Sample records for surface model predictions

  1. 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.

  2. Predictive model for ice formation on superhydrophobic surfaces. (United States)

    Bahadur, Vaibhav; Mishchenko, Lidiya; Hatton, Benjamin; Taylor, J Ashley; Aizenberg, Joanna; Krupenkin, Tom


    The prevention and control of ice accumulation has important applications in aviation, building construction, and energy conversion devices. One area of active research concerns the use of superhydrophobic surfaces for preventing ice formation. The present work develops a physics-based modeling framework to predict ice formation on cooled superhydrophobic surfaces resulting from the impact of supercooled water droplets. This modeling approach analyzes the multiple phenomena influencing ice formation on superhydrophobic surfaces through the development of submodels describing droplet impact dynamics, heat transfer, and heterogeneous ice nucleation. These models are then integrated together to achieve a comprehensive understanding of ice formation upon impact of liquid droplets at freezing conditions. The accuracy of this model is validated by its successful prediction of the experimental findings that demonstrate that superhydrophobic surfaces can fully prevent the freezing of impacting water droplets down to surface temperatures of as low as -20 to -25 °C. The model can be used to study the influence of surface morphology, surface chemistry, and fluid and thermal properties on dynamic ice formation and identify parameters critical to achieving icephobic surfaces. The framework of the present work is the first detailed modeling tool developed for the design and analysis of surfaces for various ice prevention/reduction strategies. © 2011 American Chemical Society

  3. Improvement of a land surface model for accurate prediction of surface energy and water balances

    International Nuclear Information System (INIS)

    Katata, Genki


    In order to predict energy and water balances between the biosphere and atmosphere accurately, sophisticated schemes to calculate evaporation and adsorption processes in the soil and cloud (fog) water deposition on vegetation were implemented in the one-dimensional atmosphere-soil-vegetation model including CO 2 exchange process (SOLVEG2). Performance tests in arid areas showed that the above schemes have a significant effect on surface energy and water balances. The framework of the above schemes incorporated in the SOLVEG2 and instruction for running the model are documented. With further modifications of the model to implement the carbon exchanges between the vegetation and soil, deposition processes of materials on the land surface, vegetation stress-growth-dynamics etc., the model is suited to evaluate an effect of environmental loads to ecosystems by atmospheric pollutants and radioactive substances under climate changes such as global warming and drought. (author)

  4. Models for prediction of global solar radiation on horizontal surface ...

    African Journals Online (AJOL)

    The estimation of global solar radiation continues to play a fundamental role in solar engineering systems and applications. This paper compares various models for estimating the average monthly global solar radiation on horizontal surface for Akure, Nigeria, using solar radiation and sunshine duration data covering years ...

  5. Improved Modeling and Prediction of Surface Wave Amplitudes (United States)


    data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented... advantages of the membrane surface wave technique are that 1) it is orders of magnitude faster than 3-dimensional finite-difference; and 2) it...0.5 km depth. Although the CMT sources should more accurately reproduce the observed signals from each event, they have two disadvantages : 1) in the

  6. Surface Complexation Modeling in Variable Charge Soils: Prediction of Cadmium Adsorption

    Directory of Open Access Journals (Sweden)

    Giuliano Marchi


    Full Text Available ABSTRACT Intrinsic equilibrium constants for 22 representative Brazilian Oxisols were estimated from a cadmium adsorption experiment. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. Intrinsic equilibrium constants were optimized by FITEQL and by hand calculation using Visual MINTEQ in sweep mode, and Excel spreadsheets. Data from both models were incorporated into Visual MINTEQ. Constants estimated by FITEQL and incorporated in Visual MINTEQ software failed to predict observed data accurately. However, FITEQL raw output data rendered good results when predicted values were directly compared with observed values, instead of incorporating the estimated constants into Visual MINTEQ. Intrinsic equilibrium constants optimized by hand calculation and incorporated in Visual MINTEQ reliably predicted Cd adsorption reactions on soil surfaces under changing environmental conditions.

  7. An Analytical Model for Prediction of Magnetic Flux Leakage from Surface Defects in Ferromagnetic Tubes

    Directory of Open Access Journals (Sweden)

    Suresh V.


    Full Text Available In this paper, an analytical model is proposed to predict magnetic flux leakage (MFL signals from the surface defects in ferromagnetic tubes. The analytical expression consists of elliptic integrals of first kind based on the magnetic dipole model. The radial (Bz component of leakage fields is computed from the cylindrical holes in ferromagnetic tubes. The effectiveness of the model has been studied by analyzing MFL signals as a function of the defect parameters and lift-off. The model predicted results are verified with experimental results and a good agreement is observed between the analytical and the experimental results. This analytical expression could be used for quick prediction of MFL signals and also input data for defect reconstructions in inverse MFL problem.

  8. Assessing Confidence in Pliocene Sea Surface Temperatures to Evaluate Predictive Models (United States)

    Dowsett, Harry J.; Robinson, Marci M.; Haywood, Alan M.; Hill, Daniel J.; Dolan, Aisling. M.; Chan, Wing-Le; Abe-Ouchi, Ayako; Chandler, Mark A.; Rosenbloom, Nan A.; Otto-Bliesner, Bette L.; hide


    In light of mounting empirical evidence that planetary warming is well underway, the climate research community looks to palaeoclimate research for a ground-truthing measure with which to test the accuracy of future climate simulations. Model experiments that attempt to simulate climates of the past serve to identify both similarities and differences between two climate states and, when compared with simulations run by other models and with geological data, to identify model-specific biases. Uncertainties associated with both the data and the models must be considered in such an exercise. The most recent period of sustained global warmth similar to what is projected for the near future occurred about 3.33.0 million years ago, during the Pliocene epoch. Here, we present Pliocene sea surface temperature data, newly characterized in terms of level of confidence, along with initial experimental results from four climate models. We conclude that, in terms of sea surface temperature, models are in good agreement with estimates of Pliocene sea surface temperature in most regions except the North Atlantic. Our analysis indicates that the discrepancy between the Pliocene proxy data and model simulations in the mid-latitudes of the North Atlantic, where models underestimate warming shown by our highest-confidence data, may provide a new perspective and insight into the predictive abilities of these models in simulating a past warm interval in Earth history.This is important because the Pliocene has a number of parallels to present predictions of late twenty-first century climate.

  9. Assessing confidence in Pliocene sea surface temperatures to evaluate predictive models (United States)

    Dowsett, Harry J.; Robinson, Marci M.; Haywood, Alan M.; Hill, Daniel J.; Dolan, Aisling M.; Stoll, Danielle K.; Chan, Wing-Le; Abe-Ouchi, Ayako; Chandler, Mark A.; Rosenbloom, Nan A.; Otto-Bliesner, Bette L.; Bragg, Fran J.; Lunt, Daniel J.; Foley, Kevin M.; Riesselman, Christina R.


    In light of mounting empirical evidence that planetary warming is well underway, the climate research community looks to palaeoclimate research for a ground-truthing measure with which to test the accuracy of future climate simulations. Model experiments that attempt to simulate climates of the past serve to identify both similarities and differences between two climate states and, when compared with simulations run by other models and with geological data, to identify model-specific biases. Uncertainties associated with both the data and the models must be considered in such an exercise. The most recent period of sustained global warmth similar to what is projected for the near future occurred about 3.3–3.0 million years ago, during the Pliocene epoch. Here, we present Pliocene sea surface temperature data, newly characterized in terms of level of confidence, along with initial experimental results from four climate models. We conclude that, in terms of sea surface temperature, models are in good agreement with estimates of Pliocene sea surface temperature in most regions except the North Atlantic. Our analysis indicates that the discrepancy between the Pliocene proxy data and model simulations in the mid-latitudes of the North Atlantic, where models underestimate warming shown by our highest-confidence data, may provide a new perspective and insight into the predictive abilities of these models in simulating a past warm interval in Earth history. This is important because the Pliocene has a number of parallels to present predictions of late twenty-first century climate.

  10. Evaluation of a Regression Prediction Model for Surface Roughness of Wood-Polyethylene Composite (wpc) (United States)

    Shi, Wenyong; Ma, Yan; Yang, Chunmei; Jiang, Bin; Li, Zhe

    Milling processing is an important way to obtain wood-polyethylene composite (WPC) end products. In order to improve the processing efficiency and surface quality of WPC and meet the practical application requirements, this paper focussed on morphology and roughness of the WPC-milled surface and studied surface quality changes under different cutting parameters and milling methods through multi-parameters milling experiments. The milling surface morphology and roughness of WPC were analyzed and measured during cut-in, cutting and cut-out sections. It also revealed the affect rule of different cutting parameters and milling methods on milled surface morphology and roughness. The results show that the milling surface roughness of WPC products with wood powder content of 70% is significantly larger than the one whose wood powder content is 60%, and defects such as holes are also relatively more. Finally, a surface roughness prediction model was established based on the mathematical regression method and its multi-factor simulation was carried out. A comparative analysis of predictive and experimental values was performed to verify the reliability of the model. It could also provide theoretical guidance and technical guarantee for high processing quality of WPC milling and cutting.

  11. Improving weather predictability by including land-surface model parameter uncertainty (United States)

    Orth, Rene; Dutra, Emanuel; Pappenberger, Florian


    The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogenous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF's land-surface model HTESSEL we present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. We select 6 poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally we investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs we find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. We demonstrate the robustness of our findings by comparing multiple best performing parameter sets and multiple randomly chosen parameter sets. We find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, we construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system. Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by

  12. 3D transient model to predict temperature and ablated areas during laser processing of metallic surfaces

    Directory of Open Access Journals (Sweden)

    Babak. B. Naghshine


    Full Text Available Laser processing is one of the most popular small-scale patterning methods and has many applications in semiconductor device fabrication and biomedical engineering. Numerical modelling of this process can be used for better understanding of the process, optimization, and predicting the quality of the final product. An accurate 3D model is presented here for short laser pulses that can predict the ablation depth and temperature distribution on any section of the material in a minimal amount of time. In this transient model, variations of thermal properties, plasma shielding, and phase change are considered. Ablation depth was measured using a 3D optical profiler. Calculated depths are in good agreement with measured values on laser treated titanium surfaces. The proposed model can be applied to a wide range of materials and laser systems.

  13. Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions (United States)

    van Hooidonk, R.; Huber, M.


    Future widespread coral bleaching and subsequent mortality has been projected using sea surface temperature (SST) data derived from global, coupled ocean-atmosphere general circulation models (GCMs). While these models possess fidelity in reproducing many aspects of climate, they vary in their ability to correctly capture such parameters as the tropical ocean seasonal cycle and El Niño Southern Oscillation (ENSO) variability. Such weaknesses most likely reduce the accuracy of predicting coral bleaching, but little attention has been paid to the important issue of understanding potential errors and biases, the interaction of these biases with trends, and their propagation in predictions. To analyze the relative importance of various types of model errors and biases in predicting coral bleaching, various intra- and inter-annual frequency bands of observed SSTs were replaced with those frequencies from 24 GCMs 20th century simulations included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. Subsequent thermal stress was calculated and predictions of bleaching were made. These predictions were compared with observations of coral bleaching in the period 1982-2007 to calculate accuracy using an objective measure of forecast quality, the Peirce skill score (PSS). Major findings are that: (1) predictions are most sensitive to the seasonal cycle and inter-annual variability in the ENSO 24-60 months frequency band and (2) because models tend to understate the seasonal cycle at reef locations, they systematically underestimate future bleaching. The methodology we describe can be used to improve the accuracy of bleaching predictions by characterizing the errors and uncertainties involved in the predictions.

  14. Ocean surface waves in Hurricane Ike (2008) and Superstorm Sandy (2012): Coupled model predictions and observations (United States)

    Chen, Shuyi S.; Curcic, Milan


    Forecasting hurricane impacts of extreme winds and flooding requires accurate prediction of hurricane structure and storm-induced ocean surface waves days in advance. The waves are complex, especially near landfall when the hurricane winds and water depth varies significantly and the surface waves refract, shoal and dissipate. In this study, we examine the spatial structure, magnitude, and directional spectrum of hurricane-induced ocean waves using a high resolution, fully coupled atmosphere-wave-ocean model and observations. The coupled model predictions of ocean surface waves in Hurricane Ike (2008) over the Gulf of Mexico and Superstorm Sandy (2012) in the northeastern Atlantic and coastal region are evaluated with the NDBC buoy and satellite altimeter observations. Although there are characteristics that are general to ocean waves in both hurricanes as documented in previous studies, wave fields in Ike and Sandy possess unique properties due mostly to the distinct wind fields and coastal bathymetry in the two storms. Several processes are found to significantly modulate hurricane surface waves near landfall. First, the phase speed and group velocities decrease as the waves become shorter and steeper in shallow water, effectively increasing surface roughness and wind stress. Second, the bottom-induced refraction acts to turn the waves toward the coast, increasing the misalignment between the wind and waves. Third, as the hurricane translates over land, the left side of the storm center is characterized by offshore winds over very short fetch, which opposes incoming swell. Landfalling hurricanes produce broader wave spectra overall than that of the open ocean. The front-left quadrant is most complex, where the combination of windsea, swell propagating against the wind, increasing wind-wave stress, and interaction with the coastal topography requires a fully coupled model to meet these challenges in hurricane wave and surge prediction.

  15. Response surface and neural network based predictive models of cutting temperature in hard turning

    Directory of Open Access Journals (Sweden)

    Mozammel Mia


    Full Text Available The present study aimed to develop the predictive models of average tool-workpiece interface temperature in hard turning of AISI 1060 steels by coated carbide insert. The Response Surface Methodology (RSM and Artificial Neural Network (ANN were employed to predict the temperature in respect of cutting speed, feed rate and material hardness. The number and orientation of the experimental trials, conducted in both dry and high pressure coolant (HPC environments, were planned using full factorial design. The temperature was measured by using the tool-work thermocouple. In RSM model, two quadratic equations of temperature were derived from experimental data. The analysis of variance (ANOVA and mean absolute percentage error (MAPE were performed to suffice the adequacy of the models. In ANN model, 80% data were used to train and 20% data were employed for testing. Like RSM, herein, the error analysis was also conducted. The accuracy of the RSM and ANN model was found to be ⩾99%. The ANN models exhibit an error of ∼5% MAE for testing data. The regression coefficient was found to be greater than 99.9% for both dry and HPC. Both these models are acceptable, although the ANN model demonstrated a higher accuracy. These models, if employed, are expected to provide a better control of cutting temperature in turning of hardened steel.

  16. Comparisons Between Model Predictions and Spectral Measurements of Charged and Neutral Particles on the Martian Surface (United States)

    Kim, Myung-Hee Y.; Cucinotta, Francis A.; Zeitlin, Cary; Hassler, Donald M.; Ehresmann, Bent; Rafkin, Scot C. R.; Wimmer-Schweingruber, Robert F.; Boettcher, Stephan; Boehm, Eckart; Guo, Jingnan; hide


    Detailed measurements of the energetic particle radiation environment on the surface of Mars have been made by the Radiation Assessment Detector (RAD) on the Curiosity rover since August 2012. RAD is a particle detector that measures the energy spectrum of charged particles (10 to approx. 200 MeV/u) and high energy neutrons (approx 8 to 200 MeV). The data obtained on the surface of Mars for 300 sols are compared to the simulation results using the Badhwar-O'Neill galactic cosmic ray (GCR) environment model and the high-charge and energy transport (HZETRN) code. For the nuclear interactions of primary GCR through Mars atmosphere and Curiosity rover, the quantum multiple scattering theory of nuclear fragmentation (QMSFRG) is used. For describing the daily column depth of atmosphere, daily atmospheric pressure measurements at Gale Crater by the MSL Rover Environmental Monitoring Station (REMS) are implemented into transport calculations. Particle flux at RAD after traversing varying depths of atmosphere depends on the slant angles, and the model accounts for shielding of the RAD "E" dosimetry detector by the rest of the instrument. Detailed comparisons between model predictions and spectral data of various particle types provide the validation of radiation transport models, and suggest that future radiation environments on Mars can be predicted accurately. These contributions lend support to the understanding of radiation health risks to astronauts for the planning of various mission scenarios

  17. Prediction of iodide adsorption on oxides by surface complexation modeling with spectroscopic confirmation. (United States)

    Nagata, Takahiro; Fukushi, Keisuke; Takahashi, Yoshio


    A deficiency in environmental iodine can cause a number of health problems. Understanding how iodine is sequestered by materials is helpful for evaluating and developing methods for minimizing human health effects related to iodine. In addition, (129)I is considered to be strategically important for safety assessment of underground radioactive waste disposal. To assess the long-term stability of disposed radioactive waste, an understanding of (129)I adsorption on geologic materials is essential. Therefore, the adsorption of I(-) on naturally occurring oxides is of environmental concern. The surface charges of hydrous ferric oxide (HFO) in NaI electrolyte solutions were measured by potentiometric acid-base titration. The surface charge data were analyzed by means of an extended triple-layer model (ETLM) for surface complexation modeling to obtain the I(-) adsorption reaction and its equilibrium constant. The adsorption of I(-) was determined to be an outer-sphere process from ETLM analysis, which was consistent with independent X-ray absorption near-edge structure (XANES) observation of I(-) adsorbed on HFO. The adsorption equilibrium constants for I(-) on beta-TiO(2) and gamma-Al(2)O(3) were also evaluated by analyzing the surface charge data of these oxides in NaI solution as reported in the literature. Comparison of these adsorption equilibrium constants for HFO, beta-TiO(2), and gamma-Al(2)O(3) based on site-occupancy standard states permitted prediction of I(-) adsorption equilibrium constants for all oxides by means of the Born solvation theory. The batch adsorption data for I(-) on HFO and amorphous aluminum oxide were reasonably reproduced by ETLM with the predicted equilibrium constants, confirming the validity of the present approach. Using the predicted adsorption equilibrium constants, we calculated distribution coefficient (K(d)) values for I(-) adsorption on common soil minerals as a function of pH and ionic strength.

  18. Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning

    Directory of Open Access Journals (Sweden)

    Muammer Nalbant


    Full Text Available Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of different insert nose radii of cutting tools (0.4, 0.8, 1.2 mm, various depth of cuts (0.75, 1.25, 1.75, 2.25, 2.75 mm, and different feedrates (100, 130, 160, 190, 220 mm/min on the surface quality of the AISI 1030 steel workpieces have been investigated using multiple regression analysis and artificial neural networks (ANN. Regression analysis and neural network-based models used for the prediction of surface roughness were compared for various cutting conditions in turning. The data set obtained from the measurements of surface roughness was employed to and tests the neural network model. The trained neural network models were used in predicting surface roughness for cutting conditions. A comparison of neural network models with regression model was carried out. Coefficient of determination was 0.98 in multiple regression model. The scaled conjugate gradient (SCG model with 9 neurons in hidden layer has produced absolute fraction of variance (R2 values of 0.999 for the training data, and 0.998 for the test data. Predictive neural network model showed better predictions than various regression models for surface roughness. However, both methods can be used for the prediction of surface roughness in turning.

  19. Power Prediction Model for Turning EN-31 Steel Using Response Surface Methodology

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    M. Hameedullah


    Full Text Available Power consumption in turning EN-31 steel (a material that is most extensively used in automotive industry with tungstencarbide tool under different cutting conditions was experimentally investigated. The experimental runs were planned accordingto 24+8 added centre point factorial design of experiments, replicated thrice. The data collected was statisticallyanalyzed using Analysis of Variance technique and first order and second order power consumption prediction models weredeveloped by using response surface methodology (RSM. It is concluded that second-order model is more accurate than thefirst-order model and fit well with the experimental data. The model can be used in the automotive industries for decidingthe cutting parameters for minimum power consumption and hence maximum productivity

  20. Biological and environmental surface interactions of nanomaterials: characterization, modeling, and prediction. (United States)

    Chen, Ran; Riviere, Jim E


    The understanding of nano-bio interactions is deemed essential in the design, application, and safe handling of nanomaterials. Proper characterization of the intrinsic physicochemical properties, including their size, surface charge, shape, and functionalization, is needed to consider the fate or impact of nanomaterials in biological and environmental systems. The characterizations of their interactions with surrounding chemical species are often hindered by the complexity of biological or environmental systems, and the drastically different surface physicochemical properties among a large population of nanomaterials. The complexity of these interactions is also due to the diverse ligands of different chemical properties present in most biomacromolecules, and multiple conformations they can assume at different conditions to minimize their conformational free energy. Often these interactions are collectively determined by multiple physical or chemical forces, including electrostatic forces, hydrogen bonding, and hydrophobic forces, and calls for multidimensional characterization strategies, both experimentally and computationally. Through these characterizations, the understanding of the roles surface physicochemical properties of nanomaterials and their surface interactions with biomacromolecules can play in their applications in biomedical and environmental fields can be obtained. To quantitatively decipher these physicochemical surface interactions, computational methods, including physical, statistical, and pharmacokinetic models, can be used for either analyses of large amounts of experimental characterization data, or theoretical prediction of the interactions, and consequent biological behavior in the body after administration. These computational methods include molecular dynamics simulation, structure-activity relationship models such as biological surface adsorption index, and physiologically-based pharmacokinetic models. WIREs Nanomed Nanobiotechnol 2017

  1. Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction (United States)

    Santanello, Joseph A.; Kumar, Sujay; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Zhou, Shuija


    Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

  2. Semianalytical model predicting transfer of volatile pollutants from groundwater to the soil surface. (United States)

    Atteia, Olivier; Höhener, Patrick


    Volatilization of toxic organic contaminants from groundwater to the soil surface is often considered an important pathway in risk analysis. Most of the risk models use simplified linear solutions that may overpredict the volatile flux. Although complex numerical models have been developed, their use is restricted to experienced users and for sites where field data are known in great detail. We present here a novel semianalytical model running on a spreadsheet that simulates the volatilization flux and vertical concentration profile in a soil based on the Van Genuchten functions. These widely used functions describe precisely the gas and water saturations and movement in the capillary fringe. The analytical model shows a good accuracy over several orders of magnitude when compared to a numerical model and laboratory data. The effect of barometric pumping is also included in the semianalytical formulation, although the model predicts that barometric pumping is often negligible. A sensitivity study predicts significant fluxes in sandy vadose zones and much smaller fluxes in other soils. Fluxes are linked to the dimensionless Henry's law constant H for H < 0.2 and increase by approximately 20% when temperature increases from 5 to 25 degrees C.

  3. A simple mathematical model to predict sea surface temperature over the northwest Indian Ocean (United States)

    Noori, Roohollah; Abbasi, Mahmud Reza; Adamowski, Jan Franklin; Dehghani, Majid


    A novel and simple mathematical model was developed in this study to enhance the capacity of a reduced-order model based on eigenvectors (RMEV) to predict sea surface temperature (SST) in the northwest portion of the Indian Ocean, including the Persian and Oman Gulfs and Arabian Sea. Developed using only the first two of 12,416 possible modes, the enhanced RMEV closely matched observed daily optimum interpolation SST (DOISST) values. Spatial distribution of the first mode indicated the greatest variations in DOISST occurred in the Persian Gulf. Also, the slightly increasing trend in the temporal component of the first mode observed in the study area over the last 34 years properly reflected the impact of climate change and rising DOISST. Given its simplicity and high level of accuracy, the enhanced RMEV can be applied to forecast DOISST in oceans, which the poor forecasting performance and large computational-time of other numerical models may not allow.

  4. High-Resolution Specification of the Land and Ocean Surface for Improving Regional Mesoscale Model Predictions (United States)

    Case, Jonathan L.; Lazarus, Steven M.; Splitt, Michael E.; Crosson, William L.; Lapenta, William M.; Jedlovec, Gary J.; Peters-Lidard, Christa D.


    The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many meteorological processes. High-resolution, accurate representations of surface properties such as sea-surface temperature (SST), soil temperature and moisture content, ground fluxes, and vegetation are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of sensible weather. The NASA Short-term Prediction Research and Transition (SPoRT) Center has been conducting separate studies to examine the impacts of high-resolution land-surface initialization data from the Goddard Space Flight Center Land Information System (LIS) on subsequent WRF forecasts, as well as the influence of initializing WRF with SST composites derived from the MODIS instrument. This current project addresses the combined impacts of using high-resolution lower boundary data over both land (LIS data) and water (MODIS SSTs) on the subsequent daily WRF forecasts over Florida during May 2004. For this experiment, the WRF model is configured to run on a nested domain with 9- km and 3-kin grid spacing, centered on the Florida peninsula and adjacent coastal waters of the Gulf of Mexico and Atlantic Ocean. A control configuration of WRF is established to take all initial condition data from the NCEP Eta model. Meanwhile, two WRF experimental runs are configured to use high-resolution initialization data from (1) LIS land-surface data only, and (2) a combination of LIS data and high-resolution MODIS SST composites. The experiment involves running 24-hour simulations of the control WRF configuration, the MS-initialized WRF, and the LIS+MODIS-initialized WRF daily for the entire month of May 2004. All atmospheric data for initial and boundary conditions for the Control, LIS, and LIS+MODIS runs come from the NCEP Eta model on a 40-km grid. Verification statistics are generated at land surface observation sites and buoys, and the impacts

  5. Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

    Energy Technology Data Exchange (ETDEWEB)

    Reddy, M Mohan; Gorin, Alexander [School of Engineering and Science, Curtin University of Technology, Sarawak (Malaysia); Abou-El-Hossein, K A, E-mail: [Mechanical and Aeronautical Department, Nelson Mandela Metropolitan University, Port Elegebeth, 6031 (South Africa)


    Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

  6. Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

    International Nuclear Information System (INIS)

    Reddy, M Mohan; Gorin, Alexander; Abou-El-Hossein, K A


    Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

  7. Ensemble Data Assimilation to Characterize Surface-Layer Errors In Numerical Weather Prediction Models (United States)

    Hacker, Joshua; Angevine, Wayne


    Experiments with the single-column implementation of the Weather Research and Forecasting mesoscale model provide a basis for deducing land-atmosphere coupling errors in the model. Coupling occurs both through heat and moisture fluxes through the land-atmosphere interface and roughness sub-layer, and turbulent heat, moisture, and momentum fluxes through the atmospheric surface layer. This work primarily addresses the turbulent fluxes, which are parameterized following Monin-Obukhov similarity theory applied to the atmospheric surface layer. By combining ensemble data assimilation and parameter estimation, the model error can be characterized. Ensemble data assimilation of 2-m temperature and water vapor mixing ratio, and 10-m wind components, forces the model to follow observations during a month-long simulation for a column over the well-instrumented ARM Central Facility near Lamont, OK. One-hour errors in predicted observations are systematically small but non-zero, and the systematic errors measure bias as a function of local time of day. Analysis increments for state elements nearby (15-m AGL) can be too small or have the wrong sign, indicating systematically biased covariances and model error. Experiments using the ensemble filter to objectively estimate a parameter controlling the thermal land-atmosphere coupling show that the parameter adapts to offset the model errors, but that the errors cannot be eliminated. Results suggest either structural error or further parametric error that may be difficult to estimate. Experiments omitting atypical observations such as soil and flux measurements lead to qualitatively similar deductions, showing potential for assimilating common in-situ observations as an inexpensive framework for deducing and isolating model errors. We finish by presenting recent results from a deeper examination of the second-moment ensemble statistics, which demonstrate the effect of assimilation on the coupling through the stability function in

  8. Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models (United States)

    Suzuki, Kazuyoshi; Zupanski, Milija


    In this study, we investigate the uncertainties associated with land surface processes in an ensemble predication context. Specifically, we compare the uncertainties produced by a coupled atmosphere-land modeling system with two different land surface models, the Noah- MP land surface model (LSM) and the Noah LSM, by using the Maximum Likelihood Ensemble Filter (MLEF) data assimilation system as a platform for ensemble prediction. We carried out 24-hour prediction simulations in Siberia with 32 ensemble members beginning at 00:00 UTC on 5 March 2013. We then compared the model prediction uncertainty of snow depth and solid precipitation with observation-based research products and evaluated the standard deviation of the ensemble spread. The prediction skill and ensemble spread exhibited high positive correlation for both LSMs, indicating a realistic uncertainty estimation. The inclusion of a multiple snowlayer model in the Noah-MP LSM was beneficial for reducing the uncertainties of snow depth and snow depth change compared to the Noah LSM, but the uncertainty in daily solid precipitation showed minimal difference between the two LSMs. The impact of LSM choice in reducing temperature uncertainty was limited to surface layers of the atmosphere. In summary, we found that the more sophisticated Noah-MP LSM reduces uncertainties associated with land surface processes compared to the Noah LSM. Thus, using prediction models with improved skill implies improved predictability and greater certainty of prediction.

  9. Using surrogate modeling in the prediction of fibrinogen adsorption onto polymer surfaces. (United States)

    Smith, Jack R; Knight, Doyle; Kohn, Joachim; Rasheed, Khaled; Weber, Norbert; Kholodovych, Vladyslav; Welsh, William J


    We present a Surrogate (semiempirical) Model for prediction of protein adsorption onto the surfaces of biodegradable polymers that have been designed for tissue engineering applications. The protein used in these studies, fibrinogen, is known to play a key role in blood clotting. Therefore, fibrinogen adsorption dictates the performance of implants exposed to blood. The Surrogate Model combines molecular modeling, machine learning and an Artificial Neural Network. This novel approach includes an accounting for experimental error using a Monte Carlo analysis. Briefly, measurements of human fibrinogen adsorption were obtained for 45 polymers. A total of 106 molecular descriptors were generated for each polymer. Of these, 102 descriptors were computed using the Molecular Operating Environment (MOE) software based upon the polymer chemical structures, two represented different monomer types, and two were measured experimentally. The Surrogate Model was developed in two stages. In the first stage, the three descriptors with the highest correlation to adsorption were determined by calculating the information gain of each descriptor. Here a Monte Carlo approach enabled a direct assessment of the effect of the experimental uncertainty on the results. The three highest-ranking descriptors, defined as those with the highest information gain for the sample set, were then selected as the input variables for the second stage, an Artificial Neural Network (ANN) to predict fibrinogen adsorption. The ANN was trained using one-half of the experimental data set (the training set) selected at random. The effect of experimental error on predictive capability was again explored using a Monte Carlo analysis. The accuracy of the ANN was assessed by comparison of the predicted values for fibrinogen adsorption with the experimental data for the remaining polymers (the validation set). The mean value of the Pearson correlation coefficient for the validation data sets was 0.54 +/- 0.12. The

  10. Prediction of lake surface temperature using the air2water model: guidelines, challenges, and future perspectives

    Directory of Open Access Journals (Sweden)

    Sebastiano Piccolroaz


    Full Text Available Water temperature plays a primary role in controlling a wide range of physical, geochemical and ecological processes in lakes, with considerable influences on lake water quality and ecosystem functioning. Being able to reliably predict water temperature is therefore a desired goal, which stimulated the development of models of different type and complexity, ranging from simple regression-based models to more sophisticated process-based numerical models. However, both types of models suffer of some limitations: the first are not able to address some fundamental physical processes as e.g., thermal stratification, while the latter generally require a large amount of data in input, which are not always available. In this work, lake surface temperature is simulated by means of air2water, a hybrid physically-based/statistical model, which is able to provide a robust, predictive understanding of LST dynamics knowing air temperature only. This model showed performances that are comparable with those obtained by using process based models (a root mean square error on the order of 1°C, at daily scale, while retaining the simplicity and parsimony of regression-based models, thus making it a good candidate for long-term applications.The aim of the present work is to provide the reader with useful and practical guidelines for proper use of the air2water model and for critical analysis of results. Two case studies have been selected for the analysis: Lake Superior and Lake Erie. These are clear and emblematic examples of a deep and a shallow temperate lake characterized by markedly different thermal responses to external forcing, thus are ideal for making the results of the analysis the most general and comprehensive. Particular attention is paid to assessing the influence of missing data on model performance, and to evaluating when an observed time series is sufficiently informative for proper model calibration or, conversely, data are too scarce thus

  11. Prediction of viscosities and surface tensions of fuels using a new corresponding states model

    DEFF Research Database (Denmark)

    Queimada, A.J.; Rolo, L.I.; Caco, A.I.


    While some properties of diesels are cheap, easy and fast to measure, such as densities, others such as surface tensions and viscosities are expensive and time consuming. A new approach that uses some basic information such as densities to predict viscosities and surface tensions is here proposed...

  12. Surface quality prediction model of nano-composite ceramics in ultrasonic vibration-assisted ELID mirror grinding

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Bo; Chen, Fan; Jia, Xiao-feng; Zhao, Chong-yang; Wang, Xiao-bo [Henan Polytechnic University, Jiaozuo (China)


    Ultrasonic vibration-assisted Electrolytic in-process dressing (ELID) grinding is a highly efficient and highly precise machining method. The surface quality prediction model in ultrasonic vibration-assisted ELID mirror grinding was studied. First, the interaction between grits and workpiece surface was analyzed according to kinematic mechanics, and the surface roughness model was developed. The variations in surface roughness under different parameters was subsequently calculated and analyzed by MATLAB. Results indicate that compared with the ordinary ELID grinding, ultrasonic vibration-assisted ELID grinding is superior, because it has more stable and better surface quality and has an improved range of ductile machining.

  13. Prediction of strong earthquake motions on rock surface using evolutionary process models

    International Nuclear Information System (INIS)

    Kameda, H.; Sugito, M.


    Stochastic process models are developed for prediction of strong earthquake motions for engineering design purposes. Earthquake motions with nonstationary frequency content are modeled by using the concept of evolutionary processes. Discussion is focused on the earthquake motions on bed rocks which are important for construction of nuclear power plants in seismic regions. On this basis, two earthquake motion prediction models are developed, one (EMP-IB Model) for prediction with given magnitude and epicentral distance, and the other (EMP-IIB Model) to account for the successive fault ruptures and the site location relative to the fault of great earthquakes. (Author) [pt

  14. Validation of asphalt mixture pavement skid prediction model and development of skid prediction model for surface treatments. (United States)


    Pavement skid resistance is primarily a function of the surface texture, which includes both microtexture and macrotexture. Earlier, under the Texas Department of Transportation (TxDOT) Research Project 0-5627, the researchers developed a method to p...

  15. An analytical model for force prediction in ball nose micro milling of inclined surfaces

    DEFF Research Database (Denmark)

    Bissacco, Giuliano; Hansen, Hans Nørgaard; De Chiffre, Leonardo


    Ball nose micro milling is a key process for the generation of free form surfaces and inclined surfaces often present in mould inserts for micro replication. This paper presents a new cutting force model for ball nose micro milling that is capable of taking into account the effect of the edge rad...... radius and the effect of the surface topography due to the previous milling passes. The model is completely analytical can be applied to ball end micro milling of slanted surfaces for any value of the surface inclination angle relative to the tool axis....

  16. Surface roughness prediction model in end milling of Al/SiC p MMC ...

    African Journals Online (AJOL)

    This research focuses on study and analyses of surface quality improvement in end milling operation of Al/SiCp metal matrix composite. These materials are selected as they are most widely used in automobile and aerospace industry. This research paper develops an improved mathematical model for surface roughness ...

  17. Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L.

    Directory of Open Access Journals (Sweden)

    J. Prakash Maran


    Full Text Available In this study, a comparative approach was made between artificial neural network (ANN and response surface methodology (RSM to predict the mass transfer parameters of osmotic dehydration of papaya. The effects of process variables such as temperature, osmotic solution concentration and agitation speed on water loss, weight reduction, and solid gain during osmotic dehydration were investigated using a three-level three-factor Box-Behnken experimental design. Same design was utilized to train a feed-forward multilayered perceptron (MLP ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of root mean square error (RMSE, mean absolute error (MAE, standard error of prediction (SEP, model predictive error (MPE, chi square statistic (χ2, and coefficient of determination (R2 based on the validation data set. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.

  18. Surface tensions of multi-component mixed inorganic/organic aqueous systems of atmospheric significance: measurements, model predictions and importance for cloud activation predictions

    Directory of Open Access Journals (Sweden)

    D. O. Topping


    Full Text Available In order to predict the physical properties of aerosol particles, it is necessary to adequately capture the behaviour of the ubiquitous complex organic components. One of the key properties which may affect this behaviour is the contribution of the organic components to the surface tension of aqueous particles in the moist atmosphere. Whilst the qualitative effect of organic compounds on solution surface tensions has been widely reported, our quantitative understanding on mixed organic and mixed inorganic/organic systems is limited. Furthermore, it is unclear whether models that exist in the literature can reproduce the surface tension variability for binary and higher order multi-component organic and mixed inorganic/organic systems of atmospheric significance. The current study aims to resolve both issues to some extent. Surface tensions of single and multiple solute aqueous solutions were measured and compared with predictions from a number of model treatments. On comparison with binary organic systems, two predictive models found in the literature provided a range of values resulting from sensitivity to calculations of pure component surface tensions. Results indicate that a fitted model can capture the variability of the measured data very well, producing the lowest average percentage deviation for all compounds studied. The performance of the other models varies with compound and choice of model parameters. The behaviour of ternary mixed inorganic/organic systems was unreliably captured by using a predictive scheme and this was dependent on the composition of the solutes present. For more atmospherically representative higher order systems, entirely predictive schemes performed poorly. It was found that use of the binary data in a relatively simple mixing rule, or modification of an existing thermodynamic model with parameters derived from binary data, was able to accurately capture the surface tension variation with concentration. Thus

  19. Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense mixed conifer forest (United States)

    Marek K. Jakubowksi; Qinghua Guo; Brandon Collins; Scott Stephens; Maggi. Kelly


    We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m

  20. Solar Atmosphere to Earth's Surface: Long Lead Time dB/dt Predictions with the Space Weather Modeling Framework (United States)

    Welling, D. T.; Manchester, W.; Savani, N.; Sokolov, I.; van der Holst, B.; Jin, M.; Toth, G.; Liemohn, M. W.; Gombosi, T. I.


    The future of space weather prediction depends on the community's ability to predict L1 values from observations of the solar atmosphere, which can yield hours of lead time. While both empirical and physics-based L1 forecast methods exist, it is not yet known if this nascent capability can translate to skilled dB/dt forecasts at the Earth's surface. This paper shows results for the first forecast-quality, solar-atmosphere-to-Earth's-surface dB/dt predictions. Two methods are used to predict solar wind and IMF conditions at L1 for several real-world coronal mass ejection events. The first method is an empirical and observationally based system to estimate the plasma characteristics. The magnetic field predictions are based on the Bz4Cast system which assumes that the CME has a cylindrical flux rope geometry locally around Earth's trajectory. The remaining plasma parameters of density, temperature and velocity are estimated from white-light coronagraphs via a variety of triangulation methods and forward based modelling. The second is a first-principles-based approach that combines the Eruptive Event Generator using Gibson-Low configuration (EEGGL) model with the Alfven Wave Solar Model (AWSoM). EEGGL specifies parameters for the Gibson-Low flux rope such that it erupts, driving a CME in the coronal model that reproduces coronagraph observations and propagates to 1AU. The resulting solar wind predictions are used to drive the operational Space Weather Modeling Framework (SWMF) for geospace. Following the configuration used by NOAA's Space Weather Prediction Center, this setup couples the BATS-R-US global magnetohydromagnetic model to the Rice Convection Model (RCM) ring current model and a height-integrated ionosphere electrodynamics model. The long lead time predictions of dB/dt are compared to model results that are driven by L1 solar wind observations. Both are compared to real-world observations from surface magnetometers at a variety of geomagnetic latitudes

  1. Wetting behavior of patterned micro-pillar array predicted by an equivalent surface tension model

    International Nuclear Information System (INIS)

    Chen, Qiang; Huang, Yonghua


    Micro-pillar array is widely applied to manipulate the wettability of surfaces. Cases where liquid has infiltrated such pillar arrays completely are drawing increased attention in miniaturized systems. An equivalent surface tension model is proposed to characterize the driving force of liquid evolution in patterned micro-pillar arrays after the Young-Laplace equation and surface energy analysis are applied on both the pillar unit and bulk liquid levels. The effects of local menisci induced from the wetting of pillars are bounded and treated as 'equivalent liquid-vapor surface tension', through which the bulk liquid profile is then obtained based on the principle of minimal surface energy. The model is found to be computationally efficient and can be easily obtained through numerical methods. A typical sample case is presented to demonstrate its advantages and simplicity. The bulk profile that considers the effects of pillar array is compared with the result without pillars. The influencing effects, including apparent tilt angle, pillar spacing, and pillar shape, are addressed.

  2. Molecular surface area based predictive models for the adsorption and diffusion of disperse dyes in polylactic acid matrix. (United States)

    Xu, Suxin; Chen, Jiangang; Wang, Bijia; Yang, Yiqi


    Two predictive models were presented for the adsorption affinities and diffusion coefficients of disperse dyes in polylactic acid matrix. Quantitative structure-sorption behavior relationship would not only provide insights into sorption process, but also enable rational engineering for desired properties. The thermodynamic and kinetic parameters for three disperse dyes were measured. The predictive model for adsorption affinity was based on two linear relationships derived by interpreting the experimental measurements with molecular structural parameters and compensation effect: ΔH° vs. dye size and ΔS° vs. ΔH°. Similarly, the predictive model for diffusion coefficient was based on two derived linear relationships: activation energy of diffusion vs. dye size and logarithm of pre-exponential factor vs. activation energy of diffusion. The only required parameters for both models are temperature and solvent accessible surface area of the dye molecule. These two predictive models were validated by testing the adsorption and diffusion properties of new disperse dyes. The models offer fairly good predictive ability. The linkage between structural parameter of disperse dyes and sorption behaviors might be generalized and extended to other similar polymer-penetrant systems. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. Previously published midazolam-alfentanil response surface model cannot predict patient response well in gastrointestinal endoscopy sedation. (United States)

    Liou, Jing-Yang; Ting, Chien-Kun; Huang, Yu-Ying; Tsou, Mei-Yung


    A response surface model is a mathematical model used to predict multiple-drug pharmacodynamic interactions. With the use of a previously published volunteer model, we tested the accuracy of the midazolam-alfentanil response surface model during gastrointestinal endoscopy. We enrolled 35 adult patients scheduled for combined endoscopic procedures. Patients were sedated with intravenous midazolam and alfentanil, and monitored with real-time auditory evoked potential. Sedation Observer's Assessment of Alertness/Sedation (OAA/S) scores were recorded by an independent observer every 2 minutes. Patients with OAA/S scores of ≥ 4 were designated as "awake". Pharmacokinetic profiles were calculated using the TIVA trainer. The published response surface model was modified to make estimations more reasonable. Patient response (OAA/S score ≥ 4 or response during gastrointestinal endoscopic procedure sedation. Accuracy in predicting an OAA/S score of response ranged from 0.04% to 2.94% at the time of arousal (OAA/S score ≥ 4) and from 0.24% to 15.55% when the patient was asleep (OAA/S score response of patients undergoing sedated gastrointestinal endoscopic procedures. Future model parameter adjustments are required. Copyright © 2016. Published by Elsevier Taiwan LLC.

  4. Modeling and predicting abrasive wear behaviour of poly oxy methylenes using response surface methodolgy and neural networks

    Directory of Open Access Journals (Sweden)

    A. Sagbas


    Full Text Available In this study, abrasive wear behaviour of poly oxy methylenes (POM under various testing conditions was investigated. A central composite design (CCD was used to describe response and to estimate the parameters in the model. Response surface methodology (RSM was adopted to obtain an empirical model of wear loss as a function of applied load and sliding distance. Also, a neural network (NN model was developed for the prediction and testing of the results. Finally, a comparison was made between the results obtained from RSM and NN.

  5. Correlative assessment of two predictive soil hydrology models with measured surface soil geochemistry (United States)

    Filley, T. R.; Li, M.; Le, P. V.; Kumar, P.; Yan, Q.; Papanicolaou, T.; Hou, T.; Wang, J.


    Spatial variability of surface soil organic matter on the hill slope scale is strongly influenced by topographic variation, especially in sloping terrains, where the coupled effects of soil moisture and texture are principle drivers for stabilization and decomposition. Topographic wetness index (TWI) calculations have shown reasonable correlations with soil organic carbon (SOC) content at broad spatial scales. However, due to inherent limitations of the "depression filling" approach, traditional TWI methods are generally ineffectual at capturing how small-scale micro-topographic ( 1m2) variation controls water dynamics and, subsequently, poorly correlate to surface soil biogeochmical measures. For TWI models to capture biogeochmical controls at the scales made possible by LiDAR data they need to incoportate the dynamic connection between soil moisture, local climate, edaphic properties, and micro-topographic variability. We present the results of a study correlating surface soil geochemical data across field sites in the Upper Sangamon River Basin (USRB) in Central Illinois, USA with a range of land use types to SAGA TWI and a newly developed Dynamic Topographic Wetness Index (DTWI). The DTWI for all field sites were obtained from the probability distribution of long-term stochastically modeled soil moisture in between wilting point (WP) and field capacity (FC) using Dhara modeling framework. Whereas the SAGA TWI showed no correlation with soil geochemistry measures across the site-specific data, the DTWI, within a site, was strongly, positively correlated with soil nitrogen, organic carbon, and δ15N at three of the six sites and revealed controls potentially related to connectivity to local drainage paths. Overall, this study indicates that soil moisture derived by DTWI may offer a significant improvement in generating estimates in long-term soil moisture, and subsequently, soil biogeochemistry dynamics at a crucial landscape scale.

  6. Connecting membrane fluidity and surface charge to pore-forming antimicrobial peptides resistance by an ANN-based predictive model. (United States)

    Mehla, Jitender; Sood, S K


    Efficiency of antibacterial chemotherapy is gradually more challenged by the emergence of pathogenic strains exhibiting high levels of antibiotic resistance. Pore-forming antimicrobial peptides (PF-AMPs) such as alamethicin (Alm) are therefore in the focus of extensive research efforts. In the present study, an artificial neural network (ANN)-based quantitative structure-activity relationship (SAR) modeling of membrane phospholipids vs. PF-AMPs, in context to membrane fluidity and surface charge, was carried out. We observed that the potency of PF-AMPs depends on the fatty acyl chain and polar head group of phospholipids. Alm showed surface interactions with zwitterionic phospholipids however could penetrate deeper inside the hydrophobic core of anionic membranes. Here, the resistance developed in bacterial cells was coupled to membrane fluidity and surface charge, and simultaneously, these principles could be applied for combating resistance against PF-AMPs. The correlation coefficient between observed CR and predicted CR using ANN was found to be 0.757. Thus, ANN could be used as a reliable modeling method for predicting CR, given the structure of the biomimetic membrane in terms of membrane fluidity and surface charge. Fully explored mechanisms of resistance, a forward modeling step in the design cycle of AMPs, can be cross-linked to the inward modeling using ANN to complete the peptide design cycle. The SAR between membrane phospholipids and PF-AMPs could furnish valuable information regarding their design to provide us efficacious peptides against premier pathogens. So far, this is the only report available to predict and quantify interactions of PF-AMPs with membrane phospholipids.

  7. Surface Temperature Variation Prediction Model Using Real-Time Weather Forecasts (United States)

    Karimi, M.; Vant-Hull, B.; Nazari, R.; Khanbilvardi, R.


    Combination of climate change and urbanization are heating up cities and putting the lives of millions of people in danger. More than half of the world's total population resides in cities and urban centers. Cities are experiencing urban Heat Island (UHI) effect. Hotter days are associated with serious health impacts, heart attaches and respiratory and cardiovascular diseases. Densely populated cities like Manhattan, New York can be affected by UHI impact much more than less populated cities. Even though many studies have been focused on the impact of UHI and temperature changes between urban and rural air temperature, not many look at the temperature variations within a city. These studies mostly use remote sensing data or typical measurements collected by local meteorological station networks. Local meteorological measurements only have local coverage and cannot be used to study the impact of UHI in a city and remote sensing data such as MODIS, LANDSAT and ASTER have with very low resolution which cannot be used for the purpose of this study. Therefore, predicting surface temperature in urban cities using weather data can be useful.Three months of Field campaign in Manhattan were used to measure spatial and temporal temperature variations within an urban setting by placing 10 fixed sensors deployed to measure temperature, relative humidity and sunlight. Fixed instrument shelters containing relative humidity, temperature and illumination sensors were mounted on lampposts in ten different locations in Manhattan (Vant-Hull et al, 2014). The shelters were fixed 3-4 meters above the ground for the period of three months from June 23 to September 20th of 2013 making measurements with the interval of 3 minutes. These high resolution temperature measurements and three months of weather data were used to predict temperature variability from weather forecasts. This study shows that the amplitude of spatial and temporal variation in temperature for each day can be predicted

  8. Predictive Models for Different Roughness Parameters During Machining Process of Peek Composites Using Response Surface Methodology

    Directory of Open Access Journals (Sweden)

    Mata-Cabrera Francisco


    Full Text Available Polyetheretherketone (PEEK composite belongs to a group of high performance thermoplastic polymers and is widely used in structural components. To improve the mechanical and tribological properties, short fibers are added as reinforcement to the material. Due to its functional properties and potential applications, it’s impor- tant to investigate the machinability of non-reinforced PEEK (PEEK, PEEK rein- forced with 30% of carbon fibers (PEEK CF30, and reinforced PEEK with 30% glass fibers (PEEK GF30 to determine the optimal conditions for the manufacture of the parts. The present study establishes the relationship between the cutting con- ditions (cutting speed and feed rate and the roughness (Ra , Rt , Rq , Rp , by develop- ing second order mathematical models. The experiments were planned as per full factorial design of experiments and an analysis of variance has been performed to check the adequacy of the models. These state the adequacy of the derived models to obtain predictions for roughness parameters within ranges of parameters that have been investigated during the experiments. The experimental results show that the most influence of the cutting parameters is the feed rate, furthermore, proved that glass fiber reinforcements produce a worse machinability.

  9. Improving groundwater predictions utilizing seasonal precipitation forecasts from general circulation models forced with sea surface temperature forecasts (United States)

    Almanaseer, Naser; Sankarasubramanian, A.; Bales, Jerad


    Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly groundwater levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models [principle component regression (PCR) and canonical correlation analysis (CCA)] for predicting groundwater and streamflow at both seasonal and monthly timescales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using

  10. Using isotopes to improve impact and hydrological predictions of land-surface schemes in global climate models

    International Nuclear Information System (INIS)

    McGuffie, K.; Henderson-Sellers, A.


    Global climate model (GCM) predictions of the impact of large-scale land-use change date back to 1984 as do the earliest isotopic studies of large-basin hydrology. Despite this coincidence in interest and geography, with both papers focussed on the Amazon, there have been few studies that have tried to exploit isotopic information with the goal of improving climate model simulations of the land-surface. In this paper we analyze isotopic results from the IAEA global data base specifically with the goal of identifying signatures of potential value for improving global and regional climate model simulations of the land-surface. Evaluation of climate model predictions of the impacts of deforestation of the Amazon has been shown to be of significance by recent results which indicate impacts occurring distant from the Amazon i.e. tele-connections causing climate change elsewhere around the globe. It is suggested that these could be similar in magnitude and extent to the global impacts of ENSO events. Validation of GCM predictions associated with Amazonian deforestation are increasingly urgently required because of the additional effects of other aspects of climate change, particularly synergies occurring between forest removal and greenhouse gas increases, especially CO 2 . Here we examine three decades distributions of deuterium excess across the Amazon and use the results to evaluate the relative importance of the fractionating (partial evaporation) and non-fractionating (transpiration) processes. These results illuminate GCM scenarios of importance to the regional climate and hydrology: (i) the possible impact of increased stomatal resistance in the rainforest caused by higher levels of atmospheric CO2 [4]; and (ii) the consequences of the combined effects of deforestation and global warming on the regions climate and hydrology

  11. Surface roughness prediction model in end milling of Al/SiCp MMC ...

    African Journals Online (AJOL)


    Keywords: Surface roughness (Ra), Response surface method (RSM), End milling, Metal matrix composites. DOI: 1. Introduction. The recent advancements in the CNC machine tool technology and the wide availability in manufacturing of mechanical components made it possible to ...

  12. Development of a new mathematical model for prediction of surface subsidence due to inclined coal-seam mining

    Energy Technology Data Exchange (ETDEWEB)

    Asadi, A.; Shahriar, K.; Goshtasbi, K.; Najm, K. [Islam Azad University, Tehran (Iran). Dept. of Mining Engineering


    Subsidence phenomenon as an unwanted consequence of underground mining can cause problems for environment and surface structures in mine area. Surface subsidence prediction for inclined and steep seams has been given less attention than horizontal seams due to the difficulties involved in the extraction of such coal-seams. This paper introduces a new profile function method for prediction of surface subsidence due to inclined coal-seam mining. The results of calculation with the new function indicate that the predicted value has good agreement with the measured data.

  13. High-resolution mapping and modelling of surface albedo in Norwegian boreal forests: from remotely sensed data to predictions (United States)

    Cherubini, Francesco; Hu, Xiangping; Vezhapparambu, Sajith; Stromman, Anders


    Surface albedo, a key parameter of the Earth's climate system, has high variability in space, time, and land cover and its parameterization is among the most important variables in climate models. The lack of extensive estimates for model improvement is one of the main limitations for accurately quantifying the influence of surface albedo changes on the planetary radiation balance. We use multi-year satellite retrievals of MODIS surface albedo (MCD43A3), high resolution land cover maps, and meteorological records to characterize albedo variations in Norway across latitude, seasons, land cover type, and topography. We then use this dataset to elaborate semi-empirical models to predict albedo values as a function of tree species, age, volume and climate variables like temperature and snow water equivalents (SWE). Given the complexity of the dataset and model formulation, we apply an innovative non-linear programming approach simultaneously coupled with linear un-mixing. The MODIS albedo products are at a resolution of about 500 m and 8 days. The land cover maps provide vegetation structure information on relative abundance of tree species, age, and biomass volumes at 16 m resolution (for both deciduous and coniferous species). Daily observations of meteorological information on air temperature and SWE are produced at 1 km resolution from interpolation of meteorological weather stations in Norway. These datasets have different resolution and projection, and are harmonized by identifying, for each MODIS pixel, the intersecting land cover polygons and the percentage area of the MODIS pixel represented by each land cover type. We then filter the subplots according to the following criteria: i) at least 96% of the total pixel area is covered by a single land cover class (either forest or cropland); ii) if forest area, at least 98% of the forest area is covered by spruce, deciduous or pine. Forested pixels are then categorized as spruce, deciduous, or pine dominant if the

  14. A thermodynamic model for predicting surface melting and overheating of different crystal planes in BCC, FCC and HCP pure metallic thin films

    International Nuclear Information System (INIS)

    Jahangir, Vafa; Riahifar, Reza; Sahba Yaghmaee, Maziar


    In order to predict as well as study the surface melting phenomena in contradiction to surface overheating, a generalized thermodynamics model including the surface free energy of solid and the melt state along with the interfacial energy of solid–liquid (melt on substrate) has been introduced. In addition, the effect of different crystal structures of surfaces in fcc, bcc and hcp metals was included in surface energies as well as in the atomistic model. These considerations lead us to predict surface melting and overheating as two contradictory melting phenomena. The results of the calculation are demonstrated on the example of Pb and Al thin films in three groups of (100), (110) and (111) surface planes. Our conclusions show good agreement with experimental results and other theoretical investigations. Moreover, a computational algorithm has been developed which enables users to investigate the surface melt or overheating of single component metallic thin film with variable crystal structures and different crystalline planes. This model and developed software can be used for studying all related surface phenomena. - Highlights: • Investigating the surface melting and overheating phenomena • Effect of crystal orientations, surface energies, geometry and different atomic surface layers • Developing a computational algorithm and its related code (free-software SMSO-Ver1) • Thickness and orientation of surface plane dominate the surface melting or overheating. • Total excess surface energy as a function of thickness and temperature explains melting.

  15. Experimental Validation of Surrogate Models for Predicting the Draping of Physical Interpolating Surfaces

    DEFF Research Database (Denmark)

    Christensen, Esben Toke; Lund, Erik; Lindgaard, Esben


    hypercube approach. This sampling method allows for generating a space filling and high-quality sample plan that respects mechanical constraints of the variable shape mould systems. Through the benchmark study, it is found that mechanical freeplay in the modeled system is severely detrimental......This paper concerns the experimental validation of two surrogate models through a benchmark study involving two different variable shape mould prototype systems. The surrogate models in question are different methods based on kriging and proper orthogonal decomposition (POD), which were developed...... in previous work. Measurement data used in the benchmark study are obtained using digital image correlation (DIC). For determining the variable shape mould configurations used for the training, and test sets used in the study, sampling is carried out using a novel constrained nested orthogonal maximin Latin...

  16. On the contribution of lakes in predicting near-surface temperature in a global weather forecasting model

    Directory of Open Access Journals (Sweden)

    T. Stockdale


    Full Text Available The impact of lakes in numerical weather prediction is investigated in a set of global simulations performed with the ECMWF Integrated Forecasting System (IFS. A Fresh shallow-water Lake model (FLake is introduced allowing the coupling of both resolved and subgrid lakes (those that occupy less than 50% of a grid-box to the IFS atmospheric model. Global fields for the lake ancillary conditions (namely lake cover and lake depth, as well as initial conditions for the lake physical state, have been derived to initialise the forecast experiments. The procedure for initialising the lake variables is described and verified with particular emphasis on the importance of surface water temperature and freezing conditions. The response of short-range near surface temperature to the representation of lakes is examined in a set of forecast experiments covering one full year. It is shown that the impact of subgrid lakes is beneficial, reducing forecast error over the Northern territories of Canada and over Scandinavia particularly in spring and summer seasons. This is mainly attributed to the lake thermal effect, which delays the temperature response to seasonal radiation forcing.

  17. Modeling mechanical signals on the surface of µCT and CAD based rapid prototype scaffold models to predict (early stage) tissue development. (United States)

    Hendrikson, W J; van Blitterswijk, C A; Verdonschot, N; Moroni, L; Rouwkema, J


    In the field of tissue engineering, mechano-regulation theories have been applied to help predict tissue development in tissue engineering scaffolds in the past. For this, finite element models (FEMs) were used to predict the distribution of strains within a scaffold. However, the strains reported in these studies are volumetric strains of the material or strains developed in the extracellular matrix occupying the pore space. The initial phase of cell attachment and growth on the biomaterial surface has thus far been neglected. In this study, we present a model that determines the magnitude of biomechanical signals on the biomaterial surface, enabling us to predict cell differentiation stimulus values at this initial stage. Results showed that magnitudes of the 2D strain--termed surface strain--were lower when compared to the 3D volumetric strain or the conventional octahedral shear strain as used in current mechano-regulation theories. Results of both µCT and CAD derived FEMs from the same scaffold were compared. Strain and fluid shear stress distributions, and subsequently the cell differentiation stimulus, were highly dependent on the pore shape. CAD models were not able to capture the distributions seen in the µCT FEM. The calculated mechanical stimuli could be combined with current mechanobiological models resulting in a tool to predict cell differentiation in the initial phase of tissue engineering. Although experimental data is still necessary to properly link mechanical signals to cell behavior in this specific setting, this model is an important step towards optimizing scaffold architecture and/or stimulation regimes. © 2014 Wiley Periodicals, Inc.

  18. Optimization of solvation models for predicting the structure of surface loops in proteins. (United States)

    Das, B; Meirovitch, H


    A novel procedure for optimizing the atomic solvation parameters (ASPs) sigma(i) developed recently for cyclic peptides is extended to surface loops in proteins. The loop is free to move, whereas the protein template is held fixed in its X-ray structure. The energy is E(tot) = E(FF)(epsilon = nr) + summation operator sigma(i)A(i), where E(FF)(epsilon = nr) is the force-field energy of the loop-loop and loop-template interactions, epsilon = nr is a distance-dependent dielectric constant, and n is an additional parameter to be optimized. A(i) is the solvent-accessible surface area of atom i. The optimal sigma(i) and n are those for which the loop structure with the global minimum of E(tot)(n, sigma(i)) becomes the experimental X-ray structure. Thus, the ASPs depend on the force field and are optimized in the protein environment, unlike commonly used ASPs such as those of Wesson and Eisenberg (Protein Sci 1992;1:227-235). The latter are based on the free energy of transfer of small molecules from the gas phase to water and have been traditionally combined with various force fields without further calibration. We found that for loops the all-atom AMBER force field performed better than OPLS and CHARMM22. Two sets of ASPs [based on AMBER (n = 2)], optimized independently for loops 64-71 and 89-97 of ribonuclease A, were similar and thus enabled the definition of a best-fit set. All these ASPs were negative (hydrophilic), including those for carbon. Very good (i.e., small) root-mean-square-deviation values from the X-ray loop structure were obtained with the three sets of ASPs, suggesting that the best-fit set would be transferable to loops in other proteins as well. The structure of loop 13-24 is relatively stretched and was insensitive to the effect of the ASPs. Copyright 2001 Wiley-Liss, Inc.

  19. Using a coupled groundwater/surface-water model to predict climate-change impacts to lakes in the Trout Lake Watershed, northern Wisconsin (United States)

    Hunt, Randall; Walker, John F.; Markstrom, Steven L.; Hay, Lauren E.; Doherty, John; Webb, Richard M.T.; Semmens, Darius J.


    A major focus of the U.S. Geological Survey’s Trout Lake Water, Energy, and Biogeochemical Budgets (WEBB) project is the development of a watershed model to allow predictions of hydrologic response to future conditions including land-use and climate change. The coupled groundwater/surface-water model GSFLOW was chosen for this purpose because it could easily incorporate an existing groundwater flow model and it provides for simulation of surface-water processes.

  20. Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model

    Directory of Open Access Journals (Sweden)

    François Counillon


    Full Text Available We document a pilot stochastic re-analysis computed by assimilating sea surface temperature (SST anomalies into the ocean component of the coupled Norwegian Climate Prediction Model (NorCPM for the period 1950–2010 (doi: 10.11582/2016.00002. NorCPM is based on the Norwegian Earth System Model and uses the ensemble Kalman filter for data assimilation (DA. Here, we assimilate SST from the stochastic HadISST2 historical reconstruction. The accuracy, reliability and drift are investigated using both assimilated and independent observations. NorCPM is slightly overdispersive against assimilated observations but shows stable performance through the analysis period. It demonstrates skills against independent measurements: sea surface height, heat and salt content, in particular in the Equatorial and North Pacific, the North Atlantic Subpolar Gyre (SPG region and the Nordic Seas. Furthermore, NorCPM provides a reliable monitoring of the SPG index and represents the vertical temperature variability there, in good agreement with observations. The monitoring of the Atlantic meridional overturning circulation is also encouraging. The benefit of using a flow-dependent assimilation method and constructing the covariance in isopycnal coordinates are investigated in the SPG region. Isopycnal coordinates discretisation is found to better capture the vertical structure than standard depth-coordinate discretisation, because it leads to a deeper influence of the assimilated surface observations. The vertical covariance shows a pronounced seasonal and decadal variability that highlights the benefit of flow-dependent DA method. This study demonstrates the potential of NorCPM to compute an ocean re-analysis for the 19th and 20th centuries when SST observations are available.

  1. Ultrasonic vibration-assisted pelleting of wheat straw: a predictive model for energy consumption using response surface methodology. (United States)

    Song, Xiaoxu; Zhang, Meng; Pei, Z J; Wang, Donghai


    Cellulosic biomass can be used as a feedstock for biofuel manufacturing. Pelleting of cellulosic biomass can increase its bulk density and thus improve its storability and reduce the feedstock transportation costs. Ultrasonic vibration-assisted (UV-A) pelleting can produce biomass pellets whose density is comparable to that processed by traditional pelleting methods (e.g. extruding, briquetting, and rolling). This study applied response surface methodology to the development of a predictive model for the energy consumption in UV-A pelleting of wheat straw. Effects of pelleting pressure, ultrasonic power, sieve size, and pellet weight were investigated. This study also optimized the process parameters to minimize the energy consumption in UV-A pelleting using response surface methodology. Optimal conditions to minimize the energy consumption were the following: ultrasonic power at 20%, sieve size at 4 mm, and pellet weight at 1g, and the minimum energy consumption was 2.54 Wh. Copyright © 2013 Elsevier B.V. All rights reserved.

  2. Hydrological land surface modelling

    DEFF Research Database (Denmark)

    Ridler, Marc-Etienne Francois

    and disaster management. The objective of this study is to develop and investigate methods to reduce hydrological model uncertainty by using supplementary data sources. The data is used either for model calibration or for model updating using data assimilation. Satellite estimates of soil moisture and surface......Recent advances in integrated hydrological and soil-vegetation-atmosphere transfer (SVAT) modelling have led to improved water resource management practices, greater crop production, and better flood forecasting systems. However, uncertainty is inherent in all numerical models ultimately leading...... hydrological and tested by assimilating synthetic hydraulic head observations in a catchment in Denmark. Assimilation led to a substantial reduction of model prediction error, and better model forecasts. Also, a new assimilation scheme is developed to downscale and bias-correct coarse satellite derived soil...

  3. Hydrological land surface modelling

    DEFF Research Database (Denmark)

    Ridler, Marc-Etienne Francois

    Recent advances in integrated hydrological and soil-vegetation-atmosphere transfer (SVAT) modelling have led to improved water resource management practices, greater crop production, and better flood forecasting systems. However, uncertainty is inherent in all numerical models ultimately leading...... and disaster management. The objective of this study is to develop and investigate methods to reduce hydrological model uncertainty by using supplementary data sources. The data is used either for model calibration or for model updating using data assimilation. Satellite estimates of soil moisture and surface...... hydrological and tested by assimilating synthetic hydraulic head observations in a catchment in Denmark. Assimilation led to a substantial reduction of model prediction error, and better model forecasts. Also, a new assimilation scheme is developed to downscale and bias-correct coarse satellite derived soil...

  4. Impact of Soil Moisture Assimilation on Land Surface Model Spin-Up and Coupled LandAtmosphere Prediction (United States)

    Santanello, Joseph A., Jr.; Kumar, Sujay V.; Peters-Lidard, Christa D.; Lawston, P.


    Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASAs Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spin-up can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux- PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

  5. 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

  6. 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

  7. Prediction of surface distress using neural networks (United States)

    Hamdi, Hadiwardoyo, Sigit P.; Correia, A. Gomes; Pereira, Paulo; Cortez, Paulo


    Road infrastructures contribute to a healthy economy throughout a sustainable distribution of goods and services. A road network requires appropriately programmed maintenance treatments in order to keep roads assets in good condition, providing maximum safety for road users under a cost-effective approach. Surface Distress is the key element to identify road condition and may be generated by many different factors. In this paper, a new approach is aimed to predict Surface Distress Index (SDI) values following a data-driven approach. Later this model will be accordingly applied by using data obtained from the Integrated Road Management System (IRMS) database. Artificial Neural Networks (ANNs) are used to predict SDI index using input variables related to the surface of distress, i.e., crack area and width, pothole, rutting, patching and depression. The achieved results show that ANN is able to predict SDI with high correlation factor (R2 = 0.996%). Moreover, a sensitivity analysis was applied to the ANN model, revealing the influence of the most relevant input parameters for SDI prediction, namely rutting (59.8%), crack width (29.9%) and crack area (5.0%), patching (3.0%), pothole (1.7%) and depression (0.3%).

  8. 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.

  9. Analytical Modeling for Mechanical Strength Prediction with Raman Spectroscopy and Fractured Surface Morphology of Novel Coconut Shell Powder Reinforced: Epoxy Composites (United States)

    Singh, Savita; Singh, Alok; Sharma, Sudhir Kumar


    In this paper, an analytical modeling and prediction of tensile and flexural strength of three dimensional micro-scaled novel coconut shell powder (CSP) reinforced epoxy polymer composites have been reported. The novel CSP has a specific mixing ratio of different coconut shell particle size. A comparison is made between obtained experimental strength and modified Guth model. The result shows a strong evidence for non-validation of modified Guth model for strength prediction. Consequently, a constitutive modeled equation named Singh model has been developed to predict the tensile and flexural strength of this novel CSP reinforced epoxy composite. Moreover, high resolution Raman spectrum shows that 40 % CSP reinforced epoxy composite has high dielectric constant to become an alternative material for capacitance whereas fractured surface morphology revealed that a strong bonding between novel CSP and epoxy polymer for the application as light weight composite materials in engineering.

  10. Impact of chemical lateral boundary conditions in a regional air quality forecast model on surface ozone predictions during stratospheric intrusions (United States)

    Pendlebury, Diane; Gravel, Sylvie; Moran, Michael D.; Lupu, Alexandru


    A regional air quality forecast model, GEM-MACH, is used to examine the conditions under which a limited-area air quality model can accurately forecast near-surface ozone concentrations during stratospheric intrusions. Periods in 2010 and 2014 with known stratospheric intrusions over North America were modelled using four different ozone lateral boundary conditions obtained from a seasonal climatology, a dynamically-interpolated monthly climatology, global air quality forecasts, and global air quality reanalyses. It is shown that the mean bias and correlation in surface ozone over the course of a season can be improved by using time-varying ozone lateral boundary conditions, particularly through the correct assignment of stratospheric vs. tropospheric ozone along the western lateral boundary (for North America). Part of the improvement in surface ozone forecasts results from improvements in the characterization of near-surface ozone along the lateral boundaries that then directly impact surface locations near the boundaries. However, there is an additional benefit from the correct characterization of the location of the tropopause along the western lateral boundary such that the model can correctly simulate stratospheric intrusions and their associated exchange of ozone from stratosphere to troposphere. Over a three-month period in spring 2010, the mean bias was seen to improve by as much as 5 ppbv and the correlation by 0.1 depending on location, and on the form of the chemical lateral boundary condition.


    Directory of Open Access Journals (Sweden)

    M.A. Maleque


    Full Text Available This paper develops a single order mathematical model for correlating various electrical discharge machining (EDM parameters and performance characteristics by utilizing relevant experimental data obtained through experimentation. In addition to the effect of peak ampere, the effect of pulse on time and pulse off time on surface roughness has also been investigated. Experiments have been conducted on titanium alloy Ti-6Al-4V with a copper electrode retaining negative polarity as per the Design of Experiments. Response surface methodology techniques are utilized to develop the mathematical model as well as to optimize the EDM parameters. An analysis of variance has been performed for the validity test of fit and adequacy of the proposed models. It can be seen that increasing pulse on time causes a fine surface until a certain value, beyond which the surface finish deteriorates. The excellent surface finish is investigated in this study for the case of pulse on time below 80 µs. This result acts as a guide for selecting the required process outputs and most economic industrial machining conditions for optimizing the input factors.

  12. Surface complexation modeling for predicting solid phase arsenic concentrations in the sediments of the Mississippi River Valley alluvial aquifer, Arkansas, USA (United States)

    Sharif, M.S.U.; Davis, R.K.; Steele, K.F.; Kim, B.; Hays, P.D.; Kresse, T.M.; Fazio, J.A.


    The potential health impact of As in drinking water supply systems in the Mississippi River Valley alluvial aquifer in the state of Arkansas, USA is significant. In this context it is important to understand the occurrence, distribution and mobilization of As in the Mississippi River Valley alluvial aquifer. Application of surface complexation models (SCMs) to predict the sorption behavior of As and hydrous Fe oxides (HFO) in the laboratory has increased in the last decade. However, the application of SCMs to predict the sorption of As in natural sediments has not often been reported, and such applications are greatly constrained by the lack of site-specific model parameters. Attempts have been made to use SCMs considering a component additivity (CA) approach which accounts for relative abundances of pure phases in natural sediments, followed by the addition of SCM parameters individually for each phase. Although few reliable and internally consistent sorption databases related to HFO exist, the use of SCMs using laboratory-derived sorption databases to predict the mobility of As in natural sediments has increased. This study is an attempt to evaluate the ability of the SCMs using the geochemical code PHREEQC to predict solid phase As in the sediments of the Mississippi River Valley alluvial aquifer in Arkansas. The SCM option of the double-layer model (DLM) was simulated using ferrihydrite and goethite as sorbents quantified from chemical extractions, calculated surface-site densities, published surface properties, and published laboratory-derived sorption constants for the sorbents. The model results are satisfactory for shallow wells (10.6. m below ground surface), where the redox condition is relatively oxic or mildly suboxic. However, for the deep alluvial aquifer (21-36.6. m below ground surface) where the redox condition is suboxic to anoxic, the model results are unsatisfactory. ?? 2011 Elsevier Ltd.

  13. 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...

  14. 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.

  15. 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.

  16. Nitrogen and phosphorus retention in surface waters: an inter-comparison of predictions by catchment models of different complexity

    Czech Academy of Sciences Publication Activity Database

    Hejzlar, Josef; Anthony, S.; Arheimer, B.; Behrendt, H.; Bouraoui, F.; Grizzetti, B.; Groenendijk, P.; Jeuken, M.H.J.L.; Johnsson, H.; Lo Porto, A.; Kronvang, B.; Panagopoulos, Y.; Siderius, C.; Silgram, M.; Venohr, M.; Žaloudík, Jiří


    Roč. 11, č. 3 (2009), s. 584-593 ISSN 1464-0325 R&D Projects: GA AV ČR(CZ) 1QS600170504 Grant - others:EC(XE) EVK1-2001-00062 Institutional research plan: CEZ:AV0Z60170517 Keywords : catchment modelling * phosphorus and nitrogen retention in surface waters * diffuse sources * source apportionment * MONERIS * EveNFlow * TRK * SWAT * NL-CAT Subject RIV: DJ - Water Pollution ; Quality Impact factor: 2.225, year: 2009

  17. 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...

  18. Using the Regional Ocean Modelling System (ROMS to improve the sea surface temperature predictions of the MERCATOR Ocean System

    Directory of Open Access Journals (Sweden)

    Pedro Costa


    Full Text Available Global models are generally capable of reproducing the observed trends in the globally averaged sea surface temperature (SST. However, the global models do not perform as well on regional scales. Here, we present an ocean forecast system based on the Regional Ocean Modelling System (ROMS, the boundary conditions come from the MERCATOR ocean system for the North Atlantic (1/6° horizontal resolution. The system covers the region of the northwestern Iberian Peninsula with a horizontal resolution of 1/36°, forced with the Weather Research and Forecasting Model (WRF and the Soil Water Assessment Tool (SWAT. The ocean model results from the regional ocean model are validated using real-time SST and observations from the MeteoGalicia, INTECMAR and Puertos Del Estado real-time observational networks. The validation results reveal that over a one-year period the mean absolute error of the SST is less than 1°C, and several sources of measured data reveal that the errors decrease near the coast. This improvement is related to the inclusion of local forcing not present in the boundary condition model.

  19. 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.

  20. Modeling mechanical signals on the surface of µCT and CAD based rapid prototype scaffold models to predict (early stage) tissue development

    NARCIS (Netherlands)

    Hendrikson, W.J.; van Blitterswijk, Clemens; Verdonschot, Nicolaas Jacobus Joseph; Moroni, Lorenzo; Rouwkema, Jeroen


    In the field of tissue engineering, mechano-regulation theories have been applied to help predict tissue development in tissue engineering scaffolds in the past. For this, finite element models (FEMs) were used to predict the distribution of strains within a scaffold. However, the strains reported

  1. Modeling mechanical signals on the surface of microCT and CAD based rapid prototype scaffold models to predict (early stage) tissue development

    NARCIS (Netherlands)

    Hendrikson, W.J.; Blitterswijk, C.A. van; Verdonschot, N.J.; Moroni, L.; Rouwkema, J.


    In the field of tissue engineering, mechano-regulation theories have been applied to help predict tissue development in tissue engineering scaffolds in the past. For this, finite element models (FEMs) were used to predict the distribution of strains within a scaffold. However, the strains reported

  2. Integration of nitrogen dynamics into the Noah-MP land surface model v1.1 for climate and environmental predictions

    International Nuclear Information System (INIS)

    Cai, X.; Zhang, X.


    Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next-generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. Here in this study, we add a capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This model development incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station – a Long Term Ecological Research site within the US Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.

  3. [Fire behavior of ground surface fuels in Pinus koraiensis and Quercus mongolica mixed forest under no wind and zero slope condition: a prediction with extended Rothermel model]. (United States)

    Zhang, Ji-Li; Liu, Bo-Fei; Chu, Teng-Fei; Di, Xue-Ying; Jin, Sen


    A laboratory burning experiment was conducted to measure the fire spread speed, residual time, reaction intensity, fireline intensity, and flame length of the ground surface fuels collected from a Korean pine (Pinus koraiensis) and Mongolian oak (Quercus mongolica) mixed stand in Maoer Mountains of Northeast China under the conditions of no wind, zero slope, and different moisture content, load, and mixture ratio of the fuels. The results measured were compared with those predicted by the extended Rothermel model to test the performance of the model, especially for the effects of two different weighting methods on the fire behavior modeling of the mixed fuels. With the prediction of the model, the mean absolute errors of the fire spread speed and reaction intensity of the fuels were 0.04 m X min(-1) and 77 kW X m(-2), their mean relative errors were 16% and 22%, while the mean absolute errors of residual time, fireline intensity and flame length were 15.5 s, 17.3 kW X m(-1), and 9.7 cm, and their mean relative errors were 55.5%, 48.7%, and 24%, respectively, indicating that the predicted values of residual time, fireline intensity, and flame length were lower than the observed ones. These errors could be regarded as the lower limits for the application of the extended Rothermel model in predicting the fire behavior of similar fuel types, and provide valuable information for using the model to predict the fire behavior under the similar field conditions. As a whole, the two different weighting methods did not show significant difference in predicting the fire behavior of the mixed fuels by extended Rothermel model. When the proportion of Korean pine fuels was lower, the predicted values of spread speed and reaction intensity obtained by surface area weighting method and those of fireline intensity and flame length obtained by load weighting method were higher; when the proportion of Korean pine needles was higher, the contrary results were obtained.

  4. Predicting 3D lip shapes using facial surface EMG

    NARCIS (Netherlands)

    Eskes, Merijn; van Alphen, Maarten J. A.; Balm, Alfons J. M.; Smeele, Ludi E.; Brandsma, Dieta; van der Heijden, Ferdinand


    Aim The aim of this study is to prove that facial surface electromyography (sEMG) conveys sufficient information to predict 3D lip shapes. High sEMG predictive accuracy implies we could train a neural control model for activation of biomechanical models by simultaneously recording sEMG signals and

  5. Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa (United States)

    Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh


    Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

  6. The retrospective prediction of El Nino-southern oscillation from 1881 to 2000 by a hybrid coupled model: (I) Sea surface temperature assimilation with ensemble Kalman filter

    Energy Technology Data Exchange (ETDEWEB)

    Deng, Ziwang; Tang, Youmin; Zhou, Xiaobing [University of Northern British Columbia, Environmental Science and Engineering, Prince George, BC (Canada)


    In this study, we assimilated sea surface temperature (SST) data of the past 120 years into an oceanic general circulation model (OGCM) for El Nino-southern oscillation (ENSO) retrospective predictions using ensemble Kalman filter (EnKF). It was found that the ensemble covariance matrix in EnKF can act as a time-variant transfer operator to project the SST corrections onto the subsurface temperatures effectively when initial perturbations of ensemble were constructed using vertically coherent random fields. As such the increments of subsurface temperatures can be obtained via the transfer operator during assimilation cycles. The results show that the SST assimilation improves the model simulation skills significantly, not only for the SST anomalies over the whole assimilated domain, but also for the subsurface temperature anomalies of the upper 100 m over the tropical Pacific off the equator. Along the equator, the improvement of the assimilation is confined within the mixing layer because strong upwelling motions there prevent the downward transfer of SST information. The retrospective prediction skills of ENSO over the past 120 years from 1881 to 2000 were significantly improved by the SST assimilation at all leads of 1-12 months, especially for the 3-6 months leads, compared with those initialized by the control run without assimilation. The skilful predictions by the assimilation allow us to further study ENSO predictability using this coupled model. (orig.)

  7. Response Surface Modeling Using Multivariate Orthogonal Functions (United States)

    Morelli, Eugene A.; DeLoach, Richard


    A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

  8. Examining the Impacts of High-Resolution Land Surface Initialization on Model Predictions of Convection in the Southeastern U.S. (United States)

    Case, Jonathan L.; Kumar, Sujay V.; Santos, Pablo; Medlin, Jeffrey M.; Jedlovec, Gary J.


    One of the most challenging weather forecast problems in the southeastern U.S. is daily summertime pulse convection. During the summer, atmospheric flow and forcing are generally weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. Numerical simulations of pulse convection usually have low skill, even in local predictions at high resolution, due to the inherent chaotic nature of these precipitation systems. Forecast errors can arise from assumptions within physics parameterizations, model resolution limitations, as well as uncertainties in both the initial state of the atmosphere and land surface variables such as soil moisture and temperature. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture and temperature, ground fluxes, and vegetation are necessary to better simulate the interactions between the land surface and atmosphere, and ultimately improve predictions of local circulations and summertime pulse convection. The NASA Short-term Prediction Research and Transition (SPORT) Center has been conducting studies to examine the impacts of high-resolution land surface initialization data generated by offline simulations of the NASA Land Informatiot System (LIS) on subsequent numerical forecasts using the Weather Research and Forecasting (WRF) model (Case et al. 2008, to appear in the Journal of Hydrometeorology). Case et al. presents improvements to simulated sea breezes and surface verification statistics over Florida by initializing WRF with land surface variables from an offline LIS spin-up run, conducted on the exact WRF domain and resolution. The current project extends the previous work over Florida, focusing on selected case studies of typical pulse convection over the southeastern U.S., with an emphasis on improving local short-term WRF

  9. 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.

  10. General predictive model of friction behavior regimes for metal contacts based on the formation stability and evolution of nanocrystalline surface films.

    Energy Technology Data Exchange (ETDEWEB)

    Argibay, Nicolas [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Cheng, Shengfeng [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Sawyer, W. G. [Univ. of Florida, Gainesville, FL (United States); Michael, Joseph R. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Chandross, Michael E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)


    The prediction of macro-scale friction and wear behavior based on first principles and material properties has remained an elusive but highly desirable target for tribologists and material scientists alike. Stochastic processes (e.g. wear), statistically described parameters (e.g. surface topography) and their evolution tend to defeat attempts to establish practical general correlations between fundamental nanoscale processes and macro-scale behaviors. We present a model based on microstructural stability and evolution for the prediction of metal friction regimes, founded on recently established microstructural deformation mechanisms of nanocrystalline metals, that relies exclusively on material properties and contact stress models. We show through complementary experimental and simulation results that this model overcomes longstanding practical challenges and successfully makes accurate and consistent predictions of friction transitions for a wide range of contact conditions. This framework not only challenges the assumptions of conventional causal relationships between hardness and friction, and between friction and wear, but also suggests a pathway for the design of higher performance metal alloys.

  11. Generalised empirical method for predicting surface subsidence

    International Nuclear Information System (INIS)

    Zhang, M.; Bhattacharyya, A.K.


    Based on a simplified strata parameter, i.e. the ratio of total thickness of the strong rock beds in an overburden to the overall thickness of the overburden, a Generalised Empirical Method (GEM) is described for predicting the maximum subsidence and the shape of a complete transverse subsidence profile due to a single completely extracted longwall panel. In the method, a nomogram for predicting the maximum surface subsidence is first developed from the data collected from subsidence measurements worldwide. Then, a method is developed for predicting the shapes of complete transfer subsidence profiles for a horizontal seam and ground surface and is verified by case studies. 13 refs., 9 figs., 2 tabs

  12. Response surface model predictions of emergence and response to pain in the recovery room: an evaluation of patients emerging from an isoflurane and fentanyl anesthetic (United States)

    Syroid, Noah D.; Johnson, Ken B.; Pace, Nathan L.; Westenskow, Dwayne R.; Tyler, Diane; Brühschwein, Frederike; Albert, Robert W.; Roalstad, Shelly; Costy-Bennett, Samuel; Egan, Talmage D.


    Introduction Sevoflurane - remifentanil interaction models that predict responsiveness and response to painful stimuli have been evaluated in patients undergoing elective surgery. Preliminary evaluations of model predictions were found to be consistent with observations in patients anesthetized with sevoflurane, remifentanil and fentanyl. The present study explored the feasibility of adapting the predictions of sevoflurane-remifentanil interaction models to an isoflurane-fentanyl anesthetic. We hypothesized that model predictions adapted for isoflurane and fentanyl are consistent with observed patient responses and are similar to the predictions observed in our prior work with sevoflurane-remifentanil/fentanyl anesthetics. Methods Twenty-five patients scheduled for elective surgery received a fentanyl-isoflurane anesthetic. Model predictions of unresponsiveness were recorded at emergence and predictions of a response to noxious stimulus were recorded when patients first required analgesics in the recovery room. Model predictions were compared to observations with graphical and temporal analyses. Results were also compared to our prior predictions following a sevoflurane-remifentanil/fentanyl anesthetic. Results While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (≥ 99%). Following termination of the anesthetic, model predictions of responsiveness well described the actual fraction of patients observed to be responsive during emergence. Half of the patients awoke within 2 minutes of the 50% model predicted probability of unresponsiveness; 70% awoke within 4 minutes. Similarly, predictions of a response to a noxious stimulus were consistent with the number of patients who required fentanyl in the recovery room. Model predictions following an isoflurane-fentanyl anesthetic were similar to model predictions following a sevoflurane-remifentanil/fentanyl anesthetic. Discussion Results confirmed our study

  13. Surface drift prediction in the Adriatic Sea using hyper-ensemble statistics on atmospheric, ocean and wave models: Uncertainties and probability distribution areas (United States)

    Rixen, M.; Ferreira-Coelho, E.; Signell, R.


    Despite numerous and regular improvements in underlying models, surface drift prediction in the ocean remains a challenging task because of our yet limited understanding of all processes involved. Hence, deterministic approaches to the problem are often limited by empirical assumptions on underlying physics. Multi-model hyper-ensemble forecasts, which exploit the power of an optimal local combination of available information including ocean, atmospheric and wave models, may show superior forecasting skills when compared to individual models because they allow for local correction and/or bias removal. In this work, we explore in greater detail the potential and limitations of the hyper-ensemble method in the Adriatic Sea, using a comprehensive surface drifter database. The performance of the hyper-ensembles and the individual models are discussed by analyzing associated uncertainties and probability distribution maps. Results suggest that the stochastic method may reduce position errors significantly for 12 to 72??h forecasts and hence compete with pure deterministic approaches. ?? 2007 NATO Undersea Research Centre (NURC).

  14. The prediction of BRDFs from surface profile measurements

    International Nuclear Information System (INIS)

    Church, E.L.; Takacs, P.Z.; Leonard, T.A.


    This paper discusses methods of predicting the BRDF of smooth surfaces from profile measurements of their surface finish. The conversion of optical profile data to the BRDF at the same wavelength is essentially independent of scattering models, while the conversion of mechanical measurements, and wavelength scaling in general, are model dependent. Procedures are illustrated for several surfaces, including two from the recent HeNe BRDF round robin, and results are compared with measured data. Reasonable agreement is found except for surfaces which involve significant scattering from isolated surface defects which are poorly sampled in the profile data

  15. Application of Box-Behnken Design and Response Surface Methodology for Surface Roughness Prediction Model of CP-Ti Powder Metallurgy Components Through WEDM (United States)

    Das, Arunangsu; Sarkar, Susenjit; Karanjai, Malobika; Sutradhar, Goutam


    The present work was undertaken to investigate and characterize the machining parameters (such as surface roughness, etc.) of uni-axially pressed commercially pure titanium sintered powder metallurgy components. Powder was uni-axially pressed at designated pressure of 840 MPa to form cylindrical samples and the green compacts were sintered at 0.001 mbar for about 4 h with sintering temperature varying from 1350 to 1450 °C. The influence of the sintering temperature, pulse-on and pulse-off time at wire-EDM on the surface roughness of the preforms has been investigated thoroughly. Experiments were conducted under different machining parameters in a CNC operated wire-cut EDM. The surface roughness of the machined surface was measured and critically analysed. The optimum surface roughness was achieved under the conditions of 6 μs pulse-on time, 9 μs pulse-off time and at sintering temperature of 1450 °C.

  16. Application of Box-Behnken Design and Response Surface Methodology for Surface Roughness Prediction Model of CP-Ti Powder Metallurgy Components Through WEDM (United States)

    Das, Arunangsu; Sarkar, Susenjit; Karanjai, Malobika; Sutradhar, Goutam


    The present work was undertaken to investigate and characterize the machining parameters (such as surface roughness, etc.) of uni-axially pressed commercially pure titanium sintered powder metallurgy components. Powder was uni-axially pressed at designated pressure of 840 MPa to form cylindrical samples and the green compacts were sintered at 0.001 mbar for about 4 h with sintering temperature varying from 1350 to 1450 °C. The influence of the sintering temperature, pulse-on and pulse-off time at wire-EDM on the surface roughness of the preforms has been investigated thoroughly. Experiments were conducted under different machining parameters in a CNC operated wire-cut EDM. The surface roughness of the machined surface was measured and critically analysed. The optimum surface roughness was achieved under the conditions of 6 μs pulse-on time, 9 μs pulse-off time and at sintering temperature of 1450 °C.

  17. Calibration of a PHREEQC-based geochemical model to predict surface water discharge from an operating uranium mill in the Athabasca Basin

    International Nuclear Information System (INIS)

    Mahoney, J.; Ryan, F.


    A PHREEQC based geochemical model has been developed to predict impacts from the McClean Lake Mill discharges through three lakes in the Athabasca Basin, Saskatchewan, Canada. The model is primarily a mixing calculation that uses site specific water balances and water compositions from five sources: 1) two water treatment plants, 2) waters from pit dewatering wells, 3) run-off into the lakes from surface waters, 4) ambient lake compositions, and 5) precipitation (rain and snow) onto the pit lake surface. The model allows for the discharge of these waters into the first lake, which then flows into another nearby lake and finally into a third larger lake. Water losses through evaporation and the impact of subsequent evapoconcentration processes are included in the model. PHREEQC has numerous mass transfer options including mixing, user specified reactions, equilibration with gas and solid phases, and surface complexation. Thus this program is ideally suited to this application. Preparation of such a complicated model is facilitated by an EXCEL Spreadsheet, which converts the water balance into appropriately formatted mixing proportions and to prepare portions of the PHREEQC input file in a format directly useable by PHREEQC. This allows for a high level of flexibility, while reducing transcription errors. For each scenario, the model path involves mixing of the waters in the first lake, followed by evapoconcentration, equilibration of the resulting solution with gas phases, including carbon dioxide and oxygen and with minerals and surfaces. The resultant composition is mixed in the second lake with more surface water, lake water and precipitation, and then re-equilibrated. This water represents the flow into the final lake; further mixing/dilution is accommodated; chemical equilibration may also occur. Because of the numerous steps and processes that define the pathway, each annual step requires approximately 200 lines of input in PHREEQC. Models used in the initial

  18. Prediction of Acute Radiation Mucositis using an Oral Mucosal Dose Surface Model in Carbon Ion Radiotherapy for Head and Neck Tumors.

    Directory of Open Access Journals (Sweden)

    Atsushi Musha

    Full Text Available To evaluate the dose-response relationship for development of acute radiation mucositis (ARM using an oral mucosal dose surface model (OMDS-model in carbon ion radiotherapy (C-ion RT for head and neck tumors.Thirty-nine patients receiving C-ion RT for head and neck cancer were evaluated for ARM (once per week for 6 weeks according to the Common Terminology Criteria for Adverse Events (CTCAE, version 4.0, and the Radiation Therapy Oncology Group (RTOG scoring systems. The irradiation schedule typically used was 64 Gy [relative biological effectiveness (RBE] in 16 fractions for 4 weeks. Maximum point doses in the palate and tongue were compared with ARM in each patient.The location of the ARM coincided with the high-dose area in the OMDS-model. There was a clear dose-response relationship between maximum point dose and ARM grade assessed using the RTOG criteria but not the CTCAE. The threshold doses for grade 2-3 ARM in the palate and tongue were 43.0 Gy(RBE and 54.3 Gy(RBE, respectively.The OMDS-model was useful for predicting the location and severity of ARM. Maximum point doses in the model correlated well with grade 2-3 ARM.

  19. 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...

  20. Deterministic prediction of surface wind speed variations

    Directory of Open Access Journals (Sweden)

    G. V. Drisya


    Full Text Available Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.

  1. Single-layer model for surface roughness. (United States)

    Carniglia, C K; Jensen, D G


    Random roughness of an optical surface reduces its specular reflectance and transmittance by the scattering of light. The reduction in reflectance can be modeled by a homogeneous layer on the surface if the refractive index of the layer is intermediate to the indices of the media on either side of the surface. Such a layer predicts an increase in the transmittance of the surface and therefore does not provide a valid model for the effects of scatter on the transmittance. Adding a small amount of absorption to the layer provides a model that predicts a reduction in both reflectance and transmittance. The absorbing layer model agrees with the predictions of a scalar scattering theory for a layer with a thickness that is twice the rms roughness of the surface. The extinction coefficient k for the layer is proportional to the thickness of the layer.

  2. 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


    Directory of Open Access Journals (Sweden)

    S. D. PHILIP


    Full Text Available Response surface methodology has been used to study the effects of the machining parameters such as spindle speed, feed rate and axial depth of cut on surface roughness of duplex stainless steel in end milling operation. Dry milling experiments were conducted with three levels of spindle speed, feed rate and axial depth of cut. A mathematical model has been developed to predict the surface roughness in terms of the machining parameters using Box-Behnken design response surface methodology. The adequacy of the model was verified using analysis of variance. The prediction equation shows that the feed rate is the most important factor that influences the surface roughness followed by axial depth of cut and spindle speed. The validity of the model was verified by conducting the confirmation experiment.

  4. 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...

  5. An Improved MUSIC Model for Gibbsite Surfaces

    Energy Technology Data Exchange (ETDEWEB)

    Mitchell, Scott C.; Bickmore, Barry R.; Tadanier, Christopher J.; Rosso, Kevin M.


    Here we use gibbsite as a model system with which to test a recently published, bond-valence method for predicting intrinsic pKa values for surface functional groups on oxides. At issue is whether the method is adequate when valence parameters for the functional groups are derived from ab initio structure optimization of surfaces terminated by vacuum. If not, ab initio molecular dynamics (AIMD) simulations of solvated surfaces (which are much more computationally expensive) will have to be used. To do this, we had to evaluate extant gibbsite potentiometric titration data that where some estimate of edge and basal surface area was available. Applying BET and recently developed atomic force microscopy methods, we found that most of these data sets were flawed, in that their surface area estimates were probably wrong. Similarly, there may have been problems with many of the titration procedures. However, one data set was adequate on both counts, and we applied our method of surface pKa int prediction to fitting a MUSIC model to this data with considerable success—several features of the titration data were predicted well. However, the model fit was certainly not perfect, and we experienced some difficulties optimizing highly charged, vacuum-terminated surfaces. Therefore, we conclude that we probably need to do AIMD simulations of solvated surfaces to adequately predict intrinsic pKa values for surface functional groups.

  6. Prediction of Protein Structure Using Surface Accessibility Data. (United States)

    Hartlmüller, Christoph; Göbl, Christoph; Madl, Tobias


    An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). This method exploits the distance-to-surface information encoded in the sPRE data in the chemical shift-based CS-Rosetta de novo structure prediction framework to generate reliable structural models. For several proteins, it is demonstrated that surface accessibility data is an excellent measure of the correct protein fold in the early stages of the computational folding algorithm and significantly improves accuracy and convergence of the standard Rosetta structure prediction approach. © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

  7. Molecular Dynamics Simulations for Predicting Surface Wetting

    Directory of Open Access Journals (Sweden)

    Jing Chen


    Full Text Available The investigation of wetting of a solid surface by a liquid provides important insights; the contact angle of a liquid droplet on a surface provides a quantitative measurement of this interaction and the degree of attraction or repulsion of that liquid type by the solid surface. Molecular dynamics (MD simulations are a useful way to examine the behavior of liquids on solid surfaces on a nanometer scale. Thus, we surveyed the state of this field, beginning with the fundamentals of wetting calculations to an examination of the different MD methodologies used. We highlighted some of the advantages and disadvantages of the simulations, and look to the future of computer modeling to understand wetting and other liquid-solid interaction phenomena.

  8. Assessing the Impact of Surface and Upper-Air Observations on the Forecast Skill of the ACCESS Numerical Weather Prediction Model over Australia

    Directory of Open Access Journals (Sweden)

    Sergei Soldatenko


    Full Text Available The impact of the Australian Bureau of Meteorology’s in situ observations (land and sea surface observations, upper air observations by radiosondes, pilot balloons, wind profilers, and aircraft observations on the short-term forecast skill provided by the ACCESS (Australian Community Climate and Earth-System Simulator global numerical weather prediction (NWP system is evaluated using an adjoint-based method. This technique makes use of the adjoint perturbation forecast model utilized within the 4D-Var assimilation system, and is able to calculate the individual impact of each assimilated observation in a cycling NWP system. The results obtained show that synoptic observations account for about 60% of the 24-h forecast error reduction, with the remainder accounted for by aircraft (12.8%, radiosondes (10.5%, wind profilers (3.9%, pilot balloons (2.8%, buoys (1.7% and ships (1.2%. In contrast, the largest impact per observation is from buoys and aircraft. Overall, all observation types have a positive impact on the 24-h forecast skill. Such results help to support the decision-making process regarding the evolution of the observing network, particularly at the national level. Consequently, this 4D-Var-based approach has great potential as a tool to assist the design and running of an efficient and effective observing network.

  9. Chondritic Models of 4 Vesta: Comparison of Data from the Dawn Mission with Predicted Internal Structure and Surface Composition/Mineralogy (United States)

    Toplis, M. J.; Mizzon, H.; Forni, O.; Monnereau, M.; Barrat, J-A.; Prettyman, T. H.; McSween, H. Y.; McCoy, T. J.; Mittlefehldt, D. W.; De Sanctis, M. C.; hide


    While the HEDs provide an extremely useful basis for interpreting data from the Dawn mission, there is no guarantee that they provide a complete vision of all possible crustal (and possibly mantle) lithologies that are exposed at the surface of Vesta. With this in mind, an alternative approach is to identify plausible bulk compositions and use mass-balance and geochemical modelling to predict possible internal structures and crust/mantle compositions and mineralogies. While such models must be consistent with known HED samples, this approach has the potential to extend predictions to thermodynamically plausible rock types that are not necessarily present in the HED collection. Nine chondritic bulk compositions are considered (CI, CV, CO, CM, H, L, LL, EH, EL). For each, relative proportions and densities of the core, mantle, and crust are quantified. This calculation is complicated by the fact that iron may occur in metallic form (in the core) and/or in oxidized form (in the mantle and crust). However, considering that the basaltic crust has the composition of Juvinas and assuming that this crust is in thermodynamic equilibrium with the residual mantle, it is possible to calculate a single solution to this problem for a given bulk composition. Of the nine bulk compositions tested, solutions corresponding to CI and LL groups predicted a negative metal fraction and were not considered further. Solutions for enstatite chondrites imply significant oxidation relative to the starting materials and these solutions too are considered unlikely. For the remaining bulk compositions, the relative proportion of crust to bulk silicate is typically in the range 15 to 20% corresponding to crustal thicknesses of 15 to 20 km for a porosity-free Vesta-sized body. The mantle is predicted to be largely dominated by olivine (greater than 85%) for carbonaceous chondrites, but to be a roughly equal mixture of olivine and pyroxene for ordinary chondrite precursors. All bulk compositions

  10. Linear predictability: A sea surface height case study (United States)

    Sonnewald, Maike; Wunsch, Carl; Heimbach, Patrick


    A benchmark of linear predictive skill of global sea surface height (SSH or η) is presented, complementing more complicated studies of η predictive skill. Twenty years of the ECCOv4 state estimate (1992-2012) are used, fitting ARMA(n,m) models where the order is chosen by the Akaike and Bayesian Information Criteria (AIC and BIC). The prediction on the basis of monthly detrended data shows skill generally of the order of a few months, with isolated regions of twelve months or more. With the trend, the predictive skill increases, particularly in the south Pacific. Annually averaged data are also used, although the time-series are too short to assess the variability. Including a linear trend as part of the signal results in some enhanced predictability.

  11. Modeling conoid surfaces


    Velimirović Ljubica S.; Stanković Mića S.; Radivojević Grozdana


    In tins paper we consider conoid surfaces as frequently used surfaces in building techniques, mainly as daring roof structures. Different types of conoids are presented using the programme package Mathematica. We describe the generation of conoids and by means of parametric representation we get their graphics. The geometric approach offers a wide range of possibilities in the research of complicated spatial surface systems.

  12. Comment on 'Modelling of surface energies of elemental crystals'

    International Nuclear Information System (INIS)

    Li Jinping; Luo Xiaoguang; Hu Ping; Dong Shanliang


    Jiang et al (2004 J. Phys.: Condens. Matter 16 521) present a model based on the traditional broken-bond model for predicting surface energies of elemental crystals. It is found that bias errors can be produced in calculating the coordination numbers of surface atoms, especially in the prediction of high-Miller-index surface energies. (comment)

  13. 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...

  14. BION web server: predicting non-specifically bound surface ions. (United States)

    Petukh, Marharyta; Kimmet, Taylor; Alexov, Emil


    Ions are essential component of the cell and frequently are found bound to various macromolecules, in particular to proteins. A binding of an ion to a protein greatly affects protein's biophysical characteristics and needs to be taken into account in any modeling approach. However, ion's bounded positions cannot be easily revealed experimentally, especially if they are loosely bound to macromolecular surface. Here, we report a web server, the BION web server, which addresses the demand for tools of predicting surface bound ions, for which specific interactions are not crucial; thus, they are difficult to predict. The BION is easy to use web server that requires only coordinate file to be inputted, and the user is provided with various, but easy to navigate, options. The coordinate file with predicted bound ions is displayed on the output and is available for download.

  15. 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...

  16. Seasonal Prediction of Surface Air Temperature across Vietnam Using the Regional Climate Model Version 4.2 (RegCM4.2)


    Phan Van, Tan; Van Nguyen, Hiep; Trinh Tuan, Long; Nguyen Quang, Trung; Ngo-Duc, Thanh; Laux, Patrick; Nguyen Xuan, Thanh


    To investigate the ability of dynamical seasonal climate predictions for Vietnam, the RegCM4.2 is employed to perform seasonal prediction of 2 m mean (T2m), maximum (Tx), and minimum (Tn) air temperature for the period from January 2012 to November 2013 by downscaling the NCEP Climate Forecast System (CFS) data. For model bias correction, the model and observed climatology is constructed using the CFS reanalysis and observed temperatures over Vietnam for the period 1980–2010, respectively. Th...

  17. On the predictability of land surface fluxes from meteorological variables (United States)

    Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.


    Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.


    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.

  19. Modelling land surface - atmosphere interactions

    DEFF Research Database (Denmark)

    Rasmussen, Søren Højmark

    related to inaccurate land surface modelling, e.g. enhanced warm bias in warm dry summer months. Coupling the regional climate model to a hydrological model shows the potential of improving the surface flux simulations in dry periods and the 2 m air temperature in general. In the dry periods......The study is investigates modelling of land surface – atmosphere interactions in context of fully coupled climatehydrological model. With a special focus of under what condition a fully coupled model system is needed. Regional climate model inter-comparison projects as ENSEMBLES have shown bias...

  20. 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.

  1. 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.

  2. 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...

  3. 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...

  4. Alternative model of random surfaces

    International Nuclear Information System (INIS)

    Ambartzumian, R.V.; Sukiasian, G.S.; Savvidy, G.K.; Savvidy, K.G.


    We analyse models of triangulated random surfaces and demand that geometrically nearby configurations of these surfaces must have close actions. The inclusion of this principle drives us to suggest a new action, which is a modified Steiner functional. General arguments, based on the Minkowski inequality, shows that the maximal distribution to the partition function comes from surfaces close to the sphere. (orig.)

  5. Modeling of ESD events from polymeric surfaces

    Energy Technology Data Exchange (ETDEWEB)

    Pfeifer, Kent Bryant


    Transient electrostatic discharge (ESD) events are studied to assemble a predictive model of discharge from polymer surfaces. An analog circuit simulation is produced and its response is compared to various literature sources to explore its capabilities and limitations. Results suggest that polymer ESD events can be predicted to within an order of magnitude. These results compare well to empirical findings from other sources having similar reproducibility.

  6. 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.

  7. Simplified Approach to Predicting Rough Surface Transition (United States)

    Boyle, Robert J.; Stripf, Matthias


    Turbine vane heat transfer predictions are given for smooth and rough vanes where the experimental data show transition moving forward on the vane as the surface roughness physical height increases. Consiste nt with smooth vane heat transfer, the transition moves forward for a fixed roughness height as the Reynolds number increases. Comparison s are presented with published experimental data. Some of the data ar e for a regular roughness geometry with a range of roughness heights, Reynolds numbers, and inlet turbulence intensities. The approach ta ken in this analysis is to treat the roughness in a statistical sense , consistent with what would be obtained from blades measured after e xposure to actual engine environments. An approach is given to determ ine the equivalent sand grain roughness from the statistics of the re gular geometry. This approach is guided by the experimental data. A roughness transition criterion is developed, and comparisons are made with experimental data over the entire range of experimental test co nditions. Additional comparisons are made with experimental heat tran sfer data, where the roughness geometries are both regular as well a s statistical. Using the developed analysis, heat transfer calculatio ns are presented for the second stage vane of a high pressure turbine at hypothetical engine conditions.

  8. Liquid surface model for carbon nanotube energetics

    DEFF Research Database (Denmark)

    Solov'yov, Ilia; Mathew, Maneesh; Solov'yov, Andrey V.


    In the present paper we developed a model for calculating the energy of single-wall carbon nanotubes of arbitrary chirality. This model, which we call as the liquid surface model, predicts the energy of a nanotube with relative error less than 1% once its chirality and the total number of atoms a...... the calculated energies we determine the elastic properties of the single-wall carbon nanotubes (Young modulus, curvature constant) and perform a comparison with available experimental measurements and earlier theoretical predictions....

  9. Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS)

    International Nuclear Information System (INIS)

    Mostafaei, Mostafa; Javadikia, Hossein; Naderloo, Leila


    Biodiesel is as an alternative petro-diesel fuel produced from the renewable resources. The use of novel technologies such as ultrasound technology for biodiesel production intensifies the reaction and reduces the process cost. The present study is aimed to evaluate and compare the prediction and simulating efficiency of the response surface methodology (RSM) and adaptive Neuro-fuzzy inference system (ANFIS) approaches for modeling the transesterification yield achieved in ultrasonic reactor. The influence of independent variables (reactor diameter, liquid height and ultrasound intensity) on the conversion of fatty acid methyl esters (FAME) was investigated by Box-Behnken design of RSM and two ANFIS approaches (hybrid and back-propagation optimization methods). All models were compared statistically based on the training and validation data set by the coefficient of determination (R2), root mean squares error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean relative percent deviation (MRPD). The calculated R2 for RSM and two ANFIS models were 0.9669, 0.9812 and 0.9808, respectively. All models indicated good predictions, however, the ANFIS models were more precise compared to the RSM model, which proves that the ANFIS is a powerful tool for modeling and optimizing FAME production in ultrasound reactor. - Highlights: • The ultrasound assisted FAME conversion was modelled using RSM and ANFIS approaches. • The scatter diagrams indicate the models accurately predicted the reaction yield. • The ANFIS model (hybrid) has higher R 2 (0.9812) compared to the RSM model. • The predicted deviations and residual values are relatively small for ANFIS model. • ANFIS model was more accurate for predicting ultrasound assisted FAME conversion.

  10. Modelling land surface - atmosphere interactions

    DEFF Research Database (Denmark)

    Rasmussen, Søren Højmark

    The study is investigates modelling of land surface – atmosphere interactions in context of fully coupled climatehydrological model. With a special focus of under what condition a fully coupled model system is needed. Regional climate model inter-comparison projects as ENSEMBLES have shown bias...

  11. Uncertainty-based calibration and prediction with a stormwater surface accumulation-washoff model based on coverage of sampled Zn, Cu, Pb and Cd field data

    DEFF Research Database (Denmark)

    Lindblom, Erik Ulfson; Ahlman, S.; Mikkelsen, Peter Steen


    95% model prediction bounds. A positive correlation between the dry deposition and the dry (wind) removal rates was revealed as well as a negative correlation between the wet removal (wash-off) rate and the ratio between the dry deposition and wind removal rates, which determines the maximum pool......A dynamic conceptual and lumped accumulation wash-off model (SEWSYS) is uncertainty-calibrated with Zn, Cu, Pb and Cd field data from an intensive, detailed monitoring campaign. We use the generalized linear uncertainty estimation (GLUE) technique in combination with the Metropolis algorithm, which...... allows identifying a range of behavioral model parameter sets. The small catchment size and nearness of the rain gauge justified excluding the hydrological model parameters from the uncertainty assessment. Uniform, closed prior distributions were heuristically specified for the dry and wet removal...

  12. Prediction and Migration of Surface-related Resonant Multiples

    KAUST Repository

    Guo, Bowen


    Surface-related resonant multiples can be migrated to achieve better resolution than migrating primary reflections. We now derive the formula for migrating surface-related resonant multiples, and show its super-resolution characteristics. Moreover, a method is proposed to predict surface-related resonant multiples with zero-offset primary reflections. The prediction can be used to indentify and extract the true resonant multiple from other events. Both synthetic and field data are used to validate this prediction.

  13. Foundations of elastoplasticity subloading surface model

    CERN Document Server

    Hashiguchi, Koichi


    This book is the standard text book of elastoplasticity in which the elastoplasticity theory is comprehensively described from the conventional theory for the monotonic loading to the unconventional theory for the cyclic loading behavior. Explanations of vector-tensor analysis and continuum mechanics are provided first as a foundation for elastoplasticity theory, covering various strain and stress measures and their rates with their objectivities. Elastoplasticity has been highly developed by the creation and formulation of the subloading surface model which is the unified fundamental law for irreversible mechanical phenomena in solids. The assumption that the interior of the yield surface is an elastic domain is excluded in order to describe the plastic strain rate due to the rate of stress inside the yield surface in this model aiming at the prediction of cyclic loading behavior, although the yield surface enclosing the elastic domain is assumed in all the elastoplastic models other than the subloading surf...

  14. 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

  15. Can foot anthropometric measurements predict dynamic plantar surface contact area?

    Directory of Open Access Journals (Sweden)

    Collins Natalie


    Full Text Available Abstract Background Previous studies have suggested that increased plantar surface area, associated with pes planus, is a risk factor for the development of lower extremity overuse injuries. The intent of this study was to determine if a single or combination of foot anthropometric measures could be used to predict plantar surface area. Methods Six foot measurements were collected on 155 subjects (97 females, 58 males, mean age 24.5 ± 3.5 years. The measurements as well as one ratio were entered into a stepwise regression analysis to determine the optimal set of measurements associated with total plantar contact area either including or excluding the toe region. The predicted values were used to calculate plantar surface area and were compared to the actual values obtained dynamically using a pressure sensor platform. Results A three variable model was found to describe the relationship between the foot measures/ratio and total plantar contact area (R2 = 0.77, p R2 = 0.76, p Conclusion The results of this study indicate that the clinician can use a combination of simple, reliable, and time efficient foot anthropometric measurements to explain over 75% of the plantar surface contact area, either including or excluding the toe region.

  16. Development of a regression model to predict copper toxicity to Daphnia magna and site-specific copper criteria across multiple surface-water drainages in an arid landscape. (United States)

    Fulton, Barry A; Meyer, Joseph S


    The water effect ratio (WER) procedure developed by the US Environmental Protection Agency is commonly used to derive site-specific criteria for point-source metal discharges into perennial waters. However, experience is limited with this method in the ephemeral and intermittent systems typical of arid climates. The present study presents a regression model to develop WER-based site-specific criteria for a network of ephemeral and intermittent streams influenced by nonpoint sources of Cu in the southwestern United States. Acute (48-h) Cu toxicity tests were performed concurrently with Daphnia magna in site water samples and hardness-matched laboratory waters. Median effect concentrations (EC50s) for Cu in site water samples (n=17) varied by more than 12-fold, and the range of calculated WER values was similar. Statistically significant (α=0.05) univariate predictors of site-specific Cu toxicity included (in sequence of decreasing significance) dissolved organic carbon (DOC), hardness/alkalinity ratio, alkalinity, K, and total dissolved solids. A multiple-regression model developed from a combination of DOC and alkalinity explained 85% of the toxicity variability in site water samples, providing a strong predictive tool that can be used in the WER framework when site-specific criteria values are derived. The biotic ligand model (BLM) underpredicted toxicity in site waters by more than 2-fold. Adjustments to the default BLM parameters improved the model's performance but did not provide a better predictive tool compared with the regression model developed from DOC and alkalinity. © 2014 SETAC.

  17. 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.

  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. 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

  20. Sediment Transport Model for a Surface Irrigation System


    Mailapalli, Damodhara R.; Raghuwanshi, Narendra S.; Singh, Rajendra


    Controlling irrigation-induced soil erosion is one of the important issues of irrigation management and surface water impairment. Irrigation models are useful in managing the irrigation and the associated ill effects on agricultural environment. In this paper, a physically based surface irrigation model was developed to predict sediment transport in irrigated furrows by integrating an irrigation hydraulic model with a quasi-steady state sediment transport model to predict sediment load in fur...

  1. Accurate prediction of peptide binding sites on protein surfaces.

    Directory of Open Access Journals (Sweden)

    Evangelia Petsalaki


    Full Text Available Many important protein-protein interactions are mediated by the binding of a short peptide stretch in one protein to a large globular segment in another. Recent efforts have provided hundreds of examples of new peptides binding to proteins for which a three-dimensional structure is available (either known experimentally or readily modeled but where no structure of the protein-peptide complex is known. To address this gap, we present an approach that can accurately predict peptide binding sites on protein surfaces. For peptides known to bind a particular protein, the method predicts binding sites with great accuracy, and the specificity of the approach means that it can also be used to predict whether or not a putative or predicted peptide partner will bind. We used known protein-peptide complexes to derive preferences, in the form of spatial position specific scoring matrices, which describe the binding-site environment in globular proteins for each type of amino acid in bound peptides. We then scan the surface of a putative binding protein for sites for each of the amino acids present in a peptide partner and search for combinations of high-scoring amino acid sites that satisfy constraints deduced from the peptide sequence. The method performed well in a benchmark and largely agreed with experimental data mapping binding sites for several recently discovered interactions mediated by peptides, including RG-rich proteins with SMN domains, Epstein-Barr virus LMP1 with TRADD domains, DBC1 with Sir2, and the Ago hook with Argonaute PIWI domain. The method, and associated statistics, is an excellent tool for predicting and studying binding sites for newly discovered peptides mediating critical events in biology.

  2. Prediction of water droplet evaporation on zircaloy surface

    International Nuclear Information System (INIS)

    Lee, Chi Young; In, Wang Kee


    In the present experimental study, the prediction of water droplet evaporation on a zircaloy surface was investigated using various initial droplet sizes. To the best of our knowledge, this may be the first valuable effort for understanding the details of water droplet evaporation on a zircaloy surface. The initial contact diameters of the water droplets tested ranged from 1.76 to 3.41 mm. The behavior (i.e., time-dependent droplet volume, contact angle, droplet height, and contact diameter) and mode-transition time of the water droplet evaporation were strongly influenced by the initial droplet size. Using the normalized contact angle (θ*) and contact diameter (d*), the transitions between evaporation modes were successfully expressed by a single curve, and their criteria were proposed. To predict the temporal droplet volume change and evaporation rate, the range of θ* > 0.25 and d* > 0.9, which mostly covered the whole evaporation period and the initial contact diameter remained almost constant during evaporation, was targeted. In this range, the previous contact angle functions for the evaporation model underpredicted the experimental data. A new contact angle function of a zircaloy surface was empirically proposed, which represented the present experimental data within a reasonable degree of accuracy. (author)

  3. Response surface modeling to predict fluid loss from beef strip loins and steaks injected with salt and phosphate with or without a dehydrated beef protein water binding adjunct. (United States)

    Lowder, Austin C; Goad, Carla L; Lou, Xingqiu; Morgan, J Brad; Koh, Chern Lin; Deakins, Alisha Parsons; Mireles DeWitt, Christina A


    This study was conducted using response surface methodology to predict fluid loss from injected beef strip steaks as influenced by levels of salt and sodium phosphates (SP) in the injection brine. Also, a beef-based dehydrated beef protein (DBP) water binding ingredient was evaluated. Paired U.S. select beef strip loins were quartered before being injected with 110% of initial weight with brines containing various concentrations of salt and SP (CON) or salt, SP and 5% DBP. Steaks were sliced, overwrapped and stored in the dark for 4d. Purge values ranged from 0.6% to 4.6% for CON and 0.3% to 2.1% for DBP. Fluid losses when accounting for the fluid lost from injection to slicing were as high as 6.8% for CON brines, but only 2.8% for DBP brines. The equations generated here and the DBP product could help producers achieve acceptable purge while reducing sodium use. Copyright © 2012 Elsevier Ltd. All rights reserved.

  4. Hydrologic modeling in a marsh-mangrove ecotone: Predicting wetland surface water and salinity response to restoration in the Ten Thousand Islands region of Florida, USA (United States)

    Michot, B.D.; Meselhe, E.A.; Krauss, Ken W.; Shrestha, Surendra; From, Andrew S.; Patino, Eduardo


    At the fringe of Everglades National Park in southwest Florida, United States, the Ten Thousand Islands National Wildlife Refuge (TTINWR) habitat has been heavily affected by the disruption of natural freshwater flow across the Tamiami Trail (U.S. Highway 41). As the Comprehensive Everglades Restoration Plan (CERP) proposes to restore the natural sheet flow from the Picayune Strand Restoration Project area north of the highway, the impact of planned measures on the hydrology in the refuge needs to be taken into account. The objective of this study was to develop a simple, computationally efficient mass balance model to simulate the spatial and temporal patterns of water level and salinity within the area of interest. This model could be used to assess the effects of the proposed management decisions on the surface water hydrological characteristics of the refuge. Surface water variations are critical to the maintenance of wetland processes. The model domain is divided into 10 compartments on the basis of their shared topography, vegetation, and hydrologic characteristics. A diversion of +10% of the discharge recorded during the modeling period was simulated in the primary canal draining the Picayune Strand forest north of the Tamiami Trail (Faka Union Canal) and this discharge was distributed as overland flow through the refuge area. Water depths were affected only modestly. However, in the northern part of the refuge, the hydroperiod, i.e., the duration of seasonal flooding, was increased by 21 days (from 115 to 136 days) for the simulation during the 2008 wet season, with an average water level rise of 0.06 m. The average salinity over a two-year period in the model area just south of Tamiami Trail was reduced by approximately 8 practical salinity units (psu) (from 18 to 10 psu), whereas the peak dry season average was reduced from 35 to 29 psu (by 17%). These salinity reductions were even larger with greater flow diversions (+20%). Naturally, the reduction

  5. 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

  6. Empirical model for estimating the surface roughness of machined ...

    African Journals Online (AJOL)

    Michael Horsfall

    Regression Analysis to construct a prediction model for surface roughness such that once the process parameters (cutting speed, feed, depth of cut, Nose. Radius and Speed) are given, the surface roughness can be predicted. The work piece material was EN8 which was processed by carbide-inserted tool conducted on ...

  7. Prediction of average annual surface temperature for both flexible and rigid pavements

    Directory of Open Access Journals (Sweden)

    Karthikeyan LOGANATHAN


    Full Text Available The surface temperature of pavements is a critical attribute during pavement design. Surface temperature must be measured at locations of interest based on time-consuming field tests. The key idea of this study is to develop a temperature profile model to predict the surface temperature of flexible and rigid pavements based on weather parameters. Determination of surface temperature with traditional techniques and sensors are replaced by a newly developed method. The method includes the development of a regression model to predict the average annual surface temperature based on weather parameters such as ambient air temperature, relative humidity, wind speed, and precipitation. Detailed information about temperature and other parameters are extracted from the Federal Highway Administration's (FHWA Long Term Pavement Performance (LTPP online database. The study was conducted on 61 pavement sections in the state of Alabama for a 10-year period. The developed model would predict the average annual surface temperature based on the known weather parameters. The predicted surface temperature model for asphalt pavements was very reliable and can be utilized while designing a pavement. The study was also conducted on seven rigid pavement sections in Alabama to predict their surface temperature, in which a successful model was developed. The outcome of this study would help the transportation agencies by saving time and effort invested in expensive field tests to measure the surface temperature of pavements.

  8. Predicting 3D lip shapes using facial surface EMG.

    Directory of Open Access Journals (Sweden)

    Merijn Eskes

    Full Text Available The aim of this study is to prove that facial surface electromyography (sEMG conveys sufficient information to predict 3D lip shapes. High sEMG predictive accuracy implies we could train a neural control model for activation of biomechanical models by simultaneously recording sEMG signals and their associated motions.With a stereo camera set-up, we recorded 3D lip shapes and simultaneously performed sEMG measurements of the facial muscles, applying principal component analysis (PCA and a modified general regression neural network (GRNN to link the sEMG measurements to 3D lip shapes. To test reproducibility, we conducted our experiment on five volunteers, evaluating several sEMG features and window lengths in unipolar and bipolar configurations in search of the optimal settings for facial sEMG.The errors of the two methods were comparable. We managed to predict 3D lip shapes with a mean accuracy of 2.76 mm when using the PCA method and 2.78 mm when using modified GRNN. Whereas performance improved with shorter window lengths, feature type and configuration had little influence.

  9. 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.

  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. 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.

  12. 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.

  13. Measuring and modeling surface sorption dynamics of organophosphate flame retardants on impervious surfaces (United States)

    U.S. Environmental Protection Agency — The data presented in this data file is a product of a journal publication. The dataset contains measured and model predicted OPFRs gas-phase and surface-phase...

  14. Robust Prediction of High Lift Using Surface Vorticity, Phase II (United States)

    National Aeronautics and Space Administration — FlightStream has been developed a fast, accurate, aerodynamic prediction code based on vorticity computations on the surface of an aircraft. The code, though still a...

  15. Enhanced Prediction of Gear Tooth Surface Fatigue Life, Phase I (United States)

    National Aeronautics and Space Administration — Sentient will develop an enhanced prediction of gear tooth surface fatigue life with rigorous analysis of the tribological phenomena that contribute to pitting...

  16. Modeling and analysis for surface roughness and material removal ...

    African Journals Online (AJOL)


    terms of cutting parameters is also developed using regression modeling. The results indicate that the developed model is suitable for prediction of surface roughness and material removal rate in machining of unidirectional glass fiber reinforced plastics (UD-GFRP) composites. The predicted values and measured values ...

  17. Surface EXAFS - A mathematical model

    International Nuclear Information System (INIS)

    Bateman, J.E.


    Extended X-ray absorption fine structure (EXAFS) studies are a powerful technique for studying the chemical environment of specific atoms in a molecular or solid matrix. The study of the surface layers of 'thick' materials introduces special problems due to the different escape depths of the various primary and secondary emission products which follow X-ray absorption. The processes are governed by the properties of the emitted fluorescent photons or electrons and of the material. Their interactions can easily destroy the linear relation between the detected signal and the absorption cross-section. Also affected are the probe depth within the surface and the background superimposed on the detected emission signal. A general mathematical model of the escape processes is developed which permits the optimisation of the detection modality (X-rays or electrons) and the experimental variables to suit the composition of any given surface under study

  18. Prediction of Ductile Fracture Surface Roughness Scaling

    DEFF Research Database (Denmark)

    Needleman, Alan; Tvergaard, Viggo; Bouchaud, Elisabeth


    . Ductile crack growth in a thin strip under mode I, overall plane strain, small scale yielding conditions is analyzed. Although overall plane strain loading conditions are prescribed, full 3D analyses are carried out to permit modeling of the three dimensional material microstructure and of the resulting...... three dimensional stress and deformation states that develop in the fracture process region. An elastic-viscoplastic constitutive relation for a progressively cavitating plastic solid is used to model the material. Two populations of second phase particles are represented: large inclusions with low...... strength, which result in large voids near the crack tip at an early stage, and small second phase particles, which require large strains before cavities nucleate. The larger inclusions are represented discretely and various three dimensional distributions of the larger particles are considered...

  19. Land surface impacts on subseasonal and seasonal predictability (United States)

    Guo, Zhichang; Dirmeyer, Paul A.; DelSole, Tim


    This paper shows that realistically initialized land surface states enhance atmospheric predictability significantly out to two-to-three months during summer. The spatial structure of the impact of land initialization on atmospheric predictability can be explained by the simultaneous influence of soil moisture memory time and land surface-evapotranspiration coupling strength. A proxy for this impact based on soil moisture and evaporation anomalies is proposed. The results also show that the impact of the land surface on atmospheric predictability varies with season: enhancement of predictability is relatively small during boreal spring and autumn, and reaches a maximum during boreal summer. Remarkably, the predictability of atmospheric temperature and precipitation increases with lead time from spring to summer. This increase is diagnosed as a “transfer” of predictability from land to atmosphere: during spring, the soil moisture predictability is high, but this predictability does not impact the atmosphere due to lack of land-atmosphere coupling; during summer, the coupling increases, thereby transferring the predictability from land to atmosphere.

  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. Geospatial application of the Water Erosion Prediction Project (WEPP) Model (United States)

    D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot


    The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltration, runoff, ET) component, which subsequently impacts the rest of the...

  2. AMMA Land surface Model Intercomparison Project (ALMIP) (United States)

    Boone, A. A.; Derosnay, P.


    Extreme climatic variability has afflicted West Africa over the last half century, which has resulted in significant socio-economic consequences for the people of this region. There is therefore a need to improve seasonal to inter-annual prediction of the West-African monsoon (WAM), however, difficulties modeling the WAM arise from both the paucity of observations at sufficient space-time resolutions, and due to the complex interactions between the biosphere, atmosphere and hydrosphere over this region. In particular, there is evidence that the land surface influences the variability of the WAM over a wide range of spatio-temporal scales. A critical aspect of this coupling is the feedback between the regional atmospheric circulation and the strong meridional surface flux gradients of mass and energy. One of the main goals of the African Monsoon Multi-disciplinary Analysis (AMMA) Project is to obtain a better understanding of the physical processes influencing the West-African Monsoon (WAM) on daily to inter-annual timescales. An improved comprehension of the relevant land surface processes is being addressed through the construction of a multi-scale atmospheric and land surface parameter forcing database using a variety of sources; numerical weather prediction forecast data, remote sensing products and local scale observations. The goal of this database is to drive land surface, vegetation and hydrological models over a range of spatial scales (local to regional) in order to gain better insights into the attendant processes. This goal is being met under the auspices of the AMMA Land surface Model Intercomparison Project (ALMIP). In the recently completed Phase 1 of this project, an ensemble of state-of-the-art land surface schemes have been run in "off-line" mode (i.e. decoupled from an atmospheric model) at a regional scale over western Africa for four annual cycles (2002-5). In this talk, intercomparison results will be presented. In addition, results from a

  3. Olkiluoto surface and near-surface hydrological modelling in 2010

    International Nuclear Information System (INIS)

    Karvonen, T.


    The modeling approaches carried out with the Olkiluoto surface hydrological model (SHYD) include palaeohydrological evolution of the Olkiluoto Island, examination of the boundary condition at the geosphere-biosphere interface zone, simulations related to infiltration experiment, prediction of the influence of ONKALO on hydraulic head in shallow and deep bedrock and optimisation of the shallow monitoring network. A so called short-term prediction system was developed for continuous updating of the estimated drawdowns caused by ONKALO. The palaeohydrological simulations were computed for a period starting from the time when the highest hills on Olkiluoto Island rose above sea level around 2 500 years ago. The input data needed in the model were produced by the UNTAMO-toolbox. The groundwater flow evolution is primarily driven by the postglacial land uplift and the uncertainty in the land uplift model is the biggest single factor that influences the accuracy of the results. The consistency of the boundary condition at the geosphere-biosphere interface zone (GBIZ) was studied during 2010. The comparison carried out during 2010 showed that pressure head profiles computed with the SHYD model and deep groundwater flow model FEFTRA are in good agreement with each other in the uppermost 100 m of the bedrock. This implies that flux profiles computed with the two approaches are close to each other and hydraulic heads computed at level z=0 m with the SHYD can be used as head boundary condition in the deep groundwater flow model FEFTRA. The surface hydrological model was used to analyse the results of the infiltration experiment. Increase in bedrock recharge inside WCA explains around 60-63 % from the amount of water pumped from OL-KR14 and 37-40 % of the water pumped from OL-KR14 flows towards pumping section via the hydrogeological zones. Pumping from OL-KR14 has only a minor effect on heads and fluxes in zones HZ19A and HZ19C compared to responses caused by leakages into

  4. A Velocity Prediction Procedure for Sailing Yachts with a hydrodynamic Model based on integrated fully coupled RANSE-Free-Surface Simulations

    NARCIS (Netherlands)

    Boehm, C.


    One of the most important tools in today's sailing yacht design is the Velocity Prediction Program (VPP). VPPs calculate boat speed from the equilibrium of aero- and hydrodynamic flow forces. Consequently their accuracy is linked to the accuracy of the aero- and hydrodynamic data used to represent a

  5. Experimental High-Resolution Land Surface Prediction System for the Vancouver 2010 Winter Olympic Games (United States)

    Belair, S.; Bernier, N.; Tong, L.; Mailhot, J.


    The 2010 Winter Olympic and Paralympic Games will take place in Vancouver, Canada, from 12 to 28 February 2010 and from 12 to 21 March 2010, respectively. In order to provide the best possible guidance achievable with current state-of-the-art science and technology, Environment Canada is currently setting up an experimental numerical prediction system for these special events. This system consists of a 1-km limited-area atmospheric model that will be integrated for 16h, twice a day, with improved microphysics compared with the system currently operational at the Canadian Meteorological Centre. In addition, several new and original tools will be used to adapt and refine predictions near and at the surface. Very high-resolution two-dimensional surface systems, with 100-m and 20-m grid size, will cover the Vancouver Olympic area. Using adaptation methods to improve the forcing from the lower-resolution atmospheric models, these 2D surface models better represent surface processes, and thus lead to better predictions of snow conditions and near-surface air temperature. Based on a similar strategy, a single-point model will be implemented to better predict surface characteristics at each station of an observing network especially installed for the 2010 events. The main advantage of this single-point system is that surface observations are used as forcing for the land surface models, and can even be assimilated (although this is not expected in the first version of this new tool) to improve initial conditions of surface variables such as snow depth and surface temperatures. Another adaptation tool, based on 2D stationnary solutions of a simple dynamical system, will be used to produce near-surface winds on the 100-m grid, coherent with the high- resolution orography. The configuration of the experimental numerical prediction system will be presented at the conference, together with preliminary results for winter 2007-2008.

  6. Modeling surface imperfections in thin films and nanostructured surfaces

    DEFF Research Database (Denmark)

    Hansen, Poul-Erik; Madsen, J. S.; Jensen, S. A.


    Accurate scatterometry and ellipsometry characterization of non-perfect thin films and nanostructured surfaces are challenging. Imperfections like surface roughness make the associated modelling and inverse problem solution difficult due to the lack of knowledge about the imperfection...

  7. Predicting Near-Surface Meteorological Variations over Different Vegetation Types

    NARCIS (Netherlands)

    Hutjes, R.W.A.; Klaassen, W.; Kruijt, B.; Veen, A.W.L.


    Meteorological conditions close to a surface are strongly influenced by the properties of the surface itself. As a result, input data for models calculating evaporation of surfaces differing from the measurement site need to be transformed. A transformation scheme proposed previously is tested on

  8. Lunar surface vehicle model competition (United States)


    During Fall and Winter quarters, Georgia Tech's School of Mechanical Engineering students designed machines and devices related to Lunar Base construction tasks. These include joint projects with Textile Engineering students. Topics studied included lunar environment simulator via drop tower technology, lunar rated fasteners, lunar habitat shelter, design of a lunar surface trenching machine, lunar support system, lunar worksite illumination (daytime), lunar regolith bagging system, sunlight diffusing tent for lunar worksite, service apparatus for lunar launch vehicles, lunar communication/power cables and teleoperated deployment machine, lunar regolith bag collection and emplacement device, soil stabilization mat for lunar launch/landing site, lunar rated fastening systems for robotic implementation, lunar surface cable/conduit and automated deployment system, lunar regolith bagging system, and lunar rated fasteners and fastening systems. A special topics team of five Spring quarter students designed and constructed a remotely controlled crane implement for the SKITTER model.

  9. On a model for the prediction of the friction coefficient in mixed lubrication based on a load-sharing concapt with measured surface roughness

    NARCIS (Netherlands)

    Akchurin, Aydar; Bosman, Rob; Lugt, Pieter Martin; van Drogen, Mark


    A new model was developed for the simulation of the friction coefficient in lubricated sliding line contacts. A half-space-based contact algorithm was linked with a numerical elasto-hydrodynamic lubrication solver using the load-sharing concept. The model was compared with an existing asperity-based

  10. Development of the TOXSWA model for predicting the behaviour of pesticides in surface water; proceedings of a workshop, held on November 8, 1994 at Wageningen, The Netherlands

    NARCIS (Netherlands)

    Crum, S.J.H.; Deneer, J.W.


    In an international workshop the state of the art of the aquatic fate model TOXSWA was presented. This report gives an account of the presentations and discussions of the workshop. Model concepts of TOXSWA are explained and preliminary results of the sensitivity analysis are given. Next, the TOXSWA

  11. Estimated venous return surface and cardiac output curve precisely predicts new hemodynamics after volume change. (United States)

    Sugimachi, Masaru; Sunagawa, Kenji; Uemura, Kazunori; Kamiya, Atsunori; Shimizu, Shuji; Inagaki, Masashi; Shishido, Toshiaki


    In our extended Guyton's model, the ability of heart to pump blood is characterized by a cardiac output curve and the ability of vasculature to pool blood by a venous return surface. These intersect in a three-dimensional coordinate system at the operating right atrial pressure, left atrial pressure, and cardiac output. The baseline cardiac output curve and venous return surface and their changes after volume change would predict new hemodynamics. The invasive methods needed to precisely characterize cardiac output curve and venous return surface led us to aim at estimating cardiac output curve and venous return surface from a single hemodynamic measurement. Using the average values for two logarithmic function parameters, and for two slopes of a surface, we were able to estimate cardiac output curve and venous return surface. The estimated curve and surface predicted new hemodynamics after volume change precisely.

  12. 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...

  13. Chondritic Models of 4 Vesta: Comparison of Predicted Internal Structure and Surface Composition/Mineralogy with Data from the Dawn Mission (United States)

    Toplis, M. J.; Mizzon, H.; Forni, O.; Monnereau, M.; Barrat, J-A.; Prettyman, T. H.; McSween, H. Y.; McCoy, T. J.; Mittlefehldt, D. W.; De Sanctis, M. C.; hide


    Understanding the physical and chemical processes which led to the formation of the terrestrial planets remains one of the principal challenges of the Earth and planetary science communities. However, direct traces of the earliest stages of planet building have generally been wiped out on larger bodies such as the Earth or Mars, obscuring our view of how that process occurred. On the other hand, the planet building process would appear to have been arrested prematurely in the region between Mars and Jupiter, now populated by several hundred thousand compositionally diverse objects that escaped accretion into larger planets. Of these, the asteroid 4 Vesta is of particular interest as it is large (520 km diameter), and known to have a basaltic surface dominated by pyroxenes [1, 2]. Furthermore, visible-IR spectra of Vesta obtained by ground and space-based telescopes are remarkably similar to laboratory spectra measured on meteorites of the Howardite-Eucrite-Diogenite clan (HED), leading to the paradigm that the HEDs came from Vesta [2]. Geochemical and petrological studies of the HEDs confirm the differentiated nature of the near-surface region of their parent body, and imply that crust extraction occurred well within the first 10Ma of solar system history [3]. Vesta is therefore a prime target for studies that aim to constrain the earliest stages of planet building, and for that reason it is currently the subject of the Dawn mission [4].

  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. Atmospheric modelling for seasonal prediction at the CSIR

    CSIR Research Space (South Africa)

    Landman, WA


    Full Text Available by observed monthly sea-surface temperature (SST) and sea-ice fields. The AGCM is the conformal-cubic atmospheric model (CCAM) administered by the Council for Scientific and Industrial Research. Since the model is forced with observed rather than predicted...

  16. 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

  17. Parameter optimization for surface flux transport models (United States)

    Whitbread, T.; Yeates, A. R.; Muñoz-Jaramillo, A.; Petrie, G. J. D.


    Accurate prediction of solar activity calls for precise calibration of solar cycle models. Consequently we aim to find optimal parameters for models which describe the physical processes on the solar surface, which in turn act as proxies for what occurs in the interior and provide source terms for coronal models. We use a genetic algorithm to optimize surface flux transport models using National Solar Observatory (NSO) magnetogram data for Solar Cycle 23. This is applied to both a 1D model that inserts new magnetic flux in the form of idealized bipolar magnetic regions, and also to a 2D model that assimilates specific shapes of real active regions. The genetic algorithm searches for parameter sets (meridional flow speed and profile, supergranular diffusivity, initial magnetic field, and radial decay time) that produce the best fit between observed and simulated butterfly diagrams, weighted by a latitude-dependent error structure which reflects uncertainty in observations. Due to the easily adaptable nature of the 2D model, the optimization process is repeated for Cycles 21, 22, and 24 in order to analyse cycle-to-cycle variation of the optimal solution. We find that the ranges and optimal solutions for the various regimes are in reasonable agreement with results from the literature, both theoretical and observational. The optimal meridional flow profiles for each regime are almost entirely within observational bounds determined by magnetic feature tracking, with the 2D model being able to accommodate the mean observed profile more successfully. Differences between models appear to be important in deciding values for the diffusive and decay terms. In like fashion, differences in the behaviours of different solar cycles lead to contrasts in parameters defining the meridional flow and initial field strength.

  18. Landform and surface attributes for prediction of rodent burrows in ...

    African Journals Online (AJOL)

    Previous studies suggest that rodent burrows, a proxy for rodent population are important for predicting plague risk areas. However, studies that link landform, surface attributes and rodent burrows in the Western Usambara Mountains in Tanzania are scanty. Therefore, this study was conducted in plague endemic area of ...

  19. Artificial neural network accurately predicts hepatitis B surface antigen seroclearance.

    Directory of Open Access Journals (Sweden)

    Ming-Hua Zheng

    Full Text Available BACKGROUND & AIMS: Hepatitis B surface antigen (HBsAg seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB. This study aimed to develop artificial neural networks (ANNs that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables. METHODS: Data from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion, and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC. RESULTS: Serum quantitative HBsAg (qHBsAg and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P<0.001 and were used for ANN/LRM-HBsAg seroclearance building, whereas, qHBsAg reduction was not associated with ANN-HBsAg seroconversion (P = 0.197 and LRM-HBsAg seroconversion was solely based on qHBsAg (P = 0.01. For HBsAg seroclearance, AUROCs of ANN were 0.96, 0.93 and 0.95 for the training, testing and genotype B subgroups respectively. They were significantly higher than those of LRM, qHBsAg and HBV DNA (all P<0.05. Although the performance of ANN-HBsAg seroconversion (AUROC 0.757 was inferior to that for HBsAg seroclearance, it tended to be better than those of LRM, qHBsAg and HBV DNA. CONCLUSIONS: ANN identifies spontaneous HBsAg seroclearance in HBeAg-negative CHB patients with better accuracy, on the basis of easily available serum data. More useful predictors for HBsAg seroconversion are still needed to be explored in the future.

  20. Micro-mechanical studies on graphite strength prediction models (United States)

    Kanse, Deepak; Khan, I. A.; Bhasin, V.; Vaze, K. K.


    The influence of type of loading and size-effects on the failure strength of graphite were studied using Weibull model. It was observed that this model over-predicts size effect in tension. However, incorporation of grain size effect in Weibull model, allows a more realistic simulation of size effects. Numerical prediction of strength of four-point bend specimen was made using the Weibull parameters obtained from tensile test data. Effective volume calculations were carried out and subsequently predicted strength was compared with experimental data. It was found that Weibull model can predict mean flexural strength with reasonable accuracy even when grain size effect was not incorporated. In addition, the effects of microstructural parameters on failure strength were analyzed using Rose and Tucker model. Uni-axial tensile, three-point bend and four-point bend strengths were predicted using this model and compared with the experimental data. It was found that this model predicts flexural strength within 10%. For uni-axial tensile strength, difference was 22% which can be attributed to less number of tests on tensile specimens. In order to develop failure surface of graphite under multi-axial state of stress, an open ended hollow tube of graphite was subjected to internal pressure and axial load and Batdorf model was employed to calculate failure probability of the tube. Bi-axial failure surface was generated in the first and fourth quadrant for 50% failure probability by varying both internal pressure and axial load.

  1. Predicting Nanocrystal Shape through Consideration of Surface-Ligand Interactions

    KAUST Repository

    Bealing, Clive R.


    Density functional calculations for the binding energy of oleic acid-based ligands on Pb-rich {100} and {111} facets of PbSe nanocrystals determine the surface energies as a function of ligand coverage. Oleic acid is expected to bind to the nanocrystal surface in the form of lead oleate. The Wulff construction predicts the thermodynamic equilibrium shape of the PbSe nanocrystals. The equilibrium shape is a function of the ligand surface coverage, which can be controlled by changing the concentration of oleic acid during synthesis. The different binding energy of the ligand on the {100} and {111} facets results in different equilibrium ligand coverages on the facets, and a transition in the equilibrium shape from octahedral to cubic is predicted when increasing the ligand concentration during synthesis. © 2012 American Chemical Society.

  2. 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...

  3. Response Predicting LTCC Firing Shrinkage: A Response Surface Analysis Study

    Energy Technology Data Exchange (ETDEWEB)

    Girardi, Michael; Barner, Gregg; Lopez, Cristie; Duncan, Brent; Zawicki, Larry


    The Low Temperature Cofired Ceramic (LTCC) technology is used in a variety of applications including military/space electronics, wireless communication, MEMS, medical and automotive electronics. The use of LTCC is growing due to the low cost of investment, short development time, good electrical and mechanical properties, high reliability, and flexibility in design integration (3 dimensional (3D) microstructures with cavities are possible)). The dimensional accuracy of the resulting x/y shrinkage of LTCC substrates is responsible for component assembly problems with the tolerance effect that increases in relation to the substrate size. Response Surface Analysis was used to predict product shrinkage based on specific process inputs (metal loading, layer count, lamination pressure, and tape thickness) with the ultimate goal to optimize manufacturing outputs (NC files, stencils, and screens) in achieving the final product design the first time. Three (3) regression models were developed for the DuPont 951 tape system with DuPont 5734 gold metallization based on green tape thickness.

  4. Development of Forest Drought Index and Forest Water Use Prediction in Gyeonggi Province, Korea Using High-Resolution Weather Research and Forecast Data and Localized JULES Land Surface Model (United States)

    Lee, H.; Park, J.; Cho, S.; Lee, S. J.; Kim, H. S.


    Forest determines the amount of water available to low land ecosystems, which use the rest of water after evapotranspiration by forests. Substantial increase of drought, especially for seasonal drought, has occurred in Korea due to climate change, recently. To cope with this increasing crisis, it is necessary to predict the water use of forest. In our study, forest water use in the Gyeonggi Province in Korea was estimated using high-resolution (spatial and temporal) meteorological forecast data and localized Joint UK Land Environment Simulator (JULES) which is one of the widely used land surface models. The modeled estimation was used for developing forest drought index. The localization of the model was conducted by 1) refining the existing two tree plant functional types (coniferous and deciduous trees) into five (Quercus spp., other deciduous tree spp., Pinus spp., Larix spp., and other coniferous spp.), 2) correcting moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) through data assimilation with in situ measured LAI, and 3) optimizing the unmeasured plant physiological parameters (e.g. leaf nitrogen contents, nitrogen distribution within canopy, light use efficiency) based on sensitivity analysis of model output values. The high-resolution (hourly and 810 × 810 m) National Center for AgroMeteorology-Land-Atmosphere Modeling Package (NCAM-LAMP) data were employed as meteorological input data in JULES. The plant functional types and soil texture of each grid cell in the same resolution with that of NCAM-LAMP was also used. The performance of the localized model in estimating forest water use was verified by comparison with the multi-year sapflow measurements and Eddy covariance data of Taehwa Mountain site. Our result can be used as referential information to estimate the forest water use change by the climate change. Moreover, the drought index can be used to foresee the drought condition and prepare to it.

  5. 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...

  6. 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...

  7. 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...

  8. 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.

  9. 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, ...

  10. 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...

  11. 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.

  12. 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.

  13. Climate predictability and prediction skill on seasonal time scales over South America from CHFP models (United States)

    Osman, Marisol; Vera, C. S.


    This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to

  14. Surface-complexation models for sorption onto heterogeneous surfaces

    International Nuclear Information System (INIS)

    Harvey, K.B.


    This report provides a description of the discrete-logK spectrum model, together with a description of its derivation, and of its place in the larger context of surface-complexation modelling. The tools necessary to apply the discrete-logK spectrum model are discussed, and background information appropriate to this discussion is supplied as appendices. (author)

  15. Predicting nucleic acid binding interfaces from structural models of proteins. (United States)

    Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael


    The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

  16. A Surface Pressure Altitude Prediction Model (United States)


    45.43 -9.28 107 161200 GENOA/SESTRI, IY 44.42 -8.85 2 161580 PISA/SAN GUISTO , IY 43.67 -10.38 2 162300 PESCARA, IY 42.43 -14.20 10 162420 ROME...Number: 161580 Name: PISA/SAN GUISTO , IY Effect of Station Location (c): -3.710 Effect of Interaction of Station Location and Month (d): Jan Feb Mar Apr

  17. Surface acoustic wave probe implant for predicting epileptic seizures (United States)

    Gopalsami, Nachappa [Naperville, IL; Kulikov, Stanislav [Sarov, RU; Osorio, Ivan [Leawood, KS; Raptis, Apostolos C [Downers Grove, IL


    A system and method for predicting and avoiding a seizure in a patient. The system and method includes use of an implanted surface acoustic wave probe and coupled RF antenna to monitor temperature of the patient's brain, critical changes in the temperature characteristic of a precursor to the seizure. The system can activate an implanted cooling unit which can avoid or minimize a seizure in the patient.

  18. 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…

  19. 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...

  20. Fault type predictions from stress distributions on planetary surfaces - Importance of fault initiation depth (United States)

    Golombek, M. P.


    The prediction of fault type on planetary surfaces from model stresses calculated at depth is discussed. These fault-type predictions yield different faults than those predicted using the surface criteria commonly employed in geophysical models. For elastic-plate flexure models of mascon loading on the moon, stresses calculated at the surface predict the occurrence of strike-slip faulting at the radial distance where grabens are found. Normal faults bounding lunar grabens and thrust faults responsible for wrinkle ridges are analyzed. It is found that the former initiate at the mechanical discontinuity that separates the breccia of the megaregolith from in situ fractured rock and that the latter initiate at the mechanical discontinuity between basalt layers and the underlying basin floor. The difference between elastic constants for the outer few kilometers of brecciated megaregolith and the underlying lunar lithosphere are evaluated. Superposing nonisotropic stresses resulting from the weight of overburden to the depth of the relevant mechanical discontinuity yield stresses that predict wrinkle ridges in the basin centers and grabens outside the basin margin, and eliminate the predicted zone of strike-slip faults.

  1. Surface Flux Modeling for Air Quality Applications

    Directory of Open Access Journals (Sweden)

    Limei Ran


    Full Text Available For many gasses and aerosols, dry deposition is an important sink of atmospheric mass. Dry deposition fluxes are also important sources of pollutants to terrestrial and aquatic ecosystems. The surface fluxes of some gases, such as ammonia, mercury, and certain volatile organic compounds, can be upward into the air as well as downward to the surface and therefore should be modeled as bi-directional fluxes. Model parameterizations of dry deposition in air quality models have been represented by simple electrical resistance analogs for almost 30 years. Uncertainties in surface flux modeling in global to mesoscale models are being slowly reduced as more field measurements provide constraints on parameterizations. However, at the same time, more chemical species are being added to surface flux models as air quality models are expanded to include more complex chemistry and are being applied to a wider array of environmental issues. Since surface flux measurements of many of these chemicals are still lacking, resistances are usually parameterized using simple scaling by water or lipid solubility and reactivity. Advances in recent years have included bi-directional flux algorithms that require a shift from pre-computation of deposition velocities to fully integrated surface flux calculations within air quality models. Improved modeling of the stomatal component of chemical surface fluxes has resulted from improved evapotranspiration modeling in land surface models and closer integration between meteorology and air quality models. Satellite-derived land use characterization and vegetation products and indices are improving model representation of spatial and temporal variations in surface flux processes. This review describes the current state of chemical dry deposition modeling, recent progress in bi-directional flux modeling, synergistic model development research with field measurements, and coupling with meteorological land surface models.

  2. Seasonal sea surface temperature anomaly prediction for coastal ecosystems (United States)

    Stock, Charles A.; Pegion, Kathy; Vecchi, Gabriel A.; Alexander, Michael A.; Tommasi, Desiree; Bond, Nicholas A.; Fratantoni, Paula S.; Gudgel, Richard G.; Kristiansen, Trond; O'Brien, Todd D.; Xue, Yan; Yang, Xiasong


    Sea surface temperature (SST) anomalies are often both leading indicators and important drivers of marine resource fluctuations. Assessment of the skill of SST anomaly forecasts within coastal ecosystems accounting for the majority of global fish yields, however, has been minimal. This reflects coarse global forecast system resolution and past emphasis on the predictability of ocean basin-scale SST variations. This paper assesses monthly to inter-annual SST anomaly predictions in coastal "Large Marine Ecosystems" (LMEs). We begin with an analysis of 7 well-observed LMEs adjacent to the United States and then examine how mechanisms responsible for prediction skill in these systems are reflected in predictions for LMEs globally. Historical SST anomaly estimates from the 1/4° daily Optimal Interpolation Sea Surface Temperature reanalysis (OISST.v2) were first found to be highly consistent with in-situ measurements for 6 of the 7 U.S. LMEs. Thirty years of retrospective forecasts from climate forecast systems developed at NOAA's Geophysical Fluid Dynamics Laboratory (CM2.5-FLOR) and the National Center for Environmental Prediction (CFSv2) were then assessed against OISST.v2. Forecast skill varied widely by LME, initialization month, and lead but there were many cases of high skill that also exceeded that of a persistence forecast, some at leads greater than 6 months. Mechanisms underlying skill above persistence included accurate simulation of (a) seasonal transitions between less predictable locally generated and more predictable basin-scale SST variability; (b) seasonal transitions between different basin-scale influences; (c) propagation of SST anomalies across seasons through sea ice; and (d) re-emergence of previous anomalies upon the breakdown of summer stratification. Globally, significant skill above persistence across many tropical systems arises via mechanisms (a) and (b). Combinations of all four mechanisms contribute to less prevalent but nonetheless

  3. Factors Predicting the Ocular Surface Response to Desiccating Environmental Stress (United States)

    Alex, Anastasia; Edwards, Austin; Hays, J. Daniel; Kerkstra, Michelle; Shih, Amanda; de Paiva, Cintia S.; Pflugfelder, Stephen C.


    Purpose. To identify factors predicting the ocular surface response to experimental desiccating stress. Methods. The ocular surfaces of both eyes of 15 normal and 10 dry eye subjects wearing goggles were exposed to a controlled desiccating environment (15%–25% relative humidity and 2–5 L/min airflow) for 90 minutes. Eye irritation symptoms, blink rate, tear meniscus dimensions, noninvasive (RBUT) and invasive tear break-up time, and corneal fluorescein and conjunctival lissamine green-dye staining were recorded before and after desiccating stress. Pre- and postexposure measurements were compared, and Pearson correlations between clinical parameters before and after desiccating stress were calculated. Results. Corneal and conjunctival dye staining significantly increased in all subjects following 90-minute exposure to desiccating environment, and the magnitude of change was similar in normal and dry eye subjects; except superior cornea staining was greater in dry eye. Irritation severity in the desiccating environment was associated with baseline dye staining, baseline tear meniscus height, and blink rate after 45 minutes. Desiccation-induced change in corneal fluorescein staining was inversely correlated to baseline tear meniscus width, whereas change in total ocular surface dye staining was inversely correlated to baseline dye staining, RBUT, and tear meniscus height and width. Blink rate from 30 to 90 minutes in desiccating environment was higher in the dry eye than normal group. Blink rate significantly correlated to baseline corneal fluorescein staining and environmental-induced change in corneal fluorescein staining. Conclusions. Ocular surface dye staining increases in response to desiccating stress. Baseline ocular surface dye staining, tear meniscus height, and blink rate predict severity of ocular surface dye staining following exposure to a desiccating environment. PMID:23572103

  4. Macrobenthic species response surfaces along estuarine gradients: prediction by logistic regression

    NARCIS (Netherlands)

    Ysebaert, T.; Meire, P.; Herman, P.M.J.; Verbeek, H.


    This study aims at contributing to the development of statistical models to predict macrobenthic species response to environmental conditions in estuarine ecosystems. Ecological response surfaces are derived for 10 estuarine macrobenthic species. Logistic regression is applied on a large data set,

  5. Minimal Model Theory for Log Surfaces


    Fujino, Osamu


    We discuss the log minimal model theory for log surfaces. We show that the log minimal model program, the finite generation of log canonical rings, and the log abundance theorem for log surfaces hold true under assumptions weaker than the usual framework of the log minimal model theory.

  6. New approaches to predicting surface fuel moisture in south east Australian forests (United States)

    Sheridan, Gary; Nyman, Petter; Hawthorne, Sandra; Bovill, William; Walsh, Sean; Baillie, Craig; Duff, Thomas; Tolhurst, Kevin; Lane, Patrick


    The capacity to predict of the moisture content (FMC) of fine surface fuels in mountainous south east Australian forests has improved dramatically in recent years due to the convergence of several new technologies, including i) improved process-based account-keeping type FMC models, ii) improved understanding and representation of topographic effects (aspect, drainage position, elevation) on surface fuel and soil moisture, iii) improved methods for downscaling weather variables (eg. rainfall/throughfall, short-wave radiation) using digital elevation models and airborne LIDaR, and, iv) new in-situ sensor technologies (fuelsticks, capacitance sensors, Ibuttons) for continuously monitoring surface fuels and within-litter micro-climate conditions, generating datasets of unprecedented temporal resolution and continuity for model development and testing under real field conditions across a broad range of forests, landscapes and climates. In this study the combined improvements in predictive capacity were quantified by comparing the field FMC observations with predictions from traditional, widely used operational FMC models, and with two new process-based models, including improved spatial parameterisation provided by the new technologies outlined above. The results are interpreted in the context of planned-burning decision making and outcomes, and bushfire modelling and management. The initial results showed that the new approaches to FMC prediction offered substantial improvements over the traditional methods and could be reasonably implemented at operational scales.

  7. Modeling the Acid-Base Properties of Montmorillonite Edge Surfaces. (United States)

    Tournassat, Christophe; Davis, James A; Chiaberge, Christophe; Grangeon, Sylvain; Bourg, Ian C


    The surface reactivity of clay minerals remains challenging to characterize because of a duality of adsorption surfaces and mechanisms that does not exist in the case of simple oxide surfaces: edge surfaces of clay minerals have a variable proton surface charge arising from hydroxyl functional groups, whereas basal surfaces have a permanent negative charge arising from isomorphic substitutions. Hence, the relationship between surface charge and surface potential on edge surfaces cannot be described using the Gouy-Chapman relation, because of a spillover of negative electrostatic potential from the basal surface onto the edge surface. While surface complexation models can be modified to account for these features, a predictive fit of experimental data was not possible until recently, because of uncertainty regarding the densities and intrinsic pK a values of edge functional groups. Here, we reexamine this problem in light of new knowledge on intrinsic pK a values obtained over the past decade using ab initio molecular dynamics simulations, and we propose a new formalism to describe edge functional groups. Our simulation results yield reasonable predictions of the best available experimental acid-base titration data.

  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. A robust empirical seasonal prediction of winter NAO and surface climate. (United States)

    Wang, L; Ting, M; Kushner, P J


    A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how accurately models can predict the NAO. Most dynamical models, however, have limited skill in seasonal forecasts of the winter NAO. A new empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill than current dynamical models. The empirical model provides robust and skilful prediction of the December-January-February (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions of sea-ice concentration, stratospheric circulation, and sea-surface temperature. The predictability is, for the most part, derived from the relatively long persistence of sea ice in the autumn. The lower stratospheric circulation and sea-surface temperature appear to play more indirect roles through a series of feedbacks among systems driving NAO evolution. This MLR model also provides skilful seasonal outlooks of winter surface temperature and precipitation over many regions of Eurasia and eastern North America.

  10. 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.

  11. 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...

  12. 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 ...

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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…

  5. Dynamical modeling of surface tension

    International Nuclear Information System (INIS)

    Brackbill, J.U.; Kothe, D.B.


    In a recent review it is said that free-surface flows ''represent some of the difficult remaining challenges in computational fluid dynamics''. There has been progress with the development of new approaches to treating interfaces, such as the level-set method and the improvement of older methods such as the VOF method. A common theme of many of the new developments has been the regularization of discontinuities at the interface. One example of this approach is the continuum surface force (CSF) formulation for surface tension, which replaces the surface stress given by Laplace's equation by an equivalent volume force. Here, we describe how CSF might be made more useful. Specifically, we consider a derivation of the CSF equations from a minimization of surface energy as outlined by Jacqmin. This reformulation suggests that if one eliminates the computation of curvature in terms of a unit normal vector, parasitic currents may be eliminated For this reformulation to work, it is necessary that transition region thickness be controlled. Various means for this, in addition to the one discussed by Jacqmin are discussed

  6. 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

  7. Biological Surface Adsorption Index of Nanomaterials: Modelling Surface Interactions of Nanomaterials with Biomolecules. (United States)

    Chen, Ran; Riviere, Jim E


    Quantitative analysis of the interactions between nanomaterials and their surrounding environment is crucial for safety evaluation in the application of nanotechnology as well as its development and standardization. In this chapter, we demonstrate the importance of the adsorption of surrounding molecules onto the surface of nanomaterials by forming biocorona and thus impact the bio-identity and fate of those materials. We illustrate the key factors including various physical forces in determining the interaction happening at bio-nano interfaces. We further discuss the mathematical endeavors in explaining and predicting the adsorption phenomena, and propose a new statistics-based surface adsorption model, the Biological Surface Adsorption Index (BSAI), to quantitatively analyze the interaction profile of surface adsorption of a large group of small organic molecules onto nanomaterials with varying surface physicochemical properties, first employing five descriptors representing the surface energy profile of the nanomaterials, then further incorporating traditional semi-empirical adsorption models to address concentration effects of solutes. These Advancements in surface adsorption modelling showed a promising development in the application of quantitative predictive models in biological applications, nanomedicine, and environmental safety assessment of nanomaterials.

  8. 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...

  9. 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.

  10. Predicted transport of pyrethroid insecticides from an urban landscape to surface water. (United States)

    Jorgenson, Brant; Fleishman, Erica; Macneale, Kate H; Schlenk, Daniel; Scholz, Nathaniel L; Spromberg, Julann A; Werner, Inge; Weston, Donald P; Xiao, Qingfu; Young, Thomas M; Zhang, Minghua


    The authors developed a simple screening-level model of exposure of aquatic species to pyrethroid insecticides for the lower American River watershed (California, USA). The model incorporated both empirically derived washoff functions based on existing, small-scale precipitation simulations and empirical data on pyrethroid insecticide use and watershed properties for Sacramento County, California, USA. The authors calibrated the model to in-stream monitoring data and used it to predict daily river pyrethroid concentration from 1995 through 2010. The model predicted a marked increase in pyrethroid toxic units starting in 2000, coincident with an observed watershed-wide increase in pyrethroid use. After 2000, approximately 70% of the predicted total toxic unit exposure in the watershed was associated with the pyrethroids bifenthrin and cyfluthrin. Pyrethroid applications for aboveground structural pest control on the basis of suspension concentrate categorized product formulations accounted for greater than 97% of the predicted total toxic unit exposure. Projected application of mitigation strategies, such as curtailment of structural perimeter band and barrier treatments as recently adopted by the California Department of Pesticide Regulation, reduced predicted total toxic unit exposure by 84%. The model also predicted that similar reductions in surface-water concentrations of pyrethroids could be achieved through a switch from suspension concentrate-categorized products to emulsifiable concentrate-categorized products without restrictions on current-use practice. Even with these mitigation actions, the predicted concentration of some pyrethroids would continue to exceed chronic aquatic life criteria. © 2013 SETAC.

  11. 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.

  12. Predicting the effects of measures to reduce eutrophication in surface water in rural areas - a case study

    NARCIS (Netherlands)

    Hendriks, R.F.A.; Kolk, van der J.W.H.


    The effectiveness of measures to reduce nutrient concentrations in surface water was predicted by a combination of a nutrient leaching model for groundwater and a nutrient simulation model for surface water. Scenarios were formulated based on several measures. Different combinations of drainage

  13. 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.

  14. 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.

  15. 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....

  16. 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.

  17. 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

  18. Predicting wettability behavior of fluorosilica coated metal surface using optimum neural network (United States)

    Taghipour-Gorjikolaie, Mehran; Valipour Motlagh, Naser


    The interaction between variables, which are effective on the surface wettability, is very complex to predict the contact angles and sliding angles of liquid drops. In this paper, in order to solve this complexity, artificial neural network was used to develop reliable models for predicting the angles of liquid drops. Experimental data are divided into training data and testing data. By using training data and feed forward structure for the neural network and using particle swarm optimization for training the neural network based models, the optimum models were developed. The obtained results showed that regression index for the proposed models for the contact angles and sliding angles are 0.9874 and 0.9920, respectively. As it can be seen, these values are close to unit and it means the reliable performance of the models. Also, it can be inferred from the results that the proposed model have more reliable performance than multi-layer perceptron and radial basis function based models.

  19. Dynamic Factor Models for the Volatility Surface

    DEFF Research Database (Denmark)

    van der Wel, Michel; Ozturk, Sait R.; Dijk, Dick van

    The implied volatility surface is the collection of volatilities implied by option contracts for different strike prices and time-to-maturity. We study factor models to capture the dynamics of this three-dimensional implied volatility surface. Three model types are considered to examine desirable...

  20. Surface Ship Shock Modeling and Simulation: Two-Dimensional Analysis

    Directory of Open Access Journals (Sweden)

    Young S. Shin


    Full Text Available The modeling and simulation of the response of a surface ship system to underwater explosion requires an understanding of many different subject areas. These include the process of underwater explosion events, shock wave propagation, explosion gas bubble behavior and bubble-pulse loading, bulk and local cavitation, free surface effect, fluid-structure interaction, and structural dynamics. This paper investigates the effects of fluid-structure interaction and cavitation on the response of a surface ship using USA-NASTRAN-CFA code. First, the one-dimensional Bleich-Sandler model is used to validate the approach, and second, the underwater shock response of a two-dimensional mid-section model of a surface ship is predicted with a surrounding fluid model using a constitutive equation of a bilinear fluid which does not allow transmission of negative pressures.

  1. 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

  2. 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


    Energy Technology Data Exchange (ETDEWEB)

    Lauer, E J


    The model assumes that an 'initiating event' results in positive ions on the surface near the anode and reverses the direction of the normal component of electric field so that electrons in vacuum are attracted to the dielectric locally. A sequence of surface electron avalanches progresses in steps from the anode to the cathode. For 200 kV across 1 cm, the spacing of avalanches is predicted to be about 13 microns. The time for avalanches to step from the anode to the cathode is predicted to be about a ns.

  4. Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum (United States)

    Yueh, Simon H.


    Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

  5. 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.

  6. Sediment Transport Model for a Surface Irrigation System

    Directory of Open Access Journals (Sweden)

    Damodhara R. Mailapalli


    Full Text Available Controlling irrigation-induced soil erosion is one of the important issues of irrigation management and surface water impairment. Irrigation models are useful in managing the irrigation and the associated ill effects on agricultural environment. In this paper, a physically based surface irrigation model was developed to predict sediment transport in irrigated furrows by integrating an irrigation hydraulic model with a quasi-steady state sediment transport model to predict sediment load in furrow irrigation. The irrigation hydraulic model simulates flow in a furrow irrigation system using the analytically solved zero-inertial overland flow equations and 1D-Green-Ampt, 2D-Fok, and Kostiakov-Lewis infiltration equations. Performance of the sediment transport model was evaluated for bare and cropped furrow fields. The results indicated that the sediment transport model can predict the initial sediment rate adequately, but the simulated sediment rate was less accurate for the later part of the irrigation event. Sensitivity analysis of the parameters of the sediment module showed that the soil erodibility coefficient was the most influential parameter for determining sediment load in furrow irrigation. The developed modeling tool can be used as a water management tool for mitigating sediment loss from the surface irrigated fields.

  7. Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks. (United States)

    Al Haidan, Ali; Abu-Hammad, Osama; Dar-Odeh, Najla


    Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

  8. Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ali Al Haidan


    Full Text Available Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

  9. 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....

  10. 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....

  11. 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%

  12. Novel structures of oxygen adsorbed on a Zr(0001) surface predicted from first principles

    Energy Technology Data Exchange (ETDEWEB)

    Gao, Bo [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Beijing computational science research center, Beijing,100084 (China); Wang, Jianyun [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Lv, Jian [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); College of Materials Science and Engineering, Jilin University, Changchun, 130012 (China); Gao, Xingyu [Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, 100088 (China); CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088 (China); Zhao, Yafan [CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088 (China); Wang, Yanchao, E-mail: [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Beijing computational science research center, Beijing,100084 (China); College of Materials Science and Engineering, Jilin University, Changchun, 130012 (China); Song, Haifeng, E-mail: [Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, 100088 (China); CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088 (China); Ma, Yanming [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Beijing computational science research center, Beijing,100084 (China)


    Highlights: • Two stable structures of O adsorbed on a Zr(0001) surface are predicted with SLAM. • A stable structure of O adsorbed on a Zr(0001) surface is proposed with MLAM. • The calculated work function change is agreement with experimental value. - Abstract: The structures of O atoms adsorbed on a metal surface influence the metal properties significantly. Thus, studying O chemisorption on a Zr surface is of great interest. We investigated O adsorption on a Zr(0001) surface using our newly developed structure-searching method combined with first-principles calculations. A novel structural prototype with a unique combination of surface face-centered cubic (SFCC) and surface hexagonal close-packed (SHCP) O adsorption sites was predicted using a single-layer adsorption model (SLAM) for a 0.5 and 1.0 monolayer (ML) O coverage. First-principles calculations based on the SLAM revealed that the new predicted structures are energetically favorable compared with the well-known SFCC structures for a low O coverage (0.5 and 1.0 ML). Furthermore, on basis of our predicted SFCC + SHCP structures, a new structure within multi-layer adsorption model (MLAM) was proposed to be more stable at the O coverage of 1.0 ML, in which adsorbed O atoms occupy the SFCC + SHCP sites and the substitutional octahedral sites. The calculated work functions indicate that the SFCC + SHCP configuration has the lowest work function of all known structures at an O coverage of 0.5 ML within the SLAM, which agrees with the experimental trend of work function with variation in O coverage.

  13. 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

  14. Bag model with diffuse surface

    International Nuclear Information System (INIS)

    Phatak, S.C.


    The constraint of a sharp bag boundary in the bag model is relaxed in the present work. This has been achieved by replacing the square-well potential of the bag model by a smooth scalar potential and introducing a term similar to the bag pressure term. The constraint of the conservation of the energy-momentum tensor is used to obtain an expression for the added bag pressure term. The model is then used to determine the static properties of the nucleon. The calculation shows that the rms charge radius and the nucleon magnetic moment are larger than the corresponding bag model values. Also, the axial vector coupling constant and the πNN coupling constant are in better agreement with the experimental values

  15. 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.

  16. 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.

  17. Modeling and Prediction of Soil Water Vapor Sorption Isotherms

    DEFF Research Database (Denmark)

    Arthur, Emmanuel; Tuller, Markus; Moldrup, Per


    Soil water vapor sorption isotherms describe the relationship between water activity (aw) and moisture content along adsorption and desorption paths. The isotherms are important for modeling numerous soil processes and are also used to estimate several soil (specific surface area, clay content.......93) for a wide range of soils; and (ii) develop and test regression models for estimating the isotherms from clay content. Preliminary results show reasonable fits of the majority of the investigated empirical and theoretical models to the measured data although some models were not capable to fit both sorption...... directions accurately. Evaluation of the developed prediction equations showed good estimation of the sorption/desorption isotherms for tested soils....

  18. Finite Element Simulation of Shot Peening: Prediction of Residual Stresses and Surface Roughness (United States)

    Gariépy, Alexandre; Perron, Claude; Bocher, Philippe; Lévesque, Martin

    Shot peening is a surface treatment that consists of bombarding a ductile surface with numerous small and hard particles. Each impact creates localized plastic strains that permanently stretch the surface. Since the underlying material constrains this stretching, compressive residual stresses are generated near the surface. This process is commonly used in the automotive and aerospace industries to improve fatigue life. Finite element analyses can be used to predict residual stress profiles and surface roughness created by shot peening. This study investigates further the parameters and capabilities of a random impact model by evaluating the representative volume element and the calculated stress distribution. Using an isotropic-kinematic hardening constitutive law to describe the behaviour of AA2024-T351 aluminium alloy, promising results were achieved in terms of residual stresses.

  19. Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms (United States)

    Jian, Jhih-Wei; Elumalai, Pavadai; Pitti, Thejkiran; Wu, Chih Yuan; Tsai, Keng-Chang; Chang, Jeng-Yih; Peng, Hung-Pin; Yang, An-Suei


    Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites. PMID:27513851

  20. 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...

  1. Comparison of Predictive Modeling Methods of Aircraft Landing Speed (United States)

    Diallo, Ousmane H.


    Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

  2. On the sources of global land surface hydrologic predictability

    Directory of Open Access Journals (Sweden)

    S. Shukla


    Full Text Available Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months comes from knowledge of initial hydrologic conditions (IHCs and seasonal climate forecast skill (FS. In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC macroscale hydrology model, one based on ensemble streamflow prediction (ESP and another based on Reverse-ESP (Rev-ESP, both for a 47 yr re-forecast period (1961–2007. We compare cumulative runoff (CR, soil moisture (SM and snow water equivalent (SWE forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings and estimate the ratio of root mean square error (RMSE of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS

  3. Three-model ensemble wind prediction in southern Italy

    Directory of Open Access Journals (Sweden)

    R. C. Torcasio


    Full Text Available Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013 three-model ensemble (TME experiment for wind prediction is considered. The models employed, run operationally at National Research Council – Institute of Atmospheric Sciences and Climate (CNR-ISAC, are RAMS (Regional Atmospheric Modelling System, BOLAM (BOlogna Limited Area Model, and MOLOCH (MOdello LOCale in H coordinates. The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System. Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System of the ECMWF (European Centre for Medium-Range Weather Forecast for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  4. Three-model ensemble wind prediction in southern Italy (United States)

    Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo


    Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  5. 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.

  6. Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models (United States)

    Curtis, Gary P.; Lu, Dan; Ye, Ming


    While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. This study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. These reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Limitations of applying MLBMA to the

  7. Prediction of the surface roughness of AA6082 flow-formed tubes by design of experiments

    International Nuclear Information System (INIS)

    Srinivasulu, M.; Komaraiah, M.; Rao, C. S. Krishna Prasada


    Flow forming is a modern, chipless metal forming process that is employed for the production of thin-walled seamless tubes. Experiments are conducted on AA6082 alloy pre-forms to flow form into thin-walled tubes on a CNC flow-forming machine with a single roller. Design of experiments is used to predict the surface roughness of flow-formed tubes. The process parameters selected for this study are the roller axial feed, mandrel speed, and roller radius. A standard response surface methodology (RSM) called the Box Behnken design is used to perform the experimental runs. The regression model developed by RSM successfully predicts the surface roughness of AA6082 flow-formed tubes within the range of the selected process parameters.

  8. Prediction of the surface roughness of AA6082 flow-formed tubes by design of experiments

    Energy Technology Data Exchange (ETDEWEB)

    Srinivasulu, M. [Government Polytechnic for Women Badangpet, Hyderabad (India); Komaraiah, M. [Sreenidhi Institute of Science and Technology, Hyderabad (India); Rao, C. S. Krishna Prasada [Bharat Dynamics Limited, Hyderabad (India)


    Flow forming is a modern, chipless metal forming process that is employed for the production of thin-walled seamless tubes. Experiments are conducted on AA6082 alloy pre-forms to flow form into thin-walled tubes on a CNC flow-forming machine with a single roller. Design of experiments is used to predict the surface roughness of flow-formed tubes. The process parameters selected for this study are the roller axial feed, mandrel speed, and roller radius. A standard response surface methodology (RSM) called the Box Behnken design is used to perform the experimental runs. The regression model developed by RSM successfully predicts the surface roughness of AA6082 flow-formed tubes within the range of the selected process parameters.

  9. 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.

  10. Estimation of shape model parameters for 3D surfaces

    DEFF Research Database (Denmark)

    Erbou, Søren Gylling Hemmingsen; Darkner, Sune; Fripp, Jurgen


    is applied to a database of 3D surfaces from a section of the porcine pelvic bone extracted from 33 CT scans. A leave-one-out validation shows that the parameters of the first 3 modes of the shape model can be predicted with a mean difference within [-0.01,0.02] from the true mean, with a standard deviation......Statistical shape models are widely used as a compact way of representing shape variation. Fitting a shape model to unseen data enables characterizing the data in terms of the model parameters. In this paper a Gauss-Newton optimization scheme is proposed to estimate shape model parameters of 3D...... surfaces using distance maps, which enables the estimation of model parameters without the requirement of point correspondence. For applications with acquisition limitations such as speed and cost, this formulation enables the fitting of a statistical shape model to arbitrarily sampled data. The method...


    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. Oxidative stress prediction: A preliminary approach using a response surface based technique. (United States)

    Sierra, M; Bragg-Gonzalo, L; Grasa, J; Muñoz, M J; González, D; Miana-Mena, F J


    A response surface was built to predict the lipid peroxidation level, generated in an iron-ascorbate in vitro model, of any organ, which is correlated with the oxidative stress injury in biological membranes. Oxidative stress studies are numerous, usually performed on laboratory animals. However, ethical concerns require validated methods to reduce the use of laboratory animals. The response surface described here is a validated method to replace animals. Tissue samples of rabbit liver, kidney, heart, skeletal muscle and brain were oxidized with different concentrations of FeCl 3 (0.1 to 8mM) and ascorbate (0.1mM), during different periods of time (0 to 90min) at 37°C. Experimental data obtained, with lipid content and antioxidant activity of each organ, allowed constructing a multidimensional surface capable of predicting, by interpolation, the lipid peroxidation level of any organ defined by its antioxidant activity and fat content, when exposed to different oxidant conditions. To check the predictive potential of the technique, two more experiments were carried out. First, in vitro oxidation data from lung tissue were collected. Second, the antioxidant capacity of kidney homogenates was modified by adding melatonin. Then, the response surface generated could predict lipid peroxidation levels produced in these new situations. The potential of this technique could be reinforced using collaborative databases to reduce the number of animals in experimental procedures. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Durability and life prediction modeling in polyimide composites (United States)

    Binienda, Wieslaw K.


    Sudden appearance of cracks on a macroscopically smooth surface of brittle materials due to cooling or drying shrinkage is a phenomenon related to many engineering problems. Although conventional strength theories can be used to predict the necessary condition for crack appearance, they are unable to predict crack spacing and depth. On the other hand, fracture mechanics theory can only study the behavior of existing cracks. The theory of crack initiation can be summarized into three conditions, which is a combination of a strength criterion and laws of energy conservation, the average crack spacing and depth can thus be determined. The problem of crack initiation from the surface of an elastic half plane is solved and compares quite well with available experimental evidence. The theory of crack initiation is also applied to concrete pavements. The influence of cracking is modeled by the additional compliance according to Okamura's method. The theoretical prediction by this structural mechanics type of model correlates very well with the field observation. The model may serve as a theoretical foundation for future pavement joint design. The initiation of interactive cracks of quasi-brittle material is studied based on a theory of cohesive crack model. These cracks may grow simultaneously, or some of them may close during certain stages. The concept of crack unloading of cohesive crack model is proposed. The critical behavior (crack bifurcation, maximum loads) of the cohesive crack model are characterized by rate equations. The post-critical behavior of crack initiation is also studied.

  15. Evaluation of Deep Learning Models for Predicting CO2 Flux (United States)

    Halem, M.; Nguyen, P.; Frankel, D.


    Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

  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. First principles predictions of electron tunneling rates between atoms and crystalline surfaces (United States)

    Neidfeldt, Keith

    Charge transfer is a critical process that controls many important reactions such as photosynthesis, corrosion, and catalysis. We developed a quantitative method for calculating charge transfer rates using periodic density functional theory (DFT). This approach allows us to model from first principles the interaction between an adsorbate and arbitrary material surfaces. By deconvoluting the projected density of states of the ionization level of the atom, we can determine its width, which is proportional to the charge transfer rate. These rates can be used to predict important properties such as adsorbate excited state lifetimes and neutralization fractions for scattered ions. By comparing neutralization fractions for Li scattering off of Al(001) to experimental data, we validated our first principles method of predicting charge transfer rates. While our results are consistent with the classic Langmuir-Gurney (LG) model of adsorption for nearly-free-electron-like metal surfaces, we find several important deviations caused by the actual electronic structure of more complicated material surfaces. For example, we find that the d-band of transition metal surfaces mediates an intra-atomic hybridization of the Li ionization level. Secondly, we find that surface-projected band gaps (e.g., in Cu(111)) enhance the lifetimes of alkali atoms above surfaces containing such band gaps. In addition, our method allows us to also study atoms interacting with non-metallic surfaces where the LG model does not apply. For example, we find that alkali charge transfer rates are controlled by dangling bonds on covalently-bonded surfaces (e.g., Si(001)-(2xl)) instead of by the traditional image potential.

  19. Predicting the Wear of High Friction Surfacing Aggregate

    Directory of Open Access Journals (Sweden)

    David Woodward


    Full Text Available High friction surfacing (HFS is a specialist type of road coating with very high skid resistance. It is used in the UK at locations where there is significant risk of serious or fatal accidents. This paper considers the aggregate used in HFS. Calcined bauxite is the only aggregate that provides the highest levels of skid resistance over the longest period. No naturally occurring aggregate has been found to give a comparable level of in-service performance. This paper reviews the historical development of HFS in the UK relating to aggregate. In-service performance is predicted in the laboratory using the Wear test which subjects test specimens to an estimated 5–8 years simulated trafficking. Examples are given of Wear test data. They illustrate why calcined bauxite performs better than natural aggregate. They show how the amount of calcined bauxite can be reduced by blending with high skid resistant natural aggregates. Data from the Wear test can be related to every HFS laboratory experiment and road trial carried out in the UK for over the last 50 years. Anyone considering the prediction of HFS performance needs to carefully consider the data given in this paper with any other test method currently being considered or used to investigate HFS.

  20. 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...

  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. 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....

  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. A physically based model of global freshwater surface temperature (United States)

    van Beek, Ludovicus P. H.; Eikelboom, Tessa; van Vliet, Michelle T. H.; Bierkens, Marc F. P.


    Temperature determines a range of physical properties of water and exerts a strong control on surface water biogeochemistry. Thus, in freshwater ecosystems the thermal regime directly affects the geographical distribution of aquatic species through their growth and metabolism and indirectly through their tolerance to parasites and diseases. Models used to predict surface water temperature range between physically based deterministic models and statistical approaches. Here we present the initial results of a physically based deterministic model of global freshwater surface temperature. The model adds a surface water energy balance to river discharge modeled by the global hydrological model PCR-GLOBWB. In addition to advection of energy from direct precipitation, runoff, and lateral exchange along the drainage network, energy is exchanged between the water body and the atmosphere by shortwave and longwave radiation and sensible and latent heat fluxes. Also included are ice formation and its effect on heat storage and river hydraulics. We use the coupled surface water and energy balance model to simulate global freshwater surface temperature at daily time steps with a spatial resolution of 0.5° on a regular grid for the period 1976-2000. We opt to parameterize the model with globally available data and apply it without calibration in order to preserve its physical basis with the outlook of evaluating the effects of atmospheric warming on freshwater surface temperature. We validate our simulation results with daily temperature data from rivers and lakes (U.S. Geological Survey (USGS), limited to the USA) and compare mean monthly temperatures with those recorded in the Global Environment Monitoring System (GEMS) data set. Results show that the model is able to capture the mean monthly surface temperature for the majority of the GEMS stations, while the interannual variability as derived from the USGS and NOAA data was captured reasonably well. Results are poorest for

  11. Wrinkling Prediction in Deep Drawing by Using Response Surface Methodology and Artificial Neural Network


    Rafizadeh, Hossein; Azimifar, Farhad; Foode, Puya; Foudeh, Mohammad Reza; Keymanesh, Mohammad


    The objective of this study is to predict influences of tooling parameters such as die and punch radius, blank holder force and friction coefficient between the die and the blank surfaces in a deep drawing process on the wrinkling height in aluminium AA5754 by using the response surface methodology (RSM) and an artificial neural network (ANN). The 3D finite element method (FEM), i.e. the Abaqus software, is employed to model the deep drawing process. In order to investigate the accuracy of th...

  12. 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

  13. 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.

  14. Brain surface motion imaging to predict adhesions between meningiomas and the brain surface

    Energy Technology Data Exchange (ETDEWEB)

    Taoka, Toshiaki; Yamatani, Yuya; Akashi, Toshiaki; Miyasaka, Toshiteru; Emura, Tomoko; Kichikawa, Kimihiko [Nara Medical University, Department of Radiology, Nara (Japan); Yamada, Syuichi; Nakase, Hiroyuki [Nara Medical University, Department of Neurosurgery, Nara (Japan)


    ''Brain surface motion imaging'' (BSMI) is the subtraction of pulse-gated, 3D, heavily T2-weighted image of two different phases of cerebrospinal fluid (CSF) pulsation, which enables the assessment of the dynamics of brain surface pulsatile motion. The purpose of this study was to evaluate the feasibility of this imaging method for providing presurgical information about adhesions between meningiomas and the brain surface. Eighteen cases with surgically resected meningioma in whom BSMI was presurgically obtained were studied. BSMI consisted of two sets of pulse-gated, 3D, heavily T2-weighted, fast spin echo scans. Images of the systolic phase and the diastolic phase were obtained, and subtraction was performed with 3D motion correction. We analyzed the presence of band-like texture surrounding the tumor and judged the degree of motion discrepancy as ''total,'' ''partial,'' or ''none.'' The correlation between BSMI and surgical findings was evaluated. For cases with partial adhesions, agreements in the locations of the adhesions were also evaluated. On presurgical BSMI, no motion discrepancy was seen in eight cases, partial in six cases, and total in four cases. These presurgical predictions about adhesions and surgical findings agreed in 13 cases (72.2%). The locations of adhesions agreed in five of six cases with partial adhesions. In the current study, BSMI could predict brain and meningioma adhesions correctly in 72.2% of cases, and adhesion location could also be predicted. This imaging method appears to provide presurgical information about brain/meningioma adhesions. (orig.)

  15. 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.)

  16. 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.

  17. 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

  18. Surface Adsorption in Nonpolarizable Atomic Models. (United States)

    Whitmer, Jonathan K; Joshi, Abhijeet A; Carlton, Rebecca J; Abbott, Nicholas L; de Pablo, Juan J


    Many ionic solutions exhibit species-dependent properties, including surface tension and the salting-out of proteins. These effects may be loosely quantified in terms of the Hofmeister series, first identified in the context of protein solubility. Here, our interest is to develop atomistic models capable of capturing Hofmeister effects rigorously. Importantly, we aim to capture this dependence in computationally cheap "hard" ionic models, which do not exhibit dynamic polarization. To do this, we have performed an investigation detailing the effects of the water model on these properties. Though incredibly important, the role of water models in simulation of ionic solutions and biological systems is essentially unexplored. We quantify this via the ion-dependent surface attraction of the halide series (Cl, Br, I) and, in so doing, determine the relative importance of various hypothesized contributions to ionic surface free energies. Importantly, we demonstrate surface adsorption can result in hard ionic models combined with a thermodynamically accurate representation of the water molecule (TIP4Q). The effect observed in simulations of iodide is commensurate with previous calculations of the surface potential of mean force in rigid molecular dynamics and polarizable density-functional models. Our calculations are direct simulation evidence of the subtle but sensitive role of water thermodynamics in atomistic simulations.

  19. Modeling and Inversion of Scattered Surface waves

    NARCIS (Netherlands)

    Riyanti, C.D.


    In this thesis, we present a modeling method based on a domain-type integral representation for waves propagating along the surface of the Earth which have been scattered in the vicinity of the source or the receivers. Using this model as starting point, we formulate an inversion scheme to estimate

  20. Advances in land modeling of KIAPS based on the Noah Land Surface Model (United States)

    Koo, Myung-Seo; Baek, Sunghye; Seol, Kyung-Hee; Cho, Kyoungmi


    As of 2013, the Noah Land Surface Model (LSM) version 2.7.1 was implemented in a new global model being developed at the Korea Institute of Atmospheric Prediction Systems (KIAPS). This land surface scheme is further refined in two aspects, by adding new physical processes and by updating surface input parameters. Thus, the treatment of glacier land, sea ice, and snow cover are addressed more realistically. Inconsistencies in the amount of absorbed solar flux at ground level by the land surface and radiative processes are rectified. In addition, new parameters are available by using 1-km land cover data, which had usually not been possible at a global scale. Land surface albedo/emissivity climatology is newly created using Moderate-Resolution Imaging Spectroradiometer (MODIS) satellitebased data and adjusted parameterization. These updates have been applied to the KIAPS-developed model and generally provide a positive impact on near-surface weather forecasting.

  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. Prediction and Optimization of Residual Stresses on Machined Surface and Sub-Surface in MQL Turning (United States)

    Ji, Xia; Zou, Pan; Li, Beizhi; Rajora, Manik; Shao, Yamin; Liang, Steven Y.

    Residual stress in the machined surface and subsurface is affected by materials, machining conditions, and tool geometry and can affect the component life and service quality significantly. Empirical or numerical experiments are commonly used for determining residual stresses but these are very expensive. There has been an increase in the utilization of minimum quantity lubrication (MQL) in recent years in order to reduce the cost and tool/part handling efforts, while its effect on machined part residual stress, although important, has not been explored. This paper presents a hybrid neural network that is trained using Simulated Annealing (SA) and Levenberg-Marquardt Algorithm (LM) in order to predict the values of residual stresses in cutting and radial direction on the surface and within the work piece after the MQL face turning process. Once the ANN has successfully been trained, an optimization procedure, using Genetic Algorithm (GA), is applied in order to find the best cutting conditions in order to minimize the surface tensile residual stresses and maximize the compressive residual stresses within the work piece. The optimization results show that the usage of MQL decreases the surface tensile residual stresses and increases the compressive residual stresses within the work piece.

  3. Variational assimilation of land surface temperature observations for enhanced river flow predictions (United States)

    Ercolani, Giulia; Castelli, Fabio


    Data assimilation (DA) has the potential of improving hydrologic forecasts. However, many issues arise in case it is employed for spatially distributed hydrologic models that describes processes in various compartments: large dimensionality of the inverse problem, layers governed by different equations, non-linear and discontinuous model structure, complex topology of domains such as surface drainage and river network.On the other hand, integrated models offer the possibility of improving prediction of specific states by exploiting observations of quantities belonging to other compartments. In terms of forecasting river discharges, and hence for their enhancement, soil moisture is a key variable, since it determines the partitioning of rainfall into infiltration and surface runoff. However, soil moisture measurements are affected by issues that could prevent a successful DA and an actual improvement of discharge predictions.In-situ measurements suffer a dramatic spatial scarcity, while observations from satellite are barely accurate and provide spatial information only at a very coarse scale (around 40 km).Hydrologic models that explicitly represent land surface processes of coupled water and energy balance provide a valid alternative to direct DA of soil moisture.They gives the possibility of inferring soil moisture states through DA of remotely sensed Land Surface Temperature (LST), whose measurements are more accurate and with a higher spatial resolution in respect to those of soil moisture. In this work we present the assimilation of LST data in a hydrologic model (Mobidic) that is part of the operational forecasting chain for the Arno river, central Italy, with the aim of improving flood predictions. Mobidic is a raster based, continuous in time and distributed in space hydrologic model, with coupled mass and energy balance at the surface and coupled groundwater and surface hydrology. The variational approach is adopted for DA, since it requires less

  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. Using advanced surface complexation models for modelling soil chemistry under forests: Solling forest, Germany

    International Nuclear Information System (INIS)

    Bonten, Luc T.C.; Groenenberg, Jan E.; Meesenburg, Henning; Vries, Wim de


    Various dynamic soil chemistry models have been developed to gain insight into impacts of atmospheric deposition of sulphur, nitrogen and other elements on soil and soil solution chemistry. Sorption parameters for anions and cations are generally calibrated for each site, which hampers extrapolation in space and time. On the other hand, recently developed surface complexation models (SCMs) have been successful in predicting ion sorption for static systems using generic parameter sets. This study reports the inclusion of an assemblage of these SCMs in the dynamic soil chemistry model SMARTml and applies this model to a spruce forest site in Solling Germany. Parameters for SCMs were taken from generic datasets and not calibrated. Nevertheless, modelling results for major elements matched observations well. Further, trace metals were included in the model, also using the existing framework of SCMs. The model predicted sorption for most trace elements well. - Highlights: → Surface complexation models can be well applied in field studies. → Soil chemistry under a forest site is adequately modelled using generic parameters. → The model is easily extended with extra elements within the existing framework. → Surface complexation models can show the linkages between major soil chemistry and trace element behaviour. - Surface complexation models with generic parameters make calibration of sorption superfluous in dynamic modelling of deposition impacts on soil chemistry under nature areas.

  6. New Temperature-based Models for Predicting Global Solar Radiation

    International Nuclear Information System (INIS)

    Hassan, Gasser E.; Youssef, M. Elsayed; Mohamed, Zahraa E.; Ali, Mohamed A.; Hanafy, Ahmed A.


    Highlights: • New temperature-based models for estimating solar radiation are investigated. • The models are validated against 20-years measured data of global solar radiation. • The new temperature-based model shows the best performance for coastal sites. • The new temperature-based model is more accurate than the sunshine-based models. • The new model is highly applicable with weather temperature forecast techniques. - Abstract: This study presents new ambient-temperature-based models for estimating global solar radiation as alternatives to the widely used sunshine-based models owing to the unavailability of sunshine data at all locations around the world. Seventeen new temperature-based models are established, validated and compared with other three models proposed in the literature (the Annandale, Allen and Goodin models) to estimate the monthly average daily global solar radiation on a horizontal surface. These models are developed using a 20-year measured dataset of global solar radiation for the case study location (Lat. 30°51′N and long. 29°34′E), and then, the general formulae of the newly suggested models are examined for ten different locations around Egypt. Moreover, the local formulae for the models are established and validated for two coastal locations where the general formulae give inaccurate predictions. Mostly common statistical errors are utilized to evaluate the performance of these models and identify the most accurate model. The obtained results show that the local formula for the most accurate new model provides good predictions for global solar radiation at different locations, especially at coastal sites. Moreover, the local and general formulas of the most accurate temperature-based model also perform better than the two most accurate sunshine-based models from the literature. The quick and accurate estimations of the global solar radiation using this approach can be employed in the design and evaluation of performance for

  7. Thermodynamic Modeling of Surface Tension of Aqueous Electrolyte Solution by Competitive Adsorption Model

    Directory of Open Access Journals (Sweden)

    Mohamad Javad Kamali


    Full Text Available Thermodynamic modeling of surface tension of different electrolyte systems in presence of gas phase is studied. Using the solid-liquid equilibrium, Langmuir gas-solid adsorption, and ENRTL activity coefficient model, the surface tension of electrolyte solutions is calculated. The new model has two adjustable parameters which could be determined by fitting the experimental surface tension of binary aqueous electrolyte solution in single temperature. Then the values of surface tension for other temperatures in binary and ternary system of aqueous electrolyte solution are predicted. The average absolute deviations for calculation of surface tension of binary and mixed electrolyte systems by new model are 1.98 and 1.70%, respectively.

  8. Surface physics theoretical models and experimental methods

    CERN Document Server

    Mamonova, Marina V; Prudnikova, I A


    The demands of production, such as thin films in microelectronics, rely on consideration of factors influencing the interaction of dissimilar materials that make contact with their surfaces. Bond formation between surface layers of dissimilar condensed solids-termed adhesion-depends on the nature of the contacting bodies. Thus, it is necessary to determine the characteristics of adhesion interaction of different materials from both applied and fundamental perspectives of surface phenomena. Given the difficulty in obtaining reliable experimental values of the adhesion strength of coatings, the theoretical approach to determining adhesion characteristics becomes more important. Surface Physics: Theoretical Models and Experimental Methods presents straightforward and efficient approaches and methods developed by the authors that enable the calculation of surface and adhesion characteristics for a wide range of materials: metals, alloys, semiconductors, and complex compounds. The authors compare results from the ...

  9. A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly (United States)

    Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong


    This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

  10. Land-surface modelling in hydrological perspective

    DEFF Research Database (Denmark)

    Overgaard, Jesper; Rosbjerg, Dan; Butts, M.B.


    The purpose of this paper is to provide a review of the different types of energy-based land-surface models (LSMs) and discuss some of the new possibilities that will arise when energy-based LSMs are combined with distributed hydrological modelling. We choose to focus on energy-based approaches......, and the difficulties inherent in various evaluation procedures are presented. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the perspectives of such efforts are discussed....

  11. Dynamical seasonal prediction of summer sea surface temperatures in the Great Barrier Reef (United States)

    Spillman, C. M.; Alves, O.


    Coral bleaching is a serious problem threatening the world coral reef systems, triggered by high sea surface temperatures (SST) which are becoming more prevalent as a result of global warming. Seasonal forecasts from coupled ocean-atmosphere models can be used to predict anomalous SST months in advance. In this study, we assess the ability of the Australian Bureau of Meteorology seasonal forecast model (POAMA) to forecast SST anomalies in the Great Barrier Reef, Australia, with particular focus on the major 1998 and 2002 bleaching events. Advance warning of potential bleaching events allows for the implementation of management strategies to minimise reef damage. This study represents the first attempt to apply a dynamical seasonal model to the problem of coral bleaching and predict SST over a reef system for up to 6 months lead-time, a potentially invaluable tool for reef managers.

  12. Predicting future glacial lakes in Austria using different modelling approaches (United States)

    Otto, Jan-Christoph; Helfricht, Kay; Prasicek, Günther; Buckel, Johannes; Keuschnig, Markus


    Glacier retreat is one of the most apparent consequences of temperature rise in the 20th and 21th centuries in the European Alps. In Austria, more than 240 new lakes have formed in glacier forefields since the Little Ice Age. A similar signal is reported from many mountain areas worldwide. Glacial lakes can constitute important environmental and socio-economic impacts on high mountain systems including water resource management, sediment delivery, natural hazards, energy production and tourism. Their development significantly modifies the landscape configuration and visual appearance of high mountain areas. Knowledge on the location, number and extent of these future lakes can be used to assess potential impacts on high mountain geo-ecosystems and upland-lowland interactions. Information on new lakes is critical to appraise emerging threads and potentials for society. The recent development of regional ice thickness models and their combination with high resolution glacier surface data allows predicting the topography below current glaciers by subtracting ice thickness from glacier surface. Analyzing these modelled glacier bed surfaces reveals overdeepenings that represent potential locations for future lakes. In order to predict the location of future glacial lakes below recent glaciers in the Austrian Alps we apply different ice thickness models using high resolution terrain data and glacier outlines. The results are compared and validated with ice thickness data from geophysical surveys. Additionally, we run the models on three different glacier extents provided by the Austrian Glacier Inventories from 1969, 1998 and 2006. Results of this historical glacier extent modelling are compared to existing glacier lakes and discussed focusing on geomorphological impacts on lake evolution. We discuss model performance and observed differences in the results in order to assess the approach for a realistic prediction of future lake locations. The presentation delivers

  13. 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

  14. 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.

  15. 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

  16. Estimation and prediction under local volatility jump-diffusion model (United States)

    Kim, Namhyoung; Lee, Younhee


    Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.

  17. 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...

  18. A mathematical look at a physical power prediction model

    DEFF Research Database (Denmark)

    Landberg, L.


    This article takes a mathematical look at a physical model used to predict the power produced from wind farms. The reason is to see whether simple mathematical expressions can replace the original equations and to give guidelines as to where simplifications can be made and where they cannot....... The article shows that there is a linear dependence between the geostrophic wind and the local wind at the surface, but also that great care must be taken in the selection of the simple mathematical models, since physical dependences play a very important role, e.g. through the dependence of the turning...

  19. 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....

  20. 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....

  1. 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...

  2. Use of Ensemble Numerical Weather Prediction Data for Inversely Determining Atmospheric Refractivity in Surface Ducting Conditions (United States)

    Greenway, D. P.; Hackett, E.


    Under certain atmospheric refractivity conditions, propagated electromagnetic waves (EM) can become trapped between the surface and the bottom of the atmosphere's mixed layer, which is referred to as surface duct propagation. Being able to predict the presence of these surface ducts can reap many benefits to users and developers of sensing technologies and communication systems because they significantly influence the performance of these systems. However, the ability to directly measure or model a surface ducting layer is challenging due to the high spatial resolution and large spatial coverage needed to make accurate refractivity estimates for EM propagation; thus, inverse methods have become an increasingly popular way of determining atmospheric refractivity. This study uses data from the Coupled Ocean/Atmosphere Mesoscale Prediction System developed by the Naval Research Laboratory and instrumented helicopter (helo) measurements taken during the Wallops Island Field Experiment to evaluate the use of ensemble forecasts in refractivity inversions. Helo measurements and ensemble forecasts are optimized to a parametric refractivity model, and three experiments are performed to evaluate whether incorporation of ensemble forecast data aids in more timely and accurate inverse solutions using genetic algorithms. The results suggest that using optimized ensemble members as an initial population for the genetic algorithms generally enhances the accuracy and speed of the inverse solution; however, use of the ensemble data to restrict parameter search space yields mixed results. Inaccurate results are related to parameterization of the ensemble members' refractivity profile and the subsequent extraction of the parameter ranges to limit the search space.

  3. Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing. (United States)

    Rekik, Islem; Li, Gang; Lin, Weili; Shen, Dinggang


    Longitudinal neuroimaging analysis methods have remarkably advanced our understanding of early postnatal brain development. However, learning predictive models to trace forth the evolution trajectories of both normal and abnormal cortical shapes remains broadly absent. To fill this critical gap, we pioneered the first prediction model for longitudinal developing cortical surfaces in infants using a spatiotemporal current-based learning framework solely from the baseline cortical surface. In this paper, we detail this prediction model and even further improve its performance by introducing two key variants. First, we use the varifold metric to overcome the limitations of the current metric for surface registration that was used in our preliminary study. We also extend the conventional varifold-based surface registration model for pairwise registration to a spatiotemporal surface regression model. Second, we propose a morphing process of the baseline surface using its topographic attributes such as normal direction and principal curvature sign. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we use the proposed varifold-based shape regression model to estimate geodesic cortical shape evolution trajectories for each training subject. We then build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we select the best learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints, based on similarity metrics between this baseline surface and the learnt baseline population average surface atlas. We used a leave-one-out cross validation method to predict the inner cortical surface shape at 3, 6, 9 and 12 months of age from the baseline cortical surface shape at birth. Our

  4. Minimal model for spoof acoustoelastic surface states

    DEFF Research Database (Denmark)

    Christensen, Johan; Liang, Z.; Willatzen, Morten


    Similar to textured perfect electric conductors for electromagnetic waves sustaining artificial or spoof surface plasmons we present an equivalent phenomena for the case of sound. Aided by a minimal model that is able to capture the complex wave interaction of elastic cavity modes and airborne...... sound radiation in perfect rigid panels, we construct designer acoustoelastic surface waves that are entirely controlled by the geometrical environment. Comparisons to results obtained by full-wave simu- lations confirm the feasibility of the model and we demonstrate illustrative examples...

  5. 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.

  6. Prediction of lateral surface, volume and sphericity of pomegranate using MLP artificial neural network

    Directory of Open Access Journals (Sweden)

    A Rohani


    Full Text Available Introduction: Fast and accurate determination of geometrical properties of agricultural products has many applications in agricultural operations like planting, cultivating, harvesting and post-harvesting. Calculations related to storing, shipping and storage-coating materials as well as peeling time and surface-microbial concentrations are some applications of estimating product volume and surface area. Sphericity is also a parameter by which the shape differences between fruits, vegetables, grains and seeds can be quantified. This parameter is important in grading systems and inspecting rolling capability of agricultural products. Bayram presented a new dimensional method and equation to calculate the sphericity of certain shapesand some granular food materials (Bayram, 2005. Kumar and Mathew proposed atheoretically soundmethod for estimating the surface area of ellipsoidal food materials (Kumar and Mathew, 2003. Clayton et al. used non-linear regression models for calculation of apple surface area using the fruit mass or volume (Clayton et al., 1995. Humeida and Hobani predicted surface area and volume of pomegranates based on the weight and geometrical diametermean (Humeida and Hobani, 1993. Wang and Nguang designeda low cost sensor system to automatically compute the volume and surface area of axi-symmetricagricultural products such as eggs, lemons, limes and tamarillos (Wang and Nguang, 2007. The main objective of this study was to investigate the potential of Artificial Neural Network (ANN technique as an alternative method to predict the volume, surface area and sphericity of pomegranates. Materials and methods: The water displacement method (WDM was used for measuring the actual volume of pomegranates. Also, the sphericity and surface area are computed by using analytical methods. In this study, the neural MLP models were designed based upon the three nominal diameters of pomegranatesas variable inputs, while the output model consisted

  7. Improvements to a Response Surface Thermal Model for Orion (United States)

    Miller, Stephen W.; Walker, William Q.


    A study was performed to determine if a Design of Experiments (DOE)/Response Surface Methodology could be applied to on-orbit thermal analysis and produce a set of Response Surface Equations (RSE) that predict Orion vehicle temperatures within 10 F. The study used the Orion Outer Mold Line model. Five separate factors were identified for study: yaw, pitch, roll, beta angle, and the environmental parameters. Twenty-three external Orion components were selected and their minimum and maximum temperatures captured over a period of two orbits. Thus, there are 46 responses. A DOE case matrix of 145 runs was developed. The data from these cases were analyzed to produce a fifth order RSE for each of the temperature responses. For the 145 cases in the DOE matrix, the agreement between the engineering data and the RSE predictions was encouraging with 40 of the 46 RSEs predicting temperatures within the goal band. However, the verification cases showed most responses did not meet the 10 F goal. After reframing the focus of the study to better align the RSE development with the purposes of the model, a set of RSEs for both the minimum and maximum radiator temperatures was produced which predicted the engineering model output within +/-4 F. Therefore, with the correct application of the DOE/RSE methodology, RSEs can be developed that provide analysts a fast and easy way to screen large numbers of environments and assess proposed changes to the RSE factors.

  8. Improvement of Surface Temperature Prediction Using SVR with MOGREPS Data for Short and Medium range over South Korea (United States)

    Lim, S. J.; Choi, R. K.; Ahn, K. D.; Ha, J. C.; Cho, C. H.


    As the Korea Meteorology Administration (KMA) has operated Met Office Global and Regional Ensemble Prediction System (MOGREPS) with introduction of Unified Model (UM), many attempts have been made to improve predictability in temperature forecast in last years. In this study, post-processing method of MOGREPS for surface temperature prediction is developed with machine learning over 52 locations in South Korea. Past 60-day lag time was used as a training phase of Support Vector Regression (SVR) method for surface temperature forecast model. The selected inputs for SVR are followings: date and surface temperatures from Numerical Weather prediction (NWP), such as GDAPS, individual 24 ensemble members, mean and median of ensemble members for every 3hours for 12 days.To verify the reliability of SVR-based ensemble prediction (SVR-EP), 93 days are used (from March 1 to May 31, 2014). The result yielded improvement of SVR-EP by RMSE value of 16 % throughout entire prediction period against conventional ensemble prediction (EP). In particular, short range predictability of SVR-EP resulted in 18.7% better RMSE for 1~3 day forecast. The mean temperature bias between SVR-EP and EP at all test locations showed around 0.36°C and 1.36°C, respectively. SVR-EP is currently extending for more vigorous sensitivity test, such as increasing training phase and optimizing machine learning model.

  9. Prediction of material removal rate and surface roughness for wire electrical discharge machining of nickel using response surface methodology

    Directory of Open Access Journals (Sweden)

    Thangam Chinnadurai


    Full Text Available This study focuses on investigating the effects of process parameters, namely, Peak current (Ip, Pulse on time (Ton, Pulse off time (Toff, Water pressure (Wp, Wire feed rate (Wf, Wire tension (Wt, Servo voltage (Sv and Servo feed setting (Sfs, on the Material Removal Rate (MRR and Surface Roughness (SR for Wire electrical discharge machining (Wire-EDM of nickel using Taguchi method. Response Surface Methodology (RSM is adopted to evolve mathematical relationships between the wire cutting process parameters and the output variables of the weld joint to determine the welding input parameters that lead to the desired optimal wire cutting quality. Besides, using response surface plots, the interaction effects of process parameters on the responses are analyzed and discussed. The statistical software Mini-tab is used to establish the design and to obtain the regression equations. The developed mathematical models are tested by analysis-of-variance (ANOVA method to check their appropriateness and suitability. Finally, a comparison is made between measured and calculated results, which are in good agreement. This indicates that the developed models can predict the responses accurately and precisely within the limits of cutting parameter being used.

  10. Prediction of material removal rate and surface roughness for wire electrical discharge machining of nickel using response surface methodology

    Energy Technology Data Exchange (ETDEWEB)

    Chinnadurai, T.; Vendan, S.A.


    This study focuses on investigating the effects of process parameters, namely, Peak current (Ip), Pulse on time (Ton), Pulse off time (Toff), Water pressure (Wp), Wire feed rate (Wf), Wire tension (Wt), Servo voltage (Sv) and Servo feed setting (Sfs), on the Material Removal Rate (MRR) and Surface Roughness (SR) for Wire electrical discharge machining (Wire-EDM) of nickel using Taguchi method. Response Surface Methodology (RSM) is adopted to evolve mathematical relationships between the wire cutting process parameters and the output variables of the weld joint to determine the welding input parameters that lead to the desired optimal wire cutting quality. Besides, using response surface plots, the interaction effects of process parameters on the responses are analyzed and discussed. The statistical software Mini-tab is used to establish the design and to obtain the regression equations. The developed mathematical models are tested by analysis-of-variance (ANOVA) method to check their appropriateness and suitability. Finally, a comparison is made between measured and calculated results, which are in good agreement. This indicates that the developed models can predict the responses accurately and precisely within the limits of cutting parameter being used. (Author)

  11. 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

  12. Prediction of Sea Surface Temperature Using Long Short-Term Memory (United States)

    Zhang, Qin; Wang, Hui; Dong, Junyu; Zhong, Guoqiang; Sun, Xin


    This letter adopts long short-term memory(LSTM) to predict sea surface temperature(SST), which is the first attempt, to our knowledge, to use recurrent neural network to solve the problem of SST prediction, and to make one week and one month daily prediction. We formulate the SST prediction problem as a time series regression problem. LSTM is a special kind of recurrent neural network, which introduces gate mechanism into vanilla RNN to prevent the vanished or exploding gradient problem. It has strong ability to model the temporal relationship of time series data and can handle the long-term dependency problem well. The proposed network architecture is composed of two kinds of layers: LSTM layer and full-connected dense layer. LSTM layer is utilized to model the time series relationship. Full-connected layer is utilized to map the output of LSTM layer to a final prediction. We explore the optimal setting of this architecture by experiments and report the accuracy of coastal seas of China to confirm the effectiveness of the proposed method. In addition, we also show its online updated characteristics.

  13. Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model (United States)

    Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long


    This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  20. Surface complexation modeling of americium sorption onto volcanic tuff. (United States)

    Ding, M; Kelkar, S; Meijer, A


    Results of a surface complexation model (SCM) for americium sorption on volcanic rocks (devitrified and zeolitic tuff) are presented. The model was developed using PHREEQC and based on laboratory data for americium sorption on quartz. Available data for sorption of americium on quartz as a function of pH in dilute groundwater can be modeled with two surface reactions involving an americium sulfate and an americium carbonate complex. It was assumed in applying the model to volcanic rocks from Yucca Mountain, that the surface properties of volcanic rocks can be represented by a quartz surface. Using groundwaters compositionally representative of Yucca Mountain, americium sorption distribution coefficient (Kd, L/Kg) values were calculated as function of pH. These Kd values are close to the experimentally determined Kd values for americium sorption on volcanic rocks, decreasing with increasing pH in the pH range from 7 to 9. The surface complexation constants, derived in this study, allow prediction of sorption of americium in a natural complex system, taking into account the inherent uncertainty associated with geochemical conditions that occur along transport pathways. Published by Elsevier Ltd.

  1. Prediction Activities at NASA's Global Modeling and Assimilation Office (United States)

    Schubert, Siegfried


    The Global Modeling and Assimilation Office (GMAO) is a core NASA resource for the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems that are used for addressing the weather and climate research questions identified in NASA's science mission. In fact, the GMAO is actively engaged in addressing one of NASA's science mission s key questions concerning how well transient climate variations can be understood and predicted. At weather time scales the GMAO is developing ultra-high resolution global climate models capable of resolving high impact weather systems such as hurricanes. The ability to resolve the detailed characteristics of weather systems within a global framework greatly facilitates addressing fundamental questions concerning the link between weather and climate variability. At sub-seasonal time scales, the GMAO is engaged in research and development to improve the use of land information (especially soil moisture), and in the improved representation and initialization of various sub-seasonal atmospheric variability (such as the MJO) that evolves on time scales longer than weather and involves exchanges with both the land and ocean The GMAO has a long history of development for advancing the seasonal-to-interannual (S-I) prediction problem using an older version of the coupled atmosphere-ocean general circulation model (AOGCM). This includes the development of an Ensemble Kalman Filter (EnKF) to facilitate the multivariate assimilation of ocean surface altimetry, and an EnKF developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The importance of decadal variability, especially that associated with long-term droughts is well recognized by the

  2. Comparison of Approaches to the Prediction of Surface Wave Phase Velocity (United States)

    Godfrey, K. E.; Dalton, C. A.; Hjorleifsdottir, V.; Ekstrom, G.


    Global seismic models provide crucial information about the state, composition, and dynamics of the Earth's interior, and in the shallow mantle these models are primarily constrained by observations of surface waves. Models developed by different groups have been constructed using different data sets and different techniques. While these models exhibit good agreement on the long-wavelength features, there is less consistency in the patterns and amplitude of smaller-scale heterogeneity. Here we investigate how approximations in the theoretical treatment of wave propagation and excitation influence the interpretation of measured phase delays and the tomographic images that result from inverting them. Synthetic seismograms were generated using SPECFEM3D_GLOBE for 42 earthquakes, 134 receiver locations, and two 3-D models of elastic Earth structure: S362ANI (Kustowski et al., 2008) and a rougher model constructed by adding realistic small-scale structure to S362ANI. Fundamental-mode Rayleigh and Love wave phase delays in the period range 35-250 seconds were measured using the approach of Ekström et al. (1997), for which PREM is the assumed reference Earth model. These measurements were compared to phase-delay predictions generated for the great-circle ray approximation, exact ray theory, and finite-frequency theory. We find that for both 3-D earth models exact ray theory provides the best fit to the measurements at short periods. At longer periods finite frequency theory provides the best fit. For the smooth earth model, the differences in fit for the various predictions are less significant at long periods than at shorter periods. The differences at long periods become more significant with increasing model roughness. In all cases, the agreement between predictions and measurements is best for paths located away from nodes in the source radiation pattern. The ability of the measured phase delays to recover the input Earth models is assessed through tests that explore

  3. 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

  4. A predictive model for indoor radon occurrences - A first approximation

    International Nuclear Information System (INIS)

    LeGrand, H.E.


    Knowledge of how radon gas is transmitted in the shallow ground environment and how it emanates into buildings is grossly incomplete. Admittedly, some excellent research studies have been made and some general associations between certain aspects of the environment and radon occurrences in buildings are recognized. Yet, a technique for precisely predicting the radon concentrations indoors is not likely to be developed soon. As knowledge increases, successive approximations toward a final predictive model may be required. An early approximation of a predictive model for indoor radon is presented in this paper. It applies specifically to the crystalline rock region of the eastern United States, but it should have some application on a broader basis. The predictive model described focuses on understanding the wide-ranging permeability characteristics in the soil and rock fracture system. Radon is thought to accrete in confined subsurface air and moves under ground to low-pressure places, such as house niched in hill sloped. Driving forces for the air-laden and entrapped radon gas are considered to be a rising water table and infiltrating moisture from the land surface

  5. Global modelling of Cryptosporidium in surface water (United States)

    Vermeulen, Lucie; Hofstra, Nynke


    Introduction Waterborne pathogens that cause diarrhoea, such as Cryptosporidium, pose a health risk all over the world. In many regions quantitative information on pathogens in surface water is unavailable. Our main objective is to model Cryptosporidium concentrations in surface waters worldwide. We present the GloWPa-Crypto model and use the model in a scenario analysis. A first exploration of global Cryptosporidium emissions to surface waters has been published by Hofstra et al. (2013). Further work has focused on modelling emissions of Cryptosporidium and Rotavirus to surface waters from human sources (Vermeulen et al 2015, Kiulia et al 2015). A global waterborne pathogen model can provide valuable insights by (1) providing quantitative information on pathogen levels in data-sparse regions, (2) identifying pathogen hotspots, (3) enabling future projections under global change scenarios and (4) supporting decision making. Material and Methods GloWPa-Crypto runs on a monthly time step and represents conditions for approximately the year 2010. The spatial resolution is a 0.5 x 0.5 degree latitude x longitude grid for the world. We use livestock maps ( combined with literature estimates to calculate spatially explicit livestock Cryptosporidium emissions. For human Cryptosporidium emissions, we use UN population estimates, the WHO/UNICEF JMP sanitation country data and literature estimates of wastewater treatment. We combine our emissions model with a river routing model and data from the VIC hydrological model ( to calculate concentrations in surface water. Cryptosporidium survival during transport depends on UV radiation and water temperature. We explore pathogen emissions and concentrations in 2050 with the new Shared Socio-economic Pathways (SSPs) 1 and 3. These scenarios describe plausible future trends in demographics, economic development and the degree of global integration. Results and

  6. 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

  7. Applying horizontal diffusion on pressure surface to mesoscale models on terrain-following coordinates (United States)

    Hann-Ming Henry Juang; Ching-Teng Lee; Yongxin Zhang; Yucheng Song; Ming-Chin Wu; Yi-Leng Chen; Kevin Kodama; Shyh-Chin Chen


    The National Centers for Environmental Prediction regional spectral model and mesoscale spectral model (NCEP RSM/MSM) use a spectral computation on perturbation. The perturbation is defined as a deviation between RSM/MSM forecast value and their outer model or analysis value on model sigma-coordinate surfaces. The horizontal diffusion used in the models applies...

  8. Prediction of the adhesive behavior of bio-inspired functionally graded materials against rough surfaces

    Directory of Open Access Journals (Sweden)

    Chen Peijian


    Full Text Available Roughness effect and adhesion properties are important characteristics to be accessed in the development of functionally graded materials for biological and biomimetic applications, particularly for the hierarchical composition in biomimetic gecko robot. A multi-asperities adhesion model to predict the adhesive forces is presented in this work. The effect of surface roughness and graded material properties, which significantly alter the adhesive strength between contact bodies, can be simultaneously considered in the generalized model. It is found that proper interfacial strength can be controlled by adjusting surface roughness σ / R, graded exponent k and material parameter E*R / Δγ. The results should be helpful in the design of new biomimetic materials and useful in application of micro functional instruments.

  9. Vision models for 3D surfaces (United States)

    Mitra, Sunanda


    Different approaches to computational stereo to represent human stereo vision have been developed over the past two decades. The Marr-Poggio theory of human stereo vision is probably the most widely accepted model of the human stereo vision. However, recently developed motion stereo models which use a sequence of images taken by either a moving camera or a moving object provide an alternative method of achieving multi-resolution matching without the use of Laplacian of Gaussian operators. While using image sequences, the baseline between two camera positions for a image pair is changed for the subsequent image pair so as to achieve different resolution for each image pair. Having different baselines also avoids the inherent occlusion problem in stereo vision models. The advantage of using multi-resolution images acquired by camera positioned at different baselines over those acquired by LOG operators is that one does not have to encounter spurious edges often created by zero-crossings in the LOG operated images. Therefore in designing a computer vision system, a motion stereo model is more appropriate than a stereo vision model. However, in some applications where only a stereo pair of images are available, recovery of 3D surfaces of natural scenes are possible in a computationally efficient manner by using cepstrum matching and regularization techniques. Section 2 of this paper describes a motion stereo model using multi-scale cepstrum matching for the detection of disparity between image pairs in a sequence of images and subsequent recovery of 3D surfaces from depth-map obtained by a non convergent triangulation technique. Section 3 presents a 3D surface recovery technique from a stereo pair using cepstrum matching for disparity detection and cubic B-splines for surface smoothing. Section 4 contains the results of 3D surface recovery using both of the techniques mentioned above. Section 5 discusses the merit of 2D cepstrum matching and cubic B

  10. [Application of three compartment model and response surface model to clinical anesthesia using Microsoft Excel]. (United States)

    Abe, Eiji; Abe, Mari


    With the spread of total intravenous anesthesia, clinical pharmacology has become more important. We report Microsoft Excel file applying three compartment model and response surface model to clinical anesthesia. On the Microsoft Excel sheet, propofol, remifentanil and fentanyl effect-site concentrations are predicted (three compartment model), and probabilities of no response to prodding, shaking, surrogates of painful stimuli and laryngoscopy are calculated using predicted effect-site drug concentration. Time-dependent changes in these calculated values are shown graphically. Recent development in anesthetic drug interaction studies are remarkable, and its application to clinical anesthesia with this Excel file is simple and helpful for clinical anesthesia.

  11. A model of the ideal molecular surface (United States)

    Henson, Bryan; Smilowitz, Laura


    We utilize two manifestations of the phenomena of the quasiliquid phase on the surface of molecular crystals to formulate a universal thermodynamic theory describing the thickness of the layer as a function of the liquid phase activity. We use direct measurements of the liquid thickness as a function of temperature and measurements of the acceleration of thermal decomposition as a function of temperature approaching the melting point to illustrate the mechanism. We show that given the existence of a liquid phase below the melting point the ideal liquid activity is necessarily a fixed function of the free energies of sublimation and vaporization. We use this activity to create a reduced formula for the liquid thickness generally applicable to the molecular surface. We provide a prediction of the mechanism and kinetics of quasiliquid formation and show that the phase exists as a metastable kinetic steady state. We show that to first order the principle controlling feature of the system is the configurational entropy of the liquid/solid interface, rather than the specifics of the surface potential energy. This is analogous to other bulk colligative phenomena such as ideal gas and solution theories, and is thus an ideal, universal formulation of inherent, thermodynamically driven, surface disorder.

  12. Surface Complexation Modelling in Metal-Mineral-Bacteria Systems (United States)

    Johnson, K. J.; Fein, J. B.


    The reactive surfaces of bacteria and minerals can determine the fate, transport, and bioavailability of aqueous heavy metal cations. Geochemical models are instrumental in accurately accounting for the partitioning of the metals between mineral surfaces and bacteria cell walls. Previous research has shown that surface complexation modelling (SCM) is accurate in two-component systems (metal:mineral and metal:bacteria); however, the ability of SCMs to account for metal distribution in mixed metal-mineral-bacteria systems has not been tested. In this study, we measure aqueous Cd distributions in water-bacteria-mineral systems, and compare these observations with predicted distributions based on a surface complexation modelling approach. We measured Cd adsorption in 2- and 3-component batch adsorption experiments. In the 2-component experiments, we measured the extent of adsorption of 10 ppm aqueous Cd onto either a bacterial or hydrous ferric oxide sorbent. The metal:bacteria experiments contained 1 g/L (wet wt.) of B. subtilis, and were conducted as a function of pH; the metal:mineral experiments were conducted as a function of both pH and HFO content. Two types of 3-component Cd adsorption experiments were also conducted in which both mineral powder and bacteria were present as sorbents: 1) one in which the HFO was physically but not chemically isolated from the system using sealed dialysis tubing, and 2) others where the HFO, Cd and B. subtilis were all in physical contact. The dialysis tubing approach enabled the direct determination of the concentration of Cd on each sorbing surface, after separation and acidification of each sorbent. The experiments indicate that both bacteria and mineral surfaces can dominate adsorption in the system, depending on pH and bacteria:mineral ratio. The stability constants, determined using the data from the 2-component systems, along with those for other surface and aqueous species in the systems, were used with FITEQL to

  13. Building predictive models of soil particle-size distribution

    Directory of Open Access Journals (Sweden)

    Alessandro Samuel-Rosa


    Full Text Available Is it possible to build predictive models (PMs of soil particle-size distribution (psd in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index. The PMs explained more than half of the data variance. This performance is similar to (or even better than that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd of soils in regions of complex geology.

  14. 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.

  15. [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.

  16. Nuclear surface vibrations in bag models

    International Nuclear Information System (INIS)

    Tomio, L.


    The main difficulties found in the hadron bag models are reviewed from the original version of the MIT bag model. Following, with the aim to answer two of the main difficulties in bag models, viz., the parity and the divergence illness, a dynamical model is presented. In the model, the confinement surface of the quarks (bag) is treated like a real physical object which interacts with the quarks and is exposed to vibrations. The model is applied to the nucleon, being observed that his spectrum, in the first excited levels, can be reproduced with resonable precision and obeying to the correct parity order. In the same way that in a similar work of Brown et al., it is observed to be instrumental the inclusion of the effect due to pions. (L.C.) [pt

  17. Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

    Directory of Open Access Journals (Sweden)

    Tien Du Duc


    Full Text Available The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl. For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.

  18. 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.

  19. 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)

  20. Grain Surface Models and Data for Astrochemistry (United States)

    Cuppen, H. M.; Walsh, C.; Lamberts, T.; Semenov, D.; Garrod, R. T.; Penteado, E. M.; Ioppolo, S.


    The cross-disciplinary field of astrochemistry exists to understand the formation, destruction, and survival of molecules in astrophysical environments. Molecules in space are synthesized via a large variety of gas-phase reactions, and reactions on dust-grain surfaces, where the surface acts as a catalyst. A broad consensus has been reached in the astrochemistry community on how to suitably treat gas-phase processes in models, and also on how to present the necessary reaction data in databases; however, no such consensus has yet been reached for grain-surface processes. A team of {˜}25 experts covering observational, laboratory and theoretical (astro)chemistry met in summer of 2014 at the Lorentz Center in Leiden with the aim to provide solutions for this problem and to review the current state-of-the-art of grain surface models, both in terms of technical implementation into models as well as the most up-to-date information available from experiments and chemical computations. This review builds on the results of this workshop and gives an outlook for future directions.

  1. 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.

  2. Tumor Surface Regularity at MR Imaging Predicts Survival and Response to Surgery in Patients with Glioblastoma. (United States)

    Pérez-Beteta, Julián; Molina-García, David; Ortiz-Alhambra, José A; Fernández-Romero, Antonio; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M; Meléndez, Bárbara; Rodríguez de Lope, Ángel; Moreno de la Presa, Raquel; Iglesias Bayo, Lidia; Barcia, Juan A; Martino, Juan; Velásquez, Carlos; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Revert, Antonio; Arana, Estanislao; Pérez-García, Víctor M


    Purpose To evaluate the prognostic and predictive value of surface-derived imaging biomarkers obtained from contrast material-enhanced volumetric T1-weighted pretreatment magnetic resonance (MR) imaging sequences in patients with glioblastoma multiforme. Materials and Methods A discovery cohort from five local institutions (165 patients; mean age, 62 years ± 12 [standard deviation]; 43% women and 57% men) and an independent validation cohort (51 patients; mean age, 60 years ± 12; 39% women and 61% men) from The Cancer Imaging Archive with volumetric T1-weighted pretreatment contrast-enhanced MR imaging sequences were included in the study. Clinical variables such as age, treatment, and survival were collected. After tumor segmentation and image processing, tumor surface regularity, measuring how much the tumor surface deviates from a sphere of the same volume, was obtained. Kaplan-Meier, Cox proportional hazards, correlations, and concordance indexes were used to compare variables and patient subgroups. Results Surface regularity was a powerful predictor of survival in the discovery (P = .005, hazard ratio [HR] = 1.61) and validation groups (P = .05, HR = 1.84). Multivariate analysis selected age and surface regularity as significant variables in a combined prognostic model (P surface regularity was a predictor of survival for patients who underwent complete resection (P = .01, HR = 1.90). Tumors with irregular surfaces did not benefit from total over subtotal resections (P = .57, HR = 1.17), but those with regular surfaces did (P = .004, HR = 2.07). Conclusion The surface regularity obtained from high-resolution contrast-enhanced pretreatment volumetric T1-weighted MR images is a predictor of survival in patients with glioblastoma. It may help in classifying patients for surgery. © RSNA, 2018 Online supplemental material is available for this article.

  3. Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction (United States)

    Howard, Réka; Carriquiry, Alicia L.; Beavis, William D.


    An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits comprised of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability). Possible values for these factors and the number of combinations of the factor levels that influence the performance of GP methods can be large. Thus, efficient methods for identifying combinations of factor levels that produce most accurate GPs is needed. Herein, we employ response surface methods (RSMs) to find the experimental conditions that produce the most accurate GPs. We illustrate RSM with an example of simulated doubled haploid populations and identify the combination of factors that maximize the difference between prediction accuracies of best linear unbiased prediction (BLUP) and support vector machine (SVM) GP methods. The greatest impact on the response is due to the genetic architecture of the population, heritability of the trait, and the sample size. When epistasis is responsible for all of the genotypic variance and heritability is equal to one and the sample size of the training population is large, the advantage of using the SVM method vs. the BLUP method is greatest. However, except for values close to the maximum, most of the response surface shows little difference between the methods. We also determined that the conditions resulting in the greatest prediction accuracy for BLUP occurred when genetic architecture consists solely of additive effects, and heritability is equal to one. PMID

  4. Improving Permafrost Hydrology Prediction Through Data-Model Integration (United States)

    Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.


    The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

  5. Probabilistic predictive modelling of carbon nanocomposites for medical implants design. (United States)

    Chua, Matthew; Chui, Chee-Kong


    Modelling of the mechanical properties of carbon nanocomposites based on input variables like percentage weight of Carbon Nanotubes (CNT) inclusions is important for the design of medical implants and other structural scaffolds. Current constitutive models for the mechanical properties of nanocomposites may not predict well due to differences in conditions, fabrication techniques and inconsistencies in reagents properties used across industries and laboratories. Furthermore, the mechanical properties of the designed products are not deterministic, but exist as a probabilistic range. A predictive model based on a modified probabilistic surface response algorithm is proposed in this paper to address this issue. Tensile testing of three groups of different CNT weight fractions of carbon nanocomposite samples displays scattered stress-strain curves, with the instantaneous stresses assumed to vary according to a normal distribution at a specific strain. From the probabilistic density function of the experimental data, a two factors Central Composite Design (CCD) experimental matrix based on strain and CNT weight fraction input with their corresponding stress distribution was established. Monte Carlo simulation was carried out on this design matrix to generate a predictive probabilistic polynomial equation. The equation and method was subsequently validated with more tensile experiments and Finite Element (FE) studies. The method was subsequently demonstrated in the design of an artificial tracheal implant. Our algorithm provides an effective way to accurately model the mechanical properties in implants of various compositions based on experimental data of samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Explanatory models for ecological response surfaces

    International Nuclear Information System (INIS)

    Jager, H.I.; Overton, W.S.


    Understanding the spatial organization of ecological systems is a fundamental part of ecosystem study. While discovering the causal relationships of this organization is an important goal, our purpose of spatial description on a regional scale is best met by use of explanatory variables that are somewhat removed from the mechanistic causal level. Regional level understanding is best obtained from explanatory variables that reflect spatial gradients at the regional scale and from categorical variables that describe the discrete constituents of (statistical) populations, such as lakes. In this paper, we use a regression model to predict lake acid neutralizing capacity (ANC) based on environmental predictor variables over a large region. These predictions are used to produce model-based population estimates. Two key features of our modeling approach are that is honors the spatial context and the design of the sample data. The spatial context of the data are brought into the analysis of model residuals through the interpretation of residual maps and semivariograms. The sampling design is taken into account by including stratification variables from the design in the model. This ensures that the model applies to a real population of lakes (the target population), rather than whatever hypothetical population the sample is a random sample of

  7. 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.


    Directory of Open Access Journals (Sweden)

    R. Boesch


    Full Text Available The application of extended digital surface models often reveals, that despite an acceptable global accuracy for a given dataset, the local accuracy of the model can vary in a wide range. For high resolution applications which cover the spatial extent of a whole country, this can be a major drawback. Within the Swiss National Forest Inventory (NFI, two digital surface models are available, one derived from LiDAR point data and the other from aerial images. Automatic photogrammetric image matching with ADS80 aerial infrared images with 25cm and 50cm resolution is used to generate a surface model (ADS-DSM with 1m resolution covering whole switzerland (approx. 41000 km2. The spatially corresponding LiDAR dataset has a global point density of 0.5 points per m2 and is mainly used in applications as interpolated grid with 2m resolution (LiDAR-DSM. Although both surface models seem to offer a comparable accuracy from a global view, local analysis shows significant differences. Both datasets have been acquired over several years. Concerning LiDAR-DSM, different flight patterns and inconsistent quality control result in a significantly varying point density. The image acquisition of the ADS-DSM is also stretched over several years and the model generation is hampered by clouds, varying illumination and shadow effects. Nevertheless many classification and feature extraction applications requiring high resolution data depend on the local accuracy of the used surface model, therefore precise knowledge of the local data quality is essential. The commercial photogrammetric software NGATE (part of SOCET SET generates the image based surface model (ADS-DSM and delivers also a map with figures of merit (FOM of the matching process for each calculated height pixel. The FOM-map contains matching codes like high slope, excessive shift or low correlation. For the generation of the LiDAR-DSM only first- and last-pulse data was available. Therefore only the point

  9. Description and application of the combined surface and groundwater flow model MOGROW

    NARCIS (Netherlands)

    Querner, E.P.


    In the Netherlands shallow groundwater tables prevail in many parts, such that groundwater and surface water are closely interlinked. Thus the use of a combined groundwater and surface water model is necessary to predict the effect of certain measures on a regional scale. Therefore the model MOGROW

  10. Finite element modeling of surface subsidence induced by underground coal mining

    International Nuclear Information System (INIS)

    Su, D.W.H.


    The ability to predict the effects of longwall mining on topography and surface structures is important for any coal company in making permit applications and anticipating potential mining problems. The sophisticated finite element model described and evaluated in this paper is based upon five years of underground and surface observations and evolutionary development of modeling techniques and attributes. The model provides a very powerful tool to address subsidence and other ground control questions. The model can be used to calculate postmining stress and strain conditions at any horizon between the mine and the ground surface. This holds the promise of assisting in the prediction of mining-related hydrological effects

  11. 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

  12. Prediction of Ablation Rates from Solid Surfaces Exposed to High Temperature Gas Flow (United States)

    Akyuzlu, Kazim M.; Coote, David


    ablation. Two different ablation models are proposed to determine the heat loss from the solid surface due to the ablation of the solid material. Both of them are physics based. Various numerical simulations were carried out using both models to predict the temperature distribution in the solid and in the gas flow, and then predict the ablation rates at a typical NTR motor hydrogen gas temperature and pressure. Solid mass loss rate per foot of a pipe was also calculated from these predictions. The results are presented for fully developed turbulent flow conditions in a sample SS pipe with a 6 inch diameter.

  13. 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

  14. 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...

  15. Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology. (United States)

    Huang, Jen-Ching; Chang, Ho; Kuo, Chin-Guo; Li, Jeen-Fong; You, Yong-Chin


    Atomic force microscopy (AFM) was used for visualization of a nano-oxidation technique performed on diamond-like carbon (DLC) thin film. Experiments of the nano-oxidation technique of the DLC thin film include those on nano-oxidation points and nano-oxidation lines. The feature sizes of the DLC thin film, including surface morphology, depth, and width, were explored after application of a nano-oxidation technique to the DLC thin film under different process parameters. A databank for process parameters and feature sizes of thin films was then established, and multiple regression analysis (MRA) and a back-propagation neural network (BPN) were used to carry out the algorithm. The algorithmic results are compared with the feature sizes acquired from experiments, thus obtaining a prediction model of the nano-oxidation technique of the DLC thin film. The comparative results show that the prediction accuracy of BPN is superior to that of MRA. When the BPN algorithm is used to predict nano-point machining, the mean absolute percentage errors (MAPE) of depth, left side, and right side are 8.02%, 9.68%, and 7.34%, respectively. When nano-line machining is being predicted, the MAPEs of depth, left side, and right side are 4.96%, 8.09%, and 6.77%, respectively. The obtained data can also be used to predict cross-sectional morphology in the DLC thin film treated with a nano-oxidation process.

  16. 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...

  17. Modeling superhydrophobic surfaces comprised of random roughness (United States)

    Samaha, M. A.; Tafreshi, H. Vahedi; Gad-El-Hak, M.


    We model the performance of superhydrophobic surfaces comprised of randomly distributed roughness that resembles natural surfaces, or those produced via random deposition of hydrophobic particles. Such a fabrication method is far less expensive than ordered-microstructured fabrication. The present numerical simulations are aimed at improving our understanding of the drag reduction effect and the stability of the air-water interface in terms of the microstructure parameters. For comparison and validation, we have also simulated the flow over superhydrophobic surfaces made up of aligned or staggered microposts for channel flows as well as streamwise or spanwise ridge configurations for pipe flows. The present results are compared with other theoretical and experimental studies. The numerical simulations indicate that the random distribution of surface roughness has a favorable effect on drag reduction, as long as the gas fraction is kept the same. The stability of the meniscus, however, is strongly influenced by the average spacing between the roughness peaks, which needs to be carefully examined before a surface can be recommended for fabrication. Financial support from DARPA, contract number W91CRB-10-1-0003, is acknowledged.

  18. 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...

  19. Prediction of fog/visibility over India using NWP Model (United States)

    Singh, Aditi; George, John P.; Iyengar, Gopal Raman


    Frequent occurrence of fog in different parts of northern India is common during the winter months of December and January. Low visibility conditions due to fog disrupt normal public life. Visibility conditions heavily affect both surface and air transport. A number of flights are either diverted or cancelled every year during the winter season due to low visibility conditions, experienced at different airports of north India. Thus, fog and visibility forecasts over plains of north India become very important during winter months. This study aims to understand the ability of a NWP model (NCMRWF, Unified Model, NCUM) with a diagnostic visibility scheme to forecast visibility over plains of north India. The present study verifies visibility forecasts obtained from NCUM against the INSAT-3D fog images and visibility observations from the METAR reports of different stations in the plains of north India. The study shows that the visibility forecast obtained from NCUM can provide reasonably good indication of the spatial extent of fog in advance of one day. The fog intensity is also predicted fairly well. The study also verifies the simple diagnostic model for fog which is driven by NWP model forecast of surface relative humidity and wind speed. The performance of NWP model forecast of visibility is found comparable to that from simple fog model driven by NWP forecast of relative humidity and wind speed.

  20. A multi-surface plasticity model for ductile fracture simulations (United States)

    Keralavarma, Shyam M.


    The growth and coalescence of micro-voids in a material undergoing ductile fracture depends strongly on the loading path. Void growth occurs by diffuse plasticity in the material and is sensitive to the hydrostatic stress, while void coalescence occurs by the localization of plastic deformation in the inter-void ligaments under a combination of normal and shear stresses on the localization plane. In this paper, a micromechanics-based plasticity model is developed for an isotropic porous material, accounting for both diffuse and localized modes of plasticity at the micro-scale. A multi-surface approach is adopted, and two existing plasticity models that separately account for the two modes of yielding, above, are synthesized to propose an effective isotropic yield criterion and associated state evolution equations. The yield criterion is validated by comparison with quasi-exact numerical yield loci computed using a finite elements based limit analysis procedure. It is shown that the new criterion is in better agreement with the numerical loci than the Gurson model, particularly for large values of the porosity for which the loading path dependence of the yield stress is well predicted by the new model. Even at small porosities, it is shown that the new model predicts marginally lower yield stresses under low triaxiality shear dominated loadings compared to the Gurson model, in agreement with the numerical limit analysis data. Predictions for the strains to the onset of coalescence under proportional loading, obtained by numerically integrating the model, indicate that void coalescence tends to occur at relatively small plastic strain and porosity levels under shear dominated loadings. Implications on the prediction of ductility using the new model in fracture simulations are discussed.

  1. Decadal prediction skill using a high-resolution climate model (United States)

    Monerie, Paul-Arthur; Coquart, Laure; Maisonnave, Éric; Moine, Marie-Pierre; Terray, Laurent; Valcke, Sophie


    The ability of a high-resolution coupled atmosphere-ocean general circulation model (with a horizontal resolution of a quarter of a degree in the ocean and of about 0.5° in the atmosphere) to predict the annual means of temperature, precipitation, sea-ice volume and extent is assessed based on initialized hindcasts over the 1993-2009 period. Significant skill in predicting sea surface temperatures is obtained, especially over the North Atlantic, the tropical Atlantic and the Indian Ocean. The Sea Ice Extent and volume are also reasonably predicted in winter (March) and summer (September). The model skill is mainly due to the external forcing associated with well-mixed greenhouse gases. A decrease in the global warming rate associated with a negative phase of the Pacific Decadal Oscillation is simulated by the model over a suite of 10-year periods when initialized from starting dates between 1999 and 2003. The model ability to predict regional change is investigated by focusing on the mid-90's Atlantic Ocean subpolar gyre warming. The model simulates the North Atlantic warming associated with a meridional heat transport increase, a strengthening of the North Atlantic current and a deepening of the mixed layer over the Labrador Sea. The atmosphere plays a role in the warming through a modulation of the North Atlantic Oscillation: a negative sea level pressure anomaly, located south of the subpolar gyre is associated with a wind speed decrease over the subpolar gyre. This leads to a reduced oceanic heat-loss and favors a northward displacement of anomalously warm and salty subtropical water that both concur to the subpolar gyre warming. We finally conclude that the subpolar gyre warming is mainly triggered by ocean dynamics with a possible contribution of atmospheric circulation favoring its persistence.

  2. Core surface flow modelling from high-resolution secular variation

    DEFF Research Database (Denmark)

    Holme, R.; Olsen, Nils


    -flux hypothesis, but the spectrum of the SV implies that a conclusive test of frozen-flux is not possible. We parametrize the effects of diffusion as an expected misfit in the flow prediction due to departure from the frozen-flux hypothesis; at low spherical harmonic degrees, this contribution dominates...... the expected departure of the SV predictions from flow to the observed SV, while at high degrees the SV model uncertainty is dominant. We construct fine-scale core surface flows to model the SV. Flow non-uniqueness is a serious problem because the flows are sufficiently small scale to allow flow around non......-series of magnetic data and better parametrization of the external magnetic field....

  3. Computational prediction of heat transfer to gas turbine nozzle guide vanes with roughened surfaces

    Energy Technology Data Exchange (ETDEWEB)

    Guo, S.M.; Jones, T.V. [Univ. of Oxford (United Kingdom). Dept. of Engineering Science; Lock, G.D. [Univ. of Bath (United Kingdom). Dept. of Mechanical Engineering; Dancer, S.N. [Rolls-Royce PLC, Derby (United Kingdom)


    The local Mach number and heat transfer coefficient over the aerofoil surfaces and endwalls of a transonic gas turbine nozzle guide vane have been calculated. the computations were performed by solving the time-averaged Navier-Stokes equations using a fully three-dimensional computational code (CFDS), which is well established at Rolls-Royce. A model to predict the effects of roughness has been incorporated into CFDS and heat transfer levels have been calculated for both hydraulically smooth and transitionally rough surfaces. The roughness influences the calculations in two ways; first the mixing length at a certain height above the surface is increased; second the wall function used to reconcile the wall condition with the first grid point above the wall is also altered. The first involves a relatively straightforward shift of the origin in the van Driest damping function description, the second requires an integration of the momentum equation across the wall layer. A similar treatment applies to the energy equation. The calculations are compared with experimental contours of heat transfer coefficient obtained using both thin-film gages and the transient liquid crystal technique. Measurements were performed using both hydraulically smooth and roughened surfaces, and at engine-representative Mach and Reynolds numbers. The heat transfer results are discussed and interpreted in terms of surface-shear flow visualization using oil and dye techniques.

  4. Variations in FASST Predictions of Soil Surface Temperatures

    National Research Council Canada - National Science Library

    Peck, Lindamae


    ..., initial volumetric soil moisture content, bulk density of the dry soil material, albedo (sunny days), and porosity. The thermal conductivity of the dry soil material has a minor effect on predicted soil temperature...

  5. A Theoretical Model for the Prediction of Siphon Breaking Phenomenon

    Energy Technology Data Exchange (ETDEWEB)

    Bae, Youngmin; Kim, Young-In; Seo, Jae-Kwang; Kim, Keung Koo; Yoon, Juhyeon [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)


    A siphon phenomenon or siphoning often refers to the movement of liquid from a higher elevation to a lower one through a tube in an inverted U shape (whose top is typically located above the liquid surface) under the action of gravity, and has been used in a variety of reallife applications such as a toilet bowl and a Greedy cup. However, liquid drainage due to siphoning sometimes needs to be prevented. For example, a siphon breaker, which is designed to limit the siphon effect by allowing the gas entrainment into a siphon line, is installed in order to maintain the pool water level above the reactor core when a loss of coolant accident (LOCA) occurs in an open-pool type research reactor. In this paper, we develop a theoretical model to predict the siphon breaking phenomenon. In this paper, a theoretical model to predict the siphon breaking phenomenon is developed. It is shown that the present model predicts well the fundamental features of the siphon breaking phenomenon and undershooting height.

  6. Using Google Earth Surface Metrics to Predict Plant Species Richness in a Complex Landscape

    Directory of Open Access Journals (Sweden)

    Sebastián Block


    Full Text Available Google Earth provides a freely available, global mosaic of high-resolution imagery from different sensors that has become popular in environmental and ecological studies. However, such imagery lacks the near-infrared band often used in studying vegetation, thus its potential for estimating vegetation properties remains unclear. In this study, we assess the potential of Google Earth imagery to describe and predict vegetation attributes. Further, we compare it to the potential of SPOT imagery, which has additional spectral information. We measured basal area, vegetation height, crown cover, density of individuals, and species richness in 60 plots in the oak forests of a complex volcanic landscape in central Mexico. We modelled each vegetation attribute as a function of surface metrics derived from Google Earth and SPOT images, and selected the best-supported linear models from each source. Total species richness was the best-described and predicted variable: the best Google Earth-based model explained nearly as much variation in species richness as its SPOT counterpart (R2 = 0.44 and 0.51, respectively. However, Google Earth metrics emerged as poor predictors of all remaining vegetation attributes, whilst SPOT metrics showed potential for predicting vegetation height. We conclude that Google Earth imagery can be used to estimate species richness in complex landscapes. As it is freely available, Google Earth can broaden the use of remote sensing by researchers and managers in low-income tropical countries where most biodiversity hotspots are found.

  7. The molar surface Gibbs energy and prediction of surface tension of [Cnpy][DCA] (n = 3, 4, 5)

    International Nuclear Information System (INIS)

    Xing, Nannan; Dai, Bing; Ma, Xiaoxue; Wei, Jie; Pan, Yi; Guan, Wei


    Highlights: • The molar surface Gibbs energy, g s was put forword. • A new Eötvös equation is obtained. The molar surface enthalpy, h, is a temperature-independent constant. • By using g s and n D , the surface tensions of [C n py][DCA] (n = 3, 4, 5) were estimated. - Abstract: Three pyridinium-based ionic liquids of [C n py][DCA] (n = 3, 4, 5) (N-alkyl-pyridinium dicyanamide) were prepared and characterized by 1 H NMR ( 1 H nuclear magnetic resonance) spectroscopy, 13 C NMR ( 13 C nuclear magnetic resonance) spectroscopy. Their densities, surface tensions and refractive indices were measured at different temperatures. The molar surface Gibbs energy, g s , critical temperature, T c and Eötvös empirical parameter related to polarity, k E , were also calculated. In terms of the concept of molar surface Gibbs energy, g s , a new Eötvös equation was obtained. It is found that the slope of the new Eötvös equation is the molar surface entropy of the ILs and the intercept is the molar surface enthalpy which is a temperature-independent constant. By using the refractive index and the molar surface Gibbs energy, an equation to predict surface tension of the ILs was derived and the predicted values of the surface tension of [C n py][DCA] (n = 3, 4, 5) are all most the same with the corresponding experimental values.

  8. Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices (United States)

    Funk, C.; Hoell, A.; Shukla, S.; Bladé, I.; Liebmann, B.; Roberts, J. B.; Robertson, F. R.; Husak, G.


    In eastern East Africa (the southern Ethiopia, eastern Kenya and southern Somalia region), poor boreal spring (long wet season) rains in 1999, 2000, 2004, 2007, 2008, 2009, and 2011 contributed to severe food insecurity and high levels of malnutrition. Predicting rainfall deficits in this region on seasonal and decadal time frames can help decision makers implement disaster risk reduction measures while guiding climate-smart adaptation and agricultural development. Building on recent research that links more frequent East African droughts to a stronger Walker circulation, resulting from warming in the Indo-Pacific warm pool and an increased east-to-west sea surface temperature (SST) gradient in the western Pacific, we show that the two dominant modes of East African boreal spring rainfall variability are tied to SST fluctuations in the western central Pacific and central Indian Ocean, respectively. Variations in these two rainfall modes can thus be predicted using two SST indices - the western Pacific gradient (WPG) and central Indian Ocean index (CIO), with our statistical forecasts exhibiting reasonable cross-validated skill (rcv ≈ 0.6). In contrast, the current generation of coupled forecast models show no skill during the long rains. Our SST indices also appear to capture most of the major recent drought events such as 2000, 2009 and 2011. Predictions based on these simple indices can be used to support regional forecasting efforts and land surface data assimilations to help inform early warning and guide climate outlooks.

  9. Modeling heat efficiency, flow and scale-up in the corotating disc scraped surface heat exchanger

    DEFF Research Database (Denmark)

    Friis, Alan; Szabo, Peter; Karlson, Torben


    A comparison of two different scale corotating disc scraped surface heat exchangers (CDHE) was performed experimentally. The findings were compared to predictions from a finite element model. We find that the model predicts well the flow pattern of the two CDHE's investigated. The heat transfer...... performance predicted by the model agrees well with experimental observations for the laboratory scale CDHE whereas the overall heat transfer in the scaled-up version was not in equally good agreement. The lack of the model to predict the heat transfer performance in scale-up leads us to identify the key...

  10. 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.

  11. Multipoint contact modeling of nanoparticle manipulation on rough surface

    Energy Technology Data Exchange (ETDEWEB)

    Zakeri, M., E-mail:; Faraji, J.; Kharazmi, M. [University of Tabriz, School of Engineering Emerging Technologies (Iran, Islamic Republic of)


    In this paper, the atomic force microscopy (AFM)-based 2-D pushing of nano/microparticles investigated on rough substrate by assuming a multipoint contact model. First, a new contact model was extracted and presented based on the geometrical profiles of Rumpf, Rabinovich and George models and the contact mechanics theories of JKR and Schwartz, to model the adhesion forces and the deformations in the multipoint contact of rough surfaces. The geometry of a rough surface was defined by two main parameters of asperity height (size of roughness) and asperity wavelength (compactness of asperities distribution). Then, the dynamic behaviors of nano/microparticles with radiuses in range of 50–500 nm studied during their pushing on rough substrate with a hexagonal or square arrangement of asperities. Dynamic behavior of particles were simulated and compared by assuming multipoint and single-point contact schemes. The simulation results show that the assumption of multipoint contact has a considerable influence on determining the critical manipulation force. Additionally, the assumption of smooth surfaces or single-point contact leads to large error in the obtained results. According to the results of previous research, it anticipated that a particles with the radius less than about 550 nm start to slide on smooth substrate; but by using multipoint contact model, the predicted behavior changed, and particles with radii of smaller than 400 nm begin to slide on rough substrate for different height of asperities, at first.

  12. Two adaptive radiative transfer schemes for numerical weather prediction models

    Directory of Open Access Journals (Sweden)

    V. Venema


    Full Text Available Radiative transfer calculations in atmospheric models are computationally expensive, even if based on simplifications such as the δ-two-stream approximation. In most weather prediction models these parameterisation schemes are therefore called infrequently, accepting additional model error due to the persistence assumption between calls. This paper presents two so-called adaptive parameterisation schemes for radiative transfer in a limited area model: A perturbation scheme that exploits temporal correlations and a local-search scheme that mainly takes advantage of spatial correlations. Utilising these correlations and with similar computational resources, the schemes are able to predict the surface net radiative fluxes more accurately than a scheme based on the persistence assumption. An important property of these adaptive schemes is that their accuracy does not decrease much in case of strong reductions in the number of calls to the δ-two-stream scheme. It is hypothesised that the core idea can also be employed in parameterisation schemes for other processes and in other dynamical models.

  13. TH-CD-207A-05: Lung Surface Deformation Vector Fields Prediction by Monitoring Respiratory Surrogate Signals

    International Nuclear Information System (INIS)

    Nasehi Tehrani, J; Wang, J; McEwan, A


    Purpose: In this study, we developed and evaluated a method for predicting lung surface deformation vector fields (SDVFs) based on surrogate signals such as chest and abdomen motion at selected locations and spirometry measurements. Methods: A Patient-specific 3D triangular surface mesh of the lung region at end-expiration (EE) phase was obtained by threshold-based segmentation method. For each patient, a spirometer recorded the flow volume changes of the lungs; and 192 selected points at a regular spacing of 2cm X 2cm matrix points over a total area of 34cm X 24cm on the surface of chest and abdomen was used to detect chest wall motions. Preprocessing techniques such as QR factorization with column pivoting (QRCP) were employed to remove redundant observations of the chest and abdominal area. To create a statistical model between the lung surface and the corresponding surrogate signals, we developed a predictive model based on canonical ridge regression (CRR). Two unique weighting vectors were selected for each vertex on the surface of the lung, and they were optimized during the training process using the all other phases of 4D-CT except the end-inspiration (EI) phase. These parameters were employed to predict the vertices locations of a testing data set, which was the EI phase of 4D-CT. Results: For ten lung cancer patients, the deformation vector field of each vertex of lung surface mesh was estimated from the external motion at selected positions on the chest wall surface plus spirometry measurements. The average estimation of 98th percentile of error was less than 1 mm (AP= 0.85, RL= 0.61, and SI= 0.82). Conclusion: The developed predictive model provides a non-invasive approach to derive lung boundary condition. Together with personalized biomechanical respiration modelling, the proposed model can be used to derive the lung tumor motion during radiation therapy accurately from non-invasive measurements.

  14. TH-CD-207A-05: Lung Surface Deformation Vector Fields Prediction by Monitoring Respiratory Surrogate Signals

    Energy Technology Data Exchange (ETDEWEB)

    Nasehi Tehrani, J; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States); McEwan, A [The University of Sydney, Sydney, New South Wales (Australia)


    Purpose: In this study, we developed and evaluated a method for predicting lung surface deformation vector fields (SDVFs) based on surrogate signals such as chest and abdomen motion at selected locations and spirometry measurements. Methods: A Patient-specific 3D triangular surface mesh of the lung region at end-expiration (EE) phase was obtained by threshold-based segmentation method. For each patient, a spirometer recorded the flow volume changes of the lungs; and 192 selected points at a regular spacing of 2cm X 2cm matrix points over a total area of 34cm X 24cm on the surface of chest and abdomen was used to detect chest wall motions. Preprocessing techniques such as QR factorization with column pivoting (QRCP) were employed to remove redundant observations of the chest and abdominal area. To create a statistical model between the lung surface and the corresponding surrogate signals, we developed a predictive model based on canonical ridge regression (CRR). Two unique weighting vectors were selected for each vertex on the surface of the lung, and they were optimized during the training process using the all other phases of 4D-CT except the end-inspiration (EI) phase. These parameters were employed to predict the vertices locations of a testing data set, which was the EI phase of 4D-CT. Results: For ten lung cancer patients, the deformation vector field of each vertex of lung surface mesh was estimated from the external motion at selected positions on the chest wall surface plus spirometry measurements. The average estimation of 98th percentile of error was less than 1 mm (AP= 0.85, RL= 0.61, and SI= 0.82). Conclusion: The developed predictive model provides a non-invasive approach to derive lung boundary condition. Together with personalized biomechanical respiration modelling, the proposed model can be used to derive the lung tumor motion during radiation therapy accurately from non-invasive measurements.

  15. A prediction of mars seismicity from surface faulting (United States)

    Golombek, M.P.; Banerdt, W.B.; Tanaka, K.L.; Tralli, D.M.


    The shallow seismicity of Mars has been estimated by measurement of the total slip on faults visible on the surface of the planet throughout geologic time. Seismicity was calibrated with estimates based on surface structures on the moon and measured lunar seismicity that includes the entire seismogenic lithosphere. Results indicate that Mars is seismically active today, with a sufficient number of detectable marsquakes to allow seismic investigations of its interior.

  16. Evaluation of Sub-Zonal Airflow Models for the Prediction of Local Interior Boundary Conditions

    DEFF Research Database (Denmark)

    Steskens, Paul W. M. H.; Janssen, Hans; Rode, Carsten


    and applicability of the sub-zonal airflow model to predict the local indoor environmental conditions, as well as the local surface transfer coefficients near building components. Two test cases were analyzed for, respectively, natural and forced convection in a room. The simulation results predicted from the sub...

  17. 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.

  18. 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.

  19. 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 ...

  20. Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

    International Nuclear Information System (INIS)

    Kovalenko, Andriy


    Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology

  1. Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems (United States)

    Kovalenko, Andriy


    Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology

  2. 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 ...

  3. Why did the bear cross the road? Comparing the performance of multiple resistance surfaces and connectivity modeling methods (United States)

    Samuel A. Cushman; Jesse S. Lewis; Erin L. Landguth


    There have been few assessments of the performance of alternative resistance surfaces, and little is known about how connectivity modeling approaches differ in their ability to predict organism movements. In this paper, we evaluate the performance of four connectivity modeling approaches applied to two resistance surfaces in predicting the locations of highway...

  4. 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...

  5. Frequency and wavelength prediction of ultrasonic induced liquid surface waves. (United States)

    Mahravan, Ehsan; Naderan, Hamid; Damangir, Ebrahim


    A theoretical investigation of parametric excitation of liquid free surface by a high frequency sound wave is preformed, using potential flow theory. Pressure and velocity distributions, resembling the sound wave, are applied to the free surface of the liquid. It is found that for impinging wave two distinct capillary frequencies will be excited: One of them is the same as the frequency of the sound wave, and the other is equal to the natural frequency corresponding to a wavenumber equal to the horizontal wavenumber of the sound wave. When the wave propagates in vertical direction, mathematical formulation leads to an equation, which has resonance frequency equal to half of the excitation frequency. This can explain an important contradiction between the frequency and the wavelength of capillary waves in the two cases of normal and inclined interaction of the sound wave and the free surface of the liquid. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Prediction of fluid velocity slip at solid surfaces

    DEFF Research Database (Denmark)

    Hansen, Jesper Schmidt; Todd, Billy; Daivis, Peter


    methods, it allows us to directly compute the intrinsic wall-fluid friction coefficient rather than an empirical friction coefficient that includes all sources of friction for planar shear flow. The slip length predicted by our method is in excellent agreement with the slip length obtained from direct...

  7. Predicting fire severity using surface fuels and moisture (United States)

    Pamela G. Sikkink; Robert E. Keane


    Fire severity classifications have been used extensively in fire management over the last 30 years to describe specific environmental or ecological impacts of fire on fuels, vegetation, wildlife, and soils in recently burned areas. New fire severity classifications need to be more objective, predictive, and ultimately more useful to fire management and planning. Our...

  8. Simplified Predictive Models for CO2 Sequestration Performance Assessment (United States)

    Mishra, Srikanta; RaviGanesh, Priya; Schuetter, Jared; Mooney, Douglas; He, Jincong; Durlofsky, Louis


    We present results from an ongoing research project that seeks to develop and validate a portfolio of simplified modeling approaches that will enable rapid feasibility and risk assessment for CO2 sequestration in deep saline formation. The overall research goal is to provide tools for predicting: (a) injection well and formation pressure buildup, and (b) lateral and vertical CO2 plume migration. Simplified modeling approaches that are being developed in this research fall under three categories: (1) Simplified physics-based modeling (SPM), where only the most relevant physical processes are modeled, (2) Statistical-learning based modeling (SLM), where the simulator is replaced with a "response surface", and (3) Reduced-order method based modeling (RMM), where mathematical approximations reduce the computational burden. The system of interest is a single vertical well injecting supercritical CO2 into a 2-D layered reservoir-caprock system with variable layer permeabilities. In the first category (SPM), we use a set of well-designed full-physics compositional simulations to understand key processes and parameters affecting pressure propagation and buoyant plume migration. Based on these simulations, we have developed correlations for dimensionless injectivity as a function of the slope of fractional-flow curve, variance of layer permeability values, and the nature of vertical permeability arrangement. The same variables, along with a modified gravity number, can be used to develop a correlation for the total storage efficiency within the CO2 plume footprint. In the second category (SLM), we develop statistical "proxy models" using the simulation domain described previously with two different approaches: (a) classical Box-Behnken experimental design with a quadratic response surface fit, and (b) maximin Latin Hypercube sampling (LHS) based design with a Kriging metamodel fit using a quadratic trend and Gaussian correlation structure. For roughly the same number of

  9. Climate Prediction Center (CPC) Global Land Surface Air Temperature Analysis (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A station observation-based global land monthly mean surface air temperature dataset at 0.5 0.5 latitude-longitude resolution for the period from 1948 to the present...

  10. Climate Prediction Center (CPC) Global Land Surface Air Temperature Analysis (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A station observation-based global land monthly mean surface air temperature dataset at 0.5 x 0.5 latitude-longitude resolution for the period from 1948 to the...

  11. 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

  12. Climate modelling, uncertainty and responses to predictions of change

    International Nuclear Information System (INIS)

    Henderson-Sellers, A.


    Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can't yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes

  13. A two-parameter model to predict fracture in the transition

    International Nuclear Information System (INIS)

    DeAquino, C.T.; Landes, J.D.; McCabe, D.E.


    A model is proposed that uses a numerical characterization of the crack tip stress field modified by the J - Q constraint theory and a weak link assumption to predict fracture behavior in the transition for reactor vessel steels. This model predicts the toughness scatter band for a component model from a toughness scatter band measured on a test specimen geometry. The model has been applied previously to two-dimensional through cracks. Many applications to actual components structures involve three-dimensional surface flaws. These cases require a more difficult level of analysis and need additional information. In this paper, both the current model for two-dimensional cracks and an approach needed to extend the model for the prediction of transition fracture behavior in three-dimensional surface flaws are discussed. Examples are presented to show how the model can be applied and in some cases to compare with other test results. (author). 13 refs., 7 figs

  14. 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

  15. Constraining Agricultural Irrigation Surface Energy Budget Feedbacks in Atmospheric Models (United States)

    Aufforth, M. E.; Desai, A. R.; Suyker, A.


    The expansion and modernization of irrigation increased the relevance of knowing the effects it has on regional weather and climate feedbacks. We conducted a set of observationally-constrained simulations determining the result irrigation exhibits on the surface energy budget, the atmospheric boundary layer, and regional precipitation feedbacks. Eddy covariance flux tower observations were analyzed from two irrigated and one rain-fed corn/soybean rotation sites located near Mead, Nebraska. The evaluated time period covered the summer growing months of June, July, and August (JJA) during the years when corn grew at all three sites. As a product of higher continuous surface moisture availability, the irrigated crops had significantly higher amounts of energy partitioned towards latent heating than the non-irrigated site. The daily average peak of latent heating at the rain-fed site occurred before the irrigated sites and was approximately 45 W/m2 lower. Land surface models were evaluated on their ability to reproduce these effects, including those used in numerical weather prediction and those used in agricultural carbon cycle projection. Model structure, mechanisms, and parameters that best represent irrigation-surface energy impacts will be compared and discussed.

  16. Multi-year predictability in a coupled general circulation model

    Energy Technology Data Exchange (ETDEWEB)

    Power, Scott; Colman, Rob [Bureau of Meteorology Research Centre, Melbourne, VIC (Australia)


    Multi-year to decadal variability in a 100-year integration of a BMRC coupled atmosphere-ocean general circulation model (CGCM) is examined. The fractional contribution made by the decadal component generally increases with depth and latitude away from surface waters in the equatorial Indo-Pacific Ocean. The relative importance of decadal variability is enhanced in off-equatorial ''wings'' in the subtropical eastern Pacific. The model and observations exhibit ''ENSO-like'' decadal patterns. Analytic results are derived, which show that the patterns can, in theory, occur in the absence of any predictability beyond ENSO time-scales. In practice, however, modification to this stochastic view is needed to account for robust differences between ENSO-like decadal patterns and their interannual counterparts. An analysis of variability in the CGCM, a wind-forced shallow water model, and a simple mixed layer model together with existing and new theoretical results are used to improve upon this stochastic paradigm and to provide a new theory for the origin of decadal ENSO-like patterns like the Interdecadal Pacific Oscillation and Pacific Decadal Oscillation. In this theory, ENSO-driven wind-stress variability forces internal equatorially-trapped Kelvin waves that propagate towards the eastern boundary. Kelvin waves can excite reflected internal westward propagating equatorially-trapped Rossby waves (RWs) and coastally-trapped waves (CTWs). CTWs have no impact on the off-equatorial sub-surface ocean outside the coastal wave guide, whereas the RWs do. If the frequency of the incident wave is too high, then only CTWs are excited. At lower frequencies, both CTWs and RWs can be excited. The lower the frequency, the greater the fraction of energy transmitted to RWs. This lowers the characteristic frequency of variability off the equator relative to its equatorial counterpart. Both the eastern boundary interactions and the accumulation of

  17. Evaluation of the WAMME model surface fluxes using results from the AMMA land-surface model intercomparison project

    Energy Technology Data Exchange (ETDEWEB)

    Boone, Aaron Anthony [GAME-CNRM, Meteo-France, Toulouse (France); Poccard-Leclercq, Isabelle [Universite de Nantes, LETG-Geolittomer, Nantes (France); Xue, Yongkang; Feng, Jinming [University of California at Los Angeles, Los Angeles, CA (United States); Rosnay, Patricia de [European Centre for Medium Range Weather Forecasting, Reading (United Kingdom)


    The West African monsoon (WAM) circulation and intensity have been shown to be influenced by the land surface in numerous numerical studies using regional scale and global scale atmospheric climate models (RCMs and GCMs, respectively) over the last several decades. The atmosphere-land surface interactions are modulated by the magnitude of the north-south gradient of the low level moist static energy, which is highly correlated with the steep latitudinal gradients of the vegetation characteristics and coverage, land use, and soil properties over this zone. The African Multidisciplinary Monsoon Analysis (AMMA) has organised comprehensive activities in data collection and modelling to further investigate the significance land-atmosphere feedbacks. Surface energy fluxes simulated by an ensemble of land surface models from AMMA Land-surface Model Intercomparison Project (ALMIP) have been used as a proxy for the best estimate of the ''real world'' values in order to evaluate GCM and RCM simulations under the auspices of the West African Monsoon Modelling Experiment (WAMME) project, since such large-scale observations do not exist. The ALMIP models have been forced in off-line mode using forcing based on a mixture of satellite, observational, and numerical weather prediction data. The ALMIP models were found to agree well over the region where land-atmosphere coupling is deemed to be most important (notably the Sahel), with a high signal to noise ratio (generally from 0.7 to 0.9) in the ensemble and a inter-model coefficient of variation between 5 and 15%. Most of the WAMME models simulated spatially averaged net radiation values over West Africa which were consistent with the ALMIP estimates, however, the partitioning of this energy between sensible and latent heat fluxes was significantly different: WAMME models tended to simulate larger (by nearly a factor of two) monthly latent heat fluxes than ALMIP. This results due to a positive precipitation

  18. 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...

  19. 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

  20. 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

  1. Improving prediction of pesticide drift deposition on water surfaces on the Colombian highlands (United States)

    Garcia-Santos, Glenda; Feola, Giusppe; Binder, Claudia R.


    Assessment of the environmental risk of new agricultural pesticides in Colombia may be highly underestimated. Control and registration of new pesticides is regulated by the Andean technical manual, which calculates the expected drift concentration expressed as the percentage of the applied dose. The calculations are based on tables with drift values depending on the crop type and the distance of the water surface from the field border based on the model proposed by Ganzelmeier et al. (1995). However, in the Andean region, the application techniques, agricultural practices and meteorological conditions usually significantly differ from those for which the model was designed. Therefore, the use of the tables based on Ganzelmeier et al. (1995) in the Andean manual might be producing an important disagreement between the actual and the expected pesticide concentration in water surfaces close to the treated field. The objectives of this study were i) to improve the understanding of the main factors influencing the mass fluxes (i.e. drift deposition), ii) to assess the suitability of the model proposed in the Andean technical manual for potato crops and other existing models under meteorological conditions and application practices of smallholding potato growers on the Colombian Andes, and iii) to propose a new model of drift deposition which performs better under such conditions. The study was carried out among smallholder potato producers in the Department of Boyaca, Colombia. 12 trials were carried out with a local farmer using his knapsack sprayer. Spray relative drift deposition was measured using the tracer Uranine. The main factors influencing drift were distance from the field border, wind speed and vapour pressure deficit. The data obtained in this study demonstrated that drift calculated with the Andean technical manual's tables was highly underestimated (>200 %). A similar underestimation was observed also for other existing models. However, calibration of

  2. 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.

  3. 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.

  4. Surface response model for quasielastic scattering

    International Nuclear Information System (INIS)

    Esbensen, H.


    The description of nucleon-nucleus inelastic scattering in terms of single-scattering has been very successful at intermediate energies. Nuclear structure is the most dominant feature at low excitations and forward scattering, and the Distorted Wave Impulse Approximation (DWIA) has been the most useful technique to extract structure information. The conventional DWIA has also been applied to quasielastic scattering. However, this method is very time-consuming at large scattering angles, since many different excitations of different multipolarities contribute to the inelastic cross section. It has therefore been useful to develop an approximate treatment that contains the main physics of quasielastic scattering. In the following the author will try to establish the connection between the DWIA and the much simpler Surface Response Model. The author will give a short description of the Random Phase Approximation that is used to calculate the nuclear response, and illustrate the spin-isospin dependence of the nucleon-nucleon t-matrix interaction, which is used to generate the excitations of the target nucleus. Finally, some of the applications of the surface response model to (p,p'), (p,n) and ( 3 H,t) reactions are reviewed. 19 refs., 5 figs

  5. Bioadhesion to model thermally responsive surfaces (United States)

    Andrzejewski, Brett Paul

    This dissertation focuses on the characterization of two surfaces: mixed self-assembled monolayers (SAMs) of hexa(ethylene glycol) and alkyl thiolates (mixed SAM) and poly(N-isopropylacrylamide) (PNIPAAm). The synthesis of hexa(ethylene gylcol) alkyl thiol (C11EG 6OH) is presented along with the mass spectrometry and nuclear magnetic resonance results. The gold substrates were imaged prior to SAM formation with atomic force micrscopy (AFM). Average surface roughness of the gold substrate was 0.44 nm, 0.67 nm, 1.65 nm for 15, 25 and 60 nm gold thickness, respectively. The height of the mixed SAM was measured by ellipsometry and varied from 13 to 28°A depending on surface mole fraction of C11EG6OH. The surface mole fraction of C11EG6OH for the mixed SAM was determined by X-ray photoelectron spectroscopy (XPS) with optimal thermal responsive behavior in the range of 0.4 to 0.6. The mixed SAM surface was confirmed to be thermally responsive by contact angle goniometry, 35° at 28°C and ˜55° at 40°C. In addition, the mixed SAM surfaces were confirmed to be thermally responsive for various aqueous mediums by tensiometry. Factors such as oxygen, age, and surface mole fraction and how they affect the thermal responsive of the mixed SAM are discussed. Lastly, rat fibroblasts were grown on the mixed SAM and imaged by phase contrast microscopy to show inhibition of attachment at temperatures below the molecular transition. Qualitative and quantitative measurements of the fibroblast adhesion data are provided that support the hypothesis of the mixed SAM exhibits a dominantly non-fouling molecular conformation at 25°C whereas it exhibits a dominantly fouling molecular conformation at 40°C. The adhesion of six model proteins: bovine serum albumin, collagen, pyruvate kinase, cholera toxin subunit B, ribonuclease, and lysozyme to the model thermally responsive mixed SAM were examined using AFM. All six proteins possessed adhesion to the pure component alkyl thiol, in

  6. 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.

  7. 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

  8. Predicting fatigue crack initiation through image-based micromechanical modeling

    International Nuclear Information System (INIS)

    Cheong, K.-S.; Smillie, Matthew J.; Knowles, David M.


    The influence of individual grain orientation on early fatigue crack initiation in a four-point bend fatigue test was investigated numerically and experimentally. The 99.99% aluminium test sample was subjected to high cycle fatigue (HCF) and the top surface microstructure within the inner span of the sample was characterized using electron-beam backscattering diffraction (EBSD). Applying a finite-element submodelling approach, the microstructure was digitally reconstructed and refined studies carried out in regions where fatigue damage was observed. The constitutive behaviour of aluminium was described by a crystal plasticity model which considers the evolution of dislocations and accumulation of edge dislocation dipoles. Using an energy-based approach to quantify fatigue damage, the model correctly predicts regions in grains where early fatigue crack initiation was observed. The tendency for fatigue cracks to initiate in these grains appears to be strongly linked to the orientations of the grains relative to the direction of loading - grains less favourably aligned with respect to the loading direction appear more susceptible to fatigue crack initiation. The limitations of this modelling approach are also highlighted and discussed, as some grains predicted to initiate cracks did not show any visible signs of fatigue cracking in the same locations during testing

  9. 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.

  10. 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

  11. Permeability surface area product analysis in malignant brain edema prediction - A pilot study. (United States)

    Volny, O; Cimflova, P; Lee, T-Y; Menon, B K; d'Esterre, C D


    Using an extended CT perfusion acquisition (150s), we sought to determine the association between perfusion parameters and malignant edema after ischemic stroke. Patients (from prospective study PROVE-IT, NCT02184936) with terminal internal carotid artery±proximal middle cerebral occlusion were involved. CTA was assessed for clot location and status of leptomeningeal collaterals. The following CTP parameters were calculated within the ischemic territory and contralaterally: permeability surface area product (PS), cerebral blood flow (CBF) and cerebral blood volume (CBV). PS was calculated using the adiabatic approximation to the Johnson and Wilson model. Outcome was evaluated by midline shift and infarction volume on follow-up imaging. Of 200 patients enrolled, 7 patients (3.5%) had midline shift≥5mm (2 excluded for poor-quality scans). Five patients with midline shift and 5 matched controls were analysed. There was no significant difference in mean PS, CBF and CBV within the ischemic territory between the two groups. A CBV threshold of 1.7ml/100g had the highest AUC=0.72, 95% CI=0.54-0.90 for early midline shift prediction, sensitivity and specificity were 0.83 and 0.67 respectively. Our preliminary results did not show significant differences in permeability surface area analysis if analysed for complete ischemic region. CBV parameter had the highest accuracy and there was a trend for the mean PS values for midline shift prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Improving Weather Research and Forecasting Model Initial Conditions via Surface Pressure Analysis (United States)


    boundary layer (ABL). It predicts turbulent kinetic energy (TKE) and is a Mellor-Yamada Level 2.5 turbulence closure model. As in Lee et al. (2012...cumulus parameterization (Kain 2004) is employed. For radiation , the Rapid Radiative Transfer Model (RRTM) (Mlawer et al. 1997) is used for...longwave and the Dudhia scheme (Dudhia 1989) for shortwave . The Noah land surface model (Chen and Dudhia 2001) is used to represent land surface processes


    Directory of Open Access Journals (Sweden)

    H. Sadeq


    Full Text Available In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades. It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.


    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.

  15. Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

    International Nuclear Information System (INIS)

    Zhong Jian; Dong Gang; Sun Yimei; Zhang Zhaoyang; Wu Yuqin


    The present work reports the development of nonlinear time series prediction method of genetic algorithm (GA) with singular spectrum analysis (SSA) for forecasting the surface wind of a point station in the South China Sea (SCS) with scatterometer observations. Before the nonlinear technique GA is used for forecasting the time series of surface wind, the SSA is applied to reduce the noise. The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique. The predictions have been compared with persistence forecasts in terms of root mean square error. The predicted surface wind with GA and SSA made up to four days (longer for some point station) in advance have been found to be significantly superior to those made by persistence model. This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin. (paper)

  16. Predicting Biological Age from a Skin Surface Capacitive Analysis (United States)

    Bevilacqua, Alessandro; Gherardi, Alessandro; Ferri, Massimo

    The skin is the largest (and the most exposed) organ of the body both in terms of surface area and weight. Its care is of great importance for both aesthetics and health issues. Often, the skin appearance gives us information about the skin health status as well as hints at the biological age. Therefore, the skin surface characterization is of great significance for dermatologists as well as for cosmetic scientists in order to evaluate the effectiveness of medical or cosmetic treatments. So far, no in vivo measurements regarding skin topography characterization could be achieved routinely to evaluate skin aging. This work describes how a portable capacitive device, normally used for fingerprint acquisition, can be utilized to achieve measures of skin aging routinely. The capacitive images give a high resolution (50 μm) representation of skin topography, in terms of wrinkles and cells. In this work, we have addressed the latter: through image segmentation techniques, cells have been localized and identified and a feature related to their area distribution has been generated. Accurate experiments accomplished in vivo show how the feature we conceived is linearly related to skin aging. Besides, since this finding has been achieved using a low cost portable device, this could boost research in this field as well as open doors to an application based on an embedded system.

  17. Prediction of protein retention times in hydrophobic interaction chromatography by robust statistical characterization of their atomic-level surface properties. (United States)

    Hanke, Alexander T; Klijn, Marieke E; Verhaert, Peter D E M; van der Wielen, Luuk A M; Ottens, Marcel; Eppink, Michel H M; van de Sandt, Emile J A X


    The correlation between the dimensionless retention times (DRT) of proteins in hydrophobic interaction chromatography (HIC) and their surface properties were investigated. A ternary atomic-level hydrophobicity scale was used to calculate the distribution of local average hydrophobicity across the proteins surfaces. These distributions were characterized by robust descriptive statistics to reduce their sensitivity to small changes in the three-dimensional structure. The applicability of these statistics for the prediction of protein retention behaviour was looked into. A linear combination of robust statistics describing the central tendency, heterogeneity and frequency of highly hydrophobic clusters was found to have a good predictive capability (R2  = 0.78), when combined a factor to account for protein size differences. The achieved error of prediction was 35% lower than for a similar model based on a description of the protein surface on an amino acid level. This indicates that a robust and mathematically simple model based on an atomic description of the protein surface can be used for the prediction of the retention behaviour of conformationally stable globular proteins with a well determined 3D structure in HIC. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:372-381, 2016. © 2016 American Institute of Chemical Engineers.

  18. Generic global regression models for growth prediction of Salmonella in ground pork and pork cuts

    DEFF Research Database (Denmark)

    Buschhardt, Tasja; Hansen, Tina Beck; Bahl, Martin Iain


    Introduction and Objectives Models for the prediction of bacterial growth in fresh pork are primarily developed using two-step regression (i.e. primary models followed by secondary models). These models are also generally based on experiments in liquids or ground meat and neglect surface growth....... It has been shown that one-step global regressions can result in more accurate models and that bacterial growth on intact surfaces can substantially differ from growth in liquid culture. Material and Methods We used a global-regression approach to develop predictive models for the growth of Salmonella...... for three pork matrices: on the surface of shoulder (neck) and hind part (ham), and in ground pork. We conducted five experimental trials and inoculated essentially sterile pork pieces with a Salmonella cocktail (n = 192). Inoculated meat was aerobically incubated at 4 °C, 7 °C, 12 °C, and 16 °C for 96 h...

  19. 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.

  20. Water on hydrophobic surfaces: Mechanistic modeling of hydrophobic interaction chromatography. (United States)

    Wang, Gang; Hahn, Tobias; Hubbuch, Jürgen


    Mechanistic models are successfully used for protein purification process development as shown for ion-exchange column chromatography (IEX). Modeling and simulation of hydrophobic interaction chromatography (HIC) in the column mode has been seldom reported. As a combination of these two techniques is often encountered in biopharmaceutical purification steps, accurate modeling of protein adsorption in HIC is a core issue for applying holistic model-based process development, especially in the light of the Quality by Design (QbD) approach. In this work, a new mechanistic isotherm model for HIC is derived by consideration of an equilibrium between well-ordered water molecules and bulk-like ordered water molecules on the hydrophobic surfaces of protein and ligand. The model's capability of describing column chromatography experiments is demonstrated with glucose oxidase, bovine serum albumin (BSA), and lysozyme on Capto™ Phenyl (high sub) as model system. After model calibration from chromatograms of bind-and-elute experiments, results were validated with batch isotherms and prediction of further gradient elution chromatograms. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. 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

  2. Using dose-surface maps to predict radiation-induced rectal bleeding: a neural network approach (United States)

    Buettner, Florian; Gulliford, Sarah L.; Webb, Steve; Partridge, Mike


    The incidence of late-toxicities after radiotherapy can be modelled based on the dose delivered to the organ under consideration. Most predictive models reduce the dose distribution to a set of dose-volume parameters and do not take the spatial distribution of the dose into account. The aim of this study was to develop a classifier predicting radiation-induced rectal bleeding using all available information on the dose to the rectal wall. The dose was projected on a two-dimensional dose-surface map (DSM) by virtual rectum-unfolding. These DSMs were used as inputs for a classification method based on locally connected neural networks. In contrast to fully connected conventional neural nets, locally connected nets take the topology of the input into account. In order to train the nets, data from 329 patients from the RT01 trial (ISRCTN 47772397) were split into ten roughly equal parts. By using nine of these parts as a training set and the remaining part as an independent test set, a ten-fold cross-validation was performed. Ensemble learning was used and 250 nets were built from randomly selected patients from the training set. Out of these 250 nets, an ensemble of expert nets was chosen. The performances of the full ensemble and of the expert ensemble were quantified by using receiver-operator-characteristic (ROC) curves. In order to quantify the predictive power of the shape, ensembles of fully connected conventional neural nets based on dose-surface histograms (DSHs) were generated and their performances were quantified. The expert ensembles performed better than or equally as well as the full ensembles. The area under the ROC curve for the DSM-based expert ensemble was 0.64. The area under the ROC curve for the DSH-based expert ensemble equalled 0.59. This difference in performance indicates that not only volumetric, but also morphological aspects of the dose distribution are correlated to rectal bleeding after radiotherapy. Thus, the shape of the dose

  3. Using dose-surface maps to predict radiation-induced rectal bleeding: a neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Buettner, Florian; Gulliford, Sarah L; Webb, Steve; Partridge, Mike [Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT (United Kingdom)], E-mail:


    The incidence of late-toxicities after radiotherapy can be modelled based on the dose delivered to the organ under consideration. Most predictive models reduce the dose distribution to a set of dose-volume parameters and do not take the spatial distribution of the dose into account. The aim of this study was to develop a classifier predicting radiation-induced rectal bleeding using all available information on the dose to the rectal wall. The dose was projected on a two-dimensional dose-surface map (DSM) by virtual rectum-unfolding. These DSMs were used as inputs for a classification method based on locally connected neural networks. In contrast to fully connected conventional neural nets, locally connected nets take the topology of the input into account. In order to train the nets, data from 329 patients from the RT01 trial (ISRCTN 47772397) were split into ten roughly equal parts. By using nine of these parts as a training set and the remaining part as an independent test set, a ten-fold cross-validation was performed. Ensemble learning was used and 250 nets were built from randomly selected patients from the training set. Out of these 250 nets, an ensemble of expert nets was chosen. The performances of the full ensemble and of the expert ensemble were quantified by using receiver-operator-characteristic (ROC) curves. In order to quantify the predictive power of the shape, ensembles of fully connected conventional neural nets based on dose-surface histograms (DSHs) were generated and their performances were quantified. The expert ensembles performed better than or equally as well as the full ensembles. The area under the ROC curve for the DSM-based expert ensemble was 0.64. The area under the ROC curve for the DSH-based expert ensemble equalled 0.59. This difference in performance indicates that not only volumetric, but also morphological aspects of the dose distribution are correlated to rectal bleeding after radiotherapy. Thus, the shape of the dose

  4. Modelling Monsoons: Understanding and Predicting Current and Future Behaviour

    Energy Technology Data Exchange (ETDEWEB)

    Turner, A; Sperber, K R; Slingo, J M; Meehl, G A; Mechoso, C R; Kimoto, M; Giannini, A


    The global monsoon system is so varied and complex that understanding and predicting its diverse behaviour remains a challenge that will occupy modellers for many years to come. Despite the difficult task ahead, an improved monsoon modelling capability has been realized through the inclusion of more detailed physics of the climate system and higher resolution in our numerical models. Perhaps the most crucial improvement to date has been the development of coupled ocean-atmosphere models. From subseasonal to interdecadal timescales, only through the inclusion of air-sea interaction can the proper phasing and teleconnections of convection be attained with respect to sea surface temperature variations. Even then, the response to slow variations in remote forcings (e.g., El Nino-Southern Oscillation) does not result in a robust solution, as there are a host of competing modes of variability that must be represented, including those that appear to be chaotic. Understanding the links between monsoons and land surface processes is not as mature as that explored regarding air-sea interactions. A land surface forcing signal appears to dominate the onset of wet season rainfall over the North American monsoon region, though the relative role of ocean versus land forcing remains a topic of investigation in all the monsoon systems. Also, improved forecasts have been made during periods in which additional sounding observations are available for data assimilation. Thus, there is untapped predictability that can only be attained through the development of a more comprehensive observing system for all monsoon regions. Additionally, improved parameterizations - for example, of convection, cloud, radiation, and boundary layer schemes as well as land surface processes - are essential to realize the full potential of monsoon predictability. Dynamical considerations require ever increased horizontal resolution (probably to 0.5 degree or higher) in order to resolve many monsoon features


    Directory of Open Access Journals (Sweden)

    J. Mu


    Full Text Available In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method.

  6. Prediction of the Acoustic Field Associated with Instability Wave Source Model for a Compressible Jet (United States)

    Golubev, Vladimir; Mankbadi, Reda R.; Dahl, Milo D.; Kiraly, L. James (Technical Monitor)


    This paper provides preliminary results of the study of the acoustic radiation from the source model representing spatially-growing instability waves in a round jet at high speeds. The source model is briefly discussed first followed by the analysis of the produced acoustic directivity pattern. Two integral surface techniques are discussed and compared for prediction of the jet acoustic radiation field.

  7. Quantitative relationships for the prediction of the vapor pressure of some hydrocarbons from the van der Waals molecular surface

    Directory of Open Access Journals (Sweden)

    Olariu Tudor


    Full Text Available A quantitative structure - property relationship (QSPR modeling of vapor pressure at 298.15 K, expressed as log (VP / Pa was performed for a series of 84 hydrocarbons (63 alkanes and 21 cycloalkanes using the van der Waals (vdW surface area, SW/Å2, calculated by the Monte Carlo method, as the molecular descriptor. The QSPR model developed from the subset of 63 alkanes (C1-C16, deemed as the training set, was successfully used for the prediction of the log (VP / Pa values of the 21 cycloalkanes, which was the external prediction (test subset. A QSPR model was also developed for a series composed of all 84 hydrocarbons. Both QSPR models were statistically tested for their ability to fit the data and for prediction. The results showed that the vdW molecular surface used as molecular descriptor (MD explains the variance of the majority of the log (VP / Pa values in this series of 84 hydrocarbons. This MD describes very well the intermolecular forces that hold neutral molecules together. The clear physical meaning of the molecular surface values, SW/Å2, could explain the success of the QSPR models obtained with a single structural molecular descriptor.

  8. 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…

  9. 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 ...

  10. Design of a Three Surfaces R/C Aircraft Model

    Directory of Open Access Journals (Sweden)

    D. P. Coiro


    Full Text Available Design of a three lifting surfaces radio-controlled model has been carried out at Dipartimento di Progettazione Aeronautica (DPA by the authors in the last year. The model is intended to be a UAV prototype and is now under construction. The main goal of this small aircraft's design is to check the influence of the canard surface on the aircraft's aerodynamic characteristics and flight behavior, especially at high angles of attack. The aircraft model is also intended to be a flying platform to test sensors, measurement and acquisition systems for research purposes and a valid and low-cost teaching instrument for flight dynamics and flight maneuvering. The aircraft has been designed to fly with and without canard, and all problems relative to aircraft balance and stability have been carefully analyzed and solved. The innovative configuration and the mixed wooden-composite material structure has been obtained with very simple shapes and all the design is focused on realizing a low-cost model. A complete aerodynamic analysis of the configuration up to high angles of attack and a preliminary aircraft stability and performance prediction will be presented.

  11. A surface hydrology model for regional vector borne disease models (United States)

    Tompkins, Adrian; Asare, Ernest; Bomblies, Arne; Amekudzi, Leonard


    Small, sun-lit temporary pools that form during the rainy season are important breeding sites for many key mosquito vectors responsible for the transmission of malaria and other diseases. The representation of this surface hydrology in mathematical disease models is challenging, due to their small-scale, dependence on the terrain and the difficulty of setting soil parameters. Here we introduce a model that represents the temporal evolution of the aggregate statistics of breeding sites in a single pond fractional coverage parameter. The model is based on a simple, geometrical assumption concerning the terrain, and accounts for the processes of surface runoff, pond overflow, infiltration and evaporation. Soil moisture, soil properties and large-scale terrain slope are accounted for using a calibration parameter that sets the equivalent catchment fraction. The model is calibrated and then evaluated using in situ pond measurements in Ghana and ultra-high (10m) resolution explicit simulations for a village in Niger. Despite the model's simplicity, it is shown to reproduce the variability and mean of the pond aggregate water coverage well for both locations and validation techniques. Example malaria simulations for Uganda will be shown using this new scheme with a generic calibration setting, evaluated using district malaria case data. Possible methods for implementing regional calibration will be briefly discussed.

  12. Modeling global distribution of agricultural insecticides in surface waters. (United States)

    Ippolito, Alessio; Kattwinkel, Mira; Rasmussen, Jes J; Schäfer, Ralf B; Fornaroli, Riccardo; Liess, Matthias


    Agricultural insecticides constitute a major driver of animal biodiversity loss in freshwater ecosystems. However, the global extent of their effects and the spatial extent of exposure remain largely unknown. We applied a spatially explicit model to estimate the potential for agricultural insecticide runoff into streams. Water bodies within 40% of the global land surface were at risk of insecticide runoff. We separated the influence of natural factors and variables under human control determining insecticide runoff. In the northern hemisphere, insecticide runoff presented a latitudinal gradient mainly driven by insecticide application rate; in the southern hemisphere, a combination of daily rainfall intensity, terrain slope, agricultural intensity and insecticide application rate determined the process. The model predicted the upper limit of observed insecticide exposure measured in water bodies (n = 82) in five different countries reasonably well. The study provides a global map of hotspots for insecticide contamination guiding future freshwater management and conservation efforts. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. An Empirical Jet-Surface Interaction Noise Model with Temperature and Nozzle Aspect Ratio Effects (United States)

    Brown, Cliff


    An empirical model for jet-surface interaction (JSI) noise produced by a round jet near a flat plate is described and the resulting model evaluated. The model covers unheated and hot jet conditions (1 less than or equal to jet total temperature ratio less than or equal to 2.7) in the subsonic range (0.5 less than or equal to M(sub a) less than or equal to 0.9), surface lengths 0.6 less than or equal to (axial distance from jet exit to surface trailing edge (inches)/nozzle exit diameter) less than or equal to 10, and surface standoff distances (0 less than or equal to (radial distance from jet lipline to surface (inches)/axial distance from jet exit to surface trailing edge (inches)) less than or equal to 1) using only second-order polynomials to provide predictable behavior. The JSI noise model is combined with an existing jet mixing noise model to produce exhaust noise predictions. Fit quality metrics and comparisons to between the predicted and experimental data indicate that the model is suitable for many system level studies. A first-order correction to the JSI source model that accounts for the effect of nozzle aspect ratio is also explored. This correction is based on changes to the potential core length and frequency scaling associated with rectangular nozzles up to 8:1 aspect ratio. However, more work is needed to refine these findings into a formal model.

  14. Nonlinear model predictive control of managed pressure drilling. (United States)

    Nandan, Anirudh; Imtiaz, Syed


    A new design of nonlinear model predictive controller (NMPC) is proposed for managed pressure drilling (MPD) system. The NMPC is based on output feedback control architecture and employs offset-free formulation proposed in [1]. NMPC uses active set method for computing control inputs. The controller implements an automatic switching from constant bottom hole pressure (CBHP) regulation to flow control mode in the event of a reservoir kick. In the flow control mode the controller automatically raises the bottom hole pressure setpoint, and thereby keeps the reservoir fluid flow to the surface within a tunable threshold. This is achieved by exploiting constraint handling capability of NMPC. In addition to kick mitigation the controller demonstrated good performance in containing the bottom hole pressure (BHP) during the pipe connection sequence. The controller also delivered satisfactory performance in the presence of measurement noise and uncertainty in the system. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Development of residual stress prediction model in pipe weldment

    Energy Technology Data Exchange (ETDEWEB)

    Eom, Yun Yong; Lim, Se Young; Choi, Kang Hyeuk; Cho, Young Sam; Lim, Jae Hyuk [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)


    When Leak Before Break(LBB) concepts is applied to high energy piping of nuclear power plants, residual weld stresses is a important variable. The main purpose of his research is to develop the numerical model which can predict residual weld stresses. Firstly, basic theories were described which need to numerical analysis of welding parts. Before the analysis of pipe, welding of a flat plate was analyzed and compared. Appling the data of used pipes, thermal/mechanical analysis were accomplished and computed temperature gradient and residual stress distribution. For thermal analysis, proper heat flux was regarded as the heat source and convection/radiation heat transfer were considered at surfaces. The residual stresses were counted from the computed temperature gradient and they were compared and verified with a result of another research.

  16. Prediction of hydrocarbon surface seepage potential using infiltrometer data (United States)

    Connors, J. J.; Jackson, J. L.; Engle, R. A.; Connors, J. L.


    Environmental regulations addressing above-ground storage tank (AST) spill control activities typically require owners/operators to demonstrate that local soil permeability values are low enough to adequately contain released liquids while emergency-response procedures are conducted. Frequently, geotechnical borings and soil samples/analyses, and/or monitoring well slug-test analyses, are used to provide hydraulic conductivity data for the required calculations. While these techniques are useful in assessing hydrological characteristics of the subsurface, they do not always assess the uppermost surface soil layer, where the bulk of the containment can occur. This layer may have been subject to long-term permeability-reduction by activities such as compaction by vehicular and foot traffic, micro-coatings by hydrophobic pollutants, etc. This presentation explores the usefulness of dual-ring infiltrometers, both in field and bench-scale tests, to rapidly acquire actual hydraulic conductivity values of surficial soil layers, which can be much lower than subsurface values determined using more traditional downhole geotechnical and hydrogeological approaches.

  17. Master Sintering Surface: A practical approach to its construction and utilization for Spark Plasma Sintering prediction

    Directory of Open Access Journals (Sweden)

    Pouchly V.


    Full Text Available The sintering is a complex thermally activated process, thus any prediction of sintering behaviour is very welcome not only for industrial purposes. Presented paper shows the possibility of densification prediction based on concept of Master Sintering Surface (MSS for pressure assisted Spark Plasma Sintering (SPS. User friendly software for evaluation of the MSS is presented. The concept was used for densification prediction of alumina ceramics sintered by SPS.

  18. Analytical prediction of sub-surface thermal history in translucent tissue phantoms during plasmonic photo-thermotherapy (PPTT). (United States)

    Dhar, Purbarun; Paul, Anup; Narasimhan, Arunn; Das, Sarit K


    Knowledge of thermal history and/or distribution in biological tissues during laser based hyperthermia is essential to achieve necrosis of tumour/carcinoma cells. A semi-analytical model to predict sub-surface thermal distribution in translucent, soft, tissue mimics has been proposed. The model can accurately predict the spatio-temporal temperature variations along depth and the anomalous thermal behaviour in such media, viz. occurrence of sub-surface temperature peaks. Based on optical and thermal properties, the augmented temperature and shift of the peak positions in case of gold nanostructure mediated tissue phantom hyperthermia can be predicted. Employing inverse approach, the absorption coefficient of nano-graphene infused tissue mimics is determined from the peak temperature and found to provide appreciably accurate predictions along depth. Furthermore, a simplistic, dimensionally consistent correlation to theoretically determine the position of the peak in such media is proposed and found to be consistent with experiments and computations. The model shows promise in predicting thermal distribution induced by lasers in tissues and deduction of therapeutic hyperthermia parameters, thereby assisting clinical procedures by providing a priori estimates. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Modelling the growth of Listeria monocytogenes on the surface of smear- or mould-ripened cheese

    Directory of Open Access Journals (Sweden)

    Sol eSchvartzman


    Full Text Available Surface-ripened cheeses are matured by means of manual or mechanical technologies posing a risk of cross-contamination, if any cheeses are contaminated with Listeria monocytogenes. In predictive microbiology, primary models are used to describe microbial responses, such as growth rate over time and secondary models explain how those responses change with environmental factors. In this way, primary models were used to assess the growth rate of L. monocytogenes during ripening of the cheeses and the secondary models to test how much the growth rate was affected by either the pH and/or the water activity (aw of the cheeses. The two models combined can be used to predict outcomes. The purpose of these experiments was to test three primary (the modified Gompertz equation, the Baranyi and Roberts model and the Logistic model and three secondary (the Cardinal model, the Ratowski model and the Presser model mathematical models in order to define which combination of models would best predict the growth of L. monocytogenes on the surface of artificially contaminated surface-ripened cheeses. Growth on the surface of the cheese was assessed and modelled. The primary models were firstly fitted to the data and the effects of pH and aw on the growth rate (μmax were incorporated and assessed one by one with the secondary models. The Logistic primary model by itself did not show a better fit of the data among the other primary models tested, but the inclusion of the Cardinal secondary model improved the final fit. The aw was not related to the growth of Listeria. This study suggests that surface-ripened cheese should be separately regulated within EU microbiological food legislation and results expressed as counts per surface area rather than per gram.

  20. Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation

    Directory of Open Access Journals (Sweden)

    Matthias Eifler


    Full Text Available Standard compliant parameter calculation in surface topography analysis takes the manufacturing process into account. Thus, the measurement technician can be supported with automated suggestions for preprocessing, filtering and evaluation of the measurement data based on the character of the surface topography. Artificial neuronal networks (ANN are one approach for the recognition or classification of technical surfaces. However the required set of training data for ANN is often not available, especially when data acquisition is time consuming or expensive—as e.g., measuring surface topography. Thus, generation of artificial (simulated data becomes of interest. An approach from time series analysis is chosen and examined regarding its suitability for the description of technical surfaces: the ARMAsel model, an approach for time series modelling which is capable of choosing the statistical model with the smallest prediction error and the best number of coefficients for a certain surface. With a reliable model which features the relevant stochastic properties of a surface, a generation of training data for classifiers of artificial neural networks is possible. Based on the determined ARMA-coefficients from the ARMAsel-approach, with only few measured datasets many different artificial surfaces can be generated which can be used for training classifiers of an artificial neural network. In doing so, an improved calculation of the model input data for the generation of artificial surfaces is possible as the training data generation is based on actual measurement data. The trained artificial neural network is tested with actual measurement data of surfaces that were manufactured with varying manufacturing methods and a recognition rate of the according manufacturing principle between 60% and 78% can be determined. This means that based on only few measured datasets, stochastic surface information of various manufacturing principles can be extracted

  1. Predicting Fire Season Severity in South America Using Sea Surface Temperature Anomalies (United States)

    Chen, Yang; Randerson, James T.; Morton, Douglas C.; Jin, Yufang; DeFries, Ruth S.; Collatz, George J.; Kasibhatla, Prasad S.; Giglio, Louis; Jin, Yufang; Marlier, Miriam


    Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. Here we investigated the relationship between year-to-year changes in satellite-derived estimates of fire activity in South America and sea surface temperature (SST) anomalies. We found that the Oceanic Ni o Index (ONI) was correlated with interannual fire activity in the eastern Amazon whereas the Atlantic Multidecadal Oscillation (AMO) index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model that predicted regional annual fire season severity (FSS) with 3-5 month lead times. Our approach provides the foundation for an early warning system for forecasting the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for mitigation of greenhouse gas and air pollutant emissions.

  2. Prediction and correlation of surface tension of naphtha reformate and crude oil

    Energy Technology Data Exchange (ETDEWEB)

    Darwish, E.I.; Al-Sahhaf, T.A.; Fahim, M.A. [Kuwait University (Kuwait). Chemical Engineering Dept.


    Six methods were tested for the prediction of the surface tension of naphtha reformate and crude oil fractions. The corresponding-state method gave the lowest deviation from experimental values for single cuts. The surface tension-density correlation method gave the lowest deviation for a blend of several cuts. 12 refs., 14 tabs.

  3. Colloid filtration in surface dense vegetation: experimental results and theoretical predictions. (United States)

    Wu, Lei; Muñoz-Carpena, Rafael; Gao, Bin; Yang, Wen; Pachepsky, Yakov A


    Understanding colloid and colloid-facilitated contaminant transport in overland flow through dense vegetation is important to protect water quality in the environment, especially for water bodies receiving agricultural and urban runoff. In previous studies, a single-stem efficiency theory for rigid and clean stem systems was developed to predict colloid filtration by plant stems of vegetation in laminar overland flow. Hence, in order to improve the accuracy of the single-stem efficiency theory to real dense vegetation system, we incorporated the effect of natural organic matter (NOM) on the filtration of colloids by stems. Laboratory dense vegetation flow chamber experiments and model simulations were used to determine the kinetic deposition (filtration) rate of colloids under various conditions. The results show that, in addition to flow hydrodynamics and solution chemistry, steric repulsion afforded by NOM layer on the plants stem surface also plays a significant role in controlling colloid deposition on vegetation in overland flow. For the first time, a refined single-stem efficiency theory with considerations of the NOM effect is developed that describes the experimental data with good accuracy. This theory can be used to not only help construct and refine mathematical models of colloid transport in real vegetation systems in overland flow, but also inform the development of theories of colloid deposition on NOM-coated surfaces in natural, engineered, and biomedical systems.

  4. 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.

  6. 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...

  7. 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.

  8. 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…

  9. Predictive SIRT dosimetry based on a territorial model

    Directory of Open Access Journals (Sweden)

    Nadine Spahr


    Full Text Available Abstract Background In the planning of selective internal radiation therapy (SIRT for liver cancer treatment, one major aspect is to determine the prescribed activity and to estimate the resulting absorbed dose inside normal liver and tumor tissue. An optimized partition model for SIRT dosimetry based on arterial liver territories is proposed. This model is dedicated to characterize the variability of dose within the whole liver. For an arbitrary partition, the generalized absorbed dose is derived from the classical partition model. This enables to consider normal liver partitions for each arterial perfusion supply area and one partition for each tumor for activity and dose calculation. The proposed method excludes a margin of 11 mm emitting range around tumor volumes from normal liver to investigate the impact on activity calculation. Activity and dose calculation was performed for five patients using the body-surface-area (BSA method, the classical and territorial partition model. Results The territorial model reaches smaller normal liver doses and significant higher tumor doses compared to the classical partition model. The exclusion of a small region around tumors has a significant impact on mean liver dose. Determined tumor activities for the proposed method are higher in all patients when limited by normal liver dose. Activity calculation based on BSA achieves in all cases the lowest amount. Conclusions The territorial model provides a more local and patient-individual dose distribution in normal liver taking into account arterial supply areas. This proposed arterial liver territory-based partition model may be used for SPECT-independent activity calculation and dose prediction under the condition of an artery-based simulation for particle distribution.

  10. Predictions and Verification of an Isotope Marine Boundary Layer Model (United States)

    Feng, X.; Posmentier, E. S.; Sonder, L. J.; Fan, N.


    A one-dimensional (1D), steady state isotope marine boundary layer (IMBL) model is constructed. The model includes meteorologically important features absent in Craig and Gordon type models, namely height-dependent diffusion/mixing and convergence of subsiding external air. Kinetic isotopic fractionation results from this height-dependent diffusion which starts as pure molecular diffusion at the air-water interface and increases linearly with height due to turbulent mixing. The convergence permits dry, isotopically depleted air subsiding adjacent to the model column to mix into ambient air. In δD-δ18O space, the model results fill a quadrilateral, of which three sides represent 1) vapor in equilibrium with various sea surface temperatures (SSTs) (high d18O boundary of quadrilateral); 2) mixture of vapor in equilibrium with seawater and vapor in the subsiding air (lower boundary depleted in both D and 18O); and 3) vapor that has experienced the maximum possible kinetic fractionation (high δD upper boundary). The results can be plotted in d-excess vs. δ18O space, indicating that these processes all cause variations in d-excess of MBL vapor. In particular, due to relatively high d-excess in the descending air, mixing of this air into the MBL causes an increase in d-excess, even without kinetic isotope fractionation. The model is tested by comparison with seven datasets of marine vapor isotopic ratios, with excellent correspondence; >95% of observational data fall within the quadrilateral area predicted by the model. The distribution of observations also highlights the significant influence of vapor from the nearby converging descending air on isotopic variations in the MBL. At least three factors may explain the affect the isotopic composition of precipitation. The model can be applied to modern as well as paleo- climate conditions.

  11. 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.

  12. Multi-Site Validation of the SWAT Model on the Bani Catchment: Model Performance and Predictive Uncertainty

    Directory of Open Access Journals (Sweden)

    Jamilatou Chaibou Begou


    Full Text Available The objective of this study was to assess the performance and predictive uncertainty of the Soil and Water Assessment Tool (SWAT model on the Bani River Basin, at catchment and subcatchment levels. The SWAT model was calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE approach. Potential Evapotranspiration (PET and biomass were considered in the verification of model outputs accuracy. Global Sensitivity Analysis (GSA was used for identifying important model parameters. Results indicated a good performance of the global model at daily as well as monthly time steps with adequate predictive uncertainty. PET was found to be overestimated but biomass was better predicted in agricultural land and forest. Surface runoff represents the dominant process on streamflow generation in that region. Individual calibration at subcatchment scale yielded better performance than when the global parameter sets were applied. These results are very useful and provide a support to further studies on regionalization to make prediction in ungauged basins.

  13. Modeling and optimization of surface roughness in single point incremental forming process

    Directory of Open Access Journals (Sweden)

    Suresh Kurra


    Full Text Available Single point incremental forming (SPIF is a novel and potential process for sheet metal prototyping and low volume production applications. This article is focuses on the development of predictive models for surface roughness estimation in SPIF process. Surface roughness in SPIF has been modeled using three different techniques namely, Artificial Neural Networks (ANN, Support Vector Regression (SVR and Genetic Programming (GP. In the development of these predictive models, tool diameter, step depth, wall angle, feed rate and lubricant type have been considered as model variables. Arithmetic mean surface roughness (Ra and maximum peak to valley height (Rz are used as response variables to assess the surface roughness of incrementally formed parts. The data required to generate, compare and evaluate the proposed models have been obtained from SPIF experiments performed on Computer Numerical Control (CNC milling machine using Box–Behnken design. The developed models are having satisfactory goodness of fit in predicting the surface roughness. Further, the GP model has been used for optimization of Ra and Rz using genetic algorithm. The optimum process parameters for minimum surface roughness in SPIF have been obtained and validated with the experiments and found highly satisfactory results within 10% error.

  14. Prediction of Carbohydrate Binding Sites on Protein Surfaces with 3-Dimensional Probability Density Distributions of Interacting Atoms (United States)

    Tsai, Keng-Chang; Jian, Jhih-Wei; Yang, Ei-Wen; Hsu, Po-Chiang; Peng, Hung-Pin; Chen, Ching-Tai; Chen, Jun-Bo; Chang, Jeng-Yih; Hsu, Wen-Lian; Yang, An-Suei


    Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall

  15. Heterogeneous nucleation of ice on model carbon surfaces (United States)

    Molinero, V.; Lupi, L.; Hudait, A.


    Carbonaceous particles account for 10% of the particulate matter in the atmosphere. The experimental investigation of heterogeneous freezing of water droplets by carbonaceous particles reveals widespread ice freezing temperatures. The origin of the soot and its oxidation and aging modulate its ice nucleation ability, however, it is not known which structural and chemical characteristics of soot account for the variability in ice nucleation efficiency. We find that atomically flat carbon surfaces promote heterogeneous nucleation of ice, while molecularly rough surfaces with the same hydrophobicity do not. We investigate a large set of graphitic surfaces of various dimensions and radii of curvature consistent with those of soot in experiments, and find that variations in nanostructures alone could account for the spread in the freezing temperatures of ice on soot in experiments. A characterization of the nanostructure of soot is needed to predict its ice nucleation efficiency. Atmospheric oxidation and aging of soot modulates its ice nucleation ability. It has been suggested that an increase in the ice nucleation ability of aged soot results from an increase in the hydrophilicity of the surfaces upon oxidation. Oxidation, however, also impacts the nanostructure of soot, making it difficult to assess the separate effects of soot nanostructure and hydrophilicity in experiments. We investigate the effect of changes in hydrophilicity of model graphitic surfaces on the freezing temperature of ice. Our results indicate that the hydrophilicity of the surface is not in general a good predictor of ice nucleation ability. We find a correlation between the ability of a surface to promote nucleation of ice and the layering of liquid water at the surface. The results of this work suggest that ordering of liquid water in contact with the surface plays an important role in the heterogeneous ice nucleation mechanism. References: L. Lupi, A. Hudait and V. Molinero, J. Am. Chem. Soc

  16. Prediction of the antiglycation activity of polysaccharides from Benincasa hispida using a response surface methodology. (United States)

    Jiang, Xiang; Kuang, Fei; Kong, Fansheng; Yan, Chunyan


    Benincasa hispida is a popular vegetable in China. Our previous experiments suggested that polysaccharides isolated from B. hispida fruits (PBH) have antiglycation effect and DPPH free radical scavenging activity. Ultrasonic treatments can be used to extract polysaccharides from Benincasa hispida (PBH). The aim of this study was to investigate the correlation between the ultrasonic treatment conditions and the antiglycation activity of PBH. A mathematical model was generated with an artificial neural network (ANN) toolbox from MATLAB to analyze the effects of ultrasonic treatment conditions on antiglycation activity. The response surface plots showed relationships between ultrasonic extraction conditions and bioactivity. The R(2) value of the model was 0.9919, which suggested good fitness of the neural network. The application of genetic algorithms showed that the optimal ultrasonic extraction conditions resulted in the highest antiglycation activity for PBH. These were 150W, 46°C, and 35min. These conditions produced a predicted antiglycation activity of 41.2%; the actual activity was 40.9% under optimal conditions. This is very close to the predicted value. The experimental data indicated that the PBH possessed both antiglycation and antioxidant activities. The maximum actual value of antiglycation was 101.7% that of the positive control, and the PBH inhibited the DPPH free radicals with an EC50 value of 0.98mg/mL. This is 66.2% that of ascorbic acid. These results explained the observations that B. hispida can decrease glucose levels in diabetic patients. The experimental results also showed that the ANN could be used for optimization and prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Analysis of turbulence and surface growth models on the estimation of soot level in ethylene non-premixed flames (United States)

    Yunardi, Y.; Munawar, Edi; Rinaldi, Wahyu; Razali, Asbar; Iskandar, Elwina; Fairweather, M.


    Soot prediction in a combustion system has become a subject of attention, as many factors influence its accuracy. An accurate temperature prediction will likely yield better soot predictions, since the inception, growth and destruction of the soot are affected by the temperature. This paper reported the study on the influences of turbulence closure and surface growth models on the prediction of soot levels in turbulent flames. The results demonstrated that a substantial distinction was observed in terms of temperature predictions derived using the k-ɛ and the Reynolds stress models, for the two ethylene flames studied here amongst the four types of surface growth rate model investigated, the assumption of the soot surface growth rate proportional to the particle number density, but independent on the surface area of soot particles, f ( A s ) = ρ N s , yields in closest agreement with the radial data. Without any adjustment to the constants in the surface growth term, other approaches where the surface growth directly proportional to the surface area and square root of surface area, f ( A s ) = A s and f ( A s ) = √ A s , result in an under- prediction of soot volume fraction. These results suggest that predictions of soot volume fraction are sensitive to the modelling of surface growth.

  18. Estimation of the solubility parameters of model plant surfaces and agrochemicals: a valuable tool for understanding plant surface interactions. (United States)

    Khayet, Mohamed; Fernández, Victoria


    Most aerial plant parts are covered with a hydrophobic lipid-rich cuticle, which is the interface between the plant organs and the surrounding environment. Plant surfaces may have a high degree of hydrophobicity because of the combined effects of surface chemistry and roughness. The physical and chemical complexity of the plant cuticle limits the development of models that explain its internal structure and interactions with surface-applied agrochemicals. In this article we introduce a thermodynamic method for estimating the solubilities of model plant surface constituents and relating them to the effects of agrochemicals. Following the van Krevelen and Hoftyzer method, we calculated the solubility parameters of three model plant species and eight compounds that differ in hydrophobicity and polarity. In addition, intact tissues were examined by scanning electron microscopy and the surface free energy, polarity, solubility parameter and work of adhesion of each were calculated from contact angle measurements of three liquids with different polarities. By comparing the affinities between plant surface constituents and agrochemicals derived from (a) theoretical calculations and (b) contact angle measurements we were able to distinguish the physical effect of surface roughness from the effect of the chemical nature of the epicuticular waxes. A solubility parameter model for plant surfaces is proposed on the basis of an increasing gradient from the cuticular surface towards the underlying cell wall. The procedure enabled us to predict the interactions among agrochemicals, plant surfaces, and cuticular and cell wall components, and promises to be a useful tool for improving our understanding of biological surface interactions.

  19. Estimation of the solubility parameters of model plant surfaces and agrochemicals: a valuable tool for understanding plant surface interactions (United States)


    Background Most aerial plant parts are covered with a hydrophobic lipid-rich cuticle, which is the interface between the plant organs and the surrounding environment. Plant surfaces may have a high degree of hydrophobicity because of the combined effects of surface chemistry and roughness. The physical and chemical complexity of the plant cuticle limits the development of models that explain its internal structure and interactions with surface-applied agrochemicals. In this article we introduce a thermodynamic method for estimating the solubilities of model plant surface constituents and relating them to the effects of agrochemicals. Results Following the van Krevelen and Hoftyzer method, we calculated the solubility parameters of three model plant species and eight compounds that differ in hydrophobicity and polarity. In addition, intact tissues were examined by scanning electron microscopy and the surface free energy, polarity, solubility parameter and work of adhesion of each were calculated from contact angle measurements of three liquids with different polarities. By comparing the affinities between plant surface constituents and agrochemicals derived from (a) theoretical calculations and (b) contact angle measurements we were able to distinguish the physical effect of surface roughness from the effect of the chemical nature of the epicuticular waxes. A solubility parameter model for plant surfaces is proposed on the basis of an increasing gradient from the cuticular surface towards the underlying cell wall. Conclusions The procedure enabled us to predict the interactions among agrochemicals, plant surfaces, and cuticular and cell wall components, and promises to be a useful tool for improving our understanding of biological surface interactions. PMID:23151272

  20. 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...

  1. 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.

  2. 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

  3. RIPPLE: A new model for incompressible flows with free surfaces (United States)

    Kothe, D. B.; Mjolsness, R. C.


    A new free surface flow model, RIPPLE, is summarized. RIPPLE obtains finite difference solutions for incompressible flow problems having strong surface tension forces at free surfaces of arbitrarily complex topology. The key innovation is the Continuum Surface Force (CSF) model which represents surface tension as a (strongly) localized volume force. Other features include a high-order momentum advection model, a volume-of-fluid free surface treatment, and an efficient two-step projection solution method. RIPPLE'S unique capabilities are illustrated with two example problems: low-gravity jet-induced tank flow, and the collision and coalescence of two cylindrical rods.

  4. RIPPLE - A new model for incompressible flows with free surfaces (United States)

    Kothe, D. B.; Mjolsness, R. C.


    A new free surface flow model, RIPPLE, is summarized. RIPPLE obtains finite difference solutions for incompressible flow problems having strong surface tension forces at free surfaces of arbitrarily complex topology. The key innovation is the continuum surface force model which represents surface tension as a (strongly) localized volume force. Other features include a higher-order momentum advection model, a volume-of-fluid free surface treatment, and an efficient two-step projection solution method. RIPPLE's unique capabilities are illustrated with two example problems: low-gravity jet-induced tank flow, and the collision and coalescence of two cylindrical rods.

  5. 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 ...

  6. 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.

  7. Development of corresponding states model for estimation of the surface tension of chemical compounds

    DEFF Research Database (Denmark)

    Gharagheizi, Farhad; Eslamimanesh, Ali; Sattari, Mehdi


    The gene expression programming (GEP) strategy is applied for presenting two corresponding states models to represent/predict the surface tension of about 1,700 compounds (mostly organic) from 75 chemical families at various temperatures collected from the DIPPR 801 database. The models parameter...

  8. 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...

  9. 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

  10. 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...

  11. 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.

  12. 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.

  13. Decadal prediction skill in a multi-model ensemble

    Energy Technology Data Exchange (ETDEWEB)

    Oldenborgh, Geert Jan van; Wouters, Bert; Hazeleger, Wilco [Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, De Bilt (Netherlands); Doblas-Reyes, Francisco J. [Institucio Catalana de Recerca i Estudis Avancats (ICREA) and Institut Catala de Ciencies del Clima (IC3), Barcelona (Spain)


    Decadal climate predictions may have skill due to predictable components in boundary conditions (mainly greenhouse gas concentrations but also tropospheric and stratospheric aerosol distributions) and initial conditions (mainly the ocean state). We investigate the skill of temperature and precipitation hindcasts from a multi-model ensemble of four climate forecast systems based on coupled ocean-atmosphere models. Regional variations in skill with and without trend are compared with similarly analysed uninitialised experiments to separate the trend due to monotonically increasing forcings from fluctuations around the trend due to the ocean initial state and aerosol forcings. In temperature most of the skill in both multi-model ensembles comes from the externally forced trends. The rise of the global mean temperature is represented well in the initialised hindcasts, but variations around the trend show little skill beyond the first year due to the absence of volcanic aerosols in the hindcasts and the unpredictability of ENSO. The models have non-trivial skill in hindcasts of North Atlantic sea surface temperature beyond the trend. This skill is highest in the northern North Atlantic in initialised experiments and in the subtropical North Atlantic in uninitialised simulations. A similar result is found in the Pacific Ocean, although the signal is less clear. The uninitialised simulations have good skill beyond the trend in the western North Pacific. The initialised experiments show some skill in the decadal ENSO region in the eastern Pacific, in agreement with previous studies. However, the results in this study are not statistically significant (p {approx} 0.1) by themselves. The initialised models also show some skill in forecasting 4-year mean Sahel rainfall at lead times of 1 and 5 years, in agreement with the observed teleconnection from the Atlantic Ocean. Again, the skill is not statistically significant (p {approx} 0.2). Furthermore, uninitialised simulations

  14. 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 ...

  15. 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

  16. 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...

  17. 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 ...

  18. 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

  19. 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.

  20. 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