Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.

Directory of Open Access Journals (Sweden)

Niklas Berliner

Full Text Available Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.

Enhancement of a Turbulence Sub-Model for More Accurate Predictions of Vertical Stratifications in 3D Coastal and Estuarine Modeling

Directory of Open Access Journals (Sweden)

Wenrui Huang

2010-03-01

Full Text Available This paper presents an improvement of the Mellor and Yamada's 2nd order turbulence model in the Princeton Ocean Model (POM for better predictions of vertical stratifications of salinity in estuaries. The model was evaluated in the strongly stratified estuary, Apalachicola River, Florida, USA. The three-dimensional hydrodynamic model was applied to study the stratified flow and salinity intrusion in the estuary in response to tide, wind, and buoyancy forces. Model tests indicate that model predictions over estimate the stratification when using the default turbulent parameters. Analytic studies of density-induced and wind-induced flows indicate that accurate estimation of vertical eddy viscosity plays an important role in describing vertical profiles. Initial model revision experiments show that the traditional approach of modifying empirical constants in the turbulence model leads to numerical instability. In order to improve the performance of the turbulence model while maintaining numerical stability, a stratification factor was introduced to allow adjustment of the vertical turbulent eddy viscosity and diffusivity. Sensitivity studies indicate that the stratification factor, ranging from 1.0 to 1.2, does not cause numerical instability in Apalachicola River. Model simulations show that increasing the turbulent eddy viscosity by a stratification factor of 1.12 results in an optimal agreement between model predictions and observations in the case study presented in this study. Using the proposed stratification factor provides a useful way for coastal modelers to improve the turbulence model performance in predicting vertical turbulent mixing in stratified estuaries and coastal waters.

Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model

International Nuclear Information System (INIS)

ZareNezhad, Bahman; Aminian, Ali

2011-01-01

This paper presents a new approach based on using an artificial neural network (ANN) model for predicting the acid dew points of the combustion gases in process and power plants. The most important acidic combustion gases namely, SO 3 , SO 2 , NO 2 , HCl and HBr are considered in this investigation. Proposed Network is trained using the Levenberg-Marquardt back propagation algorithm and the hyperbolic tangent sigmoid activation function is applied to calculate the output values of the neurons of the hidden layer. According to the network's training, validation and testing results, a three layer neural network with nine neurons in the hidden layer is selected as the best architecture for accurate prediction of the acidic combustion gases dew points over wide ranges of acid and moisture concentrations. The proposed neural network model can have significant application in predicting the condensation temperatures of different acid gases to mitigate the corrosion problems in stacks, pollution control devices and energy recovery systems.

Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

Science.gov (United States)

Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

2018-03-01

Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error model predicts the exposure of

Inverse and Predictive Modeling

Energy Technology Data Exchange (ETDEWEB)

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

2017-09-27

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.

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

International Nuclear Information System (INIS)

Katata, Genki

2009-02-01

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)

Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

Science.gov (United States)

Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

2017-04-01

Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

Towards Accurate Prediction of Unbalance Response, Oil Whirl and Oil Whip of Flexible Rotors Supported by Hydrodynamic Bearings

Directory of Open Access Journals (Sweden)

Rob Eling

2016-09-01

Full Text Available Journal bearings are used to support rotors in a wide range of applications. In order to ensure reliable operation, accurate analyses of these rotor-bearing systems are crucial. Coupled analysis of the rotor and the journal bearing is essential in the case that the rotor is flexible. The accuracy of prediction of the model at hand depends on its comprehensiveness. In this study, we construct three bearing models of increasing modeling comprehensiveness and use these to predict the response of two different rotor-bearing systems. The main goal is to evaluate the correlation with measurement data as a function of modeling comprehensiveness: 1D versus 2D pressure prediction, distributed versus lumped thermal model, Newtonian versus non-Newtonian fluid description and non-mass-conservative versus mass-conservative cavitation description. We conclude that all three models predict the existence of critical speeds and whirl for both rotor-bearing systems. However, the two more comprehensive models in general show better correlation with measurement data in terms of frequency and amplitude. Furthermore, we conclude that a thermal network model comprising temperature predictions of the bearing surroundings is essential to obtain accurate predictions. The results of this study aid in developing accurate and computationally-efficient models of flexible rotors supported by plain journal bearings.

Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

Energy Technology Data Exchange (ETDEWEB)

Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

2008-10-20

One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

Accurate lithography simulation model based on convolutional neural networks

Science.gov (United States)

Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

2017-07-01

Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Science.gov (United States)

Nemati, Shamim; Holder, Andre; Razmi, Fereshteh; Stanley, Matthew D; Clifford, Gari D; Buchman, Timothy G

2018-04-01

Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. Observational cohort study. Academic medical center from January 2013 to December 2015. Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. None. High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the

Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models.

Science.gov (United States)

Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

2015-09-18

Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

Accurate wavelength prediction of photonic crystal resonant reflection and applications in refractive index measurement

DEFF Research Database (Denmark)

Hermannsson, Pétur Gordon; Vannahme, Christoph; Smith, Cameron L. C.

2014-01-01

and superstrate materials. The importance of accounting for material dispersion in order to obtain accurate simulation results is highlighted, and a method for doing so using an iterative approach is demonstrated. Furthermore, an application for the model is demonstrated, in which the material dispersion......In the past decade, photonic crystal resonant reflectors have been increasingly used as the basis for label-free biochemical assays in lab-on-a-chip applications. In both designing and interpreting experimental results, an accurate model describing the optical behavior of such structures...... is essential. Here, an analytical method for precisely predicting the absolute positions of resonantly reflected wavelengths is presented. The model is experimentally verified to be highly accurate using nanoreplicated, polymer-based photonic crystal grating reflectors with varying grating periods...

A Novel Fibrosis Index Comprising a Non-Cholesterol Sterol Accurately Predicts HCV-Related Liver Cirrhosis

DEFF Research Database (Denmark)

Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin

2014-01-01

of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive...

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

Directory of Open Access Journals (Sweden)

Jing Lu

2014-11-01

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.

Highly Accurate Prediction of Jobs Runtime Classes

OpenAIRE

Reiner-Benaim, Anat; Grabarnick, Anna; Shmueli, Edi

2016-01-01

Separating the short jobs from the long is a known technique to improve scheduling performance. In this paper we describe a method we developed for accurately predicting the runtimes classes of the jobs to enable this separation. Our method uses the fact that the runtimes can be represented as a mixture of overlapping Gaussian distributions, in order to train a CART classifier to provide the prediction. The threshold that separates the short jobs from the long jobs is determined during the ev...

Predictive modeling of complications.

Science.gov (United States)

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

2016-09-01

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.

The MIDAS touch for Accurately Predicting the Stress-Strain Behavior of Tantalum

Energy Technology Data Exchange (ETDEWEB)

Jorgensen, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2016-03-02

Testing the behavior of metals in extreme environments is not always feasible, so material scientists use models to try and predict the behavior. To achieve accurate results it is necessary to use the appropriate model and material-specific parameters. This research evaluated the performance of six material models available in the MIDAS database [1] to determine at which temperatures and strain-rates they perform best, and to determine to which experimental data their parameters were optimized. Additionally, parameters were optimized for the Johnson-Cook model using experimental data from Lassila et al [2].

Non-isothermal kinetics model to predict accurate phase transformation and hardness of 22MnB5 boron steel

Energy Technology Data Exchange (ETDEWEB)

Bok, H.-H.; Kim, S.N.; Suh, D.W. [Graduate Institute of Ferrous Technology, POSTECH, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongsangbuk-do (Korea, Republic of); Barlat, F., E-mail: f.barlat@postech.ac.kr [Graduate Institute of Ferrous Technology, POSTECH, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongsangbuk-do (Korea, Republic of); Lee, M.-G., E-mail: myounglee@korea.ac.kr [Department of Materials Science and Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul (Korea, Republic of)

2015-02-25

A non-isothermal phase transformation kinetics model obtained by modifying the well-known JMAK approach is proposed for application to a low carbon boron steel (22MnB5) sheet. In the modified kinetics model, the parameters are functions of both temperature and cooling rate, and can be identified by a numerical optimization method. Moreover, in this approach the transformation start and finish temperatures are variable instead of the constants that depend on chemical composition. These variable reference temperatures are determined from the measured CCT diagram using dilatation experiments. The kinetics model developed in this work captures the complex transformation behavior of the boron steel sheet sample accurately. In particular, the predicted hardness and phase fractions in the specimens subjected to a wide range of cooling rates were validated by experiments.

Rapid and accurate prediction and scoring of water molecules in protein binding sites.

Directory of Open Access Journals (Sweden)

Gregory A Ross

Full Text Available Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

Accurate predictions for the LHC made easy

CERN Multimedia

CERN. Geneva

2014-01-01

The data recorded by the LHC experiments is of a very high quality. To get the most out of the data, precise theory predictions, including uncertainty estimates, are needed to reduce as much as possible theoretical bias in the experimental analyses. Recently, significant progress has been made in computing Next-to-Leading Order (NLO) computations, including matching to the parton shower, that allow for these accurate, hadron-level predictions. I shall discuss one of these efforts, the MadGraph5_aMC@NLO program, that aims at the complete automation of predictions at the NLO accuracy within the SM as well as New Physics theories. I’ll illustrate some of the theoretical ideas behind this program, show some selected applications to LHC physics, as well as describe the future plans.

XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

Science.gov (United States)

Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

2013-12-23

Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

Differential contribution of visual and auditory information to accurately predict the direction and rotational motion of a visual stimulus.

Science.gov (United States)

Park, Seoung Hoon; Kim, Seonjin; Kwon, MinHyuk; Christou, Evangelos A

2016-03-01

Vision and auditory information are critical for perception and to enhance the ability of an individual to respond accurately to a stimulus. However, it is unknown whether visual and auditory information contribute differentially to identify the direction and rotational motion of the stimulus. The purpose of this study was to determine the ability of an individual to accurately predict the direction and rotational motion of the stimulus based on visual and auditory information. In this study, we recruited 9 expert table-tennis players and used table-tennis service as our experimental model. Participants watched recorded services with different levels of visual and auditory information. The goal was to anticipate the direction of the service (left or right) and the rotational motion of service (topspin, sidespin, or cut). We recorded their responses and quantified the following outcomes: (i) directional accuracy and (ii) rotational motion accuracy. The response accuracy was the accurate predictions relative to the total number of trials. The ability of the participants to predict the direction of the service accurately increased with additional visual information but not with auditory information. In contrast, the ability of the participants to predict the rotational motion of the service accurately increased with the addition of auditory information to visual information but not with additional visual information alone. In conclusion, this finding demonstrates that visual information enhances the ability of an individual to accurately predict the direction of the stimulus, whereas additional auditory information enhances the ability of an individual to accurately predict the rotational motion of stimulus.

Risk prediction model: Statistical and artificial neural network approach

Science.gov (United States)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

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.

A Real-Time Accurate Model and Its Predictive Fuzzy PID Controller for Pumped Storage Unit via Error Compensation

Directory of Open Access Journals (Sweden)

Jianzhong Zhou

2017-12-01

Full Text Available Model simulation and control of pumped storage unit (PSU are essential to improve the dynamic quality of power station. Only under the premise of the PSU models reflecting the actual transient process, the novel control method can be properly applied in the engineering. The contributions of this paper are that (1 a real-time accurate equivalent circuit model (RAECM of PSU via error compensation is proposed to reconcile the conflict between real-time online simulation and accuracy under various operating conditions, and (2 an adaptive predicted fuzzy PID controller (APFPID based on RAECM is put forward to overcome the instability of conventional control under no-load conditions with low water head. Respectively, all hydraulic factors in pipeline system are fully considered based on equivalent lumped-circuits theorem. The pretreatment, which consists of improved Suter-transformation and BP neural network, and online simulation method featured by two iterative loops are synthetically proposed to improve the solving accuracy of the pump-turbine. Moreover, the modified formulas for compensating error are derived with variable-spatial discretization to improve the accuracy of the real-time simulation further. The implicit RadauIIA method is verified to be more suitable for PSUGS owing to wider stable domain. Then, APFPID controller is constructed based on the integration of fuzzy PID and the model predictive control. Rolling prediction by RAECM is proposed to replace rolling optimization with its computational speed guaranteed. Finally, the simulation and on-site measurements are compared to prove trustworthy of RAECM under various running conditions. Comparative experiments also indicate that APFPID controller outperforms other controllers in most cases, especially low water head conditions. Satisfying results of RAECM have been achieved in engineering and it provides a novel model reference for PSUGS.

Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

Science.gov (United States)

Reynolds, Sheila M.; Bilmes, Jeff A.; Noble, William Stafford

2010-01-01

DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence—301 base pairs, centered at the position to be scored—with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

Directory of Open Access Journals (Sweden)

Sheila M Reynolds

2010-07-01

Full Text Available DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the

Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

Science.gov (United States)

Reynolds, Sheila M; Bilmes, Jeff A; Noble, William Stafford

2010-07-08

DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance

OpenAIRE

Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos

2016-01-01

At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the model, or the effort required to ma...

Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots.

Science.gov (United States)

Hajdin, Christine E; Bellaousov, Stanislav; Huggins, Wayne; Leonard, Christopher W; Mathews, David H; Weeks, Kevin M

2013-04-02

A pseudoknot forms in an RNA when nucleotides in a loop pair with a region outside the helices that close the loop. Pseudoknots occur relatively rarely in RNA but are highly overrepresented in functionally critical motifs in large catalytic RNAs, in riboswitches, and in regulatory elements of viruses. Pseudoknots are usually excluded from RNA structure prediction algorithms. When included, these pairings are difficult to model accurately, especially in large RNAs, because allowing this structure dramatically increases the number of possible incorrect folds and because it is difficult to search the fold space for an optimal structure. We have developed a concise secondary structure modeling approach that combines SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) experimental chemical probing information and a simple, but robust, energy model for the entropic cost of single pseudoknot formation. Structures are predicted with iterative refinement, using a dynamic programming algorithm. This melded experimental and thermodynamic energy function predicted the secondary structures and the pseudoknots for a set of 21 challenging RNAs of known structure ranging in size from 34 to 530 nt. On average, 93% of known base pairs were predicted, and all pseudoknots in well-folded RNAs were identified.

An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

Science.gov (United States)

Wang, Shiyao; Deng, Zhidong; Yin, Gang

2016-02-24

A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

Directory of Open Access Journals (Sweden)

Shiyao Wang

2016-02-01

Full Text Available A high-performance differential global positioning system (GPS  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU/dead reckoning (DR data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

Science.gov (United States)

Diallo, Ousmane H.

2012-01-01

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.

NNLOPS accurate predictions for $W^+W^-$ production arXiv

CERN Document Server

Re, Emanuele; Zanderighi, Giulia

We present novel predictions for the production of $W^+W^-$ pairs in hadron collisions that are next-to-next-to-leading order accurate and consistently matched to a parton shower (NNLOPS). All diagrams that lead to the process $pp\\to e^- \\bar \

Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers

Science.gov (United States)

Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes

2018-06-01

Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3-1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that can guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and a highly economical path to determining the RI values for a wide range of organic polymers.

Nonlinear chaotic model for predicting storm surges

Directory of Open Access Journals (Sweden)

M. Siek

2010-09-01

Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians.

Science.gov (United States)

Ntaios, G; Gioulekas, F; Papavasileiou, V; Strbian, D; Michel, P

2016-11-01

ASTRAL, SEDAN and DRAGON scores are three well-validated scores for stroke outcome prediction. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated. Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. An estimate was considered accurate if it was within 95% confidence intervals of actual outcome. In all, 244 participants from 32 different countries responded assessing 720 real scenarios and 2636 outcomes. The majority of physicians' estimates were inaccurate (1422/2636, 53.9%). 400 (56.8%) of physicians' estimates about the percentage probability of 3-month modified Rankin score (mRS) > 2 were accurate compared with 609 (86.5%) of ASTRAL score estimates (P DRAGON score estimates (P DRAGON score estimates (P DRAGON and SEDAN scores predict outcome of acute ischaemic stroke patients with higher accuracy compared to physicians interested in stroke. © 2016 EAN.

Accurate anisotropic material modelling using only tensile tests for hot and cold forming

Science.gov (United States)

Abspoel, M.; Scholting, M. E.; Lansbergen, M.; Neelis, B. M.

2017-09-01

Accurate material data for simulations require a lot of effort. Advanced yield loci require many different kinds of tests and a Forming Limit Curve (FLC) needs a large amount of samples. Many people use simple material models to reduce the effort of testing, however some models are either not accurate enough (i.e. Hill’48), or do not describe new types of materials (i.e. Keeler). Advanced yield loci describe the anisotropic materials behaviour accurately, but are not widely adopted because of the specialized tests, and data post-processing is a hurdle for many. To overcome these issues, correlations between the advanced yield locus points (biaxial, plane strain and shear) and mechanical properties have been investigated. This resulted in accurate prediction of the advanced stress points using only Rm, Ag and r-values in three directions from which a Vegter yield locus can be constructed with low effort. FLC’s can be predicted with the equations of Abspoel & Scholting depending on total elongation A80, r-value and thickness. Both predictive methods are initially developed for steel, aluminium and stainless steel (BCC and FCC materials). The validity of the predicted Vegter yield locus is investigated with simulation and measurements on both hot and cold formed parts and compared with Hill’48. An adapted specimen geometry, to ensure a homogeneous temperature distribution in the Gleeble hot tensile test, was used to measure the mechanical properties needed to predict a hot Vegter yield locus. Since for hot material, testing of stress states other than uniaxial is really challenging, the prediction for the yield locus adds a lot of value. For the hot FLC an A80 sample with a homogeneous temperature distribution is needed which is due to size limitations not possible in the Gleeble tensile tester. Heating the sample in an industrial type furnace and tensile testing it in a dedicated device is a good alternative to determine the necessary parameters for the FLC

PVT characterization and viscosity modeling and prediction of crude oils

DEFF Research Database (Denmark)

Cisneros, Eduardo Salvador P.; Dalberg, Anders; Stenby, Erling Halfdan

2004-01-01

In previous works, the general, one-parameter friction theory (f-theory), models have been applied to the accurate viscosity modeling of reservoir fluids. As a base, the f-theory approach requires a compositional characterization procedure for the application of an equation of state (EOS), in most...... pressure, is also presented. The combination of the mass characterization scheme presented in this work and the f-theory, can also deliver accurate viscosity modeling results. Additionally, depending on how extensive the compositional characterization is, the approach,presented in this work may also...... deliver accurate viscosity predictions. The modeling approach presented in this work can deliver accurate viscosity and density modeling and prediction results over wide ranges of reservoir conditions, including the compositional changes induced by recovery processes such as gas injection....

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

International Nuclear Information System (INIS)

Liu, Guangming; Ouyang, Minggao; Lu, Languang; Li, Jianqiu; Hua, Jianfeng

2015-01-01

Highlights: • An energy prediction (EP) method is introduced for battery E RDE determination. • EP determines E RDE through coupled prediction of future states, parameters, and output. • The PAEP combines parameter adaptation and prediction to update model parameters. • The PAEP provides improved E RDE accuracy compared with DC and other EP methods. - Abstract: In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (E RDE ) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available E RDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the E RDE directly to the current state of charge (SOC). To enhance the E RDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E RDE prediction horizon, and the E RDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E RDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the E RDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online E RDE prediction. The correlation of SOC estimation and E RDE calculation is then discussed to illustrate the

A New Approach for Accurate Prediction of Liquid Loading of Directional Gas Wells in Transition Flow or Turbulent Flow

Directory of Open Access Journals (Sweden)

Ruiqing Ming

2017-01-01

Full Text Available Current common models for calculating continuous liquid-carrying critical gas velocity are established based on vertical wells and laminar flow without considering the influence of deviation angle and Reynolds number on liquid-carrying. With the increase of the directional well in transition flow or turbulent flow, the current common models cannot accurately predict the critical gas velocity of these wells. So we built a new model to predict continuous liquid-carrying critical gas velocity for directional well in transition flow or turbulent flow. It is shown from sensitivity analysis that the correction coefficient is mainly influenced by Reynolds number and deviation angle. With the increase of Reynolds number, the critical liquid-carrying gas velocity increases first and then decreases. And with the increase of deviation angle, the critical liquid-carrying gas velocity gradually decreases. It is indicated from the case calculation analysis that the calculation error of this new model is less than 10%, where accuracy is much higher than those of current common models. It is demonstrated that the continuous liquid-carrying critical gas velocity of directional well in transition flow or turbulent flow can be predicted accurately by using this new model.

Highly accurate prediction of food challenge outcome using routinely available clinical data.

Science.gov (United States)

DunnGalvin, Audrey; Daly, Deirdre; Cullinane, Claire; Stenke, Emily; Keeton, Diane; Erlewyn-Lajeunesse, Mich; Roberts, Graham C; Lucas, Jane; Hourihane, Jonathan O'B

2011-03-01

Serum specific IgE or skin prick tests are less useful at levels below accepted decision points. We sought to develop and validate a model to predict food challenge outcome by using routinely collected data in a diverse sample of children considered suitable for food challenge. The proto-algorithm was generated by using a limited data set from 1 service (phase 1). We retrospectively applied, evaluated, and modified the initial model by using an extended data set in another center (phase 2). Finally, we prospectively validated the model in a blind study in a further group of children undergoing food challenge for peanut, milk, or egg in the second center (phase 3). Allergen-specific models were developed for peanut, egg, and milk. Phase 1 (N = 429) identified 5 clinical factors associated with diagnosis of food allergy by food challenge. In phase 2 (N = 289), we examined the predictive ability of 6 clinical factors: skin prick test, serum specific IgE, total IgE minus serum specific IgE, symptoms, sex, and age. In phase 3 (N = 70), 97% of cases were accurately predicted as positive and 94% as negative. Our model showed an advantage in clinical prediction compared with serum specific IgE only, skin prick test only, and serum specific IgE and skin prick test (92% accuracy vs 57%, and 81%, respectively). Our findings have implications for the improved delivery of food allergy-related health care, enhanced food allergy-related quality of life, and economized use of health service resources by decreasing the number of food challenges performed. Copyright © 2011 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Interpreting Disruption Prediction Models to Improve Plasma Control

Science.gov (United States)

Parsons, Matthew

2017-10-01

In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.

Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models

Science.gov (United States)

Plant, Nathaniel G.; Holland, K. Todd

2011-01-01

Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.

Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

Science.gov (United States)

Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

2017-09-01

Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

Science.gov (United States)

Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

2018-02-01

Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

Factors Influencing the Predictive Power of Models for Predicting Mortality and/or Heart Failure Hospitalization in Patients With Heart Failure

NARCIS (Netherlands)

Ouwerkerk, Wouter; Voors, Adriaan A.; Zwinderman, Aeilko H.

2014-01-01

The present paper systematically reviews and compares existing prediction models in order to establish the strongest variables, models, and model characteristics in patients with heart failure predicting outcome. To improve decision making accurately predicting mortality and heart-failure

Improving medical decisions for incapacitated persons: does focusing on "accurate predictions" lead to an inaccurate picture?

Science.gov (United States)

Kim, Scott Y H

2014-04-01

The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients' preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%-80% reliability of people's preferences for future medical decisions--a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed.

New and Accurate Predictive Model for the Efficacy of Extracorporeal Shock Wave Therapy in Managing Patients With Chronic Plantar Fasciitis.

Science.gov (United States)

Yin, Mengchen; Chen, Ni; Huang, Quan; Marla, Anastasia Sulindro; Ma, Junming; Ye, Jie; Mo, Wen

2017-12-01

Youden index was .4243, .3003, and .7189, respectively. The Hosmer-Lemeshow test showed a good fitting of the predictive model, with an overall accuracy of 89.6%. This study establishes a new and accurate predictive model for the efficacy of ESWT in managing patients with chronic plantar fasciitis. The use of these parameters, in the form of a predictive model for ESWT efficacy, has the potential to improve decision-making in the application of ESWT. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Probability-based collaborative filtering model for predicting gene–disease associations

OpenAIRE

Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-01-01

Background Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. Methods We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our mo...

Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

International Nuclear Information System (INIS)

Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; Lilienfeld, O. Anatole von; Müller, Klaus-Robert; Tkatchenko, Alexandre

2015-01-01

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the 'holy grail' of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

Science.gov (United States)

Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

2016-09-01

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. © 2016 American Society of Plant Biologists. All rights reserved.

Models for predicting fuel consumption in sagebrush-dominated ecosystems

Science.gov (United States)

Clinton S. Wright

2013-01-01

Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentate Nutt.) ecosystems....

Towards more accurate wind and solar power prediction by improving NWP model physics

Science.gov (United States)

Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo

2014-05-01

The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions1

Science.gov (United States)

Zuñiga, Cristal; Li, Chien-Ting; Zielinski, Daniel C.; Guarnieri, Michael T.; Antoniewicz, Maciek R.; Zengler, Karsten

2016-01-01

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

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.

2016-01-01

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

Techniques for discrimination-free predictive models (Chapter 12)

NARCIS (Netherlands)

Kamiran, F.; Calders, T.G.K.; Pechenizkiy, M.; Custers, B.H.M.; Calders, T.G.K.; Schermer, B.W.; Zarsky, T.Z.

2013-01-01

In this chapter, we give an overview of the techniques developed ourselves for constructing discrimination-free classifiers. In discrimination-free classification the goal is to learn a predictive model that classifies future data objects as accurately as possible, yet the predicted labels should be

Probability-based collaborative filtering model for predicting gene-disease associations.

Science.gov (United States)

Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-12-28

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

Accurate, model-based tuning of synthetic gene expression using introns in S. cerevisiae.

Directory of Open Access Journals (Sweden)

Ido Yofe

2014-06-01

Full Text Available Introns are key regulators of eukaryotic gene expression and present a potentially powerful tool for the design of synthetic eukaryotic gene expression systems. However, intronic control over gene expression is governed by a multitude of complex, incompletely understood, regulatory mechanisms. Despite this lack of detailed mechanistic understanding, here we show how a relatively simple model enables accurate and predictable tuning of synthetic gene expression system in yeast using several predictive intron features such as transcript folding and sequence motifs. Using only natural Saccharomyces cerevisiae introns as regulators, we demonstrate fine and accurate control over gene expression spanning a 100 fold expression range. These results broaden the engineering toolbox of synthetic gene expression systems and provide a framework in which precise and robust tuning of gene expression is accomplished.

Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation)

Energy Technology Data Exchange (ETDEWEB)

Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Pesaran, A.

2014-02-01

Predictive models of capacity and power fade must consider a multiplicity of degradation modes experienced by Li-ion batteries in the automotive environment. Lacking accurate models and tests, lifetime uncertainty must presently be absorbed by overdesign and excess warranty costs. To reduce these costs and extend life, degradation models are under development that predict lifetime more accurately and with less test data. The lifetime models provide engineering feedback for cell, pack and system designs and are being incorporated into real-time control strategies.

Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

NARCIS (Netherlands)

Kayastha, N.

2014-01-01

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

Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

Science.gov (United States)

Rey, M.; Nikitin, A. V.; Tyuterev, V.

2014-06-01

Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

Improved predictive modeling of white LEDs with accurate luminescence simulation and practical inputs with TracePro opto-mechanical design software

Science.gov (United States)

Tsao, Chao-hsi; Freniere, Edward R.; Smith, Linda

2009-02-01

The use of white LEDs for solid-state lighting to address applications in the automotive, architectural and general illumination markets is just emerging. LEDs promise greater energy efficiency and lower maintenance costs. However, there is a significant amount of design and cost optimization to be done while companies continue to improve semiconductor manufacturing processes and begin to apply more efficient and better color rendering luminescent materials such as phosphor and quantum dot nanomaterials. In the last decade, accurate and predictive opto-mechanical software modeling has enabled adherence to performance, consistency, cost, and aesthetic criteria without the cost and time associated with iterative hardware prototyping. More sophisticated models that include simulation of optical phenomenon, such as luminescence, promise to yield designs that are more predictive - giving design engineers and materials scientists more control over the design process to quickly reach optimum performance, manufacturability, and cost criteria. A design case study is presented where first, a phosphor formulation and excitation source are optimized for a white light. The phosphor formulation, the excitation source and other LED components are optically and mechanically modeled and ray traced. Finally, its performance is analyzed. A blue LED source is characterized by its relative spectral power distribution and angular intensity distribution. YAG:Ce phosphor is characterized by relative absorption, excitation and emission spectra, quantum efficiency and bulk absorption coefficient. Bulk scatter properties are characterized by wavelength dependent scatter coefficients, anisotropy and bulk absorption coefficient.

Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers

DEFF Research Database (Denmark)

Lundegaard, Claus; Lund, Ole; Nielsen, Morten

2008-01-01

Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate...

Heart rate during basketball game play and volleyball drills accurately predicts oxygen uptake and energy expenditure.

Science.gov (United States)

Scribbans, T D; Berg, K; Narazaki, K; Janssen, I; Gurd, B J

2015-09-01

There is currently little information regarding the ability of metabolic prediction equations to accurately predict oxygen uptake and exercise intensity from heart rate (HR) during intermittent sport. The purpose of the present study was to develop and, cross-validate equations appropriate for accurately predicting oxygen cost (VO2) and energy expenditure from HR during intermittent sport participation. Eleven healthy adult males (19.9±1.1yrs) were recruited to establish the relationship between %VO2peak and %HRmax during low-intensity steady state endurance (END), moderate-intensity interval (MOD) and high intensity-interval exercise (HI), as performed on a cycle ergometer. Three equations (END, MOD, and HI) for predicting %VO2peak based on %HRmax were developed. HR and VO2 were directly measured during basketball games (6 male, 20.8±1.0 yrs; 6 female, 20.0±1.3yrs) and volleyball drills (12 female; 20.8±1.0yrs). Comparisons were made between measured and predicted VO2 and energy expenditure using the 3 equations developed and 2 previously published equations. The END and MOD equations accurately predicted VO2 and energy expenditure, while the HI equation underestimated, and the previously published equations systematically overestimated VO2 and energy expenditure. Intermittent sport VO2 and energy expenditure can be accurately predicted from heart rate data using either the END (%VO2peak=%HRmax x 1.008-17.17) or MOD (%VO2peak=%HRmax x 1.2-32) equations. These 2 simple equations provide an accessible and cost-effective method for accurate estimation of exercise intensity and energy expenditure during intermittent sport.

Energy based prediction models for building acoustics

DEFF Research Database (Denmark)

Brunskog, Jonas

2012-01-01

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....

Prediction of retention times in comprehensive two-dimensional gas chromatography using thermodynamic models.

Science.gov (United States)

McGinitie, Teague M; Harynuk, James J

2012-09-14

A method was developed to accurately predict both the primary and secondary retention times for a series of alkanes, ketones and alcohols in a flow-modulated GC×GC system. This was accomplished through the use of a three-parameter thermodynamic model where ΔH, ΔS, and ΔC(p) for an analyte's interaction with the stationary phases in both dimensions are known. Coupling this thermodynamic model with a time summation calculation it was possible to accurately predict both (1)t(r) and (2)t(r) for all analytes. The model was able to predict retention times regardless of the temperature ramp used, with an average error of only 0.64% for (1)t(r) and an average error of only 2.22% for (2)t(r). The model shows promise for the accurate prediction of retention times in GC×GC for a wide range of compounds and is able to utilize data collected from 1D experiments. Copyright © 2012 Elsevier B.V. All rights reserved.

PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.

Directory of Open Access Journals (Sweden)

Jaroslav Bendl

2014-01-01

Full Text Available Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

Science.gov (United States)

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

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

Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling

Directory of Open Access Journals (Sweden)

Eric R. Edelman

2017-06-01

Full Text Available For efficient utilization of operating rooms (ORs, accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT. We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT. TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related

Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling.

Science.gov (United States)

Edelman, Eric R; van Kuijk, Sander M J; Hamaekers, Ankie E W; de Korte, Marcel J M; van Merode, Godefridus G; Buhre, Wolfgang F F A

2017-01-01

For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA) physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT). We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT). TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related benefits.

Predictive Models of Li-ion Battery Lifetime (Presentation)

Energy Technology Data Exchange (ETDEWEB)

Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Shi, Y.; Pesaran, A.

2014-09-01

Predictive models of Li-ion battery reliability must consider a multiplicity of electrochemical, thermal and mechanical degradation modes experienced by batteries in application environments. Complicating matters, Li-ion batteries can experience several path dependent degradation trajectories dependent on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. Lacking accurate models and tests, lifetime uncertainty must be absorbed by overdesign and warranty costs. Degradation models are needed that predict lifetime more accurately and with less test data. Models should also provide engineering feedback for next generation battery designs. This presentation reviews both multi-dimensional physical models and simpler, lumped surrogate models of battery electrochemical and mechanical degradation. Models are compared with cell- and pack-level aging data from commercial Li-ion chemistries. The analysis elucidates the relative importance of electrochemical and mechanical stress-induced degradation mechanisms in real-world operating environments. Opportunities for extending the lifetime of commercial battery systems are explored.

Evaluation of wave runup predictions from numerical and parametric models

Science.gov (United States)

Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.

2014-01-01

Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

Development of a noise prediction model based on advanced fuzzy approaches in typical industrial workrooms.

Science.gov (United States)

Aliabadi, Mohsen; Golmohammadi, Rostam; Khotanlou, Hassan; Mansoorizadeh, Muharram; Salarpour, Amir

2014-01-01

Noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. One of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, advanced fuzzy approaches were employed to develop relatively accurate models for predicting noise in noisy industrial workrooms. The data were collected from 60 industrial embroidery workrooms in the Khorasan Province, East of Iran. The main acoustic and embroidery process features that influence the noise were used to develop prediction models using MATLAB software. Multiple regression technique was also employed and its results were compared with those of fuzzy approaches. Prediction errors of all prediction models based on fuzzy approaches were within the acceptable level (lower than one dB). However, Neuro-fuzzy model (RMSE=0.53dB and R2=0.88) could slightly improve the accuracy of noise prediction compared with generate fuzzy model. Moreover, fuzzy approaches provided more accurate predictions than did regression technique. The developed models based on fuzzy approaches as useful prediction tools give professionals the opportunity to have an optimum decision about the effectiveness of acoustic treatment scenarios in embroidery workrooms.

Accuracy assessment of landslide prediction models

International Nuclear Information System (INIS)

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

2014-01-01

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

Towards more accurate and reliable predictions for nuclear applications

International Nuclear Information System (INIS)

Goriely, S.

2015-01-01

The need for nuclear data far from the valley of stability, for applications such as nuclear astrophysics or future nuclear facilities, challenges the robustness as well as the predictive power of present nuclear models. Most of the nuclear data evaluation and prediction are still performed on the basis of phenomenological nuclear models. For the last decades, important progress has been achieved in fundamental nuclear physics, making it now feasible to use more reliable, but also more complex microscopic or semi-microscopic models in the evaluation and prediction of nuclear data for practical applications. In the present contribution, the reliability and accuracy of recent nuclear theories are discussed for most of the relevant quantities needed to estimate reaction cross sections and beta-decay rates, namely nuclear masses, nuclear level densities, gamma-ray strength, fission properties and beta-strength functions. It is shown that nowadays, mean-field models can be tuned at the same level of accuracy as the phenomenological models, renormalized on experimental data if needed, and therefore can replace the phenomenogical inputs in the prediction of nuclear data. While fundamental nuclear physicists keep on improving state-of-the-art models, e.g. within the shell model or ab initio models, nuclear applications could make use of their most recent results as quantitative constraints or guides to improve the predictions in energy or mass domain that will remain inaccessible experimentally. (orig.)

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

Science.gov (United States)

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions.

Science.gov (United States)

Deng, Xin; Gumm, Jordan; Karki, Suman; Eickholt, Jesse; Cheng, Jianlin

2015-07-07

Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

Directory of Open Access Journals (Sweden)

Xin Deng

2015-07-01

Full Text Available Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

An updated prediction model of the global risk of cardiovascular disease in HIV-positive persons

DEFF Research Database (Denmark)

Friis-Møller, Nina; Ryom, Lene; Smith, Colette

2016-01-01

,663 HIV-positive persons from 20 countries in Europe and Australia, who were free of CVD at entry into the Data-collection on Adverse Effects of Anti-HIV Drugs (D:A:D) study. Cox regression models (full and reduced) were developed that predict the risk of a global CVD endpoint. The predictive performance...... significantly predicted risk more accurately than the recalibrated Framingham model (Harrell's c-statistic of 0.791, 0.783 and 0.766 for the D:A:D full, D:A:D reduced, and Framingham models respectively; p models also more accurately predicted five-year CVD-risk for key prognostic subgroups...... to quantify risk and to guide preventive care....

Rare Plants of Southeastern Hardwood Forests and the Role of Predictive Modeling

International Nuclear Information System (INIS)

Imm, D.W.; Shealy, H.E. Jr.; McLeod, K.W.; Collins, B.

2001-01-01

Habitat prediction models for rare plants can be useful when large areas must be surveyed or populations must be established. Investigators developed a habitat prediction model for four species of Southeastern hardwood forests. These four examples suggest that models based on resource and vegetation characteristics can accurately predict habitat, but only when plants are strongly associated with these variables and the scale of modeling coincides with habitat size

Deformation, Failure, and Fatigue Life of SiC/Ti-15-3 Laminates Accurately Predicted by MAC/GMC

Science.gov (United States)

Bednarcyk, Brett A.; Arnold, Steven M.

2002-01-01

NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) (ref.1) has been extended to enable fully coupled macro-micro deformation, failure, and fatigue life predictions for advanced metal matrix, ceramic matrix, and polymer matrix composites. Because of the multiaxial nature of the code's underlying micromechanics model, GMC--which allows the incorporation of complex local inelastic constitutive models--MAC/GMC finds its most important application in metal matrix composites, like the SiC/Ti-15-3 composite examined here. Furthermore, since GMC predicts the microscale fields within each constituent of the composite material, submodels for local effects such as fiber breakage, interfacial debonding, and matrix fatigue damage can and have been built into MAC/GMC. The present application of MAC/GMC highlights the combination of these features, which has enabled the accurate modeling of the deformation, failure, and life of titanium matrix composites.

Group contribution modelling for the prediction of safety-related and environmental properties

DEFF Research Database (Denmark)

Frutiger, Jerome; Abildskov, Jens; Sin, Gürkan

warming potential and ozone depletion potential. Process safety studies and environmental assessments rely on accurate property data. Safety data such as flammability limits, heat of combustion or auto ignition temperature play an important role in quantifying the risk of fire and explosions among others......We present a new set of property prediction models based on group contributions to predict major safety-related and environmental properties for organic compounds. The predicted list of properties includes lower and upper flammability limits, heat of combustion, auto ignition temperature, global...... models like group contribution (GC) models can estimate data. However, the estimation needs to be accurate, reliable and as little time-consuming as possible so that the models can be used on the fly. In this study the Marrero and Gani group contribution (MR GC) method has been used to develop the models...

Development of estrogen receptor beta binding prediction model using large sets of chemicals.

Science.gov (United States)

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

2017-11-03

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

Tectonic predictions with mantle convection models

Science.gov (United States)

Coltice, Nicolas; Shephard, Grace E.

2018-04-01

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

An Anisotropic Hardening Model for Springback Prediction

Science.gov (United States)

Zeng, Danielle; Xia, Z. Cedric

2005-08-01

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.

An Anisotropic Hardening Model for Springback Prediction

International Nuclear Information System (INIS)

Zeng, Danielle; Xia, Z. Cedric

2005-01-01

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

A probabilistic model to predict clinical phenotypic traits from genome sequencing.

Science.gov (United States)

Chen, Yun-Ching; Douville, Christopher; Wang, Cheng; Niknafs, Noushin; Yeo, Grace; Beleva-Guthrie, Violeta; Carter, Hannah; Stenson, Peter D; Cooper, David N; Li, Biao; Mooney, Sean; Karchin, Rachel

2014-09-01

Genetic screening is becoming possible on an unprecedented scale. However, its utility remains controversial. Although most variant genotypes cannot be easily interpreted, many individuals nevertheless attempt to interpret their genetic information. Initiatives such as the Personal Genome Project (PGP) and Illumina's Understand Your Genome are sequencing thousands of adults, collecting phenotypic information and developing computational pipelines to identify the most important variant genotypes harbored by each individual. These pipelines consider database and allele frequency annotations and bioinformatics classifications. We propose that the next step will be to integrate these different sources of information to estimate the probability that a given individual has specific phenotypes of clinical interest. To this end, we have designed a Bayesian probabilistic model to predict the probability of dichotomous phenotypes. When applied to a cohort from PGP, predictions of Gilbert syndrome, Graves' disease, non-Hodgkin lymphoma, and various blood groups were accurate, as individuals manifesting the phenotype in question exhibited the highest, or among the highest, predicted probabilities. Thirty-eight PGP phenotypes (26%) were predicted with area-under-the-ROC curve (AUC)>0.7, and 23 (15.8%) of these were statistically significant, based on permutation tests. Moreover, in a Critical Assessment of Genome Interpretation (CAGI) blinded prediction experiment, the models were used to match 77 PGP genomes to phenotypic profiles, generating the most accurate prediction of 16 submissions, according to an independent assessor. Although the models are currently insufficiently accurate for diagnostic utility, we expect their performance to improve with growth of publicly available genomics data and model refinement by domain experts.

In vitro transcription accurately predicts lac repressor phenotype in vivo in Escherichia coli

Directory of Open Access Journals (Sweden)

Matthew Almond Sochor

2014-07-01

Full Text Available A multitude of studies have looked at the in vivo and in vitro behavior of the lac repressor binding to DNA and effector molecules in order to study transcriptional repression, however these studies are not always reconcilable. Here we use in vitro transcription to directly mimic the in vivo system in order to build a self consistent set of experiments to directly compare in vivo and in vitro genetic repression. A thermodynamic model of the lac repressor binding to operator DNA and effector is used to link DNA occupancy to either normalized in vitro mRNA product or normalized in vivo fluorescence of a regulated gene, YFP. An accurate measurement of repressor, DNA and effector concentrations were made both in vivo and in vitro allowing for direct modeling of the entire thermodynamic equilibrium. In vivo repression profiles are accurately predicted from the given in vitro parameters when molecular crowding is considered. Interestingly, our measured repressor–operator DNA affinity differs significantly from previous in vitro measurements. The literature values are unable to replicate in vivo binding data. We therefore conclude that the repressor-DNA affinity is much weaker than previously thought. This finding would suggest that in vitro techniques that are specifically designed to mimic the in vivo process may be necessary to replicate the native system.

Short-term wind power prediction based on LSSVM–GSA model

International Nuclear Information System (INIS)

Yuan, Xiaohui; Chen, Chen; Yuan, Yanbin; Huang, Yuehua; Tan, Qingxiong

2015-01-01

Highlights: • A hybrid model is developed for short-term wind power prediction. • The model is based on LSSVM and gravitational search algorithm. • Gravitational search algorithm is used to optimize parameters of LSSVM. • Effect of different kernel function of LSSVM on wind power prediction is discussed. • Comparative studies show that prediction accuracy of wind power is improved. - Abstract: Wind power forecasting can improve the economical and technical integration of wind energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to forecast wind power accurately. For the purpose of utilizing wind power to the utmost extent, it is very important to make an accurate prediction of the output power of a wind farm under the premise of guaranteeing the security and the stability of the operation of the power system. In this paper, a hybrid model (LSSVM–GSA) based on the least squares support vector machine (LSSVM) and gravitational search algorithm (GSA) is proposed to forecast the short-term wind power. As the kernel function and the related parameters of the LSSVM have a great influence on the performance of the prediction model, the paper establishes LSSVM model based on different kernel functions for short-term wind power prediction. And then an optimal kernel function is determined and the parameters of the LSSVM model are optimized by using GSA. Compared with the Back Propagation (BP) neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction. Therefore, the proposed LSSVM–GSA is a better model for short-term wind power prediction

Modeling of capacitor charging dynamics in an energy harvesting system considering accurate electromechanical coupling effects

Science.gov (United States)

Bagheri, Shahriar; Wu, Nan; Filizadeh, Shaahin

2018-06-01

This paper presents an iterative numerical method that accurately models an energy harvesting system charging a capacitor with piezoelectric patches. The constitutive relations of piezoelectric materials connected with an external charging circuit with a diode bridge and capacitors lead to the electromechanical coupling effect and the difficulty of deriving accurate transient mechanical response, as well as the charging progress. The proposed model is built upon the Euler-Bernoulli beam theory and takes into account the electromechanical coupling effects as well as the dynamic process of charging an external storage capacitor. The model is validated through experimental tests on a cantilever beam coated with piezoelectric patches. Several parametric studies are performed and the functionality of the model is verified. The efficiency of power harvesting system can be predicted and tuned considering variations in different design parameters. Such a model can be utilized to design robust and optimal energy harvesting system.

A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2017-11-01

Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

An Extrapolation of a Radical Equation More Accurately Predicts Shelf Life of Frozen Biological Matrices.

Science.gov (United States)

De Vore, Karl W; Fatahi, Nadia M; Sass, John E

2016-08-01

Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry. Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. We compared the ability to predict analyte recovery during frozen storage using 3 separate strategies: traditional accelerated studies with Arrhenius modeling, and extrapolation of recovery at 20% of shelf life using either ordinary least squares or a radical equation y = B1x(0.5) + B0. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk. However, the majority of analytes followed a rate of change that slowed over time, which was fit best to a radical equation of the form y = B1x(0.5) + B0. Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method. Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. © 2016 American Association for Clinical Chemistry.

Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

Science.gov (United States)

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

2015-01-01

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

Generating Facial Expressions Using an Anatomically Accurate Biomechanical Model.

Science.gov (United States)

Wu, Tim; Hung, Alice; Mithraratne, Kumar

2014-11-01

This paper presents a computational framework for modelling the biomechanics of human facial expressions. A detailed high-order (Cubic-Hermite) finite element model of the human head was constructed using anatomical data segmented from magnetic resonance images. The model includes a superficial soft-tissue continuum consisting of skin, the subcutaneous layer and the superficial Musculo-Aponeurotic system. Embedded within this continuum mesh, are 20 pairs of facial muscles which drive facial expressions. These muscles were treated as transversely-isotropic and their anatomical geometries and fibre orientations were accurately depicted. In order to capture the relative composition of muscles and fat, material heterogeneity was also introduced into the model. Complex contact interactions between the lips, eyelids, and between superficial soft tissue continuum and deep rigid skeletal bones were also computed. In addition, this paper investigates the impact of incorporating material heterogeneity and contact interactions, which are often neglected in similar studies. Four facial expressions were simulated using the developed model and the results were compared with surface data obtained from a 3D structured-light scanner. Predicted expressions showed good agreement with the experimental data.

Accuracy of some simple models for predicting particulate interception and retention in agricultural systems

International Nuclear Information System (INIS)

Pinder, J.E. III; McLeod, K.W.; Adriano, D.C.

1989-01-01

The accuracy of three radionuclide transfer models for predicting the interception and retention of airborne particles by agricultural crops was tested using Pu-bearing aerosols released to the atmosphere from nuclear fuel facilities on the U.S. Department of Energy's Savannah River Plant, near Aiken, SC. The models evaluated were: (1) NRC, the model defined in U.S. Nuclear Regulatory Guide 1.109; (2) FOOD, a model similar to the NRC model that also predicts concentrations in grains; and (3) AGNS, a model developed from the NRC model for the southeastern United States. Plutonium concentrations in vegetation and grain were predicted from measured deposition rates and compared to concentrations observed in the field. Crops included wheat, soybeans, corn and cabbage. Although predictions of the three models differed by less than a factor of 4, they showed different abilities to predict concentrations observed in the field. The NRC and FOOD models consistently underpredicted the observed Pu concentrations for vegetation. The AGNS model was a more accurate predictor of Pu concentrations for vegetation. Both the FOOD and AGNS models accurately predicted the Pu concentrations for grains

A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

Directory of Open Access Journals (Sweden)

Magdalena Ydreborg

Full Text Available Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI in the paper, was based on the model: Log-odds (predicting cirrhosis = -12.17+ (age × 0.11 + (BMI (kg/m(2 × 0.23 + (D7-lathosterol (μg/100 mg cholesterol×(-0.013 + (Platelet count (x10(9/L × (-0.018 + (Prothrombin-INR × 3.69. The area under the ROC curve (AUROC for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96. The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98. In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding.

Science.gov (United States)

Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

2017-01-01

Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

A model for predicting lung cancer response to therapy

International Nuclear Information System (INIS)

Seibert, Rebecca M.; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.

2007-01-01

treatment. Conclusions: The LWR model accurately predicted final tumor volume for all 20 lung cancer lesions. These predictions were made using only 8 days' worth of observations from early in the treatment. Because the predictions are accurate with quantified uncertainty, they could eventually be used to optimize treatment

Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-BandOpenMote Hardware.

Science.gov (United States)

Daneels, Glenn; Municio, Esteban; Van de Velde, Bruno; Ergeerts, Glenn; Weyn, Maarten; Latré, Steven; Famaey, Jeroen

2018-02-02

The Time-Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4e amendment aims to improve reliability and energy efficiency in industrial and other challenging Internet-of-Things (IoT) environments. This paper presents an accurate and up-to-date energy consumption model for devices using this IEEE 802.15.4e TSCH mode. The model identifies all network-related CPU and radio state changes, thus providing a precise representation of the device behavior and an accurate prediction of its energy consumption. Moreover, energy measurements were performed with a dual-band OpenMote device, running the OpenWSN firmware. This allows the model to be used for devices using 2.4 GHz, as well as 868 MHz. Using these measurements, several network simulations were conducted to observe the TSCH energy consumption effects in end-to-end communication for both frequency bands. Experimental verification of the model shows that it accurately models the consumption for all possible packet sizes and that the calculated consumption on average differs less than 3% from the measured consumption. This deviation includes measurement inaccuracies and the variations of the guard time. As such, the proposed model is very suitable for accurate energy consumption modeling of TSCH networks.

The prediction of intelligence in preschool children using alternative models to regression.

Science.gov (United States)

Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

2011-12-01

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs

DEFF Research Database (Denmark)

Will, Sebastian; Joshi, Tejal; Hofacker, Ivo L.

2012-01-01

Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing...... methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based...... on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used nc...

A consensus approach for estimating the predictive accuracy of dynamic models in biology.

Science.gov (United States)

Villaverde, Alejandro F; Bongard, Sophia; Mauch, Klaus; Müller, Dirk; Balsa-Canto, Eva; Schmid, Joachim; Banga, Julio R

2015-04-01

Mathematical models that predict the complex dynamic behaviour of cellular networks are fundamental in systems biology, and provide an important basis for biomedical and biotechnological applications. However, obtaining reliable predictions from large-scale dynamic models is commonly a challenging task due to lack of identifiability. The present work addresses this challenge by presenting a methodology for obtaining high-confidence predictions from dynamic models using time-series data. First, to preserve the complex behaviour of the network while reducing the number of estimated parameters, model parameters are combined in sets of meta-parameters, which are obtained from correlations between biochemical reaction rates and between concentrations of the chemical species. Next, an ensemble of models with different parameterizations is constructed and calibrated. Finally, the ensemble is used for assessing the reliability of model predictions by defining a measure of convergence of model outputs (consensus) that is used as an indicator of confidence. We report results of computational tests carried out on a metabolic model of Chinese Hamster Ovary (CHO) cells, which are used for recombinant protein production. Using noisy simulated data, we find that the aggregated ensemble predictions are on average more accurate than the predictions of individual ensemble models. Furthermore, ensemble predictions with high consensus are statistically more accurate than ensemble predictions with large variance. The procedure provides quantitative estimates of the confidence in model predictions and enables the analysis of sufficiently complex networks as required for practical applications. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Multi-model analysis in hydrological prediction

Science.gov (United States)

Lanthier, M.; Arsenault, R.; Brissette, F.

2017-12-01

Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been

Nonparametric Tree-Based Predictive Modeling of Storm Outages on an Electric Distribution Network.

Science.gov (United States)

He, Jichao; Wanik, David W; Hartman, Brian M; Anagnostou, Emmanouil N; Astitha, Marina; Frediani, Maria E B

2017-03-01

This article compares two nonparametric tree-based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high-resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2-km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree-leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources. © 2016 Society for Risk Analysis.

Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

Science.gov (United States)

Daigle, Matthew John; Goebel, Kai Frank

2010-01-01

Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

Towards accurate performance prediction of a vertical axis wind turbine operating at different tip speed ratios

NARCIS (Netherlands)

Rezaeiha, A.; Kalkman, I.; Blocken, B.J.E.

2017-01-01

Accurate prediction of the performance of a vertical-axis wind turbine (VAWT) using CFD simulation requires the employment of a sufficiently fine azimuthal increment (dθ) combined with a mesh size at which essential flow characteristics can be accurately resolved. Furthermore, the domain size needs

Predicting turns in proteins with a unified model.

Directory of Open Access Journals (Sweden)

Qi Song

Full Text Available MOTIVATION: Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. RESULTS: In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i using newly exploited features of structural evolution information (secondary structure and shape string of protein based on structure homologies, (ii considering all types of turns in a unified model, and (iii practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.

Towards Relaxing the Spherical Solar Radiation Pressure Model for Accurate Orbit Predictions

Science.gov (United States)

Lachut, M.; Bennett, J.

2016-09-01

The well-known cannonball model has been used ubiquitously to capture the effects of atmospheric drag and solar radiation pressure on satellites and/or space debris for decades. While it lends itself naturally to spherical objects, its validity in the case of non-spherical objects has been debated heavily for years throughout the space situational awareness community. One of the leading motivations to improve orbit predictions by relaxing the spherical assumption, is the ongoing demand for more robust and reliable conjunction assessments. In this study, we explore the orbit propagation of a flat plate in a near-GEO orbit under the influence of solar radiation pressure, using a Lambertian BRDF model. Consequently, this approach will account for the spin rate and orientation of the object, which is typically determined in practice using a light curve analysis. Here, simulations will be performed which systematically reduces the spin rate to demonstrate the point at which the spherical model no longer describes the orbital elements of the spinning plate. Further understanding of this threshold would provide insight into when a higher fidelity model should be used, thus resulting in improved orbit propagations. Therefore, the work presented here is of particular interest to organizations and researchers that maintain their own catalog, and/or perform conjunction analyses.

Genomic prediction in a nuclear population of layers using single-step models.

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Yan, Yiyuan; Wu, Guiqin; Liu, Aiqiao; Sun, Congjiao; Han, Wenpeng; Li, Guangqi; Yang, Ning

2018-02-01

Single-step genomic prediction method has been proposed to improve the accuracy of genomic prediction by incorporating information of both genotyped and ungenotyped animals. The objective of this study is to compare the prediction performance of single-step model with a 2-step models and the pedigree-based models in a nuclear population of layers. A total of 1,344 chickens across 4 generations were genotyped by a 600 K SNP chip. Four traits were analyzed, i.e., body weight at 28 wk (BW28), egg weight at 28 wk (EW28), laying rate at 38 wk (LR38), and Haugh unit at 36 wk (HU36). In predicting offsprings, individuals from generation 1 to 3 were used as training data and females from generation 4 were used as validation set. The accuracies of predicted breeding values by pedigree BLUP (PBLUP), genomic BLUP (GBLUP), SSGBLUP and single-step blending (SSBlending) were compared for both genotyped and ungenotyped individuals. For genotyped females, GBLUP performed no better than PBLUP because of the small size of training data, while the 2 single-step models predicted more accurately than the PBLUP model. The average predictive ability of SSGBLUP and SSBlending were 16.0% and 10.8% higher than the PBLUP model across traits, respectively. Furthermore, the predictive abilities for ungenotyped individuals were also enhanced. The average improvements of prediction abilities were 5.9% and 1.5% for SSGBLUP and SSBlending model, respectively. It was concluded that single-step models, especially the SSGBLUP model, can yield more accurate prediction of genetic merits and are preferable for practical implementation of genomic selection in layers. © 2017 Poultry Science Association Inc.

Risk terrain modeling predicts child maltreatment.

Science.gov (United States)

Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

2016-12-01

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Accurate prediction of the enthalpies of formation for xanthophylls.

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Lii, Jenn-Huei; Liao, Fu-Xing; Hu, Ching-Han

2011-11-30

This study investigates the applications of computational approaches in the prediction of enthalpies of formation (ΔH(f)) for C-, H-, and O-containing compounds. Molecular mechanics (MM4) molecular mechanics method, density functional theory (DFT) combined with the atomic equivalent (AE) and group equivalent (GE) schemes, and DFT-based correlation corrected atomization (CCAZ) were used. We emphasized on the application to xanthophylls, C-, H-, and O-containing carotenoids which consist of ∼ 100 atoms and extended π-delocaization systems. Within the training set, MM4 predictions are more accurate than those obtained using AE and GE; however a systematic underestimation was observed in the extended systems. ΔH(f) for the training set molecules predicted by CCAZ combined with DFT are in very good agreement with the G3 results. The average absolute deviations (AADs) of CCAZ combined with B3LYP and MPWB1K are 0.38 and 0.53 kcal/mol compared with the G3 data, and are 0.74 and 0.69 kcal/mol compared with the available experimental data, respectively. Consistency of the CCAZ approach for the selected xanthophylls is revealed by the AAD of 2.68 kcal/mol between B3LYP-CCAZ and MPWB1K-CCAZ. Copyright © 2011 Wiley Periodicals, Inc.

Comparison of pause predictions of two sequence-dependent transcription models

International Nuclear Information System (INIS)

Bai, Lu; Wang, Michelle D

2010-01-01

Two recent theoretical models, Bai et al (2004, 2007) and Tadigotla et al (2006), formulated thermodynamic explanations of sequence-dependent transcription pausing by RNA polymerase (RNAP). The two models differ in some basic assumptions and therefore make different yet overlapping predictions for pause locations, and different predictions on pause kinetics and mechanisms. Here we present a comprehensive comparison of the two models. We show that while they have comparable predictive power of pause locations at low NTP concentrations, the Bai et al model is more accurate than Tadigotla et al at higher NTP concentrations. The pausing kinetics predicted by Bai et al is also consistent with time-course transcription reactions, while Tadigotla et al is unsuited for this type of kinetic prediction. More importantly, the two models in general predict different pausing mechanisms even for the same pausing sites, and the Bai et al model provides an explanation more consistent with recent single molecule observations

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

Directory of Open Access Journals (Sweden)

Yong-Bi Fu

2017-07-01

Full Text Available Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

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Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

2017-01-01

Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria

Directory of Open Access Journals (Sweden)

Kimmel Marek

2011-05-01

Full Text Available Abstract Background Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome. Methods Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data. Results Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET. Conclusions The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.

Predicting acid dew point with a semi-empirical model

International Nuclear Information System (INIS)

Xiang, Baixiang; Tang, Bin; Wu, Yuxin; Yang, Hairui; Zhang, Man; Lu, Junfu

2016-01-01

Highlights: • The previous semi-empirical models are systematically studied. • An improved thermodynamic correlation is derived. • A semi-empirical prediction model is proposed. • The proposed semi-empirical model is validated. - Abstract: Decreasing the temperature of exhaust flue gas in boilers is one of the most effective ways to further improve the thermal efficiency, electrostatic precipitator efficiency and to decrease the water consumption of desulfurization tower, while, when this temperature is below the acid dew point, the fouling and corrosion will occur on the heating surfaces in the second pass of boilers. So, the knowledge on accurately predicting the acid dew point is essential. By investigating the previous models on acid dew point prediction, an improved thermodynamic correlation formula between the acid dew point and its influencing factors is derived first. And then, a semi-empirical prediction model is proposed, which is validated with the data both in field test and experiment, and comparing with the previous models.

Predicting nucleic acid binding interfaces from structural models of proteins.

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

2012-02-01

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.

Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry

DEFF Research Database (Denmark)

Mollerup, Christian Brinch; Mardal, Marie; Dalsgaard, Petur Weihe

2018-01-01

artificial neural networks (ANNs). Prediction was based on molecular descriptors, 827 RTs, and 357 CCS values from pharmaceuticals, drugs of abuse, and their metabolites. ANN models for the prediction of RT or CCS separately were examined, and the potential to predict both from a single model......Exact mass, retention time (RT), and collision cross section (CCS) are used as identification parameters in liquid chromatography coupled to ion mobility high resolution accurate mass spectrometry (LC-IM-HRMS). Targeted screening analyses are now more flexible and can be expanded for suspect...

Sweat loss prediction using a multi-model approach.

Science.gov (United States)

Xu, Xiaojiang; Santee, William R

2011-07-01

A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15-40°C, RH 25-75%), and exercise intensities (250-600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30-39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.

Statistical analysis of accurate prediction of local atmospheric optical attenuation with a new model according to weather together with beam wandering compensation system: a season-wise experimental investigation

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Arockia Bazil Raj, A.; Padmavathi, S.

2016-07-01

Atmospheric parameters strongly affect the performance of Free Space Optical Communication (FSOC) system when the optical wave is propagating through the inhomogeneous turbulent medium. Developing a model to get an accurate prediction of optical attenuation according to meteorological parameters becomes significant to understand the behaviour of FSOC channel during different seasons. A dedicated free space optical link experimental set-up is developed for the range of 0.5 km at an altitude of 15.25 m. The diurnal profile of received power and corresponding meteorological parameters are continuously measured using the developed optoelectronic assembly and weather station, respectively, and stored in a data logging computer. Measured meteorological parameters (as input factors) and optical attenuation (as response factor) of size [177147 × 4] are used for linear regression analysis and to design the mathematical model that is more suitable to predict the atmospheric optical attenuation at our test field. A model that exhibits the R2 value of 98.76% and average percentage deviation of 1.59% is considered for practical implementation. The prediction accuracy of the proposed model is investigated along with the comparative results obtained from some of the existing models in terms of Root Mean Square Error (RMSE) during different local seasons in one-year period. The average RMSE value of 0.043-dB/km is obtained in the longer range dynamic of meteorological parameters variations.

Size matters. The width and location of a ureteral stone accurately predict the chance of spontaneous passage

Energy Technology Data Exchange (ETDEWEB)

Jendeberg, Johan; Geijer, Haakan; Alshamari, Muhammed; Liden, Mats [Oerebro University Hospital, Department of Radiology, Faculty of Medicine and Health, Oerebro (Sweden); Cierzniak, Bartosz [Oerebro University, Department of Surgery, Faculty of Medicine and Health, Oerebro (Sweden)

2017-11-15

To determine how to most accurately predict the chance of spontaneous passage of a ureteral stone using information in the diagnostic non-enhanced computed tomography (NECT) and to create predictive models with smaller stone size intervals than previously possible. Retrospectively 392 consecutive patients with ureteric stone on NECT were included. Three radiologists independently measured the stone size. Stone location, side, hydronephrosis, CRP, medical expulsion therapy (MET) and all follow-up radiology until stone expulsion or 26 weeks were recorded. Logistic regressions were performed with spontaneous stone passage in 4 weeks and 20 weeks as the dependent variable. The spontaneous passage rate in 20 weeks was 312 out of 392 stones, 98% in 0-2 mm, 98% in 3 mm, 81% in 4 mm, 65% in 5 mm, 33% in 6 mm and 9% in ≥6.5 mm wide stones. The stone size and location predicted spontaneous ureteric stone passage. The side and the grade of hydronephrosis only predicted stone passage in specific subgroups. Spontaneous passage of a ureteral stone can be predicted with high accuracy with the information available in the NECT. We present a prediction method based on stone size and location. (orig.)

Modeling a multivariable reactor and on-line model predictive control.

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Yu, D W; Yu, D L

2005-10-01

A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

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Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

Disturbance observer based model predictive control for accurate atmospheric entry of spacecraft

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Wu, Chao; Yang, Jun; Li, Shihua; Li, Qi; Guo, Lei

2018-05-01

Facing the complex aerodynamic environment of Mars atmosphere, a composite atmospheric entry trajectory tracking strategy is investigated in this paper. External disturbances, initial states uncertainties and aerodynamic parameters uncertainties are the main problems. The composite strategy is designed to solve these problems and improve the accuracy of Mars atmospheric entry. This strategy includes a model predictive control for optimized trajectory tracking performance, as well as a disturbance observer based feedforward compensation for external disturbances and uncertainties attenuation. 500-run Monte Carlo simulations show that the proposed composite control scheme achieves more precise Mars atmospheric entry (3.8 km parachute deployment point distribution error) than the baseline control scheme (8.4 km) and integral control scheme (5.8 km).

Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

Science.gov (United States)

Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

2011-01-01

A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

Science.gov (United States)

Notas, George; Bariotakis, Michail; Kalogrias, Vaios; Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

2015-01-01

Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

Can crop-climate models be accurate and precise? A case study for wheat production in Denmark

DEFF Research Database (Denmark)

Montesino San Martin, Manuel; Olesen, Jørgen E.; Porter, John Roy

2015-01-01

Crop models, used to make projections of climate change impacts, differ greatly in structural detail. Complexity of model structure has generic effects on uncertainty and error propagation in climate change impact assessments. We applied Bayesian calibration to three distinctly different empirical....... Yields predicted by the mechanistic model were generally more accurate than the empirical models for extrapolated conditions. This trend does not hold for all extrapolations; mechanistic and empirical models responded differently due to their sensitivities to distinct weather features. However, higher...... suitable for generic model ensembles for near-term agricultural impact assessments of climate change....

Considerations of the Use of 3-D Geophysical Models to Predict Test Ban Monitoring Observables

Science.gov (United States)

2007-09-01

predict first P arrival times. Since this is a 3-D model, the travel times are predicted with a 3-D finite-difference code solving the eikonal equations...for the eikonal wave equation should provide more accurate predictions of travel-time from 3D models. These techniques and others are being

A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

Science.gov (United States)

Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

2018-06-01

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

Science.gov (United States)

Xie, Weihong; Yu, Yang

2017-01-01

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly. PMID:29124062

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

Directory of Open Access Journals (Sweden)

Fan Liang

2017-01-01

Full Text Available Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech.

Science.gov (United States)

Whitwell, Jennifer L; Weigand, Stephen D; Duffy, Joseph R; Strand, Edythe A; Machulda, Mary M; Senjem, Matthew L; Gunter, Jeffrey L; Lowe, Val J; Jack, Clifford R; Josephs, Keith A

2016-01-01

Beta-amyloid (Aβ) deposition can be observed in primary progressive aphasia (PPA) and progressive apraxia of speech (PAOS). While it is typically associated with logopenic PPA, there are exceptions that make predicting Aβ status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict Aβ deposition. One hundred and thirty-nine PPA (n = 97; 15 agrammatic, 53 logopenic, 13 semantic and 16 unclassified) and PAOS (n = 42) subjects were prospectively recruited into a cross-sectional study and underwent speech/language assessments, 3.0 T MRI and C11-Pittsburgh Compound B PET. The presence of Aβ was determined using a 1.5 SUVR cut-point. Atlas-based parcellation was used to calculate gray matter volumes of 42 regions-of-interest across the brain. Penalized binary logistic regression was utilized to determine what combination of MRI regions, and what combination of speech and language tests, best predicts Aβ (+) status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to Aβ status. MRI accurately classified 81% of subjects using 14 regions. Small left superior temporal and inferior parietal volumes and large left Broca's area volumes were particularly predictive of Aβ (+) status. Clinical scores accurately classified 83% of subjects using 12 tests. Phonological errors and repetition deficits, and absence of agrammatism and motor speech deficits were particularly predictive of Aβ (+) status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting Aβ deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of Aβ status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting Aβ status in unclassified PPA subjects.

Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech

Directory of Open Access Journals (Sweden)

Jennifer L. Whitwell

2016-01-01

Full Text Available Beta-amyloid (Aβ deposition can be observed in primary progressive aphasia (PPA and progressive apraxia of speech (PAOS. While it is typically associated with logopenic PPA, there are exceptions that make predicting Aβ status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict Aβ deposition. One hundred and thirty-nine PPA (n = 97; 15 agrammatic, 53 logopenic, 13 semantic and 16 unclassified and PAOS (n = 42 subjects were prospectively recruited into a cross-sectional study and underwent speech/language assessments, 3.0 T MRI and C11-Pittsburgh Compound B PET. The presence of Aβ was determined using a 1.5 SUVR cut-point. Atlas-based parcellation was used to calculate gray matter volumes of 42 regions-of-interest across the brain. Penalized binary logistic regression was utilized to determine what combination of MRI regions, and what combination of speech and language tests, best predicts Aβ (+ status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to Aβ status. MRI accurately classified 81% of subjects using 14 regions. Small left superior temporal and inferior parietal volumes and large left Broca's area volumes were particularly predictive of Aβ (+ status. Clinical scores accurately classified 83% of subjects using 12 tests. Phonological errors and repetition deficits, and absence of agrammatism and motor speech deficits were particularly predictive of Aβ (+ status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting Aβ deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of Aβ status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting Aβ status in unclassified

Biodynamic modelling and the prediction of accumulated trace metal concentrations in the polychaete Arenicola marina

International Nuclear Information System (INIS)

Casado-Martinez, M. Carmen; Smith, Brian D.; DelValls, T. Angel; Luoma, Samuel N.; Rainbow, Philip S.

2009-01-01

The use of biodynamic models to understand metal uptake directly from sediments by deposit-feeding organisms still represents a special challenge. In this study, accumulated concentrations of Cd, Zn and Ag predicted by biodynamic modelling in the lugworm Arenicola marina have been compared to measured concentrations in field populations in several UK estuaries. The biodynamic model predicted accumulated field Cd concentrations remarkably accurately, and predicted bioaccumulated Ag concentrations were in the range of those measured in lugworms collected from the field. For Zn the model showed less but still good comparability, accurately predicting Zn bioaccumulation in A. marina at high sediment concentrations but underestimating accumulated Zn in the worms from sites with low and intermediate levels of Zn sediment contamination. Therefore, it appears that the physiological parameters experimentally derived for A. marina are applicable to the conditions encountered in these environments and that the assumptions made in the model are plausible. - Biodynamic modelling predicts accumulated field concentrations of Ag, Cd and Zn in the deposit-feeding polychaete Arenicola marina.

What Is the Predictive Value of Animal Models for Vaccine Efficacy in Humans? Consideration of Strategies to Improve the Value of Animal Models.

Science.gov (United States)

Herati, Ramin Sedaghat; Wherry, E John

2018-04-02

Animal models are an essential feature of the vaccine design toolkit. Although animal models have been invaluable in delineating the mechanisms of immune function, their precision in predicting how well specific vaccines work in humans is often suboptimal. There are, of course, many obvious species differences that may limit animal models from predicting all details of how a vaccine works in humans. However, careful consideration of which animal models may have limitations should also allow more accurate interpretations of animal model data and more accurate predictions of what is to be expected in clinical trials. In this article, we examine some of the considerations that might be relevant to cross-species extrapolation of vaccine-related immune responses for the prediction of how vaccines will perform in humans. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.

Optimal Cluster Mill Pass Scheduling With an Accurate and Rapid New Strip Crown Model

International Nuclear Information System (INIS)

Malik, Arif S.; Grandhi, Ramana V.; Zipf, Mark E.

2007-01-01

Besides the requirement to roll coiled sheet at high levels of productivity, the optimal pass scheduling of cluster-type reversing cold mills presents the added challenge of assigning mill parameters that facilitate the best possible strip flatness. The pressures of intense global competition, and the requirements for increasingly thinner, higher quality specialty sheet products that are more difficult to roll, continue to force metal producers to commission innovative flatness-control technologies. This means that during the on-line computerized set-up of rolling mills, the mathematical model should not only determine the minimum total number of passes and maximum rolling speed, it should simultaneously optimize the pass-schedule so that desired flatness is assured, either by manual or automated means. In many cases today, however, on-line prediction of strip crown and corresponding flatness for the complex cluster-type rolling mills is typically addressed either by trial and error, by approximate deflection models for equivalent vertical roll-stacks, or by non-physical pattern recognition style models. The abundance of the aforementioned methods is largely due to the complexity of cluster-type mill configurations and the lack of deflection models with sufficient accuracy and speed for on-line use. Without adequate assignment of the pass-schedule set-up parameters, it may be difficult or impossible to achieve the required strip flatness. In this paper, we demonstrate optimization of cluster mill pass-schedules using a new accurate and rapid strip crown model. This pass-schedule optimization includes computations of the predicted strip thickness profile to validate mathematical constraints. In contrast to many of the existing methods for on-line prediction of strip crown and flatness on cluster mills, the demonstrated method requires minimal prior tuning and no extensive training with collected mill data. To rapidly and accurately solve the multi-contact problem

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

Directory of Open Access Journals (Sweden)

Saerom Park

Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

In silico modeling predicts drug sensitivity of patient-derived cancer cells.

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Pingle, Sandeep C; Sultana, Zeba; Pastorino, Sandra; Jiang, Pengfei; Mukthavaram, Rajesh; Chao, Ying; Bharati, Ila Sri; Nomura, Natsuko; Makale, Milan; Abbasi, Taher; Kapoor, Shweta; Kumar, Ansu; Usmani, Shahabuddin; Agrawal, Ashish; Vali, Shireen; Kesari, Santosh

2014-05-21

Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.

Chemical structure-based predictive model for methanogenic anaerobic biodegradation potential.

Science.gov (United States)

Meylan, William; Boethling, Robert; Aronson, Dallas; Howard, Philip; Tunkel, Jay

2007-09-01

Many screening-level models exist for predicting aerobic biodegradation potential from chemical structure, but anaerobic biodegradation generally has been ignored by modelers. We used a fragment contribution approach to develop a model for predicting biodegradation potential under methanogenic anaerobic conditions. The new model has 37 fragments (substructures) and classifies a substance as either fast or slow, relative to the potential to be biodegraded in the "serum bottle" anaerobic biodegradation screening test (Organization for Economic Cooperation and Development Guideline 311). The model correctly classified 90, 77, and 91% of the chemicals in the training set (n = 169) and two independent validation sets (n = 35 and 23), respectively. Accuracy of predictions of fast and slow degradation was equal for training-set chemicals, but fast-degradation predictions were less accurate than slow-degradation predictions for the validation sets. Analysis of the signs of the fragment coefficients for this and the other (aerobic) Biowin models suggests that in the context of simple group contribution models, the majority of positive and negative structural influences on ultimate degradation are the same for aerobic and methanogenic anaerobic biodegradation.

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

Science.gov (United States)

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction

Science.gov (United States)

Afan, Haitham Abdulmohsin; El-shafie, Ahmed; Mohtar, Wan Hanna Melini Wan; Yaseen, Zaher Mundher

2016-10-01

An accurate model for sediment prediction is a priority for all hydrological researchers. Many conventional methods have shown an inability to achieve an accurate prediction of suspended sediment. These methods are unable to understand the behaviour of sediment transport in rivers due to the complexity, noise, non-stationarity, and dynamism of the sediment pattern. In the past two decades, Artificial Intelligence (AI) and computational approaches have become a remarkable tool for developing an accurate model. These approaches are considered a powerful tool for solving any non-linear model, as they can deal easily with a large number of data and sophisticated models. This paper is a review of all AI approaches that have been applied in sediment modelling. The current research focuses on the development of AI application in sediment transport. In addition, the review identifies major challenges and opportunities for prospective research. Throughout the literature, complementary models superior to classical modelling.

An evolution of trauma care evaluation: A thesis on trauma registry and outcome prediction models

NARCIS (Netherlands)

Joosse, P.

2013-01-01

Outcome prediction models play an invaluable role in the evaluation and improvement of modern trauma care. Trauma registries underlying these outcome prediction models need to be accurate, complete and consistent. This thesis focused on the opportunities and limitations of trauma registries and

Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria

Directory of Open Access Journals (Sweden)

Sato Yoshiharu

2011-11-01

Full Text Available Abstract Background Many pathogens use a type III secretion system to translocate virulence proteins (called effectors in order to adapt to the host environment. To date, many prediction tools for effector identification have been developed. However, these tools are insufficiently accurate for producing a list of putative effectors that can be applied directly for labor-intensive experimental verification. This also suggests that important features of effectors have yet to be fully characterized. Results In this study, we have constructed an accurate approach to predicting secreted virulence effectors from Gram-negative bacteria. This consists of a support vector machine-based discriminant analysis followed by a simple criteria-based filtering. The accuracy was assessed by estimating the average number of true positives in the top-20 ranking in the genome-wide screening. In the validation, 10 sets of 20 training and 20 testing examples were randomly selected from 40 known effectors of Salmonella enterica serovar Typhimurium LT2. On average, the SVM portion of our system predicted 9.7 true positives from 20 testing examples in the top-20 of the prediction. Removal of the N-terminal instability, codon adaptation index and ProtParam indices decreased the score to 7.6, 8.9 and 7.9, respectively. These discrimination features suggested that the following characteristics of effectors had been uncovered: unstable N-terminus, non-optimal codon usage, hydrophilic, and less aliphathic. The secondary filtering process represented by coexpression analysis and domain distribution analysis further refined the average true positive counts to 12.3. We further confirmed that our system can correctly predict known effectors of P. syringae DC3000, strongly indicating its feasibility. Conclusions We have successfully developed an accurate prediction system for screening effectors on a genome-wide scale. We confirmed the accuracy of our system by external validation

Spectral Neugebauer-based color halftone prediction model accounting for paper fluorescence.

Science.gov (United States)

Hersch, Roger David

2014-08-20

We present a spectral model for predicting the fluorescent emission and the total reflectance of color halftones printed on optically brightened paper. By relying on extended Neugebauer models, the proposed model accounts for the attenuation by the ink halftones of both the incident exciting light in the UV wavelength range and the emerging fluorescent emission in the visible wavelength range. The total reflectance is predicted by adding the predicted fluorescent emission relative to the incident light and the pure reflectance predicted with an ink-spreading enhanced Yule-Nielsen modified Neugebauer reflectance prediction model. The predicted fluorescent emission spectrum as a function of the amounts of cyan, magenta, and yellow inks is very accurate. It can be useful to paper and ink manufacturers who would like to study in detail the contribution of the fluorescent brighteners and the attenuation of the fluorescent emission by ink halftones.

A multivariate model for predicting segmental body composition.

Science.gov (United States)

Tian, Simiao; Mioche, Laurence; Denis, Jean-Baptiste; Morio, Béatrice

2013-12-01

The aims of the present study were to propose a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured variables and to compare its predictive capacity with that of the available univariate models that predict body fat percentage (BF%). The dual-energy X-ray absorptiometry (DXA) dataset (52% men and 48% women) with White, Black and Hispanic ethnicities (1999-2004, National Health and Nutrition Examination Survey) was randomly divided into three sub-datasets: a training dataset (TRD), a test dataset (TED); a validation dataset (VAD), comprising 3835, 1917 and 1917 subjects. For each sex, several multivariate prediction models were fitted from the TRD using age, weight, height and possibly waist circumference. The most accurate model was selected from the TED and then applied to the VAD and a French DXA dataset (French DB) (526 men and 529 women) to assess the prediction accuracy in comparison with that of five published univariate models, for which adjusted formulas were re-estimated using the TRD. Waist circumference was found to improve the prediction accuracy, especially in men. For BF%, the standard error of prediction (SEP) values were 3.26 (3.75) % for men and 3.47 (3.95)% for women in the VAD (French DB), as good as those of the adjusted univariate models. Moreover, the SEP values for the prediction of body and appendicular lean masses ranged from 1.39 to 2.75 kg for both the sexes. The prediction accuracy was best for age < 65 years, BMI < 30 kg/m2 and the Hispanic ethnicity. The application of our multivariate model to large populations could be useful to address various public health issues.

An Accurate and Dynamic Computer Graphics Muscle Model

Science.gov (United States)

Levine, David Asher

1997-01-01

A computer based musculo-skeletal model was developed at the University in the departments of Mechanical and Biomedical Engineering. This model accurately represents human shoulder kinematics. The result of this model is the graphical display of bones moving through an appropriate range of motion based on inputs of EMGs and external forces. The need existed to incorporate a geometric muscle model in the larger musculo-skeletal model. Previous muscle models did not accurately represent muscle geometries, nor did they account for the kinematics of tendons. This thesis covers the creation of a new muscle model for use in the above musculo-skeletal model. This muscle model was based on anatomical data from the Visible Human Project (VHP) cadaver study. Two-dimensional digital images from the VHP were analyzed and reconstructed to recreate the three-dimensional muscle geometries. The recreated geometries were smoothed, reduced, and sliced to form data files defining the surfaces of each muscle. The muscle modeling function opened these files during run-time and recreated the muscle surface. The modeling function applied constant volume limitations to the muscle and constant geometry limitations to the tendons.

Validation and Refinement of Prediction Models to Estimate Exercise Capacity in Cancer Survivors Using the Steep Ramp Test.

Science.gov (United States)

Stuiver, Martijn M; Kampshoff, Caroline S; Persoon, Saskia; Groen, Wim; van Mechelen, Willem; Chinapaw, Mai J M; Brug, Johannes; Nollet, Frans; Kersten, Marie-José; Schep, Goof; Buffart, Laurien M

2017-11-01

To further test the validity and clinical usefulness of the steep ramp test (SRT) in estimating exercise tolerance in cancer survivors by external validation and extension of previously published prediction models for peak oxygen consumption (Vo 2peak ) and peak power output (W peak ). Cross-sectional study. Multicenter. Cancer survivors (N=283) in 2 randomized controlled exercise trials. Not applicable. Prediction model accuracy was assessed by intraclass correlation coefficients (ICCs) and limits of agreement (LOA). Multiple linear regression was used for model extension. Clinical performance was judged by the percentage of accurate endurance exercise prescriptions. ICCs of SRT-predicted Vo 2peak and W peak with these values as obtained by the cardiopulmonary exercise test were .61 and .73, respectively, using the previously published prediction models. 95% LOA were ±705mL/min with a bias of 190mL/min for Vo 2peak and ±59W with a bias of 5W for W peak . Modest improvements were obtained by adding body weight and sex to the regression equation for the prediction of Vo 2peak (ICC, .73; 95% LOA, ±608mL/min) and by adding age, height, and sex for the prediction of W peak (ICC, .81; 95% LOA, ±48W). Accuracy of endurance exercise prescription improved from 57% accurate prescriptions to 68% accurate prescriptions with the new prediction model for W peak . Predictions of Vo 2peak and W peak based on the SRT are adequate at the group level, but insufficiently accurate in individual patients. The multivariable prediction model for W peak can be used cautiously (eg, supplemented with a Borg score) to aid endurance exercise prescription. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

SHMF: Interest Prediction Model with Social Hub Matrix Factorization

Directory of Open Access Journals (Sweden)

Chaoyuan Cui

2017-01-01

Full Text Available With the development of social networks, microblog has become the major social communication tool. There is a lot of valuable information such as personal preference, public opinion, and marketing in microblog. Consequently, research on user interest prediction in microblog has a positive practical significance. In fact, how to extract information associated with user interest orientation from the constantly updated blog posts is not so easy. Existing prediction approaches based on probabilistic factor analysis use blog posts published by user to predict user interest. However, these methods are not very effective for the users who post less but browse more. In this paper, we propose a new prediction model, which is called SHMF, using social hub matrix factorization. SHMF constructs the interest prediction model by combining the information of blogs posts published by both user and direct neighbors in user’s social hub. Our proposed model predicts user interest by integrating user’s historical behavior and temporal factor as well as user’s friendships, thus achieving accurate forecasts of user’s future interests. The experimental results on Sina Weibo show the efficiency and effectiveness of our proposed model.

New tips for structure prediction by comparative modeling

Science.gov (United States)

Rayan, Anwar

2009-01-01

Comparative modelling is utilized to predict the 3-dimensional conformation of a given protein (target) based on its sequence alignment to experimentally determined protein structure (template). The use of such technique is already rewarding and increasingly widespread in biological research and drug development. The accuracy of the predictions as commonly accepted depends on the score of sequence identity of the target protein to the template. To assess the relationship between sequence identity and model quality, we carried out an analysis of a set of 4753 sequence and structure alignments. Throughout this research, the model accuracy was measured by root mean square deviations of Cα atoms of the target-template structures. Surprisingly, the results show that sequence identity of the target protein to the template is not a good descriptor to predict the accuracy of the 3-D structure model. However, in a large number of cases, comparative modelling with lower sequence identity of target to template proteins led to more accurate 3-D structure model. As a consequence of this study, we suggest new tips for improving the quality of omparative models, particularly for models whose target-template sequence identity is below 50%. PMID:19255646

Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

Science.gov (United States)

Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

2015-01-01

Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

Predictive modelling using neuroimaging data in the presence of confounds.

Science.gov (United States)

Rao, Anil; Monteiro, Joao M; Mourao-Miranda, Janaina

2017-04-15

When training predictive models from neuroimaging data, we typically have available non-imaging variables such as age and gender that affect the imaging data but which we may be uninterested in from a clinical perspective. Such variables are commonly referred to as 'confounds'. In this work, we firstly give a working definition for confound in the context of training predictive models from samples of neuroimaging data. We define a confound as a variable which affects the imaging data and has an association with the target variable in the sample that differs from that in the population-of-interest, i.e., the population over which we intend to apply the estimated predictive model. The focus of this paper is the scenario in which the confound and target variable are independent in the population-of-interest, but the training sample is biased due to a sample association between the target and confound. We then discuss standard approaches for dealing with confounds in predictive modelling such as image adjustment and including the confound as a predictor, before deriving and motivating an Instance Weighting scheme that attempts to account for confounds by focusing model training so that it is optimal for the population-of-interest. We evaluate the standard approaches and Instance Weighting in two regression problems with neuroimaging data in which we train models in the presence of confounding, and predict samples that are representative of the population-of-interest. For comparison, these models are also evaluated when there is no confounding present. In the first experiment we predict the MMSE score using structural MRI from the ADNI database with gender as the confound, while in the second we predict age using structural MRI from the IXI database with acquisition site as the confound. Considered over both datasets we find that none of the methods for dealing with confounding gives more accurate predictions than a baseline model which ignores confounding, although

Prediction of lithium-ion battery capacity with metabolic grey model

International Nuclear Information System (INIS)

Chen, Lin; Lin, Weilong; Li, Junzi; Tian, Binbin; Pan, Haihong

2016-01-01

Given the popularity of Lithium-ion batteries in EVs (electric vehicles), predicting the capacity quickly and accurately throughout a battery's full life-time is still a challenging issue for ensuring the reliability of EVs. This paper proposes an approach in predicting the varied capacity with discharge cycles based on metabolic grey theory and consider issues from two perspectives: 1) three metabolic grey models will be presented, including MGM (metabolic grey model), MREGM (metabolic Residual-error grey model), and MMREGM (metabolic Markov-residual-error grey model); 2) the universality of these models will be explored under different conditions (such as various discharge rates and temperatures). Furthermore, the research findings in this paper demonstrate the excellent performance of the prediction depending on the three models; however, the precision of the MREGM model is inferior compared to the others. Therefore, we have obtained the conclusion in which the MGM model and the MMREGM model have excellent performances in predicting the capacity under a variety of load conditions, even using few data points for modeling. Also, the universality of the metabolic grey prediction theory is verified by predicting the capacity of batteries under different discharge rates and different temperatures. - Highlights: • The metabolic mechanism is introduced in a grey system for capacity prediction. • Three metabolic grey models are presented and studied. • The universality of these models under different conditions is assessed. • A few data points are required for predicting the capacity with these models.

DATA MINING METHODOLOGY FOR DETERMINING THE OPTIMAL MODEL OF COST PREDICTION IN SHIP INTERIM PRODUCT ASSEMBLY

Directory of Open Access Journals (Sweden)

Damir Kolich

2016-03-01

Full Text Available In order to accurately predict costs of the thousands of interim products that are assembled in shipyards, it is necessary to use skilled engineers to develop detailed Gantt charts for each interim product separately which takes many hours. It is helpful to develop a prediction tool to estimate the cost of interim products accurately and quickly without the need for skilled engineers. This will drive down shipyard costs and improve competitiveness. Data mining is used extensively for developing prediction models in other industries. Since ships consist of thousands of interim products, it is logical to develop a data mining methodology for a shipyard or any other manufacturing industry where interim products are produced. The methodology involves analysis of existing interim products and data collection. Pre-processing and principal component analysis is done to make the data “user-friendly” for later prediction processing and the development of both accurate and robust models. The support vector machine is demonstrated as the better model when there are a lower number of tuples. However as the number of tuples is increased to over 10000, then the artificial neural network model is recommended.

Predicting artificailly drained areas by means of selective model ensemble

DEFF Research Database (Denmark)

Møller, Anders Bjørn; Beucher, Amélie; Iversen, Bo Vangsø

. The approaches employed include decision trees, discriminant analysis, regression models, neural networks and support vector machines amongst others. Several models are trained with each method, using variously the original soil covariates and principal components of the covariates. With a large ensemble...... out since the mid-19th century, and it has been estimated that half of the cultivated area is artificially drained (Olesen, 2009). A number of machine learning approaches can be used to predict artificially drained areas in geographic space. However, instead of choosing the most accurate model....... The study aims firstly to train a large number of models to predict the extent of artificially drained areas using various machine learning approaches. Secondly, the study will develop a method for selecting the models, which give a good prediction of artificially drained areas, when used in conjunction...

Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence

Science.gov (United States)

Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd

2018-04-01

Air overpressure (AOp) resulting from blasting can cause damage and nuisance to nearby civilians. Thus, it is important to be able to predict AOp accurately. In this study, 8 different Artificial Neural Network (ANN) were developed for the purpose of prediction of AOp. The ANN models were trained using different variants of Particle Swarm Optimization (PSO) algorithm. AOp predictions were also made using an empirical equation, as suggested by United States Bureau of Mines (USBM), to serve as a benchmark. In order to develop the models, 76 blasting operations in Hulu Langat were investigated. All the ANN models were found to outperform the USBM equation in three performance metrics; root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Using a performance ranking method, MSO-Rand-Mut was determined to be the best prediction model for AOp with a performance metric of RMSE=2.18, MAPE=1.73% and R2=0.97. The result shows that ANN models trained using PSO are capable of predicting AOp with great accuracy.

Enhancing pavement performance prediction models for the Illinois Tollway System

OpenAIRE

Laxmikanth Premkumar; William R. Vavrik

2016-01-01

Accurate pavement performance prediction represents an important role in prioritizing future maintenance and rehabilitation needs, and predicting future pavement condition in a pavement management system. The Illinois State Toll Highway Authority (Tollway) with over 2000 lane miles of pavement utilizes the condition rating survey (CRS) methodology to rate pavement performance. Pavement performance models developed in the past for the Illinois Department of Transportation (IDOT) are used by th...

A new algebraic turbulence model for accurate description of airfoil flows

Science.gov (United States)

Xiao, Meng-Juan; She, Zhen-Su

2017-11-01

We report a new algebraic turbulence model (SED-SL) based on the SED theory, a symmetry-based approach to quantifying wall turbulence. The model specifies a multi-layer profile of a stress length (SL) function in both the streamwise and wall-normal directions, which thus define the eddy viscosity in the RANS equation (e.g. a zero-equation model). After a successful simulation of flat plate flow (APS meeting, 2016), we report here further applications of the model to the flow around airfoil, with significant improvement of the prediction accuracy of the lift (CL) and drag (CD) coefficients compared to other popular models (e.g. BL, SA, etc.). Two airfoils, namely RAE2822 airfoil and NACA0012 airfoil, are computed for over 50 cases. The results are compared to experimental data from AGARD report, which shows deviations of CL bounded within 2%, and CD within 2 counts (10-4) for RAE2822 and 6 counts for NACA0012 respectively (under a systematic adjustment of the flow conditions). In all these calculations, only one parameter (proportional to the Karmen constant) shows slight variation with Mach number. The most remarkable outcome is, for the first time, the accurate prediction of the drag coefficient. The other interesting outcome is the physical interpretation of the multi-layer parameters: they specify the corresponding multi-layer structure of turbulent boundary layer; when used together with simulation data, the SED-SL enables one to extract physical information from empirical data, and to understand the variation of the turbulent boundary layer.

End-of-Discharge and End-of-Life Prediction in Lithium-Ion Batteries with Electrochemistry-Based Aging Models

Science.gov (United States)

Daigle, Matthew; Kulkarni, Chetan S.

2016-01-01

As batteries become increasingly prevalent in complex systems such as aircraft and electric cars, monitoring and predicting battery state of charge and state of health becomes critical. In order to accurately predict the remaining battery power to support system operations for informed operational decision-making, age-dependent changes in dynamics must be accounted for. Using an electrochemistry-based model, we investigate how key parameters of the battery change as aging occurs, and develop models to describe aging through these key parameters. Using these models, we demonstrate how we can (i) accurately predict end-of-discharge for aged batteries, and (ii) predict the end-of-life of a battery as a function of anticipated usage. The approach is validated through an experimental set of randomized discharge profiles.

ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

Energy Technology Data Exchange (ETDEWEB)

Visel, Axel; Blow, Matthew J.; Li, Zirong; Zhang, Tao; Akiyama, Jennifer A.; Holt, Amy; Plajzer-Frick, Ingrid; Shoukry, Malak; Wright, Crystal; Chen, Feng; Afzal, Veena; Ren, Bing; Rubin, Edward M.; Pennacchio, Len A.

2009-02-01

A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.

Influence of covariate distribution on the predictive performance of pharmacokinetic models in paediatric research

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Piana, Chiara; Danhof, Meindert; Della Pasqua, Oscar

2014-01-01

Aims The accuracy of model-based predictions often reported in paediatric research has not been thoroughly characterized. The aim of this exercise is therefore to evaluate the role of covariate distributions when a pharmacokinetic model is used for simulation purposes. Methods Plasma concentrations of a hypothetical drug were simulated in a paediatric population using a pharmacokinetic model in which body weight was correlated with clearance and volume of distribution. Two subgroups of children were then selected from the overall population according to a typical study design, in which pre-specified body weight ranges (10–15 kg and 30–40 kg) were used as inclusion criteria. The simulated data sets were then analyzed using non-linear mixed effects modelling. Model performance was assessed by comparing the accuracy of AUC predictions obtained for each subgroup, based on the model derived from the overall population and by extrapolation of the model parameters across subgroups. Results Our findings show that systemic exposure as well as pharmacokinetic parameters cannot be accurately predicted from the pharmacokinetic model obtained from a population with a different covariate range from the one explored during model building. Predictions were accurate only when a model was used for prediction in a subgroup of the initial population. Conclusions In contrast to current practice, the use of pharmacokinetic modelling in children should be limited to interpolations within the range of values observed during model building. Furthermore, the covariate point estimate must be kept in the model even when predictions refer to a subset different from the original population. PMID:24433411

On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models

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Karagiannis, Georgios; Lin, Guang

2017-08-01

For many real systems, several computer models may exist with different physics and predictive abilities. To achieve more accurate simulations/predictions, it is desirable for these models to be properly combined and calibrated. We propose the Bayesian calibration of computer model mixture method which relies on the idea of representing the real system output as a mixture of the available computer model outputs with unknown input dependent weight functions. The method builds a fully Bayesian predictive model as an emulator for the real system output by combining, weighting, and calibrating the available models in the Bayesian framework. Moreover, it fits a mixture of calibrated computer models that can be used by the domain scientist as a mean to combine the available computer models, in a flexible and principled manner, and perform reliable simulations. It can address realistic cases where one model may be more accurate than the others at different input values because the mixture weights, indicating the contribution of each model, are functions of the input. Inference on the calibration parameters can consider multiple computer models associated with different physics. The method does not require knowledge of the fidelity order of the models. We provide a technique able to mitigate the computational overhead due to the consideration of multiple computer models that is suitable to the mixture model framework. We implement the proposed method in a real-world application involving the Weather Research and Forecasting large-scale climate model.

Development of a Skin Burn Predictive Model adapted to Laser Irradiation

Science.gov (United States)

Sonneck-Museux, N.; Scheer, E.; Perez, L.; Agay, D.; Autrique, L.

2016-12-01

Laser technology is increasingly used, and it is crucial for both safety and medical reasons that the impact of laser irradiation on human skin can be accurately predicted. This study is mainly focused on laser-skin interactions and potential lesions (burns). A mathematical model dedicated to heat transfers in skin exposed to infrared laser radiations has been developed. The model is validated by studying heat transfers in human skin and simultaneously performing experimentations an animal model (pig). For all experimental tests, pig's skin surface temperature is recorded. Three laser wavelengths have been tested: 808 nm, 1940 nm and 10 600 nm. The first is a diode laser producing radiation absorbed deep within the skin. The second wavelength has a more superficial effect. For the third wavelength, skin is an opaque material. The validity of the developed models is verified by comparison with experimental results (in vivo tests) and the results of previous studies reported in the literature. The comparison shows that the models accurately predict the burn degree caused by laser radiation over a wide range of conditions. The results show that the important parameter for burn prediction is the extinction coefficient. For the 1940 nm wavelength especially, significant differences between modeling results and literature have been observed, mainly due to this coefficient's value. This new model can be used as a predictive tool in order to estimate the amount of injury induced by several types (couple power-time) of laser aggressions on the arm, the face and on the palm of the hand.

Development of Interpretable Predictive Models for BPH and Prostate Cancer.

Science.gov (United States)

Bermejo, Pablo; Vivo, Alicia; Tárraga, Pedro J; Rodríguez-Montes, J A

2015-01-01

Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. Statistical dependence with PC and BPH was found for prostate volume (P-value BPH prediction. PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced.

Simplified versus geometrically accurate models of forefoot anatomy to predict plantar pressures: A finite element study.

Science.gov (United States)

Telfer, Scott; Erdemir, Ahmet; Woodburn, James; Cavanagh, Peter R

2016-01-25

Integration of patient-specific biomechanical measurements into the design of therapeutic footwear has been shown to improve clinical outcomes in patients with diabetic foot disease. The addition of numerical simulations intended to optimise intervention design may help to build on these advances, however at present the time and labour required to generate and run personalised models of foot anatomy restrict their routine clinical utility. In this study we developed second-generation personalised simple finite element (FE) models of the forefoot with varying geometric fidelities. Plantar pressure predictions from barefoot, shod, and shod with insole simulations using simplified models were compared to those obtained from CT-based FE models incorporating more detailed representations of bone and tissue geometry. A simplified model including representations of metatarsals based on simple geometric shapes, embedded within a contoured soft tissue block with outer geometry acquired from a 3D surface scan was found to provide pressure predictions closest to the more complex model, with mean differences of 13.3kPa (SD 13.4), 12.52kPa (SD 11.9) and 9.6kPa (SD 9.3) for barefoot, shod, and insole conditions respectively. The simplified model design could be produced in 3h in the case of the more detailed model, and solved on average 24% faster. FE models of the forefoot based on simplified geometric representations of the metatarsal bones and soft tissue surface geometry from 3D surface scans may potentially provide a simulation approach with improved clinical utility, however further validity testing around a range of therapeutic footwear types is required. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dynamic Travel Time Prediction Models for Buses Using Only GPS Data

Directory of Open Access Journals (Sweden)

Wei Fan

2015-01-01

Full Text Available Providing real-time and accurate travel time information of transit vehicles can be very helpful as it assists passengers in planning their trips to minimize waiting times. The purpose of this research is to develop and compare dynamic travel time prediction models which can provide accurate prediction of bus travel time in order to give real-time information at a given downstream bus stop using only global positioning system (GPS data. Historical Average (HA, Kalman Filtering (KF and Artificial Neural Network (ANN models are considered and developed in this paper. A case has been studied by making use of the three models. Promising results are obtained from the case study, indicating that the models can be used to implement an Advanced Public Transport System. The implementation of this system could assist transit operators in improving the reliability of bus services, thus attracting more travelers to transit vehicles and helping relieve congestion. The performances of the three models were assessed and compared with each other under two criteria: overall prediction accuracy and robustness. It was shown that the ANN outperformed the other two models in both aspects. In conclusion, it is shown that bus travel time information can be reasonably provided using only arrival and departure time information at stops even in the absence of traffic-stream data.

Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis.

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Masso, Majid; Vaisman, Iosif I

2008-09-15

Accurate predictive models for the impact of single amino acid substitutions on protein stability provide insight into protein structure and function. Such models are also valuable for the design and engineering of new proteins. Previously described methods have utilized properties of protein sequence or structure to predict the free energy change of mutants due to thermal (DeltaDeltaG) and denaturant (DeltaDeltaG(H2O)) denaturations, as well as mutant thermal stability (DeltaT(m)), through the application of either computational energy-based approaches or machine learning techniques. However, accuracy associated with applying these methods separately is frequently far from optimal. We detail a computational mutagenesis technique based on a four-body, knowledge-based, statistical contact potential. For any mutation due to a single amino acid replacement in a protein, the method provides an empirical normalized measure of the ensuing environmental perturbation occurring at every residue position. A feature vector is generated for the mutant by considering perturbations at the mutated position and it's ordered six nearest neighbors in the 3-dimensional (3D) protein structure. These predictors of stability change are evaluated by applying machine learning tools to large training sets of mutants derived from diverse proteins that have been experimentally studied and described. Predictive models based on our combined approach are either comparable to, or in many cases significantly outperform, previously published results. A web server with supporting documentation is available at http://proteins.gmu.edu/automute.

Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

Science.gov (United States)

Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

2016-01-01

Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

Accurate cut-offs for predicting endoscopic activity and mucosal healing in Crohn's disease with fecal calprotectin

Directory of Open Access Journals (Sweden)

Juan María Vázquez-Morón

Full Text Available Background: Fecal biomarkers, especially fecal calprotectin, are useful for predicting endoscopic activity in Crohn's disease; however, the cut-off point remains unclear. The aim of this paper was to analyze whether faecal calprotectin and M2 pyruvate kinase are good tools for generating highly accurate scores for the prediction of the state of endoscopic activity and mucosal healing. Methods: The simple endoscopic score for Crohn's disease and the Crohn's disease activity index was calculated for 71 patients diagnosed with Crohn's. Fecal calprotectin and M2-PK were measured by the enzyme-linked immunosorbent assay test. Results: A fecal calprotectin cut-off concentration of ≥ 170 µg/g (sensitivity 77.6%, specificity 95.5% and likelihood ratio +17.06 predicts a high probability of endoscopic activity, and a fecal calprotectin cut-off of ≤ 71 µg/g (sensitivity 95.9%, specificity 52.3% and likelihood ratio -0.08 predicts a high probability of mucosal healing. Three clinical groups were identified according to the data obtained: endoscopic activity (calprotectin ≥ 170, mucosal healing (calprotectin ≤ 71 and uncertainty (71 > calprotectin < 170, with significant differences in endoscopic values (F = 26.407, p < 0.01. Clinical activity or remission modified the probabilities of presenting endoscopic activity (100% vs 89% or mucosal healing (75% vs 87% in the diagnostic scores generated. M2-PK was insufficiently accurate to determine scores. Conclusions: The highly accurate scores for fecal calprotectin provide a useful tool for interpreting the probabilities of presenting endoscopic activity or mucosal healing, and are valuable in the specific clinical context.

A Method for Driving Route Predictions Based on Hidden Markov Model

Directory of Open Access Journals (Sweden)

Ning Ye

2015-01-01

Full Text Available We present a driving route prediction method that is based on Hidden Markov Model (HMM. This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

An accurate fatigue damage model for welded joints subjected to variable amplitude loading

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Aeran, A.; Siriwardane, S. C.; Mikkelsen, O.; Langen, I.

2017-12-01

Researchers in the past have proposed several fatigue damage models to overcome the shortcomings of the commonly used Miner’s rule. However, requirements of material parameters or S-N curve modifications restricts their practical applications. Also, application of most of these models under variable amplitude loading conditions have not been found. To overcome these restrictions, a new fatigue damage model is proposed in this paper. The proposed model can be applied by practicing engineers using only the S-N curve given in the standard codes of practice. The model is verified with experimentally derived damage evolution curves for C 45 and 16 Mn and gives better agreement compared to previous models. The model predicted fatigue lives are also in better correlation with experimental results compared to previous models as shown in earlier published work by the authors. The proposed model is applied to welded joints subjected to variable amplitude loadings in this paper. The model given around 8% shorter fatigue lives compared to Eurocode given Miner’s rule. This shows the importance of applying accurate fatigue damage models for welded joints.

Time dependent patient no-show predictive modelling development.

Science.gov (United States)

Huang, Yu-Li; Hanauer, David A

2016-05-09

Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

Fourier and non-Fourier bio-heat transfer models to predict ex vivo temperature response to focused ultrasound heating

Science.gov (United States)

Li, Chenghai; Miao, Jiaming; Yang, Kexin; Guo, Xiasheng; Tu, Juan; Huang, Pintong; Zhang, Dong

2018-05-01

Although predicting temperature variation is important for designing treatment plans for thermal therapies, research in this area is yet to investigate the applicability of prevalent thermal conduction models, such as the Pennes equation, the thermal wave model of bio-heat transfer, and the dual phase lag (DPL) model. To address this shortcoming, we heated a tissue phantom and ex vivo bovine liver tissues with focused ultrasound (FU), measured the temperature response, and compared the results with those predicted by these models. The findings show that, for a homogeneous-tissue phantom, the initial temperature increase is accurately predicted by the Pennes equation at the onset of FU irradiation, although the prediction deviates from the measured temperature with increasing FU irradiation time. For heterogeneous liver tissues, the predicted response is closer to the measured temperature for the non-Fourier models, especially the DPL model. Furthermore, the DPL model accurately predicts the temperature response in biological tissues because it increases the phase lag, which characterizes microstructural thermal interactions. These findings should help to establish more precise clinical treatment plans for thermal therapies.

Nonhydrostatic and surfbeat model predictions of extreme wave run-up in fringing reef environments

Science.gov (United States)

Lashley, Christopher H.; Roelvink, Dano; van Dongeren, Ap R.; Buckley, Mark L.; Lowe, Ryan J.

2018-01-01

The accurate prediction of extreme wave run-up is important for effective coastal engineering design and coastal hazard management. While run-up processes on open sandy coasts have been reasonably well-studied, very few studies have focused on understanding and predicting wave run-up at coral reef-fronted coastlines. This paper applies the short-wave resolving, Nonhydrostatic (XB-NH) and short-wave averaged, Surfbeat (XB-SB) modes of the XBeach numerical model to validate run-up using data from two 1D (alongshore uniform) fringing-reef profiles without roughness elements, with two objectives: i) to provide insight into the physical processes governing run-up in such environments; and ii) to evaluate the performance of both modes in accurately predicting run-up over a wide range of conditions. XBeach was calibrated by optimizing the maximum wave steepness parameter (maxbrsteep) in XB-NH and the dissipation coefficient (alpha) in XB-SB) using the first dataset; and then applied to the second dataset for validation. XB-NH and XB-SB predictions of extreme wave run-up (Rmax and R2%) and its components, infragravity- and sea-swell band swash (SIG and SSS) and shoreline setup (), were compared to observations. XB-NH more accurately simulated wave transformation but under-predicted shoreline setup due to its exclusion of parameterized wave-roller dynamics. XB-SB under-predicted sea-swell band swash but overestimated shoreline setup due to an over-prediction of wave heights on the reef flat. Run-up (swash) spectra were dominated by infragravity motions, allowing the short-wave (but not wave group) averaged model (XB-SB) to perform comparably well to its more complete, short-wave resolving (XB-NH) counterpart. Despite their respective limitations, both modes were able to accurately predict Rmax and R2%.

Development of dual stream PCRTM-SOLAR for fast and accurate radiative transfer modeling in the cloudy atmosphere with solar radiation

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Yang, Q.; Liu, X.; Wu, W.; Kizer, S.; Baize, R. R.

2016-12-01

Fast and accurate radiative transfer model is the key for satellite data assimilation and observation system simulation experiments for numerical weather prediction and climate study applications. We proposed and developed a dual stream PCRTM-SOLAR model which may simulate radiative transfer in the cloudy atmosphere with solar radiation quickly and accurately. Multi-scattering of multiple layers of clouds/aerosols is included in the model. The root-mean-square errors are usually less than 5x10-4 mW/cm2.sr.cm-1. The computation speed is 3 to 4 orders of magnitude faster than the medium speed correlated-k option MODTRAN5. This model will enable a vast new set of scientific calculations that were previously limited due to the computational expenses of available radiative transfer models.

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

Science.gov (United States)

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

Science.gov (United States)

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes. PMID:26294903

Babcock and Wilcox model for predicting in-reactor densification

International Nuclear Information System (INIS)

Buescher, B.J.; Pegram, J.W.

1975-06-01

The B and W fuel densification model is used to describe the extent and kinetics of in-reactor densification in B and W production fuel. The model and approach are qualified against an extensive data base available through B and W's participation in the EEI Fuel Densification Program. Out-of-reactor resintering tests on representative pellets from each batch of fuel are used to provide input parameters to the B and W densification model. The B and W densification model predicts in-reactor densification very accurately for pellets operated at heat rates above 5 kW/ft and with considerable conservation for pellets operated at heat rates less than 5 kW/ft. This model represents a technically rigorous and conservative basis for predicting the extent and kinetics of in-reactor densification. 9 references. (U.S.)

Development of a Diagnostic Prediction Model for Conductive Conditions in Neonates Using Wideband Acoustic Immittance.

Science.gov (United States)

Myers, Joshua; Kei, Joseph; Aithal, Sreedevi; Aithal, Venkatesh; Driscoll, Carlie; Khan, Asaduzzaman; Manuel, Alehandrea; Joseph, Anjali; Malicka, Alicja N

2018-03-03

Wideband acoustic immittance (WAI) is an emerging test of middle-ear function with potential applications for neonates in screening and diagnostic settings. Previous large-scale diagnostic accuracy studies have assessed the performance of WAI against evoked otoacoustic emissions, but further research is needed using a more stringent reference standard. Research into suitable quantitative techniques to analyze the large volume of data produced by WAI is still in its infancy. Prediction models are an attractive method for analysis of multivariate data because they provide individualized probabilities that a subject has the condition. A clinically useful prediction model must accurately discriminate between normal and abnormal cases and be well calibrated (i.e., give accurate predictions). The present study aimed to develop a diagnostic prediction model for detecting conductive conditions in neonates using WAI. A stringent reference standard was created by combining results of high-frequency tympanometry and distortion product otoacoustic emissions. High-frequency tympanometry and distortion product otoacoustic emissions were performed on both ears of 629 healthy neonates to assess outer- and middle-ear function. Wideband absorbance and complex admittance (magnitude and phase) were measured at frequencies ranging from 226 to 8000 Hz in each neonate at ambient pressure using a click stimulus. Results from one ear of each neonate were used to develop the prediction model. WAI results were used as logistic regression predictors to model the probability that an ear had outer/middle-ear dysfunction. WAI variables were modeled both linearly and nonlinearly, to test whether allowing nonlinearity improved model fit and thus calibration. The best-fitting model was validated using the opposite ears and with bootstrap resampling. The best-fitting model used absorbance at 1000 and 2000 Hz, admittance magnitude at 1000 and 2000 Hz, and admittance phase at 1000 and 4000 Hz modeled

Accurate modeling and maximum power point detection of ...

African Journals Online (AJOL)

Accurate modeling and maximum power point detection of photovoltaic ... Determination of MPP enables the PV system to deliver maximum available power. ..... adaptive artificial neural network: Proposition for a new sizing procedure.

A deep learning-based multi-model ensemble method for cancer prediction.

Science.gov (United States)

Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

2018-01-01

Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

An evolutionary model-based algorithm for accurate phylogenetic breakpoint mapping and subtype prediction in HIV-1.

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Sergei L Kosakovsky Pond

2009-11-01

Full Text Available Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1 are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (approximately 5% fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance

Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

Science.gov (United States)

Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

2017-09-01

This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Science.gov (United States)

Xie, Tian; Grossman, Jeffrey C.

2018-04-01

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?

Directory of Open Access Journals (Sweden)

Simionescu Mihaela

2015-06-01

Full Text Available This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component.

DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

Directory of Open Access Journals (Sweden)

Zhiheng Wang

Full Text Available The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

Database and prediction model for CANDU pressure tube diameter

Energy Technology Data Exchange (ETDEWEB)

Jung, J.Y.; Park, J.H. [Korea Atomic Energy Research Inst., Daejeon (Korea, Republic of)

2014-07-01

The pressure tube (PT) diameter is basic data in evaluating the CCP (critical channel power) of a CANDU reactor. Since the CCP affects the operational margin directly, an accurate prediction of the PT diameter is important to assess the operational margin. However, the PT diameter increases by creep owing to the effects of irradiation by neutron flux, stress, and reactor operating temperatures during the plant service period. Thus, it has been necessary to collect the measured data of the PT diameter and establish a database (DB) and develop a prediction model of PT diameter. Accordingly, in this study, a DB for the measured PT diameter data was established and a neural network (NN) based diameter prediction model was developed. The established DB included not only the measured diameter data but also operating conditions such as the temperature, pressure, flux, and effective full power date. The currently developed NN based diameter prediction model considers only extrinsic variables such as the operating conditions, and will be enhanced to consider the effect of intrinsic variables such as the micro-structure of the PT material. (author)

Enhancing pavement performance prediction models for the Illinois Tollway System

Directory of Open Access Journals (Sweden)

Laxmikanth Premkumar

2016-01-01

Full Text Available Accurate pavement performance prediction represents an important role in prioritizing future maintenance and rehabilitation needs, and predicting future pavement condition in a pavement management system. The Illinois State Toll Highway Authority (Tollway with over 2000 lane miles of pavement utilizes the condition rating survey (CRS methodology to rate pavement performance. Pavement performance models developed in the past for the Illinois Department of Transportation (IDOT are used by the Tollway to predict the future condition of its network. The model projects future CRS ratings based on pavement type, thickness, traffic, pavement age and current CRS rating. However, with time and inclusion of newer pavement types there was a need to calibrate the existing pavement performance models, as well as, develop models for newer pavement types.This study presents the results of calibrating the existing models, and developing new models for the various pavement types in the Illinois Tollway network. The predicted future condition of the pavements is used in estimating its remaining service life to failure, which is of immediate use in recommending future maintenance and rehabilitation requirements for the network. Keywords: Pavement performance models, Remaining life, Pavement management

Effect of heteroscedasticity treatment in residual error models on model calibration and prediction uncertainty estimation

Science.gov (United States)

Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli

2017-11-01

The heteroscedasticity treatment in residual error models directly impacts the model calibration and prediction uncertainty estimation. This study compares three methods to deal with the heteroscedasticity, including the explicit linear modeling (LM) method and nonlinear modeling (NL) method using hyperbolic tangent function, as well as the implicit Box-Cox transformation (BC). Then a combined approach (CA) combining the advantages of both LM and BC methods has been proposed. In conjunction with the first order autoregressive model and the skew exponential power (SEP) distribution, four residual error models are generated, namely LM-SEP, NL-SEP, BC-SEP and CA-SEP, and their corresponding likelihood functions are applied to the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin, China. Results show that the LM-SEP yields the poorest streamflow predictions with the widest uncertainty band and unrealistic negative flows. The NL and BC methods can better deal with the heteroscedasticity and hence their corresponding predictive performances are improved, yet the negative flows cannot be avoided. The CA-SEP produces the most accurate predictions with the highest reliability and effectively avoids the negative flows, because the CA approach is capable of addressing the complicated heteroscedasticity over the study basin.

Medium- and Long-term Prediction of LOD Change with the Leap-step Autoregressive Model

Science.gov (United States)

Liu, Q. B.; Wang, Q. J.; Lei, M. F.

2015-09-01

It is known that the accuracies of medium- and long-term prediction of changes of length of day (LOD) based on the combined least-square and autoregressive (LS+AR) decrease gradually. The leap-step autoregressive (LSAR) model is more accurate and stable in medium- and long-term prediction, therefore it is used to forecast the LOD changes in this work. Then the LOD series from EOP 08 C04 provided by IERS (International Earth Rotation and Reference Systems Service) is used to compare the effectiveness of the LSAR and traditional AR methods. The predicted series resulted from the two models show that the prediction accuracy with the LSAR model is better than that from AR model in medium- and long-term prediction.

Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint

Energy Technology Data Exchange (ETDEWEB)

Raszmann, Emma; Baker, Kyri; Shi, Ying; Christensen, Dane

2017-02-22

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.

Technical Note: Using experimentally determined proton spot scanning timing parameters to accurately model beam delivery time.

Science.gov (United States)

Shen, Jiajian; Tryggestad, Erik; Younkin, James E; Keole, Sameer R; Furutani, Keith M; Kang, Yixiu; Herman, Michael G; Bues, Martin

2017-10-01

To accurately model the beam delivery time (BDT) for a synchrotron-based proton spot scanning system using experimentally determined beam parameters. A model to simulate the proton spot delivery sequences was constructed, and BDT was calculated by summing times for layer switch, spot switch, and spot delivery. Test plans were designed to isolate and quantify the relevant beam parameters in the operation cycle of the proton beam therapy delivery system. These parameters included the layer switch time, magnet preparation and verification time, average beam scanning speeds in x- and y-directions, proton spill rate, and maximum charge and maximum extraction time for each spill. The experimentally determined parameters, as well as the nominal values initially provided by the vendor, served as inputs to the model to predict BDTs for 602 clinical proton beam deliveries. The calculated BDTs (T BDT ) were compared with the BDTs recorded in the treatment delivery log files (T Log ): ∆t = T Log -T BDT . The experimentally determined average layer switch time for all 97 energies was 1.91 s (ranging from 1.9 to 2.0 s for beam energies from 71.3 to 228.8 MeV), average magnet preparation and verification time was 1.93 ms, the average scanning speeds were 5.9 m/s in x-direction and 19.3 m/s in y-direction, the proton spill rate was 8.7 MU/s, and the maximum proton charge available for one acceleration is 2.0 ± 0.4 nC. Some of the measured parameters differed from the nominal values provided by the vendor. The calculated BDTs using experimentally determined parameters matched the recorded BDTs of 602 beam deliveries (∆t = -0.49 ± 1.44 s), which were significantly more accurate than BDTs calculated using nominal timing parameters (∆t = -7.48 ± 6.97 s). An accurate model for BDT prediction was achieved by using the experimentally determined proton beam therapy delivery parameters, which may be useful in modeling the interplay effect and patient throughput. The model may

Accurate Modeling Method for Cu Interconnect

Science.gov (United States)

Yamada, Kenta; Kitahara, Hiroshi; Asai, Yoshihiko; Sakamoto, Hideo; Okada, Norio; Yasuda, Makoto; Oda, Noriaki; Sakurai, Michio; Hiroi, Masayuki; Takewaki, Toshiyuki; Ohnishi, Sadayuki; Iguchi, Manabu; Minda, Hiroyasu; Suzuki, Mieko

This paper proposes an accurate modeling method of the copper interconnect cross-section in which the width and thickness dependence on layout patterns and density caused by processes (CMP, etching, sputtering, lithography, and so on) are fully, incorporated and universally expressed. In addition, we have developed specific test patterns for the model parameters extraction, and an efficient extraction flow. We have extracted the model parameters for 0.15μm CMOS using this method and confirmed that 10%τpd error normally observed with conventional LPE (Layout Parameters Extraction) was completely dissolved. Moreover, it is verified that the model can be applied to more advanced technologies (90nm, 65nm and 55nm CMOS). Since the interconnect delay variations due to the processes constitute a significant part of what have conventionally been treated as random variations, use of the proposed model could enable one to greatly narrow the guardbands required to guarantee a desired yield, thereby facilitating design closure.

Detailed physical properties prediction of pure methyl esters for biodiesel combustion modeling

International Nuclear Information System (INIS)

An, H.; Yang, W.M.; Maghbouli, A.; Chou, S.K.; Chua, K.J.

2013-01-01

Highlights: ► Group contribution methods from molecular level have been used for the prediction. ► Complete prediction of the physical properties for 5 methyl esters has been done. ► The predicted results can be very useful for biodiesel combustion modeling. ► Various models have been compared and the best model has been identified. ► Predicted properties are over large temperature ranges with excellent accuracies. -- Abstract: In order to accurately simulate the fuel spray, atomization, combustion and emission formation processes of a diesel engine fueled with biodiesel, adequate knowledge of biodiesel’s physical properties is desired. The objective of this work is to do a detailed physical properties prediction for the five major methyl esters of biodiesel for combustion modeling. The physical properties considered in this study are: normal boiling point, critical properties, vapor pressure, and latent heat of vaporization, liquid density, liquid viscosity, liquid thermal conductivity, gas diffusion coefficients and surface tension. For each physical property, the best prediction model has been identified, and very good agreements have been obtained between the predicted results and the published data where available. The calculated results can be used as key references for biodiesel combustion modeling.

Prediction of selected Indian stock using a partitioning–interpolation based ARIMA–GARCH model

Directory of Open Access Journals (Sweden)

C. Narendra Babu

2015-07-01

Full Text Available Accurate long-term prediction of time series data (TSD is a very useful research challenge in diversified fields. As financial TSD are highly volatile, multi-step prediction of financial TSD is a major research problem in TSD mining. The two challenges encountered are, maintaining high prediction accuracy and preserving the data trend across the forecast horizon. The linear traditional models such as autoregressive integrated moving average (ARIMA and generalized autoregressive conditional heteroscedastic (GARCH preserve data trend to some extent, at the cost of prediction accuracy. Non-linear models like ANN maintain prediction accuracy by sacrificing data trend. In this paper, a linear hybrid model, which maintains prediction accuracy while preserving data trend, is proposed. A quantitative reasoning analysis justifying the accuracy of proposed model is also presented. A moving-average (MA filter based pre-processing, partitioning and interpolation (PI technique are incorporated by the proposed model. Some existing models and the proposed model are applied on selected NSE India stock market data. Performance results show that for multi-step ahead prediction, the proposed model outperforms the others in terms of both prediction accuracy and preserving data trend.

Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

Science.gov (United States)

Voorhies, Coerte V.; Conrad, Joy

1996-01-01

The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field

Remaining Useful Life Prediction of Gas Turbine Engine using Autoregressive Model

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Ahsan Shazaib

2017-01-01

Full Text Available Gas turbine (GT engines are known for their high availability and reliability and are extensively used for power generation, marine and aero-applications. Maintenance of such complex machines should be done proactively to reduce cost and sustain high availability of the GT. The aim of this paper is to explore the use of autoregressive (AR models to predict remaining useful life (RUL of a GT engine. The Turbofan Engine data from NASA benchmark data repository is used as case study. The parametric investigation is performed to check on any effect of changing model parameter on modelling accuracy. Results shows that a single sensory data cannot accurately predict RUL of GT and further research need to be carried out by incorporating multi-sensory data. Furthermore, the predictions made using AR model seems to give highly pessimistic values for RUL of GT.

Research on a Novel Kernel Based Grey Prediction Model and Its Applications

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Xin Ma

2016-01-01

Full Text Available The discrete grey prediction models have attracted considerable interest of research due to its effectiveness to improve the modelling accuracy of the traditional grey prediction models. The autoregressive GM(1,1 model, abbreviated as ARGM(1,1, is a novel discrete grey model which is easy to use and accurate in prediction of approximate nonhomogeneous exponential time series. However, the ARGM(1,1 is essentially a linear model; thus, its applicability is still limited. In this paper a novel kernel based ARGM(1,1 model is proposed, abbreviated as KARGM(1,1. The KARGM(1,1 has a nonlinear function which can be expressed by a kernel function using the kernel method, and its modelling procedures are presented in details. Two case studies of predicting the monthly gas well production are carried out with the real world production data. The results of KARGM(1,1 model are compared to the existing discrete univariate grey prediction models, including ARGM(1,1, NDGM(1,1,k, DGM(1,1, and NGBMOP, and it is shown that the KARGM(1,1 outperforms the other four models.

Cluster abundance in chameleon f ( R ) gravity I: toward an accurate halo mass function prediction

Energy Technology Data Exchange (ETDEWEB)

Cataneo, Matteo; Rapetti, David [Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen, Juliane Maries Vej 30, 2100 Copenhagen (Denmark); Lombriser, Lucas [Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ (United Kingdom); Li, Baojiu, E-mail: matteoc@dark-cosmology.dk, E-mail: drapetti@dark-cosmology.dk, E-mail: llo@roe.ac.uk, E-mail: baojiu.li@durham.ac.uk [Institute for Computational Cosmology, Department of Physics, Durham University, South Road, Durham DH1 3LE (United Kingdom)

2016-12-01

We refine the mass and environment dependent spherical collapse model of chameleon f ( R ) gravity by calibrating a phenomenological correction inspired by the parameterized post-Friedmann framework against high-resolution N -body simulations. We employ our method to predict the corresponding modified halo mass function, and provide fitting formulas to calculate the enhancement of the f ( R ) halo abundance with respect to that of General Relativity (GR) within a precision of ∼< 5% from the results obtained in the simulations. Similar accuracy can be achieved for the full f ( R ) mass function on the condition that the modeling of the reference GR abundance of halos is accurate at the percent level. We use our fits to forecast constraints on the additional scalar degree of freedom of the theory, finding that upper bounds competitive with current Solar System tests are within reach of cluster number count analyses from ongoing and upcoming surveys at much larger scales. Importantly, the flexibility of our method allows also for this to be applied to other scalar-tensor theories characterized by a mass and environment dependent spherical collapse.

A Comparative Study of Spectral Auroral Intensity Predictions From Multiple Electron Transport Models

Science.gov (United States)

Grubbs, Guy; Michell, Robert; Samara, Marilia; Hampton, Donald; Hecht, James; Solomon, Stanley; Jahn, Jorg-Micha

2018-01-01

It is important to routinely examine and update models used to predict auroral emissions resulting from precipitating electrons in Earth's magnetotail. These models are commonly used to invert spectral auroral ground-based images to infer characteristics about incident electron populations when in situ measurements are unavailable. In this work, we examine and compare auroral emission intensities predicted by three commonly used electron transport models using varying electron population characteristics. We then compare model predictions to same-volume in situ electron measurements and ground-based imaging to qualitatively examine modeling prediction error. Initial comparisons showed differences in predictions by the GLobal airglOW (GLOW) model and the other transport models examined. Chemical reaction rates and radiative rates in GLOW were updated using recent publications, and predictions showed better agreement with the other models and the same-volume data, stressing that these rates are important to consider when modeling auroral processes. Predictions by each model exhibit similar behavior for varying atmospheric constants, energies, and energy fluxes. Same-volume electron data and images are highly correlated with predictions by each model, showing that these models can be used to accurately derive electron characteristics and ionospheric parameters based solely on multispectral optical imaging data.

Predicting Proliferation: High Reliability Forecasting Models of Nuclear Proliferation as a Policy & Analytical Aid

OpenAIRE

Center on Contemporary Conflict; Gartzke, Erik

2015-01-01

Performer: University of California at San Diego Project Lead: Erik Gartzke Project Cost: $121,000 FY15-16 Objective: Scholars have spent decades studying and explaining nuclear proliferation. This project will develop a model to predict the behavior of states regarding their pursuit and acquisition of nuclear weapons. An accurate prediction model will allow for action against potential suppliers, interdiction of nuclear trade, intelligence collection on covert nuclea...

Dinucleotide controlled null models for comparative RNA gene prediction.

Science.gov (United States)

Gesell, Tanja; Washietl, Stefan

2008-05-27

Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz

Dinucleotide controlled null models for comparative RNA gene prediction

Directory of Open Access Journals (Sweden)

Gesell Tanja

2008-05-01

Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require

Evaluation of burst pressure prediction models for line pipes

Energy Technology Data Exchange (ETDEWEB)

Zhu, Xian-Kui, E-mail: zhux@battelle.org [Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43201 (United States); Leis, Brian N. [Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43201 (United States)

2012-01-15

Accurate prediction of burst pressure plays a central role in engineering design and integrity assessment of oil and gas pipelines. Theoretical and empirical solutions for such prediction are evaluated in this paper relative to a burst pressure database comprising more than 100 tests covering a variety of pipeline steel grades and pipe sizes. Solutions considered include three based on plasticity theory for the end-capped, thin-walled, defect-free line pipe subjected to internal pressure in terms of the Tresca, von Mises, and ZL (or Zhu-Leis) criteria, one based on a cylindrical instability stress (CIS) concept, and a large group of analytical and empirical models previously evaluated by Law and Bowie (International Journal of Pressure Vessels and Piping, 84, 2007: 487-492). It is found that these models can be categorized into either a Tresca-family or a von Mises-family of solutions, except for those due to Margetson and Zhu-Leis models. The viability of predictions is measured via statistical analyses in terms of a mean error and its standard deviation. Consistent with an independent parallel evaluation using another large database, the Zhu-Leis solution is found best for predicting burst pressure, including consideration of strain hardening effects, while the Tresca strength solutions including Barlow, Maximum shear stress, Turner, and the ASME boiler code provide reasonably good predictions for the class of line-pipe steels with intermediate strain hardening response. - Highlights: Black-Right-Pointing-Pointer This paper evaluates different burst pressure prediction models for line pipes. Black-Right-Pointing-Pointer The existing models are categorized into two major groups of Tresca and von Mises solutions. Black-Right-Pointing-Pointer Prediction quality of each model is assessed statistically using a large full-scale burst test database. Black-Right-Pointing-Pointer The Zhu-Leis solution is identified as the best predictive model.

Evaluation of burst pressure prediction models for line pipes

International Nuclear Information System (INIS)

Zhu, Xian-Kui; Leis, Brian N.

2012-01-01

Accurate prediction of burst pressure plays a central role in engineering design and integrity assessment of oil and gas pipelines. Theoretical and empirical solutions for such prediction are evaluated in this paper relative to a burst pressure database comprising more than 100 tests covering a variety of pipeline steel grades and pipe sizes. Solutions considered include three based on plasticity theory for the end-capped, thin-walled, defect-free line pipe subjected to internal pressure in terms of the Tresca, von Mises, and ZL (or Zhu-Leis) criteria, one based on a cylindrical instability stress (CIS) concept, and a large group of analytical and empirical models previously evaluated by Law and Bowie (International Journal of Pressure Vessels and Piping, 84, 2007: 487–492). It is found that these models can be categorized into either a Tresca-family or a von Mises-family of solutions, except for those due to Margetson and Zhu-Leis models. The viability of predictions is measured via statistical analyses in terms of a mean error and its standard deviation. Consistent with an independent parallel evaluation using another large database, the Zhu-Leis solution is found best for predicting burst pressure, including consideration of strain hardening effects, while the Tresca strength solutions including Barlow, Maximum shear stress, Turner, and the ASME boiler code provide reasonably good predictions for the class of line-pipe steels with intermediate strain hardening response. - Highlights: ► This paper evaluates different burst pressure prediction models for line pipes. ► The existing models are categorized into two major groups of Tresca and von Mises solutions. ► Prediction quality of each model is assessed statistically using a large full-scale burst test database. ► The Zhu-Leis solution is identified as the best predictive model.

Model for prediction of strip temperature in hot strip steel mill

International Nuclear Information System (INIS)

Panjkovic, Vladimir

2007-01-01

Proper functioning of set-up models in a hot strip steel mill requires reliable prediction of strip temperature. Temperature prediction is particularly important for accurate calculation of rolling force because of strong dependence of yield stress and strip microstructure on temperature. A comprehensive model was developed to replace an obsolete model in the Western Port hot strip mill of BlueScope Steel. The new model predicts the strip temperature evolution from the roughing mill exit to the finishing mill exit. It takes into account the radiative and convective heat losses, forced flow boiling and film boiling of water at strip surface, deformation heat in the roll gap, frictional sliding heat, heat of scale formation and the heat transfer between strip and work rolls through an oxide layer. The significance of phase transformation was also investigated. Model was tested with plant measurements and benchmarked against other models in the literature, and its performance was very good

Model for prediction of strip temperature in hot strip steel mill

Energy Technology Data Exchange (ETDEWEB)

Panjkovic, Vladimir [BlueScope Steel, TEOB, 1 Bayview Road, Hastings Vic. 3915 (Australia)]. E-mail: Vladimir.Panjkovic@BlueScopeSteel.com

2007-10-15

Proper functioning of set-up models in a hot strip steel mill requires reliable prediction of strip temperature. Temperature prediction is particularly important for accurate calculation of rolling force because of strong dependence of yield stress and strip microstructure on temperature. A comprehensive model was developed to replace an obsolete model in the Western Port hot strip mill of BlueScope Steel. The new model predicts the strip temperature evolution from the roughing mill exit to the finishing mill exit. It takes into account the radiative and convective heat losses, forced flow boiling and film boiling of water at strip surface, deformation heat in the roll gap, frictional sliding heat, heat of scale formation and the heat transfer between strip and work rolls through an oxide layer. The significance of phase transformation was also investigated. Model was tested with plant measurements and benchmarked against other models in the literature, and its performance was very good.

Estimation and prediction under local volatility jump-diffusion model

Science.gov (United States)

Kim, Namhyoung; Lee, Younhee

2018-02-01

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.

Reliability of Degree-Day Models to Predict the Development Time of Plutella xylostella (L.) under Field Conditions.

Science.gov (United States)

Marchioro, C A; Krechemer, F S; de Moraes, C P; Foerster, L A

2015-12-01

The diamondback moth, Plutella xylostella (L.), is a cosmopolitan pest of brassicaceous crops occurring in regions with highly distinct climate conditions. Several studies have investigated the relationship between temperature and P. xylostella development rate, providing degree-day models for populations from different geographical regions. However, there are no data available to date to demonstrate the suitability of such models to make reliable projections on the development time for this species in field conditions. In the present study, 19 models available in the literature were tested regarding their ability to accurately predict the development time of two cohorts of P. xylostella under field conditions. Only 11 out of the 19 models tested accurately predicted the development time for the first cohort of P. xylostella, but only seven for the second cohort. Five models correctly predicted the development time for both cohorts evaluated. Our data demonstrate that the accuracy of the models available for P. xylostella varies widely and therefore should be used with caution for pest management purposes.

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine.

Science.gov (United States)

Ramakrishnan, Sridhar; Wesensten, Nancy J; Kamimori, Gary H; Moon, James E; Balkin, Thomas J; Reifman, Jaques

2016-10-01

Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies. We hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg). The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% caffeine resulted in improved predictions (after caffeine consumption) by up to 70%. The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance. © 2016 Associated Professional Sleep Societies, LLC.

Underwater Sound Propagation Modeling Methods for Predicting Marine Animal Exposure.

Science.gov (United States)

Hamm, Craig A; McCammon, Diana F; Taillefer, Martin L

2016-01-01

The offshore exploration and production (E&P) industry requires comprehensive and accurate ocean acoustic models for determining the exposure of marine life to the high levels of sound used in seismic surveys and other E&P activities. This paper reviews the types of acoustic models most useful for predicting the propagation of undersea noise sources and describes current exposure models. The severe problems caused by model sensitivity to the uncertainty in the environment are highlighted to support the conclusion that it is vital that risk assessments include transmission loss estimates with statistical measures of confidence.

Accurate wind farm development and operation. Advanced wake modelling

Energy Technology Data Exchange (ETDEWEB)

Brand, A.; Bot, E.; Ozdemir, H. [ECN Unit Wind Energy, P.O. Box 1, NL 1755 ZG Petten (Netherlands); Steinfeld, G.; Drueke, S.; Schmidt, M. [ForWind, Center for Wind Energy Research, Carl von Ossietzky Universitaet Oldenburg, D-26129 Oldenburg (Germany); Mittelmeier, N. REpower Systems SE, D-22297 Hamburg (Germany))

2013-11-15

The ability is demonstrated to calculate wind farm wakes on the basis of ambient conditions that were calculated with an atmospheric model. Specifically, comparisons are described between predicted and observed ambient conditions, and between power predictions from three wind farm wake models and power measurements, for a single and a double wake situation. The comparisons are based on performance indicators and test criteria, with the objective to determine the percentage of predictions that fall within a given range about the observed value. The Alpha Ventus site is considered, which consists of a wind farm with the same name and the met mast FINO1. Data from the 6 REpower wind turbines and the FINO1 met mast were employed. The atmospheric model WRF predicted the ambient conditions at the location and the measurement heights of the FINO1 mast. May the predictability of the wind speed and the wind direction be reasonable if sufficiently sized tolerances are employed, it is fairly impossible to predict the ambient turbulence intensity and vertical shear. Three wind farm wake models predicted the individual turbine powers: FLaP-Jensen and FLaP-Ainslie from ForWind Oldenburg, and FarmFlow from ECN. The reliabilities of the FLaP-Ainslie and the FarmFlow wind farm wake models are of equal order, and higher than FLaP-Jensen. Any difference between the predictions from these models is most clear in the double wake situation. Here FarmFlow slightly outperforms FLaP-Ainslie.

Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

Directory of Open Access Journals (Sweden)

B. M. Brentan

2017-01-01

Full Text Available Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA and machine learning powerful algorithms such as Self-Organizing Maps (SOMs and Random Forest (RF. We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine

Science.gov (United States)

Ramakrishnan, Sridhar; Wesensten, Nancy J.; Kamimori, Gary H.; Moon, James E.; Balkin, Thomas J.; Reifman, Jaques

2016-01-01

Study Objectives: Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies. Methods: We hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg). Results: The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% caffeine resulted in improved predictions (after caffeine consumption) by up to 70%. Conclusions: The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance. Citation: Ramakrishnan S, Wesensten NJ, Kamimori GH, Moon JE, Balkin TJ, Reifman J. A unified model of performance for predicting the effects of sleep and caffeine. SLEEP 2016;39(10):1827–1841. PMID:27397562

Issues and Importance of "Good" Starting Points for Nonlinear Regression for Mathematical Modeling with Maple: Basic Model Fitting to Make Predictions with Oscillating Data

Science.gov (United States)

Fox, William

2012-01-01

The purpose of our modeling effort is to predict future outcomes. We assume the data collected are both accurate and relatively precise. For our oscillating data, we examined several mathematical modeling forms for predictions. We also examined both ignoring the oscillations as an important feature and including the oscillations as an important…

Predicting birth weight with conditionally linear transformation models.

Science.gov (United States)

Möst, Lisa; Schmid, Matthias; Faschingbauer, Florian; Hothorn, Torsten

2016-12-01

Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs. © The Author(s) 2014.

Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.

Directory of Open Access Journals (Sweden)

Yiming Hu

2017-06-01

Full Text Available Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

Effect of computational grid on accurate prediction of a wind turbine rotor using delayed detached-eddy simulations

Energy Technology Data Exchange (ETDEWEB)

Bangga, Galih; Weihing, Pascal; Lutz, Thorsten; Krämer, Ewald [University of Stuttgart, Stuttgart (Germany)

2017-05-15

The present study focuses on the impact of grid for accurate prediction of the MEXICO rotor under stalled conditions. Two different blade mesh topologies, O and C-H meshes, and two different grid resolutions are tested for several time step sizes. The simulations are carried out using Delayed detached-eddy simulation (DDES) with two eddy viscosity RANS turbulence models, namely Spalart- Allmaras (SA) and Menter Shear stress transport (SST) k-ω. A high order spatial discretization, WENO (Weighted essentially non- oscillatory) scheme, is used in these computations. The results are validated against measurement data with regards to the sectional loads and the chordwise pressure distributions. The C-H mesh topology is observed to give the best results employing the SST k-ω turbulence model, but the computational cost is more expensive as the grid contains a wake block that increases the number of cells.

Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models

Science.gov (United States)

Field, Scott E.; Galley, Chad R.; Hesthaven, Jan S.; Kaye, Jason; Tiglio, Manuel

2014-07-01

We propose a solution to the problem of quickly and accurately predicting gravitational waveforms within any given physical model. The method is relevant for both real-time applications and more traditional scenarios where the generation of waveforms using standard methods can be prohibitively expensive. Our approach is based on three offline steps resulting in an accurate reduced order model in both parameter and physical dimensions that can be used as a surrogate for the true or fiducial waveform family. First, a set of m parameter values is determined using a greedy algorithm from which a reduced basis representation is constructed. Second, these m parameters induce the selection of m time values for interpolating a waveform time series using an empirical interpolant that is built for the fiducial waveform family. Third, a fit in the parameter dimension is performed for the waveform's value at each of these m times. The cost of predicting L waveform time samples for a generic parameter choice is of order O(mL+mcfit) online operations, where cfit denotes the fitting function operation count and, typically, m ≪L. The result is a compact, computationally efficient, and accurate surrogate model that retains the original physics of the fiducial waveform family while also being fast to evaluate. We generate accurate surrogate models for effective-one-body waveforms of nonspinning binary black hole coalescences with durations as long as 105M, mass ratios from 1 to 10, and for multiple spherical harmonic modes. We find that these surrogates are more than 3 orders of magnitude faster to evaluate as compared to the cost of generating effective-one-body waveforms in standard ways. Surrogate model building for other waveform families and models follows the same steps and has the same low computational online scaling cost. For expensive numerical simulations of binary black hole coalescences, we thus anticipate extremely large speedups in generating new waveforms with a

ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

Science.gov (United States)

Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

2016-05-01

At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

Usability Prediction & Ranking of SDLC Models Using Fuzzy Hierarchical Usability Model

Science.gov (United States)

Gupta, Deepak; Ahlawat, Anil K.; Sagar, Kalpna

2017-06-01

Evaluation of software quality is an important aspect for controlling and managing the software. By such evaluation, improvements in software process can be made. The software quality is significantly dependent on software usability. Many researchers have proposed numbers of usability models. Each model considers a set of usability factors but do not cover all the usability aspects. Practical implementation of these models is still missing, as there is a lack of precise definition of usability. Also, it is very difficult to integrate these models into current software engineering practices. In order to overcome these challenges, this paper aims to define the term `usability' using the proposed hierarchical usability model with its detailed taxonomy. The taxonomy considers generic evaluation criteria for identifying the quality components, which brings together factors, attributes and characteristics defined in various HCI and software models. For the first time, the usability model is also implemented to predict more accurate usability values. The proposed system is named as fuzzy hierarchical usability model that can be easily integrated into the current software engineering practices. In order to validate the work, a dataset of six software development life cycle models is created and employed. These models are ranked according to their predicted usability values. This research also focuses on the detailed comparison of proposed model with the existing usability models.

Model-based prediction of myelosuppression and recovery based on frequent neutrophil monitoring.

Science.gov (United States)

Netterberg, Ida; Nielsen, Elisabet I; Friberg, Lena E; Karlsson, Mats O

2017-08-01

To investigate whether a more frequent monitoring of the absolute neutrophil counts (ANC) during myelosuppressive chemotherapy, together with model-based predictions, can improve therapy management, compared to the limited clinical monitoring typically applied today. Daily ANC in chemotherapy-treated cancer patients were simulated from a previously published population model describing docetaxel-induced myelosuppression. The simulated values were used to generate predictions of the individual ANC time-courses, given the myelosuppression model. The accuracy of the predicted ANC was evaluated under a range of conditions with reduced amount of ANC measurements. The predictions were most accurate when more data were available for generating the predictions and when making short forecasts. The inaccuracy of ANC predictions was highest around nadir, although a high sensitivity (≥90%) was demonstrated to forecast Grade 4 neutropenia before it occurred. The time for a patient to recover to baseline could be well forecasted 6 days (±1 day) before the typical value occurred on day 17. Daily monitoring of the ANC, together with model-based predictions, could improve anticancer drug treatment by identifying patients at risk for severe neutropenia and predicting when the next cycle could be initiated.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

Science.gov (United States)

Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-08-01

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bridge Deterioration Prediction Model Based On Hybrid Markov-System Dynamic

Directory of Open Access Journals (Sweden)

Widodo Soetjipto Jojok

2017-01-01

Full Text Available Instantaneous bridge failure tends to increase in Indonesia. To mitigate this condition, Indonesia’s Bridge Management System (I-BMS has been applied to continuously monitor the condition of bridges. However, I-BMS only implements visual inspection for maintenance priority of the bridge structure component instead of bridge structure system. This paper proposes a new bridge failure prediction model based on hybrid Markov-System Dynamic (MSD. System dynamic is used to represent the correlation among bridge structure components while Markov chain is used to calculate temporal probability of the bridge failure. Around 235 data of bridges in Indonesia were collected from Directorate of Bridge the Ministry of Public Works and Housing for calculating transition probability of the model. To validate the model, a medium span concrete bridge was used as a case study. The result shows that the proposed model can accurately predict the bridge condition. Besides predicting the probability of the bridge failure, this model can also be used as an early warning system for bridge monitoring activity.

A theoretical model for predicting the Peak Cutting Force of conical picks

Directory of Open Access Journals (Sweden)

Gao Kuidong

2014-01-01

Full Text Available In order to predict the PCF (Peak Cutting Force of conical pick in rock cutting process, a theoretical model is established based on elastic fracture mechanics theory. The vertical fracture model of rock cutting fragment is also established based on the maximum tensile criterion. The relation between vertical fracture angle and associated parameters (cutting parameter  and ratio B of rock compressive strength to tensile strength is obtained by numerical analysis method and polynomial regression method, and the correctness of rock vertical fracture model is verified through experiments. Linear regression coefficient between the PCF of prediction and experiments is 0.81, and significance level less than 0.05 shows that the model for predicting the PCF is correct and reliable. A comparative analysis between the PCF obtained from this model and Evans model reveals that the result of this prediction model is more reliable and accurate. The results of this work could provide some guidance for studying the rock cutting theory of conical pick and designing the cutting mechanism.

Modeling and Prediction of Soil Water Vapor Sorption Isotherms

DEFF Research Database (Denmark)

Arthur, Emmanuel; Tuller, Markus; Moldrup, Per

2015-01-01

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....

Simple, fast and accurate two-diode model for photovoltaic modules

Energy Technology Data Exchange (ETDEWEB)

Ishaque, Kashif; Salam, Zainal; Taheri, Hamed [Faculty of Electrical Engineering, Universiti Teknologi Malaysia, UTM 81310, Skudai, Johor Bahru (Malaysia)

2011-02-15

This paper proposes an improved modeling approach for the two-diode model of photovoltaic (PV) module. The main contribution of this work is the simplification of the current equation, in which only four parameters are required, compared to six or more in the previously developed two-diode models. Furthermore the values of the series and parallel resistances are computed using a simple and fast iterative method. To validate the accuracy of the proposed model, six PV modules of different types (multi-crystalline, mono-crystalline and thin-film) from various manufacturers are tested. The performance of the model is evaluated against the popular single diode models. It is found that the proposed model is superior when subjected to irradiance and temperature variations. In particular the model matches very accurately for all important points of the I-V curves, i.e. the peak power, short-circuit current and open circuit voltage. The modeling method is useful for PV power converter designers and circuit simulator developers who require simple, fast yet accurate model for the PV module. (author)

Hybrid Prediction Model of the Temperature Field of a Motorized Spindle

Directory of Open Access Journals (Sweden)

Lixiu Zhang

2017-10-01

Full Text Available The thermal characteristics of a motorized spindle are the main determinants of its performance, and influence the machining accuracy of computer numerical control machine tools. It is important to accurately predict the thermal field of a motorized spindle during its operation to improve its thermal characteristics. This paper proposes a model to predict the temperature field of a high-speed and high-precision motorized spindle under different working conditions using a finite element model and test data. The finite element model considers the influence of the parameters of the cooling system and the lubrication system, and that of environmental conditions on the coefficient of heat transfer based on test data for the surface temperature of the motorized spindle. A genetic algorithm is used to optimize the coefficient of heat transfer of the spindle, and its temperature field is predicted using a three-dimensional model that employs this optimal coefficient. A prediction model of the 170MD30 temperature field of the motorized spindle is created and simulation data for the temperature field are compared with the test data. The results show that when the speed of the spindle is 10,000 rpm, the relative mean prediction error is 1.5%, and when its speed is 15,000 rpm, the prediction error is 3.6%. Therefore, the proposed prediction model can predict the temperature field of the motorized spindle with high accuracy.

Developing a stochastic traffic volume prediction model for public-private partnership projects

Science.gov (United States)

Phong, Nguyen Thanh; Likhitruangsilp, Veerasak; Onishi, Masamitsu

2017-11-01

Transportation projects require an enormous amount of capital investment resulting from their tremendous size, complexity, and risk. Due to the limitation of public finances, the private sector is invited to participate in transportation project development. The private sector can entirely or partially invest in transportation projects in the form of Public-Private Partnership (PPP) scheme, which has been an attractive option for several developing countries, including Vietnam. There are many factors affecting the success of PPP projects. The accurate prediction of traffic volume is considered one of the key success factors of PPP transportation projects. However, only few research works investigated how to predict traffic volume over a long period of time. Moreover, conventional traffic volume forecasting methods are usually based on deterministic models which predict a single value of traffic volume but do not consider risk and uncertainty. This knowledge gap makes it difficult for concessionaires to estimate PPP transportation project revenues accurately. The objective of this paper is to develop a probabilistic traffic volume prediction model. First, traffic volumes were estimated following the Geometric Brownian Motion (GBM) process. Monte Carlo technique is then applied to simulate different scenarios. The results show that this stochastic approach can systematically analyze variations in the traffic volume and yield more reliable estimates for PPP projects.

Development of a method to accurately calculate the Dpb and quickly predict the strength of a chemical bond

International Nuclear Information System (INIS)

Du, Xia; Zhao, Dong-Xia; Yang, Zhong-Zhi

2013-01-01

Highlights: ► A method from new respect to characterize and measure the bond strength is proposed. ► We calculate the D pb of a series of various bonds to justify our approach. ► A quite good linear relationship of the D pb with the bond lengths for series of various bonds is shown. ► Take the prediction of strengths of C–H and N–H bonds for base pairs in DNA as a practical application of our method. - Abstract: A new approach to characterize and measure bond strength has been developed. First, we propose a method to accurately calculate the potential acting on an electron in a molecule (PAEM) at the saddle point along a chemical bond in situ, denoted by D pb . Then, a direct method to quickly evaluate bond strength is established. We choose some familiar molecules as models for benchmarking this method. As a practical application, the D pb of base pairs in DNA along C–H and N–H bonds are obtained for the first time. All results show that C 7 –H of A–T and C 8 –H of G–C are the relatively weak bonds that are the injured positions in DNA damage. The significance of this work is twofold: (i) A method is developed to calculate D pb of various sizable molecules in situ quickly and accurately; (ii) This work demonstrates the feasibility to quickly predict the bond strength in macromolecules

Building predictive models of soil particle-size distribution

Directory of Open Access Journals (Sweden)

Alessandro Samuel-Rosa

2013-04-01

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.

Accurate Prediction of Coronary Artery Disease Using Bioinformatics Algorithms

Directory of Open Access Journals (Sweden)

Hajar Shafiee

2016-06-01

Full Text Available Background and Objectives: Cardiovascular disease is one of the main causes of death in developed and Third World countries. According to the statement of the World Health Organization, it is predicted that death due to heart disease will rise to 23 million by 2030. According to the latest statistics reported by Iran’s Minister of health, 3.39% of all deaths are attributed to cardiovascular diseases and 19.5% are related to myocardial infarction. The aim of this study was to predict coronary artery disease using data mining algorithms. Methods: In this study, various bioinformatics algorithms, such as decision trees, neural networks, support vector machines, clustering, etc., were used to predict coronary heart disease. The data used in this study was taken from several valid databases (including 14 data. Results: In this research, data mining techniques can be effectively used to diagnose different diseases, including coronary artery disease. Also, for the first time, a prediction system based on support vector machine with the best possible accuracy was introduced. Conclusion: The results showed that among the features, thallium scan variable is the most important feature in the diagnosis of heart disease. Designation of machine prediction models, such as support vector machine learning algorithm can differentiate between sick and healthy individuals with 100% accuracy.

Predicting germination in semi-arid wildland seedbeds II. Field validation of wet thermal-time models

Science.gov (United States)

Jennifer K. Rawlins; Bruce A. Roundy; Dennis Eggett; Nathan. Cline

2011-01-01

Accurate prediction of germination for species used for semi-arid land revegetation would support selection of plant materials for specific climatic conditions and sites. Wet thermal-time models predict germination time by summing progress toward germination subpopulation percentages as a function of temperature across intermittent wet periods or within singular wet...

Predicting growth of the healthy infant using a genome scale metabolic model

DEFF Research Database (Denmark)

Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

2017-01-01

to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body...

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

DEFF Research Database (Denmark)

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

2017-01-01

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

Prediction model for oxide thickness on aluminum alloy cladding during irradiation

International Nuclear Information System (INIS)

Kim, Yeon Soo; Hofman, G.L.; Hanan, N.A.; Snelgrove, J.L.

2003-01-01

An empirical model predicting the oxide film thickness on aluminum alloy cladding during irradiation has been developed as a function of irradiation time, temperature, heat flux, pH, and coolant flow rate. The existing models in the literature are neither consistent among themselves nor fit the measured data very well. They also lack versatility for various reactor situations such as a pH other than 5, high coolant flow rates, and fuel life longer than ∼1200 hrs. Particularly, they were not intended for use in irradiation situations. The newly developed model is applicable to these in-reactor situations as well as ex-reactor tests, and has a more accurate prediction capability. The new model demonstrated with consistent predictions to the measured data of UMUS and SIMONE fuel tests performed in the HFR, Petten, tests results from the ORR, and IRIS tests from the OSIRIS and to the data from the out-of-pile tests available in the literature as well. (author)

A Combined Hydrological and Hydraulic Model for Flood Prediction in Vietnam Applied to the Huong River Basin as a Test Case Study

Directory of Open Access Journals (Sweden)

Dang Thanh Mai

2017-11-01

Full Text Available A combined hydrological and hydraulic model is presented for flood prediction in Vietnam. This model is applied to the Huong river basin as a test case study. Observed flood flows and water surface levels of the 2002–2005 flood seasons are used for model calibration, and those of the 2006–2007 flood seasons are used for validation of the model. The physically based distributed hydrologic model WetSpa is used for predicting the generation and propagation of flood flows in the mountainous upper sub-basins, and proves to predict flood flows accurately. The Hydrologic Engineering Center River Analysis System (HEC-RAS hydraulic model is applied to simulate flood flows and inundation levels in the downstream floodplain, and also proves to predict water levels accurately. The predicted water profiles are used for mapping of inundations in the floodplain. The model may be useful in developing flood forecasting and early warning systems to mitigate losses due to flooding in Vietnam.

Comparison of the CATHENA model of Gentilly-2 end shield cooling system predictions to station data

Energy Technology Data Exchange (ETDEWEB)

Zagre, G.; Sabourin, G. [Candu Energy Inc., Montreal, Quebec (Canada); Chapados, S. [Hydro-Quebec, Montreal, Quebec (Canada)

2012-07-01

As part of the Gentilly-2 Refurbishment Project, Hydro-Quebec has elected to perform the End Shield Cooling Safety Analysis. A CATHENA model of Gentilly-2 End Shield Cooling System was developed for this purpose. This model includes new elements compared to other CANDU6 End Shield Cooling models such as a detailed heat exchanger and control logic model. In order to test the model robustness and accuracy, the model predictions were compared with plant measurements.This paper summarizes this comparison between the model predictions and the station measurements. It is shown that the CATHENA model is flexible and accurate enough to predict station measurements for critical parameters, and the detailed heat exchanger model allows reproducing station transients. (author)

Bootstrap prediction and Bayesian prediction under misspecified models

OpenAIRE

Fushiki, Tadayoshi

2005-01-01

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...

Prediction of phenanthrene uptake by plants with a partition-limited model

International Nuclear Information System (INIS)

Zhu, Lizhong; Gao, Yanzheng

2004-01-01

The performance of a partition-limited model on prediction of phenanthrene uptake by a wide variety of plant species was evaluated using a greenhouse study. The model predictions of root or shoot concentrations for tested plant species were all within an order of magnitude of the observed values. Modeled root concentrations appeared to be more accurate than modeled shoot concentrations. The differences of simulated and experimented concentrations of phenanthrene in roots and shoots of three representative plant species, including ryegrass, flowering Chinese cabbage, and three-colored amaranth, were less than 81% for roots and 103% for shoots. Results are promising in that the α pt values of the partition-limited model for root uptake of phenanthrene correlate well with root lipid contents. Additionally, a significantly positive correlation is also observed between root concentration factors (RCFs, defined as the ratio of contaminant concentrations in root and in soil on a dry weight basis) of phenanthrene and root lipid contents. Results from this study suggest that the partition-limited model may have potential applications for predicting the plant PAH concentration in contaminated sites

Accurate diffraction data integration by the EVAL15 profile prediction method : Application in chemical and biological crystallography

NARCIS (Netherlands)

Xian, X.

2009-01-01

Accurate integration of reflection intensities plays an essential role in structure determination of the crystallized compound. A new diffraction data integration method, EVAL15, is presented in this thesis. This method uses the principle of general impacts to predict ab inito three-dimensional

Prediction of oxy-coal combustion through an optimized weighted sum of gray gases model

International Nuclear Information System (INIS)

Kangwanpongpan, Tanin; Corrêa da Silva, Rodrigo; Krautz, Hans Joachim

2012-01-01

Oxy-fuel combustion is considered as one of promising options for carbon dioxide capture in future coal power plants. Currently models available in CFD codes fail to predict accurately the radiative heat transfer in oxy-fuel cases due to higher pressure of carbon dioxide and water vapor. This paper concerns numerical investigation applying three band formulations aiming an accurate prediction of radiative properties. The radiative heat transfer is calculated by discrete ordinate method coupled with a weighted sum of gray gases model. The first case relates to the domain-based approach using air-fired parameters. In the last two cases, the optimized parameters of 3 and 4 gray gases fitted to oxy-fired conditions are implemented through a non-gray gases approach. Results applying these set of parameters are evaluated through a comparison with experimental data. Discrepancies between the predicted and measured velocity and O 2 concentration are found mainly close to the burner due to shortcomings of the turbulence model and inaccurate thermochemical closure. The gas flame temperatures are better predicted by the optimized parameters for oxy-fuel conditions, which are considerably lower than the values calculated by the air-fired parameters. Similar trends are observed when the radiative heat fluxes at the lateral wall are compared.

Computer models versus reality: how well do in silico models currently predict the sensitization potential of a substance.

Science.gov (United States)

Teubner, Wera; Mehling, Anette; Schuster, Paul Xaver; Guth, Katharina; Worth, Andrew; Burton, Julien; van Ravenzwaay, Bennard; Landsiedel, Robert

2013-12-01

National legislations for the assessment of the skin sensitization potential of chemicals are increasingly based on the globally harmonized system (GHS). In this study, experimental data on 55 non-sensitizing and 45 sensitizing chemicals were evaluated according to GHS criteria and used to test the performance of computer (in silico) models for the prediction of skin sensitization. Statistic models (Vega, Case Ultra, TOPKAT), mechanistic models (Toxtree, OECD (Q)SAR toolbox, DEREK) or a hybrid model (TIMES-SS) were evaluated. Between three and nine of the substances evaluated were found in the individual training sets of various models. Mechanism based models performed better than statistical models and gave better predictivities depending on the stringency of the domain definition. Best performance was achieved by TIMES-SS, with a perfect prediction, whereby only 16% of the substances were within its reliability domain. Some models offer modules for potency; however predictions did not correlate well with the GHS sensitization subcategory derived from the experimental data. In conclusion, although mechanistic models can be used to a certain degree under well-defined conditions, at the present, the in silico models are not sufficiently accurate for broad application to predict skin sensitization potentials. Copyright © 2013 Elsevier Inc. All rights reserved.

Metal accumulation in the earthworm Lumbricus rubellus. Model predictions compared to field data

Science.gov (United States)

Veltman, K.; Huijbregts, M.A.J.; Vijver, M.G.; Peijnenburg, W.J.G.M.; Hobbelen, P.H.F.; Koolhaas, J.E.; van Gestel, C.A.M.; van Vliet, P.C.J.; Jan, Hendriks A.

2007-01-01

The mechanistic bioaccumulation model OMEGA (Optimal Modeling for Ecotoxicological Applications) is used to estimate accumulation of zinc (Zn), copper (Cu), cadmium (Cd) and lead (Pb) in the earthworm Lumbricus rubellus. Our validation to field accumulation data shows that the model accurately predicts internal cadmium concentrations. In addition, our results show that internal metal concentrations in the earthworm are less than linearly (slope < 1) related to the total concentration in soil, while risk assessment procedures often assume the biota-soil accumulation factor (BSAF) to be constant. Although predicted internal concentrations of all metals are generally within a factor 5 compared to field data, incorporation of regulation in the model is necessary to improve predictability of the essential metals such as zinc and copper. ?? 2006 Elsevier Ltd. All rights reserved.

Forward and Inverse Predictive Model for the Trajectory Tracking Control of a Lower Limb Exoskeleton for Gait Rehabilitation: Simulation modelling analysis

Science.gov (United States)

Zakaria, M. A.; Majeed, A. P. P. A.; Taha, Z.; Alim, M. M.; Baarath, K.

2018-03-01

The movement of a lower limb exoskeleton requires a reasonably accurate control method to allow for an effective gait therapy session to transpire. Trajectory tracking is a nontrivial means of passive rehabilitation technique to correct the motion of the patients’ impaired limb. This paper proposes an inverse predictive model that is coupled together with the forward kinematics of the exoskeleton to estimate the behaviour of the system. A conventional PID control system is used to converge the required joint angles based on the desired input from the inverse predictive model. It was demonstrated through the present study, that the inverse predictive model is capable of meeting the trajectory demand with acceptable error tolerance. The findings further suggest the ability of the predictive model of the exoskeleton to predict a correct joint angle command to the system.

Epidemiology of Mild Traumatic Brain Injury with Intracranial Hemorrhage: Focusing Predictive Models for Neurosurgical Intervention.

Science.gov (United States)

Orlando, Alessandro; Levy, A Stewart; Carrick, Matthew M; Tanner, Allen; Mains, Charles W; Bar-Or, David

2017-11-01

To outline differences in neurosurgical intervention (NI) rates between intracranial hemorrhage (ICH) types in mild traumatic brain injuries and help identify which ICH types are most likely to benefit from creation of predictive models for NI. A multicenter retrospective study of adult patients spanning 3 years at 4 U.S. trauma centers was performed. Patients were included if they presented with mild traumatic brain injury (Glasgow Coma Scale score 13-15) with head CT scan positive for ICH. Patients were excluded for skull fractures, "unspecified hemorrhage," or coagulopathy. Primary outcome was NI. Stepwise multivariable logistic regression models were built to analyze the independent association between ICH variables and outcome measures. The study comprised 1876 patients. NI rate was 6.7%. There was a significant difference in rate of NI by ICH type. Subdural hematomas had the highest rate of NI (15.5%) and accounted for 78% of all NIs. Isolated subarachnoid hemorrhages had the lowest, nonzero, NI rate (0.19%). Logistic regression models identified ICH type as the most influential independent variable when examining NI. A model predicting NI for isolated subarachnoid hemorrhages would require 26,928 patients, but a model predicting NI for isolated subdural hematomas would require only 328 patients. This study highlighted disparate NI rates among ICH types in patients with mild traumatic brain injury and identified mild, isolated subdural hematomas as most appropriate for construction of predictive NI models. Increased health care efficiency will be driven by accurate understanding of risk, which can come only from accurate predictive models. Copyright © 2017 Elsevier Inc. All rights reserved.

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

Directory of Open Access Journals (Sweden)

Giuliano Marchi

2015-10-01

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.

Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

Science.gov (United States)

Rahmati, Mehdi

2017-08-01

Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed

Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, Artificial Neural Network, and Time Series Forecasting Models

Directory of Open Access Journals (Sweden)

Chih-Chieh Young

2015-01-01

Full Text Available Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL in Taiwan: a three-dimensional hydrodynamic model, an artificial neural network (ANN model (back propagation neural network, BPNN, a time series forecasting (autoregressive moving average with exogenous inputs, ARMAX model, and a combined hydrodynamic and ANN model. Particularly, the black-box ANN model and physically based hydrodynamic model are coupled to more accurately predict water level fluctuation. Hourly water level data (a total of 7296 observations was collected for model calibration (training and validation. Three statistical indicators (mean absolute error, root mean square error, and coefficient of correlation were adopted to evaluate model performances. Overall, the results demonstrate that the hydrodynamic model can satisfactorily predict hourly water level changes during the calibration stage but not for the validation stage. The ANN and ARMAX models better predict the water level than the hydrodynamic model does. Meanwhile, the results from an ANN model are superior to those by the ARMAX model in both training and validation phases. The novel proposed concept using a three-dimensional hydrodynamic model in conjunction with an ANN model has clearly shown the improved prediction accuracy for the water level fluctuation.

Evaluation on the model of performance predictions for on-line monitoring system for combined-cycle power plant

International Nuclear Information System (INIS)

Kim, Si Moon

2002-01-01

This paper presents the simulation model developed to predict design and off-design performance of an actual combined cycle power plant(S-Station in Korea), which would be running combined with on-line performance monitoring system in an on-line real-time fashion. The first step in thermal performance analysis is to build an accurate performance model of the power plant, in order to achieve this goal, GateCycle program has been employed in developing the model. This developed models predict design and off-design performance with a precision of one percent over a wide range of operating conditions so that on-line real-time performance monitoring can accurately establish both current performance and expected performance and also help the operator identify problems before they would be noticed

The Prediction of Drought-Related Tree Mortality in Vegetation Models

Science.gov (United States)

Schwinning, S.; Jensen, J.; Lomas, M. R.; Schwartz, B.; Woodward, F. I.

2013-12-01

Drought-related tree die-off events at regional scales have been reported from all wooded continents and it has been suggested that their frequency may be increasing. The prediction of these drought-related die-off events from regional to global scales has been recognized as a critical need for the conservation of forest resources and improving the prediction of climate-vegetation interactions. However, there is no conceptual consensus on how to best approach the quantitative prediction of tree mortality. Current models use a variety of mechanisms to represent demographic events. Mortality is modeled to represent a number of different processes, including death by fire, wind throw, extreme temperatures, and self-thinning, and each vegetation model differs in the emphasis they place on specific mechanisms. Dynamic global vegetation models generally operate on the assumption of incremental vegetation shift due to changes in the carbon economy of plant functional types and proportional effects on recruitment, growth, competition and mortality, but this may not capture sudden and sweeping tree death caused by extreme weather conditions. We tested several different approaches to predicting tree mortality within the framework of the Sheffield Dynamic Global Vegetation Model. We applied the model to the state of Texas, USA, which in 2011 experienced extreme drought conditions, causing the death of an estimated 300 million trees statewide. We then compared predicted to actual mortality to determine which algorithms most accurately predicted geographical variation in tree mortality. We discuss implications regarding the ongoing debate on the causes of tree death.

Combining Satellite Measurements and Numerical Flood Prediction Models to Save Lives and Property from Flooding

Science.gov (United States)

Saleh, F.; Garambois, P. A.; Biancamaria, S.

2017-12-01

Floods are considered the major natural threats to human societies across all continents. Consequences of floods in highly populated areas are more dramatic with losses of human lives and substantial property damage. This risk is projected to increase with the effects of climate change, particularly sea-level rise, increasing storm frequencies and intensities and increasing population and economic assets in such urban watersheds. Despite the advances in computational resources and modeling techniques, significant gaps exist in predicting complex processes and accurately representing the initial state of the system. Improving flood prediction models and data assimilation chains through satellite has become an absolute priority to produce accurate flood forecasts with sufficient lead times. The overarching goal of this work is to assess the benefits of the Surface Water Ocean Topography SWOT satellite data from a flood prediction perspective. The near real time methodology is based on combining satellite data from a simulator that mimics the future SWOT data, numerical models, high resolution elevation data and real-time local measurement in the New York/New Jersey area.

Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM

Directory of Open Access Journals (Sweden)

Łapczyński Mariusz

2014-08-01

Full Text Available Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models, customers who purchase additional products (cross- and up-sell models or customers intending to resign from the cooperation (churn models. During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm and cluster analysis (k-means. During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.

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

Science.gov (United States)

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

2012-08-01

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

Maximum solid concentrations of coal water slurries predicted by neural network models

Energy Technology Data Exchange (ETDEWEB)

Cheng, Jun; Li, Yanchang; Zhou, Junhu; Liu, Jianzhong; Cen, Kefa

2010-12-15

The nonlinear back-propagation (BP) neural network models were developed to predict the maximum solid concentration of coal water slurry (CWS) which is a substitute for oil fuel, based on physicochemical properties of 37 typical Chinese coals. The Levenberg-Marquardt algorithm was used to train five BP neural network models with different input factors. The data pretreatment method, learning rate and hidden neuron number were optimized by training models. It is found that the Hardgrove grindability index (HGI), moisture and coalification degree of parent coal are 3 indispensable factors for the prediction of CWS maximum solid concentration. Each BP neural network model gives a more accurate prediction result than the traditional polynomial regression equation. The BP neural network model with 3 input factors of HGI, moisture and oxygen/carbon ratio gives the smallest mean absolute error of 0.40%, which is much lower than that of 1.15% given by the traditional polynomial regression equation. (author)

Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

OpenAIRE

David Ebert

2006-01-01

One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...

Modeling and prediction of human word search behavior in interactive machine translation

Science.gov (United States)

Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

2017-12-01

As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.

Prediction models and control algorithms for predictive applications of setback temperature in cooling systems

International Nuclear Information System (INIS)

Moon, Jin Woo; Yoon, Younju; Jeon, Young-Hoon; Kim, Sooyoung

2017-01-01

Highlights: • Initial ANN model was developed for predicting the time to the setback temperature. • Initial model was optimized for producing accurate output. • Optimized model proved its prediction accuracy. • ANN-based algorithms were developed and tested their performance. • ANN-based algorithms presented superior thermal comfort or energy efficiency. - Abstract: In this study, a temperature control algorithm was developed to apply a setback temperature predictively for the cooling system of a residential building during occupied periods by residents. An artificial neural network (ANN) model was developed to determine the required time for increasing the current indoor temperature to the setback temperature. This study involved three phases: development of the initial ANN-based prediction model, optimization and testing of the initial model, and development and testing of three control algorithms. The development and performance testing of the model and algorithm were conducted using TRNSYS and MATLAB. Through the development and optimization process, the final ANN model employed indoor temperature and the temperature difference between the current and target setback temperature as two input neurons. The optimal number of hidden layers, number of neurons, learning rate, and moment were determined to be 4, 9, 0.6, and 0.9, respectively. The tangent–sigmoid and pure-linear transfer function was used in the hidden and output neurons, respectively. The ANN model used 100 training data sets with sliding-window method for data management. Levenberg-Marquart training method was employed for model training. The optimized model had a prediction accuracy of 0.9097 root mean square errors when compared with the simulated results. Employing the ANN model, ANN-based algorithms maintained indoor temperatures better within target ranges. Compared to the conventional algorithm, the ANN-based algorithms reduced the duration of time, in which the indoor temperature

Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models

Directory of Open Access Journals (Sweden)

Scott E. Field

2014-07-01

Full Text Available We propose a solution to the problem of quickly and accurately predicting gravitational waveforms within any given physical model. The method is relevant for both real-time applications and more traditional scenarios where the generation of waveforms using standard methods can be prohibitively expensive. Our approach is based on three offline steps resulting in an accurate reduced order model in both parameter and physical dimensions that can be used as a surrogate for the true or fiducial waveform family. First, a set of m parameter values is determined using a greedy algorithm from which a reduced basis representation is constructed. Second, these m parameters induce the selection of m time values for interpolating a waveform time series using an empirical interpolant that is built for the fiducial waveform family. Third, a fit in the parameter dimension is performed for the waveform’s value at each of these m times. The cost of predicting L waveform time samples for a generic parameter choice is of order O(mL+mc_{fit} online operations, where c_{fit} denotes the fitting function operation count and, typically, m≪L. The result is a compact, computationally efficient, and accurate surrogate model that retains the original physics of the fiducial waveform family while also being fast to evaluate. We generate accurate surrogate models for effective-one-body waveforms of nonspinning binary black hole coalescences with durations as long as 10^{5}M, mass ratios from 1 to 10, and for multiple spherical harmonic modes. We find that these surrogates are more than 3 orders of magnitude faster to evaluate as compared to the cost of generating effective-one-body waveforms in standard ways. Surrogate model building for other waveform families and models follows the same steps and has the same low computational online scaling cost. For expensive numerical simulations of binary black hole coalescences, we thus anticipate extremely large speedups in

Western Validation of a Novel Gastric Cancer Prognosis Prediction Model in US Gastric Cancer Patients.

Science.gov (United States)

Woo, Yanghee; Goldner, Bryan; Son, Taeil; Song, Kijun; Noh, Sung Hoon; Fong, Yuman; Hyung, Woo Jin

2018-03-01

A novel prediction model for accurate determination of 5-year overall survival of gastric cancer patients was developed by an international collaborative group (G6+). This prediction model was created using a single institution's database of 11,851 Korean patients and included readily available and clinically relevant factors. Already validated using external East Asian cohorts, its applicability in the American population was yet to be determined. Using the Surveillance, Epidemiology, and End Results (SEER) dataset, 2014 release, all patients diagnosed with gastric adenocarcinoma who underwent surgical resection between 2002 and 2012, were selected. Characteristics for analysis included: age, sex, depth of tumor invasion, number of positive lymph nodes, total lymph nodes retrieved, presence of distant metastasis, extent of resection, and histology. Concordance index (C-statistic) was assessed using the novel prediction model and compared with the prognostic index, the seventh edition of the TNM staging system. Of the 26,019 gastric cancer patients identified from the SEER database, 15,483 had complete datasets. Validation of the novel prediction tool revealed a C-statistic of 0.762 (95% CI 0.754 to 0.769) compared with the seventh TNM staging model, C-statistic 0.683 (95% CI 0.677 to 0.689), (p prediction model for gastric cancer in the American patient population. Its superior prediction of the 5-year survival of gastric cancer patients in a large Western cohort strongly supports its global applicability. Importantly, this model allows for accurate prognosis for an increasing number of gastric cancer patients worldwide, including those who received inadequate lymphadenectomy or underwent a noncurative resection. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Confronting species distribution model predictions with species functional traits.

Science.gov (United States)

Wittmann, Marion E; Barnes, Matthew A; Jerde, Christopher L; Jones, Lisa A; Lodge, David M

2016-02-01

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(-1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = -0.26, 95% CI (-0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.

Predictive Local Composition Models for Solid/Liquid Equilibrium in n-Alkane Systems: Wilson Equation for Multicomponent Systems

DEFF Research Database (Denmark)

Coutinho, João A.P.; Stenby, Erling Halfdan

1996-01-01

The predictive local composition model is applied to multicomponent hydrocarbon systems with long-chain n-alkanes as solutes. The results show that it can successfully be extended to highorder systems and accurately predict the solid appearance temperature, also known as cloud point, in solutions...

Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.

Science.gov (United States)

Onogi, Akio; Watanabe, Maya; Mochizuki, Toshihiro; Hayashi, Takeshi; Nakagawa, Hiroshi; Hasegawa, Toshihiro; Iwata, Hiroyoshi

2016-04-01

It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model. Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype-environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder-Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in

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

Science.gov (United States)

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

2016-01-01

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

Predicting accurate absolute binding energies in aqueous solution

DEFF Research Database (Denmark)

Jensen, Jan Halborg

2015-01-01

Recent predictions of absolute binding free energies of host-guest complexes in aqueous solution using electronic structure theory have been encouraging for some systems, while other systems remain problematic. In this paper I summarize some of the many factors that could easily contribute 1-3 kcal......-represented by continuum models. While I focus on binding free energies in aqueous solution the approach also applies (with minor adjustments) to any free energy difference such as conformational or reaction free energy differences or activation free energies in any solvent....

A study of modelling simplifications in ground vibration predictions for railway traffic at grade

Science.gov (United States)

Germonpré, M.; Degrande, G.; Lombaert, G.

2017-10-01

Accurate computational models are required to predict ground-borne vibration due to railway traffic. Such models generally require a substantial computational effort. Therefore, much research has focused on developing computationally efficient methods, by either exploiting the regularity of the problem geometry in the direction along the track or assuming a simplified track structure. This paper investigates the modelling errors caused by commonly made simplifications of the track geometry. A case study is presented investigating a ballasted track in an excavation. The soil underneath the ballast is stiffened by a lime treatment. First, periodic track models with different cross sections are analyzed, revealing that a prediction of the rail receptance only requires an accurate representation of the soil layering directly underneath the ballast. A much more detailed representation of the cross sectional geometry is required, however, to calculate vibration transfer from track to free field. Second, simplifications in the longitudinal track direction are investigated by comparing 2.5D and periodic track models. This comparison shows that the 2.5D model slightly overestimates the track stiffness, while the transfer functions between track and free field are well predicted. Using a 2.5D model to predict the response during a train passage leads to an overestimation of both train-track interaction forces and free field vibrations. A combined periodic/2.5D approach is therefore proposed in this paper. First, the dynamic axle loads are computed by solving the train-track interaction problem with a periodic model. Next, the vibration transfer to the free field is computed with a 2.5D model. This combined periodic/2.5D approach only introduces small modelling errors compared to an approach in which a periodic model is used in both steps, while significantly reducing the computational cost.

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Science.gov (United States)

Drzewiecki, Wojciech

2016-12-01

In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

Performance of Reynolds Averaged Navier-Stokes Models in Predicting Separated Flows: Study of the Hump Flow Model Problem

Science.gov (United States)

Cappelli, Daniele; Mansour, Nagi N.

2012-01-01

Separation can be seen in most aerodynamic flows, but accurate prediction of separated flows is still a challenging problem for computational fluid dynamics (CFD) tools. The behavior of several Reynolds Averaged Navier-Stokes (RANS) models in predicting the separated ow over a wall-mounted hump is studied. The strengths and weaknesses of the most popular RANS models (Spalart-Allmaras, k-epsilon, k-omega, k-omega-SST) are evaluated using the open source software OpenFOAM. The hump ow modeled in this work has been documented in the 2004 CFD Validation Workshop on Synthetic Jets and Turbulent Separation Control. Only the baseline case is treated; the slot flow control cases are not considered in this paper. Particular attention is given to predicting the size of the recirculation bubble, the position of the reattachment point, and the velocity profiles downstream of the hump.

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

Science.gov (United States)

Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have

Accurate Modeling of Ionospheric Electromagnetic Fields Generated by a Low Altitude VLF Transmitter

Science.gov (United States)

2009-03-31

AFRL-RV-HA-TR-2009-1055 Accurate Modeling of Ionospheric Electromagnetic Fields Generated by a Low Altitude VLF Transmitter ...m (or even 500 m) at mid to high latitudes . At low latitudes , the FDTD model exhibits variations that make it difficult to determine a reliable...Scientific, Final 3. DATES COVERED (From - To) 02-08-2006 – 31-12-2008 4. TITLE AND SUBTITLE Accurate Modeling of Ionospheric Electromagnetic Fields

A variable capacitance based modeling and power capability predicting method for ultracapacitor

Science.gov (United States)

Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang

2018-01-01

Methods of accurate modeling and power capability predicting for ultracapacitors are of great significance in management and application of lithium-ion battery/ultracapacitor hybrid energy storage system. To overcome the simulation error coming from constant capacitance model, an improved ultracapacitor model based on variable capacitance is proposed, where the main capacitance varies with voltage according to a piecewise linear function. A novel state-of-charge calculation approach is developed accordingly. After that, a multi-constraint power capability prediction is developed for ultracapacitor, in which a Kalman-filter-based state observer is designed for tracking ultracapacitor's real-time behavior. Finally, experimental results verify the proposed methods. The accuracy of the proposed model is verified by terminal voltage simulating results under different temperatures, and the effectiveness of the designed observer is proved by various test conditions. Additionally, the power capability prediction results of different time scales and temperatures are compared, to study their effects on ultracapacitor's power capability.

Predictive Uncertainty Estimation in Water Demand Forecasting Using the Model Conditional Processor

Directory of Open Access Journals (Sweden)

Amos O. Anele

2018-04-01

Full Text Available In a previous paper, a number of potential models for short-term water demand (STWD prediction have been analysed to find the ones with the best fit. The results obtained in Anele et al. (2017 showed that hybrid models may be considered as the accurate and appropriate forecasting models for STWD prediction. However, such best single valued forecast does not guarantee reliable and robust decisions, which can be properly obtained via model uncertainty processors (MUPs. MUPs provide an estimate of the full predictive densities and not only the single valued expected prediction. Amongst other MUPs, the purpose of this paper is to use the multi-variate version of the model conditional processor (MCP, proposed by Todini (2008, to demonstrate how the estimation of the predictive probability conditional to a number of relatively good predictive models may improve our knowledge, thus reducing the predictive uncertainty (PU when forecasting into the unknown future. Through the MCP approach, the probability distribution of the future water demand can be assessed depending on the forecast provided by one or more deterministic forecasting models. Based on an average weekly data of 168 h, the probability density of the future demand is built conditional on three models’ predictions, namely the autoregressive-moving average (ARMA, feed-forward back propagation neural network (FFBP-NN and hybrid model (i.e., combined forecast from ARMA and FFBP-NN. The results obtained show that MCP may be effectively used for real-time STWD prediction since it brings out the PU connected to its forecast, and such information could help water utilities estimate the risk connected to a decision.

Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis.

Science.gov (United States)

Byham-Gray, Laura D; Parrott, J Scott; Peters, Emily N; Fogerite, Susan Gould; Hand, Rosa K; Ahrens, Sean; Marcus, Andrea Fleisch; Fiutem, Justin J

2017-03-01

Hypermetabolism is theorized in patients diagnosed with chronic kidney disease who are receiving maintenance hemodialysis (MHD). We aimed to distinguish key disease-specific determinants of resting energy expenditure to create a predictive energy equation that more precisely establishes energy needs with the intent of preventing protein-energy wasting. For this 3-year multisite cross-sectional study (N = 116), eligible participants were diagnosed with chronic kidney disease and were receiving MHD for at least 3 months. Predictors for the model included weight, sex, age, C-reactive protein (CRP), glycosylated hemoglobin, and serum creatinine. The outcome variable was measured resting energy expenditure (mREE). Regression modeling was used to generate predictive formulas and Bland-Altman analyses to evaluate accuracy. The majority were male (60.3%), black (81.0%), and non-Hispanic (76.7%), and 23% were ≥65 years old. After screening for multicollinearity, the best predictive model of mREE ( R 2 = 0.67) included weight, age, sex, and CRP. Two alternative models with acceptable predictability ( R 2 = 0.66) were derived with glycosylated hemoglobin or serum creatinine. Based on Bland-Altman analyses, the maintenance hemodialysis equation that included CRP had the best precision, with the highest proportion of participants' predicted energy expenditure classified as accurate (61.2%) and with the lowest number of individuals with underestimation or overestimation. This study confirms disease-specific factors as key determinants of mREE in patients on MHD and provides a preliminary predictive energy equation. Further prospective research is necessary to test the reliability and validity of this equation across diverse populations of patients who are receiving MHD.

A new solar power output prediction based on hybrid forecast engine and decomposition model.

Science.gov (United States)

Zhang, Weijiang; Dang, Hongshe; Simoes, Rolando

2018-06-12

Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Fast and accurate calculation of dilute quantum gas using Uehling–Uhlenbeck model equation

Energy Technology Data Exchange (ETDEWEB)

Yano, Ryosuke, E-mail: ryosuke.yano@tokiorisk.co.jp

2017-02-01

The Uehling–Uhlenbeck (U–U) model equation is studied for the fast and accurate calculation of a dilute quantum gas. In particular, the direct simulation Monte Carlo (DSMC) method is used to solve the U–U model equation. DSMC analysis based on the U–U model equation is expected to enable the thermalization to be accurately obtained using a small number of sample particles and the dilute quantum gas dynamics to be calculated in a practical time. Finally, the applicability of DSMC analysis based on the U–U model equation to the fast and accurate calculation of a dilute quantum gas is confirmed by calculating the viscosity coefficient of a Bose gas on the basis of the Green–Kubo expression and the shock layer of a dilute Bose gas around a cylinder.

A simple, fast, and accurate thermodynamic-based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part III: Retention time prediction on target column.

Science.gov (United States)

Hou, Siyuan; Stevenson, Keisean A J M; Harynuk, James J

2018-03-27

This is the third part of a three-part series of papers. In Part I, we presented a method for determining the actual effective geometry of a reference column as well as the thermodynamic-based parameters of a set of probe compounds in an in-house mixture. Part II introduced an approach for estimating the actual effective geometry of a target column by collecting retention data of the same mixture of probe compounds on the target column and using their thermodynamic parameters, acquired on the reference column, as a bridge between both systems. Part III, presented here, demonstrates the retention time transfer and prediction from the reference column to the target column using experimental data for a separate mixture of compounds. To predict the retention time of a new compound, we first estimate its thermodynamic-based parameters on the reference column (using geometric parameters determined previously). The compound's retention time on a second column (of previously determined geometry) is then predicted. The models and the associated optimization algorithms were tested using simulated and experimental data. The accuracy of predicted retention times shows that the proposed approach is simple, fast, and accurate for retention time transfer and prediction between gas chromatography columns. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

International Nuclear Information System (INIS)

Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke

2015-01-01

attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries

The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

Energy Technology Data Exchange (ETDEWEB)

Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke, E-mail: ksheng@mednet.ucla.edu [Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90024 (United States)

2015-11-15

attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.

The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery.

Science.gov (United States)

Yu, Victoria Y; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A; Sheng, Ke

2015-11-01

errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.

Mid- and long-term runoff predictions by an improved phase-space reconstruction model

International Nuclear Information System (INIS)

Hong, Mei; Wang, Dong; Wang, Yuankun; Zeng, Xiankui; Ge, Shanshan; Yan, Hengqian; Singh, Vijay P.

2016-01-01

In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall–runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ‘‘wet years and dry years predictability barrier,’’ to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff. - Highlights: • The improved phase-space reconstruction model of monthly runoff is established. • Two variables (temperature and rainfall) are incorporated

Mid- and long-term runoff predictions by an improved phase-space reconstruction model

Energy Technology Data Exchange (ETDEWEB)

Hong, Mei [Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and oceanography, PLA University of Science and Technology, Nanjing (China); Wang, Dong, E-mail: wangdong@nju.edu.cn [Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210093 (China); Wang, Yuankun; Zeng, Xiankui [Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210093 (China); Ge, Shanshan; Yan, Hengqian [Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and oceanography, PLA University of Science and Technology, Nanjing (China); Singh, Vijay P. [Department of Biological and Agricultural Engineering Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843 (United States)

2016-07-15

In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall–runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ‘‘wet years and dry years predictability barrier,’’ to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff. - Highlights: • The improved phase-space reconstruction model of monthly runoff is established. • Two variables (temperature and rainfall) are incorporated

Long Range Aircraft Trajectory Prediction

OpenAIRE

Magister, Tone

2009-01-01

The subject of the paper is the improvement of the aircraft future trajectory prediction accuracy for long-range airborne separation assurance. The strategic planning of safe aircraft flights and effective conflict avoidance tactics demand timely and accurate conflict detection based upon future four–dimensional airborne traffic situation prediction which is as accurate as each aircraft flight trajectory prediction. The improved kinematics model of aircraft relative flight considering flight ...

Crop Yield Predictions - High Resolution Statistical Model for Intra-season Forecasts Applied to Corn in the US

Science.gov (United States)

Cai, Y.

2017-12-01

Accurately forecasting crop yields has broad implications for economic trading, food production monitoring, and global food security. However, the variation of environmental variables presents challenges to model yields accurately, especially when the lack of highly accurate measurements creates difficulties in creating models that can succeed across space and time. In 2016, we developed a sequence of machine-learning based models forecasting end-of-season corn yields for the US at both the county and national levels. We combined machine learning algorithms in a hierarchical way, and used an understanding of physiological processes in temporal feature selection, to achieve high precision in our intra-season forecasts, including in very anomalous seasons. During the live run, we predicted the national corn yield within 1.40% of the final USDA number as early as August. In the backtesting of the 2000-2015 period, our model predicts national yield within 2.69% of the actual yield on average already by mid-August. At the county level, our model predicts 77% of the variation in final yield using data through the beginning of August and improves to 80% by the beginning of October, with the percentage of counties predicted within 10% of the average yield increasing from 68% to 73%. Further, the lowest errors are in the most significant producing regions, resulting in very high precision national-level forecasts. In addition, we identify the changes of important variables throughout the season, specifically early-season land surface temperature, and mid-season land surface temperature and vegetation index. For the 2017 season, we feed 2016 data to the training set, together with additional geospatial data sources, aiming to make the current model even more precise. We will show how our 2017 US corn yield forecasts converges in time, which factors affect the yield the most, as well as present our plans for 2018 model adjustments.

Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models.

Science.gov (United States)

Yock, Adam D; Rao, Arvind; Dong, Lei; Beadle, Beth M; Garden, Adam S; Kudchadker, Rajat J; Court, Laurence E

2014-05-01

The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: -11.6%-23.8%) and 14.6% (range: -7.3%-27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: -6.8%-40.3%) and 13.1% (range: -1.5%-52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: -11.1%-20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.

Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models

International Nuclear Information System (INIS)

Yock, Adam D.; Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.

2014-01-01

Purpose: The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Methods: Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. Results: In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: −11.6%–23.8%) and 14.6% (range: −7.3%–27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: −6.8%–40.3%) and 13.1% (range: −1.5%–52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: −11.1%–20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. Conclusions: A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography

Helicopter Rotor Load Prediction Using a Geometrically Exact Beam with Multicomponent Model

DEFF Research Database (Denmark)

Lee, Hyun-Ku; Viswamurthy, S.R.; Park, Sang Chul

2010-01-01

In this paper, an accurate structural dynamic analysis was developed for a helicopter rotor system including rotor control components, which was coupled to various aerodynamic and wake models in order to predict an aeroelastic response and the loads acting on the rotor. Its blade analysis was based...... rotor-blade/control-system model was loosely coupled with various inflow and wake models in order to simulate both hover and forward-flight conditions. The resulting rotor blade response and pitch link loads are in good agreement with those predicted byCAMRADII. The present analysis features both model...... on an intrinsic formulation of moving beams implemented in the time domain. The rotor control system was modeled as a combination of rigid and elastic components. A multicomponent analysis was then developed by coupling the beam finite element model with the rotor control system model to obtain a complete rotor-blade/control...

Winnerless competition principle and prediction of the transient dynamics in a Lotka-Volterra model

Science.gov (United States)

Afraimovich, Valentin; Tristan, Irma; Huerta, Ramon; Rabinovich, Mikhail I.

2008-12-01

Predicting the evolution of multispecies ecological systems is an intriguing problem. A sufficiently complex model with the necessary predicting power requires solutions that are structurally stable. Small variations of the system parameters should not qualitatively perturb its solutions. When one is interested in just asymptotic results of evolution (as time goes to infinity), then the problem has a straightforward mathematical image involving simple attractors (fixed points or limit cycles) of a dynamical system. However, for an accurate prediction of evolution, the analysis of transient solutions is critical. In this paper, in the framework of the traditional Lotka-Volterra model (generalized in some sense), we show that the transient solution representing multispecies sequential competition can be reproducible and predictable with high probability.

A random forest based risk model for reliable and accurate prediction of receipt of transfusion in patients undergoing percutaneous coronary intervention.

Directory of Open Access Journals (Sweden)

Hitinder S Gurm

Full Text Available BACKGROUND: Transfusion is a common complication of Percutaneous Coronary Intervention (PCI and is associated with adverse short and long term outcomes. There is no risk model for identifying patients most likely to receive transfusion after PCI. The objective of our study was to develop and validate a tool for predicting receipt of blood transfusion in patients undergoing contemporary PCI. METHODS: Random forest models were developed utilizing 45 pre-procedural clinical and laboratory variables to estimate the receipt of transfusion in patients undergoing PCI. The most influential variables were selected for inclusion in an abbreviated model. Model performance estimating transfusion was evaluated in an independent validation dataset using area under the ROC curve (AUC, with net reclassification improvement (NRI used to compare full and reduced model prediction after grouping in low, intermediate, and high risk categories. The impact of procedural anticoagulation on observed versus predicted transfusion rates were assessed for the different risk categories. RESULTS: Our study cohort was comprised of 103,294 PCI procedures performed at 46 hospitals between July 2009 through December 2012 in Michigan of which 72,328 (70% were randomly selected for training the models, and 30,966 (30% for validation. The models demonstrated excellent calibration and discrimination (AUC: full model  = 0.888 (95% CI 0.877-0.899, reduced model AUC = 0.880 (95% CI, 0.868-0.892, p for difference 0.003, NRI = 2.77%, p = 0.007. Procedural anticoagulation and radial access significantly influenced transfusion rates in the intermediate and high risk patients but no clinically relevant impact was noted in low risk patients, who made up 70% of the total cohort. CONCLUSIONS: The risk of transfusion among patients undergoing PCI can be reliably calculated using a novel easy to use computational tool (https://bmc2.org/calculators/transfusion. This risk prediction

A novel Bayesian hierarchical model for road safety hotspot prediction.

Science.gov (United States)

Fawcett, Lee; Thorpe, Neil; Matthews, Joseph; Kremer, Karsten

2017-02-01

model. We conclude that our model accurately predicts future accident counts, with point estimates from the predictive distribution matching observed counts extremely well. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).

Science.gov (United States)

Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

2014-01-01

Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

Predicting the natural flow regime: Models for assessing hydrological alteration in streams

Science.gov (United States)

Carlisle, D.M.; Falcone, J.; Wolock, D.M.; Meador, M.R.; Norris, R.H.

2009-01-01

Understanding the extent to which natural streamflow characteristics have been altered is an important consideration for ecological assessments of streams. Assessing hydrologic condition requires that we quantify the attributes of the flow regime that would be expected in the absence of anthropogenic modifications. The objective of this study was to evaluate whether selected streamflow characteristics could be predicted at regional and national scales using geospatial data. Long-term, gaged river basins distributed throughout the contiguous US that had streamflow characteristics representing least disturbed or near pristine conditions were identified. Thirteen metrics of the magnitude, frequency, duration, timing and rate of change of streamflow were calculated using a 20-50 year period of record for each site. We used random forests (RF), a robust statistical modelling approach, to develop models that predicted the value for each streamflow metric using natural watershed characteristics. We compared the performance (i.e. bias and precision) of national- and regional-scale predictive models to that of models based on landscape classifications, including major river basins, ecoregions and hydrologic landscape regions (HLR). For all hydrologic metrics, landscape stratification models produced estimates that were less biased and more precise than a null model that accounted for no natural variability. Predictive models at the national and regional scale performed equally well, and substantially improved predictions of all hydrologic metrics relative to landscape stratification models. Prediction error rates ranged from 15 to 40%, but were 25% for most metrics. We selected three gaged, non-reference sites to illustrate how predictive models could be used to assess hydrologic condition. These examples show how the models accurately estimate predisturbance conditions and are sensitive to changes in streamflow variability associated with long-term land-use change. We also

Allele-sharing models: LOD scores and accurate linkage tests.

Science.gov (United States)

Kong, A; Cox, N J

1997-11-01

Starting with a test statistic for linkage analysis based on allele sharing, we propose an associated one-parameter model. Under general missing-data patterns, this model allows exact calculation of likelihood ratios and LOD scores and has been implemented by a simple modification of existing software. Most important, accurate linkage tests can be performed. Using an example, we show that some previously suggested approaches to handling less than perfectly informative data can be unacceptably conservative. Situations in which this model may not perform well are discussed, and an alternative model that requires additional computations is suggested.

Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model

Directory of Open Access Journals (Sweden)

Jun Lin

2018-01-01

Full Text Available The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer’s operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA and a generalized regression neural network (GRNN using an improved fruit fly optimization algorithm (FFOA to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM and gray model (GM methods.

Thermophysical properties of liquid UO2, ZrO2 and corium by molecular dynamics and predictive models

International Nuclear Information System (INIS)

Kim, Woong Kee; Shim, Ji Hoon; Kaviany Massoud

2016-01-01

The analysis of such accidents (fate of the melt), requires accurate corium thermophysical properties data up to 5000 K. In addition, the initial corium melt superheat melt, determined from such properties, are key in predicting the fuel-coolant interactions (FCIs) and convection and retention of corium in accident scenarios, e.g., core-melt down corium discharge from reactor pressure vessels and spreading in external core-catcher. Due to the high temperatures, data on molten corium and its constituents are limited, so there are much data scatters and mostly extrapolations (even from solid state) have been used. Here we predict the thermophysical properties of molten UO 2 and ZrO 2 using classical molecular dynamics (MD) simulations (properties of corium are predicted using the mixture theories and UO 2 and ZrO 2 properties). The thermophysical properties (density, compressibility, heat capacity, viscosity and surface tension) of liquid UO 2 and ZrO 2 are predicted using classical molecular dynamics simulations, up to 5000 K. For atomic interactions, the CRG and the Teter potential models are found most appropriate. The liquid behavior is verified with the random motion of the constituent atoms and the pair-distribution functions, starting with the solid phase and raising the temperature to realize liquid phase. The viscosity and thermal conductivity are calculated with the Green-Kubo autocorrelation decay formulae and compared with the predictive models of Andrade and Bridgman. For liquid UO 2 , the CRG model gives satisfactory MD predictions. For ZrO 2 , the density is reliably predicted with the CRG potential model, while the compressibility and viscosity are more accurately predicted by the Teter model

Fast integration-based prediction bands for ordinary differential equation models.

Science.gov (United States)

Hass, Helge; Kreutz, Clemens; Timmer, Jens; Kaschek, Daniel

2016-04-15

To gain a deeper understanding of biological processes and their relevance in disease, mathematical models are built upon experimental data. Uncertainty in the data leads to uncertainties of the model's parameters and in turn to uncertainties of predictions. Mechanistic dynamic models of biochemical networks are frequently based on nonlinear differential equation systems and feature a large number of parameters, sparse observations of the model components and lack of information in the available data. Due to the curse of dimensionality, classical and sampling approaches propagating parameter uncertainties to predictions are hardly feasible and insufficient. However, for experimental design and to discriminate between competing models, prediction and confidence bands are essential. To circumvent the hurdles of the former methods, an approach to calculate a profile likelihood on arbitrary observations for a specific time point has been introduced, which provides accurate confidence and prediction intervals for nonlinear models and is computationally feasible for high-dimensional models. In this article, reliable and smooth point-wise prediction and confidence bands to assess the model's uncertainty on the whole time-course are achieved via explicit integration with elaborate correction mechanisms. The corresponding system of ordinary differential equations is derived and tested on three established models for cellular signalling. An efficiency analysis is performed to illustrate the computational benefit compared with repeated profile likelihood calculations at multiple time points. The integration framework and the examples used in this article are provided with the software package Data2Dynamics, which is based on MATLAB and freely available at http://www.data2dynamics.org helge.hass@fdm.uni-freiburg.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e

Structure-Activity Relationship Models for Rat Carcinogenesis and Assessing the Role Mutagens Play in Model Predictivity

Science.gov (United States)

Carrasquer, C. Alex; Batey, Kaylind; Qamar, Shahid; Cunningham, Albert R.; Cunningham, Suzanne L.

2016-01-01

We previously demonstrated that fragment based cat-SAR carcinogenesis models consisting solely of mutagenic or non-mutagenic carcinogens varied greatly in terms of their predictive accuracy. This led us to investigate how well the rat cancer cat-SAR model predicted mutagens and non-mutagens in their learning set. Four rat cancer cat-SAR models were developed: Complete Rat, Transgender Rat, Male Rat, and Female Rat, with leave-one-out (LOO) validation concordance values of 69%, 74%, 67%, and 73%, respectively. The mutagenic carcinogens produced concordance values in the range of 69–76% as compared to only 47–53% for non-mutagenic carcinogens. As a surrogate for mutagenicity comparisons between single site and multiple site carcinogen SAR models was analyzed. The LOO concordance values for models consisting of 1-site, 2-site, and 4+-site carcinogens were 66%, 71%, and 79%, respectively. As expected, the proportion of mutagens to non-mutagens also increased, rising from 54% for 1-site to 80% for 4+-site carcinogens. This study demonstrates that mutagenic chemicals, in both SAR learning sets and test sets, are influential in assessing model accuracy. This suggests that SAR models for carcinogens may require a two-step process in which mutagenicity is first determined before carcinogenicity can be accurately predicted. PMID:24697549

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

Science.gov (United States)

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Development of Predictive Models of Injury for the Lower Extremity, Lumbar, and Thoracic Spine after discharge from Physical Rehabilitation

Science.gov (United States)

2017-10-01

prediction models will vary by age and sex . Hypothesis 3: A multi-factorial prediction model that accurately predicts risk of new and recurring injuries, as...cleared to return to duty from an injury is of great importance. The purpose of this project is to determine if performance on a battery of...balance screens, measures of power, demographic data and biopsychosocial measures. • Injury data will be collected through self -report, profile data, and

Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction II: Nonplanar Molecules.

Science.gov (United States)

Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

2017-11-14

The crystal structure prediction (CSP) of a given compound from its molecular diagram is a fundamental challenge in computational chemistry with implications in relevant technological fields. A key component of CSP is the method to calculate the lattice energy of a crystal, which allows the ranking of candidate structures. This work is the second part of our investigation to assess the potential of the exchange-hole dipole moment (XDM) dispersion model for crystal structure prediction. In this article, we study the relatively large, nonplanar, mostly flexible molecules in the first five blind tests held by the Cambridge Crystallographic Data Centre. Four of the seven experimental structures are predicted as the energy minimum, and thermal effects are demonstrated to have a large impact on the ranking of at least another compound. As in the first part of this series, delocalization error affects the results for a single crystal (compound X), in this case by detrimentally overstabilizing the π-conjugated conformation of the monomer. Overall, B86bPBE-XDM correctly predicts 16 of the 21 compounds in the five blind tests, a result similar to the one obtained using the best CSP method available to date (dispersion-corrected PW91 by Neumann et al.). Perhaps more importantly, the systems for which B86bPBE-XDM fails to predict the experimental structure as the energy minimum are mostly the same as with Neumann's method, which suggests that similar difficulties (absence of vibrational free energy corrections, delocalization error,...) are not limited to B86bPBE-XDM but affect GGA-based DFT-methods in general. Our work confirms B86bPBE-XDM as an excellent option for crystal energy ranking in CSP and offers a guide to identify crystals (organic salts, conjugated flexible systems) where difficulties may appear.

A Predictive Model for Yeast Cell Polarization in Pheromone Gradients.

Science.gov (United States)

Muller, Nicolas; Piel, Matthieu; Calvez, Vincent; Voituriez, Raphaël; Gonçalves-Sá, Joana; Guo, Chin-Lin; Jiang, Xingyu; Murray, Andrew; Meunier, Nicolas

2016-04-01

Budding yeast cells exist in two mating types, a and α, which use peptide pheromones to communicate with each other during mating. Mating depends on the ability of cells to polarize up pheromone gradients, but cells also respond to spatially uniform fields of pheromone by polarizing along a single axis. We used quantitative measurements of the response of a cells to α-factor to produce a predictive model of yeast polarization towards a pheromone gradient. We found that cells make a sharp transition between budding cycles and mating induced polarization and that they detect pheromone gradients accurately only over a narrow range of pheromone concentrations corresponding to this transition. We fit all the parameters of the mathematical model by using quantitative data on spontaneous polarization in uniform pheromone concentration. Once these parameters have been computed, and without any further fit, our model quantitatively predicts the yeast cell response to pheromone gradient providing an important step toward understanding how cells communicate with each other.

A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

Science.gov (United States)

Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon

2016-03-01

Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD  =  1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be

Evaluation of model quality predictions in CASP9

KAUST Repository

Kryshtafovych, Andriy

2011-01-01

CASP has been assessing the state of the art in the a priori estimation of accuracy of protein structure prediction since 2006. The inclusion of model quality assessment category in CASP contributed to a rapid development of methods in this area. In the last experiment, 46 quality assessment groups tested their approaches to estimate the accuracy of protein models as a whole and/or on a per-residue basis. We assessed the performance of these methods predominantly on the basis of the correlation between the predicted and observed quality of the models on both global and local scales. The ability of the methods to identify the models closest to the best one, to differentiate between good and bad models, and to identify well modeled regions was also analyzed. Our evaluations demonstrate that even though global quality assessment methods seem to approach perfection point (weighted average per-target Pearson\\'s correlation coefficients are as high as 0.97 for the best groups), there is still room for improvement. First, all top-performing methods use consensus approaches to generate quality estimates, and this strategy has its own limitations. Second, the methods that are based on the analysis of individual models lag far behind clustering techniques and need a boost in performance. The methods for estimating per-residue accuracy of models are less accurate than global quality assessment methods, with an average weighted per-model correlation coefficient in the range of 0.63-0.72 for the best 10 groups.

A Bayesian antedependence model for whole genome prediction.

Science.gov (United States)

Yang, Wenzhao; Tempelman, Robert J

2012-04-01

Hierarchical mixed effects models have been demonstrated to be powerful for predicting genomic merit of livestock and plants, on the basis of high-density single-nucleotide polymorphism (SNP) marker panels, and their use is being increasingly advocated for genomic predictions in human health. Two particularly popular approaches, labeled BayesA and BayesB, are based on specifying all SNP-associated effects to be independent of each other. BayesB extends BayesA by allowing a large proportion of SNP markers to be associated with null effects. We further extend these two models to specify SNP effects as being spatially correlated due to the chromosomally proximal effects of causal variants. These two models, that we respectively dub as ante-BayesA and ante-BayesB, are based on a first-order nonstationary antedependence specification between SNP effects. In a simulation study involving 20 replicate data sets, each analyzed at six different SNP marker densities with average LD levels ranging from r(2) = 0.15 to 0.31, the antedependence methods had significantly (P 0. 24) with differences exceeding 3%. A cross-validation study was also conducted on the heterogeneous stock mice data resource (http://mus.well.ox.ac.uk/mouse/HS/) using 6-week body weights as the phenotype. The antedependence methods increased cross-validation prediction accuracies by up to 3.6% compared to their classical counterparts (P benchmark data sets and demonstrated that the antedependence methods were more accurate than their classical counterparts for genomic predictions, even for individuals several generations beyond the training data.

A model to predict multivessel coronary artery disease from the exercise thallium-201 stress test

International Nuclear Information System (INIS)

Pollock, S.G.; Abbott, R.D.; Boucher, C.A.; Watson, D.D.; Kaul, S.

1991-01-01

The aim of this study was to (1) determine whether nonimaging variables add to the diagnostic information available from exercise thallium-201 images for the detection of multivessel coronary artery disease; and (2) to develop a model based on the exercise thallium-201 stress test to predict the presence of multivessel disease. The study populations included 383 patients referred to the University of Virginia and 325 patients referred to the Massachusetts General Hospital for evaluation of chest pain. All patients underwent both cardiac catheterization and exercise thallium-201 stress testing between 1978 and 1981. In the University of Virginia cohort, at each level of thallium-201 abnormality (no defects, one defect, more than one defect), ST depression and patient age added significantly in the detection of multivessel disease. Logistic regression analysis using data from these patients identified three independent predictors of multivessel disease: initial thallium-201 defects, ST depression, and age. A model was developed to predict multivessel disease based on these variables. As might be expected, the risk of multivessel disease predicted by the model was similar to that actually observed in the University of Virginia population. More importantly, however, the model was accurate in predicting the occurrence of multivessel disease in the unrelated population studied at the Massachusetts General Hospital. It is, therefore, concluded that (1) nonimaging variables (age and exercise-induced ST depression) add independent information to thallium-201 imaging data in the detection of multivessel disease; and (2) a model has been developed based on the exercise thallium-201 stress test that can accurately predict the probability of multivessel disease in other populations

Blasting Vibration Safety Criterion Analysis with Equivalent Elastic Boundary: Based on Accurate Loading Model

Directory of Open Access Journals (Sweden)

Qingwen Li

2015-01-01

Full Text Available In the tunnel and underground space engineering, the blasting wave will attenuate from shock wave to stress wave to elastic seismic wave in the host rock. Also, the host rock will form crushed zone, fractured zone, and elastic seismic zone under the blasting loading and waves. In this paper, an accurate mathematical dynamic loading model was built. And the crushed zone as well as fractured zone was considered as the blasting vibration source thus deducting the partial energy for cutting host rock. So this complicated dynamic problem of segmented differential blasting was regarded as an equivalent elastic boundary problem by taking advantage of Saint-Venant’s Theorem. At last, a 3D model in finite element software FLAC3D accepted the constitutive parameters, uniformly distributed mutative loading, and the cylindrical attenuation law to predict the velocity curves and effective tensile curves for calculating safety criterion formulas of surrounding rock and tunnel liner after verifying well with the in situ monitoring data.

Predictive simulation of bidirectional Glenn shunt using a hybrid blood vessel model.

Science.gov (United States)

Li, Hao; Leow, Wee Kheng; Chiu, Ing-Sh

2009-01-01

This paper proposes a method for performing predictive simulation of cardiac surgery. It applies a hybrid approach to model the deformation of blood vessels. The hybrid blood vessel model consists of a reference Cosserat rod and a surface mesh. The reference Cosserat rod models the blood vessel's global bending, stretching, twisting and shearing in a physically correct manner, and the surface mesh models the surface details of the blood vessel. In this way, the deformation of blood vessels can be computed efficiently and accurately. Our predictive simulation system can produce complex surgical results given a small amount of user inputs. It allows the surgeon to easily explore various surgical options and evaluate them. Tests of the system using bidirectional Glenn shunt (BDG) as an application example show that the results produc by the system are similar to real surgical results.

Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

Science.gov (United States)

Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

2018-05-01

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine

Directory of Open Access Journals (Sweden)

Hang-cheong Wong

2012-01-01

Full Text Available Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM, to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN and decremental least-squares support vector machine (DLSSVM. Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.

Prediction of microsegregation and pitting corrosion resistance of austenitic stainless steel welds by modelling

Energy Technology Data Exchange (ETDEWEB)

Vilpas, M. [VTT Manufacturing Technology, Espoo (Finland). Materials and Structural Integrity

1999-07-01

The present study focuses on the ability of several computer models to accurately predict the solidification, microsegregation and pitting corrosion resistance of austenitic stainless steel weld metals. Emphasis was given to modelling the effect of welding speed on solute redistribution and ultimately to the prediction of weld pitting corrosion resistance. Calculations were experimentally verified by applying autogenous GTA- and laser processes over the welding speed range of 0.1 to 5 m/min for several austenitic stainless steel grades. Analytical and computer aided models were applied and linked together for modelling the solidification behaviour of welds. The combined use of macroscopic and microscopic modelling is a unique feature of this work. This procedure made it possible to demonstrate the effect of weld pool shape and the resulting solidification parameters on microsegregation and pitting corrosion resistance. Microscopic models were also used separately to study the role of welding speed and solidification mode in the development of microsegregation and pitting corrosion resistance. These investigations demonstrate that the macroscopic model can be implemented to predict solidification parameters that agree well with experimentally measured values. The linked macro-micro modelling was also able to accurately predict segregation profiles and CPT-temperatures obtained from experiments. The macro-micro simulations clearly showed the major roles of weld composition and welding speed in determining segregation and pitting corrosion resistance while the effect of weld shape variations remained negligible. The microscopic dendrite tip and interdendritic models were applied to welds with good agreement with measured segregation profiles. Simulations predicted that weld inhomogeneity can be substantially decreased with increasing welding speed resulting in a corresponding improvement in the weld pitting corrosion resistance. In the case of primary austenitic

High Quality Model Predictive Control for Single Phase Grid Connected Photovoltaic Inverters

DEFF Research Database (Denmark)

Zangeneh Bighash, Esmaeil; Sadeghzadeh, Seyed Mohammad; Ebrahimzadeh, Esmaeil

2018-01-01

Single phase grid-connected inverters with LCL filter are widely used to connect the photovoltaic systems to the utility grid. Among the presented control schemes, predictive control methods are faster and more accurate but are more complex to implement. Recently, the model-predictive control...... algorithm for single-phase inverter has been presented, where the algorithm implementation is straightforward. In the proposed approach, all switching states are tested in each switching period to achieve the control objectives. However, since the number of the switching states in single-phase inverter...... is low, the inverter output current has a high total harmonic distortions. In order to reduce the total harmonic distortions of the injected current, this paper presents a high-quality model-predictive control for one of the newest structure of the grid connected photovoltaic inverter, i.e., HERIC...

Refining Sunrise/set Prediction Models by Accounting for the Effects of Refraction

Science.gov (United States)

Wilson, Teresa; Bartlett, Jennifer L.

2016-01-01

Current atmospheric models used to predict the times of sunrise and sunset have an error of one to four minutes at mid-latitudes (0° - 55° N/S). At higher latitudes, slight changes in refraction may cause significant discrepancies, including determining even whether the Sun appears to rise or set. While different components of refraction are known, how they affect predictions of sunrise/set has not yet been quantified. A better understanding of the contributions from temperature profile, pressure, humidity, and aerosols, could significantly improve the standard prediction. Because sunrise/set times and meteorological data from multiple locations will be necessary for a thorough investigation of the problem, we will collect this data using smartphones as part of a citizen science project. This analysis will lead to more complete models that will provide more accurate times for navigators and outdoorsman alike.

Ability of the MACRO Model to Predict Long-Term Leaching of Metribuzin and Diketometribuzin

DEFF Research Database (Denmark)

Rosenbom, Annette E; Kjær, Jeanne; Henriksen, Trine

2009-01-01

In a regulatory context, numerical models are increasingly employed to quantify leaching of pesticides and their metabolites. Although the ability of these models to accurately simulate leaching of pesticides has been evaluated, little is known about their ability to accurately simulate long...... alternative kinetics (a two-site approach), we captured the observed leaching scenario, thus underlining the necessity of accounting for the long-term sorption and dissipation characteristics when using models to predict the risk of groundwater contamination.......-term leaching of metabolites. A Danish study on the dissipation and sorption of metribuzin, involving both monitoring and batch experiments, concluded that desorption and degradation of metribuzin and leaching of its primary metabolite diketometribuzin continued for 5-6 years after application, posing a risk...

The economic value of accurate wind power forecasting to utilities

Energy Technology Data Exchange (ETDEWEB)

Watson, S J [Rutherford Appleton Lab., Oxfordshire (United Kingdom); Giebel, G; Joensen, A [Risoe National Lab., Dept. of Wind Energy and Atmospheric Physics, Roskilde (Denmark)

1999-03-01

With increasing penetrations of wind power, the need for accurate forecasting is becoming ever more important. Wind power is by its very nature intermittent. For utility schedulers this presents its own problems particularly when the penetration of wind power capacity in a grid reaches a significant level (>20%). However, using accurate forecasts of wind power at wind farm sites, schedulers are able to plan the operation of conventional power capacity to accommodate the fluctuating demands of consumers and wind farm output. The results of a study to assess the value of forecasting at several potential wind farm sites in the UK and in the US state of Iowa using the Reading University/Rutherford Appleton Laboratory National Grid Model (NGM) are presented. The results are assessed for different types of wind power forecasting, namely: persistence, optimised numerical weather prediction or perfect forecasting. In particular, it will shown how the NGM has been used to assess the value of numerical weather prediction forecasts from the Danish Meteorological Institute model, HIRLAM, and the US Nested Grid Model, which have been `site tailored` by the use of the linearized flow model WA{sup s}P and by various Model output Statistics (MOS) and autoregressive techniques. (au)

Accurate path integration in continuous attractor network models of grid cells.

Science.gov (United States)

Burak, Yoram; Fiete, Ila R

2009-02-01

Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning. However, errors accumulate rapidly when velocity inputs are integrated in existing models of grid cell activity. To produce grid-cell-like responses, these models would require frequent resets triggered by external sensory cues. Such inadequacies, shared by various models, cast doubt on the dead-reckoning potential of the grid cell system. Here we focus on the question of accurate path integration, specifically in continuous attractor models of grid cell activity. We show, in contrast to previous models, that continuous attractor models can generate regular triangular grid responses, based on inputs that encode only the rat's velocity and heading direction. We consider the role of the network boundary in the integration performance of the network and show that both periodic and aperiodic networks are capable of accurate path integration, despite important differences in their attractor manifolds. We quantify the rate at which errors in the velocity integration accumulate as a function of network size and intrinsic noise within the network. With a plausible range of parameters and the inclusion of spike variability, our model networks can accurately integrate velocity inputs over a maximum of approximately 10-100 meters and approximately 1-10 minutes. These findings form a proof-of-concept that continuous attractor dynamics may underlie velocity integration in the dorsolateral medial entorhinal cortex. The simulations also generate pertinent upper bounds on the accuracy of integration that may be achieved by continuous attractor dynamics in the grid cell network. We suggest experiments to test the continuous attractor model and differentiate it from models in which single cells establish their responses independently of each other.

A cluster expansion model for predicting activation barrier of atomic processes

International Nuclear Information System (INIS)

Rehman, Tafizur; Jaipal, M.; Chatterjee, Abhijit

2013-01-01

We introduce a procedure based on cluster expansion models for predicting the activation barrier of atomic processes encountered while studying the dynamics of a material system using the kinetic Monte Carlo (KMC) method. Starting with an interatomic potential description, a mathematical derivation is presented to show that the local environment dependence of the activation barrier can be captured using cluster interaction models. Next, we develop a systematic procedure for training the cluster interaction model on-the-fly, which involves: (i) obtaining activation barriers for handful local environments using nudged elastic band (NEB) calculations, (ii) identifying the local environment by analyzing the NEB results, and (iii) estimating the cluster interaction model parameters from the activation barrier data. Once a cluster expansion model has been trained, it is used to predict activation barriers without requiring any additional NEB calculations. Numerical studies are performed to validate the cluster expansion model by studying hop processes in Ag/Ag(100). We show that the use of cluster expansion model with KMC enables efficient generation of an accurate process rate catalog

Accurately Detecting Students' Lies regarding Relational Aggression by Correctional Instructions

Science.gov (United States)

Dickhauser, Oliver; Reinhard, Marc-Andre; Marksteiner, Tamara

2012-01-01

This study investigates the effect of correctional instructions when detecting lies about relational aggression. Based on models from the field of social psychology, we predict that correctional instruction will lead to a less pronounced lie bias and to more accurate lie detection. Seventy-five teachers received videotapes of students' true denial…

TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction

Science.gov (United States)

Lalbakhsh, Pooia; Chen, Yi-Ping Phoebe

2017-05-01

This paper presents a transportable ant colony discrimination strategy (TACD) to predict corporate bankruptcy, a topic of vital importance that is attracting increasing interest in the field of economics. The proposed algorithm uses financial ratios to build a binary prediction model for companies with the two statuses of bankrupt and non-bankrupt. The algorithm takes advantage of an improved version of continuous ant colony optimisation (CACO) at the core, which is used to create an accurate, simple and understandable linear model for discrimination. This also enables the algorithm to work with continuous values, leading to more efficient learning and adaption by avoiding data discretisation. We conduct a comprehensive performance evaluation on three real-world data sets under a stratified cross-validation strategy. In three different scenarios, TACD is compared with 11 other bankruptcy prediction strategies. We also discuss the efficiency of the attribute selection methods used in the experiments. In addition to its simplicity and understandability, statistical significance tests prove the efficiency of TACD against the other prediction algorithms in both measures of AUC and accuracy.