Significance of predictive models/risk calculators for HBV-related hepatocellular carcinoma
Directory of Open Access Journals (Sweden)
DONG Jing
2015-06-01
Full Text Available Hepatitis B virus (HBV-related hepatocellular carcinoma (HCC is a major public health problem in Southeast Asia. In recent years, researchers from Hong Kong and Taiwan have reported predictive models or risk calculators for HBV-associated HCC by studying its natural history, which, to some extent, predicts the possibility of HCC development. Generally, risk factors of each model involve age, sex, HBV DNA level, and liver cirrhosis. This article discusses the evolution and clinical significance of currently used predictive models for HBV-associated HCC and assesses the advantages and limits of risk calculators. Updated REACH-B model and LSM-HCC model show better negative predictive values and have better performance in predicting the outcomes of patients with chronic hepatitis B (CHB. These models can be applied to stratified screening of HCC and, meanwhile, become an assessment tool for the management of CHB patients.
Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin
2016-11-01
Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.
Significance of predictive models/risk calculators for HBV-related hepatocellular carcinoma
DONG Jing
2015-01-01
Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is a major public health problem in Southeast Asia. In recent years, researchers from Hong Kong and Taiwan have reported predictive models or risk calculators for HBV-associated HCC by studying its natural history, which, to some extent, predicts the possibility of HCC development. Generally, risk factors of each model involve age, sex, HBV DNA level, and liver cirrhosis. This article discusses the evolution and clinical significa...
Research Pearls: The Significance of Statistics and Perils of Pooling. Part 2: Predictive Modeling.
Hohmann, Erik; Wetzler, Merrick J; D'Agostino, Ralph B
2017-07-01
The focus of predictive modeling or predictive analytics is to use statistical techniques to predict outcomes and/or the results of an intervention or observation for patients that are conditional on a specific set of measurements taken on the patients prior to the outcomes occurring. Statistical methods to estimate these models include using such techniques as Bayesian methods; data mining methods, such as machine learning; and classical statistical models of regression such as logistic (for binary outcomes), linear (for continuous outcomes), and survival (Cox proportional hazards) for time-to-event outcomes. A Bayesian approach incorporates a prior estimate that the outcome of interest is true, which is made prior to data collection, and then this prior probability is updated to reflect the information provided by the data. In principle, data mining uses specific algorithms to identify patterns in data sets and allows a researcher to make predictions about outcomes. Regression models describe the relations between 2 or more variables where the primary difference among methods concerns the form of the outcome variable, whether it is measured as a binary variable (i.e., success/failure), continuous measure (i.e., pain score at 6 months postop), or time to event (i.e., time to surgical revision). The outcome variable is the variable of interest, and the predictor variable(s) are used to predict outcomes. The predictor variable is also referred to as the independent variable and is assumed to be something the researcher can modify in order to see its impact on the outcome (i.e., using one of several possible surgical approaches). Survival analysis investigates the time until an event occurs. This can be an event such as failure of a medical device or death. It allows the inclusion of censored data, meaning that not all patients need to have the event (i.e., die) prior to the study's completion. Copyright © 2017 Arthroscopy Association of North America. Published by
DEFF Research Database (Denmark)
Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen
2011-01-01
Uncertainty derived from one of the process models – such as one-dimensional secondary settling tank (SST) models – can impact the output of the other process models, e.g., biokinetic (ASM1), as well as the integrated wastewater treatment plant (WWTP) models. The model structure and parameter...... uncertainty of settler models can therefore propagate, and add to the uncertainties in prediction of any plant performance criteria. Here we present an assessment of the relative significance of secondary settling model performance in WWTP simulations. We perform a global sensitivity analysis (GSA) based....... The outcome of this study contributes to a better understanding of uncertainty in WWTPs, and explicitly demonstrates the significance of secondary settling processes that are crucial elements of model prediction under dry and wet-weather loading conditions....
International Nuclear Information System (INIS)
Lee, S. C.; Bankoff, S. G.
1998-01-01
The predictive models for the Onset of Significant Void (OSV) in forced-convection subcooled boiling are reviewed and compared with extensive data. Three analytical models and seven empirical correlations are considered in this paper. These models and correlations are put onto a common basis and are compared, again on a common basis, with a variety of data. The evaluation of their range of validity and applicability under various operating conditions are discussed. The results show that the correlations of Saha-Zuber (1974) seems to be the best model to predict OSV in vertical subcooled boiling flow
Directory of Open Access Journals (Sweden)
D. O. Topping
2007-01-01
Full Text Available In order to predict the physical properties of aerosol particles, it is necessary to adequately capture the behaviour of the ubiquitous complex organic components. One of the key properties which may affect this behaviour is the contribution of the organic components to the surface tension of aqueous particles in the moist atmosphere. Whilst the qualitative effect of organic compounds on solution surface tensions has been widely reported, our quantitative understanding on mixed organic and mixed inorganic/organic systems is limited. Furthermore, it is unclear whether models that exist in the literature can reproduce the surface tension variability for binary and higher order multi-component organic and mixed inorganic/organic systems of atmospheric significance. The current study aims to resolve both issues to some extent. Surface tensions of single and multiple solute aqueous solutions were measured and compared with predictions from a number of model treatments. On comparison with binary organic systems, two predictive models found in the literature provided a range of values resulting from sensitivity to calculations of pure component surface tensions. Results indicate that a fitted model can capture the variability of the measured data very well, producing the lowest average percentage deviation for all compounds studied. The performance of the other models varies with compound and choice of model parameters. The behaviour of ternary mixed inorganic/organic systems was unreliably captured by using a predictive scheme and this was dependent on the composition of the solutes present. For more atmospherically representative higher order systems, entirely predictive schemes performed poorly. It was found that use of the binary data in a relatively simple mixing rule, or modification of an existing thermodynamic model with parameters derived from binary data, was able to accurately capture the surface tension variation with concentration. Thus
Wiener, Martin; Acland, Katharine M; Shaw, Helen M; Soong, Seng-Jaw; Lin, Hui-Yi; Chen, Dung-Tsa; Scolyer, Richard A; Winstanley, Julie B; Thompson, John F
2010-08-01
Completion lymph node dissection (CLND) following positive sentinel node biopsy (SNB) for melanoma detects additional nonsentinel node (NSN) metastases in approximately 20% of cases. This study aimed to establish whether NSN status can be predicted, to determine its effect on survival, and to develop survival tree models for the sentinel node (SN) positive population. Sydney Melanoma Unit (SMU) patients with at least 1 positive SN, meeting inclusion criteria and treated between October 1992 and June 2005, were identified from the Unit database. Survival characteristics, potential predictors of survival, and NSN status were assessed using the Kaplan-Meier method, Cox regression model, and logistic regression analyses, respectively. Classification tree analysis was performed to identify groups with distinctly different survival characteristics. A total of 323 SN-positive melanoma patients met the inclusion criteria. On multivariate analysis, age, gender, primary tumor thickness, mitotic rate, number of positive NSNs, or total number of positive nodes were statistically significant predictors of survival. NSN metastasis, found at CLND in 19% of patients, was only predicted to a statistically significant degree by ulceration. Multivariate analyses demonstrated that survival was more closely related to number of positive NSNs than total number of positive nodes. Classification tree analysis revealed 4 prognostically distinct survival groups. Patients with NSN metastases could not be reliably identified prior to CLND. Prognosis following CLND was more closely related to number of positive NSNs than total number of positive nodes. Classification tree analysis defined distinctly different survival groups more accurately than use of single-factor analysis.
Analysis of significant factors for dengue fever incidence prediction.
Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak
2016-04-16
Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting
Cultural Resource Predictive Modeling
2017-10-01
refining formal, inductive predictive models is the quality of the archaeological and environmental data. To build models efficiently, relevant...geomorphology, and historic information . Lessons Learned: The original model was focused on the identification of prehistoric resources. This...system but uses predictive modeling informally . For example, there is no probability for buried archaeological deposits on the Burton Mesa, but there is
Predictive modeling of complications.
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.
B. Roozenbeek (Bob); H.F. Lingsma (Hester); F.E. Lecky (Fiona); J. Lu (Juan); J. Weir (James); I. Butcher (Isabella); G.S. McHugh (Gillian); G.D. Murray (Gordon); P. Perel (Pablo); A.I.R. Maas (Andrew); E.W. Steyerberg (Ewout)
2012-01-01
textabstractObjective: The International Mission on Prognosis and Analysis of Clinical Trials and Corticoid Randomisation After Significant Head injury prognostic models predict outcome after traumatic brain injury but have not been compared in large datasets. The objective of this is study is to
Archaeological predictive model set.
2015-03-01
This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...
Levy, R.; Mcginness, H.
1976-01-01
Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.
Zephyr - the prediction models
DEFF Research Database (Denmark)
Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg
2001-01-01
utilities as partners and users. The new models are evaluated for five wind farms in Denmark as well as one wind farm in Spain. It is shown that the predictions based on conditional parametric models are superior to the predictions obatined by state-of-the-art parametric models.......This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Danish...
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.
Melanoma risk prediction models
Directory of Open Access Journals (Sweden)
Nikolić Jelena
2014-01-01
Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were
Predictive Surface Complexation Modeling
Energy Technology Data Exchange (ETDEWEB)
Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences
2016-11-29
Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO_{2} and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.
Candidate Prediction Models and Methods
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik
2005-01-01
This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...
Predictive Maintenance (PdM) Centralization for Significant Energy Savings
Energy Technology Data Exchange (ETDEWEB)
Smith, Dale
2010-09-15
Cost effective predictive maintenance (PdM) technologies and basic energy calculations can mine energy savings form processes or maintenance activities. Centralizing and packaging this information correctly empowers facility maintenance and reliability professionals to build financial justification and support for strategies and personnel to weather global economic downturns and competition. Attendees will learn how to: Systematically build a 'pilot project' for applying PdM and tracking systems; Break down a typical electrical bill to calculate energy savings; Use return on investment (ROI) calculations to identify the best and highest value options, strategies and tips for substantiating your energy reduction maintenance strategies.
Confidence scores for prediction models
DEFF Research Database (Denmark)
Gerds, Thomas Alexander; van de Wiel, MA
2011-01-01
In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation......, then rival strategies can still be compared based on repeated bootstraps of the same data. Often, however, the overall performance of rival strategies is similar and it is thus difficult to decide for one model. Here, we investigate the variability of the prediction models that results when the same...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...
Exploring the significance of human mobility patterns in social link prediction
Alharbi, Basma Mohammed
2014-01-01
Link prediction is a fundamental task in social networks. Recently, emphasis has been placed on forecasting new social ties using user mobility patterns, e.g., investigating physical and semantic co-locations for new proximity measure. This paper explores the effect of in-depth mobility patterns. Specifically, we study individuals\\' movement behavior, and quantify mobility on the basis of trip frequency, travel purpose and transportation mode. Our hybrid link prediction model is composed of two modules. The first module extracts mobility patterns, including travel purpose and mode, from raw trajectory data. The second module employs the extracted patterns for link prediction. We evaluate our method on two real data sets, GeoLife [15] and Reality Mining [5]. Experimental results show that our hybrid model significantly improves the accuracy of social link prediction, when comparing to primary topology-based solutions. Copyright 2014 ACM.
Modelling vocal anatomy's significant effect on speech
de Boer, B.
2010-01-01
This paper investigates the effect of larynx position on the articulatory abilities of a humanlike vocal tract. Previous work has investigated models that were built to resemble the anatomy of existing species or fossil ancestors. This has led to conflicting conclusions about the relation between
Hybrid approaches to physiologic modeling and prediction
Olengü, Nicholas O.; Reifman, Jaques
2005-05-01
This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.
Bootstrap prediction and Bayesian prediction under misspecified models
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 models in complex terrain
DEFF Research Database (Denmark)
Marti, I.; Nielsen, Torben Skov; Madsen, Henrik
2001-01-01
The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... the performance of HIRLAM in particular with respect to wind predictions. To estimate the performance of the model two spatial resolutions (0,5 Deg. and 0.2 Deg.) and different sets of HIRLAM variables were used to predict wind speed and energy production. The predictions of energy production for the wind farms...... are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production...
MODEL PREDICTIVE CONTROL FUNDAMENTALS
African Journals Online (AJOL)
2012-07-02
Jul 2, 2012 ... Linear MPC. 1. Uses linear model: ˙x = Ax + Bu. 2. Quadratic cost function: F = xT Qx + uT Ru. 3. Linear constraints: Hx + Gu < 0. 4. Quadratic program. Nonlinear MPC. 1. Nonlinear model: ˙x = f(x, u). 2. Cost function can be nonquadratic: F = (x, u). 3. Nonlinear constraints: h(x, u) < 0. 4. Nonlinear program.
Modelling bankruptcy prediction models in Slovak companies
Directory of Open Access Journals (Sweden)
Kovacova Maria
2017-01-01
Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.
Melanoma Risk Prediction Models
Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Predictive models of moth development
Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...
Predictive Models and Computational Embryology
EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...
Predictions models with neural nets
Directory of Open Access Journals (Sweden)
Vladimír Konečný
2008-01-01
Full Text Available The contribution is oriented to basic problem trends solution of economic pointers, using neural networks. Problems include choice of the suitable model and consequently configuration of neural nets, choice computational function of neurons and the way prediction learning. The contribution contains two basic models that use structure of multilayer neural nets and way of determination their configuration. It is postulate a simple rule for teaching period of neural net, to get most credible prediction.Experiments are executed with really data evolution of exchange rate Kč/Euro. The main reason of choice this time series is their availability for sufficient long period. In carry out of experiments the both given basic kind of prediction models with most frequent use functions of neurons are verified. Achieve prediction results are presented as in numerical and so in graphical forms.
Return Predictability, Model Uncertainty, and Robust Investment
DEFF Research Database (Denmark)
Lukas, Manuel
Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....
Fingerprint verification prediction model in hand dermatitis.
Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah
2015-07-01
Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.
Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System.
Doyle, Andy; Katz, Graham; Summers, Kristen; Ackermann, Chris; Zavorin, Ilya; Lim, Zunsik; Muthiah, Sathappan; Butler, Patrick; Self, Nathan; Zhao, Liang; Lu, Chang-Tien; Khandpur, Rupinder Paul; Fayed, Youssef; Ramakrishnan, Naren
2014-12-01
Developed under the Intelligence Advanced Research Project Activity Open Source Indicators program, Early Model Based Event Recognition using Surrogates (EMBERS) is a large-scale big data analytics system for forecasting significant societal events, such as civil unrest events on the basis of continuous, automated analysis of large volumes of publicly available data. It has been operational since November 2012 and delivers approximately 50 predictions each day for countries of Latin America. EMBERS is built on a streaming, scalable, loosely coupled, shared-nothing architecture using ZeroMQ as its messaging backbone and JSON as its wire data format. It is deployed on Amazon Web Services using an entirely automated deployment process. We describe the architecture of the system, some of the design tradeoffs encountered during development, and specifics of the machine learning models underlying EMBERS. We also present a detailed prospective evaluation of EMBERS in forecasting significant societal events in the past 2 years.
What do saliency models predict?
Koehler, Kathryn; Guo, Fei; Zhang, Sheng; Eckstein, Miguel P.
2014-01-01
Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements. PMID:24618107
Spatial Economics Model Predicting Transport Volume
Directory of Open Access Journals (Sweden)
Lu Bo
2016-10-01
Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.
Hengl, Tomislav; Heuvelink, Gerard B. M.; Kempen, Bas; Leenaars, Johan G. B.; Walsh, Markus G.; Shepherd, Keith D.; Sila, Andrew; MacMillan, Robert A.; Mendes de Jesus, Jorge; Tamene, Lulseged; Tondoh, Jérôme E.
2015-01-01
80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008–2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management—organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15–75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological
Hengl, Tomislav; Heuvelink, Gerard B M; Kempen, Bas; Leenaars, Johan G B; Walsh, Markus G; Shepherd, Keith D; Sila, Andrew; MacMillan, Robert A; Mendes de Jesus, Jorge; Tamene, Lulseged; Tondoh, Jérôme E
2015-01-01
80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological
Evaluation of CASP8 model quality predictions
Cozzetto, Domenico
2009-01-01
The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.
Bayesian Test of Significance for Conditional Independence: The Multinomial Model
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Pablo de Morais Andrade
2014-03-01
Full Text Available Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of probabilistic graphical models, which includes Bayesian network models, conditional independence tests are especially important for the task of learning the probabilistic graphical model structure from data. In this paper, we propose the full Bayesian significance test for tests of conditional independence for discrete datasets. The full Bayesian significance test is a powerful Bayesian test for precise hypothesis, as an alternative to the frequentist’s significance tests (characterized by the calculation of the p-value.
Predicting Community College Outcomes: Does High School CTE Participation Have a Significant Effect?
Dietrich, Cecile; Lichtenberger, Eric; Kamalludeen, Rosemaliza
2016-01-01
This study explored the relative importance of participation in high school career and technical education (CTE) programs in predicting community college outcomes. A hierarchical generalized linear model (HGLM) was used to predict community college outcome attainment among a random sample of direct community college entrants. Results show that…
Modelling language evolution: Examples and predictions
Gong, Tao; Shuai, Lan; Zhang, Menghan
2014-06-01
We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.
Khan, Imran; Ansari, Irfan A; Singh, Pratichi; Dass J, Febin Prabhu
2017-09-01
The phosphatase and tensin homolog (PTEN) gene plays a crucial role in signal transduction by negatively regulating the PI3K signaling pathway. It is the most frequent mutated gene in many human-related cancers. Considering its critical role, a functional analysis of missense mutations of PTEN gene was undertaken in this study. Thirty five nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of the PTEN gene were selected for our in silico investigation, and five nsSNPs (G129E, C124R, D252G, H61D, and R130G) were found to be deleterious based on combinatorial predictions of different computational tools. Moreover, molecular dynamics (MD) simulation was performed to investigate the conformational variation between native and all the five mutant PTEN proteins having predicted deleterious nsSNPs. The results of MD simulation of all mutant models illustrated variation in structural attributes such as root-mean-square deviation, root-mean-square fluctuation, radius of gyration, and total energy; which depicts the structural stability of PTEN protein. Furthermore, mutant PTEN protein structures also showed a significant variation in the solvent accessible surface area and hydrogen bond frequencies from the native PTEN structure. In conclusion, results of this study have established the deleterious effect of the all the five predicted nsSNPs on the PTEN protein structure. Thus, results of the current study can pave a new platform to sort out nsSNPs that can be undertaken for the confirmation of their phenotype and their correlation with diseased status in case of control studies. © 2016 International Union of Biochemistry and Molecular Biology, Inc.
Maas, M; Dam-Deisz, W D C; van Roon, A M; Takumi, K; van der Giessen, J W B
2014-12-15
The emergence of the zoonotic tapeworm Echinococcus multilocularis, causative agent of alveolar echinococcosis (AE), poses a public health risk. A previously designed risk map model predicted a spread of E. multilocularis and increasing numbers of alveolar echinococcosis patients in the province of Limburg, The Netherlands. This study was designed to determine trends in the prevalence and worm burden of E. multilocularis in foxes in a popular recreational area in the southern part of Limburg to assess the risk of infection for humans and to study the prevalence of E. multilocularis in dogs in the adjacent city of Maastricht. Thirty-seven hunted red foxes were tested by the intestinal scraping technique and nested PCR on colon content. Additionally, 142 fecal samples of domestic dogs from Maastricht were analyzed by qPCR for the presence of E. multilocularis. In foxes, a significantly increased prevalence of 59% (95% confidence interval 43-74%) was found, compared to the prevalence of 11% (95% CI 7-18%) in 2005-2006. Average worm burden increased to 37 worms per fox, the highest since the first detection, but consistent with the prediction about the parasite population for this region. Updated prediction on the number of AE cases did not lead to an increase in previous estimates of human AE cases up to 2018. No dogs in the city of Maastricht tested positive, but results of questionnaires showed that deworming schemes were inadequate, especially in dogs that were considered at risk for infection. Copyright © 2014 Elsevier B.V. All rights reserved.
Predicting Intentions of a Familiar Significant Other Beyond the Mirror Neuron System
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Stephanie Cacioppo
2017-08-01
Full Text Available Inferring intentions of others is one of the most intriguing issues in interpersonal interaction. Theories of embodied cognition and simulation suggest that this mechanism takes place through a direct and automatic matching process that occurs between an observed action and past actions. This process occurs via the reactivation of past self-related sensorimotor experiences within the inferior frontoparietal network (including the mirror neuron system, MNS. The working model is that the anticipatory representations of others' behaviors require internal predictive models of actions formed from pre-established, shared representations between the observer and the actor. This model suggests that observers should be better at predicting intentions performed by a familiar actor, rather than a stranger. However, little is known about the modulations of the intention brain network as a function of the familiarity between the observer and the actor. Here, we combined functional magnetic resonance imaging (fMRI with a behavioral intention inference task, in which participants were asked to predict intentions from three types of actors: A familiar actor (their significant other, themselves (another familiar actor, and a non-familiar actor (a stranger. Our results showed that the participants were better at inferring intentions performed by familiar actors than non-familiar actors and that this better performance was associated with greater activation within and beyond the inferior frontoparietal network i.e., in brain areas related to familiarity (e.g., precuneus. In addition, and in line with Hebbian principles of neural modulations, the more the participants reported being cognitively close to their partner, the less the brain areas associated with action self-other comparison (e.g., inferior parietal lobule, attention (e.g., superior parietal lobule, recollection (hippocampus, and pair bond (ventral tegmental area, VTA were recruited, suggesting that the
Caries risk assessment models in caries prediction
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Amila Zukanović
2013-11-01
Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.
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Carol C. Choo
2018-04-01
Full Text Available An important risk factor for suicide assessment is the suicide precipitant. This study explores suicide attempt precipitants across the lifespan. Three years of medical records related to suicide attempters who were admitted to the emergency department of a large teaching hospital in Singapore were subjected to analysis. These cases were divided into three age groups: Adolescence, Early Adulthood, and Middle Adulthood. A total of 540 cases were examined (70.9% females; 63.7% Chinese, 13.7% Malays, 15.9% Indians, whose ages ranged from 12 to 62. There were eight cases above the age of 65 years which were excluded from the analysis. Significant differences were found in precipitants for suicide attempts across the lifespan. Middle adults had relatively fewer relationship problems, and adolescents had comparatively fewer financial and medical problems. The models to predict medically severe attempts across the age groups using suicide precipitants were not significant. The findings were discussed in regards to implications in suicide assessment and primary prevention in Singapore, as well as limitations and recommendations for future research.
Kossobokov, V.G.; Romashkova, L.L.; Keilis-Borok, V. I.; Healy, J.H.
1999-01-01
Algorithms M8 and MSc (i.e., the Mendocino Scenario) were used in a real-time intermediate-term research prediction of the strongest earthquakes in the Circum-Pacific seismic belt. Predictions are made by M8 first. Then, the areas of alarm are reduced by MSc at the cost that some earthquakes are missed in the second approximation of prediction. In 1992-1997, five earthquakes of magnitude 8 and above occurred in the test area: all of them were predicted by M8 and MSc identified correctly the locations of four of them. The space-time volume of the alarms is 36% and 18%, correspondingly, when estimated with a normalized product measure of empirical distribution of epicenters and uniform time. The statistical significance of the achieved results is beyond 99% both for M8 and MSc. For magnitude 7.5 + , 10 out of 19 earthquakes were predicted by M8 in 40% and five were predicted by M8-MSc in 13% of the total volume considered. This implies a significance level of 81% for M8 and 92% for M8-MSc. The lower significance levels might result from a global change in seismic regime in 1993-1996, when the rate of the largest events has doubled and all of them become exclusively normal or reversed faults. The predictions are fully reproducible; the algorithms M8 and MSc in complete formal definitions were published before we started our experiment [Keilis-Borok, V.I., Kossobokov, V.G., 1990. Premonitory activation of seismic flow: Algorithm M8, Phys. Earth and Planet. Inter. 61, 73-83; Kossobokov, V.G., Keilis-Borok, V.I., Smith, S.W., 1990. Localization of intermediate-term earthquake prediction, J. Geophys. Res., 95, 19763-19772; Healy, J.H., Kossobokov, V.G., Dewey, J.W., 1992. A test to evaluate the earthquake prediction algorithm, M8. U.S. Geol. Surv. OFR 92-401]. M8 is available from the IASPEI Software Library [Healy, J.H., Keilis-Borok, V.I., Lee, W.H.K. (Eds.), 1997. Algorithms for Earthquake Statistics and Prediction, Vol. 6. IASPEI Software Library]. ?? 1999 Elsevier
Charry, Jose D; Tejada, Jorman H; Pinzon, Miguel A; Tejada, Wilson A; Ochoa, Juan D; Falla, Manuel; Tovar, Jesus H; Cuellar-Bahamón, Ana M; Solano, Juan P
2017-05-01
Traumatic brain injury (TBI) is of public health interest and produces significant mortality and disability in Colombia. Calculators and prognostic models have been developed to establish neurologic outcomes. We tested prognostic models (the Marshall computed tomography [CT] score, International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT), and Corticosteroid Randomization After Significant Head Injury) for 14-day mortality, 6-month mortality, and 6-month outcome in patients with TBI at a university hospital in Colombia. A 127-patient cohort with TBI was treated in a regional trauma center in Colombia over 2 years and bivariate and multivariate analyses were used. Discriminatory power of the models, their accuracy, and precision was assessed by both logistic regression and area under the receiver operating characteristic curve (AUC). Shapiro-Wilk, χ 2 , and Wilcoxon test were used to compare real outcomes in the cohort against predicted outcomes. The group's median age was 33 years, and 84.25% were male. The injury severity score median was 25, and median Glasgow Coma Scale motor score was 3. Six-month mortality was 29.13%. Six-month unfavorable outcome was 37%. Mortality prediction by Marshall CT score was 52.8%, P = 0.104 (AUC 0.585; 95% confidence interval [CI] 0 0.489-0.681), the mortality prediction by CRASH prognosis calculator was 59.9%, P < 0.001 (AUC 0.706; 95% CI 0.590-0.821), and the unfavorable outcome prediction by IMPACT was 77%, P < 0.048 (AUC 0.670; 95% CI 0.575-0.763). In a university hospital in Colombia, the Marshall CT score, IMPACT, and Corticosteroid Randomization After Significant Head Injury models overestimated the adverse neurologic outcome in patients with severe head trauma. Copyright © 2017 Elsevier Inc. All rights reserved.
Link Prediction via Sparse Gaussian Graphical Model
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Liangliang Zhang
2016-01-01
Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.
Significance of high resolution GHRSST on prediction of Indian Summer Monsoon
Jangid, Buddhi Prakash; Kumar, Prashant; Raju, Attada; Kumar, Raj
2017-05-01
In this study, the Weather Research and Forecasting (WRF) model was used to assess the importance of very high resolution sea surface temperature (SST) on seasonal rainfall prediction. Two different SST datasets available from the National Centers for Environmental Prediction (NCEP) global model analysis and merged satellite product from Group for High Resolution SST (GHRSST) are used as a lower boundary condition in the WRF model for the Indian Summer Monsoon (ISM) 2010. Before using NCEP SST and GHRSST for model simulation, an initial verification of NCEP SST and GHRSST are performed with buoy measurements. It is found that approximately 0.4 K root mean square difference (RMSD) in GHRSST and NCEP SST when compared with buoy observations available over the Indian Ocean during 01 May to 30 September 2010. Our analyses suggest that use of GHRSST as lower boundary conditions in the WRF model improve the low level temperature, moisture, wind speed and rainfall prediction over ISM region. Moreover, temporal evolution of surface parameters such as temperature, moisture and wind speed forecasts associated with monsoon is also improved with GHRSST forcing as a lower boundary condition. Interestingly, rainfall prediction is improved with the use of GHRSST over the Western Ghats, which mostly not simulated in the NCEP SST based experiment.
Significance of High Resolution GHRSST on prediction of Indian Summer Monsoon
Jangid, Buddhi Prakash
2017-02-24
In this study, the Weather Research and Forecasting (WRF) model was used to assess the importance of very high resolution sea surface temperature (SST) on seasonal rainfall prediction. Two different SST datasets available from the National Centers for Environmental Prediction (NCEP) global model analysis and merged satellite product from Group for High Resolution SST (GHRSST) are used as a lower boundary condition in the WRF model for the Indian Summer Monsoon (ISM) 2010. Before using NCEP SST and GHRSST for model simulation, an initial verification of NCEP SST and GHRSST are performed with buoy measurements. It is found that approximately 0.4 K root mean square difference (RMSD) in GHRSST and NCEP SST when compared with buoy observations available over the Indian Ocean during 01 May to 30 September 2010. Our analyses suggest that use of GHRSST as lower boundary conditions in the WRF model improve the low level temperature, moisture, wind speed and rainfall prediction over ISM region. Moreover, temporal evolution of surface parameters such as temperature, moisture and wind speed forecasts associated with monsoon is also improved with GHRSST forcing as a lower boundary condition. Interestingly, rainfall prediction is improved with the use of GHRSST over the Western Ghats, which mostly not simulated in the NCEP SST based experiment.
Electrostatic ion thrusters - towards predictive modeling
Energy Technology Data Exchange (ETDEWEB)
Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)
2014-02-15
The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
A statistical model for predicting muscle performance
Byerly, Diane Leslie De Caix
The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing
Iowa calibration of MEPDG performance prediction models.
2013-06-01
This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...
Model complexity control for hydrologic prediction
Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.
2008-01-01
A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore
PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION
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Narciso Ysac Avila Serrano
2009-06-01
Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (Pâ‰¤ 0.05 among cultivars. PaceÃ±o and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients â‰¥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (Pâ‰¤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.
Maslekar, S; Gardiner, A B; Monson, J R T; Duthie, G S
2010-12-01
Artificial neural networks (ANNs) are computer programs used to identify complex relations within data. Routine predictions of presence of colorectal pathology based on population statistics have little meaning for individual patient. This results in large number of unnecessary lower gastrointestinal endoscopies (LGEs - colonoscopies and flexible sigmoidoscopies). We aimed to develop a neural network algorithm that can accurately predict presence of significant pathology in patients attending routine outpatient clinics for gastrointestinal symptoms. Ethics approval was obtained and the study was monitored according to International Committee on Harmonisation - Good Clinical Practice (ICH-GCP) standards. Three-hundred patients undergoing LGE prospectively completed a specifically developed questionnaire, which included 40 variables based on clinical symptoms, signs, past- and family history. Complete data sets of 100 patients were used to train the ANN; the remaining data was used for internal validation. The primary output used was positive finding on LGE, including polyps, cancer, diverticular disease or colitis. For external validation, the ANN was applied to data from 50 patients in primary care and also compared with the predictions of four clinicians. Clear correlation between actual data value and ANN predictions were found (r = 0.931; P = 0.0001). The predictive accuracy of ANN was 95% in training group and 90% (95% CI 84-96) in the internal validation set and this was significantly higher than the clinical accuracy (75%). ANN also showed high accuracy in the external validation group (89%). Artificial neural networks offer the possibility of personal prediction of outcome for individual patients presenting in clinics with colorectal symptoms, making it possible to make more appropriate requests for lower gastrointestinal endoscopy. © 2010 The Authors. Colorectal Disease © 2010 The Association of Coloproctology of Great Britain and Ireland.
Bayesian variable order Markov models: Towards Bayesian predictive state representations
Dimitrakakis, C.
2009-01-01
We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more
Staying Power of Churn Prediction Models
Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.
In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging
Evaluation of the US Army fallout prediction model
International Nuclear Information System (INIS)
Pernick, A.; Levanon, I.
1987-01-01
The US Army fallout prediction method was evaluated against an advanced fallout prediction model--SIMFIC (Simplified Fallout Interpretive Code). The danger zone areas of the US Army method were found to be significantly greater (up to a factor of 8) than the areas of corresponding radiation hazard as predicted by SIMFIC. Nonetheless, because the US Army's method predicts danger zone lengths that are commonly shorter than the corresponding hot line distances of SIMFIC, the US Army's method is not reliably conservative
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Lianlian Pan
2018-01-01
Full Text Available It would be important to predict type 2 diabetes mellitus (T2DM and diabetic nephropathy (DN. This study was aimed at evaluating the predicting significance of hemostatic parameters for T2DM and DN. Plasma coagulation and hematologic parameters before treatment were measured in 297 T2DM patients. The risk factors and their predicting power were evaluated. T2DM patients without complications exhibited significantly different activated partial thromboplastin time (aPTT, platelet (PLT, and D-dimer (D-D levels compared with controls (P<0.01. Fibrinogen (FIB, PLT, and D-D increased in DN patients compared with those without complications (P<0.001. Both aPTT and PLT were the independent risk factors for T2DM (OR: 1.320 and 1.211, P<0.01, resp., and FIB and PLT were the independent risk factors for DN (OR: 1.611 and 1.194, P<0.01, resp.. The area under ROC curve (AUC of aPTT and PLT was 0.592 and 0.647, respectively, with low sensitivity in predicting T2DM. AUC of FIB was 0.874 with high sensitivity (85% and specificity (76% for DN, and that of PLT was 0.564, with sensitivity (60% and specificity (89% based on the cutoff values of 3.15 g/L and 245 × 109/L, respectively. This study suggests that hemostatic parameters have a low predicting value for T2DM, whereas fibrinogen is a powerful predictor for DN.
Dhillon, Sandeep K.; JosephTawil; Goldstein, Baruch; Eslava-Manchego, Dayana; Singh, Jagdeep; Hanon, Sam; Schweitzer, Paul; Bergmann, Steven R.
2012-01-01
Background Cardiac rhythm monitoring is widely applied on hospitalized patients. However, its value has not been evaluated systematically. Methods This study considered the utility of our institutional telemetry guidelines in predicting clinically significant arrhythmias. A retrospective analysis was performed of 562 patients admitted to the telemetry unit. A total of 1932 monitoring days were evaluated. Patients were divided into 2 groups based on telemetry guidelines: “telemetry indicated” and “telemetry not indicated”. Results Differences in arrhythmia event rates and pre-defined clinical significance were determined. One hundred and forty-four (34%) vs. 16 (11%) patients had at least one arrhythmic event in the “telemetry indicated” group compared with the “telemetry not indicated” group, respectively (P = 0.001). No patient in the “telemetry not indicated” group had a clinically significant arrhythmia. In contrast, of patients in the “telemetry indicated” group who had at least one arrhythmic event, 36% were considered clinically significant (P telemetry guidelines to allocate this resource appropriately and predict clinically significant arrhythmias. PMID:28357019
Comparison of Prediction-Error-Modelling Criteria
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...
Artificial Neural Network Model for Predicting Compressive
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Salim T. Yousif
2013-05-01
Full Text Available Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.
Energy Technology Data Exchange (ETDEWEB)
Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)
2012-07-01
The understanding and mitigation of downhole vibration has been a heavily researched subject in the oil industry as it results in more expensive drilling operations, as vibrations significantly diminish the amount of effective drilling energy available to the bit and generate forces that can push the bit or the Bottom Hole Assembly (BHA) off its concentric axis of rotation, producing high magnitude impacts with the borehole wall. In order to drill ahead, a sufficient amount of energy must be supplied by the rig to overcome the resistance of the drilling system, including the reactive torque of the system, drag forces, fluid pressure losses and energy dissipated by downhole vibrations, then providing the bit with the energy required to fail the rock. If the drill string enters resonant modes of vibration, not only does it decreases the amount of available energy to drill, but increases the potential for catastrophic downhole equipment and drilling bit failures. In this sense, the mitigation of downhole vibrations will result in faster, smoother, and cheaper drilling operations. A software tool using Finite Element Analysis (FEA) has been developed to provide better understanding of downhole vibration phenomena in drilling environments. The software tool calculates the response of the drilling system at various input conditions, based on the design of the wellbore along with the geometry of the Bottom Hole Assembly (BHA) and the drill string. It identifies where undesired levels of resonant vibration will be driven by certain combinations of specific drilling parameters, and also which combinations of drilling parameters will result in lower levels of vibration, so the least shocks, the highest penetration rate and the lowest cost per foot can be achieved. With the growing performance of personal computers, complex software systems modeling the drilling vibrations using FEA has been accessible to a wider audience of field users, further complimenting with real time
Calibration of PMIS pavement performance prediction models.
2012-02-01
Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...
Predictive Model Assessment for Count Data
National Research Council Canada - National Science Library
Czado, Claudia; Gneiting, Tilmann; Held, Leonhard
2007-01-01
.... In case studies, we critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. Key words: Calibration...
Modeling and Prediction Using Stochastic Differential Equations
DEFF Research Database (Denmark)
Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp
2016-01-01
deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs......) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described....... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...
Predictive models for arteriovenous fistula maturation.
Al Shakarchi, Julien; McGrogan, Damian; Van der Veer, Sabine; Sperrin, Matthew; Inston, Nicholas
2016-05-07
Haemodialysis (HD) is a lifeline therapy for patients with end-stage renal disease (ESRD). A critical factor in the survival of renal dialysis patients is the surgical creation of vascular access, and international guidelines recommend arteriovenous fistulas (AVF) as the gold standard of vascular access for haemodialysis. Despite this, AVFs have been associated with high failure rates. Although risk factors for AVF failure have been identified, their utility for predicting AVF failure through predictive models remains unclear. The objectives of this review are to systematically and critically assess the methodology and reporting of studies developing prognostic predictive models for AVF outcomes and assess them for suitability in clinical practice. Electronic databases were searched for studies reporting prognostic predictive models for AVF outcomes. Dual review was conducted to identify studies that reported on the development or validation of a model constructed to predict AVF outcome following creation. Data were extracted on study characteristics, risk predictors, statistical methodology, model type, as well as validation process. We included four different studies reporting five different predictive models. Parameters identified that were common to all scoring system were age and cardiovascular disease. This review has found a small number of predictive models in vascular access. The disparity between each study limits the development of a unified predictive model.
Model Predictive Control Fundamentals | Orukpe | Nigerian Journal ...
African Journals Online (AJOL)
Model Predictive Control (MPC) has developed considerably over the last two decades, both within the research control community and in industries. MPC strategy involves the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, ...
Unreachable Setpoints in Model Predictive Control
DEFF Research Database (Denmark)
Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp
2008-01-01
In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optim...
Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M
2015-07-01
Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.
Iype, Elizabeth Mathew; Jagad, Vijay; Nochikattil, Santhosh Kumar; Varghese, Bipin T; Sebastian, Paul
2016-02-01
Intraoperative management of thyroid gland in laryngeal and hypopharyngeal cancer is controversial. The objectives of this study were to determine the incidence of thyroid gland invasion in patients undergoing surgery for laryngeal or hypopharyngeal carcinoma, to assess predictive factors and to assess the prognosis in patients with and without thyroid gland invasion. One hundred and thirty-three patients who underwent surgery for carcinoma larynx and hypopharynx from 2006 to 2010 were reviewed retrospectively. Surgical specimens were examined to determine the incidence of thyroid gland invasion and predictive factors were analysed. The recurrence rate and the survival in patients with and without thyroid gland invasion were also analysed. Out of the 133 patients with carcinoma larynx and hypopharynx who underwent surgery, histological thyroid gland invasion was observed in 28/133 (21%) patients. Significant relationship was found between histological thyroid gland invasion and preoperative evidence of thyroid cartilage erosion by CT scan and also when gross thyroid gland involvement observed during surgery. There is significant association between thyroid gland invasion when there is upper oesophageal or subglottic involvement. After analysing the retrospective data from our study, we would like to suggest that thyroid gland need not be removed routinely in all laryngectomies, unless there is advanced disease with thyroid cartilage erosion and gross thyroid gland involvement or disease with significant subglottic or oesophageal involvement.
2013-01-01
Background Both eating disorders (EDs) and body dysmorphic disorder (BDD) are disorders of body image. This study aimed to assess the presence, predictive utility, and impact of clinical features commonly associated with BDD in women with EDs. Methods Participants recruited from two non-clinical cohorts of women, symptomatic and asymptomatic of EDs, completed a survey on ED (EDE-Q) and BDD (BDDE-SR) psychopathology, psychological distress (K-10), and quality of life (SF-12). Results A strong correlation was observed between the total BDDE-SR and the global EDE-Q scores (r = 0.79, p 0.05) measured appearance checking, reassurance-seeking, camouflaging, comparison-making, and social avoidance. In addition to these behaviors, inspection of sensitivity (Se) and specificity (Sp) revealed that BDDE-SR items measuring preoccupation and dissatisfaction with appearance were most predictive of ED cases (Se and Sp > 0.60). Higher total BDDE-SR scores were associated with greater distress on the K-10 and poorer quality of life on the SF-12 (all p < 0.01). Conclusions Clinical features central to the model of BDD are common in, predictive of, and associated with impairment in women with EDs. Practice implications are that these features be included in the assessment and treatment of EDs. PMID:24999401
Urinalysis vs urine protein–creatinine ratio to predict significant proteinuria in pregnancy
Dwyer, BK; Gorman, M; Carroll, IR; Druzin, M
2009-01-01
Objective To compare the urine protein–creatinine ratio with urinalysis to predict significant proteinuria (≥300 mg per day). Study Design A total of 116 paired spot urine samples and 24-h urine collections were obtained prospectively from women at risk for preeclampsia. Urine protein–creatinine ratio and urinalysis were compared to the 24-h urine collection. Result The urine protein–creatinine ratio had better discriminatory power than urinalysis: the receiver operating characteristic curve had a greater area under the curve, 0.89 (95% confidence interval (CI) 0.83 to 0.95) vs 0.71 (95% CI 0.64 to 0.77, Purine protein–creatinine ratio (cutoff ≥0.28) is more sensitive than urinalysis (cutoff ≥1+): 66 vs 41%, P = 0.001 (with 95 and 100% specificity, respectively). Furthermore, the urine protein–creatinine ratio predicted the absence or presence of proteinuria in 64% of patients; urinalysis predicted this in only 19%. Conclusion The urine protein–creatinine ratio is a better screening test. It provides early information for more patients. PMID:18288120
Urinalysis vs urine protein-creatinine ratio to predict significant proteinuria in pregnancy.
Dwyer, B K; Gorman, M; Carroll, I R; Druzin, M
2008-07-01
To compare the urine protein-creatinine ratio with urinalysis to predict significant proteinuria (>or=300 mg per day). A total of 116 paired spot urine samples and 24-h urine collections were obtained prospectively from women at risk for preeclampsia. Urine protein-creatinine ratio and urinalysis were compared to the 24-h urine collection. The urine protein-creatinine ratio had better discriminatory power than urinalysis: the receiver operating characteristic curve had a greater area under the curve, 0.89 (95% confidence interval (CI) 0.83 to 0.95) vs 0.71 (95% CI 0.64 to 0.77, Purine protein-creatinine ratio (cutoff >or=0.28) is more sensitive than urinalysis (cutoff >or=1+): 66 vs 41%, P=0.001 (with 95 and 100% specificity, respectively). Furthermore, the urine protein-creatinine ratio predicted the absence or presence of proteinuria in 64% of patients; urinalysis predicted this in only 19%. The urine protein-creatinine ratio is a better screening test. It provides early information for more patients.
Evaluating the Predictive Value of Growth Prediction Models
Murphy, Daniel L.; Gaertner, Matthew N.
2014-01-01
This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…
Model predictive control classical, robust and stochastic
Kouvaritakis, Basil
2016-01-01
For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...
Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance.
Li, Bian; Mendenhall, Jeffrey L; Kroncke, Brett M; Taylor, Keenan C; Huang, Hui; Smith, Derek K; Vanoye, Carlos G; Blume, Jeffrey D; George, Alfred L; Sanders, Charles R; Meiler, Jens
2017-10-01
An emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity. In this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants. Based on this data set, we completed a detailed quantitative analysis on the sequence conservation patterns of subdomains of KCNQ1 and the distribution of pathogenic variants therein. We found that conserved subdomains generally are critical for channel function and are enriched with dysfunctional variants. Using this experimentally validated data set, we trained a neural network, designated Q1VarPred, specifically for predicting the functional impact of KCNQ1 variants of unknown significance. The estimated predictive performance of Q1VarPred in terms of Matthew's correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0.884, respectively, superior to the performance of 8 previous methods tested in parallel. Q1VarPred is publicly available as a web server at http://meilerlab.org/q1varpred. Although a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools. © 2017 American Heart Association, Inc.
Risk prediction model: Statistical and artificial neural network approach
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.
Validation of a tuber blight (Phytophthora infestans) prediction model
Potato tuber blight caused by Phytophthora infestans accounts for significant losses in storage. There is limited published quantitative data on predicting tuber blight. We validated a tuber blight prediction model developed in New York with cultivars Allegany, NY 101, and Katahdin using independent...
A Global Model for Bankruptcy Prediction.
Alaminos, David; Del Castillo, Agustín; Fernández, Manuel Ángel
2016-01-01
The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.
Heuristic Modeling for TRMM Lifetime Predictions
Jordan, P. S.; Sharer, P. J.; DeFazio, R. L.
1996-01-01
Analysis time for computing the expected mission lifetimes of proposed frequently maneuvering, tightly altitude constrained, Earth orbiting spacecraft have been significantly reduced by means of a heuristic modeling method implemented in a commercial-off-the-shelf spreadsheet product (QuattroPro) running on a personal computer (PC). The method uses a look-up table to estimate the maneuver frequency per month as a function of the spacecraft ballistic coefficient and the solar flux index, then computes the associated fuel use by a simple engine model. Maneuver frequency data points are produced by means of a single 1-month run of traditional mission analysis software for each of the 12 to 25 data points required for the table. As the data point computations are required only a mission design start-up and on the occasion of significant mission redesigns, the dependence on time consuming traditional modeling methods is dramatically reduced. Results to date have agreed with traditional methods to within 1 to 1.5 percent. The spreadsheet approach is applicable to a wide variety of Earth orbiting spacecraft with tight altitude constraints. It will be particularly useful to such missions as the Tropical Rainfall Measurement Mission scheduled for launch in 1997, whose mission lifetime calculations are heavily dependent on frequently revised solar flux predictions.
Massive Predictive Modeling using Oracle R Enterprise
CERN. Geneva
2014-01-01
R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...
Predictive Model of Systemic Toxicity (SOT)
In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...
Testicular Cancer Risk Prediction Models
Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Pancreatic Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Colorectal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Prostate Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Bladder Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Esophageal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Cervical Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Breast Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Lung Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Liver Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Ovarian Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Using a Prediction Model to Manage Cyber Security Threats.
Jaganathan, Venkatesh; Cherurveettil, Priyesh; Muthu Sivashanmugam, Premapriya
2015-01-01
Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.
Using a Prediction Model to Manage Cyber Security Threats
Directory of Open Access Journals (Sweden)
Venkatesh Jaganathan
2015-01-01
Full Text Available Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.
The significance of collateral vessels, as seen on chest CT, in predicting SVC obstruction
International Nuclear Information System (INIS)
Yeouk, Young Soo; Kim, Sung Jin; Bae, Il Hun; Kim, Jae Youn; Hwang, Seung Min; Han, Gi Seok; Park, Kil Sun; Kim, Dae Young
1998-01-01
To evaluate the significance of collateral veins, as seen on chest CT, in the diagnosis of superior vena cava obstruction. We retrospectively the records of 81 patients in whom collateral veins were seen on chest CT. On spiral CT(n=49), contrast material was infused via power injector, and on conventional CT(n=32), 50 ml bolus infusion was followed by 50 ml drip infusion. Obstruction of the SVC was evaluated on chest CT; if, however, evaluation of the SVC of its major tributaries was difficult, as in five cases, the patient underwent SVC phlebography. Collateral vessels were assigned to one of ten categories. On conventional CT, the jugular venous arch in the only collateral vessel to predict SVC obstruction; on spiral CT, however, collateral vessels are not helpful in the diagnosis of SVC obstruction, but are a nonspecific finding. (author). 12 refs., 2 tab., 2 figs
Posterior Predictive Model Checking in Bayesian Networks
Crawford, Aaron
2014-01-01
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…
Liu, Fang-Teng; Dong, Qing; Gao, Hui; Zhu, Zheng-Ming
2017-06-20
Urothelial Carcinoma Associated 1 (UCA1) was an originally identified lncRNA in bladder cancer. Previous studies have reported that UCA1 played a significant role in various types of cancer. This study aimed to clarify the prognostic value of UCA1 in digestive system cancers. The meta-analysis of 15 studies were included, comprising 1441 patients with digestive system cancers. The pooled results of 14 studies indicated that high expression of UCA1 was significantly associated with poorer OS in patients with digestive system cancers (HR: 1.89, 95 % CI: 1.52-2.26). In addition, UCA1 could be as an independent prognostic factor for predicting OS of patients (HR: 1.85, 95 % CI: 1.45-2.25). The pooled results of 3 studies indicated a significant association between UCA1 and DFS in patients with digestive system cancers (HR = 2.50; 95 % CI = 1.30-3.69). Statistical significance was also observed in subgroup meta-analysis. Furthermore, the clinicopathological values of UCA1 were discussed in esophageal cancer, colorectal cancer and pancreatic cancer. A comprehensive retrieval was performed to search studies evaluating the prognostic value of UCA1 in digestive system cancers. Many databases were involved, including PubMed, Web of Science, Embase and Chinese National Knowledge Infrastructure and Wanfang database. Quantitative meta-analysis was performed with standard statistical methods and the prognostic significance of UCA1 in digestive system cancers was qualified. Elevated level of UCA1 indicated the poor clinical outcome for patients with digestive system cancers. It may serve as a new biomarker related to prognosis in digestive system cancers.
Predicting and Modeling RNA Architecture
Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice
2011-01-01
SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963
Multiple Steps Prediction with Nonlinear ARX Models
Zhang, Qinghua; Ljung, Lennart
2007-01-01
NLARX (NonLinear AutoRegressive with eXogenous inputs) models are frequently used in black-box nonlinear system identication. Though it is easy to make one step ahead prediction with such models, multiple steps prediction is far from trivial. The main difficulty is that in general there is no easy way to compute the mathematical expectation of an output conditioned by past measurements. An optimal solution would require intensive numerical computations related to nonlinear filltering. The pur...
Predictability of extreme values in geophysical models
Directory of Open Access Journals (Sweden)
A. E. Sterk
2012-09-01
Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.
Model complexity control for hydrologic prediction
Schoups, G.; van de Giesen, N. C.; Savenije, H. H. G.
2008-12-01
A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore needed. We compare three model complexity control methods for hydrologic prediction, namely, cross validation (CV), Akaike's information criterion (AIC), and structural risk minimization (SRM). Results show that simulation of water flow using non-physically-based models (polynomials in this case) leads to increasingly better calibration fits as the model complexity (polynomial order) increases. However, prediction uncertainty worsens for complex non-physically-based models because of overfitting of noisy data. Incorporation of physically based constraints into the model (e.g., storage-discharge relationship) effectively bounds prediction uncertainty, even as the number of parameters increases. The conclusion is that overparameterization and equifinality do not lead to a continued increase in prediction uncertainty, as long as models are constrained by such physical principles. Complexity control of hydrologic models reduces parameter equifinality and identifies the simplest model that adequately explains the data, thereby providing a means of hydrologic generalization and classification. SRM is a promising technique for this purpose, as it (1) provides analytic upper bounds on prediction uncertainty, hence avoiding the computational burden of CV, and (2) extends the applicability of classic methods such as AIC to finite data. The main hurdle in applying SRM is the need for an a priori estimation of the complexity of the hydrologic model, as measured by its Vapnik-Chernovenkis (VC) dimension. Further research is needed in this area.
Quantifying predictive accuracy in survival models.
Lirette, Seth T; Aban, Inmaculada
2017-12-01
For time-to-event outcomes in medical research, survival models are the most appropriate to use. Unlike logistic regression models, quantifying the predictive accuracy of these models is not a trivial task. We present the classes of concordance (C) statistics and R 2 statistics often used to assess the predictive ability of these models. The discussion focuses on Harrell's C, Kent and O'Quigley's R 2 , and Royston and Sauerbrei's R 2 . We present similarities and differences between the statistics, discuss the software options from the most widely used statistical analysis packages, and give a practical example using the Worcester Heart Attack Study dataset.
Predictive power of nuclear-mass models
Directory of Open Access Journals (Sweden)
Yu. A. Litvinov
2013-12-01
Full Text Available Ten different theoretical models are tested for their predictive power in the description of nuclear masses. Two sets of experimental masses are used for the test: the older set of 2003 and the newer one of 2011. The predictive power is studied in two regions of nuclei: the global region (Z, N ≥ 8 and the heavy-nuclei region (Z ≥ 82, N ≥ 126. No clear correlation is found between the predictive power of a model and the accuracy of its description of the masses.
Multiresolution wavelet-ANN model for significant wave height forecasting.
Digital Repository Service at National Institute of Oceanography (India)
Deka, P.C.; Mandal, S.; Prahlada, R.
(ANN) modeling. The transformed output data are used as inputs to ANN models. Various decomposition levels have been tried for a db3 wavelet to obtain optimal results. It is found that the performance of hybrid WLNN is better than that of ANN when lead...
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
Xu, Zhenjun; Pan, Jun; Chen, Tao; Zhou, Qing; Wang, Qiang; Cao, Hailong; Fan, Fudong; Luo, Xuan; Ge, Min; Wang, Dongjin
2017-12-18
Our goal was to establish a prediction score and protocol for the preoperative prediction of significant coronary artery disease (CAD) in patients with rheumatic valvular heart disease. Using multivariate logistic regression analysis, we validated the model based on 490 patients without a history of myocardial infarction and who underwent preoperative screening coronary angiography. Significant CAD was defined as ≥50% narrowing of the diameter of the lumen of the left main coronary artery or ≥70% narrowing of the diameter of the lumen of the left anterior descending coronary artery, left circumflex artery or right coronary artery. Significant CAD was present in 9.8% of patients. Age, smoking, diabetes mellitus, diastolic blood pressure, low-density lipoprotein cholesterol and ischaemia evident on an electrocardiogram were independently associated with significant CAD and were entered into the multivariate model. According to the logistic regression predictive risk score, preoperative coronary angiography is recommended in (i) postmenopausal women between 50 and 59 years of age with ≥9.1% logistic regression predictive risk score; (ii) postmenopausal women who are ≥60 years old with a logistic regression predictive risk score ≥6.6% and (iii) men ≥50 years old whose logistic regression predictive risk score was ≥2.8%. Based on this predictive model, 246 (50.2%) preoperative coronary angiograms could be safely avoided. The negative predictive value of the model was 98.8% (246 of 249). This model was accurate for the preoperative prediction of significant CAD in patients with rheumatic valvular heart disease. This model must be validated in larger cohorts and various populations.
Predictive validation of an influenza spread model.
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Ayaz Hyder
Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve
Predictive Validation of an Influenza Spread Model
Hyder, Ayaz; Buckeridge, David L.; Leung, Brian
2013-01-01
Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive
North Atlantic climate model bias influence on multiyear predictability
Wu, Y.; Park, T.; Park, W.; Latif, M.
2018-01-01
The influences of North Atlantic biases on multiyear predictability of unforced surface air temperature (SAT) variability are examined in the Kiel Climate Model (KCM). By employing a freshwater flux correction over the North Atlantic to the model, which strongly alleviates both North Atlantic sea surface salinity (SSS) and sea surface temperature (SST) biases, the freshwater flux-corrected integration depicts significantly enhanced multiyear SAT predictability in the North Atlantic sector in comparison to the uncorrected one. The enhanced SAT predictability in the corrected integration is due to a stronger and more variable Atlantic Meridional Overturning Circulation (AMOC) and its enhanced influence on North Atlantic SST. Results obtained from preindustrial control integrations of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the findings obtained from the KCM: models with large North Atlantic biases tend to have a weak AMOC influence on SAT and exhibit a smaller SAT predictability over the North Atlantic sector.
Assessment of performance of survival prediction models for cancer prognosis
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Chen Hung-Chia
2012-07-01
Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in
Posterior predictive checking of multiple imputation models.
Nguyen, Cattram D; Lee, Katherine J; Carlin, John B
2015-07-01
Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the model and the data. The aim of this study was to evaluate the performance of the posterior predictive p-value as an imputation diagnostic. Using simulation methods, we deliberately misspecified imputation models to determine whether posterior predictive p-values were effective in identifying these problems. When estimating the regression parameter of interest, we found that more extreme p-values were associated with poorer imputation model performance, although the results highlighted that traditional thresholds for classical p-values do not apply in this context. A shortcoming of the PPC method was its reduced ability to detect misspecified models with increasing amounts of missing data. Despite the limitations of posterior predictive p-values, they appear to have a valuable place in the imputer's toolkit. In addition to automated checking using p-values, we recommend imputers perform graphical checks and examine other summaries of the test quantity distribution. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Predicting Protein Secondary Structure with Markov Models
DEFF Research Database (Denmark)
Fischer, Paul; Larsen, Simon; Thomsen, Claus
2004-01-01
we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....
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....
Comparative Study of Bancruptcy Prediction Models
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Isye Arieshanti
2013-09-01
Full Text Available Early indication of bancruptcy is important for a company. If companies aware of potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%
Prediction Models for Dynamic Demand Response
Energy Technology Data Exchange (ETDEWEB)
Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.
2015-11-02
As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D^{2}R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D^{2}R, which we address in this paper. Our first contribution is the formal definition of D^{2}R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D^{2}R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D^{2}R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D^{2}R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D^{2}R. Also, prediction models require just few days’ worth of data indicating that small amounts of
Are animal models predictive for humans?
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Greek Ray
2009-01-01
Full Text Available Abstract It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics.
Sahle, Berhe W; Owen, Alice J; Chin, Ken Lee; Reid, Christopher M
2017-09-01
Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models. EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution. Copyright © 2017 Elsevier Inc. All rights reserved.
Model predictive controller design of hydrocracker reactors
GÖKÇE, Dila
2014-01-01
This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...
Evaluation of wave runup predictions from numerical and parametric models
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.
Preoperative prediction model of outcome after cholecystectomy for symptomatic gallstones
DEFF Research Database (Denmark)
Borly, L; Anderson, I B; Bardram, Linda
1999-01-01
and sonography evaluated gallbladder motility, gallstones, and gallbladder volume. Preoperative variables in patients with or without postcholecystectomy pain were compared statistically, and significant variables were combined in a logistic regression model to predict the postoperative outcome. RESULTS: Eighty...... and by the absence of 'agonizing' pain and of symptoms coinciding with pain (P model 15 of 18 predicted patients had postoperative pain (PVpos = 0.83). Of 62 patients predicted as having no pain postoperatively, 56 were pain-free (PVneg = 0.90). Overall accuracy...... was 89%. CONCLUSION: From this prospective study a model based on preoperative symptoms was developed to predict postcholecystectomy pain. Since intrastudy reclassification may give too optimistic results, the model should be validated in future studies....
Comparing National Water Model Inundation Predictions with Hydrodynamic Modeling
Egbert, R. J.; Shastry, A.; Aristizabal, F.; Luo, C.
2017-12-01
The National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts, runoff, and other variables for 2.7 million reaches along the National Hydrography Dataset for the continental United States. NWM applies Muskingum-Cunge channel routing which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain better estimates of streamflow and stage in rivers especially for applications such as flood inundation mapping. Simulation Program for River NeTworks (SPRNT) is a fully dynamic model for large scale river networks that solves the full nonlinear Saint-Venant equations for 1D flow and stage height in river channel networks with non-uniform bathymetry. For the current work, the steady-state version of the SPRNT model was leveraged. An evaluation on SPRNT's and NWM's abilities to predict inundation was conducted for the record flood of Hurricane Matthew in October 2016 along the Neuse River in North Carolina. This event was known to have been influenced by backwater effects from the Hurricane's storm surge. Retrospective NWM discharge predictions were converted to stage using synthetic rating curves. The stages from both models were utilized to produce flood inundation maps using the Height Above Nearest Drainage (HAND) method which uses the local relative heights to provide a spatial representation of inundation depths. In order to validate the inundation produced by the models, Sentinel-1A synthetic aperture radar data in the VV and VH polarizations along with auxiliary data was used to produce a reference inundation map. A preliminary, binary comparison of the inundation maps to the reference, limited to the five HUC-12 areas of Goldsboro, NC, yielded that the flood inundation accuracies for NWM and SPRNT were 74.68% and 78.37%, respectively. The differences for all the relevant test statistics including accuracy, true positive rate, true negative rate, and positive predictive value were found
Multi-Model Ensemble Wake Vortex Prediction
Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.
2015-01-01
Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.
Ma, Gaoxiang; Wang, Qiaoyan; Lv, Chunye; Qiang, Fulin; Hua, Qiuhan; Chu, Haiyan; Du, Mulong; Tong, Na; Jiang, Yejuan; Wang, Meilin; Zhang, Zhengdong; Wang, Jian; Gong, Weida
2015-12-01
Although some studies have assessed the prognostic value of HOTAIR in patients with digestive system tumors, the relationship between the HOTAIR and outcome of digestive system tumors remains unknown. The PubMed was searched to identify the eligible studies. Here, we performed a meta-analysis with 11 studies, including a total of 903 cases. Pooled hazard ratios (HRs) and 95 % confidence interval (CI) of HOTAIR for cancer survival were calculated. We found that the pooled HR elevated HOTAIR expression in tumor tissues was 2.36 (95 % CI 1.88-2.97) compared with patients with low HOTAIR expression. Moreover, subgroup analysis revealed that HOTAIR overexpression was also markedly associated with short survival for esophageal squamous cell carcinoma (HR 2.19, 95 % CI 1.62-2.94) and gastric cancer (HR 1.66, 95 % CI 1.02-2.68). In addition, up-regulated HOTAIR was significantly related to survival of digestive system cancer among the studies with more follow-up time (follow time ≥ 5 years) (HR 2.51, 95 % CI 1.99-3.17). When stratified by HR resource and number of patients, the result indicated consistent results with the overall analysis. Subgroup analysis on ethnicities did not change the prognostic influence of elevated HOTAIR expression. Additionally, we conducted an independent validation cohort including 71 gastric cancer cases, in which patients with up-regulated HOTAIR expression had an unfavorable outcome with HR of 2.10 (95 % CI 1.10-4.03). The results suggest that aberrant HOTAIR expression may serve as a candidate positive marker to predict the prognosis of patients with carcinoma of digestive system.
Mirmehrabi, Mahmoud; Rohani, Sohrab; Perry, Luisa
2006-04-01
A new activity coefficient model was developed from excess Gibbs free energy in the form G(ex) = cA(a) x(1)(b)...x(n)(b). The constants of the proposed model were considered to be function of solute and solvent dielectric constants, Hildebrand solubility parameters and specific volumes of solute and solvent molecules. The proposed model obeys the Gibbs-Duhem condition for activity coefficient models. To generalize the model and make it as a purely predictive model without any adjustable parameters, its constants were found using the experimental activity coefficient and physical properties of 20 vapor-liquid systems. The predictive capability of the proposed model was tested by calculating the activity coefficients of 41 binary vapor-liquid equilibrium systems and showed good agreement with the experimental data in comparison with two other predictive models, the UNIFAC and Hildebrand models. The only data used for the prediction of activity coefficients, were dielectric constants, Hildebrand solubility parameters, and specific volumes of the solute and solvent molecules. Furthermore, the proposed model was used to predict the activity coefficient of an organic compound, stearic acid, whose physical properties were available in methanol and 2-butanone. The predicted activity coefficient along with the thermal properties of the stearic acid were used to calculate the solubility of stearic acid in these two solvents and resulted in a better agreement with the experimental data compared to the UNIFAC and Hildebrand predictive models.
PREDICTIVE CAPACITY OF ARCH FAMILY MODELS
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Raphael Silveira Amaro
2016-03-01
Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.
A revised prediction model for natural conception.
Bensdorp, Alexandra J; van der Steeg, Jan Willem; Steures, Pieternel; Habbema, J Dik F; Hompes, Peter G A; Bossuyt, Patrick M M; van der Veen, Fulco; Mol, Ben W J; Eijkemans, Marinus J C
2017-06-01
One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis was to assess whether additional predictors can refine the Hunault model and extend its applicability. Consecutive subfertile couples with unexplained and mild male subfertility presenting in fertility clinics were asked to participate in a prospective cohort study. We constructed a multivariable prediction model with the predictors from the Hunault model and new potential predictors. The primary outcome, natural conception leading to an ongoing pregnancy, was observed in 1053 women of the 5184 included couples (20%). All predictors of the Hunault model were selected into the revised model plus an additional seven (woman's body mass index, cycle length, basal FSH levels, tubal status,history of previous pregnancies in the current relationship (ongoing pregnancies after natural conception, fertility treatment or miscarriages), semen volume, and semen morphology. Predictions from the revised model seem to concur better with observed pregnancy rates compared with the Hunault model; c-statistic of 0.71 (95% CI 0.69 to 0.73) compared with 0.59 (95% CI 0.57 to 0.61). Copyright © 2017. Published by Elsevier Ltd.
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Tülay Tos
2012-12-01
CONClUSION: This data led us to conclude that in our country there is still need for more accurate and standardized method to predict abnormal cases with higher sensitivity and specificiy to decrease invasive procedures.
Dietary information improves cardiovascular disease risk prediction models.
Baik, I; Cho, N H; Kim, S H; Shin, C
2013-01-01
Data are limited on cardiovascular disease (CVD) risk prediction models that include dietary predictors. Using known risk factors and dietary information, we constructed and evaluated CVD risk prediction models. Data for modeling were from population-based prospective cohort studies comprised of 9026 men and women aged 40-69 years. At baseline, all were free of known CVD and cancer, and were followed up for CVD incidence during an 8-year period. We used Cox proportional hazard regression analysis to construct a traditional risk factor model, an office-based model, and two diet-containing models and evaluated these models by calculating Akaike information criterion (AIC), C-statistics, integrated discrimination improvement (IDI), net reclassification improvement (NRI) and calibration statistic. We constructed diet-containing models with significant dietary predictors such as poultry, legumes, carbonated soft drinks or green tea consumption. Adding dietary predictors to the traditional model yielded a decrease in AIC (delta AIC=15), a 53% increase in relative IDI (P-value for IDI NRI (category-free NRI=0.14, P NRI (category-free NRI=0.08, P<0.01) compared with the office-based model. The calibration plots for risk prediction demonstrated that the inclusion of dietary predictors contributes to better agreement in persons at high risk for CVD. C-statistics for the four models were acceptable and comparable. We suggest that dietary information may be useful in constructing CVD risk prediction models.
Osman, Marisol; Vera, C. S.
2017-10-01
This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to
Modelling the predictive performance of credit scoring
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Shi-Wei Shen
2013-07-01
Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.
Model Predictive Control of Sewer Networks
DEFF Research Database (Denmark)
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik
2016-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....... and controlled have thus become essential factors for efficient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona...
Bayesian Predictive Models for Rayleigh Wind Speed
DEFF Research Database (Denmark)
Shahirinia, Amir; Hajizadeh, Amin; Yu, David C
2017-01-01
predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior......One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view...
Comparison of two ordinal prediction models
DEFF Research Database (Denmark)
Kattan, Michael W; Gerds, Thomas A
2015-01-01
system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....
Uncertainties in model-based outcome predictions for treatment planning
International Nuclear Information System (INIS)
Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry
2001-01-01
Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment
Predictive analytics can support the ACO model.
Bradley, Paul
2012-04-01
Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.
Predictive modeling in homogeneous catalysis: a tutorial
Maldonado, A.G.; Rothenberg, G.
2010-01-01
Predictive modeling has become a practical research tool in homogeneous catalysis. It can help to pinpoint ‘good regions’ in the catalyst space, narrowing the search for the optimal catalyst for a given reaction. Just like any other new idea, in silico catalyst optimization is accepted by some
Model predictive control of smart microgrids
DEFF Research Database (Denmark)
Hu, Jiefeng; Zhu, Jianguo; Guerrero, Josep M.
2014-01-01
required to realise high-performance of distributed generations and will realise innovative control techniques utilising model predictive control (MPC) to assist in coordinating the plethora of generation and load combinations, thus enable the effective exploitation of the clean renewable energy sources...
Feedback model predictive control by randomized algorithms
Batina, Ivo; Stoorvogel, Antonie Arij; Weiland, Siep
2001-01-01
In this paper we present a further development of an algorithm for stochastic disturbance rejection in model predictive control with input constraints based on randomized algorithms. The algorithm presented in our work can solve the problem of stochastic disturbance rejection approximately but with
A Robustly Stabilizing Model Predictive Control Algorithm
Ackmece, A. Behcet; Carson, John M., III
2007-01-01
A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.
Hierarchical Model Predictive Control for Resource Distribution
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob
2010-01-01
This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...
Model Predictive Control based on Finite Impulse Response Models
DEFF Research Database (Denmark)
Prasath, Guru; Jørgensen, John Bagterp
2008-01-01
We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations ...
Disease prediction models and operational readiness.
Directory of Open Access Journals (Sweden)
Courtney D Corley
Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology
Model output statistics applied to wind power prediction
Energy Technology Data Exchange (ETDEWEB)
Joensen, A.; Giebel, G.; Landberg, L. [Risoe National Lab., Roskilde (Denmark); Madsen, H.; Nielsen, H.A. [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)
1999-03-01
Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.
Maas, M.; Dam-Deisz, W.D.C.; Roon, van A.M.; Takumi, K.; Giessen, van der J.W.B.
2014-01-01
The emergence of the zoonotic tapeworm Echinococcus multilocularis, causative agent ofalveolar echinococcosis (AE), poses a public health risk. A previously designed risk mapmodel predicted a spread of E. multilocularis and increasing numbers of alveolar echinococ-cosis patients in the province of
Tucker, Jalie A.; Vuchinich, Rudy E.; Black, Bethany C.; Rippens, Paula D.
2006-01-01
This study investigated whether a behavioral economic index of the value of rewards available over different time horizons improved prediction of drinking outcomes beyond established biopsychosocial predictors. Preferences for immediate drinking versus more delayed rewards made possible by saving money were determined from expenditures prior to…
Gouw, ASH; van den Heuvel, MC; van den Berg, AP; Slooff, NJH; de Jong, KP; Poppema, S
2002-01-01
Background. Chronic rejection (CR) in liver allografts shows a rapid onset and progressive course, leading to graft failure within the first year after transplantation. Most cases are preceded by episodes of acute cellular rejection (AR), but histological features predictive for the transition
On the necessity and biological significance of threshold-free regulon prediction outputs.
Rigali, Sébastien; Nivelle, Renaud; Tocquin, Pierre
2015-02-01
The in silico prediction of cis-acting elements in a genome is an efficient way to quickly obtain an overview of the biological processes controlled by a trans-acting factor, and connections between regulatory networks. Several regulon prediction web tools are available, designed to identify DNA motifs predicted to be bound by transcription factors using position weight matrix-based algorithms. In this paper we expose and discuss the conflicting objectives of software creators (bioinformaticians) and software users (biologists), who aim for reliable and exhaustive prediction outputs, respectively. Software makers, concerned with providing tools that minimise the number of false positive hits, often impose a stringent threshold score for a sequence to be included in the list of the putative cis-acting sites. This rigidity eventually results in the identification of strongly reliable but largely straightforward sites, i.e. those associated with genes already anticipated to be targeted by the studied transcription factor. Importantly, this biased identification of strongly bound sequences contrasts with the biological reality where, in many circumstances, a weak DNA-protein interaction is required for the appropriate gene's expression. We show here a series of transcriptionally controlled systems involving weakly bound cis-acting elements that could never have been discovered because of the policy of preventing software users from modifying the screening parameters. Proposing only trustworthy prediction outputs thus prevents biologists from fully utilising their knowledge background and deciding to analyse statistically irrelevant hits that could nonetheless be potentially involved in subtle, unexpected, though essential cis-trans relationships.
Characterizing Attention with Predictive Network Models.
Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M
2017-04-01
Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.
Genetic models of homosexuality: generating testable predictions
Gavrilets, Sergey; Rice, William R
2006-01-01
Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism. PMID:17015344
Prediction models : the right tool for the right problem
Kappen, Teus H.; Peelen, Linda M.
2016-01-01
PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to
Neuro-fuzzy modeling in bankruptcy prediction
Directory of Open Access Journals (Sweden)
Vlachos D.
2003-01-01
Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.
Embryo quality predictive models based on cumulus cells gene expression
Directory of Open Access Journals (Sweden)
Devjak R
2016-06-01
Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.
Directory of Open Access Journals (Sweden)
Sue F. Phelps
2016-03-01
of the faculty choices. The p-value for this relationship was less than 0.0001, also indicating that the result was not by chance. A quadratic model plotted alongside the previous linear model follows a similar pattern. The p-value of the comparison is 0.0002, which indicates the quadratic model’s fit cannot be explained by random chance. Main Results – The authors point out three outstanding findings. First, the match rate between faculty valuations and bibliometric scores for serials is 65%. This exceeds the 50% rate that would indicate random association, but also indicates a statistically significant difference between faculty and bibliometric valuations. Secondly, the match rate with the bibliometric scores for titles that faculty chose to keep (73% was higher than those they chose to cancel (54%. Thirdly, the match rate increased with higher bibliometric scores. Conclusions – Though the authors identify only a modest degree of similarity between faculty and bibliometric valuations of serials, it is noted that there is more agreement in the higher valued serials than the lower valued serials. With that in mind, librarians might focus faculty review on the lower scoring titles in the future, taking into consideration that unique faculty interests may drive selection at that level and would need to be balanced with the mission of the library.
[Hyperspectrum based prediction model for nitrogen content of apple flowers].
Zhu, Xi-Cun; Zhao, Geng-Xing; Wang, Ling; Dong, Fang; Lei, Tong; Zhan, Bing
2010-02-01
The present paper aims to quantitatively retrieve nitrogen content in apple flowers, so as to provide an important basis for apple informationization management. By using ASD FieldSpec 3 field spectrometer, hyperspectral reflectivity of 120 apple flower samples in full-bloom stage was measured and their nitrogen contents were analyzed. Based on the apple flower original spectrum and first derivative spectral characteristics, correlation analysis was carried out between apple flowers original spectrum and first derivative spectrum reflectivity and nitrogen contents, so as to determine the sensitive bands. Based on characteristic spectral parameters, prediction models were built, optimized and tested. The results indicated that the nitrogen content of apple was very significantly negatively correlated with the original spectral reflectance in the 374-696, 1 340-1 890 and 2 052-2 433 nm, while in 736-913 nm they were very significantly positively correlated; the first derivative spectrum in 637-675 nm was very significantly negatively correlated, and in 676-746 nm was very significantly positively correlated. All the six spectral parameters established were significantly correlated with the nitrogen content of apple flowers. Through further comparison and selection, the prediction models built with original spectral reflectance of 640 and 676 nm were determined as the best for nitrogen content prediction of apple flowers. The test results showed that the coefficients of determination (R2) of the two models were 0.825 8 and 0.893 6, the total root mean square errors (RMSE) were 0.732 and 0.638 6, and the slopes were 0.836 1 and 1.019 2 respectively. Therefore the models produced desired results for nitrogen content prediction of apple flowers with average prediction accuracy of 92.9% and 94.0%. This study will provide theoretical basis and technical support for rapid apple flower nitrogen content prediction and nutrition diagnosis.
Predictive Models for Carcinogenicity and Mutagenicity ...
Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t
Yu, Zhiyuan; Zheng, Jun; Ali, Hasan; Guo, Rui; Li, Mou; Wang, Xiaoze; Ma, Lu; Li, Hao; You, Chao
2017-11-01
Hematoma expansion is related to poor outcome in spontaneous intracerebral hemorrhage (ICH). Recently, a non-enhanced computed tomography (CT) based finding, termed the 'satellite sign', was reported to be a novel predictor for poor outcome in spontaneous ICH. However, it is still unclear whether the presence of the satellite sign is related to hematoma expansion. Initial computed tomography angiography (CTA) was conducted within 6h after ictus. Satellite sign on non-enhanced CT and spot sign on CTA were detected by two independent reviewers. The sensitivity and specificity of both satellite sign and spot sign were calculated. Receiver-operator analysis was conducted to evaluate their predictive accuracy for hematoma expansion. This study included 153 patients. Satellite sign was detected in 58 (37.91%) patients and spot sign was detected in 38 (24.84%) patients. Among 37 patients with hematoma expansion, 22 (59.46%) had satellite sign and 23 (62.16%) had spot sign. The sensitivity and specificity of satellite sign for prediction of hematoma expansion were 59.46% and 68.97%, respectively. The sensitivity and specificity of spot sign were 62.16% and 87.07%, respectively. The area under the curve (AUC) of satellite sign was 0.642 and the AUC of spot sign was 0.746. (P=0.157) CONCLUSION: Our results suggest that the satellite sign is an independent predictor for hematoma expansion in spontaneous ICH. Although spot sign has the higher predictive accuracy, satellite sign is still an acceptable predictor for hematoma expansion when CTA is unavailable. Copyright © 2017 Elsevier B.V. All rights reserved.
Disease Prediction Models and Operational Readiness
Energy Technology Data Exchange (ETDEWEB)
Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.
2014-03-19
INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the
Nonlinear model predictive control theory and algorithms
Grüne, Lars
2017-01-01
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...
The predictive performance and stability of six species distribution models.
Duan, Ren-Yan; Kong, Xiao-Quan; Huang, Min-Yi; Fan, Wei-Yi; Wang, Zhi-Gao
2014-01-01
Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (pSDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.
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.
A predictive model for dimensional errors in fused deposition modeling
DEFF Research Database (Denmark)
Stolfi, A.
2015-01-01
values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....
A predictive model for dimensional errors in fused deposition modeling
DEFF Research Database (Denmark)
Stolfi, A.
2015-01-01
This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....
Predictive Modeling in Actinide Chemistry and Catalysis
Energy Technology Data Exchange (ETDEWEB)
Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-05-16
These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.
Predictive modelling of evidence informed teaching
Zhang, Dell; Brown, C.
2017-01-01
In this paper, we analyse the questionnaire survey data collected from 79 English primary schools about the situation of evidence informed teaching, where the evidences could come from research journals or conferences. Specifically, we build a predictive model to see what external factors could help to close the gap between teachers’ belief and behaviour in evidence informed teaching, which is the first of its kind to our knowledge. The major challenge, from the data mining perspective, is th...
A Predictive Model for Cognitive Radio
2006-09-14
response in a given situation. Vadde et al. interest and produce a model for prediction of the response. have applied response surface methodology and...34 2000. [3] K. K. Vadde and V. R. Syrotiuk, "Factor interaction on service configurations to those that best meet our communication delivery in mobile ad...resulting set of configurations randomly or apply additional 2004. screening criteria. [4] K. K. Vadde , M.-V. R. Syrotiuk, and D. C. Montgomery
Predictions of titanium alloy properties using thermodynamic modeling tools
Zhang, F.; Xie, F.-Y.; Chen, S.-L.; Chang, Y. A.; Furrer, D.; Venkatesh, V.
2005-12-01
Thermodynamic modeling tools have become essential in understanding the effect of alloy chemistry on the final microstructure of a material. Implementation of such tools to improve titanium processing via parameter optimization has resulted in significant cost savings through the elimination of shop/laboratory trials and tests. In this study, a thermodynamic modeling tool developed at CompuTherm, LLC, is being used to predict β transus, phase proportions, phase chemistries, partitioning coefficients, and phase boundaries of multicomponent titanium alloys. This modeling tool includes Pandat, software for multicomponent phase equilibrium calculations, and PanTitanium, a thermodynamic database for titanium alloys. Model predictions are compared with experimental results for one α-β alloy (Ti-64) and two near-β alloys (Ti-17 and Ti-10-2-3). The alloying elements, especially the interstitial elements O, N, H, and C, have been shown to have a significant effect on the β transus temperature, and are discussed in more detail herein.
Tectonic predictions with mantle convection models
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
Predictive Modeling of the CDRA 4BMS
Coker, Robert F.; Knox, James C.
2016-01-01
As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.
Use of data mining to predict significant factors and benefits of bilateral cochlear implantation.
Ramos-Miguel, Angel; Perez-Zaballos, Teresa; Perez, Daniel; Falconb, Juan Carlos; Ramosb, Angel
2015-11-01
Data mining (DM) is a technique used to discover pattern and knowledge from a big amount of data. It uses artificial intelligence, automatic learning, statistics, databases, etc. In this study, DM was successfully used as a predictive tool to assess disyllabic speech test performance in bilateral implanted patients with a success rate above 90%. 60 bilateral sequentially implanted adult patients were included in the study. The DM algorithms developed found correlations between unilateral medical records and Audiological test results and bilateral performance by establishing relevant variables based on two DM techniques: the classifier and the estimation. The nearest neighbor algorithm was implemented in the first case, and the linear regression in the second. The results showed that patients with unilateral disyllabic test results below 70% benefited the most from a bilateral implantation. Finally, it was observed that its benefits decrease as the inter-implant time increases.
Predictive Modeling by the Cerebellum Improves Proprioception
Bhanpuri, Nasir H.; Okamura, Allison M.
2013-01-01
Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance. PMID:24005283
Significance of collateral vessels on the prediction of superior vena cava syndrome on CT
International Nuclear Information System (INIS)
Kim, Hyun Sook; Kim, Hyung Jin; Lee, Hyeng Gon; Ahn, In Oak; Chung, Sung Hoon
1993-01-01
Although visible collateral vessels on computed tomography (CT) has been considered as an important finding in superior vena cava (SVC) syndrome, there is no systematical analysis concerning correlation between the CT evidence of collateral vessels and clinical evidence of SVC syndrome. The purpose of this study is to evaluate how accurately we predict the clinical presence of SVC syndrome by the collateral vessels in patients with apparent SVC obstruction in CT. Forty seven patients having a CT evidence of obstruction or compression of SVC and/or its major tributaries were included in this study. Lung cancer was the most common underlying disease (n=40). The enhanced CT scans were obtained through either arm vein using a combined bolus and drip-infusion technique. Analyzing the CT scans, we particularly paid attention to the site and pattern of venous compromise, presence of collateral vessels, and if present, their location, without knowing whether symptoms and sign were present or nor, and then compared them with clinical data by a thorough review of charts, To verify the frequency of visible collateral vessels in normal subjects, we also evaluated the CT scans of 50 patients without mediastinal disease and clinical SVC syndrome as a control group. On CT, collateral vessels were found in 24 patients, among whom three patient had a single collateral and 21 patients had two or more collateral channels. There were two false positive cases, in which clinically overt SVC syndrome appeared 10 days and three months after CT examination respectively, and one false negative case. The presence of collateral vessels on CT, respectively, and one false negative case. The presence of collateral vessels on CT, regardless of the number and location of collateral vessels and pattern of venous obstruction, was a good clue for predicting the presence of clinical SVC syndrome with the sensitivity and the specificity of 95.7% and 91.7%, respectively. In control group, collateral
Model for predicting the injury severity score.
Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi
2015-07-01
To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.
Prediction of Chemical Function: Model Development and ...
The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi
Gamma-Ray Pulsars Models and Predictions
Harding, A K
2001-01-01
Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...
A prediction model for Clostridium difficile recurrence
Directory of Open Access Journals (Sweden)
Francis D. LaBarbera
2015-02-01
Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.
Evaluating predictive models of software quality
International Nuclear Information System (INIS)
Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D
2014-01-01
Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.
A generative model for predicting terrorist incidents
Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger
2017-05-01
A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations
Predicting third molar surgery operative time: a validated model.
Susarla, Srinivas M; Dodson, Thomas B
2013-01-01
The purpose of the present study was to develop and validate a statistical model to predict third molar (M3) operative time. This was a prospective cohort study consisting of a sample of subjects presenting for M3 removal. The demographic, anatomic, and operative variables were recorded for each subject. Using an index sample of randomly selected subjects, a multiple linear regression model was generated to predict the operating time. A nonoverlapping group of randomly selected subjects (validation sample) was used to assess model accuracy. P≤.05 was considered significant. The sample was composed of 150 subjects (n) who had 450 (k) M3s removed. The index sample (n=100 subjects, k=313 M3s extracted) had a mean age of 25.4±10.0 years. The mean extraction time was 6.4±7.0 minutes. The multiple linear regression model included M3 location, Winter's classification, tooth morphology, number of teeth extracted, procedure type, and surgical experience (R2=0.58). No statistically significant differences were seen between the index sample and the validation sample (n=50, k=137) for any of the study variables. Compared with the index model, the β-coefficients of the validation model were similar in direction and magnitude for most variables. Compared with the observed extraction time for all teeth in the sample, the predicted extraction time was not significantly different (P=.16). Fair agreement was seen between the β-coefficients for our multiple models in the index and validation populations, with no significant difference in the predicted and observed operating times. Copyright © 2013 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.
GA-ARMA Model for Predicting IGS RTS Corrections
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Mingyu Kim
2017-01-01
Full Text Available The global navigation satellite system (GNSS is widely used to estimate user positions. For precise positioning, users should correct for GNSS error components such as satellite orbit and clock errors as well as ionospheric delay. The international GNSS service (IGS real-time service (RTS can be used to correct orbit and clock errors in real-time. Since the IGS RTS provides real-time corrections via the Internet, intermittent data loss can occur due to software or hardware failures. We propose applying a genetic algorithm autoregressive moving average (GA-ARMA model to predict the IGS RTS corrections during data loss periods. The RTS orbit and clock corrections are predicted up to 900 s via the GA-ARMA model, and the prediction accuracies are compared with the results from a generic ARMA model. The orbit prediction performance of the GA-ARMA is nearly equivalent to that of ARMA, but GA-ARMA’s clock prediction performance is clearly better than that of ARMA, achieving a 32% error reduction. Predicted RTS corrections are applied to the broadcast ephemeris, and precise point positioning accuracies are compared. GA-ARMA shows a significant accuracy improvement over ARMA, particularly in terms of vertical positioning.
Prediction of gas compressibility factor using intelligent models
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Mohamad Mohamadi-Baghmolaei
2015-10-01
Full Text Available The gas compressibility factor, also known as Z-factor, plays the determinative role for obtaining thermodynamic properties of gas reservoir. Typically, empirical correlations have been applied to determine this important property. However, weak performance and some limitations of these correlations have persuaded the researchers to use intelligent models instead. In this work, prediction of Z-factor is aimed using different popular intelligent models in order to find the accurate one. The developed intelligent models are including Artificial Neural Network (ANN, Fuzzy Interface System (FIS and Adaptive Neuro-Fuzzy System (ANFIS. Also optimization of equation of state (EOS by Genetic Algorithm (GA is done as well. The validity of developed intelligent models was tested using 1038 series of published data points in literature. It was observed that the accuracy of intelligent predicting models for Z-factor is significantly better than conventional empirical models. Also, results showed the improvement of optimized EOS predictions when coupled with GA optimization. Moreover, of the three intelligent models, ANN model outperforms other models considering all data and 263 field data points of an Iranian offshore gas condensate with R2 of 0.9999, while the R2 for best empirical correlation was about 0.8334.
Predictive Models for Normal Fetal Cardiac Structures.
Krishnan, Anita; Pike, Jodi I; McCarter, Robert; Fulgium, Amanda L; Wilson, Emmanuel; Donofrio, Mary T; Sable, Craig A
2016-12-01
Clinicians rely on age- and size-specific measures of cardiac structures to diagnose cardiac disease. No universally accepted normative data exist for fetal cardiac structures, and most fetal cardiac centers do not use the same standards. The aim of this study was to derive predictive models for Z scores for 13 commonly evaluated fetal cardiac structures using a large heterogeneous population of fetuses without structural cardiac defects. The study used archived normal fetal echocardiograms in representative fetuses aged 12 to 39 weeks. Thirteen cardiac dimensions were remeasured by a blinded echocardiographer from digitally stored clips. Studies with inadequate imaging views were excluded. Regression models were developed to relate each dimension to estimated gestational age (EGA) by dates, biparietal diameter, femur length, and estimated fetal weight by the Hadlock formula. Dimension outcomes were transformed (e.g., using the logarithm or square root) as necessary to meet the normality assumption. Higher order terms, quadratic or cubic, were added as needed to improve model fit. Information criteria and adjusted R 2 values were used to guide final model selection. Each Z-score equation is based on measurements derived from 296 to 414 unique fetuses. EGA yielded the best predictive model for the majority of dimensions; adjusted R 2 values ranged from 0.72 to 0.893. However, each of the other highly correlated (r > 0.94) biometric parameters was an acceptable surrogate for EGA. In most cases, the best fitting model included squared and cubic terms to introduce curvilinearity. For each dimension, models based on EGA provided the best fit for determining normal measurements of fetal cardiac structures. Nevertheless, other biometric parameters, including femur length, biparietal diameter, and estimated fetal weight provided results that were nearly as good. Comprehensive Z-score results are available on the basis of highly predictive models derived from gestational
Directory of Open Access Journals (Sweden)
T. A. Pavlova
2008-01-01
Full Text Available Objective: to reveal a relationship between the impaired oxygen transport and the sectoral distribution of fluids in severe concomitant injury (SCI. Subjects and methods: A follow-up covered 29 patients with SCI. The severity of the condition was evaluated using the traumatic shock scale (Dzhanelidze Research Institute of Emergency Care, Saint Petersburg. Differences were estimated between the groups of patients with varying traumatic shock and between the surviving and deceased victims. The victims were examined by the generally accepted standards of clinical and laboratory studies; noninvasive monitoring of hemodynamics and fluid distribution along the body’s aqueous sectors was made in all the patients, by applying integral tetrapolar rheovasography and two-frequency bioimpedance study (Diamant-M, Saint Petersburg. Results. The diagnostic and predictive values of determination of the cardiac index have been ascertained by the Tishchenko tetrapolar rheovasography in patients with SCI. The cardiac index of less than 3.2 l/min^m2 in the first 24 hours after referral to an intensive care unit (ICU is a predictor of mortality. The above cardiac index is consistent with the presence of evident oxygen debt along with the respective oxygen extraction coefficient of greater than 42%. With lower cardiac index, there may be associated dyshidria: hyperosmolar hyperhydration of the intravascular sector and interstice with a tendency for intracellular sector dehydration. On the basis of determination of a low cardiac index (less than 3.2 l/min^m2 in the first 24 hours after referral to the ICU, intensive care tactics aimed at preventing and correcting the above-mentioned homeostatic disorders should be chosen. Key words: severe injury, cardiac index, oxygen transport, water electrolyte disorders.
An analytical model for climatic predictions
International Nuclear Information System (INIS)
Njau, E.C.
1990-12-01
A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs
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
Data Quality Enhanced Prediction Model for Massive Plant Data
Energy Technology Data Exchange (ETDEWEB)
Park, Moon-Ghu [Nuclear Engr. Sejong Univ., Seoul (Korea, Republic of); Kang, Seong-Ki [Monitoring and Diagnosis, Suwon (Korea, Republic of); Shin, Hajin [Saint Paul Preparatory Seoul, Seoul (Korea, Republic of)
2016-10-15
This paper introduces an integrated signal preconditioning and model prediction mainly by kernel functions. The performance and benefits of the methods are demonstrated by a case study with measurement data from a power plant and its components transient data. The developed methods will be applied as a part of monitoring massive or big data platform where human experts cannot detect the fault behaviors due to too large size of the measurements. Recent extensive efforts for on-line monitoring implementation insists that a big surprise in the modeling for predicting process variables was the extent of data quality problems in measurement data especially for data-driven modeling. Bad data for training will be learned as normal and can make significant degrade in prediction performance. For this reason, the quantity and quality of measurement data in modeling phase need special care. Bad quality data must be removed from training sets to the bad data considered as normal system behavior. This paper presented an integrated structure of supervisory system for monitoring the plants or sensors performance. The quality of the data-driven model is improved with a bilateral kernel filter for preprocessing of the noisy data. The prediction module is also based on kernel regression having the same basis with noise filter. The model structure is optimized by a grouping process with nonlinear Hoeffding correlation function.
Data Quality Enhanced Prediction Model for Massive Plant Data
International Nuclear Information System (INIS)
Park, Moon-Ghu; Kang, Seong-Ki; Shin, Hajin
2016-01-01
This paper introduces an integrated signal preconditioning and model prediction mainly by kernel functions. The performance and benefits of the methods are demonstrated by a case study with measurement data from a power plant and its components transient data. The developed methods will be applied as a part of monitoring massive or big data platform where human experts cannot detect the fault behaviors due to too large size of the measurements. Recent extensive efforts for on-line monitoring implementation insists that a big surprise in the modeling for predicting process variables was the extent of data quality problems in measurement data especially for data-driven modeling. Bad data for training will be learned as normal and can make significant degrade in prediction performance. For this reason, the quantity and quality of measurement data in modeling phase need special care. Bad quality data must be removed from training sets to the bad data considered as normal system behavior. This paper presented an integrated structure of supervisory system for monitoring the plants or sensors performance. The quality of the data-driven model is improved with a bilateral kernel filter for preprocessing of the noisy data. The prediction module is also based on kernel regression having the same basis with noise filter. The model structure is optimized by a grouping process with nonlinear Hoeffding correlation function
Relative sensitivity analysis of the predictive properties of sloppy models.
Myasnikova, Ekaterina; Spirov, Alexander
2018-01-25
Commonly among the model parameters characterizing complex biological systems are those that do not significantly influence the quality of the fit to experimental data, so-called "sloppy" parameters. The sloppiness can be mathematically expressed through saturating response functions (Hill's, sigmoid) thereby embodying biological mechanisms responsible for the system robustness to external perturbations. However, if a sloppy model is used for the prediction of the system behavior at the altered input (e.g. knock out mutations, natural expression variability), it may demonstrate the poor predictive power due to the ambiguity in the parameter estimates. We introduce a method of the predictive power evaluation under the parameter estimation uncertainty, Relative Sensitivity Analysis. The prediction problem is addressed in the context of gene circuit models describing the dynamics of segmentation gene expression in Drosophila embryo. Gene regulation in these models is introduced by a saturating sigmoid function of the concentrations of the regulatory gene products. We show how our approach can be applied to characterize the essential difference between the sensitivity properties of robust and non-robust solutions and select among the existing solutions those providing the correct system behavior at any reasonable input. In general, the method allows to uncover the sources of incorrect predictions and proposes the way to overcome the estimation uncertainties.
The predictive significance of CD20 expression in B-cell lymphomas
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Horvat Mateja
2011-04-01
Full Text Available Abstract Background In our recent study, we determined the cut-off value of CD20 expression at the level of 25 000 molecules of equivalent soluble fluorochrome (MESF to be the predictor of response to rituximab containing treatment in patients with B-cell lymphomas. In 17.5% of patients, who had the level of CD20 expression below the cut-off value, the response to rituximab containing treatment was significantly worse than in the rest of the patients with the level of CD20 expression above the cut-off value. The proportion of patients with low CD20 expression who might not benefit from rituximab containing treatment was not necessarily representative. Therefore the aim of this study was to quantify the CD20 expression in a larger series of patients with B-cell lymphomas which might allow us to determine more reliably the proportion of patients with the CD20 expression below the cut-off. Methods Cytological samples of 64 diffuse large B-cell lymphomas (DLBCL, 56 follicular lymphomas (FL, 31 chronic lymphocytic leukemias (CLL, 34 mantle cell lymphomas (MCL, 18 marginal zone lymphomas (MZL and 15 B-cell lymphomas unclassified were analyzed for CD20 expression by quantitative four-color flow cytometric measurements using FACSCalibur flow cytometer (BD Biosciences. Results The range of CD20 expression in different B-cell lymphomas was very broad, varying from 2 737 to 115 623 MESF in CLL and 3 549 to 679 577 MESF in DLBCL. However, when we compared the CD20 expression in the groups of patients with DLBCL, FL, MCL, MZL, CLL and B-cell lymphomas unclassified, it was found to be significantly lower (p = 0.002 only in CLL but did not significantly differ in other lymphoma types (p = NS. Fifty-three out of 218 (24.3% patients with B-cell lymphomas had the CD20 expression below the cut-off value. Conclusions The CD20 expression in CLL is significantly lower than in most histological types of mature B-cell lymphomas in which it appears to be comparable
Web tools for predictive toxicology model building.
Jeliazkova, Nina
2012-07-01
The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.
[Endometrial cancer: Predictive models and clinical impact].
Bendifallah, Sofiane; Ballester, Marcos; Daraï, Emile
2017-12-01
In France, in 2015, endometrial cancer (CE) is the first gynecological cancer in terms of incidence and the fourth cause of cancer of the woman. About 8151 new cases and nearly 2179 deaths have been reported. Treatments (surgery, external radiotherapy, brachytherapy and chemotherapy) are currently delivered on the basis of an estimation of the recurrence risk, an estimation of lymph node metastasis or an estimate of survival probability. This risk is determined on the basis of prognostic factors (clinical, histological, imaging, biological) taken alone or grouped together in the form of classification systems, which are currently insufficient to account for the evolutionary and prognostic heterogeneity of endometrial cancer. For endometrial cancer, the concept of mathematical modeling and its application to prediction have developed in recent years. These biomathematical tools have opened a new era of care oriented towards the promotion of targeted therapies and personalized treatments. Many predictive models have been published to estimate the risk of recurrence and lymph node metastasis, but a tiny fraction of them is sufficiently relevant and of clinical utility. The optimization tracks are multiple and varied, suggesting the possibility in the near future of a place for these mathematical models. The development of high-throughput genomics is likely to offer a more detailed molecular characterization of the disease and its heterogeneity. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.
Predictive Capability Maturity Model for computational modeling and simulation.
Energy Technology Data Exchange (ETDEWEB)
Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.
2007-10-01
The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.
Predictions of models for environmental radiological assessment
International Nuclear Information System (INIS)
Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando
2011-01-01
In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for 137 Cs and 60 Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)
[A predictive model on turnover intention of nurses in Korea].
Moon, Sook Ja; Han, Sang Sook
2011-10-01
The purpose of this study was to propose and test a predictive model that could explain and predict Korean nurses' turnover intentions. A survey using a structured questionnaire was conducted with 445 nurses in Korea. Six instruments were used in this model. The data were analyzed using SPSS 15.0 and Amos 7.0 program. Based on the constructed model, organizational commitment, and burnout were found to have a significant direct effect on turnover intention of nurses. In addition, factors such as empowerment, job satisfaction, and organizational commitment were found to indirectly affect turnover intention of nurse. The final modified model yielded χ²=402.30, pturnover intention in Korean nurses. Findings from this study can be used to design appropriate strategies to further decrease the nurses' turnover intention in Korea.
Directory of Open Access Journals (Sweden)
César Hernández-Hernández
2017-06-01
Full Text Available Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.
Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.
Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F
2013-04-01
In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.
Directory of Open Access Journals (Sweden)
Steenkamp Emma
2009-01-01
Full Text Available Abstract Background Resolving the evolutionary relationships among Fungi remains challenging because of their highly variable evolutionary rates, and lack of a close phylogenetic outgroup. Nucleariida, an enigmatic group of amoeboids, have been proposed to emerge close to the fungal-metazoan divergence and might fulfill this role. Yet, published phylogenies with up to five genes are without compelling statistical support, and genome-level data should be used to resolve this question with confidence. Results Our analyses with nuclear (118 proteins and mitochondrial (13 proteins data now robustly associate Nucleariida and Fungi as neighbors, an assemblage that we term 'Holomycota'. With Nucleariida as an outgroup, we revisit unresolved deep fungal relationships. Conclusion Our phylogenomic analysis provides significant support for the paraphyly of the traditional taxon Zygomycota, and contradicts a recent proposal to include Mortierella in a phylum Mucoromycotina. We further question the introduction of separate phyla for Glomeromycota and Blastocladiomycota, whose phylogenetic positions relative to other phyla remain unresolved even with genome-level datasets. Our results motivate broad sampling of additional genome sequences from these phyla.
Combining GPS measurements and IRI model predictions
International Nuclear Information System (INIS)
Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.
2002-01-01
The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations
Mathematical models for indoor radon prediction
International Nuclear Information System (INIS)
Malanca, A.; Pessina, V.; Dallara, G.
1995-01-01
It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model
A Predictive Maintenance Model for Railway Tracks
DEFF Research Database (Denmark)
Li, Rui; Wen, Min; Salling, Kim Bang
2015-01-01
presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...... recovery on the track quality after tamping operation and (5) Tamping machine operation factors. A Danish railway track between Odense and Fredericia with 57.2 km of length is applied for a time period of two to four years in the proposed maintenance model. The total cost can be reduced with up to 50...
Directory of Open Access Journals (Sweden)
Tang H
2017-05-01
Full Text Available Hong Tang,1,* Yufeng Wu,1,* Yanru Qin,2 Hongyan Wang,1 Yongxu Jia,2 Shujun Yang,1 Suxia Luo,1 Qiming Wang11Department of Internal Medicine, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 2Department of Clinical Oncology, the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, Hong Kong, China*These authors contributed equally to this workAbstract: Despite a series of attempts during the last decades, the prognosis of esophageal squamous cell carcinoma (ESCC remains poor. Different responses of individual tumors encouraged us to look for valuable prognostic markers. As a key regulator controlling the anabolic ketogenic pathway, 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2 has been reported to play a crucial role in colorectal cancer and prostate cancer. However, its importance to ESCC has not been verified. Therefore, a large cohort retrospective study was planned, to investigate the relationship between HMGCS2 expression and ESCC prognosis. By adopting real-time polymerase chain reaction (PCR and immunohistochemical (IHC staining, HMGCS2 expression was examined in tissues of 300 ESCC patients with complete resection. Besides, the association between HMGCS2 protein expression and survival time was evaluated through chi-square test and Kaplan–Meier analysis. With the use of Cox-proportional hazards model, the prognostic impact of clinicopathologic variables and biomarker expression was evaluated. Compared with their non-tumor counterparts, HMGCS2 downregulation occurred in 65.5% and 37.6% of primary ESCCs on the mRNA and protein levels (P<0.001, respectively. On the protein level, HMGCS2 expression was associated with tumor cell differentiation (P=0.003, pT status (P=0.006, and TNM stage (P=0.010. In the down-HMGCS2 expression group, the 5-year overall survival (OS and relapse-free survival (RFS are poorer than those in the normal expression group (19 months vs 24 months, P=0.002; 13 months vs 17
Planetary wave prediction: Benefits of tropical data and global models
Somerville, R. C. J.
1985-01-01
Skillful numerical predictions of midlatitude atmospheric planetary waves generally require both tropical data for the initial conditions and a global domain for the forecast model. The lack of either adequate tropical observations or a global domain typically leads to a significant degradation of forecast skill in middle latitudes within the first one to three days of the forecast period. These effects were first discovered by numerical experimentation. They were subsequently explained theoretically, and their importance for practical forecasting was confirmed in a series of prediction experiments using FGGE data.
Hybrid multiscale modeling and prediction of cancer cell behavior.
Directory of Open Access Journals (Sweden)
Mohammad Hossein Zangooei
Full Text Available Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems.In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters.Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable.Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.
Han, Donghee; Lee, Ji Hyun; Rizvi, Asim; Gransar, Heidi; Baskaran, Lohendran; Schulman-Marcus, Joshua; Ó Hartaigh, Bríain; Lin, Fay Y; Min, James K
2018-02-01
Evaluation of resting myocardial computed tomography perfusion (CTP) by coronary CT angiography (CCTA) might serve as a useful addition for determining coronary artery disease. We aimed to evaluate the incremental benefit of resting CTP over coronary stenosis for predicting ischemia using a computational algorithm trained by machine learning methods. 252 patients underwent CCTA and invasive fractional flow reserve (FFR). CT stenosis was classified as 0%, 1-30%, 31-49%, 50-70%, and >70% maximal stenosis. Significant ischemia was defined as invasive FFR machine learning. On a per-patient basis, accuracy, sensitivity, specificity, positive predictive, and negative predictive values according to resting CTP when added to CT stenosis (>70%) for predicting ischemia were 68.3%, 52.7%, 84.6%, 78.2%, and 63.0%, respectively. Compared with CT stenosis [area under the receiver operating characteristic curve (AUC): 0.68, 95% confidence interval (CI) 0.62-0.74], the addition of resting CTP appeared to improve discrimination (AUC: 0.75, 95% CI 0.69-0.81, P value .001) and reclassification (net reclassification improvement: 0.52, P value machine learning techniques may improve the predictive utility of significant ischemia over coronary stenosis.
An Operational Model for the Prediction of Jet Blast
2012-01-09
This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...
Intra prediction based on Markov process modeling of images.
Kamisli, Fatih
2013-10-01
In recent video coding standards, intraprediction of a block of pixels is performed by copying neighbor pixels of the block along an angular direction inside the block. Each block pixel is predicted from only one or few directionally aligned neighbor pixels of the block. Although this is a computationally efficient approach, it ignores potentially useful correlation of other neighbor pixels of the block. To use this correlation, a general linear prediction approach is proposed, where each block pixel is predicted using a weighted sum of all neighbor pixels of the block. The disadvantage of this approach is the increased complexity because of the large number of weights. In this paper, we propose an alternative approach to intraprediction, where we model image pixels with a Markov process. The Markov process model accounts for the ignored correlation in standard intraprediction methods, but uses few neighbor pixels and enables a computationally efficient recursive prediction algorithm. Compared with the general linear prediction approach that has a large number of independent weights, the Markov process modeling approach uses a much smaller number of independent parameters and thus offers significantly reduced memory or computation requirements, while achieving similar coding gains with offline computed parameters.
The Significance of the Bystander Effect: Modeling, Experiments, and More Modeling
Energy Technology Data Exchange (ETDEWEB)
Brenner, David J.
2009-07-22
-term models is needed. As an example of this novel approach, we integrated a stochastic short-term initiation/inactivation/repopulation model with a deterministic two-stage long-term model. Within this new formalism, the following assumptions are implemented: radiation initiates, promotes, or kills pre-malignant cells; a pre-malignant cell generates a clone, which, if it survives, quickly reaches a size limitation; the clone subsequently grows more slowly and can eventually generate a malignant cell; the carcinogenic potential of pre-malignant cells decreases with age. The effectiveness of high-LET radiation per unit dose increases as dose rate decreases. This “inverse dose rate effect” is seen in radon-induced lung carcinogenesis. We suggest a biologically-motivated mechanism based on radiation-induced direct and bystander-effect-related risks: During radon exposure, only a fraction of cells are traversed by alpha particles. These irradiated cells have an increased probability of being initiated into a pre-malignant state. They release signals, which convert some nearby unirradiated cells to an activated state. When already pre-malignant cells are activated, their proliferation (promotion) rate increases. If a radiation dose is sufficient to activate most susceptible cells, protracting the exposure does not substantially decrease the number of activated cells, but prolongs the activated state during which pre-malignant cell proliferation is accelerated. This mechanism is implemented in a low-dose-rate extension of our carcinogenesis model, which integrates both short- and long-term modeling approaches, and was applied to radiotherapy-induced second cancer risk estimation. Model predictions adequately describe the data on radon-induced lung carcinogenesis in humans and rats, using few adjustable parameters. Conclusions about the relative importance of promotion vs. initiation for radon carcinogenesis are similar to those reported with the two-stage clonal expansion model
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...... model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model...
An international model to predict recurrent cardiovascular disease.
Wilson, Peter W F; D'Agostino, Ralph; Bhatt, Deepak L; Eagle, Kim; Pencina, Michael J; Smith, Sidney C; Alberts, Mark J; Dallongeville, Jean; Goto, Shinya; Hirsch, Alan T; Liau, Chiau-Suong; Ohman, E Magnus; Röther, Joachim; Reid, Christopher; Mas, Jean-Louis; Steg, Ph Gabriel
2012-07-01
Prediction models for cardiovascular events and cardiovascular death in patients with established cardiovascular disease are not generally available. Participants from the prospective REduction of Atherothrombosis for Continued Health (REACH) Registry provided a global outpatient population with known cardiovascular disease at entry. Cardiovascular prediction models were estimated from the 2-year follow-up data of 49,689 participants from around the world. A developmental prediction model was estimated from 33,419 randomly selected participants (2394 cardiovascular events with 1029 cardiovascular deaths) from the pool of 49,689. The number of vascular beds with clinical disease, diabetes, smoking, low body mass index, history of atrial fibrillation, cardiac failure, and history of cardiovascular event(s) <1 year before baseline examination increased risk of a subsequent cardiovascular event. Statin (hazard ratio 0.75; 95% confidence interval, 0.69-0.82) and acetylsalicylic acid therapy (hazard ratio 0.90; 95% confidence interval, 0.83-0.99) also were significantly associated with reduced risk of cardiovascular events. The prediction model was validated in the remaining 16,270 REACH subjects (1172 cardiovascular events, 494 cardiovascular deaths). Risk of cardiovascular death was similarly estimated with the same set of risk factors. Simple algorithms were developed for prediction of overall cardiovascular events and for cardiovascular death. This study establishes and validates a risk model to predict secondary cardiovascular events and cardiovascular death in outpatients with established atherothrombotic disease. Traditional risk factors, burden of disease, lack of treatment, and geographic location all are related to an increased risk of subsequent cardiovascular morbidity and cardiovascular mortality. Copyright © 2012 Elsevier Inc. All rights reserved.
Predictive Modelling of Contagious Deforestation in the Brazilian Amazon
Rosa, Isabel M. D.; Purves, Drew; Souza, Carlos; Ewers, Robert M.
2013-01-01
Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges “bottom up”, as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated–pre- and post-PPCDAM (“Plano de Ação para Proteção e Controle do Desmatamento na Amazônia”)–the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is
Predictive modelling of contagious deforestation in the Brazilian Amazon.
Rosa, Isabel M D; Purves, Drew; Souza, Carlos; Ewers, Robert M
2013-01-01
Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia")-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently
Predictive modeling: potential application in prevention services.
Wilson, Moira L; Tumen, Sarah; Ota, Rissa; Simmers, Anthony G
2015-05-01
In 2012, the New Zealand Government announced a proposal to introduce predictive risk models (PRMs) to help professionals identify and assess children at risk of abuse or neglect as part of a preventive early intervention strategy, subject to further feasibility study and trialing. The purpose of this study is to examine technical feasibility and predictive validity of the proposal, focusing on a PRM that would draw on population-wide linked administrative data to identify newborn children who are at high priority for intensive preventive services. Data analysis was conducted in 2013 based on data collected in 2000-2012. A PRM was developed using data for children born in 2010 and externally validated for children born in 2007, examining outcomes to age 5 years. Performance of the PRM in predicting administratively recorded substantiations of maltreatment was good compared to the performance of other tools reviewed in the literature, both overall, and for indigenous Māori children. Some, but not all, of the children who go on to have recorded substantiations of maltreatment could be identified early using PRMs. PRMs should be considered as a potential complement to, rather than a replacement for, professional judgment. Trials are needed to establish whether risks can be mitigated and PRMs can make a positive contribution to frontline practice, engagement in preventive services, and outcomes for children. Deciding whether to proceed to trial requires balancing a range of considerations, including ethical and privacy risks and the risk of compounding surveillance bias. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.
Model predictive control based on reduced order models applied to belt conveyor system.
Chen, Wei; Li, Xin
2016-11-01
In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Regression Model to Predict Global Solar Irradiance in Malaysia
Directory of Open Access Journals (Sweden)
Hairuniza Ahmed Kutty
2015-01-01
Full Text Available A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE, mean bias error (MBE, and the coefficient of determination (R2 with other models available from literature studies. Seven models based on single parameters (PM1 to PM7 and five multiple-parameter models (PM7 to PM12 are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.
Estimating Predictive Variance for Statistical Gas Distribution Modelling
International Nuclear Information System (INIS)
Lilienthal, Achim J.; Asadi, Sahar; Reggente, Matteo
2009-01-01
Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.
van Leeuwen, Pim J; Hayen, Andrew; Thompson, James E; Moses, Daniel; Shnier, Ron; Böhm, Maret; Abuodha, Magdaline; Haynes, Anne-Maree; Ting, Francis; Barentsz, Jelle; Roobol, Monique; Vass, Justin; Rasiah, Krishan; Delprado, Warick; Stricker, Phillip D
2017-12-01
To develop and externally validate a predictive model for detection of significant prostate cancer. Development of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate-specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling. In all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P prostate cancer. Individualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of over-detection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed. © 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.
The predictive performance and stability of six species distribution models.
Directory of Open Access Journals (Sweden)
Ren-Yan Duan
Full Text Available Predicting species' potential geographical range by species distribution models (SDMs is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials. We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05, while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05, and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points.According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.
A Computational Model for Predicting Gas Breakdown
Gill, Zachary
2017-10-01
Pulsed-inductive discharges are a common method of producing a plasma. They provide a mechanism for quickly and efficiently generating a large volume of plasma for rapid use and are seen in applications including propulsion, fusion power, and high-power lasers. However, some common designs see a delayed response time due to the plasma forming when the magnitude of the magnetic field in the thruster is at a minimum. New designs are difficult to evaluate due to the amount of time needed to construct a new geometry and the high monetary cost of changing the power generation circuit. To more quickly evaluate new designs and better understand the shortcomings of existing designs, a computational model is developed. This model uses a modified single-electron model as the basis for a Mathematica code to determine how the energy distribution in a system changes with regards to time and location. By analyzing this energy distribution, the approximate time and location of initial plasma breakdown can be predicted. The results from this code are then compared to existing data to show its validity and shortcomings. Missouri S&T APLab.
Distributed model predictive control made easy
Negenborn, Rudy
2014-01-01
The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...
Tollenaar, N.; van der Heijden, P.G.M.
2012-01-01
Using criminal population conviction histories of recent offenders, prediction mod els are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining and machine learning provide an improvement in predictive performance over classical statistical methods, namely logistic regression and linear discrim inant analysis. These models are compared ...
Predictive modeling of liquid-sodium thermal–hydraulics experiments and computations
International Nuclear Information System (INIS)
Arslan, Erkan; Cacuci, Dan G.
2014-01-01
Highlights: • We applied the predictive modeling method of Cacuci and Ionescu-Bujor (2010). • We assimilated data from sodium flow experiments. • We used computational fluid dynamics simulations of sodium experiments. • The predictive modeling method greatly reduced uncertainties in predicted results. - Abstract: This work applies the predictive modeling procedure formulated by Cacuci and Ionescu-Bujor (2010) to assimilate data from liquid-sodium thermal–hydraulics experiments in order to reduce systematically the uncertainties in the predictions of computational fluid dynamics (CFD) simulations. The predicted CFD-results for the best-estimate model parameters and results describing sodium-flow velocities and temperature distributions are shown to be significantly more precise than the original computations and experiments, in that the predicted uncertainties for the best-estimate results and model parameters are significantly smaller than both the originally computed and the experimental uncertainties
Macromolecular symmetric assembly prediction using swarm intelligence dynamic modeling.
Degiacomi, Matteo T; Dal Peraro, Matteo
2013-07-02
Proteins often assemble in multimeric complexes to perform a specific biologic function. However, trapping these high-order conformations is difficult experimentally. Therefore, predicting how proteins assemble using in silico techniques can be of great help. The size of the associated conformational space and the fact that proteins are intrinsically flexible structures make this optimization problem extremely challenging. Nonetheless, known experimental spatial restraints can guide the search process, contributing to model biologically relevant states. We present here a swarm intelligence optimization protocol able to predict the arrangement of protein symmetric assemblies by exploiting a limited amount of experimental restraints and steric interactions. Importantly, within this scheme the native flexibility of each protein subunit is taken into account as extracted from molecular dynamics (MD) simulations. We show that this is a key ingredient for the prediction of biologically functional assemblies when, upon oligomerization, subunits explore activated states undergoing significant conformational changes.
Real estate value prediction using multivariate regression models
Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav
2017-11-01
The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.
Fuzzy predictive filtering in nonlinear economic model predictive control for demand response
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.
2016-01-01
The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...
Effect of misreported family history on Mendelian mutation prediction models.
Katki, Hormuzd A
2006-06-01
People with familial history of disease often consult with genetic counselors about their chance of carrying mutations that increase disease risk. To aid them, genetic counselors use Mendelian models that predict whether the person carries deleterious mutations based on their reported family history. Such models rely on accurate reporting of each member's diagnosis and age of diagnosis, but this information may be inaccurate. Commonly encountered errors in family history can significantly distort predictions, and thus can alter the clinical management of people undergoing counseling, screening, or genetic testing. We derive general results about the distortion in the carrier probability estimate caused by misreported diagnoses in relatives. We show that the Bayes factor that channels all family history information has a convenient and intuitive interpretation. We focus on the ratio of the carrier odds given correct diagnosis versus given misreported diagnosis to measure the impact of errors. We derive the general form of this ratio and approximate it in realistic cases. Misreported age of diagnosis usually causes less distortion than misreported diagnosis. This is the first systematic quantitative assessment of the effect of misreported family history on mutation prediction. We apply the results to the BRCAPRO model, which predicts the risk of carrying a mutation in the breast and ovarian cancer genes BRCA1 and BRCA2.
Chen, Zeng-Ping; Li, Li-Mei; Yu, Ru-Qin; Littlejohn, David; Nordon, Alison; Morris, Julian; Dann, Alison S; Jeffkins, Paul A; Richardson, Mark D; Stimpson, Sarah L
2011-01-07
The development of reliable multivariate calibration models for spectroscopic instruments in on-line/in-line monitoring of chemical and bio-chemical processes is generally difficult, time-consuming and costly. Therefore, it is preferable if calibration models can be used for an extended period, without the need to replace them. However, in many process applications, changes in the instrumental response (e.g. owing to a change of spectrometer) or variations in the measurement conditions (e.g. a change in temperature) can cause a multivariate calibration model to become invalid. In this contribution, a new method, systematic prediction error correction (SPEC), has been developed to maintain the predictive abilities of multivariate calibration models when e.g. the spectrometer or measurement conditions are altered. The performance of the method has been tested on two NIR data sets (one with changes in instrumental responses, the other with variations in experimental conditions) and the outcomes compared with those of some popular methods, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS). The results show that SPEC achieves satisfactory analyte predictions with significantly lower RMSEP values than global PLS and SBC for both data sets, even when only a few standardization samples are used. Furthermore, SPEC is simple to implement and requires less information than PDS, which offers advantages for applications with limited data.
Validating predictions from climate envelope models
Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.
2013-01-01
Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.
Validating predictions from climate envelope models.
Directory of Open Access Journals (Sweden)
James I Watling
Full Text Available Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species' distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967-1971 (t1 and evaluated using occurrence data from 1998-2002 (t2. Model sensitivity (the ability to correctly classify species presences was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on
Shukla, Ravi P.; Kinter, James L.; Shin, Chul-Su
2018-03-01
This study evaluates the effect of El Niño and the Southern Oscillation (ENSO) and Madden Julian Oscillation (MJO) events on 14-day mean significant wave height (SWH) at 3 weeks lead time (Wk34) over the Western Pacific and Indian Oceans using the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2). The WAVEWATCH-3 (WW3) model is forced with daily 10m-winds predicted by a modified version of CFSv2 that is initialized with multiple ocean analyses in both January and May for 1979-2008. A significant anomaly correlation of predicted and observed SWH anomalies (SWHA) at Wk34 lead-time is found over portions of the domain, including the central western Pacific, South China Sea (SCS), Bay of Bengal (BOB) and southern Indian Ocean (IO) in January cases, and over BOB, equatorial western Pacific, the Maritime Continent and southern IO in May cases. The model successfully predicts almost all the important features of the observed composite SWHA during El Niño events in January, including negative SWHA in the central IO where westerly wind anomalies act on an easterly mean state, and positive SWHA over the southern Ocean (SO) where westerly wind anomalies act on a westerly mean state. The model successfully predicts the sign and magnitude of SWHA at Wk34 lead-time in May over the BOB and SCS in composites of combined phases-2-3 and phases-6-7 of MJO. The observed leading mode of SWHA in May and the third mode of SWHA in January are influenced by the combined effects of ENSO and MJO. Based on spatial and temporal correlations, the spatial patterns of SWHA in the model at Wk34 in both January and May are in good agreement with the observations over the equatorial western Pacific, equatorial and southern IO, and SO.
Robust Model Predictive Control of a Wind Turbine
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2012-01-01
, a new sensor is introduced in the EKF to give faster estimations. Wind speed estimation error is used to assess uncertainties in the linearized model. Significant uncertainties are considered to be in the gain of the system (B matrix of the state space model). Therefore this special structure......In this work the problem of robust model predictive control (robust MPC) of a wind turbine in the full load region is considered. A minimax robust MPC approach is used to tackle the problem. Nonlinear dynamics of the wind turbine are derived by combining blade element momentum (BEM) theory...... and first principle modeling of the turbine flexible structure. Thereafter the nonlinear model is linearized using Taylor series expansion around system operating points. Operating points are determined by effective wind speed and an extended Kalman filter (EKF) is employed to estimate this. In addition...
MOTORCYCLE CRASH PREDICTION MODEL FOR NON-SIGNALIZED INTERSECTIONS
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S. HARNEN
2003-01-01
Full Text Available This paper attempts to develop a prediction model for motorcycle crashes at non-signalized intersections on urban roads in Malaysia. The Generalized Linear Modeling approach was used to develop the model. The final model revealed that an increase in motorcycle and non-motorcycle flows entering an intersection is associated with an increase in motorcycle crashes. Non-motorcycle flow on major road had the greatest effect on the probability of motorcycle crashes. Approach speed, lane width, number of lanes, shoulder width and land use were also found to be significant in explaining motorcycle crashes. The model should assist traffic engineers to decide the need for appropriate intersection treatment that specifically designed for non-exclusive motorcycle lane facilities.
Energy Technology Data Exchange (ETDEWEB)
Hell, Michaela M., E-mail: michaela.hell@uk-erlangen.de [Department of Cardiology, University of Erlangen (Germany); Dey, Damini [Department of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Taper Building, Room A238, 8700 Beverly Boulevard, Los Angeles, CA 90048 (United States); Marwan, Mohamed; Achenbach, Stephan; Schmid, Jasmin; Schuhbaeck, Annika [Department of Cardiology, University of Erlangen (Germany)
2015-08-15
Highlights: • Overestimation of coronary lesions by coronary computed tomography angiography and subsequent unnecessary invasive coronary angiography and revascularization is a concern. • Differences in plaque characteristics and contrast density difference between hemodynamically significant and non-significant stenoses, as defined by invasive fractional flow reserve, were assessed. • At a threshold of ≥24%, contrast density difference predicted hemodynamically significant lesions with a specificity of 75%, sensitivity of 33%, PPV of 35% and NPV of 73%. • The determination of contrast density difference required less time than transluminal attenuation gradient measurement. - Abstract: Objectives: Coronary computed tomography angiography (CTA) allows the detection of obstructive coronary artery disease. However, its ability to predict the hemodynamic significance of stenoses is limited. We assessed differences in plaque characteristics and contrast density difference between hemodynamically significant and non-significant stenoses, as defined by invasive fractional flow reserve (FFR). Methods: Lesion characteristics of 59 consecutive patients (72 lesions) in whom invasive FFR was performed in at least one coronary artery with moderate to high-grade stenoses in coronary CTA were evaluated by two experienced readers. Coronary CTA data sets were acquired on a second-generation dual-source CT scanner using retrospectively ECG-gated spiral acquisition or prospectively ECG-triggered axial acquisition mode. Plaque volume and composition (non-calcified, calcified), remodeling index as well as contrast density difference (defined as the percentage decline in luminal CT attenuation/cross-sectional area over the lesion) were assessed using a semi-automatic software tool (Autoplaq). Additionally, the transluminal attenuation gradient (defined as the linear regression coefficient between intraluminal CT attenuation and length from the ostium) was determined
Using plural modeling for predicting decisions made by adaptive adversaries
International Nuclear Information System (INIS)
Buede, Dennis M.; Mahoney, Suzanne; Ezell, Barry; Lathrop, John
2012-01-01
Incorporating an appropriate representation of the likelihood of terrorist decision outcomes into risk assessments associated with weapons of mass destruction attacks has been a significant problem for countries around the world. Developing these likelihoods gets at the heart of the most difficult predictive problems: human decision making, adaptive adversaries, and adversaries about which very little is known. A plural modeling approach is proposed that incorporates estimates of all critical uncertainties: who is the adversary and what skills and resources are available to him, what information is known to the adversary and what perceptions of the important facts are held by this group or individual, what does the adversary know about the countermeasure actions taken by the government in question, what are the adversary's objectives and the priorities of those objectives, what would trigger the adversary to start an attack and what kind of success does the adversary desire, how realistic is the adversary in estimating the success of an attack, how does the adversary make a decision and what type of model best predicts this decision-making process. A computational framework is defined to aggregate the predictions from a suite of models, based on this broad array of uncertainties. A validation approach is described that deals with a significant scarcity of data.
Model for predicting mountain wave field uncertainties
Damiens, Florentin; Lott, François; Millet, Christophe; Plougonven, Riwal
2017-04-01
Studying the propagation of acoustic waves throughout troposphere requires knowledge of wind speed and temperature gradients from the ground up to about 10-20 km. Typical planetary boundary layers flows are known to present vertical low level shears that can interact with mountain waves, thereby triggering small-scale disturbances. Resolving these fluctuations for long-range propagation problems is, however, not feasible because of computer memory/time restrictions and thus, they need to be parameterized. When the disturbances are small enough, these fluctuations can be described by linear equations. Previous works by co-authors have shown that the critical layer dynamics that occur near the ground produces large horizontal flows and buoyancy disturbances that result in intense downslope winds and gravity wave breaking. While these phenomena manifest almost systematically for high Richardson numbers and when the boundary layer depth is relatively small compare to the mountain height, the process by which static stability affects downslope winds remains unclear. In the present work, new linear mountain gravity wave solutions are tested against numerical predictions obtained with the Weather Research and Forecasting (WRF) model. For Richardson numbers typically larger than unity, the mesoscale model is used to quantify the effect of neglected nonlinear terms on downslope winds and mountain wave patterns. At these regimes, the large downslope winds transport warm air, a so called "Foehn" effect than can impact sound propagation properties. The sensitivity of small-scale disturbances to Richardson number is quantified using two-dimensional spectral analysis. It is shown through a pilot study of subgrid scale fluctuations of boundary layer flows over realistic mountains that the cross-spectrum of mountain wave field is made up of the same components found in WRF simulations. The impact of each individual component on acoustic wave propagation is discussed in terms of
Model Predictive Control for an Industrial SAG Mill
DEFF Research Database (Denmark)
Ohan, Valeriu; Steinke, Florian; Metzger, Michael
2012-01-01
We discuss Model Predictive Control (MPC) based on ARX models and a simple lower order disturbance model. The advantage of this MPC formulation is that it has few tuning parameters and is based on an ARX prediction model that can readily be identied using standard technologies from system identic...
Regional differences in prediction models of lung function in Germany
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Schäper Christoph
2010-04-01
Full Text Available Abstract Background Little is known about the influencing potential of specific characteristics on lung function in different populations. The aim of this analysis was to determine whether lung function determinants differ between subpopulations within Germany and whether prediction equations developed for one subpopulation are also adequate for another subpopulation. Methods Within three studies (KORA C, SHIP-I, ECRHS-I in different areas of Germany 4059 adults performed lung function tests. The available data consisted of forced expiratory volume in one second, forced vital capacity and peak expiratory flow rate. For each study multivariate regression models were developed to predict lung function and Bland-Altman plots were established to evaluate the agreement between predicted and measured values. Results The final regression equations for FEV1 and FVC showed adjusted r-square values between 0.65 and 0.75, and for PEF they were between 0.46 and 0.61. In all studies gender, age, height and pack-years were significant determinants, each with a similar effect size. Regarding other predictors there were some, although not statistically significant, differences between the studies. Bland-Altman plots indicated that the regression models for each individual study adequately predict medium (i.e. normal but not extremely high or low lung function values in the whole study population. Conclusions Simple models with gender, age and height explain a substantial part of lung function variance whereas further determinants add less than 5% to the total explained r-squared, at least for FEV1 and FVC. Thus, for different adult subpopulations of Germany one simple model for each lung function measures is still sufficient.
Uncertainties in spatially aggregated predictions from a logistic regression model
Horssen, P.W. van; Pebesma, E.J.; Schot, P.P.
2002-01-01
This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The
Dealing with missing predictor values when applying clinical prediction models.
Janssen, K.J.; Vergouwe, Y.; Donders, A.R.T.; Harrell Jr, F.E.; Chen, Q.; Grobbee, D.E.; Moons, K.G.
2009-01-01
BACKGROUND: Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with
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.
Directory of Open Access Journals (Sweden)
Masanobu Takahashi
Full Text Available Development of robust prognostic and/or predictive biomarkers in patients with colorectal cancer (CRC is imperative for advancing treatment strategies for this disease. We aimed to determine whether expression status of certain miRNAs might have prognostic/predictive value in CRC patients treated with conventional cytotoxic chemotherapies.We studied a cohort of 273 CRC specimens from stage II/III patients treated with 5-fluorouracil-based adjuvant chemotherapy and stage IV patients subjected to 5-fluorouracil and oxaliplatin-based chemotherapy. In a screening set (n = 44, 13 of 21 candidate miRNAs were successfully quantified by multiplex quantitative RT-PCR. In the validation set comprising of the entire patient cohort, miR-148a expression status was assessed by quantitative RT-PCR, and its promoter methylation was quantified by bisulfite pyrosequencing. Lastly, we analyzed the associations between miR-148a expression and patient survival.Among the candidate miRNAs studied, miR-148a expression was most significantly down-regulated in advanced CRC tissues. In stage III and IV CRC, low miR-148a expression was associated with significantly shorter disease free-survival (DFS, a worse therapeutic response, and poor overall survival (OS. Furthermore, miR-148a methylation status correlated inversely with its expression, and was associated with worse survival in stage IV CRC. In multivariate analysis, miR-148a expression was an independent prognostic/predictive biomarker for advanced CRC patients (DFS in stage III, low vs. high expression, HR 2.11; OS in stage IV, HR 1.93.MiR-148a status has a prognostic/predictive value in advanced CRC patients treated with conventional chemotherapy, which has important clinical implications in improving therapeutic strategies and personalized management of this malignancy.
Foundation Settlement Prediction Based on a Novel NGM Model
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Peng-Yu Chen
2014-01-01
Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.
Predictive capabilities of various constitutive models for arterial tissue.
Schroeder, Florian; Polzer, Stanislav; Slažanský, Martin; Man, Vojtěch; Skácel, Pavel
2018-02-01
Aim of this study is to validate some constitutive models by assessing their capabilities in describing and predicting uniaxial and biaxial behavior of porcine aortic tissue. 14 samples from porcine aortas were used to perform 2 uniaxial and 5 biaxial tensile tests. Transversal strains were furthermore stored for uniaxial data. The experimental data were fitted by four constitutive models: Holzapfel-Gasser-Ogden model (HGO), model based on generalized structure tensor (GST), Four-Fiber-Family model (FFF) and Microfiber model. Fitting was performed to uniaxial and biaxial data sets separately and descriptive capabilities of the models were compared. Their predictive capabilities were assessed in two ways. Firstly each model was fitted to biaxial data and its accuracy (in term of R 2 and NRMSE) in prediction of both uniaxial responses was evaluated. Then this procedure was performed conversely: each model was fitted to both uniaxial tests and its accuracy in prediction of 5 biaxial responses was observed. Descriptive capabilities of all models were excellent. In predicting uniaxial response from biaxial data, microfiber model was the most accurate while the other models showed also reasonable accuracy. Microfiber and FFF models were capable to reasonably predict biaxial responses from uniaxial data while HGO and GST models failed completely in this task. HGO and GST models are not capable to predict biaxial arterial wall behavior while FFF model is the most robust of the investigated constitutive models. Knowledge of transversal strains in uniaxial tests improves robustness of constitutive models. Copyright © 2017 Elsevier Ltd. All rights reserved.
Models for predicting objective function weights in prostate cancer IMRT
International Nuclear Information System (INIS)
Boutilier, Justin J.; Lee, Taewoo; Craig, Tim; Sharpe, Michael B.; Chan, Timothy C. Y.
2015-01-01
Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR
Genomic value prediction for quantitative traits under the epistatic model
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Xu Shizhong
2011-01-01
Full Text Available Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL. The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects and marker pairs (epistatic effects to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive effects were used for prediction. When the interaction (epistatic effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding.
Predictive models for moving contact line flows
Rame, Enrique; Garoff, Stephen
2003-01-01
Modeling flows with moving contact lines poses the formidable challenge that the usual assumptions of Newtonian fluid and no-slip condition give rise to a well-known singularity. This singularity prevents one from satisfying the contact angle condition to compute the shape of the fluid-fluid interface, a crucial calculation without which design parameters such as the pressure drop needed to move an immiscible 2-fluid system through a solid matrix cannot be evaluated. Some progress has been made for low Capillary number spreading flows. Combining experimental measurements of fluid-fluid interfaces very near the moving contact line with an analytical expression for the interface shape, we can determine a parameter that forms a boundary condition for the macroscopic interface shape when Ca much les than l. This parameter, which plays the role of an "apparent" or macroscopic dynamic contact angle, is shown by the theory to depend on the system geometry through the macroscopic length scale. This theoretically established dependence on geometry allows this parameter to be "transferable" from the geometry of the measurement to any other geometry involving the same material system. Unfortunately this prediction of the theory cannot be tested on Earth.
Decadal prediction skill using a high-resolution climate model
Monerie, Paul-Arthur; Coquart, Laure; Maisonnave, Éric; Moine, Marie-Pierre; Terray, Laurent; Valcke, Sophie
2017-11-01
The ability of a high-resolution coupled atmosphere-ocean general circulation model (with a horizontal resolution of a quarter of a degree in the ocean and of about 0.5° in the atmosphere) to predict the annual means of temperature, precipitation, sea-ice volume and extent is assessed based on initialized hindcasts over the 1993-2009 period. Significant skill in predicting sea surface temperatures is obtained, especially over the North Atlantic, the tropical Atlantic and the Indian Ocean. The Sea Ice Extent and volume are also reasonably predicted in winter (March) and summer (September). The model skill is mainly due to the external forcing associated with well-mixed greenhouse gases. A decrease in the global warming rate associated with a negative phase of the Pacific Decadal Oscillation is simulated by the model over a suite of 10-year periods when initialized from starting dates between 1999 and 2003. The model ability to predict regional change is investigated by focusing on the mid-90's Atlantic Ocean subpolar gyre warming. The model simulates the North Atlantic warming associated with a meridional heat transport increase, a strengthening of the North Atlantic current and a deepening of the mixed layer over the Labrador Sea. The atmosphere plays a role in the warming through a modulation of the North Atlantic Oscillation: a negative sea level pressure anomaly, located south of the subpolar gyre is associated with a wind speed decrease over the subpolar gyre. This leads to a reduced oceanic heat-loss and favors a northward displacement of anomalously warm and salty subtropical water that both concur to the subpolar gyre warming. We finally conclude that the subpolar gyre warming is mainly triggered by ocean dynamics with a possible contribution of atmospheric circulation favoring its persistence.
Developmental prediction model for early alcohol initiation in Dutch adolescents
Geels, L.M.; Vink, J.M.; Beijsterveldt, C.E.M. van; Bartels, M.; Boomsma, D.I.
2013-01-01
Objective: Multiple factors predict early alcohol initiation in teenagers. Among these are genetic risk factors, childhood behavioral problems, life events, lifestyle, and family environment. We constructed a developmental prediction model for alcohol initiation below the Dutch legal drinking age
A grey NGM(1,1, k) self-memory coupling prediction model for energy consumption prediction.
Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling
2014-01-01
Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.
A Grey NGM(1,1, k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction
Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling
2014-01-01
Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span. PMID:25054174
Seasonal predictability of Kiremt rainfall in coupled general circulation models
Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen
2017-11-01
The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.
MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL
Directory of Open Access Journals (Sweden)
Andrey Borisovich Nikolaev
2017-09-01
Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.
Predictive Model of Surgical Time for Revision Total Hip Arthroplasty.
Wu, Albert; Weaver, Michael J; Heng, Marilyn M; Urman, Richard D
2017-07-01
Maximizing operating room utilization in orthopedic and other surgeries relies on accurate estimates of surgical control time (SCT). A variety of case and patient-specific variables can influence the duration of surgical time during revision total hip arthroplasty (THA). We hypothesized that these variables are better predictors of actual SCT (aSCT) than a surgeon's own prediction (pSCT). All revision THAs from October 2008 to September 2014 from one institution were accessed. Variables for each case included aSCT, pSCT, patient age, gender, body mass index, American Society of Anesthesiologists Physical Status class, active infection, periprosthetic fracture, bone loss, heterotopic ossification, and implantation/explantation of a well-fixed acetabular/femoral component. These were incorporated in a stepwise fashion into a multivariate regression model for aSCT with a significant cutoff of 0.15. This was compared to a univariate regression model of aSCT that only used pSCT. In total, 516 revision THAs were analyzed. After stepwise selection, patient age and American Society of Anesthesiologists Physical Status were excluded from the model. The most significant increase in aSCT was seen with implantation of a new femoral component (24.0 min), followed by explantation of a well-fixed femoral component (18.7 min) and significant bone loss (15.0 min). Overall, the multivariate model had an improved r 2 of 0.49, compared to 0.16 from only using pSCT. A multivariate regression model can assist surgeons in more accurately predicting the duration of revision THAs. The strongest predictors of increased aSCT are explantation of a well-fixed femoral component, placement of an entirely new femoral component, and presence of significant bone loss. Copyright © 2017 Elsevier Inc. All rights reserved.
Model Predictive Control-Based Fast Charging for Vehicular Batteries
Directory of Open Access Journals (Sweden)
Zhibin Song
2011-08-01
Full Text Available Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs. In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC. A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV charge method.
Predictability in models of the atmospheric circulation
Houtekamer, P.L.
1992-01-01
It will be clear from the above discussions that skill forecasts are still in their infancy. Operational skill predictions do not exist. One is still struggling to prove that skill predictions, at any range, have any quality at all. It is not clear what the statistics of the analysis error
International Nuclear Information System (INIS)
Ado, Ayman A.; Al-Swat, Khalid; Azzam, N.; Al-Faleh, Faleh; Ahmed, S.
2007-01-01
We tested the clinical utility of the platelet count, aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio, and the AST to platelet ratio index (APRI) score in predicting the presence or absence of advanced fibrosis and cirrhosis in patients with chronic hepatitis C in Saudi Arabia. Liver biopsy procedures performed on chronic hepatitis C patients in our gastroenterology unit at King Khalid University Hospital were traced form records between 1998 to 2003. The hospital computer database was then accessed and detailed laboratory parameters obtained. By plotting receiver operating characteristic curves (ROC), three selected models (platelet count, AST/ALT ratio and the APRI score) were compared in terms of the best variable to predict significant fibrosis. Two hundred and forty-six patients with hepatitis C were included in this analysis. Overall, 26% of patients had advanced fibrosis. When comparing the three above mentioned prediction models, APRI score was the one associated with the highest area under the curve (AUC) = 0.812 (95%Cl, 0.756-0.868) on the ROC curves, compared to the platelet count and AST/ALT ratio, which yielded an AUC of 0.783 (0.711-0.855) and 0.716 (0.642-0.789), respectively. The APRI score seemed to be the best predictive variable for the presence or absence of advanced fibrosis in Saudi hepatitis C patients. (author)
Directory of Open Access Journals (Sweden)
Divya Bhadoo
2015-01-01
Full Text Available Aims: Study on prognostic significance of antenatal ultrasonography and renin angiotensin system activation in predicting disease severity in posterior urethral valves. Materials and Methods: Antenatally diagnosed hydronephrosis patients were included. Postnatally, they were divided into two groups, posterior urethral valve (PUV and non-PUV. The studied parameters were: Gestational age at detection, surgical intervention, ultrasound findings, cord blood and follow up plasma renin activity (PRA values, vesico-ureteric reflux (VUR, renal scars, and glomerular filtration rate (GFR. Results: A total of 25 patients were included, 10 PUV and 15 non-PUV. All infants with PUV underwent primary valve incision. GFR was less than 60 ml/min/1.73 m 2 body surface area in 4 patients at last follow-up. Keyhole sign, oligoamnios, absent bladder cycling, and cortical cysts were not consistent findings on antenatal ultrasound in PUV. Cord blood PRA was significantly higher (P < 0.0001 in PUV compared to non-PUV patients. Gestational age at detection of hydronephrosis, cortical cysts, bladder wall thickness, and amniotic fluid index were not significantly correlated with GFR while PRA could differentiate between poor and better prognosis cases with PUV. Conclusions: Ultrasound was neither uniformly useful in diagnosing PUV antenatally, nor differentiating it from cases with non-PUV hydronephrosis. In congenital hydronephrosis, cord blood PRA was significantly higher in cases with PUV compared to non-PUV cases and fell significantly after valve ablation. Cord blood PRA could distinguish between poor and better prognosis cases with PUV.
Laudano, Melissa A.; Badani, Ketan K.; McCann, Tara R.; Mann, Mark J.; Ritch, Chad; Desai, Manisha; Benson, Mitchell C.; McKiernan, James M.
2017-01-01
OBJECTIVES To examine by race how frequently the data after radical prostatectomy translates into a substantial change in prognosis. Many nomograms exist to predict the survival outcomes using the pretreatment clinical parameters and post-treatment pathologic parameters. Race might be an important factor affecting their predictive ability. METHODS Kattan nomograms were used to calculate the pretreatment and post-radical prostatectomy 5-year progression-free probability for each patient. The difference between the nomogram scores was used to divide the patients into 3 groups. A decrease in probability of ≥15 percentage points was classified as a significant increase in the probability of recurrence, an increase of ≥15 points was classified as a significant decrease in the probability of recurrence, and an absolute change of <15 points was considered no significant change. RESULTS The data from 1709 (132 black and 1577 white) men were analyzed. Among the black men, 26.5% had an increase in the probability of recurrence, 57.6% had no change, and 15.9% had a decrease in the probability of recurrence. Among the white men, 13.8% had an increase in the probability of recurrence, 64.5% had no change, and 21.7% had a decrease in the probability of recurrence. Black men were twice as likely to have a significant increase in the probability of recurrence postoperatively compared with white men after adjusting for preoperative prostate-specific antigen level, clinical stage, and biopsy Gleason sum (odds ratio 2.0, 95% confidence interval 1.3–3.1, P = .002). CONCLUSIONS These data could assist clinicians when counseling black men regarding their treatment options according to their preoperative risk profile. PMID:19589568
Danner, Omar K; Hendren, Sandra; Santiago, Ethel; Nye, Brittany; Abraham, Prasad
2017-04-01
Enhancing the efficiency of diagnosis and treatment of severe sepsis by using physiologically-based, predictive analytical strategies has not been fully explored. We hypothesize assessment of heart-rate-to-systolic-ratio significantly increases the timeliness and accuracy of sepsis prediction after emergency department (ED) presentation. We evaluated the records of 53,313 ED patients from a large, urban teaching hospital between January and June 2015. The HR-to-systolic ratio was compared to SIRS criteria for sepsis prediction. There were 884 patients with discharge diagnoses of sepsis, severe sepsis, and/or septic shock. Variations in three presenting variables, heart rate, systolic BP and temperature were determined to be primary early predictors of sepsis with a 74% (654/884) accuracy compared to 34% (304/884) using SIRS criteria (p analytics improved the accuracy and expediency of sepsis identification via detection of variations in HR-to-systolic ratio. This approach may lead to earlier sepsis workup and life-saving interventions. Copyright © 2017 Elsevier Inc. All rights reserved.
Required Collaborative Work in Online Courses: A Predictive Modeling Approach
Smith, Marlene A.; Kellogg, Deborah L.
2015-01-01
This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…
Models for predicting compressive strength and water absorption of ...
African Journals Online (AJOL)
This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...
Predicting future glacial lakes in Austria using different modelling approaches
Otto, Jan-Christoph; Helfricht, Kay; Prasicek, Günther; Buckel, Johannes; Keuschnig, Markus
2017-04-01
Glacier retreat is one of the most apparent consequences of temperature rise in the 20th and 21th centuries in the European Alps. In Austria, more than 240 new lakes have formed in glacier forefields since the Little Ice Age. A similar signal is reported from many mountain areas worldwide. Glacial lakes can constitute important environmental and socio-economic impacts on high mountain systems including water resource management, sediment delivery, natural hazards, energy production and tourism. Their development significantly modifies the landscape configuration and visual appearance of high mountain areas. Knowledge on the location, number and extent of these future lakes can be used to assess potential impacts on high mountain geo-ecosystems and upland-lowland interactions. Information on new lakes is critical to appraise emerging threads and potentials for society. The recent development of regional ice thickness models and their combination with high resolution glacier surface data allows predicting the topography below current glaciers by subtracting ice thickness from glacier surface. Analyzing these modelled glacier bed surfaces reveals overdeepenings that represent potential locations for future lakes. In order to predict the location of future glacial lakes below recent glaciers in the Austrian Alps we apply different ice thickness models using high resolution terrain data and glacier outlines. The results are compared and validated with ice thickness data from geophysical surveys. Additionally, we run the models on three different glacier extents provided by the Austrian Glacier Inventories from 1969, 1998 and 2006. Results of this historical glacier extent modelling are compared to existing glacier lakes and discussed focusing on geomorphological impacts on lake evolution. We discuss model performance and observed differences in the results in order to assess the approach for a realistic prediction of future lake locations. The presentation delivers
Assessment of factors influencing finite element vertebral model predictions.
Jones, Alison C; Wilcox, Ruth K
2007-12-01
This study aimed to establish model construction and configuration procedures for future vertebral finite element analysis by studying convergence, sensitivity, and accuracy behaviors of semiautomatically generated models and comparing the results with manually generated models. During a previous study, six porcine vertebral bodies were imaged using a microcomputed tomography scanner and tested in axial compression to establish their stiffness and failure strength. Finite element models were built using a manual meshing method. In this study, the experimental agreement of those models was compared with that of semiautomatically generated models of the same six vertebrae. Both manually and semiautomatically generated models were assigned gray-scale-based, element-specific material properties. The convergence of the semiautomatically generated models was analyzed for the complete models along with material property and architecture control cases. A sensitivity study was also undertaken to test the reaction of the models to changes in material property values, architecture, and boundary conditions. In control cases, the element-specific material properties reduce the convergence of the models in comparison to homogeneous models. However, the full vertebral models showed strong convergence characteristics. The sensitivity study revealed a significant reaction to changes in architecture, boundary conditions, and load position, while the sensitivity to changes in material property values was proportional. The semiautomatically generated models produced stiffness and strength predictions of similar accuracy to the manually generated models with much shorter image segmentation and meshing times. Semiautomatic methods can provide a more rapid alternative to manual mesh generation techniques and produce vertebral models of similar accuracy. The representation of the boundary conditions, load position, and surrounding environment is crucial to the accurate prediction of the
Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus
In this thesis, we consider control strategies for flexible distributed energy resources in the future intelligent energy system – the Smart Grid. The energy system is a large-scale complex network with many actors and objectives in different hierarchical layers. Specifically the power system must...... significantly. A Smart Grid calls for flexible consumers that can adjust their consumption based on the amount of green energy in the grid. This requires coordination through new large-scale control and optimization algorithms. Trading of flexibility is key to drive power consumption in a sustainable direction....... In Denmark, we expect that distributed energy resources such as heat pumps, and batteries in electric vehicles will mobilize part of the needed flexibility. Our primary objectives in the thesis were threefold: 1.Simulate the components in the power system based on simple models from literature (e.g. heat...
A prognostic model for development of significant liver fibrosis in HIV-hepatitis C co-infection.
Directory of Open Access Journals (Sweden)
Nasheed Moqueet
Full Text Available Liver fibrosis progresses rapidly in HIV-Hepatitis C virus (HCV co-infected individuals partially due to heightened inflammation. Immune markers targeting stages of fibrogenesis could aid in prognosis of fibrosis.A case-cohort study was nested in the prospective Canadian Co-infection Cohort (n = 1119. HCV RNA positive individuals without fibrosis, end-stage liver disease or chronic Hepatitis B at baseline (n = 679 were eligible. A random subcohort (n = 236 was selected from those eligible. Pro-fibrogenic markers and Interferon Lambda (IFNL rs8099917 genotype were measured from first available sample in all fibrosis cases (APRI ≥ 1.5 during follow-up and the subcohort. We used Cox proportional hazards and compared Model 1 (selected clinical predictors only to Model 2 (Model 1 plus selected markers for predicting 3-year risk of liver fibrosis using weighted Harrell's C and Net Reclassification Improvement indices.113 individuals developed significant liver fibrosis over 1300 person-years (8.63 per 100 person-years 95% CI: 7.08, 10.60. Model 1 (age, sex, current alcohol use, HIV RNA, baseline APRI, HCV genotype was nested in model 2, which also included IFNL genotype and IL-8, sICAM-1, RANTES, hsCRP, and sCD14. The C indexes (95% CI for model 1 vs. model 2 were 0.720 (0.649, 0.791 and 0.756 (0.688, 0.825, respectively. Model 2 classified risk more appropriately (overall net reclassification improvement, p<0.05.Including IFNL genotype and inflammatory markers IL-8, sICAM-1, RANTES, hs-CRP, and sCD14 enabled better prediction of the 3-year risk of significant liver fibrosis over clinical predictors alone. Whether this modest improvement in prediction justifies their additional cost requires further cost-benefit analyses.
Haritoglou, Christos; Kernt, Marcus; Neubauer, Aljoscha; Gerss, Joachim; Oliveira, Carlos Manta; Kampik, Anselm; Ulbig, Michael
2014-01-01
To evaluate the predictive value of microaneurysm (MA) formation rate concerning the development of clinically significant macular edema (CSME) in patients with mild-to-moderate nonproliferative diabetic retinopathy as evaluated by an automated analysis of central field fundus 30° photographs. Two hundred and eighty-seven eyes were included in the study. Photographs obtained at Day 0, at 6, and 12 months were analyzed using the RetmarkerDR software (Critical Health SA) in a masked manner, and the MA formation rate was documented. A threshold of a calculated MA formation rate of 2 or more was chosen to consider a patient "positive." The ability to predict CSME development was then calculated for a period of up to 5 years. HbA1c values, blood pressure, or duration of diabetes were also evaluated. The study population consisted of 89 male and 59 female patients with a mean age of 57.6 years, a mean HbA1c of 7.8, and a mean duration of diabetes of 12.3 years. Forty-seven of 287 eyes (16.4%) developed CSME during follow-up. An increased MA formation rate of >2 MA was clearly associated with development of CSME. Using the automated analysis and a threshold of 2 or more new MA, the authors were able to identify 70.2% of the eyes that developed CSME during follow-up ("true positive") and using a threshold of up to 2 new MA, 71.7% of the patients that did not develop CSME ("true negative"). No significant differences concerning baseline and 1-year HbA1c levels within patient eyes that developed CSME compared with patient eyes below or over the calculated threshold of 2 MA (P = 0.554 and P = 0.890, respectively) were seen. The positive and negative predictive value was calculated to be 33% versus 92.5%, sensitivity was 70%, and specificity was 72%. Using the RetmarkerDR software, the authors were able to identify patients with higher risk to develop CSME during follow-up using a threshold of 2 or more MA formation rate. Together with the high negative predictive value, the
Model predictive control for a thermostatic controlled system
DEFF Research Database (Denmark)
Shafiei, Seyed Ehsan; Rasmussen, Henrik; Stoustrup, Jakob
2013-01-01
This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff...... temperatures are estimated by reduced order observers and evaporation temperature is regulated by an algorithmic suction pressure control scheme. The method is applied to a validated simulation benchmark. The results show that even with the thermostatic control valves, there exists significant potential...
Algorithms and Software for Predictive and Perceptual Modeling of Speech
Atti, Venkatraman
2010-01-01
From the early pulse code modulation-based coders to some of the recent multi-rate wideband speech coding standards, the area of speech coding made several significant strides with an objective to attain high quality of speech at the lowest possible bit rate. This book presents some of the recent advances in linear prediction (LP)-based speech analysis that employ perceptual models for narrow- and wide-band speech coding. The LP analysis-synthesis framework has been successful for speech coding because it fits well the source-system paradigm for speech synthesis. Limitations associated with th
How Often Is the Misfit of Item Response Theory Models Practically Significant?
Sinharay, Sandip; Haberman, Shelby J.
2014-01-01
Standard 3.9 of the Standards for Educational and Psychological Testing ([, 1999]) demands evidence of model fit when item response theory (IRT) models are employed to data from tests. Hambleton and Han ([Hambleton, R. K., 2005]) and Sinharay ([Sinharay, S., 2005]) recommended the assessment of practical significance of misfit of IRT models, but…
Regression models for predicting anthropometric measurements of ...
African Journals Online (AJOL)
measure anthropometric dimensions to predict difficult-to-measure dimensions required for ergonomic design of school furniture. A total of 143 students aged between 16 and 18 years from eight public secondary schools in Ogbomoso, Nigeria ...
FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...
African Journals Online (AJOL)
direction (σx) had a maximum value of 375MPa (tensile) and minimum value of ... These results shows that the residual stresses obtained by prediction from the finite element method are in fair agreement with the experimental results.
Probabilistic Modeling and Visualization for Bankruptcy Prediction
DEFF Research Database (Denmark)
Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara
2017-01-01
In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...... visualization to improve our understanding of the different attained performances, effectively compiling all the conducted experiments in a meaningful way. We complete our study with an entropy-based analysis that highlights the uncertainty handling properties provided by the GP, crucial for prediction tasks...
Prediction for Major Adverse Outcomes in Cardiac Surgery: Comparison of Three Prediction Models
Directory of Open Access Journals (Sweden)
Cheng-Hung Hsieh
2007-09-01
Conclusion: The Parsonnet score performed as well as the logistic regression models in predicting major adverse outcomes. The Parsonnet score appears to be a very suitable model for clinicians to use in risk stratification of cardiac surgery.
Xu, Yanyan; Sun, Hongliang; Song, Aiping; Yang, Qiang; Lu, Xiaomei; Wang, Wu
2015-12-01
The preoperative assessment of tumor grade has important clinical implications for the treatment and prognosis of patients with colorectal adenocarcinomas. The purpose of this study is to investigate the predictive significance of colorectal adenocarcinoma grade using 256-slice whole-tumor computed tomography (CT) perfusion. Fifty-three patients with proven colorectal adenocarcinomas were enrolled. All of them underwent 256-slice whole-tumor CT perfusion. They were divided into two different subgroups according to postoperative pathological results: low grade and high grade. The Kruskal-Wallis test or one-way analysis of variance was used for comparison of CT perfusion parameters between different tumor grades. Multivariant correlation between pathologic tumor stage, histologic tumor differentiation, and whole-tumor CT perfusion parameters was evaluated by Spearman rank correlation coefficient. According to receiver operating characteristic (ROC) curves, perfusion parameters including blood flow (BF), peak enhancement index (PEI), blood volume (BV), and time to peak (TTP) of 53 patients were analyzed, and the sensitivity, specificity, and accuracy of these parameters in predicting tumor grade were calculated. There were significant differences in BF and TTP between low-grade and high-grade tumors. According to the ROC curve, BF and TTP were of diagnostic significance, with the area under the curve values of 0.828 and 0.736, respectively. The diagnostic threshold of BF was 32.12 mL/min/100 g and that of TTP was 18.10 seconds. The CT perfusion parameters (BF, TTP) of first-pass 256-slice whole-tumor CT perfusion imaging can reflect tumor grade in colorectal adenocarcinoma. Copyright © 2015 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
From Predictive Models to Instructional Policies
Rollinson, Joseph; Brunskill, Emma
2015-01-01
At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…
Computer-aided and predictive models for design of controlled release of pesticides
DEFF Research Database (Denmark)
Suné, Nuria Muro; Gani, Rafiqul
2004-01-01
in this paper, together with a specific case study application to highlight its scope and significance. The paper also addresses the need for predictive models and proposes a computer aided modelling framework for achieving it through the development and introduction of reliable and predictive constitutive...... models. A group-contribution based model for one of the constitutive variables (AI solubility in polymers) is presented together with examples of application and validation....
Analytical model for local scour prediction around hydrokinetic turbine foundations
Musa, M.; Heisel, M.; Hill, C.; Guala, M.
2017-12-01
Marine and Hydrokinetic renewable energy is an emerging sustainable and secure technology which produces clean energy harnessing water currents from mostly tidal and fluvial waterways. Hydrokinetic turbines are typically anchored at the bottom of the channel, which can be erodible or non-erodible. Recent experiments demonstrated the interactions between operating turbines and an erodible surface with sediment transport, resulting in a remarkable localized erosion-deposition pattern significantly larger than those observed by static in-river construction such as bridge piers, etc. Predicting local scour geometry at the base of hydrokinetic devices is extremely important during foundation design, installation, operation, and maintenance (IO&M), and long-term structural integrity. An analytical modeling framework is proposed applying the phenomenological theory of turbulence to the flow structures that promote the scouring process at the base of a turbine. The evolution of scour is directly linked to device operating conditions through the turbine drag force, which is inferred to locally dictate the energy dissipation rate in the scour region. The predictive model is validated using experimental data obtained at the University of Minnesota's St. Anthony Falls Laboratory (SAFL), covering two sediment mobility regimes (clear water and live bed), different turbine designs, hydraulic parameters, grain size distribution and bedform types. The model is applied to a potential prototype scale deployment in the lower Mississippi River, demonstrating its practical relevance and endorsing the feasibility of hydrokinetic energy power plants in large sandy rivers. Multi-turbine deployments are further studied experimentally by monitoring both local and non-local geomorphic effects introduced by a twelve turbine staggered array model installed in a wide channel at SAFL. Local scour behind each turbine is well captured by the theoretical predictive model. However, multi
Comparisons of Faulting-Based Pavement Performance Prediction Models
Directory of Open Access Journals (Sweden)
Weina Wang
2017-01-01
Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.
Hologram QSAR model for the prediction of human oral bioavailability.
Moda, Tiago L; Montanari, Carlos A; Andricopulo, Adriano D
2007-12-15
A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure-activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q(2)=0.70, r(2)=0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.
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.
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
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.
A model to predict the beginning of the pollen season
DEFF Research Database (Denmark)
Toldam-Andersen, Torben Bo
1991-01-01
In order to predict the beginning of the pollen season, a model comprising the Utah phenoclirnatography Chill Unit (CU) and ASYMCUR-Growing Degree Hour (GDH) submodels were used to predict the first bloom in Alms, Ulttirrs and Berirln. The model relates environmental temperatures to rest completion...... and bud development. As phenologic parameter 14 years of pollen counts were used. The observed datcs for the beginning of the pollen seasons were defined from the pollen counts and compared with the model prediction. The CU and GDH submodels were used as: 1. A fixed day model, using only the GDH model...... for fruit trees are generally applicable, and give a reasonable description of the growth processes of other trees. This type of model can therefore be of value in predicting the start of the pollen season. The predicted dates were generally within 3-5 days of the observed. Finally the possibility of frost...
Comparative Evaluation of Some Crop Yield Prediction Models ...
African Journals Online (AJOL)
A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...
Comparative Evaluation of Some Crop Yield Prediction Models ...
African Journals Online (AJOL)
(1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of cowpea yield-water use and weather data were collected.
Prediction of speech intelligibility based on an auditory preprocessing model
DEFF Research Database (Denmark)
Christiansen, Claus Forup Corlin; Pedersen, Michael Syskind; Dau, Torsten
2010-01-01
Classical speech intelligibility models, such as the speech transmission index (STI) and the speech intelligibility index (SII) are based on calculations on the physical acoustic signals. The present study predicts speech intelligibility by combining a psychoacoustically validated model of auditory...
Modelling microbial interactions and food structure in predictive microbiology
Malakar, P.K.
2002-01-01
Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.
Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of
Ocean wave prediction using numerical and neural network models
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Prabaharan, N.
This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...
A Prediction Model of the Capillary Pressure J-Function.
Directory of Open Access Journals (Sweden)
W S Xu
Full Text Available The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative.
Fournier's gangrene: a model for early prediction.
Palvolgyi, Roland; Kaji, Amy H; Valeriano, Javier; Plurad, David; Rajfer, Jacob; de Virgilio, Christian
2014-10-01
Early diagnosis remains the cornerstone of management of Fournier's gangrene. As a result of variable progression of disease, identifying early predictors of necrosis becomes a diagnostic challenge. We present a scoring system based on objective admission criteria, which can help distinguish Fournier's gangrene from nonnecrotizing scrotal infections. Ninety-six patients were identified, 38 diagnosed with Fournier's gangrene and 58 diagnosed with scrotal cellulitis or abscess. Statistical analyses comparing admission vital signs, laboratory values, and imaging studies were performed and Classification and Regression Tree analysis was used to construct a scoring system. Admission heart rate greater than 110 beats/minute, serum sodium less than 135 mmol/L, blood urea nitrogen greater than 15 mg/dL, and white blood cell count greater than 15 × 10(3)/μL were significant predictors of Fournier's gangrene. Using a threshold score of two or greater, our model differentiates patients with Fournier's gangrene from those with nonnecrotizing infections with a sensitivity of 84.2 per cent. Only 34.2 per cent of patients with Fournier's gangrene had hard signs of necrotizing infection on admission, which were not observed in patients with nonnecrotizing infections. Objective admission criteria assist in distinguishing Fournier's gangrene from scrotal cellulitis or abscess. In situations in which results of the physical examination are ambiguous, this scoring system can heighten the index of suspicion for Fournier's gangrene and prompt rapid surgical intervention.
Usability Prediction & Ranking of SDLC Models Using Fuzzy Hierarchical Usability Model
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.
Statistical model based gender prediction for targeted NGS clinical panels
Directory of Open Access Journals (Sweden)
Palani Kannan Kandavel
2017-12-01
The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.
comparative analysis of two mathematical models for prediction
African Journals Online (AJOL)
Abstract. A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data ob- tained from experimental work done in this study. The models used are Scheffes and Osadebes optimization theories to predict the compressive strength of ...
Comparison of predictive models for the early diagnosis of diabetes
M. Jahani (Meysam); M. Mahdavi (Mahdi)
2016-01-01
textabstractObjectives: This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods: We used memetic algorithms to update weights and to improve
Testing and analysis of internal hardwood log defect prediction models
R. Edward. Thomas
2011-01-01
The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...
Hidden Markov Model for quantitative prediction of snowfall
Indian Academy of Sciences (India)
A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...
Demonstrating the improvement of predictive maturity of a computational model
Energy Technology Data Exchange (ETDEWEB)
Hemez, Francois M [Los Alamos National Laboratory; Unal, Cetin [Los Alamos National Laboratory; Atamturktur, Huriye S [CLEMSON UNIV.
2010-01-01
We demonstrate an improvement of predictive capability brought to a non-linear material model using a combination of test data, sensitivity analysis, uncertainty quantification, and calibration. A model that captures increasingly complicated phenomena, such as plasticity, temperature and strain rate effects, is analyzed. Predictive maturity is defined, here, as the accuracy of the model to predict multiple Hopkinson bar experiments. A statistical discrepancy quantifies the systematic disagreement (bias) between measurements and predictions. Our hypothesis is that improving the predictive capability of a model should translate into better agreement between measurements and predictions. This agreement, in turn, should lead to a smaller discrepancy. We have recently proposed to use discrepancy and coverage, that is, the extent to which the physical experiments used for calibration populate the regime of applicability of the model, as basis to define a Predictive Maturity Index (PMI). It was shown that predictive maturity could be improved when additional physical tests are made available to increase coverage of the regime of applicability. This contribution illustrates how the PMI changes as 'better' physics are implemented in the model. The application is the non-linear Preston-Tonks-Wallace (PTW) strength model applied to Beryllium metal. We demonstrate that our framework tracks the evolution of maturity of the PTW model. Robustness of the PMI with respect to the selection of coefficients needed in its definition is also studied.
Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling
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
Refining the committee approach and uncertainty prediction in hydrological modelling
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
Wind turbine control and model predictive control for uncertain systems
DEFF Research Database (Denmark)
Thomsen, Sven Creutz
as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...
Hidden Markov Model for quantitative prediction of snowfall and ...
Indian Academy of Sciences (India)
A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...
Model predictive control of a 3-DOF helicopter system using ...
African Journals Online (AJOL)
... by simulation, and its performance is compared with that achieved by linear model predictive control (LMPC). Keywords: nonlinear systems, helicopter dynamics, MIMO systems, model predictive control, successive linearization. International Journal of Engineering, Science and Technology, Vol. 2, No. 10, 2010, pp. 9-19 ...
Models for predicting fuel consumption in sagebrush-dominated ecosystems
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....
Comparative Analysis of Two Mathematical Models for Prediction of ...
African Journals Online (AJOL)
A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data obtained from experimental work done in this study. The models used are Scheffe's and Osadebe's optimization theories to predict the compressive strength of sandcrete ...
A mathematical model for predicting earthquake occurrence ...
African Journals Online (AJOL)
We consider the continental crust under damage. We use the observed results of microseism in many seismic stations of the world which was established to study the time series of the activities of the continental crust with a view to predicting possible time of occurrence of earthquake. We consider microseism time series ...
Directory of Open Access Journals (Sweden)
Nicola A. Wardrop
2014-11-01
Full Text Available The application of spatial modelling to epidemiology has increased significantly over the past decade, delivering enhanced understanding of the environmental and climatic factors affecting disease distributions and providing spatially continuous representations of disease risk (predictive maps. These outputs provide significant information for disease control programmes, allowing spatial targeting and tailored interventions. However, several factors (e.g. sampling protocols or temporal disease spread can influence predictive mapping outputs. This paper proposes a conceptual framework which defines several scenarios and their potential impact on resulting predictive outputs, using simulated data to provide an exemplar. It is vital that researchers recognise these scenarios and their influence on predictive models and their outputs, as a failure to do so may lead to inaccurate interpretation of predictive maps. As long as these considerations are kept in mind, predictive mapping will continue to contribute significantly to epidemiological research and disease control planning.
Econometric models for predicting confusion crop ratios
Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)
1979-01-01
Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.
Kim, Ok-Yeon; Chan, Johnny C. L.
2018-01-01
This study aims to predict the seasonal TC track density over the South Pacific by combining the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) dynamical prediction system with a statistical model. The hybrid dynamical-statistical model is developed for each of the three clusters that represent major groups of TC best tracks in the South Pacific. The cross validation result from the MME hybrid model demonstrates moderate but statistically significant skills to predict TC numbers across all TC clusters, with correlation coefficients of 0.4 to 0.6 between the hindcasts and observations for 1982/1983 to 2008/2009. The prediction skill in the area east of about 170°E is significantly influenced by strong El Niño, whereas the skill in the southwest Pacific region mainly comes from the linear trend of TC number. The prediction skill of TC track density is particularly high in the region where there is climatological high TC track density around the area 160°E-180° and 20°S. Since this area has a mixed response with respect to ENSO, the prediction skill of TC track density is higher in non-ENSO years compared to that in ENSO years. Even though the cross-validation prediction skill is higher in the area east of about 170°E compared to other areas, this region shows less skill for track density based on the categorical verification due to huge influences by strong El Niño years. While prediction skill of the developed methodology varies across the region, it is important that the model demonstrates skill in the area where TC activity is high. Such a result has an important practical implication—improving the accuracy of seasonal forecast and providing communities at risk with advanced information which could assist with preparedness and disaster risk reduction.
Directory of Open Access Journals (Sweden)
N. N. Babyshkina
2016-01-01
Full Text Available The identification of informative biomarkers that are able to predict prognosis and treatment response may be particularly important in triple negative breast cancer.The aim of the study was to investigate the relationship between vascular endothelial growth factor receptor VEGFR-2 expression and KDR gene polymorphisms with the efficacy of neoadjuvant chemotherapy (NAC in patients with triple negative breast cancer.Methods. The study included 70 patients with triple negative operable breast cancer (T1–3N0–3M0, who had received 2–4 cycles of FAC and CAX regimens. The pathologic complete response (pCR to treatment was determined by RECIST. VEGFR-2 expression level was evaluated using immunohistochemistry. Genotypes for KDR (rs2071559, rs2305948 were detected by a Real-time PCR.Results. The pCR rate was significantly associated with young age at diagnosis (≤50 years (p=0.0044, a high level of Ki67 expression (≥20 (p=0.0322 and with CAX regimen (p=0.0246. Additionally, all patients with pCR had the lack of VEGFR-2 expression in tumor tissue after surgery (p=0.0000. The presence of the VEGFR-2 expression (negative or positive in tumor tissue before NAC was associated with KDR rs2071559 (r=−0.297; p=0.0273. A significant correlation between the KDR rs2071559 and VEGFR-2 expression level (less than 70, 70% or more in the tumor tissue before NAC was found (r=−0.314; p=0.0297. Multivariate logistic regression analysis showed that the young age of the patients (≤50 years, the lack of VEGFR-2 expression after surgery and CAX regimen were significant predictors of NAC.Conclusion. The VEGFR-2 expression level in tumor tissue and KDR gene polymorphism can be considered as new additional molecular predictive markers of pathologic complete response to NAC in triple negative breast cancer patients.
Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki
2012-01-01
The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.
Adding propensity scores to pure prediction models fails to improve predictive performance
Directory of Open Access Journals (Sweden)
Amy S. Nowacki
2013-08-01
Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.
PEEX Modelling Platform for Seamless Environmental Prediction
Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku
2017-04-01
The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.
Validation of an Acoustic Impedance Prediction Model for Skewed Resonators
Howerton, Brian M.; Parrott, Tony L.
2009-01-01
An impedance prediction model was validated experimentally to determine the composite impedance of a series of high-aspect ratio slot resonators incorporating channel skew and sharp bends. Such structures are useful for packaging acoustic liners into constrained spaces for turbofan noise control applications. A formulation of the Zwikker-Kosten Transmission Line (ZKTL) model, incorporating the Richards correction for rectangular channels, is used to calculate the composite normalized impedance of a series of six multi-slot resonator arrays with constant channel length. Experimentally, acoustic data was acquired in the NASA Langley Normal Incidence Tube over the frequency range of 500 to 3500 Hz at 120 and 140 dB OASPL. Normalized impedance was reduced using the Two-Microphone Method for the various combinations of channel skew and sharp 90o and 180o bends. Results show that the presence of skew and/or sharp bends does not significantly alter the impedance of a slot resonator as compared to a straight resonator of the same total channel length. ZKTL predicts the impedance of such resonators very well over the frequency range of interest. The model can be used to design arrays of slot resonators that can be packaged into complex geometries heretofore unsuitable for effective acoustic treatment.
A computational model that predicts behavioral sensitivity to intracortical microstimulation
Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J.
2017-02-01
Objective. Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. Approach. We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Main results. Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R 2 = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber’s law. Significance. The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.
Predictive SIRT dosimetry based on a territorial model
Directory of Open Access Journals (Sweden)
Nadine Spahr
2017-10-01
Full Text Available Abstract Background In the planning of selective internal radiation therapy (SIRT for liver cancer treatment, one major aspect is to determine the prescribed activity and to estimate the resulting absorbed dose inside normal liver and tumor tissue. An optimized partition model for SIRT dosimetry based on arterial liver territories is proposed. This model is dedicated to characterize the variability of dose within the whole liver. For an arbitrary partition, the generalized absorbed dose is derived from the classical partition model. This enables to consider normal liver partitions for each arterial perfusion supply area and one partition for each tumor for activity and dose calculation. The proposed method excludes a margin of 11 mm emitting range around tumor volumes from normal liver to investigate the impact on activity calculation. Activity and dose calculation was performed for five patients using the body-surface-area (BSA method, the classical and territorial partition model. Results The territorial model reaches smaller normal liver doses and significant higher tumor doses compared to the classical partition model. The exclusion of a small region around tumors has a significant impact on mean liver dose. Determined tumor activities for the proposed method are higher in all patients when limited by normal liver dose. Activity calculation based on BSA achieves in all cases the lowest amount. Conclusions The territorial model provides a more local and patient-individual dose distribution in normal liver taking into account arterial supply areas. This proposed arterial liver territory-based partition model may be used for SPECT-independent activity calculation and dose prediction under the condition of an artery-based simulation for particle distribution.
Models Predicting Success of Infertility Treatment: A Systematic Review
Zarinara, Alireza; Zeraati, Hojjat; Kamali, Koorosh; Mohammad, Kazem; Shahnazari, Parisa; Akhondi, Mohammad Mehdi
2016-01-01
Background: Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn’t simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models. Methods: This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated. Results: Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems. Conclusion: Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples’ needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable. PMID:27141461
Significant uncertainty in global scale hydrological modeling from precipitation data erros
Sperna Weiland, F.; Vrugt, J.A.; Beek, van P.H.; Weerts, A.H.; Bierkens, M.F.P.
2015-01-01
In the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we
Significant uncertainty in global scale hydrological modeling from precipitation data errors
Weiland, Frederiek C. Sperna; Vrugt, Jasper A.; van Beek, Rens (L. ) P. H.|info:eu-repo/dai/nl/14749799X; Weerts, Albrecht H.; Bierkens, Marc F. P.|info:eu-repo/dai/nl/125022794
2015-01-01
In the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we
Towards a generalized energy prediction model for machine tools.
Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan
2017-04-01
Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.
Poisson Mixture Regression Models for Heart Disease Prediction
Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611
Comparison of Predictive Models for the Early Diagnosis of Diabetes.
Jahani, Meysam; Mahdavi, Mahdi
2016-04-01
This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.
Mapping the Most Significant Computer Hacking Events to a Temporal Computer Attack Model
Heerden , Renier ,; Pieterse , Heloise; Irwin , Barry
2012-01-01
Part 4: Section 3: ICT for Peace and War; International audience; This paper presents eight of the most significant computer hacking events (also known as computer attacks). These events were selected because of their unique impact, methodology, or other properties. A temporal computer attack model is presented that can be used to model computer based attacks. This model consists of the following stages: Target Identification, Reconnaissance, Attack, and Post-Attack Reconnaissance stages. The...
Applications of modeling in polymer-property prediction
Case, F. H.
1996-08-01
A number of molecular modeling techniques have been applied for the prediction of polymer properties and behavior. Five examples illustrate the range of methodologies used. A simple atomistic simulation of small polymer fragments is used to estimate drug compatibility with a polymer matrix. The analysis of molecular dynamics results from a more complex model of a swollen hydrogel system is used to study gas diffusion in contact lenses. Statistical mechanics are used to predict conformation dependent properties — an example is the prediction of liquid-crystal formation. The effect of the molecular weight distribution on phase separation in polyalkanes is predicted using thermodynamic models. In some cases, the properties of interest cannot be directly predicted using simulation methods or polymer theory. Correlation methods may be used to bridge the gap between molecular structure and macroscopic properties. The final example shows how connectivity-indices-based quantitative structure-property relationships were used to predict properties for candidate polyimids in an electronics application.
Artificial Neural Network Model for Predicting Compressive
Salim T. Yousif; Salwa M. Abdullah
2013-01-01
Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum...
A two-component rain model for the prediction of attenuation statistics
Crane, R. K.
1982-01-01
A two-component rain model has been developed for calculating attenuation statistics. In contrast to most other attenuation prediction models, the two-component model calculates the occurrence probability for volume cells or debris attenuation events. The model performed significantly better than the International Radio Consultative Committee model when used for predictions on earth-satellite paths. It is expected that the model will have applications in modeling the joint statistics required for space diversity system design, the statistics of interference due to rain scatter at attenuating frequencies, and the duration statistics for attenuation events.
Prediction of hourly solar radiation with multi-model framework
International Nuclear Information System (INIS)
Wu, Ji; Chan, Chee Keong
2013-01-01
Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others
Simplified Predictive Models for CO2 Sequestration Performance Assessment
Mishra, Srikanta; RaviGanesh, Priya; Schuetter, Jared; Mooney, Douglas; He, Jincong; Durlofsky, Louis
2014-05-01
simulations, the LHS-based meta-model yields a more robust predictive model, as verified by a k-fold cross-validation approach. In the third category (RMM), we use a reduced-order modeling procedure that combines proper orthogonal decomposition (POD) for reducing problem dimensionality with trajectory-piecewise linearization (TPWL) for extrapolating system response at new control points from a limited number of trial runs ("snapshots"). We observe significant savings in computational time with very good accuracy from the POD-TPWL reduced order model - which could be important in the context of history matching, uncertainty quantification and optimization problems. The paper will present results from our ongoing investigations, and also discuss future research directions and likely outcomes. This work was supported by U.S. Department of Energy National Energy Technology Laboratory award DE-FE0009051 and Ohio Department of Development grant D-13-02.
Posterior Predictive Model Checking for Multidimensionality in Item Response Theory
Levy, Roy; Mislevy, Robert J.; Sinharay, Sandip
2009-01-01
If data exhibit multidimensionality, key conditional independence assumptions of unidimensional models do not hold. The current work pursues posterior predictive model checking, a flexible family of model-checking procedures, as a tool for criticizing models due to unaccounted for dimensions in the context of item response theory. Factors…
Model predictive control of a crude oil distillation column
Directory of Open Access Journals (Sweden)
Morten Hovd
1999-04-01
Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.
Enhancing Flood Prediction Reliability Using Bayesian Model Averaging
Liu, Z.; Merwade, V.
2017-12-01
Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.
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
Decadal prediction skill in a multi-model ensemble
Energy Technology Data Exchange (ETDEWEB)
Oldenborgh, Geert Jan van; Wouters, Bert; Hazeleger, Wilco [Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, De Bilt (Netherlands); Doblas-Reyes, Francisco J. [Institucio Catalana de Recerca i Estudis Avancats (ICREA) and Institut Catala de Ciencies del Clima (IC3), Barcelona (Spain)
2012-04-15
Decadal climate predictions may have skill due to predictable components in boundary conditions (mainly greenhouse gas concentrations but also tropospheric and stratospheric aerosol distributions) and initial conditions (mainly the ocean state). We investigate the skill of temperature and precipitation hindcasts from a multi-model ensemble of four climate forecast systems based on coupled ocean-atmosphere models. Regional variations in skill with and without trend are compared with similarly analysed uninitialised experiments to separate the trend due to monotonically increasing forcings from fluctuations around the trend due to the ocean initial state and aerosol forcings. In temperature most of the skill in both multi-model ensembles comes from the externally forced trends. The rise of the global mean temperature is represented well in the initialised hindcasts, but variations around the trend show little skill beyond the first year due to the absence of volcanic aerosols in the hindcasts and the unpredictability of ENSO. The models have non-trivial skill in hindcasts of North Atlantic sea surface temperature beyond the trend. This skill is highest in the northern North Atlantic in initialised experiments and in the subtropical North Atlantic in uninitialised simulations. A similar result is found in the Pacific Ocean, although the signal is less clear. The uninitialised simulations have good skill beyond the trend in the western North Pacific. The initialised experiments show some skill in the decadal ENSO region in the eastern Pacific, in agreement with previous studies. However, the results in this study are not statistically significant (p {approx} 0.1) by themselves. The initialised models also show some skill in forecasting 4-year mean Sahel rainfall at lead times of 1 and 5 years, in agreement with the observed teleconnection from the Atlantic Ocean. Again, the skill is not statistically significant (p {approx} 0.2). Furthermore, uninitialised simulations
Predictive models for acute kidney injury following cardiac surgery.
Demirjian, Sevag; Schold, Jesse D; Navia, Jose; Mastracci, Tara M; Paganini, Emil P; Yared, Jean-Pierre; Bashour, Charles A
2012-03-01
Accurate prediction of cardiac surgery-associated acute kidney injury (AKI) would improve clinical decision making and facilitate timely diagnosis and treatment. The aim of the study was to develop predictive models for cardiac surgery-associated AKI using presurgical and combined pre- and intrasurgical variables. Prospective observational cohort. 25,898 patients who underwent cardiac surgery at Cleveland Clinic in 2000-2008. Presurgical and combined pre- and intrasurgical variables were used to develop predictive models. Dialysis therapy and a composite of doubling of serum creatinine level or dialysis therapy within 2 weeks (or discharge if sooner) after cardiac surgery. Incidences of dialysis therapy and the composite of doubling of serum creatinine level or dialysis therapy were 1.7% and 4.3%, respectively. Kidney function parameters were strong independent predictors in all 4 models. Surgical complexity reflected by type and history of previous cardiac surgery were robust predictors in models based on presurgical variables. However, the inclusion of intrasurgical variables accounted for all explained variance by procedure-related information. Models predictive of dialysis therapy showed good calibration and superb discrimination; a combined (pre- and intrasurgical) model performed better than the presurgical model alone (C statistics, 0.910 and 0.875, respectively). Models predictive of the composite end point also had excellent discrimination with both presurgical and combined (pre- and intrasurgical) variables (C statistics, 0.797 and 0.825, respectively). However, the presurgical model predictive of the composite end point showed suboptimal calibration (P predictive models in other cohorts is required before wide-scale application. We developed and internally validated 4 new models that accurately predict cardiac surgery-associated AKI. These models are based on readily available clinical information and can be used for patient counseling, clinical
Modeling number of claims and prediction of total claim amount
Acar, Aslıhan Şentürk; Karabey, Uǧur
2017-07-01
In this study we focus on annual number of claims of a private health insurance data set which belongs to a local insurance company in Turkey. In addition to Poisson model and negative binomial model, zero-inflated Poisson model and zero-inflated negative binomial model are used to model the number of claims in order to take into account excess zeros. To investigate the impact of different distributional assumptions for the number of claims on the prediction of total claim amount, predictive performances of candidate models are compared by using root mean square error (RMSE) and mean absolute error (MAE) criteria.
Livi, Riccardo; Guiducci, Serena; Perfetto, Federico; Ciuti, Gabriele; Grifoni, Elisa; Conforti, Letizia; Galluccio, Felice; Moggi Pignone, Alberto; Matucci Cerinic, Marco
2011-11-01
To evaluate if defective activation of renal functional reserve (RFR) in systemic sclerosis (SSc) without clinical signs of renal involvement predicts the risk of developing clinically relevant renal damage. Twenty-eight normotensive SSc patients with normal renal function and no urinary abnormalities were submitted to an intravenous amino acid load to activate RFR. Nineteen patients (six with diffuse cutaneous SSc (dcSSc)) had an RFR activation defect, while nine (two with dcSSc) showed normal RFR. All patients were followed up for 5 years, with periodic evaluation of renal function, urinary protein excretion and systemic blood pressure (BP). At admission, patients with normal RFR had lower BP than those with abnormal RFR; no age, disease duration or creatinine clearance (CCr) differences were found. Five years later, patients with abnormal RFR showed, with respect to basal values, a significantly higher CCr reduction than patients with normal RFR (mean percent decrease 15.4 ± 9.5 vs 2.6 ± 3.8, pRFR, 13 (68.4%) showed a CCr reduction of ≥ 2 ml/min/year, with a final CCr of ≤ 70 ml/min in eight cases; two patients developed microalbuminuria and 10 grade 1 or 2 systemic hypertension. Significant CCr reduction rates were found in eight patients with high BP and in five patients who remained normotensive. No patient with normal RFR had proteinuria or high BP during follow-up. Lack of RFR activation is an early sign of renal involvement in SSc, and is a harbinger of an increased risk of developing renal insufficiency and systemic hypertension.
Yokoyama, Y; Sato, S; Fukushi, Y; Sakamoto, T; Futagami, M; Saito, Y
1999-12-01
To clarify the expression of multi-drug-resistant (MDR) markers, GST-pi, c-Jun, P-glycoprotein (Pgp), and MDR-associated protein (MRP) in epithelial ovarian cancer, and to determine whether their expression is predictive of chemotherapy response and patient prognosis. Specimens of 58 epithelial ovarian cancer cases obtained at initial surgery were studied immunohistochemically using antibodies. Overall positive rates in the 58 specimens were 58.6% for GST-pi, 44.8% for c-Jun, 27.6% for Pgp, and 22.4% for MRP. The 5-year disease-free survival rate was 26.0% for patients with MRP-positive tumors and 75.2% for those with MRP-negative tumors. The prognosis for those with MRP-positive tumors was significantly poorer (p < 0.05). Patients with GST-pi-positive tumors had a significantly worse prognosis than those with GST-pi-negative tumors (51.9% vs 79.2%, p < 0.05). Multivariate analysis showed that residual tumors 2 cm or larger and MRP expression were independent prognostic factors for chemotherapy resistance. The relative risk of chemotherapy resistance in a patient with a residual tumor 2 cm or larger, positive MRP, and positive GST-pi was 10.6 times greater than the risk in a patient without these factors. MRP and GST-pi expression might be potential predictors of the response to standard chemotherapy in epithelial ovarian cancer. Their expression also might contribute to individualizing clinical trials of postoperative chemotherapy.
Model-based uncertainty in species range prediction
DEFF Research Database (Denmark)
Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel
2006-01-01
algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. Main conclusions We highlight an important source of uncertainty in assessments of the impacts of climate......Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions......, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current...
Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models
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.
Prediction Model for Gastric Cancer Incidence in Korean Population.
Eom, Bang Wool; Joo, Jungnam; Kim, Sohee; Shin, Aesun; Yang, Hye-Ryung; Park, Junghyun; Choi, Il Ju; Kim, Young-Woo; Kim, Jeongseon; Nam, Byung-Ho
2015-01-01
Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea. Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope. During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women). In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.
AN EFFICIENT PATIENT INFLOW PREDICTION MODEL FOR HOSPITAL RESOURCE MANAGEMENT
Directory of Open Access Journals (Sweden)
Kottalanka Srikanth
2017-07-01
Full Text Available There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.
Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.
Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan
2018-01-12
Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.
Prediction Model for Gastric Cancer Incidence in Korean Population.
Directory of Open Access Journals (Sweden)
Bang Wool Eom
Full Text Available Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.During a median of 11.4 years of follow-up, 19,465 (1.4% and 5,579 (0.7% newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women.In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.
Stage-specific predictive models for breast cancer survivability.
Kate, Rohit J; Nadig, Ramya
2017-01-01
Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright Â© 2016 Elsevier Ireland Ltd. All rights reserved.
Femtocells Sharing Management using mobility prediction model
Barth, Dominique; Choutri, Amira; Kloul, Leila; Marcé, Olivier
2013-01-01
Bandwidth sharing paradigm constitutes an incentive solution for the serious capacity management problem faced by operators as femtocells owners are able to offer a QoS guaranteed network access to mobile users in their femtocell coverage. In this paper, we consider a technico-economic bandwidth sharing model based on a reinforcement learning algorithm. Because such a model does not allow the convergence of the learning algorithm, due to the small size of the femtocells, the mobile users velo...
2015-01-01
progression in patients with IS. Conclusions These findings indicate that 31.5 degrees of spinal curvature when patients are at PHV is a significant predictive indicator for progression of the curve to a magnitude requiring surgery. We suggest that the curve-progression risk assessment in patients with IS should include PHV, along with measures of skeletal and non-skeletal maturities. PMID:25815057
Predictions and Verification of an Isotope Marine Boundary Layer Model
Feng, X.; Posmentier, E. S.; Sonder, L. J.; Fan, N.
2017-12-01
A one-dimensional (1D), steady state isotope marine boundary layer (IMBL) model is constructed. The model includes meteorologically important features absent in Craig and Gordon type models, namely height-dependent diffusion/mixing and convergence of subsiding external air. Kinetic isotopic fractionation results from this height-dependent diffusion which starts as pure molecular diffusion at the air-water interface and increases linearly with height due to turbulent mixing. The convergence permits dry, isotopically depleted air subsiding adjacent to the model column to mix into ambient air. In δD-δ18O space, the model results fill a quadrilateral, of which three sides represent 1) vapor in equilibrium with various sea surface temperatures (SSTs) (high d18O boundary of quadrilateral); 2) mixture of vapor in equilibrium with seawater and vapor in the subsiding air (lower boundary depleted in both D and 18O); and 3) vapor that has experienced the maximum possible kinetic fractionation (high δD upper boundary). The results can be plotted in d-excess vs. δ18O space, indicating that these processes all cause variations in d-excess of MBL vapor. In particular, due to relatively high d-excess in the descending air, mixing of this air into the MBL causes an increase in d-excess, even without kinetic isotope fractionation. The model is tested by comparison with seven datasets of marine vapor isotopic ratios, with excellent correspondence; >95% of observational data fall within the quadrilateral area predicted by the model. The distribution of observations also highlights the significant influence of vapor from the nearby converging descending air on isotopic variations in the MBL. At least three factors may explain the affect the isotopic composition of precipitation. The model can be applied to modern as well as paleo- climate conditions.
Prediction skill of rainstorm events over India in the TIGGE weather prediction models
Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.
2017-12-01
Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.
Micro-mechanical studies on graphite strength prediction models
Kanse, Deepak; Khan, I. A.; Bhasin, V.; Vaze, K. K.
2013-06-01
The influence of type of loading and size-effects on the failure strength of graphite were studied using Weibull model. It was observed that this model over-predicts size effect in tension. However, incorporation of grain size effect in Weibull model, allows a more realistic simulation of size effects. Numerical prediction of strength of four-point bend specimen was made using the Weibull parameters obtained from tensile test data. Effective volume calculations were carried out and subsequently predicted strength was compared with experimental data. It was found that Weibull model can predict mean flexural strength with reasonable accuracy even when grain size effect was not incorporated. In addition, the effects of microstructural parameters on failure strength were analyzed using Rose and Tucker model. Uni-axial tensile, three-point bend and four-point bend strengths were predicted using this model and compared with the experimental data. It was found that this model predicts flexural strength within 10%. For uni-axial tensile strength, difference was 22% which can be attributed to less number of tests on tensile specimens. In order to develop failure surface of graphite under multi-axial state of stress, an open ended hollow tube of graphite was subjected to internal pressure and axial load and Batdorf model was employed to calculate failure probability of the tube. Bi-axial failure surface was generated in the first and fourth quadrant for 50% failure probability by varying both internal pressure and axial load.
New Approaches for Channel Prediction Based on Sinusoidal Modeling
Directory of Open Access Journals (Sweden)
Ekman Torbjörn
2007-01-01
Full Text Available Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS prediction model and the associated joint least-squares (LS predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.
Predictive seismic modeling case history from the Niger delta
Energy Technology Data Exchange (ETDEWEB)
Idowu, A.O. (Nigerian National Petroleum Corp., Lagos (Nigeria))
1993-09-01
Seismic modeling techniques provide the mechanics for simulating the geology of the subsurface by depicting the impact of a propagating seismic wavefront on subsurface structures. In practice, seismic data have been used to map the geometry of events in the subsurface, mainly from reflection continuity and the character of reflection packages. In the Niger delta, recent developments in stratigraphic exploration has induced the examination of more subtle features of reflection, mainly polarity, amplitude, and waveform to define the limits of seismic resolution and hence predict the geometry of subsurface fluid and solid interfaces. The case history discussed here involved interpretative study for defining the fluid contents of prospective oil and gas leads as indicated by anomalous seismic events on a Niger delta field located in a water depth of 25 m. An appropriate source signal (5-35 Hertz minimum phase) is selected, and the wavelet is convolved with a practical geologic model to obtain a synthetic seismogram. By an interactive process involving slight modifications in the geologic model, a synthetic seismogram is ultimately derived that matches a field signal, thus providing a more accurate prediction of the geological formation under study. The technique was effect (as confirmed by later drilling) in appraising the fluid contents of the targeted pay zones encountered at gas/water, oil/water, and gas/oil/water contacts in the O field, located in the eastern offshore area of the Niger delta. The method further demonstrated that structural and stratigraphic modeling are effective tools for testing the mapability of a geologic concept and are able to evaluate the significance of reflectivity changes or anomalies on uncalibrated seismic data.
Stand diameter distribution modelling and prediction based on Richards function.
Directory of Open Access Journals (Sweden)
Ai-guo Duan
Full Text Available The objective of this study was to introduce application of the Richards equation on modelling and prediction of stand diameter distribution. The long-term repeated measurement data sets, consisted of 309 diameter frequency distributions from Chinese fir (Cunninghamia lanceolata plantations in the southern China, were used. Also, 150 stands were used as fitting data, the other 159 stands were used for testing. Nonlinear regression method (NRM or maximum likelihood estimates method (MLEM were applied to estimate the parameters of models, and the parameter prediction method (PPM and parameter recovery method (PRM were used to predict the diameter distributions of unknown stands. Four main conclusions were obtained: (1 R distribution presented a more accurate simulation than three-parametric Weibull function; (2 the parameters p, q and r of R distribution proved to be its scale, location and shape parameters, and have a deep relationship with stand characteristics, which means the parameters of R distribution have good theoretical interpretation; (3 the ordinate of inflection point of R distribution has significant relativity with its skewness and kurtosis, and the fitted main distribution range for the cumulative diameter distribution of Chinese fir plantations was 0.4∼0.6; (4 the goodness-of-fit test showed diameter distributions of unknown stands can be well estimated by applying R distribution based on PRM or the combination of PPM and PRM under the condition that only quadratic mean DBH or plus stand age are known, and the non-rejection rates were near 80%, which are higher than the 72.33% non-rejection rate of three-parametric Weibull function based on the combination of PPM and PRM.
Bayesian Age-Period-Cohort Modeling and Prediction - BAMP
Directory of Open Access Journals (Sweden)
Volker J. Schmid
2007-10-01
Full Text Available The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted.
Modeling for prediction of restrained shrinkage effect in concrete repair
International Nuclear Information System (INIS)
Yuan Yingshu; Li Guo; Cai Yue
2003-01-01
A general model of autogenous shrinkage caused by chemical reaction (chemical shrinkage) is developed by means of Arrhenius' law and a degree of chemical reaction. Models of tensile creep and relaxation modulus are built based on a viscoelastic, three-element model. Tests of free shrinkage and tensile creep were carried out to determine some coefficients in the models. Two-dimensional FEM analysis based on the models and other constitutions can predict the development of tensile strength and cracking. Three groups of patch-repaired beams were designed for analysis and testing. The prediction from the analysis shows agreement with the test results. The cracking mechanism after repair is discussed
Significance of categorization and the modeling of age related factors for radiation protection
International Nuclear Information System (INIS)
Matsuoka, Osamu
1987-01-01
It is proposed that the categorization and modelling are necessary with regard to age related factors of radionuclide metabolism for the radiation protection of the public. In order to utilize the age related information as a model for life time risk estimate of public, it is necessary to generalize and simplify it according to the categorized model patterns. Since the patterns of age related changes in various parameters of radionuclide metabolism seem to be rather simple, it is possible to categorize them into eleven types of model patterns. Among these models, five are selected as positively significant models to be considered. Examples are shown as to the fitting of representative parameters of both physiological and metabolic parameter of radionuclides into the proposed model. The range of deviation from adult standard value is also analyzed for each model. The fitting of each parameter to categorized models, and its comparative consideration provide the effective information as to the physiological basis of radionuclide metabolism. Discussions are made on the problems encountered in the application of available age related information to radiation protection of the public, i.e. distribution of categorized parameter, period of life covered, range of deviation from adult value, implication to other dosimetric and pathological models and to the final estimation. 5 refs.; 3 figs.; 4 tabs
Predicting Footbridge Response using Stochastic Load Models
DEFF Research Database (Denmark)
Pedersen, Lars; Frier, Christian
2013-01-01
Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing so...... decisions need to be made in terms of statistical distributions of walking parameters and in terms of the parameters describing the statistical distributions. The paper explores how sensitive computations of bridge response are to some of the decisions to be made in this respect. This is useful...
DEFF Research Database (Denmark)
Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen
2014-01-01
Current research focuses on predicting and mitigating the impacts of high hydraulic loadings on centralized wastewater treatment plants (WWTPs) under wet-weather conditions. The maximum permissible inflow to WWTPs depends not only on the settleability of activated sludge in secondary settling tanks...... (SSTs) but also on the hydraulic behaviour of SSTs. The present study investigates the impacts of ideal and non-ideal flow (dry and wet weather) and settling (good settling and bulking) boundary conditions on the sensitivity of WWTP model outputs to uncertainties intrinsic to the one-dimensional (1-D......) SST model structures and parameters. We identify the critical sources of uncertainty in WWTP models through global sensitivity analysis (GSA) using the Benchmark simulation model No. 1 in combination with first- and second-order 1-D SST models. The results obtained illustrate that the contribution...
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 model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C 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.
Geospatial application of the Water Erosion Prediction Project (WEPP) Model
D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot
2011-01-01
The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltration, runoff, ET) component, which subsequently impacts the rest of the...
Reduced order modelling and predictive control of multivariable ...
Indian Academy of Sciences (India)
Anuj Abraham
2018-03-16
Mar 16, 2018 ... The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE. Keywords. Predictive control; distillation column; reduced order model; dominant pole; ...
Mixed models for predictive modeling in actuarial science
Antonio, K.; Zhang, Y.
2012-01-01
We start with a general discussion of mixed (also called multilevel) models and continue with illustrating specific (actuarial) applications of this type of models. Technical details on (linear, generalized, non-linear) mixed models follow: model assumptions, specifications, estimation techniques
Consensus models to predict endocrine disruption for all ...
Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte
ARMA modeling of stochastic processes in nuclear reactor with significant detection noise
International Nuclear Information System (INIS)
Zavaljevski, N.
1992-01-01
The theoretical basis of ARMA modelling of stochastic processes in nuclear reactor was presented in a previous paper, neglecting observational noise. The identification of real reactor data indicated that in some experiments the detection noise is significant. Thus a more rigorous theoretical modelling of stochastic processes in nuclear reactor is performed. Starting from the fundamental stochastic differential equations of the Langevin type for the interaction of the detector with neutron field, a new theoretical ARMA model is developed. preliminary identification results confirm the theoretical expectations. (author)
On the selection of significant variables in a model for the deteriorating process of facades
Serrat, C.; Gibert, V.; Casas, J. R.; Rapinski, J.
2017-10-01
In previous works the authors of this paper have introduced a predictive system that uses survival analysis techniques for the study of time-to-failure in the facades of a building stock. The approach is population based, in order to obtain information on the evolution of the stock across time, and to help the manager in the decision making process on global maintenance strategies. For the decision making it is crutial to determine those covariates -like materials, morphology and characteristics of the facade, orientation or environmental conditions- that play a significative role in the progression of different failures. The proposed platform also incorporates an open source GIS plugin that includes survival and test moduli that allow the investigator to model the time until a lesion taking into account the variables collected during the inspection process. The aim of this paper is double: a) to shortly introduce the predictive system, as well as the inspection and the analysis methodologies and b) to introduce and illustrate the modeling strategy for the deteriorating process of an urban front. The illustration will be focused on the city of L’Hospitalet de Llobregat (Barcelona, Spain) in which more than 14,000 facades have been inspected and analyzed.
Scanpath Based N-Gram Models for Predicting Reading Behavior
DEFF Research Database (Denmark)
Mishra, Abhijit; Bhattacharyya, Pushpak; Carl, Michael
2013-01-01
Predicting reading behavior is a difficult task. Reading behavior depends on various linguistic factors (e.g. sentence length, structural complexity etc.) and other factors (e.g individual's reading style, age etc.). Ideally, a reading model should be similar to a language model where the model i...
Unsupervised ship trajectory modeling and prediction using compression and clustering
de Vries, G.; van Someren, M.; van Erp, M.; Stehouwer, H.; van Zaanen, M.
2009-01-01
In this paper we show how to build a model of ship trajectories in a certain maritime region and use this model to predict future ship movements. The presented method is unsupervised and based on existing compression (line-simplification) and clustering techniques. We evaluate the model with a
Prediction of annual rainfall pattern using Hidden Markov Model ...
African Journals Online (AJOL)
A hidden Markov model to predict annual rainfall pattern has been presented in this paper. The model is developed to provide necessary information for the farmers, agronomists, water resource management scientists and policy makers to enable them plan for the uncertainty of annual rainfall. The model classified annual ...
The Selection of Turbulence Models for Prediction of Room Airflow
DEFF Research Database (Denmark)
Nielsen, Peter V.
This paper discusses the use of different turbulence models and their advantages in given situations. As an example, it is shown that a simple zero-equation model can be used for the prediction of special situations as flow with a low level of turbulence. A zero-equation model with compensation...
Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad
2013-01-01
The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined...
Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach.
Fernandes, G S; Bhattacharya, A; McWilliams, D F; Ingham, S L; Doherty, M; Zhang, W
2017-03-20
Twenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit physical ability and cause distress and bears significant socioeconomic costs. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiative (OAI) cohort. A total of 1822 participants from the Nottingham community who were at risk for knee pain were followed for 12 years. Of this cohort, two-thirds (n = 1203) were used to develop the risk prediction model, and one-third (n = 619) were used to validate the model. Incident knee pain was defined as pain on most days for at least 1 month in the past 12 months. Predictors were age, sex, body mass index, pain elsewhere, prior knee injury and knee alignment. A Bayesian logistic regression model was used to determine the probability of an OR >1. The Hosmer-Lemeshow χ 2 statistic (HLS) was used for calibration, and ROC curve analysis was used for discrimination. The OAI cohort from the United States was also used to examine the performance of the model. A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration, with an HLS of 7.17 (p = 0.52) and moderate discriminative ability (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p prediction model for knee pain, regardless of underlying structural changes of knee osteoarthritis, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in individuals with a higher risk for knee osteoarthritis, and it may provide a convenient tool for use in primary care to predict the risk of knee pain in the general population.
Using Pareto points for model identification in predictive toxicology
2013-01-01
Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649
DEFF Research Database (Denmark)
Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty
constructed from geological and hydrological data. However, geophysical data are increasingly used to inform hydrogeologic models because they are collected at lower cost and much higher density than geological and hydrological data. Despite increased use of geophysics, it is still unclear whether...... the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... collecting geophysical data. At a minimum, an analysis should be conducted assuming settings that are favorable for the chosen geophysical method. If the analysis suggests that data collected by the geophysical method is unlikely to improve model prediction performance under these favorable settings...
Hybrid Corporate Performance Prediction Model Considering Technical Capability
Directory of Open Access Journals (Sweden)
Joonhyuck Lee
2016-07-01
Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.
Prediction of Chemical Function: Model Development and Application
The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...
Linear regression crash prediction models : issues and proposed solutions.
2010-05-01
The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...
FPGA implementation of predictive degradation model for engine oil lifetime
Idros, M. F. M.; Razak, A. H. A.; Junid, S. A. M. Al; Suliman, S. I.; Halim, A. K.
2018-03-01
This paper presents the implementation of linear regression model for degradation prediction on Register Transfer Logic (RTL) using QuartusII. A stationary model had been identified in the degradation trend for the engine oil in a vehicle in time series method. As for RTL implementation, the degradation model is written in Verilog HDL and the data input are taken at a certain time. Clock divider had been designed to support the timing sequence of input data. At every five data, a regression analysis is adapted for slope variation determination and prediction calculation. Here, only the negative value are taken as the consideration for the prediction purposes for less number of logic gate. Least Square Method is adapted to get the best linear model based on the mean values of time series data. The coded algorithm has been implemented on FPGA for validation purposes. The result shows the prediction time to change the engine oil.
Predictive Modeling: A New Paradigm for Managing Endometrial Cancer.
Bendifallah, Sofiane; Daraï, Emile; Ballester, Marcos
2016-03-01
With the abundance of new options in diagnostic and treatment modalities, a shift in the medical decision process for endometrial cancer (EC) has been observed. The emergence of individualized medicine and the increasing complexity of available medical data has lead to the development of several prediction models. In EC, those clinical models (algorithms, nomograms, and risk scoring systems) have been reported, especially for stratifying and subgrouping patients, with various unanswered questions regarding such things as the optimal surgical staging for lymph node metastasis as well as the assessment of recurrence and survival outcomes. In this review, we highlight existing prognostic and predictive models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. In the future, the emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches.
On the Predictiveness of Single-Field Inflationary Models
Burgess, C.P.; Trott, Michael
2014-01-01
We re-examine the predictiveness of single-field inflationary models and discuss how an unknown UV completion can complicate determining inflationary model parameters from observations, even from precision measurements. Besides the usual naturalness issues associated with having a shallow inflationary potential, we describe another issue for inflation, namely, unknown UV physics modifies the running of Standard Model (SM) parameters and thereby introduces uncertainty into the potential inflationary predictions. We illustrate this point using the minimal Higgs Inflationary scenario, which is arguably the most predictive single-field model on the market, because its predictions for $A_s$, $r$ and $n_s$ are made using only one new free parameter beyond those measured in particle physics experiments, and run up to the inflationary regime. We find that this issue can already have observable effects. At the same time, this UV-parameter dependence in the Renormalization Group allows Higgs Inflation to occur (in prin...
Predictive modeling in catalysis - from dream to reality
Maldonado, A.G.; Rothenberg, G.
2009-01-01
In silico catalyst optimization is the ultimate application of computers in catalysis. This article provides an overview of the basic concepts of predictive modeling and describes how this technique can be used in catalyst and reaction design.
Fuzzy model predictive control algorithm applied in nuclear power plant
International Nuclear Information System (INIS)
Zuheir, Ahmad
2006-01-01
The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)
Compensatory versus noncompensatory models for predicting consumer preferences
Directory of Open Access Journals (Sweden)
Anja Dieckmann
2009-04-01
Full Text Available Standard preference models in consumer research assume that people weigh and add all attributes of the available options to derive a decision, while there is growing evidence for the use of simplifying heuristics. Recently, a greedoid algorithm has been developed (Yee, Dahan, Hauser and Orlin, 2007; Kohli and Jedidi, 2007 to model lexicographic heuristics from preference data. We compare predictive accuracies of the greedoid approach and standard conjoint analysis in an online study with a rating and a ranking task. The lexicographic model derived from the greedoid algorithm was better at predicting ranking compared to rating data, but overall, it achieved lower predictive accuracy for hold-out data than the compensatory model estimated by conjoint analysis. However, a considerable minority of participants was better predicted by lexicographic strategies. We conclude that the new algorithm will not replace standard tools for analyzing preferences, but can boost the study of situational and individual differences in preferential choice processes.
Predictive Modeling of Partitioned Systems: Implementation and Applications
Latten, Christine
2014-01-01
A general mathematical methodology for predictive modeling of coupled multi-physics systems is implemented and has been applied without change to an illustrative heat conduction example and reactor physics benchmarks.
A new, accurate predictive model for incident hypertension
DEFF Research Database (Denmark)
Völzke, Henry; Fung, Glenn; Ittermann, Till
2013-01-01
Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures....
Wemheuer, Bernd; Wemheuer, Franziska; Meier, Dimitri; Billerbeck, Sara; Giebel, Helge-Ansgar; Simon, Meinhard; Scherber, Christoph; Daniel, Rolf
2017-11-05
Deciphering the ecological traits of abundant marine bacteria is a major challenge in marine microbial ecology. In the current study, we linked compositional and functional predictions to elucidate such traits for abundant bacterioplankton lineages in the North Sea. For this purpose, we investigated entire and active bacterioplankton composition along a transect ranging from the German Bight to the northern North Sea by pyrotag sequencing of bacterial 16S rRNA genes and transcripts. Functional profiles were inferred from 16S rRNA data using Tax4Fun. Bacterioplankton communities were dominated by well-known marine lineages including clusters/genera that are affiliated with the Roseobacter group and the Flavobacteria . Variations in community composition and function were significantly explained by measured environmental and microbial properties. Turnover of dissolved free amino acids (DFAA) showed the strongest correlation to community composition and function. We applied multinomial models, which enabled us to identify bacterial lineages involved in DFAA turnover. For instance, the genus Planktomarina was more abundant at higher DFAA turnover rates, suggesting its vital role in amino acid degradation. Functional predictions further indicated that Planktomarina is involved in leucine and isoleucine degradation. Overall, our results provide novel insights into the biogeochemical significance of abundant bacterioplankton lineages in the North Sea.
Directory of Open Access Journals (Sweden)
Bernd Wemheuer
2017-11-01
Full Text Available Deciphering the ecological traits of abundant marine bacteria is a major challenge in marine microbial ecology. In the current study, we linked compositional and functional predictions to elucidate such traits for abundant bacterioplankton lineages in the North Sea. For this purpose, we investigated entire and active bacterioplankton composition along a transect ranging from the German Bight to the northern North Sea by pyrotag sequencing of bacterial 16S rRNA genes and transcripts. Functional profiles were inferred from 16S rRNA data using Tax4Fun. Bacterioplankton communities were dominated by well-known marine lineages including clusters/genera that are affiliated with the Roseobacter group and the Flavobacteria. Variations in community composition and function were significantly explained by measured environmental and microbial properties. Turnover of dissolved free amino acids (DFAA showed the strongest correlation to community composition and function. We applied multinomial models, which enabled us to identify bacterial lineages involved in DFAA turnover. For instance, the genus Planktomarina was more abundant at higher DFAA turnover rates, suggesting its vital role in amino acid degradation. Functional predictions further indicated that Planktomarina is involved in leucine and isoleucine degradation. Overall, our results provide novel insights into the biogeochemical significance of abundant bacterioplankton lineages in the North Sea.
Model Predictive Control for Ethanol Steam Reformers
Li, Mingming
2014-01-01
This thesis firstly proposes a new approach of modelling an ethanol steam reformer (ESR) for producing pure hydrogen. Hydrogen has obvious benefits as an alternative for feeding the proton exchange membrane fuel cells (PEMFCs) to produce electricity. However, an important drawback is that the hydrogen distribution and storage have high cost. So the ESR is regarded as a way to overcome these difficulties. Ethanol is currently considered as a promising energy source under the res...
Haskell financial data modeling and predictive analytics
Ryzhov, Pavel
2013-01-01
This book is a hands-on guide that teaches readers how to use Haskell's tools and libraries to analyze data from real-world sources in an easy-to-understand manner.This book is great for developers who are new to financial data modeling using Haskell. A basic knowledge of functional programming is not required but will be useful. An interest in high frequency finance is essential.
Wireless model predictive control: Application to water-level system
Directory of Open Access Journals (Sweden)
Ramdane Hedjar
2016-04-01
Full Text Available This article deals with wireless model predictive control of a water-level control system. The objective of the model predictive control algorithm is to constrain the control signal inside saturation limits and maintain the water level around the desired level. Linear modeling of any nonlinear plant leads to parameter uncertainties and non-modeled dynamics in the linearized mathematical model. These uncertainties induce a steady-state error in the output response of the water level. To eliminate this steady-state error and increase the robustness of the control algorithm, an integral action is included in the closed loop. To control the water-level system remotely, the communication between the controller and the process is performed using radio channel. To validate the proposed scheme, simulation and real-time implementation of the algorithm have been conducted, and the results show the effectiveness of wireless model predictive control with integral action.
Cheng, Hao-Min; Chuang, Shao-Yuan; Wang, Jiun-Jr; Shih, Yuan-Ta; Wang, Hsin-Ning; Huang, Chi-Jung; Huang, Jui-Tzu; Sung, Shih-Hsien; Lakatta, Edward G; Yin, Frank C P; Chou, Pesus; Yeh, Chih-Jung; Bai, Chyi-Huey; Pan, Wen-Harn; Chen, Chen-Huan
2016-07-15
Numerous mechanical biomarkers derived from pulse wave analysis (PWA) have been proposed to predict cardiovascular outcomes. However, whether these biomarkers carry independent prognostic value and clinical utility beyond traditional cardiovascular risk factors hasn't been systematically evaluated. We aimed to investigate the additive utility of PWA-derived biomarkers in two independent population-based cohorts. PWA on central arterial pressure waveforms obtained from subjects without a prior history of cardiovascular diseases of two studies was conducted based on the wave transmission and reservoir-wave theory: firstly in the Kinmen study (1272 individuals, a median follow-up of 19.8years); and then in the Cardiovascular Disease Risk Factors Two-Township Study (2221 individuals, median follow-up of 10years). The incremental value of the biomarkers was evaluated by net reclassification index (NRI). In multivariate Cox analyses accounting for age, gender, body mass index, systolic blood pressure, fasting glucose, high-density- and low-density-lipoprotein cholesterol, and smoking, only systolic (SC) and diastolic rate constant (DC) of reservoir pressure could independently and consistently predict cardiovascular mortality in both cohorts and the combined cohort (SC: hazard ratio 1.18 [95% confidence interval 1.08-1.28, pprediction estimates in traditional risk prediction models were significantly more accurate when incorporating peak of reservoir pressure (NRI=0.049, p=0.0361), SC (NRI=0.043, p=0.0236) and DC (NRI=0.054, p=0.047). Of all PWA-derived biomarkers, SC and DC were consistently identified as valuable parameters for incremental cardiovascular risk prediction in two large prospective cohorts. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model
Boone, Spencer
2017-01-01
This presentation will discuss the updated Inclination Adjust Maneuver (IAM) performance prediction model that was developed for Aqua and Aura following the 2017 IAM series. This updated model uses statistical regression methods to identify potential long-term trends in maneuver parameters, yielding improved predictions when re-planning past maneuvers. The presentation has been reviewed and approved by Eric Moyer, ESMO Deputy Project Manager.
Approximating prediction uncertainty for random forest regression models
John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne
2016-01-01
Machine learning approaches such as random forest haveÂ increased for the spatial modeling and mapping of continuousÂ variables. Random forest is a non-parametric ensembleÂ approach, and unlike traditional regression approaches thereÂ is no direct quantification of prediction error. UnderstandingÂ prediction uncertainty is important when using model-basedÂ continuous maps as...
Prediction of cloud droplet number in a general circulation model
Energy Technology Data Exchange (ETDEWEB)
Ghan, S.J.; Leung, L.R. [Pacific Northwest National Lab., Richland, WA (United States)
1996-04-01
We have applied the Colorado State University Regional Atmospheric Modeling System (RAMS) bulk cloud microphysics parameterization to the treatment of stratiform clouds in the National Center for Atmospheric Research Community Climate Model (CCM2). The RAMS predicts mass concentrations of cloud water, cloud ice, rain and snow, and number concnetration of ice. We have introduced the droplet number conservation equation to predict droplet number and it`s dependence on aerosols.
The Next Page Access Prediction Using Makov Model
Deepti Razdan
2011-01-01
Predicting the next page to be accessed by the Webusers has attracted a large amount of research. In this paper, anew web usage mining approach is proposed to predict next pageaccess. It is proposed to identify similar access patterns from weblog using K-mean clustering and then Markov model is used forprediction for next page accesses. The tightness of clusters isimproved by setting similarity threshold while forming clusters.In traditional recommendation models, clustering by nonsequentiald...
Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza
2014-01-01
This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…
Accurate Holdup Calculations with Predictive Modeling & Data Integration
Energy Technology Data Exchange (ETDEWEB)
Azmy, Yousry [North Carolina State Univ., Raleigh, NC (United States). Dept. of Nuclear Engineering; Cacuci, Dan [Univ. of South Carolina, Columbia, SC (United States). Dept. of Mechanical Engineering
2017-04-03
In facilities that process special nuclear material (SNM) it is important to account accurately for the fissile material that enters and leaves the plant. Although there are many stages and processes through which materials must be traced and measured, the focus of this project is material that is “held-up” in equipment, pipes, and ducts during normal operation and that can accumulate over time into significant quantities. Accurately estimating the holdup is essential for proper SNM accounting (vis-à-vis nuclear non-proliferation), criticality and radiation safety, waste management, and efficient plant operation. Usually it is not possible to directly measure the holdup quantity and location, so these must be inferred from measured radiation fields, primarily gamma and less frequently neutrons. Current methods to quantify holdup, i.e. Generalized Geometry Holdup (GGH), primarily rely on simple source configurations and crude radiation transport models aided by ad hoc correction factors. This project seeks an alternate method of performing measurement-based holdup calculations using a predictive model that employs state-of-the-art radiation transport codes capable of accurately simulating such situations. Inverse and data assimilation methods use the forward transport model to search for a source configuration that best matches the measured data and simultaneously provide an estimate of the level of confidence in the correctness of such configuration. In this work the holdup problem is re-interpreted as an inverse problem that is under-determined, hence may permit multiple solutions. A probabilistic approach is applied to solving the resulting inverse problem. This approach rates possible solutions according to their plausibility given the measurements and initial information. This is accomplished through the use of Bayes’ Theorem that resolves the issue of multiple solutions by giving an estimate of the probability of observing each possible solution. To use
Working Towards a Risk Prediction Model for Neural Tube Defects
Agopian, A.J.; Lupo, Philip J.; Tinker, Sarah C.; Canfield, Mark A.; Mitchell, Laura E.
2015-01-01
BACKGROUND Several risk factors have been consistently associated with neural tube defects (NTDs). However, the predictive ability of these risk factors in combination has not been evaluated. METHODS To assess the predictive ability of established risk factors for NTDs, we built predictive models using data from the National Birth Defects Prevention Study, which is a large, population-based study of nonsyndromic birth defects. Cases with spina bifida or anencephaly, or both (n = 1239), and controls (n = 8494) were randomly divided into separate training (75% of cases and controls) and validation (remaining 25%) samples. Multivariable logistic regression models were constructed with the training samples. The predictive ability of these models was evaluated in the validation samples by assessing the area under the receiver operator characteristic curves. An ordinal predictive risk index was also constructed and evaluated. In addition, the ability of classification and regression tree (CART) analysis to identify subgroups of women at increased risk for NTDs in offspring was evaluated. RESULTS The predictive ability of the multivariable models was poor (area under the receiver operating curve: 0.55 for spina bifida only, 0.59 for anencephaly only, and 0.56 for anencephaly and spina bifida combined). The predictive abilities of the ordinal risk indexes and CART models were also low. CONCLUSION Current established risk factors for NTDs are insufficient for population-level prediction of a women’s risk for having affected offspring. Identification of genetic risk factors and novel nongenetic risk factors will be critical to establishing models, with good predictive ability, for NTDs. PMID:22253139
Predictive QSAR Models for the Toxicity of Disinfection Byproducts.
Qin, Litang; Zhang, Xin; Chen, Yuhan; Mo, Lingyun; Zeng, Honghu; Liang, Yanpeng
2017-10-09
Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination ( R ²) > 0.7, explained variance in leave-one-out prediction ( Q ² LOO ) and in leave-many-out prediction ( Q ² LMO ) > 0.6, variance explained in external prediction ( Q ² F1 , Q ² F2 , and Q ² F3 ) > 0.7, and concordance correlation coefficient ( CCC ) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.
Predictive QSAR Models for the Toxicity of Disinfection Byproducts
Directory of Open Access Journals (Sweden)
Litang Qin
2017-10-01
Full Text Available Several hundred disinfection byproducts (DBPs in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2 > 0.7, explained variance in leave-one-out prediction (Q2LOO and in leave-many-out prediction (Q2LMO > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3 > 0.7, and concordance correlation coefficient (CCC > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.
Nonconvex Model Predictive Control for Commercial Refrigeration
DEFF Research Database (Denmark)
Hovgaard, Tobias Gybel; Larsen, Lars F.S.; Jørgensen, John Bagterp
2013-01-01
function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimization method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out...... the iterations, which is more than fast enough to run in real-time. We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost...... capacity associated with large penetration of intermittent renewable energy sources in a future smart grid....
Evolutionary modeling and prediction of non-coding RNAs in Drosophila.
Directory of Open Access Journals (Sweden)
Robert K Bradley
2009-08-01
Full Text Available We performed benchmarks of phylogenetic grammar-based ncRNA gene prediction, experimenting with eight different models of structural evolution and two different programs for genome alignment. We evaluated our models using alignments of twelve Drosophila genomes. We find that ncRNA prediction performance can vary greatly between different gene predictors and subfamilies of ncRNA gene. Our estimates for false positive rates are based on simulations which preserve local islands of conservation; using these simulations, we predict a higher rate of false positives than previous computational ncRNA screens have reported. Using one of the tested prediction grammars, we provide an updated set of ncRNA predictions for D. melanogaster and compare them to previously-published predictions and experimental data. Many of our predictions show correlations with protein-coding genes. We found significant depletion of intergenic predictions near the 3' end of coding regions and furthermore depletion of predictions in the first intron of protein-coding genes. Some of our predictions are colocated with larger putative unannotated genes: for example, 17 of our predictions showing homology to the RFAM family snoR28 appear in a tandem array on the X chromosome; the 4.5 Kbp spanned by the predicted tandem array is contained within a FlyBase-annotated cDNA.
Maxent modelling for predicting the potential distribution of Thai Palms
DEFF Research Database (Denmark)
Tovaranonte, Jantrararuk; Barfod, Anders S.; Overgaard, Anne Blach
2011-01-01
Increasingly species distribution models are being used to address questions related to ecology, biogeography and species conservation on global and regional scales. We used the maximum entropy approach implemented in the MAXENT programme to build a habitat suitability model for Thai palms based...... overprediction of species distribution ranges. The models with the best predictive power were found by calculating the area under the curve (AUC) of receiver-operating characteristic (ROC). Here, we provide examples of contrasting predicted species distribution ranges as well as a map of modeled palm diversity...
Validation of Fatigue Modeling Predictions in Aviation Operations
Gregory, Kevin; Martinez, Siera; Flynn-Evans, Erin
2017-01-01
Bio-mathematical fatigue models that predict levels of alertness and performance are one potential tool for use within integrated fatigue risk management approaches. A number of models have been developed that provide predictions based on acute and chronic sleep loss, circadian desynchronization, and sleep inertia. Some are publicly available and gaining traction in settings such as commercial aviation as a means of evaluating flight crew schedules for potential fatigue-related risks. Yet, most models have not been rigorously evaluated and independently validated for the operations to which they are being applied and many users are not fully aware of the limitations in which model results should be interpreted and applied.
Aero-acoustic noise of wind turbines. Noise prediction models
Energy Technology Data Exchange (ETDEWEB)
Maribo Pedersen, B. [ed.
1997-12-31
Semi-empirical and CAA (Computational AeroAcoustics) noise prediction techniques are the subject of this expert meeting. The meeting presents and discusses models and methods. The meeting may provide answers to the following questions: What Noise sources are the most important? How are the sources best modeled? What needs to be done to do better predictions? Does it boil down to correct prediction of the unsteady aerodynamics around the rotor? Or is the difficult part to convert the aerodynamics into acoustics? (LN)
Predictions for mt and MW in minimal supersymmetric models
International Nuclear Information System (INIS)
Buchmueller, O.; Ellis, J.R.; Flaecher, H.; Isidori, G.
2009-12-01
Using a frequentist analysis of experimental constraints within two versions of the minimal supersymmetric extension of the Standard Model, we derive the predictions for the top quark mass, m t , and the W boson mass, m W . We find that the supersymmetric predictions for both m t and m W , obtained by incorporating all the relevant experimental information and state-of-the-art theoretical predictions, are highly compatible with the experimental values with small remaining uncertainties, yielding an improvement compared to the case of the Standard Model. (orig.)
Tollenaar, N.; Van der Heijden, P.G.M.
2013-01-01
Using criminal population criminal conviction history information, prediction models are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining
Painter, S; Cvetkovic, V; Pickett, D; Turner, D R
2002-12-15
A two-site kinetic model for solute sorption on inorganic colloids is developed. The model quantifies linear first-order sorption on two types of sites ("fast" and "slow") characterized by two pairs of rates (forward and reverse). We use the model to explore data requirements for long-term predictive calculations of colloid-facilitated transport and to evaluate laboratory kinetic sorption data of Lu et al.. Five batch sorption data sets are considered with plutonium as the tracer and montmorillonite, hematite, silica, and smectite as colloids. Using asymptotic results applicable on the time scale of limited duration experiments, a robust estimation procedure is developed for the fast-site partitioning coefficient K(C) and the slow forward rate alpha. The estimated range of K(C) is 1.1-76 L/g, and the range for alpha is 0.0017-0.02 1/h. The fast reverse rate k(r) is estimated in the range 0.012-0.1 1/h. Comparison of one-site and two-site sorption interpretations reveals the difficulty in discriminating between the two models for montmorillonite and to a lesser extent for hematite. For silica and smectite, the two-site model clearly provides a better representation of the data as compared with a single site model. Kinetic data for silica are available for different colloid concentrations (0.2 g/L and 1 g/L). For the range of experimental conditions considered, alpha appears to be independent of colloid concentration.
Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.
Yin, Wen-Jun; Yi, Yi-Hu; Guan, Xiao-Feng; Zhou, Ling-Yun; Wang, Jiang-Lin; Li, Dai-Yang; Zuo, Xiao-Cong
2017-02-03
Several models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level. The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.
Risk Prediction Models for Oral Clefts Allowing for Phenotypic Heterogeneity
Directory of Open Access Journals (Sweden)
Yalu eWen
2015-08-01
Full Text Available Oral clefts are common birth defects that have a major impact on the affected individual, their family and society. World-wide, the incidence of oral clefts is 1/700 live births, making them the most common craniofacial birth defects. The successful prediction of oral clefts may help identify sub-population at high risk, and promote new diagnostic and therapeutic strategies. Nevertheless, developing a clinically useful oral clefts risk prediction model remains a great challenge. Compelling evidences suggest the etiologies of oral clefts are highly heterogeneous, and the development of a risk prediction model with consideration of phenotypic heterogeneity may potentially improve the accuracy of a risk prediction model. In this study, we applied a previously developed statistical method to investigate the risk prediction on sub-phenotypes of oral clefts. Our results suggested subtypes of cleft lip and palate have similar genetic etiologies (AUC=0.572 with subtypes of cleft lip only (AUC=0.589, while the subtypes of cleft palate only (CPO have heterogeneous underlying mechanisms (AUCs for soft CPO and hard CPO are 0.617 and 0.623, respectively. This highlighted the potential that the hard and soft forms of CPO have their own mechanisms despite sharing some of the genetic risk factors. Comparing with conventional methods for risk prediction modeling, our method considers phenotypic heterogeneity of a disease, which potentially improves the accuracy for predicting each sub-phenotype of oral clefts.
Survival prediction model for postoperative hepatocellular carcinoma patients.
Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao
2017-09-01
This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.
A prediction model for assessing residential radon concentration in Switzerland
International Nuclear Information System (INIS)
Hauri, Dimitri D.; Huss, Anke; Zimmermann, Frank; Kuehni, Claudia E.; Röösli, Martin
2012-01-01
Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th–90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40–111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69–215 Bq/m³) in the medium category, and 219 Bq/m³ (108–427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be
Numerical modeling capabilities to predict repository performance
International Nuclear Information System (INIS)
1979-09-01
This report presents a summary of current numerical modeling capabilities that are applicable to the design and performance evaluation of underground repositories for the storage of nuclear waste. The report includes codes that are available in-house, within Golder Associates and Lawrence Livermore Laboratories; as well as those that are generally available within the industry and universities. The first listing of programs are in-house codes in the subject areas of hydrology, solute transport, thermal and mechanical stress analysis, and structural geology. The second listing of programs are divided by subject into the following categories: site selection, structural geology, mine structural design, mine ventilation, hydrology, and mine design/construction/operation. These programs are not specifically designed for use in the design and evaluation of an underground repository for nuclear waste; but several or most of them may be so used
Model Predictive Control of Wind Turbines
DEFF Research Database (Denmark)
Henriksen, Lars Christian
Wind turbines play a major role in the transformation from a fossil fuel based energy production to a more sustainable production of energy. Total-cost-of-ownership is an important parameter when investors decide in which energy technology they should place their capital. Modern wind turbines...... are controlled by pitching the blades and by controlling the electro-magnetic torque of the generator, thus slowing the rotation of the blades. Improved control of wind turbines, leading to reduced fatigue loads, can be exploited by using less materials in the construction of the wind turbine or by reducing...... the need for maintenance of the wind turbine. Either way, better total-cost-of-ownership for wind turbine operators can be achieved by improved control of the wind turbines. Wind turbine control can be improved in two ways, by improving the model on which the controller bases its design or by improving...
Comparison of Linear Prediction Models for Audio Signals
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available While linear prediction (LP has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.
Model Predictive Control of a Wave Energy Converter
DEFF Research Database (Denmark)
Andersen, Palle; Pedersen, Tom Søndergård; Nielsen, Kirsten Mølgaard
2015-01-01
In this paper reactive control and Model Predictive Control (MPC) for a Wave Energy Converter (WEC) are compared. The analysis is based on a WEC from Wave Star A/S designed as a point absorber. The model predictive controller uses wave models based on the dominating sea states combined with a model......'s are designed for each sea state using a model assuming a linear loss torque. The mean power results from two controllers are compared using both loss models. Simulation results show that MPC can outperform a reactive controller if a good model of the conversion losses is available....... connecting undisturbed wave sequences to sequences of torque. Losses in the conversion from mechanical to electrical power are taken into account in two ways. Conventional reactive controllers are tuned for each sea state with the assumption that the converter has the same efficiency back and forth. MPC...
Review of Model Predictions for Extensive Air Showers
Pierog, Tanguy
In detailed air shower simulations, the uncertainty in the prediction of shower observable for different primary particles and energies is currently dominated by differences between hadronic interaction models. With the results of the first run of the LHC, the difference between post-LHC model predictions has been reduced at the same level as experimental uncertainties of cosmic ray experiments. At the same time new types of air shower observables, like the muon production depth, have been measured, adding new constraints on hadronic models. Currently no model is able to reproduce consistently all mass composition measurements possible with the Pierre Auger Observatory for instance. We review the current model predictions for various particle production observables and their link with air shower observables and discuss the future possible improvements.
Hidden markov model for the prediction of transmembrane proteins using MATLAB.
Chaturvedi, Navaneet; Shanker, Sudhanshu; Singh, Vinay Kumar; Sinha, Dhiraj; Pandey, Paras Nath
2011-01-01
Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.
Measurements and IRI Model Predictions During the Recent Solar Minimum
Bilitza, Dieter; Brown, Steven A.; Wang, Mathew Y.; Souza, Jonas R.; Roddy, Patrick A.
2012-01-01
Cycle 23 was exceptional in that it lasted almost two years longer than its predecessors and in that it ended in an extended minimum period that proved all predictions wrong. Comparisons of the International Reference Ionosphere (IRI) with CHAMP and GRACE in-situ measurements of electron density during the minimum have revealed significant discrepancies at 400-500 km altitude. Our study investigates the causes for these discrepancies with the help of ionosonde and Planar Langmuir Probe (PLP) data from the Communications/Navigation Outage Forecasting System (C/NOFS) satellite. Our C/NOFS comparisons confirm the earlier CHAMP and GRACE results. But the ionosonde measurements of the F-peak plasma frequency (foF2) show generally good agreement throughout the whole solar cycle. At mid-latitude stations yearly averages of the data-model difference are within 10% and at low latitudes stations within 20%. The 60-70% differences found at 400-500 km altitude are not seen at the F peak. We will discuss how these seemingly contradicting results from the ionosonde and in situ data-model comparisons can be explained and which parameters need to be corrected in the IRI model.
Ensemble models on palaeoclimate to predict India's groundwater challenge
Directory of Open Access Journals (Sweden)
Partha Sarathi Datta
2013-09-01
Full Text Available In many parts of the world, freshwater crisis is largely due to increasing water consumption and pollution by rapidly growing population and aspirations for economic development, but, ascribed usually to the climate. However, limited understanding and knowledge gaps in the factors controlling climate and uncertainties in the climate models are unable to assess the probable impacts on water availability in tropical regions. In this context, review of ensemble models on δ18O and δD in rainfall and groundwater, 3H- and 14C- ages of groundwater and 14C- age of lakes sediments helped to reconstruct palaeoclimate and long-term recharge in the North-west India; and predict future groundwater challenge. The annual mean temperature trend indicates both warming/cooling in different parts of India in the past and during 1901–2010. Neither the GCMs (Global Climate Models nor the observational record indicates any significant change/increase in temperature and rainfall over the last century, and climate change during the last 1200 yrs BP. In much of the North-West region, deep groundwater renewal occurred from past humid climate, and shallow groundwater renewal from limited modern recharge over the past decades. To make water management to be more responsive to climate change, the gaps in the science of climate change need to be bridged.
Integrating predictive frameworks and cognitive models of face perception.
Trapp, Sabrina; Schweinberger, Stefan R; Hayward, William G; Kovács, Gyula
2018-02-08
The idea of a "predictive brain"-that is, the interpretation of internal and external information based on prior expectations-has been elaborated intensely over the past decade. Several domains in cognitive neuroscience have embraced this idea, including studies in perception, motor control, language, and affective, social, and clinical neuroscience. Despite the various studies that have used face stimuli to address questions related to predictive processing, there has been surprisingly little connection between this work and established cognitive models of face recognition. Here we suggest that the predictive framework can serve as an important complement of established cognitive face models. Conversely, the link to cognitive face models has the potential to shed light on issues that remain open in predictive frameworks.
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
Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during
Gok, Abdulkerim; Ngendahimana, David K; Fagerholm, Cara L; French, Roger H; Sun, Jiayang; Bruckman, Laura S
2017-01-01
Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples' responses, the change in haze (%) depended on individual samples' responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction.
Predictive modeling of coupled multi-physics systems: I. Theory
International Nuclear Information System (INIS)
Cacuci, Dan Gabriel
2014-01-01
Highlights: • We developed “predictive modeling of coupled multi-physics systems (PMCMPS)”. • PMCMPS reduces predicted uncertainties in predicted model responses and parameters. • PMCMPS treats efficiently very large coupled systems. - Abstract: This work presents an innovative mathematical methodology for “predictive modeling of coupled multi-physics systems (PMCMPS).” This methodology takes into account fully the coupling terms between the systems but requires only the computational resources that would be needed to perform predictive modeling on each system separately. The PMCMPS methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution based on a priori known mean values and uncertainties characterizing the parameters and responses for both multi-physics models. This “maximum entropy”-approximate a priori distribution is combined, using Bayes’ theorem, with the “likelihood” provided by the multi-physics simulation models. Subsequently, the posterior distribution thus obtained is evaluated using the saddle-point method to obtain analytical expressions for the optimally predicted values for the multi-physics models parameters and responses along with corresponding reduced uncertainties. Noteworthy, the predictive modeling methodology for the coupled systems is constructed such that the systems can be considered sequentially rather than simultaneously, while preserving exactly the same results as if the systems were treated simultaneously. Consequently, very large coupled systems, which could perhaps exceed available computational resources if treated simultaneously, can be treated with the PMCMPS methodology presented in this work sequentially and without any loss of generality or information, requiring just the resources that would be needed if the systems were treated sequentially
Comparison of Predictive Modeling Methods of Aircraft Landing Speed
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.
Model Predictive Control of Three Phase Inverter for PV Systems
Irtaza M. Syed; Kaamran Raahemifar
2015-01-01
This paper presents a model predictive control (MPC) of a utility interactive three phase inverter (TPI) for a photovoltaic (PV) system at commercial level. The proposed model uses phase locked loop (PLL) to synchronize the TPI with the power electric grid (PEG) and performs MPC control in a dq reference frame. TPI model consists of a boost converter (BC), maximum power point tracking (MPPT) control, and a three-leg voltage source inverter (VSI). The operational model of ...
Prediction error, ketamine and psychosis: An updated model.
Corlett, Philip R; Honey, Garry D; Fletcher, Paul C
2016-11-01
In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.
Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi
2018-02-01
To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.
Zhou, Ligang; Keung Lai, Kin; Yen, Jerome
2014-03-01
Due to the economic significance of bankruptcy prediction of companies for financial institutions, investors and governments, many quantitative methods have been used to develop effective prediction models. Support vector machine (SVM), a powerful classification method, has been used for this task; however, the performance of SVM is sensitive to model form, parameter setting and features selection. In this study, a new approach based on direct search and features ranking technology is proposed to optimise features selection and parameter setting for 1-norm and least-squares SVM models for bankruptcy prediction. This approach is also compared to the SVM models with parameter optimisation and features selection by the popular genetic algorithm technique. The experimental results on a data set with 2010 instances show that the proposed models are good alternatives for bankruptcy prediction.
A deep auto-encoder model for gene expression prediction.
Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua
2017-11-17
Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.
Jeffrey J. Barry; John M. Buffington; Peter Goodwin; John .G. King; William W. Emmett
2008-01-01
Previous studies assessing the accuracy of bed-load transport equations have considered equation performance statistically based on paired observations of measured and predicted bed-load transport rates. However, transport measurements were typically taken during low flows, biasing the assessment of equation performance toward low discharges, and because equation...
Predicting the Yield Stress of SCC using Materials Modelling
DEFF Research Database (Denmark)
Thrane, Lars Nyholm; Hasholt, Marianne Tange; Pade, Claus
2005-01-01
A conceptual model for predicting the Bingham rheological parameter yield stress of SCC has been established. The model used here is inspired by previous work of Oh et al. (1), predicting that the yield stress of concrete relative to the yield stress of paste is a function of the relative thickne...... and distribution were varied between SCC types. The results indicate that yield stress of SCC may be predicted using the model.......A conceptual model for predicting the Bingham rheological parameter yield stress of SCC has been established. The model used here is inspired by previous work of Oh et al. (1), predicting that the yield stress of concrete relative to the yield stress of paste is a function of the relative thickness...... of excess paste around the aggregate. The thickness of excess paste is itself a function of particle shape, particle size distribution, and particle packing. Seven types of SCC were tested at four different excess paste contents in order to verify the conceptual model. Paste composition and aggregate shape...
Predictive models of prolonged mechanical ventilation yield moderate accuracy.
Figueroa-Casas, Juan B; Dwivedi, Alok K; Connery, Sean M; Quansah, Raphael; Ellerbrook, Lowell; Galvis, Juan
2015-06-01
To develop a model to predict prolonged mechanical ventilation within 48 hours of its initiation. In 282 general intensive care unit patients, multiple variables from the first 2 days on mechanical ventilation and their total ventilation duration were prospectively collected. Three models accounting for early deaths were developed using different analyses: (a) multinomial logistic regression to predict duration > 7 days vs duration ≤ 7 days alive vs duration ≤ 7 days death; (b) binary logistic regression to predict duration > 7 days for the entire cohort and for survivors only, separately; and (c) Cox regression to predict time to being free of mechanical ventilation alive. Positive end-expiratory pressure, postoperative state (negatively), and Sequential Organ Failure Assessment score were independently associated with prolonged mechanical ventilation. The multinomial regression model yielded an accuracy (95% confidence interval) of 60% (53%-64%). The binary regression models yielded accuracies of 67% (61%-72%) and 69% (63%-75%) for the entire cohort and for survivors, respectively. The Cox regression model showed an equivalent to area under the curve of 0.67 (0.62-0.71). Different predictive models of prolonged mechanical ventilation in general intensive care unit patients achieve a moderate level of overall accuracy, likely insufficient to assist in clinical decisions. Copyright © 2015 Elsevier Inc. All rights reserved.
Comparison of the models of financial distress prediction
Directory of Open Access Journals (Sweden)
Jiří Omelka
2013-01-01
Full Text Available Prediction of the financial distress is generally supposed as approximation if a business entity is closed on bankruptcy or at least on serious financial problems. Financial distress is defined as such a situation when a company is not able to satisfy its liabilities in any forms, or when its liabilities are higher than its assets. Classification of financial situation of business entities represents a multidisciplinary scientific issue that uses not only the economic theoretical bases but interacts to the statistical, respectively to econometric approaches as well.The first models of financial distress prediction have originated in the sixties of the 20th century. One of the most known is the Altman’s model followed by a range of others which are constructed on more or less conformable bases. In many existing models it is possible to find common elements which could be marked as elementary indicators of potential financial distress of a company. The objective of this article is, based on the comparison of existing models of prediction of financial distress, to define the set of basic indicators of company’s financial distress at conjoined identification of their critical aspects. The sample defined this way will be a background for future research focused on determination of one-dimensional model of financial distress prediction which would subsequently become a basis for construction of multi-dimensional prediction model.
Plant water potential improves prediction of empirical stomatal models.
Directory of Open Access Journals (Sweden)
William R L Anderegg
Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.
Directory of Open Access Journals (Sweden)
Soldić-Aleksić Jasna
2009-01-01
Full Text Available Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables which are statistically significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.
Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker
2018-04-01
A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as `field' or `global' significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Daily precipitation climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is
Significance of Various Experimental Models and Assay Techniques in Cancer Diagnosis.
Ghanghoria, Raksha; Kesharwani, Prashant; Jain, Narendra K
2017-01-01
The experimental models are of vital significance to provide information regarding biological as well as genetic factors that control the phenotypic characteristics of the disease and serve as the foundation for the development of rational intervention stratagem. This review highlights the importance of experimental models in the field of cancer management. The process of pathogenesis in cancer progression, invasion and metastasis can be successfully explained by employing clinically relevant laboratory models of the disease. Cancer cell lines have been used extensively to monitor the process of cancer pathogenesis process by controlling growth regulation and chemo-sensitivity for the evaluation of novel therapeutics in both in vitro and xenograft models. The experimental models have been used for the elaboration of diagnostic or therapeutic protocols, and thus employed in preclinical studies of bioactive agents relevant for cancer prevention. The outcome of this review should provide useful information in understanding and selection of various models in accordance with the stage of cancer. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
Strifler, Lisa; Cardoso, Roberta; McGowan, Jessie; Cogo, Elise; Nincic, Vera; Khan, Paul A; Scott, Alistair; Ghassemi, Marco; MacDonald, Heather; Lai, Yonda; Treister, Victoria; Tricco, Andrea C; Straus, Sharon E
2018-04-13
To conduct a scoping review of knowledge translation (KT) theories, models and frameworks that have been used to guide dissemination or implementation of evidence-based interventions targeted to prevention and/or management of cancer or other chronic diseases. We used a comprehensive multistage search process from 2000-2016, which included traditional bibliographic database searching, searching using names of theories, models and frameworks, and cited reference searching. Two reviewers independently screened the literature and abstracted data. We found 596 studies reporting on the use of 159 KT theories, models or frameworks. A majority (87%) of the identified theories, models or frameworks were used in five or fewer studies, with 60% used once. The theories, models and frameworks were most commonly used to inform planning/design, implementation and evaluation activities, and least commonly used to inform dissemination and sustainability/scalability activities. Twenty-six were used across the full implementation spectrum (from planning/design to sustainability/scalability) either within or across studies. All were used for at least individual-level behavior change, while 48% were used for organization-level, 33% for community-level and 17% for system-level change. We found a significant number of KT theories, models and frameworks with a limited evidence base describing their use. Copyright © 2018. Published by Elsevier Inc.
Predicting the ungauged basin: model validation and realism assessment
van Emmerik, Tim; Mulder, Gert; Eilander, Dirk; Piet, Marijn; Savenije, Hubert
2015-01-01
The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of
Predicting the ungauged basin : Model validation and realism assessment
van Emmerik, Tim; Mulder, Gert; Eilander, Dirk; Piet, Marijn; Savenije, Hubert
2015-01-01
The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of
A Mathematical Model for the Prediction of Injectivity Decline | Odeh ...
African Journals Online (AJOL)
Injectivity impairment due to invasion of solid suspensions has been studied by several investigators and some modelling approaches have also been reported. Worthy of note is the development of analytical models for internal and external filtration coupled with transition time concept for predicting the overall decline in ...
Mathematical Model for Prediction of Flexural Strength of Mound ...
African Journals Online (AJOL)
The mound soil-cement blended proportions were mathematically optimized by using scheffe's approach and the optimization model developed. A computer program predicting the mix proportion for the model was written. The optimal proportion by the program was used prepare beam samples measuring 150mm x 150mm ...
Katz model prediction of Caenorhabditis elegans mutagenesis on STS-42
Cucinotta, Francis A.; Wilson, John W.; Katz, Robert; Badhwar, Gautam D.
1992-01-01
Response parameters that describe the production of recessive lethal mutations in C. elegans from ionizing radiation are obtained with the Katz track structure model. The authors used models of the space radiation environment and radiation transport to predict and discuss mutation rates for C. elegans on the IML-1 experiment aboard STS-42.
Accident Prediction Models for Akure – Ondo Carriageway, Ondo ...
African Journals Online (AJOL)
FIRST LADY
traffic exposure and intersection effects as independent variables. They suggested that the Poisson distribution allows for the relationship between exposure and crashes to be more accurately modeled as opposed to. Accident Prediction Models for Akure-Ondo Carriageway…Using Multiple Linear Regression ...
Multi-model prediction of downward short-wave radiation
Czech Academy of Sciences Publication Activity Database
Eben, Kryštof; Resler, Jaroslav; Krč, Pavel; Juruš, Pavel; Pelikán, Emil
2012-01-01
Roč. 9, - (2012), EMS2012-384 [EMS Annual Meeting /12./ and European Conference on Applied Climatology /9./. 10.09.2012-14.09.2012, Lodz] Institutional support: RVO:67985807 Keywords : multi-model prediction * NWP * model postprocessing Subject RIV: DG - Athmosphere Sciences, Meteorology
Atmospheric modelling for seasonal prediction at the CSIR
CSIR Research Space (South Africa)
Landman, WA
2014-10-01
Full Text Available by observed monthly sea-surface temperature (SST) and sea-ice fields. The AGCM is the conformal-cubic atmospheric model (CCAM) administered by the Council for Scientific and Industrial Research. Since the model is forced with observed rather than predicted...
Prediction Models and Decision Support: Chances and Challenges
Kappen, T.H.
2015-01-01
A clinical prediction model can assist doctors in arriving at the most likely diagnosis or estimating the prognosis. By utilizing various patient- and disease-related properties, such models can yield objective estimations of the risk of a disease or the probability of a certain disease course for
Validation of a multi-objective, predictive urban traffic model
Wilmink, I.R.; Haak, P. van den; Woldeab, Z.; Vreeswijk, J.
2013-01-01
This paper describes the results of the verification and validation of the ecoStrategic Model, which was developed, implemented and tested in the eCoMove project. The model uses real-time and historical traffic information to determine the current, predicted and desired state of traffic in a
Predictive ability of boiler production models | Ogundu | Animal ...
African Journals Online (AJOL)
The weekly body weight measurements of a growing strain of Ross broiler were used to compare the of ability of three mathematical models (the multi, linear, quadratic and Exponential) to predict 8 week body weight from early body measurements at weeks I, II, III, IV, V, VI and VII. The results suggest that the three models ...
Predictive modelling of noise level generated during sawing of rocks ...
Indian Academy of Sciences (India)
2016-08-26
Aug 26, 2016 ... Inﬂuence of the operating variables and rock properties on the noise level are investigated and analysed. Statistical analyses are then employed and models are built for the prediction of noise levels depending on the operating variables and the rock properties. The derived models are validated through ...
Modelling and prediction of non-stationary optical turbulence behaviour
Doelman, N.J.; Osborn, J.
2016-01-01
There is a strong need to model the temporal fluctuations in turbulence parameters, for instance for scheduling, simulation and prediction purposes. This paper aims at modelling the dynamic behaviour of the turbulence coherence length r0, utilising measurement data from the Stereo-SCIDAR instrument
Inferential ecosystem models, from network data to prediction
James S. Clark; Pankaj Agarwal; David M. Bell; Paul G. Flikkema; Alan Gelfand; Xuanlong Nguyen; Eric Ward; Jun. Yang
2011-01-01
Recent developments suggest that predictive modeling could begin to play a larger role not only for data analysis, but also for data collection. We address the example of efficient wireless sensor networks, where inferential ecosystem models can be used to weigh the value of an observation against the cost of data collection. Transmission costs make observations ââ...
Model prediction of maize yield responses to climate change in ...
African Journals Online (AJOL)
Observed data of the last three decades (1971 to 2000) from several climatological stations in north-eastern Zimbabwe and outputs from several global climate models were used. The downscaled model simulations consistently predicted a warming of between 1 and 2 ºC above the baseline period (1971-2000) at most of ...
A theoretical model for predicting neutron fluxes for cyclic Neutron ...
African Journals Online (AJOL)
A theoretical model has been developed for prediction of thermal neutron fluxes required for cyclic irradiations of a sample to obtain the same activity previously used for the detection of any radionuclide of interest. The model is suitable for radiotracer production or for long-lived neutron activation products where the ...
A model to predict the sound reflection from forests
Wunderli, J.M.; Salomons, E.M.
2009-01-01
A model is presented to predict the reflection of sound at forest edges. A single tree is modelled as a vertical cylinder. For the reflection at a cylinder an analytical solution is given based on the theory of scattering of spherical waves. The entire forest is represented by a line of cylinders
Model Predictive Control for Offset-Free Reference Tracking
Czech Academy of Sciences Publication Activity Database
Belda, Květoslav
2016-01-01
Roč. 5, č. 1 (2016), s. 8-13 ISSN 1805-3386 Institutional support: RVO:67985556 Keywords : offset-free reference tracking * predictive control * ARX model * state-space model * multi-input multi-output system * robotic system * mechatronic system Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2016/AS/belda-0458355.pdf
Multi-model ensemble schemes for predicting northeast monsoon ...
Indian Academy of Sciences (India)
An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among ...
Supervisory Model Predictive Control of the Heat Integrated Distillation Column
DEFF Research Database (Denmark)
Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted
2017-01-01
This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized contro...
Evaluation of preformance of Predictive Models for Deoxynivalenol in Wheat
Fels, van der H.J.
2014-01-01
The aim of this study was to evaluate the performance of two predictive models for deoxynivalenol contamination of wheat at harvest in the Netherlands, including the use of weather forecast data and external model validation. Data were collected in a different year and from different wheat fields
Three-model ensemble wind prediction in southern Italy
Directory of Open Access Journals (Sweden)
R. C. Torcasio
2016-03-01
Full Text Available Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013 three-model ensemble (TME experiment for wind prediction is considered. The models employed, run operationally at National Research Council – Institute of Atmospheric Sciences and Climate (CNR-ISAC, are RAMS (Regional Atmospheric Modelling System, BOLAM (BOlogna Limited Area Model, and MOLOCH (MOdello LOCale in H coordinates. The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System. Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System of the ECMWF (European Centre for Medium-Range Weather Forecast for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.
Three-model ensemble wind prediction in southern Italy
Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo
2016-03-01
Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo
2016-01-01
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Directory of Open Access Journals (Sweden)
Jaime Cuevas
2017-01-01
Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.
Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo
2017-01-05
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.
Stochastic models for predicting pitting corrosion damage of HLRW containers
International Nuclear Information System (INIS)
Henshall, G.A.
1991-10-01
Stochastic models for predicting aqueous pitting corrosion damage of high-level radioactive-waste containers are described. These models could be used to predict the time required for the first pit to penetrate a container and the increase in the number of breaches at later times, both of which would be useful in the repository system performance analysis. Monte Carlo implementations of the stochastic models are described, and predictions of induction time, survival probability and pit depth distributions are presented. These results suggest that the pit nucleation probability decreases with exposure time and that pit growth may be a stochastic process. The advantages and disadvantages of the stochastic approach, methods for modeling the effects of environment, and plans for future work are discussed
Verification and improvement of a predictive model for radionuclide migration
International Nuclear Information System (INIS)
Miller, C.W.; Benson, L.V.; Carnahan, C.L.
1982-01-01
Prediction of the rates of migration of contaminant chemical species in groundwater flowing through toxic waste repositories is essential to the assessment of a repository's capability of meeting standards for release rates. A large number of chemical transport models, of varying degrees of complexity, have been devised for the purpose of providing this predictive capability. In general, the transport of dissolved chemical species through a water-saturated porous medium is influenced by convection, diffusion/dispersion, sorption, formation of complexes in the aqueous phase, and chemical precipitation. The reliability of predictions made with the models which omit certain of these processes is difficult to assess. A numerical model, CHEMTRN, has been developed to determine which chemical processes govern radionuclide migration. CHEMTRN builds on a model called MCCTM developed previously by Lichtner and Benson
A novel Bayesian hierarchical model for road safety hotspot prediction.
Fawcett, Lee; Thorpe, Neil; Matthews, Joseph; Kremer, Karsten
2017-02-01
In this paper, we propose a Bayesian hierarchical model for predicting accident counts in future years at sites within a pool of potential road safety hotspots. The aim is to inform road safety practitioners of the location of likely future hotspots to enable a proactive, rather than reactive, approach to road safety scheme implementation. A feature of our model is the ability to rank sites according to their potential to exceed, in some future time period, a threshold accident count which may be used as a criterion for scheme implementation. Our model specification enables the classical empirical Bayes formulation - commonly used in before-and-after studies, wherein accident counts from a single before period are used to estimate counterfactual counts in the after period - to be extended to incorporate counts from multiple time periods. This allows site-specific variations in historical accident counts (e.g. locally-observed trends) to offset estimates of safety generated by a global accident prediction model (APM), which itself is used to help account for the effects of global trend and regression-to-mean (RTM). The Bayesian posterior predictive distribution is exploited to formulate predictions and to properly quantify our uncertainty in these predictions. The main contributions of our model include (i) the ability to allow accident counts from multiple time-points to inform predictions, with counts in more recent years lending more weight to predictions than counts from time-points further in the past; (ii) where appropriate, the ability to offset global estimates of trend by variations in accident counts observed locally, at a site-specific level; and (iii) the ability to account for unknown/unobserved site-specific factors which may affect accident counts. We illustrate our model with an application to accident counts at 734 potential hotspots in the German city of Halle; we also propose some simple diagnostics to validate the predictive capability of our
Directory of Open Access Journals (Sweden)
Mani Francesco
2011-01-01
Full Text Available Abstract This article investigates the prediction accuracy of an advanced deterministic propagation model in terms of channel depolarization and frequency selectivity for indoor wireless propagation. In addition to specular reflection and diffraction, the developed ray tracing tool considers penetration through dielectric blocks and/or diffuse scattering mechanisms. The sensitivity and prediction accuracy analysis is based on two measurement campaigns carried out in a warehouse and an office building. It is shown that the implementation of diffuse scattering into RT significantly increases the accuracy of the cross-polar discrimination prediction, whereas the delay-spread prediction is only marginally improved.
Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?
Torres, Leigh G; Read, Andrew J; Halpin, Patrick
2008-10-01
Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on
Kucharik, C.
2004-12-01
At the scale of individual fields, crop models have long been used to examine the interactions between soils, vegetation, the atmosphere and human management, using varied levels of numerical sophistication. While previous efforts have contributed significantly towards the advancement of modeling tools, the models themselves are not typically applied across larger continental scales due to a lack of crucial data. Furthermore, many times crop models are used to study a single quantity, process, or cycle in isolation, limiting their value in considering the important tradeoffs between competing ecosystem services such as food production, water quality, and sequestered carbon. In response to the need for a more integrated agricultural modeling approach across the continental scale, an updated agricultural version of a dynamic biosphere model (IBIS) now integrates representations of land-surface physics and soil physics, canopy physiology, terrestrial carbon and nitrogen balance, crop phenology, solute transport, and farm management into a single framework. This version of the IBIS model (Agro-IBIS) uses a short 20 to 60-minute timestep to simulate the rapid exchange of energy, carbon, water, and momentum between soils, vegetative canopies, and the atmosphere. The model can be driven either by site-specific meteorological data or by gridded climate datasets. Mechanistic crop models for corn, soybean, and wheat use physiologically-based representations of leaf photosynthesis, stomatal conductance, and plant respiration. Model validation has been performed using a variety of temporal scale data collected at the following spatial scales: (1) the precision-agriculture scale (5 m), (2) the individual field experiment scale (AmeriFlux), and (3) regional and continental scales using annual USDA county-level yield data and monthly satellite (AVHRR) observations of vegetation characteristics at 0.5 degree resolution. To date, the model has been used with great success to
International Nuclear Information System (INIS)
Sathyamoorthy, Dinesh
2014-01-01
The derivation of spatial significance is an important aspect of geospatial analysis and hence, various methods have been proposed to compute the spatial significance of entities based on spatial distances with other entities within the cluster. This paper is aimed at studying the spatial significance of mountain objects extracted from multiscale digital elevation models (DEMs). At each scale, the value of spatial significance index SSI of a mountain object is the minimum number of morphological dilation iterations required to occupy all the other mountain objects in the terrain. The mountain object with the lowest value of SSI is the spatially most significant mountain object, indicating that it has the shortest distance to the other mountain objects. It is observed that as the area of the mountain objects reduce with increasing scale, the distances between the mountain objects increase, resulting in increasing values of SSI. The results obtained indicate that the strategic location of a mountain object at the centre of the terrain is more important than its size in determining its reach to other mountain objects and thus, its spatial significance
Monthly to seasonal low flow prediction: statistical versus dynamical models
Ionita-Scholz, Monica; Klein, Bastian; Meissner, Dennis; Rademacher, Silke
2016-04-01
the Alfred Wegener Institute a purely statistical scheme to generate streamflow forecasts for several months ahead. Instead of directly using teleconnection indices (e.g. NAO, AO) the idea is to identify regions with stable teleconnections between different global climate information (e.g. sea surface temperature, geopotential height etc.) and streamflow at different gauges relevant for inland waterway transport. So-called stability (correlation) maps are generated showing regions where streamflow and climate variable from previous months are significantly correlated in a 21 (31) years moving window. Finally, the optimal forecast model is established based on a multiple regression analysis of the stable predictors. We will present current results of the aforementioned approaches with focus on the River Rhine (being one of the world's most frequented waterways and the backbone of the European inland waterway network) and the Elbe River. Overall, our analysis reveals the existence of a valuable predictability of the low flows at monthly and seasonal time scales, a result that may be useful to water resources management. Given that all predictors used in the models are available at the end of each month, the forecast scheme can be used operationally to predict extreme events and to provide early warnings for upcoming low flows.
2017-05-31
solute partition coefficient between two phases, K, for the changes in molecular interaction energies involved in transferring a chemical between the...Office of chemical safety and pollution prevention. Washington, D.C: US EPA. 2012. 46. American Society for Testing and Materials. E1963 − 09: Standard...
Models that predict standing crop of stream fish from habitat variables: 1950-85.
K.D. Fausch; C.L. Hawkes; M.G. Parsons
1988-01-01
We reviewed mathematical models that predict standing crop of stream fish (number or biomass per unit area or length of stream) from measurable habitat variables and classified them by the types of independent habitat variables found significant, by mathematical structure, and by model quality. Habitat variables were of three types and were measured on different scales...
Diverse models for the prediction of CDK4 inhibitory activity of ...
Indian Academy of Sciences (India)
Decision tree, random forest, moving average analysis (MAA), multiple linear regression (MLR), partial least square regression (PLSR) and principal component regression (PCR) were used to develop models for prediction of CDK4 inhibitory activity. The statistical significance of models was assessed through specificity, ...
A significant advantage for trapped field magnet applications—A failure of the critical state model
Weinstein, Roy; Parks, Drew; Sawh, Ravi-Persad; Carpenter, Keith; Davey, Kent
2015-10-01
Ongoing research has increased achievable field in trapped field magnets (TFMs) to multi-Tesla levels. This has greatly increased the attractiveness of TFMs for applications. However, it also increases the already very difficult problem of in situ activation and reactivation of the TFMs. The pulsed zero-field-cool (ZFC) method of activation is used in most applications because it can be accomplished with much lower power and more modest equipment than field-cool activation. The critical state model (CSM) has been a reliable theoretical tool for experimental analysis and engineering design of TFMs and their applications for over a half-century. The activating field, BA, required to fully magnetize a TFM to its maximum trappable field, BT,max, using pulsed-ZFC is predicted by CSM to be R ≡ BA/BT,max ≥ 2.0. We report here experiments on R as a function of Jc, which find a monotonic decrease of R to 1.0 as Jc increases. The reduction to R = 1.0 reduces the power needed to magnetize TFMs by about an order of magnitude. This is a critical advantage for TFM applications. The results also indicate the limits of applicability of CSM, and shed light on the physics omitted from the model. The experimental results rule out heating effects and pinning center geometry as causes of the decrease in R. A possible physical cause is proposed.
Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation
International Nuclear Information System (INIS)
Voyant, Cyril; Muselli, Marc; Paoli, Christophe; Nivet, Marie-Laure
2012-01-01
We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/ARMA is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed. -- Highlights: ► Time series forecasting with hybrid method based on the use of ALADIN numerical weather model, ANN and ARMA. ► Innovative pre-input layer selection method. ► Combination of optimized MLP and ARMA model obtained from a rule based on the analysis of hourly data series. ► Stationarity process (method and control) for the global radiation time series.
A simplified building airflow model for agent concentration prediction.
Jacques, David R; Smith, David A
2010-11-01
A simplified building airflow model is presented that can be used to predict the spread of a contaminant agent from a chemical or biological attack. If the dominant means of agent transport throughout the building is an air-handling system operating at steady-state, a linear time-invariant (LTI) model can be constructed to predict the concentration in any room of the building as a result of either an internal or external release. While the model does not capture weather-driven and other temperature-driven effects, it is suitable for concentration predictions under average daily conditions. The model is easily constructed using information that should be accessible to a building manager, supplemented with assumptions based on building codes and standard air-handling system design practices. The results of the model are compared with a popular multi-zone model for a simple building and are demonstrated for building examples containing one or more air-handling systems. The model can be used for rapid concentration prediction to support low-cost placement strategies for chemical and biological detection sensors.
Discrete fracture modelling for the Stripa tracer validation experiment predictions
International Nuclear Information System (INIS)
Dershowitz, W.; Wallmann, P.
1992-02-01
Groundwater flow and transport through three-dimensional networks of discrete fractures was modeled to predict the recovery of tracer from tracer injection experiments conducted during phase 3 of the Stripa site characterization and validation protect. Predictions were made on the basis of an updated version of the site scale discrete fracture conceptual model used for flow predictions and preliminary transport modelling. In this model, individual fractures were treated as stochastic features described by probability distributions of geometric and hydrologic properties. Fractures were divided into three populations: Fractures in fracture zones near the drift, non-fracture zone fractures within 31 m of the drift, and fractures in fracture zones over 31 meters from the drift axis. Fractures outside fracture zones are not modelled beyond 31 meters from the drift axis. Transport predictions were produced using the FracMan discrete fracture modelling package for each of five tracer experiments. Output was produced in the seven formats specified by the Stripa task force on fracture flow modelling. (au)
Predicting nucleic acid binding interfaces from structural models of proteins.
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.
Adaptive Gaussian Predictive Process Models for Large Spatial Datasets
Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.
2011-01-01
Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is determined by a judicious choice of “knots” or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial-temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. PMID:22298952
Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models.
Liu, Bowen; Ramsundar, Bharath; Kawthekar, Prasad; Shi, Jade; Gomes, Joseph; Luu Nguyen, Quang; Ho, Stephen; Sloane, Jack; Wender, Paul; Pande, Vijay
2017-10-25
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis.
Pulsatile fluidic pump demonstration and predictive model application
International Nuclear Information System (INIS)
Morgan, J.G.; Holland, W.D.
1986-04-01
Pulsatile fluidic pumps were developed as a remotely controlled method of transferring or mixing feed solutions. A test in the Integrated Equipment Test facility demonstrated the performance of a critically safe geometry pump suitable for use in a 0.1-ton/d heavy metal (HM) fuel reprocessing plant. A predictive model was developed to calculate output flows under a wide range of external system conditions. Predictive and experimental flow rates are compared for both submerged and unsubmerged fluidic pump cases
Cloud Based Metalearning System for Predictive Modeling of Biomedical Data
Directory of Open Access Journals (Sweden)
Milan Vukićević
2014-01-01
Full Text Available Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.
Models for Predicting and Explaining Citation Count of Biomedical Articles
Fu, Lawrence D.; Aliferis, Constantin
2008-01-01
The single most important bibliometric criterion for judging the impact of biomedical papers and their authors’ work is the number of citations received which is commonly referred to as “citation count”. This metric however is unavailable until several years after publication time. In the present work, we build computer models that accurately predict citation counts of biomedical publications within a deep horizon of ten years using only predictive information available at publication time. O...
Predictive Models of Li-ion Battery Lifetime
Energy Technology Data Exchange (ETDEWEB)
Smith, Kandler; Wood, Eric; Santhanagopalan, Shriram; Kim, Gi-heon; Shi, Ying; Pesaran, Ahmad
2015-06-15
It remains an open question how best to predict real-world battery lifetime based on accelerated calendar and cycle aging data from the laboratory. Multiple degradation mechanisms due to (electro)chemical, thermal, and mechanical coupled phenomena influence Li-ion battery lifetime, each with different dependence on time, cycling and thermal environment. The standardization of life predictive models would benefit the industry by reducing test time and streamlining development of system controls.
Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms
DEFF Research Database (Denmark)
Asgarpour, Masoud; Sørensen, John Dalsgaard
2018-01-01
monitoring, fault prediction and predictive maintenance of offshore wind components is defined. The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution......The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation...
The significance of using satellite imagery data only in Ecological Niche Modelling of Iberian herps
Directory of Open Access Journals (Sweden)
Neftalí Sillero
2012-12-01
Full Text Available The environmental data used to calculate ecological niche models (ENM are obtained mainly from ground-based maps (e.g., climatic interpolated surfaces. These data are often not available for less developed areas, or may be at an inappropriate scale, and thus to obtain this information requires fieldwork. An alternative source of eco-geographical data comes from satellite imagery. Three sets of ENM were calculated exclusively with variables obtained (1 from optical and radar images only and (2 from climatic and altitude maps obtained by ground-based methods. These models were compared to evaluate whether satellite imagery can accurately generate ENM. These comparisons must be made in areas with well-known species distribution and with available satellite imagery and ground-based data. Thus, the study area was the south-western part of Salamanca (Spain, using amphibian and reptiles as species models. Models’ discrimination capacity was measured with ROC plots. Models’ covariation was measured with a Spatial Spearman correlation. Four modelling techniques were used (Bioclim, Mahalanobis distance, GARP and Maxent. The results of this comparison showed that there were no significant differences between models generated using remotely sensed imagery or ground-based data. However, the models built with satellite imagery data exhibited a larger diversity of values, probably related to the higher spatial resolution of the satellite imagery. Satellite imagery can produce accurate ENM, independently of the modelling technique or the dataset used. Therefore, biogeographical analysis of species distribution in remote areas can be accurately developed only with variables from satellite imagery.
Modelling personality, plasticity and predictability in shelter dogs
2017-01-01
Behavioural assessments of shelter dogs (Canis lupus familiaris) typically comprise standardized test batteries conducted at one time point, but test batteries have shown inconsistent predictive validity. Longitudinal behavioural assessments offer an alternative. We modelled longitudinal observational data on shelter dog behaviour using the framework of behavioural reaction norms, partitioning variance into personality (i.e. inter-individual differences in behaviour), plasticity (i.e. inter-individual differences in average behaviour) and predictability (i.e. individual differences in residual intra-individual variation). We analysed data on interactions of 3263 dogs (n = 19 281) with unfamiliar people during their first month after arrival at the shelter. Accounting for personality, plasticity (linear and quadratic trends) and predictability improved the predictive accuracy of the analyses compared to models quantifying personality and/or plasticity only. While dogs were, on average, highly sociable with unfamiliar people and sociability increased over days since arrival, group averages were unrepresentative of all dogs and predictions made at the individual level entailed considerable uncertainty. Effects of demographic variables (e.g. age) on personality, plasticity and predictability were observed. Behavioural repeatability was higher one week after arrival compared to arrival day. Our results highlight the value of longitudinal assessments on shelter dogs and identify measures that could improve the predictive validity of behavioural assessments in shelters. PMID:28989764
Prediction of type A behaviour: A structural equation model
Directory of Open Access Journals (Sweden)
René van Wyk
2009-05-01
Full Text Available The predictability of Type A behaviour was measured in a sample of 375 professionals with a shortened version of the Jenkins Activity Survey (JAS. Two structural equation models were constructed with the Type A behaviour achievement sub-scale and global (total Type A as the predictor variables. The indices showed a reasonable-to-promising fit with the data. Type A achievement was reasonably predicted by service-career orientation, internal locus of control, power self-concept and economic innovation. Type A global was also predicted by internal locus of control, power self-concept and the entrepreneurial attitude of achievement and personal control.
Modelling of physical properties - databases, uncertainties and predictive power
DEFF Research Database (Denmark)
Gani, Rafiqul
Physical and thermodynamic property in the form of raw data or estimated values for pure compounds and mixtures are important pre-requisites for performing tasks such as, process design, simulation and optimization; computer aided molecular/mixture (product) design; and, product-process analysis...... in the estimated/predicted property values, how to assess the quality and reliability of the estimated/predicted property values? The paper will review a class of models for prediction of physical and thermodynamic properties of organic chemicals and their mixtures based on the combined group contribution – atom...
Uribe-Rivera, David E; Soto-Azat, Claudio; Valenzuela-Sánchez, Andrés; Bizama, Gustavo; Simonetti, Javier A; Pliscoff, Patricio
2017-07-01
Climate change is a major threat to biodiversity; the development of models that reliably predict its effects on species distributions is a priority for conservation biogeography. Two of the main issues for accurate temporal predictions from Species Distribution Models (SDM) are model extrapolation and unrealistic dispersal scenarios. We assessed the consequences of these issues on the accuracy of climate-driven SDM predictions for the dispersal-limited Darwin's frog Rhinoderma darwinii in South America. We calibrated models using historical data (1950-1975) and projected them across 40 yr to predict distribution under current climatic conditions, assessing predictive accuracy through the area under the ROC curve (AUC) and True Skill Statistics (TSS), contrasting binary model predictions against temporal-independent validation data set (i.e., current presences/absences). To assess the effects of incorporating dispersal processes we compared the predictive accuracy of dispersal constrained models with no dispersal limited SDMs; and to assess the effects of model extrapolation on the predictive accuracy of SDMs, we compared this between extrapolated and no extrapolated areas. The incorporation of dispersal processes enhanced predictive accuracy, mainly due to a decrease in the false presence rate of model predictions, which is consistent with discrimination of suitable but inaccessible habitat. This also had consequences on range size changes over time, which is the most used proxy for extinction risk from climate change. The area of current climatic conditions that was absent in the baseline conditions (i.e., extrapolated areas) represents 39% of the study area, leading to a significant decrease in predictive accuracy of model predictions for those areas. Our results highlight (1) incorporating dispersal processes can improve predictive accuracy of temporal transference of SDMs and reduce uncertainties of extinction risk assessments from global change; (2) as
Sahyoun, Maher; Korsholm, Ulrik S.; Sørensen, Jens H.; Šantl-Temkiv, Tina; Finster, Kai; Gosewinkel, Ulrich; Nielsen, Niels W.
2017-12-01
Bacterial ice-nucleating particles (INP) have the ability to facilitate ice nucleation from super-cooled cloud droplets at temperatures just below the melting point. Bacterial INP have been detected in cloud water, precipitation, and dry air, hence they may have an impact on weather and climate. In modeling studies, the potential impact of bacteria on ice nucleation and precipitation formation on global scale is still uncertain due to their small concentration compared to other types of INP, i.e. dust. Those earlier studies did not account for the yet undetected high concentration of nanoscale fragments of bacterial INP, which may be found free or attached to soil dust in the atmosphere. In this study, we investigate the sensitivity of modeled cloud ice, precipitation and global solar radiation in different weather scenarios to changes in the fraction of cloud droplets containing bacterial INP, regardless of their size. For this purpose, a module that calculates the probability of ice nucleation as a function of ice nucleation rate and bacterial INP fraction was developed and implemented in a numerical weather prediction model. The threshold value for the fraction of cloud droplets containing bacterial INP needed to produce a 1% increase in cloud ice was determined at 10-5 to 10-4. We also found that increasing this fraction causes a perturbation in the forecast, leading to significant differences in cloud ice and smaller differences in convective and total precipitation and in net solar radiation reaching the surface. These effects were most pronounced in local convective events. Our results show that bacterial INP can be considered as a trigger factor for precipitation, but not an enhancement factor.
Murari, A.; Peluso, E.; Vega, J.; Gelfusa, M.; Lungaroni, M.; Gaudio, P.; Martínez, F. J.; Contributors, JET
2017-01-01
Understanding the many aspects of tokamak physics requires the development of quite sophisticated models. Moreover, in the operation of the devices, prediction of the future evolution of discharges can be of crucial importance, particularly in the case of the prediction of disruptions, which can cause serious damage to various parts of the machine. The determination of the limits of predictability is therefore an important issue for modelling, classifying and forecasting. In all these cases, once a certain level of performance has been reached, the question typically arises as to whether all the information available in the data has been exploited, or whether there are still margins for improvement of the tools being developed. In this paper, a theoretical information approach is proposed to address this issue. The excellent properties of the developed indicator, called the prediction factor (PF), have been proved with the help of a series of numerical tests. Its application to some typical behaviour relating to macroscopic instabilities in tokamaks has shown very positive results. The prediction factor has also been used to assess the performance of disruption predictors running in real time in the JET system, including the one systematically deployed in the feedback loop for mitigation purposes. The main conclusion is that the most advanced predictors basically exploit all the information contained in the locked mode signal on which they are based. Therefore, qualitative improvements in disruption prediction performance in JET would need the processing of additional signals, probably profiles.
Dedes, I.; Dudek, J.
2018-03-01
We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.
International Nuclear Information System (INIS)
Buffa, Francesca Meteora
2002-01-01
Various hypotheses for radiation local tumor control probability (ltcp) were modeled, and assessed against local tumor control (LTC) and clonogenic assay (CA) data. For head-and-neck tumors receiving low-LET external-beam irradiation, the best model was a Poisson ltcp accounting for cell repopulation, hypoxia, and tumor volume dependence of radiosensitivity (α). This confirmed that hypoxia is limiting LTC of these tumors, with the magnitude depending upon tumor volume. However, LTC of cervical carcinoma receiving external-beam irradiation and brachytherapy was well described by a model not accounting for hypoxia. Furthermore, when the survival fraction at 2 Gy (SF 2 ) and colony forming efficiency (CFE) measured for individual patients were incorporated into this model, very good correlation with LTC was seen (p=0.0004). After multivariate analysis, this model was the best independent prognostic factor for LTC and patient survival. Furthermore, no difference in prediction was seen when a model based on birth-and-death stochastic theory was used. Two forms of hypoxia are known to be present in tumors: diffusion-limited, chronic hypoxia (CH), and acute, transient hypoxia (TH). A modeling study on WiDr multicellular spheroids showed that the CH effect on LTC is significantly lower than expected from CA. This could arise from energy charge depletion accompanying CH, reducing the number of proliferating clonogenic cells that can repair radiation damage, and thus mitigating the radioresistance of CH cells. This suggests that TH, rather than CH, may be the limiting factor for in vivo LTC. Finally, by computing ltcp using Monte Carlo calculated dose distributions, it was shown that Monte Carlo statistical noise can cause an underestimation of ltcp, with the magnitude depending upon the model hypotheses
Explicit prediction of ice clouds in general circulation models
Kohler, Martin
1999-11-01
Although clouds play extremely important roles in the radiation budget and hydrological cycle of the Earth, there are large quantitative uncertainties in our understanding of their generation, maintenance and decay mechanisms, representing major obstacles in the development of reliable prognostic cloud water schemes for General Circulation Models (GCMs). Recognizing their relative neglect in the past, both observationally and theoretically, this work places special focus on ice clouds. A recent version of the UCLA - University of Utah Cloud Resolving Model (CRM) that includes interactive radiation is used to perform idealized experiments to study ice cloud maintenance and decay mechanisms under various conditions in term of: (1) background static stability, (2) background relative humidity, (3) rate of cloud ice addition over a fixed initial time-period and (4) radiation: daytime, nighttime and no-radiation. Radiation is found to have major effects on the life-time of layer-clouds. Optically thick ice clouds decay significantly slower than expected from pure microphysical crystal fall-out (taucld = 0.9--1.4 h as opposed to no-motion taumicro = 0.5--0.7 h). This is explained by the upward turbulent fluxes of water induced by IR destabilization, which partially balance the downward transport of water by snowfall. Solar radiation further slows the ice-water decay by destruction of the inversion above cloud-top and the resulting upward transport of water. Optically thin ice clouds, on the other hand, may exhibit even longer life-times (>1 day) in the presence of radiational cooling. The resulting saturation mixing ratio reduction provides for a constant cloud ice source. These CRM results are used to develop a prognostic cloud water scheme for the UCLA-GCM. The framework is based on the bulk water phase model of Ose (1993). The model predicts cloud liquid water and cloud ice separately, and which is extended to split the ice phase into suspended cloud ice (predicted
Polycaprolactone thin-film drug delivery systems: Empirical and predictive models for device design.
Schlesinger, Erica; Ciaccio, Natalie; Desai, Tejal A
2015-12-01
To define empirical models and parameters based on theoretical equations to describe drug release profiles from two polycaprolactone thin-film drug delivery systems. Additionally, to develop a predictive model for empirical parameters based on drugs' physicochemical properties. Release profiles from a selection of drugs representing the standard pharmaceutical space in both polycaprolactone matrix and reservoir systems were determined experimentally. The proposed models were used to calculate empirical parameters describing drug diffusion and release. Observed correlations between empirical parameters and drug properties were used to develop equations to predict parameters based on drug properties. Predictive and empirical models were evaluated in the design of three prototype devices: a levonorgestrel matrix system for on-demand locally administered contraception, a timolol-maleate reservoir system for glaucoma treatment, and a primaquine-bisphosphate reservoir system for malaria prophylaxis. Proposed empirical equations accurately fit experimental data. Experimentally derived empirical parameters show significant correlations with LogP, molecular weight, and solubility. Empirical models based on predicted parameters accurately predict experimental release data for three prototype systems, demonstrating the accuracy and utility of these models. The proposed empirical models can be used to design polycaprolactone thin-film devices for target geometries and release rates. Empirical parameters can be predicted based on drug properties. Together, these models provide tools for preliminary evaluation and design of controlled-release delivery systems. Copyright © 2015. Published by Elsevier B.V.
Modeling the prediction of business intelligence system effectiveness.
Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I
2016-01-01
Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.