom reheating furnace. ► The models were validated with experimental and plant model data. ► Examine the transient furnace response to changing the furnace throughput rates. ► No significant differences found between the predictions from the 2D and 3D models.

  19. GARN2: coarse-grained prediction of 3D structure of large RNA molecules by regret minimization. (United States)

    Boudard, Mélanie; Barth, Dominique; Bernauer, Julie; Denise, Alain; Cohen, Johanne


    Predicting the 3D structure of RNA molecules is a key feature towards predicting their functions. Methods which work at atomic or nucleotide level are not suitable for large molecules. In these cases, coarse-grained prediction methods aim to predict a shape which could be refined later by using more precise methods on smaller parts of the molecule. We developed a complete method for sampling 3D RNA structure at a coarse-grained model, taking a secondary structure as input. One of the novelties of our method is that a second step extracts two best possible structures close to the native, from a set of possible structures. Although our method benefits from the first version of GARN, some of the main features on GARN2 are very different. GARN2 is much faster than the previous version and than the well-known methods of the state-of-art. Our experiments show that GARN2 can also provide better structures than the other state-of-the-art methods. GARN2 is written in Java. It is freely distributed and available at or Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email:

  20. Application of simplified models to CO2 migration and immobilization in large-scale geological systems

    KAUST Repository

    Gasda, Sarah E.


    Long-term stabilization of injected carbon dioxide (CO 2) is an essential component of risk management for geological carbon sequestration operations. However, migration and trapping phenomena are inherently complex, involving processes that act over multiple spatial and temporal scales. One example involves centimeter-scale density instabilities in the dissolved CO 2 region leading to large-scale convective mixing that can be a significant driver for CO 2 dissolution. Another example is the potentially important effect of capillary forces, in addition to buoyancy and viscous forces, on the evolution of mobile CO 2. Local capillary effects lead to a capillary transition zone, or capillary fringe, where both fluids are present in the mobile state. This small-scale effect may have a significant impact on large-scale plume migration as well as long-term residual and dissolution trapping. Computational models that can capture both large and small-scale effects are essential to predict the role of these processes on the long-term storage security of CO 2 sequestration operations. Conventional modeling tools are unable to resolve sufficiently all of these relevant processes when modeling CO 2 migration in large-scale geological systems. Herein, we present a vertically-integrated approach to CO 2 modeling that employs upscaled representations of these subgrid processes. We apply the model to the Johansen formation, a prospective site for sequestration of Norwegian CO 2 emissions, and explore the sensitivity of CO 2 migration and trapping to subscale physics. Model results show the relative importance of different physical processes in large-scale simulations. The ability of models such as this to capture the relevant physical processes at large spatial and temporal scales is important for prediction and analysis of CO 2 storage sites. © 2012 Elsevier Ltd.

  1. Prediction of insulin resistance with anthropometric measures: lessons from a large adolescent population

    Directory of Open Access Journals (Sweden)

    Wedin WK


    Full Text Available William K Wedin,1 Lizmer Diaz-Gimenez,1 Antonio J Convit1,21Department of Psychiatry, NYU School of Medicine, New York, NY, USA; 2Nathan Kline Institute, Orangeburg, NY, USAObjective: The aim of this study was to describe the minimum number of anthropometric measures that will optimally predict insulin resistance (IR and to characterize the utility of these measures among obese and nonobese adolescents.Research design and methods: Six anthropometric measures (selected from three categories: central adiposity, weight, and body composition were measured from 1298 adolescents attending two New York City public high schools. Body composition was determined by bioelectric impedance analysis (BIA. The homeostatic model assessment of IR (HOMA-IR, based on fasting glucose and insulin concentrations, was used to estimate IR. Stepwise linear regression analyses were performed to predict HOMA-IR based on the six selected measures, while controlling for age.Results: The stepwise regression retained both waist circumference (WC and percentage of body fat (BF%. Notably, BMI was not retained. WC was a stronger predictor of HOMA-IR than BMI was. A regression model using solely WC performed best among the obese II group, while a model using solely BF% performed best among the lean group. Receiver operator characteristic curves showed the WC and BF% model to be more sensitive in detecting IR than BMI, but with less specificity.Conclusion: WC combined with BF% was the best predictor of HOMA-IR. This finding can be attributed partly to the ability of BF% to model HOMA-IR among leaner participants and to the ability of WC to model HOMA-IR among participants who are more obese. BMI was comparatively weak in predicting IR, suggesting that assessments that are more comprehensive and include body composition analysis could increase detection of IR during adolescence, especially among those who are lean, yet insulin-resistant.Keywords: BMI, bioelectrical impedance

  2. Ecohydrological modeling for large-scale environmental impact assessment. (United States)

    Woznicki, Sean A; Nejadhashemi, A Pouyan; Abouali, Mohammad; Herman, Matthew R; Esfahanian, Elaheh; Hamaamin, Yaseen A; Zhang, Zhen


    Ecohydrological models are frequently used to assess the biological integrity of unsampled streams. These models vary in complexity and scale, and their utility depends on their final application. Tradeoffs are usually made in model scale, where large-scale models are useful for determining broad impacts of human activities on biological conditions, and regional-scale (e.g. watershed or ecoregion) models provide stakeholders greater detail at the individual stream reach level. Given these tradeoffs, the objective of this study was to develop large-scale stream health models with reach level accuracy similar to regional-scale models thereby allowing for impacts assessments and improved decision-making capabilities. To accomplish this, four measures of biological integrity (Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT), Family Index of Biotic Integrity (FIBI), Hilsenhoff Biotic Index (HBI), and fish Index of Biotic Integrity (IBI)) were modeled based on four thermal classes (cold, cold-transitional, cool, and warm) of streams that broadly dictate the distribution of aquatic biota in Michigan. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow and water quality in seven watersheds and the Hydrologic Index Tool was used to calculate 171 ecologically relevant flow regime variables. Unique variables were selected for each thermal class using a Bayesian variable selection method. The variables were then used in development of adaptive neuro-fuzzy inference systems (ANFIS) models of EPT, FIBI, HBI, and IBI. ANFIS model accuracy improved when accounting for stream thermal class rather than developing a global model. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. Predictive Models for Semiconductor Device Design and Processing (United States)

    Meyyappan, Meyya; Arnold, James O. (Technical Monitor)


    The device feature size continues to be on a downward trend with a simultaneous upward trend in wafer size to 300 mm. Predictive models are needed more than ever before for this reason. At NASA Ames, a Device and Process Modeling effort has been initiated recently with a view to address these issues. Our activities cover sub-micron device physics, process and equipment modeling, computational chemistry and material science. This talk would outline these efforts and emphasize the interaction among various components. The device physics component is largely based on integrating quantum effects into device simulators. We have two parallel efforts, one based on a quantum mechanics approach and the second, a semiclassical hydrodynamics approach with quantum correction terms. Under the first approach, three different quantum simulators are being developed and compared: a nonequlibrium Green's function (NEGF) approach, Wigner function approach, and a density matrix approach. In this talk, results using various codes will be presented. Our process modeling work focuses primarily on epitaxy and etching using first-principles models coupling reactor level and wafer level features. For the latter, we are using a novel approach based on Level Set theory. Sample results from this effort will also be presented.

  4. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang


    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  5. Large time periodic solutions to coupled chemotaxis-fluid models (United States)

    Jin, Chunhua


    In this paper, we deal with the time periodic problem to coupled chemotaxis-fluid models. We prove the existence of large time periodic strong solutions for the full chemotaxis-Navier-Stokes system in spatial dimension N=2, and the existence of large time periodic strong solutions for the chemotaxis-Stokes system in spatial dimension N=3. On the basis of these, the regularity of the solutions can be further improved. More precisely speaking, if the time periodic source g and the potential force \

  6. Simplicity at the cost of predictive accuracy in diffuse large B-cell lymphoma

    DEFF Research Database (Denmark)

    Biccler, Jorne; Eloranta, Sandra; de Nully Brown, Peter


    The international prognostic index (IPI) and similar models form the cornerstone of clinical assessment in newly diagnosed diffuse large B-cell lymphoma (DLBCL). While being simple and convenient to use, their inadequate use of the available clinical data is a major weakness. In this study, we...... compared performance of the International Prognostic Index (IPI) and its variations (R-IPI and NCCN-IPI) to a Cox proportional hazards (CPH) model using the same covariates in nondichotomized form. All models were tested in 4863 newly diagnosed DLBCL patients from population-based Nordic registers. The CPH...

  7. Hybrid Reynolds-Averaged/Large Eddy Simulation of the Flow in a Model SCRamjet Cavity Flameholder (United States)

    Baurle, R. A.


    Steady-state and scale-resolving simulations have been performed for flow in and around a model scramjet combustor flameholder. Experimental data available for this configuration include velocity statistics obtained from particle image velocimetry. Several turbulence models were used for the steady-state Reynolds-averaged simulations which included both linear and non-linear eddy viscosity models. The scale-resolving simulations used a hybrid Reynolds-averaged/large eddy simulation strategy that is designed to be a large eddy simulation everywhere except in the inner portion (log layer and below) of the boundary layer. Hence, this formulation can be regarded as a wall-modeled large eddy simulation. This e ort was undertaken to not only assess the performance of the hybrid Reynolds-averaged / large eddy simulation modeling approach in a flowfield of interest to the scramjet research community, but to also begin to understand how this capability can best be used to augment standard Reynolds-averaged simulations. The numerical errors were quantified for the steady-state simulations, and at least qualitatively assessed for the scale-resolving simulations prior to making any claims of predictive accuracy relative to the measurements. The steady-state Reynolds-averaged results displayed a high degree of variability when comparing the flameholder fuel distributions obtained from each turbulence model. This prompted the consideration of applying the higher-fidelity scale-resolving simulations as a surrogate "truth" model to calibrate the Reynolds-averaged closures in a non-reacting setting prior to their use for the combusting simulations. In general, the Reynolds-averaged velocity profile predictions at the lowest fueling level matched the particle imaging measurements almost as well as was observed for the non-reacting condition. However, the velocity field predictions proved to be more sensitive to the flameholder fueling rate than was indicated in the measurements.

  8. Modelling and Preliminary Prediction of Thermal Balance Test for COMS

    Directory of Open Access Journals (Sweden)

    Hyoung Yoll Jun


    Full Text Available COMS (Communication, Ocean and Meteorological Satellite is a geostationary satellite and developed by KARI for communication, ocean and meteorological observations. It will be tested under vacuum and very low temperature conditions in order to verify thermal design of COMS. The test will be performed by using KARI large thermal vacuum chamber, which was developed by KARI, and the COMS will be the first flight satellite tested in this chamber. The purposes of thermal balance test are to correlate analytical model used for design evaluation and predicting temperatures, and to verify and adjust thermal control concept. KARI has plan to use heating plates to simulate space hot condition especially for radiator panels of satellite such as north and south panels. They will be controlled from 90 K to 273 K by circulating GN2 and LN2 alternatively according to the test phases, while the main shroud of the vacuum chamber will be under constant temperature, 90 K, during all thermal balance test. This paper presents thermal modelling including test chamber, heating plates and the satellite without solar array wing and Ka-band reflectors and discusses temperature prediction during thermal balance test.

  9. Large Animal Models for Foamy Virus Vector Gene Therapy

    Directory of Open Access Journals (Sweden)

    Peter A. Horn


    Full Text Available Foamy virus (FV vectors have shown great promise for hematopoietic stem cell (HSC gene therapy. Their ability to efficiently deliver transgenes to multi-lineage long-term repopulating cells in large animal models suggests they will be effective for several human hematopoietic diseases. Here, we review FV vector studies in large animal models, including the use of FV vectors with the mutant O6-methylguanine-DNA methyltransferase, MGMTP140K to increase the number of genetically modified cells after transplantation. In these studies, FV vectors have mediated efficient gene transfer to polyclonal repopulating cells using short ex vivo transduction protocols designed to minimize the negative effects of ex vivo culture on stem cell engraftment. In this regard, FV vectors appear superior to gammaretroviral vectors, which require longer ex vivo culture to effect efficient transduction. FV vectors have also compared favorably with lentiviral vectors when directly compared in the dog model. FV vectors have corrected leukocyte adhesion deficiency and pyruvate kinase deficiency in the dog large animal model. FV vectors also appear safer than gammaretroviral vectors based on a reduced frequency of integrants near promoters and also near proto-oncogenes in canine repopulating cells. Together, these studies suggest that FV vectors should be highly effective for several human hematopoietic diseases, including those that will require relatively high percentages of gene-modified cells to achieve clinical benefit.

  10. A model to predict the beginning of the pollen season

    DEFF Research Database (Denmark)

    Toldam-Andersen, Torben Bo


    In order to predict the beginning of the pollen season, a model comprising the Utah phenoclirnatography Chill Unit (CU) and ASYMCUR-Growing Degree Hour (GDH) submodels were used to predict the first bloom in Alms, Ulttirrs and Berirln. The model relates environmental temperatures to rest completion...... and bud development. As phenologic parameter 14 years of pollen counts were used. The observed datcs for the beginning of the pollen seasons were defined from the pollen counts and compared with the model prediction. The CU and GDH submodels were used as: 1. A fixed day model, using only the GDH model...... for fruit trees are generally applicable, and give a reasonable description of the growth processes of other trees. This type of model can therefore be of value in predicting the start of the pollen season. The predicted dates were generally within 3-5 days of the observed. Finally the possibility of frost...

  11. Risk prediction model: Statistical and artificial neural network approach (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim


    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  12. Use of the LQ model with large fraction sizes results in underestimation of isoeffect doses

    International Nuclear Information System (INIS)

    Sheu, Tommy; Molkentine, Jessica; Transtrum, Mark K.; Buchholz, Thomas A.; Withers, Hubert Rodney; Thames, Howard D.; Mason, Kathy A.


    Purpose: To test the appropriateness of the linear-quadratic (LQ) model to describe survival of jejunal crypt clonogens after split doses with variable (small 1–6 Gy, large 8–13 Gy) first dose, as a model of its appropriateness for both small and large fraction sizes. Methods: C3Hf/KamLaw mice were exposed to whole body irradiation using 300 kVp X-rays at a dose rate of 1.84 Gy/min, and the number of viable jejunal crypts was determined using the microcolony assay. 14 Gy total dose was split into unequal first and second fractions separated by 4 h. Data were analyzed using the LQ model, the lethal potentially lethal (LPL) model, and a repair-saturation (RS) model. Results: Cell kill was greater in the group receiving the larger fraction first, creating an asymmetry in the plot of survival vs size of first dose, as opposed to the prediction of the LQ model of a symmetric response. There was a significant difference in the estimated βs (higher β after larger first doses), but no significant difference in the αs, when large doses were given first vs small doses first. This difference results in underestimation (based on present data by approximately 8%) of isoeffect doses using LQ model parameters based on small fraction sizes. While the LPL model also predicted a symmetric response inconsistent with the data, the RS model results were consistent with the observed asymmetry. Conclusion: The LQ model underestimates doses for isoeffective crypt-cell survival with large fraction sizes (in the present setting, >9 Gy)

  13. Evaluation of the US Army fallout prediction model

    International Nuclear Information System (INIS)

    Pernick, A.; Levanon, I.


    The US Army fallout prediction method was evaluated against an advanced fallout prediction model--SIMFIC (Simplified Fallout Interpretive Code). The danger zone areas of the US Army method were found to be significantly greater (up to a factor of 8) than the areas of corresponding radiation hazard as predicted by SIMFIC. Nonetheless, because the US Army's method predicts danger zone lengths that are commonly shorter than the corresponding hot line distances of SIMFIC, the US Army's method is not reliably conservative

  14. Comparative Evaluation of Some Crop Yield Prediction Models ...

    African Journals Online (AJOL)

    A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...

  15. Comparative Evaluation of Some Crop Yield Prediction Models ...

    African Journals Online (AJOL)

    (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of cowpea yield-water use and weather data were collected.

  16. Prediction of speech intelligibility based on an auditory preprocessing model

    DEFF Research Database (Denmark)

    Christiansen, Claus Forup Corlin; Pedersen, Michael Syskind; Dau, Torsten


    Classical speech intelligibility models, such as the speech transmission index (STI) and the speech intelligibility index (SII) are based on calculations on the physical acoustic signals. The present study predicts speech intelligibility by combining a psychoacoustically validated model of auditory...

  17. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.


    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.

    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of

  18. Ocean wave prediction using numerical and neural network models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

  19. A Prediction Model of the Capillary Pressure J-Function.

    Directory of Open Access Journals (Sweden)

    W S Xu

    Full Text Available The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative.

  20. Statistical model based gender prediction for targeted NGS clinical panels

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel


    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  1. Penalized Estimation in Large-Scale Generalized Linear Array Models

    DEFF Research Database (Denmark)

    Lund, Adam; Vincent, Martin; Hansen, Niels Richard


    Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension of the para......Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension...... of the parameter vector. A new design matrix free algorithm is proposed for computing the penalized maximum likelihood estimate for GLAMs, which, in particular, handles nondifferentiable penalty functions. The proposed algorithm is implemented and available via the R package glamlasso. It combines several ideas...

  2. Precise MRI-based stereotaxic surgery in large animal models

    DEFF Research Database (Denmark)

    Glud, A. N.; Bech, J.; Tvilling, L.

    and subcortical anatomical differences. NEW METHOD: We present a convenient method to make an MRI-visible skull fiducial for 3D MRI-based stereotaxic procedures in larger experimental animals. Plastic screws were filled with either copper-sulphate solution or MRI-visible paste from a commercially available......BACKGROUND: Stereotaxic neurosurgery in large animals is used widely in different sophisticated models, where precision is becoming more crucial as desired anatomical target regions are becoming smaller. Individually calculated coordinates are necessary in large animal models with cortical...... cranial head marker. The screw fiducials were inserted in the animal skulls and T1 weighted MRI was performed allowing identification of the inserted skull marker. RESULTS: Both types of fiducial markers were clearly visible on the MRÍs. This allows high precision in the stereotaxic space. COMPARISON...

  3. Model for large scale circulation of nuclides in nature, 1

    Energy Technology Data Exchange (ETDEWEB)

    Ohnishi, Teruaki


    A model for large scale circulation of nuclides was developed, and a computer code named COCAIN was made which simulates this circulation system-dynamically. The natural environment considered in the present paper consists of 2 atmospheres, 8 geospheres and 2 lithospheres. The biosphere is composed of 4 types of edible plants, 5 cattles and their products, 4 water biota and 16 human organs. The biosphere is assumed to be given nuclides from the natural environment mentioned above. With the use of COCAIN, two numerical case studies were carried out; the one is the study on nuclear pollution in nature by the radioactive nuclides originating from the past nuclear bomb tests, and the other is the study on the response of environment and biota to the pulse injection of nuclides into one compartment. From the former case study it was verified that this model can well explain the observation and properly simulate the large scale circulation of nuclides in nature.

  4. comparative analysis of two mathematical models for prediction

    African Journals Online (AJOL)

    Abstract. A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data ob- tained from experimental work done in this study. The models used are Scheffes and Osadebes optimization theories to predict the compressive strength of ...

  5. Comparison of predictive models for the early diagnosis of diabetes

    NARCIS (Netherlands)

    M. Jahani (Meysam); M. Mahdavi (Mahdi)


    textabstractObjectives: This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods: We used memetic algorithms to update weights and to improve

  6. Testing and analysis of internal hardwood log defect prediction models (United States)

    R. Edward. Thomas


    The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...

  7. Hidden Markov Model for quantitative prediction of snowfall

    Indian Academy of Sciences (India)

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  8. Bayesian variable order Markov models: Towards Bayesian predictive state representations

    NARCIS (Netherlands)

    Dimitrakakis, C.


    We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more

  9. Demonstrating the improvement of predictive maturity of a computational model

    Energy Technology Data Exchange (ETDEWEB)

    Hemez, Francois M [Los Alamos National Laboratory; Unal, Cetin [Los Alamos National Laboratory; Atamturktur, Huriye S [CLEMSON UNIV.


    We demonstrate an improvement of predictive capability brought to a non-linear material model using a combination of test data, sensitivity analysis, uncertainty quantification, and calibration. A model that captures increasingly complicated phenomena, such as plasticity, temperature and strain rate effects, is analyzed. Predictive maturity is defined, here, as the accuracy of the model to predict multiple Hopkinson bar experiments. A statistical discrepancy quantifies the systematic disagreement (bias) between measurements and predictions. Our hypothesis is that improving the predictive capability of a model should translate into better agreement between measurements and predictions. This agreement, in turn, should lead to a smaller discrepancy. We have recently proposed to use discrepancy and coverage, that is, the extent to which the physical experiments used for calibration populate the regime of applicability of the model, as basis to define a Predictive Maturity Index (PMI). It was shown that predictive maturity could be improved when additional physical tests are made available to increase coverage of the regime of applicability. This contribution illustrates how the PMI changes as 'better' physics are implemented in the model. The application is the non-linear Preston-Tonks-Wallace (PTW) strength model applied to Beryllium metal. We demonstrate that our framework tracks the evolution of maturity of the PTW model. Robustness of the PMI with respect to the selection of coefficients needed in its definition is also studied.

  10. Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

    NARCIS (Netherlands)

    Kayastha, N.


    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of

  11. Refining the committee approach and uncertainty prediction in hydrological modelling

    NARCIS (Netherlands)

    Kayastha, N.


    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of

  12. Wind turbine control and model predictive control for uncertain systems

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz

    as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...

  13. Hidden Markov Model for quantitative prediction of snowfall and ...

    Indian Academy of Sciences (India)

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  14. Model predictive control of a 3-DOF helicopter system using ...

    African Journals Online (AJOL)

    ... by simulation, and its performance is compared with that achieved by linear model predictive control (LMPC). Keywords: nonlinear systems, helicopter dynamics, MIMO systems, model predictive control, successive linearization. International Journal of Engineering, Science and Technology, Vol. 2, No. 10, 2010, pp. 9-19 ...

  15. Models for predicting fuel consumption in sagebrush-dominated ecosystems (United States)

    Clinton S. Wright


    Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentate Nutt.) ecosystems....

  16. Comparative Analysis of Two Mathematical Models for Prediction of ...

    African Journals Online (AJOL)

    A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data obtained from experimental work done in this study. The models used are Scheffe's and Osadebe's optimization theories to predict the compressive strength of sandcrete ...

  17. Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

    KAUST Repository

    Sang, Huiyan


    This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.

  18. A mathematical model for predicting earthquake occurrence ...

    African Journals Online (AJOL)

    We consider the continental crust under damage. We use the observed results of microseism in many seismic stations of the world which was established to study the time series of the activities of the continental crust with a view to predicting possible time of occurrence of earthquake. We consider microseism time series ...

  19. Model for predicting the injury severity score. (United States)

    Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi


    To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P  Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.

  20. Large animal models and new therapies for glycogen storage disease. (United States)

    Brooks, Elizabeth D; Koeberl, Dwight D


    Glycogen storage diseases (GSD), a unique category of inherited metabolic disorders, were first described early in the twentieth century. Since then, the biochemical and genetic bases of these disorders have been determined, and an increasing number of animal models for GSD have become available. At least seven large mammalian models have been developed for laboratory research on GSDs. These models have facilitated the development of new therapies, including gene therapy, which are undergoing clinical translation. For example, gene therapy prolonged survival and prevented hypoglycemia during fasting for greater than one year in dogs with GSD type Ia, and the need for periodic re-administration to maintain efficacy was demonstrated in that dog model. The further development of gene therapy could provide curative therapy for patients with GSD and other inherited metabolic disorders.

  1. Predicting carnivore occurrence with noninvasive surveys and occupancy modeling (United States)

    Long, Robert A.; Donovan, Therese M.; MacKay, Paula; Zielinski, William J.; Buzas, Jeffrey S.


    Terrestrial carnivores typically have large home ranges and exist at low population densities, thus presenting challenges to wildlife researchers. We employed multiple, noninvasive survey methods—scat detection dogs, remote cameras, and hair snares—to collect detection–nondetection data for elusive American black bears (Ursus americanus), fishers (Martes pennanti), and bobcats (Lynx rufus) throughout the rugged Vermont landscape. We analyzed these data using occupancy modeling that explicitly incorporated detectability as well as habitat and landscape variables. For black bears, percentage of forested land within 5 km of survey sites was an important positive predictor of occupancy, and percentage of human developed land within 5 km was a negative predictor. Although the relationship was less clear for bobcats, occupancy appeared positively related to the percentage of both mixed forest and forested wetland habitat within 1 km of survey sites. The relationship between specific covariates and fisher occupancy was unclear, with no specific habitat or landscape variables directly related to occupancy. For all species, we used model averaging to predict occurrence across the study area. Receiver operating characteristic (ROC) analyses of our black bear and fisher models suggested that occupancy modeling efforts with data from noninvasive surveys could be useful for carnivore conservation and management, as they provide insights into habitat use at the regional and landscape scale without requiring capture or direct observation of study species.

  2. Electromagnetic Model Reliably Predicts Radar Scattering Characteristics of Airborne Organisms (United States)

    Mirkovic, Djordje; Stepanian, Phillip M.; Kelly, Jeffrey F.; Chilson, Phillip B.


    The radar scattering characteristics of aerial animals are typically obtained from controlled laboratory measurements of a freshly harvested specimen. These measurements are tedious to perform, difficult to replicate, and typically yield only a small subset of the full azimuthal, elevational, and polarimetric radio scattering data. As an alternative, biological applications of radar often assume that the radar cross sections of flying animals are isotropic, since sophisticated computer models are required to estimate the 3D scattering properties of objects having complex shapes. Using the method of moments implemented in the WIPL-D software package, we show for the first time that such electromagnetic modeling techniques (typically applied to man-made objects) can accurately predict organismal radio scattering characteristics from an anatomical model: here the Brazilian free-tailed bat (Tadarida brasiliensis). The simulated scattering properties of the bat agree with controlled measurements and radar observations made during a field study of bats in flight. This numerical technique can produce the full angular set of quantitative polarimetric scattering characteristics, while eliminating many practical difficulties associated with physical measurements. Such a modeling framework can be applied for bird, bat, and insect species, and will help drive a shift in radar biology from a largely qualitative and phenomenological science toward quantitative estimation of animal densities and taxonomic identification.

  3. Modeling large underground experimental halls for the superconducting super collider

    International Nuclear Information System (INIS)

    Duan, F.; Mrugala, M.


    Geomechanical aspects of the excavation design, and analysis of two large underground experimental halls for the Superconducting Super Collider (SSC), being built in Texas, have been extensively investigated using computer modeling. Each chamber, measuring approximately 350 ft long, 110 ft wide, and 190 ft high, is to be excavated mainly through soft marl and overlying competent limestone. Wall stability is essential not only for ensuring excavation safety but also for meeting strict requirements for chamber stability over the 30-yr design life of the facility. Extensive numerical modeling has played a significant role in the selection of excavation methods, excavation sequence, and rock reinforcement systems. (Author)

  4. A Modeling & Simulation Implementation Framework for Large-Scale Simulation

    Directory of Open Access Journals (Sweden)

    Song Xiao


    Full Text Available Classical High Level Architecture (HLA systems are facing development problems for lack of supporting fine-grained component integration and interoperation in large-scale complex simulation applications. To provide efficient methods of this issue, an extensible, reusable and composable simulation framework is proposed. To promote the reusability from coarse-grained federate to fine-grained components, this paper proposes a modelling & simulation framework which consists of component-based architecture, modelling methods, and simulation services to support and simplify the process of complex simulation application construction. Moreover, a standard process and simulation tools are developed to ensure the rapid and effective development of simulation application.

  5. Simulating large cosmology surveys with calibrated halo models


    Lynn, Stuart


    In this thesis I present a novel method for constructing large scale mock galaxy and halo catalogues and apply this model to a number of important topics in modern cosmology. Traditionally such mocks are created through first evolving a high resolution particle simulation from a set of initial conditions to the present epoch, identifying bound structures and their evolution, and finally applying a semi-analytic prescription for galaxy formation. In contrast to this computatio...

  6. Shear viscosity from a large-Nc NJL model

    Energy Technology Data Exchange (ETDEWEB)

    Lang, Robert; Kaiser, Norbert [TUM Physik Department, Garching (Germany); Weise, Wolfram [ECT, Villa Tambosi, Villazzano (Italy); TUM Physik Department, Garching (Germany)


    We calculate the ratio of shear viscosity to entropy density within a large-N{sub c} Nambu-Jona-Lasinio model. A consistent treatment of the Kubo formalism incorporating the full Dirac structure of the quark self-energy from mesonic fluctuations is presented. We compare our results to common approximation schemes applied to the Kubo formalism and to the quark self-energy.

  7. Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function. (United States)

    Lerman, Caryn; Gu, Hong; Loughead, James; Ruparel, Kosha; Yang, Yihong; Stein, Elliot A


    Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. To test the hypothesis that the strength of coupling among 3 large-scale brain networks--salience, executive control, and default mode--will reflect the state of nicotine withdrawal (vs smoking satiety) and will predict abstinence-induced craving and cognitive deficits and to develop a resource allocation index (RAI) that reflects the combined strength of interactions among the 3 large-scale networks. A within-subject functional magnetic resonance imaging study in an academic medical center compared resting-state functional connectivity coherence strength after 24 hours of abstinence and after smoking satiety. We examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural functions. We included 37 healthy smoking volunteers, aged 19 to 61 years, for analyses. Twenty-four hours of abstinence vs smoking satiety. Inter-network connectivity strength (primary) and the relationship with subjective, behavioral, and neural measures of nicotine withdrawal during abstinence vs smoking satiety states (secondary). The RAI was significantly lower in the abstinent compared with the smoking satiety states (left RAI, P = .002; right RAI, P = .04), suggesting weaker inhibition between the default mode and salience networks. Weaker inter-network connectivity (reduced RAI) predicted abstinence-induced cravings to smoke (r = -0.59; P = .007) and less suppression of default mode activity during performance of a subsequent working memory task (ventromedial prefrontal cortex, r = -0.66, P = .003; posterior cingulate cortex, r = -0.65, P = .001). Alterations in coupling of the salience and default mode networks and the inability to disengage from the default mode network may be critical in cognitive/affective alterations that underlie nicotine dependence.

  8. Improved survival prediction from lung function data in a large population sample

    DEFF Research Database (Denmark)

    Miller, M.R.; Pedersen, O.F.; Lange, P.


    Studies relating tung function to survival commonly express lung function impairment as a percent of predicted but this retains age, height and sex bias. We have studied alternative methods of expressing forced expiratory volume in 1 s (FEV1) for predicting all cause and airway related lung disease...... mortality in the Copenhagen City Heart Study data. Cox regression models were derived for survival over 25 years in 13,900 subjects. Age on entry, sex, smoking status, body mass index, previous myocardial infarction and diabetes were putative predictors together with FEV1 either as raw data, standardised...... of expressing FEV1 impairment best reflects hazard for subsequent death. (C) 2008 Elsevier Ltd. All rights reserved Udgivelsesdato: 2009/3...

  9. Econometric models for predicting confusion crop ratios (United States)

    Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)


    Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

  10. Shear wave elastography of thyroid nodules for the prediction of malignancy in a large scale study

    International Nuclear Information System (INIS)

    Park, Ah Young; Son, Eun Ju; Han, Kyunghwa; Youk, Ji Hyun; Kim, Jeong-Ah; Park, Cheong Soo


    Highlights: •Elasticity indices of malignant thyroid nodules were higher than those of benign. •High elasticity indices were the independent predictors of thyroid malignancy. •SWE evaluation could be useful as adjunctive tool for thyroid cancer diagnosis. -- Abstract: Objectives: The purpose of this study is to validate the usefulness of shear wave elastography (SWE) in predicting thyroid malignancy with a large-scale quantitative SWE data. Methods: This restrospective study included 476 thyroid nodules in 453 patients who underwent gray-scale US and SWE before US-guided fine-needle aspiration biopsy (US-FNA) or surgical excision were included. Gray-scale findings and SWE elasticity indices (EIs) were retrospectively reviewed and compared between benign and malignant thyroid nodules. The optimal cut-off values of EIs for predicting malignancy were determined. The diagnostic performances of gray-scale US and SWE for predicting malignancy were analyzed. The diagnostic performance was compared between the gray-scale US findings only and the combined use of gray-scale US findings with SWEs. Results: All EIs of malignant thyroid nodules were significantly higher than those of benign nodules (p ≤ .001). The optimal cut-off value of each EI for predicting malignancy was 85.2 kPa of E mean , 94.0 kPa of E max , 54.0 kPa of E min . E mean (OR 3.071, p = .005) and E max (OR 3.015, p = .003) were the independent predictors of thyroid malignancy. Combined use of gray-scale US findings and each EI showed elevated sensitivity (95.0–95.5% vs 92.9%, p ≤ .005) and AUC (0.820–0.834 vs 0.769, p ≤ .005) for predicting malignancy, compared with the use of only gray-scale US findings. Conclusions: Quantitative parameters of SWE were the independent predictors of thyroid malignancy and SWE evaluation combined with gray-scale US was adjunctive to the diagnostic performance of gray-scale US for predicting thyroid malignancy

  11. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes (United States)

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki


    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  12. Topological σ Models and Large N Matrix Integral (United States)

    Eguchi, Tohru; Hori, Kentaro; Yang, Sung-Kil

    In this paper we describe in some detail the representation of the topological CP1 model in terms of a matrix integral which we have introduced in a previous article. We first discuss the integrable structure of the CP1 model and show that it is governed by an extension of the one-dimensional Toda hierarchy. We then introduce a matrix model which reproduces the sum over holomorphic maps from arbitrary Riemann surfaces onto CP1. We compute intersection numbers on the moduli space of curves using a geometrical method and show that the results agree with those predicted by the matrix model. We also develop a Landau-Ginzburg (LG) description of the CP1 model using a superpotential eX + et0,Q e-X given by the Lax operator of the Toda hierarchy (X is the LG field and t0,Q is the coupling constant of the Kähler class). The form of the superpotential indicates the close connection between CP1 and N=2 supersymmetric sine-Gordon theory which was noted sometime ago by several authors. We also discuss possible generalizations of our construction to other manifolds and present an LG formulation of the topological CP2 model.

  13. Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

    Directory of Open Access Journals (Sweden)

    Erasmo Cadenas


    Full Text Available Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA. This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX. This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.

  14. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki


    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  15. Large-scale prediction of drug-target interactions using protein sequences and drug topological structures

    Energy Technology Data Exchange (ETDEWEB)

    Cao Dongsheng [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Liu Shao [Xiangya Hospital, Central South University, Changsha 410008 (China); Xu Qingsong [School of Mathematical Sciences and Computing Technology, Central South University, Changsha 410083 (China); Lu Hongmei; Huang Jianhua [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Hu Qiannan [Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071 (China); Liang Yizeng, E-mail: [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China)


    Highlights: Black-Right-Pointing-Pointer Drug-target interactions are predicted using an extended SAR methodology. Black-Right-Pointing-Pointer A drug-target interaction is regarded as an event triggered by many factors. Black-Right-Pointing-Pointer Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. Black-Right-Pointing-Pointer Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug-target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug-target interactions in a timely manner. In this article, we aim at extending current structure-activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug-target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug

  16. Models Predicting Success of Infertility Treatment: A Systematic Review (United States)

    Zarinara, Alireza; Zeraati, Hojjat; Kamali, Koorosh; Mohammad, Kazem; Shahnazari, Parisa; Akhondi, Mohammad Mehdi


    Background: Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn’t simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models. Methods: This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated. Results: Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems. Conclusion: Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples’ needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable. PMID:27141461

  17. Canopy Surface Reconstruction and Tropical Forest Parameters Prediction from Airborne Laser Scanner for Large Forest Area (United States)

    Chen, Z.; Yang, Z.; Chen, Y.; Wang, C.; Qian, J.; Yang, Q.; Chen, X.; Lei, J.


    Canopy height model(CHM) and tree mean height are critical forestry parameters that many other parameters such as growth, carbon sequestration, standing timber volume, and biomass can be derived from. LiDAR is a new method used to rapidly estimate these parameters over large areas. The estimation of these parameters has been derived successfully from CHM. However, a number of challenges limit the accurate retrieval of tree height and crowns, especially in tropical forest area. In this study, an improved canopy estimation model is proposed based on dynamic moving window that applied on LiDAR point cloud data. DEM, DSM and CHM of large tropical forest area can be derived from LiDAR data effectively and efficiently.

  18. Towards a generalized energy prediction model for machine tools. (United States)

    Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan


    Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

  19. The similarity between optimal precursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation

    NARCIS (Netherlands)

    Wang, Q; Mu, M.; Dijkstra, H.A.


    The links between optimal precursor (OPR) and optimally growing initial error (OGIE) in the predictability studies of Kuroshio large meander (LM) are investigated using the Conditional Nonlinear Optimal Perturbation approach within a 1.5-layer shallow-water model. The OPR is a kind of initial

  20. Models of Eucalypt phenology predict bat population flux. (United States)

    Giles, John R; Plowright, Raina K; Eby, Peggy; Peel, Alison J; McCallum, Hamish


    Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt-focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86-93% accuracy. Negative binomial models explained 43-53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt-based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar-based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology

  1. Poisson Mixture Regression Models for Heart Disease Prediction (United States)

    Erol, Hamza


    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  2. Comparison of Predictive Models for the Early Diagnosis of Diabetes. (United States)

    Jahani, Meysam; Mahdavi, Mahdi


    This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.

  3. Simulation of large-scale rule-based models

    Energy Technology Data Exchange (ETDEWEB)

    Hlavacek, William S [Los Alamos National Laboratory; Monnie, Michael I [Los Alamos National Laboratory; Colvin, Joshua [NON LANL; Faseder, James [NON LANL


    Interactions of molecules, such as signaling proteins, with multiple binding sites and/or multiple sites of post-translational covalent modification can be modeled using reaction rules. Rules comprehensively, but implicitly, define the individual chemical species and reactions that molecular interactions can potentially generate. Although rules can be automatically processed to define a biochemical reaction network, the network implied by a set of rules is often too large to generate completely or to simulate using conventional procedures. To address this problem, we present DYNSTOC, a general-purpose tool for simulating rule-based models. DYNSTOC implements a null-event algorithm for simulating chemical reactions in a homogenous reaction compartment. The simulation method does not require that a reaction network be specified explicitly in advance, but rather takes advantage of the availability of the reaction rules in a rule-based specification of a network to determine if a randomly selected set of molecular components participates in a reaction during a time step. DYNSTOC reads reaction rules written in the BioNetGen language which is useful for modeling protein-protein interactions involved in signal transduction. The method of DYNSTOC is closely related to that of STOCHSIM. DYNSTOC differs from STOCHSIM by allowing for model specification in terms of BNGL, which extends the range of protein complexes that can be considered in a model. DYNSTOC enables the simulation of rule-based models that cannot be simulated by conventional methods. We demonstrate the ability of DYNSTOC to simulate models accounting for multisite phosphorylation and multivalent binding processes that are characterized by large numbers of reactions. DYNSTOC is free for non-commercial use. The C source code, supporting documentation and example input files are available at .

  4. Applications of modeling in polymer-property prediction (United States)

    Case, F. H.


    A number of molecular modeling techniques have been applied for the prediction of polymer properties and behavior. Five examples illustrate the range of methodologies used. A simple atomistic simulation of small polymer fragments is used to estimate drug compatibility with a polymer matrix. The analysis of molecular dynamics results from a more complex model of a swollen hydrogel system is used to study gas diffusion in contact lenses. Statistical mechanics are used to predict conformation dependent properties — an example is the prediction of liquid-crystal formation. The effect of the molecular weight distribution on phase separation in polyalkanes is predicted using thermodynamic models. In some cases, the properties of interest cannot be directly predicted using simulation methods or polymer theory. Correlation methods may be used to bridge the gap between molecular structure and macroscopic properties. The final example shows how connectivity-indices-based quantitative structure-property relationships were used to predict properties for candidate polyimids in an electronics application.

  5. Modeling and simulation of large scale stirred tank (United States)

    Neuville, John R.

    agitation of the vessel is adequate to produce a homogenous mixture but not so high that it produces excessive erosion to internal components. The main findings reported by this study were: (1) Careful consideration of the fluid yield stress characteristic is required to make predictions of fluid flow behavior. Laminar Models can predict flow patterns and stagnant regions in the tank until full movement of the flow field occurs. Power Curves and flow patterns were developed for the full scale mixing model to show the differences in expected performance of the mixing process for a broad range of fluids that exhibit Herschel--Bulkley and Bingham Plastic flow behavior. (2) The impeller power demand is independent of the flow model selection for turbulent flow fields in the region of the impeller. The laminar models slightly over predicted the agitator impeller power demand produced by turbulent models. (3) The CFD results show that the power number produced by the mixing system is independent of size. The 40 gallon model produced the same power number results as the 9300 gallon model for the same process conditions. (4) CFD Results show that the Scale-Up of fluid motion in a 40 gallon tank should compare with fluid motion at full scale, 9300 gallons by maintaining constant impeller Tip Speed.

  6. Airflow and radon transport modeling in four large buildings

    International Nuclear Information System (INIS)

    Fan, J.B.; Persily, A.K.


    Computer simulations of multizone airflow and contaminant transport were performed in four large buildings using the program CONTAM88. This paper describes the physical characteristics of the buildings and their idealizations as multizone building airflow systems. These buildings include a twelve-story multifamily residential building, a five-story mechanically ventilated office building with an atrium, a seven-story mechanically ventilated office building with an underground parking garage, and a one-story school building. The air change rates and interzonal airflows of these buildings are predicted for a range of wind speeds, indoor-outdoor temperature differences, and percentages of outdoor air intake in the supply air Simulations of radon transport were also performed in the buildings to investigate the effects of indoor-outdoor temperature difference and wind speed on indoor radon concentrations

  7. Artificial Neural Network Model for Predicting Compressive


    Salim T. Yousif; Salwa M. Abdullah


      Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum...

  8. Architectural Large Constructed Environment. Modeling and Interaction Using Dynamic Simulations (United States)

    Fiamma, P.


    How to use for the architectural design, the simulation coming from a large size data model? The topic is related to the phase coming usually after the acquisition of the data, during the construction of the model and especially after, when designers must have an interaction with the simulation, in order to develop and verify their idea. In the case of study, the concept of interaction includes the concept of real time "flows". The work develops contents and results that can be part of the large debate about the current connection between "architecture" and "movement". The focus of the work, is to realize a collaborative and participative virtual environment on which different specialist actors, client and final users can share knowledge, targets and constraints to better gain the aimed result. The goal is to have used a dynamic micro simulation digital resource that allows all the actors to explore the model in powerful and realistic way and to have a new type of interaction in a complex architectural scenario. On the one hand, the work represents a base of knowledge that can be implemented more and more; on the other hand the work represents a dealt to understand the large constructed architecture simulation as a way of life, a way of being in time and space. The architectural design before, and the architectural fact after, both happen in a sort of "Spatial Analysis System". The way is open to offer to this "system", knowledge and theories, that can support architectural design work for every application and scale. We think that the presented work represents a dealt to understand the large constructed architecture simulation as a way of life, a way of being in time and space. Architecture like a spatial configuration, that can be reconfigurable too through designing.


    Directory of Open Access Journals (Sweden)

    P. Fiamma


    Full Text Available How to use for the architectural design, the simulation coming from a large size data model? The topic is related to the phase coming usually after the acquisition of the data, during the construction of the model and especially after, when designers must have an interaction with the simulation, in order to develop and verify their idea. In the case of study, the concept of interaction includes the concept of real time "flows". The work develops contents and results that can be part of the large debate about the current connection between "architecture" and "movement". The focus of the work, is to realize a collaborative and participative virtual environment on which different specialist actors, client and final users can share knowledge, targets and constraints to better gain the aimed result. The goal is to have used a dynamic micro simulation digital resource that allows all the actors to explore the model in powerful and realistic way and to have a new type of interaction in a complex architectural scenario. On the one hand, the work represents a base of knowledge that can be implemented more and more; on the other hand the work represents a dealt to understand the large constructed architecture simulation as a way of life, a way of being in time and space. The architectural design before, and the architectural fact after, both happen in a sort of "Spatial Analysis System". The way is open to offer to this "system", knowledge and theories, that can support architectural design work for every application and scale. We think that the presented work represents a dealt to understand the large constructed architecture simulation as a way of life, a way of being in time and space. Architecture like a spatial configuration, that can be reconfigurable too through designing.

  10. Prediction of hourly solar radiation with multi-model framework

    International Nuclear Information System (INIS)

    Wu, Ji; Chan, Chee Keong


    Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

  11. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

    Directory of Open Access Journals (Sweden)

    Xiaolei Ma


    Full Text Available This paper proposes a convolutional neural network (CNN-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

  12. Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models (United States)

    Plant, Nathaniel G.; Holland, K. Todd


    Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.

  13. Uncertainty Quantification for Large-Scale Ice Sheet Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Ghattas, Omar [Univ. of Texas, Austin, TX (United States)


    This report summarizes our work to develop advanced forward and inverse solvers and uncertainty quantification capabilities for a nonlinear 3D full Stokes continental-scale ice sheet flow model. The components include: (1) forward solver: a new state-of-the-art parallel adaptive scalable high-order-accurate mass-conservative Newton-based 3D nonlinear full Stokes ice sheet flow simulator; (2) inverse solver: a new adjoint-based inexact Newton method for solution of deterministic inverse problems governed by the above 3D nonlinear full Stokes ice flow model; and (3) uncertainty quantification: a novel Hessian-based Bayesian method for quantifying uncertainties in the inverse ice sheet flow solution and propagating them forward into predictions of quantities of interest such as ice mass flux to the ocean.

  14. Predictive modeling of coupled multi-physics systems: II. Illustrative application to reactor physics

    International Nuclear Information System (INIS)

    Cacuci, Dan Gabriel; Badea, Madalina Corina


    Highlights: • We applied the PMCMPS methodology to a paradigm neutron diffusion model. • We underscore the main steps in applying PMCMPS to treat very large coupled systems. • PMCMPS reduces the uncertainties in the optimally predicted responses and model parameters. • PMCMPS is for sequentially treating coupled systems that cannot be treated simultaneously. - Abstract: This work presents paradigm applications to reactor physics of the innovative mathematical methodology for “predictive modeling of coupled multi-physics systems (PMCMPS)” developed by Cacuci (2014). This methodology enables the assimilation of experimental and computational information and computes optimally predicted responses and model parameters with reduced predicted uncertainties, taking fully into account the coupling terms between the multi-physics systems, but using only the computational resources that would be needed to perform predictive modeling on each system separately. The paradigm examples presented in this work are based on a simple neutron diffusion model, chosen so as to enable closed-form solutions with clear physical interpretations. These paradigm examples also illustrate the computational efficiency of the PMCMPS, which enables the assimilation of additional experimental information, with a minimal increase in computational resources, to reduce the uncertainties in predicted responses and best-estimate values for uncertain model parameters, thus illustrating how very large systems can be treated without loss of information in a sequential rather than simultaneous manner

  15. Posterior Predictive Model Checking for Multidimensionality in Item Response Theory (United States)

    Levy, Roy; Mislevy, Robert J.; Sinharay, Sandip


    If data exhibit multidimensionality, key conditional independence assumptions of unidimensional models do not hold. The current work pursues posterior predictive model checking, a flexible family of model-checking procedures, as a tool for criticizing models due to unaccounted for dimensions in the context of item response theory. Factors…

  16. Model predictive control of a crude oil distillation column

    Directory of Open Access Journals (Sweden)

    Morten Hovd


    Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.

  17. Improvements on GPS Location Cluster Analysis for the Prediction of Large Carnivore Feeding Activities: Ground-Truth Detection Probability and Inclusion of Activity Sensor Measures.

    Directory of Open Access Journals (Sweden)

    Kevin A Blecha

    Full Text Available Animal space use studies using GPS collar technology are increasingly incorporating behavior based analysis of spatio-temporal data in order to expand inferences of resource use. GPS location cluster analysis is one such technique applied to large carnivores to identify the timing and location of feeding events. For logistical and financial reasons, researchers often implement predictive models for identifying these events. We present two separate improvements for predictive models that future practitioners can implement. Thus far, feeding prediction models have incorporated a small range of covariates, usually limited to spatio-temporal characteristics of the GPS data. Using GPS collared cougar (Puma concolor we include activity sensor data as an additional covariate to increase prediction performance of feeding presence/absence. Integral to the predictive modeling of feeding events is a ground-truthing component, in which GPS location clusters are visited by human observers to confirm the presence or absence of feeding remains. Failing to account for sources of ground-truthing false-absences can bias the number of predicted feeding events to be low. Thus we account for some ground-truthing error sources directly in the model with covariates and when applying model predictions. Accounting for these errors resulted in a 10% increase in the number of clusters predicted to be feeding events. Using a double-observer design, we show that the ground-truthing false-absence rate is relatively low (4% using a search delay of 2-60 days. Overall, we provide two separate improvements to the GPS cluster analysis techniques that can be expanded upon and implemented in future studies interested in identifying feeding behaviors of large carnivores.

  18. Predictive Models, How good are they?

    DEFF Research Database (Denmark)

    Kasch, Helge

    role of pre-existing genetic risk factors, and the roles of underreported pre-existing pain and both the long-term follow up studies on whiplash injury, but also recently applied in a large (DANFUND) population study of more than 10,000 previously healthy subjects, now with more than 2-year follow...

  19. Enhancing Flood Prediction Reliability Using Bayesian Model Averaging (United States)

    Liu, Z.; Merwade, V.


    Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.

  20. Modelling and Measuring Flow and Wind Turbine Wakes in Large Wind Farms Offshore

    DEFF Research Database (Denmark)

    Barthelmie, Rebecca Jane; Hansen, Kurt Schaldemose; Frandsen, Sten Tronæs


    Average power losses due to wind turbine wakes are of the order of 10 to 20% of total power output in large offshore wind farms. Accurately quantifying power losses due to wakes is, therefore, an important part of overall wind farm economics. The focus of this research is to compare different types...... power losses due to wakes and loads. The research presented is part of the EC-funded UpWind project, which aims to radically improve wind turbine and wind farm models in order to continue to improve the costs of wind energy. Reducing wake losses, or even reduce uncertainties in predicting power losses...... from wakes, contributes to the overall goal of reduced costs. Here, we assess the state of the art in wake and flow modelling for offshore wind forms, the focus so for has been cases at the Horns Rev wind form, which indicate that wind form models require modification to reduce under-prediction of wake...